Military Medicine

Por, Elaine D; Selig, Daniel J; Chin, Geoffrey C; DeLuca, Jesse P; Oliver, Thomas G; Livezey, Jeffrey R

doi: 10.1093/milmed/usab098pmid: 34967404

ABSTRACTPharmacogenomics (PGx) plays a fundamental role in personalized medicine, providing an evidence-based treatment approach centered on the relationship between genomic variations and their effect on drug metabolism. Cytochrome P450 (CYP450) enzymes are responsible for the metabolism of most clinically prescribed drugs and a major source of variability in drug pharmacokinetics and pharmacodynamics. To assess the prevalence of PGx testing within the Military Health System (MHS), testing of specific CYP450 enzymes was evaluated. Data were retrospectively obtained from the Military Health System Management Analysis and Reporting Tool (M2) database. Patient demographics were identified for each test, along with TRICARE status, military treatment facility, clinic, and National Provider Identifier. A total of 929 patients received 1,833 PGx tests, predominantly composed of active duty/guard service members (N = 460; 49.5%), with highest testing rates in the army (51.5%). An even distribution in testing was observed among gender, with the highest rates in Caucasians (41.7%). Of the CYP enzymes assessed, CYP2C19 and CYP2D6 accounted for 87.8% of all PGx CYP testing. The majority of patients were tested in psychiatry clinics (N = 496; 53.4%) and primary care clinics (N = 233; 25.1%), accounting for 56.4% and 24.8% of all tests, respectively. Testing was found to be provider driven, suggesting a lack of a standardized approach to PGx and its application in patient care within the MHS. We initially recommend targeted education and revising testing labels to be more uniform and informative. Long-term recommendations include establishing pharmacy-driven protocols and point-of-care PGx testing to optimize patient outcomes.

Selig, Daniel J; Livezey, Jeffrey R; Chin, Geoffrey C; DeLuca, Jesse P; Guillory II, Walter O; Kress, Adrian T; Oliver, Thomas O; Por, Elaine D

doi: 10.1093/milmed/usab481pmid: 34967405

ABSTRACT Introduction Clinical utilization of pharmacogenomics (PGx) testing is highly institutionally dependent, and little information is known about provider practices of PGx testing in the Military Health System (MHS). In this study, we aimed to characterize Clinical Pharmacogenetics Implementation Consortium (CPIC) actionable prescription (Rx) patterns and their temporal relationship with PGx testing in the MHS. Methods Using data from the Military Health System Management Analysis and Reporting Tool (M2) database, this retrospective cohort study included all patients receiving at least one PGx test and at least one CPIC actionable Rx from January 2015 to August 2020 (845 patients, 1,471 PGx, 7,725 index CPIC actionable Rxs). Rx patterns and temporal relationships with PGx testing were characterized via descriptive statistics. Binomial regression was used to determine which patient and provider characteristics were associated with a patient receiving a PGx test within 30 days of an index Rx. Results Patients had a median of 9 index CPIC actionable Rx’s (range 1–26). Pain medications were most commonly prescribed (N = 794, 94% patients with at least 1 Rx). However, pain medication had the lowest Rx–PGx match rate (40%) compared to an average of 62% Rx–PGx match rate for all CPIC drugs. Antidepressants were also commonly prescribed (N = 668, 79.1% patients with at least 1 Rx), and antidepressants had the highest Rx–PGx match rate of 86.7%. A minority of providers (20%, N = 249) ordered the majority of PGx tests (86.1%, N = 1,266) and only 8.3% of PGx tests (N = 398) matched to a CPIC actionable drug within 30 days of the test (defined by Rxs ordered within 30 days before or after the PGx test). However, approximately 39.8% of patients (N = 317) had at least one drug match to a PGx test within 30 days. The largest predictor of whether a patient received a PGx test within 30 days of any index Rx was whether or not a specific psychiatry provider ordered the PGx test (odds ratio; OR 3.7, 95% CI 2.13–6.54, P < 0.001). Neither the CPIC level of evidence nor FDA PGx actionable or informative labels had a significant effect on PGx test timing. Conclusions PGx testing was generally limited to high Rx-drug users and was found to be an under-utilized resource. PGx testing did not typically follow CPIC guidelines. Implementing PGx testing protocols, simplifying PGx test-ordering by incorporating at minimum CYP2D6, CYP2C19, and CYP2C9 into PGx-testing panels, and unifying providers’ PGx knowledgebase in the MHS are feasible and would improve the clinical utilization of PGx tests in the MHS. INTRODUCTION Clinical implementation of pharmacogenomics (PGx) testing is likely a cost-effective solution to improve the personalization, safety, and efficacy of drug therapy.1,2 There is a large body of evidence, numerous clinical guidelines, and a multi-institutional collaboration in support of PGx testing and its clinical implementation.3–5 Consequently, there are increasing efforts to implement PGx testing across many clinical settings to optimize therapeutic efficacy and minimize adverse drug effects. However, to date, the majority of clinical PGx implementation programs are limited to large academic medical centers.6–8 Barriers to widespread adoption of PGx testing include cost of testing, complex logistics of incorporation into the electronic health record (EHR), and provider skepticism.9,10 The Military Health System (MHS) is finalizing centralization of administrative and management duties of local military treatment facilities to the Defense Health Agency and has a common EHR, and insurance reimbursement is generally not a concern.11 Therefore, the MHS is uniquely situated for successful implementation of PGx testing protocols; however, little is known about the clinical utilization of PGx testing in the MHS. For this study, we extended our previous work12 by exploring Clinical Pharmacogenetics Implementation Consortium (CPIC) Rx patterns and their temporal relationships to PGx testing in the MHS. Findings presented from this study provide detailed insight into PGx testing and the Rx of various drug classes across clinical services within the MHS. METHODS Data Collection Data were collected from the Military Health System Management Analysis and Reporting Tool (M2) database (January 2015–August 2020). Data collection and definitions of PGx testing were previously described.12 Of note, only PGx tests on CYP enzymes were included in this study. For each patient with an available pseudo-identification number, drug data was also extracted including the name, order date, strength, number of pills dispensed, clinical service, and the national provider identifier associated with the drug order. A dataset of gene–drug pairs was downloaded from the CPIC website.13 The terms gene–drug pair and Rx–pharmacogenomics match (Rx–PGx match) were used interchangeably. Drugs in the CPIC dataset were identified by the generic name. A reference list of brand names was used to search the MHS extracted data and standardize all potentially matching drugs to generic name used by CPIC. After standardizing drug names, CPIC data was linked with extracted M2 data via drugs common to both datasets. Drugs in the final analytic dataset were assigned a drug class according to their most common indication (Table S1). Drugs were classified as other if they did not fit in a major category of prescribed drugs. This protocol (DBS.2019.049) was reviewed by the Uniformed Services University’s Human Research Protections Program Office and determined not to meet the criteria defining research involving human subjects at 32 CFR 219.102 and applicable Department of Defense policy guidance. Study Design This is a retrospective cohort study including any patient that received at least one PGx test and at least one CPIC actionable drug during the study period. An index PGx test was defined as the earliest occurrence of a PGx test for a given patient in the dataset. An index Rx was defined as the earliest occurrence of a drug Rx for a given patient. Cohort entry date was defined by the minimum date of the index PGx test or earliest index Rx. Patients were followed until the maximum of the index PGx test date or latest Rx date (index or subsequent). The temporal relationship of a Rx to the PGx test was the difference in days between Rx order date and PGx test order date. There were 163 (9.5%) occasions of duplicate PGx testing on different days. Therefore, only the index PGx test was included to ensure a unique temporal relationship. Estimation of Drug Exposure and Construction of Treatment Episodes Drug exposure was estimated using a total days’ supply method.14 The total days’ supply of a given Rx was estimated by multiplying the quantity of pills of the drug supplied by the average number of times a pill would be prescribed in a day for the most common adult indication. Internal validity checks were performed to ensure that the median days’ supply was plausible for each drug class (Table S2). Within the MHS, it is common to dispense large supplies of drugs for patients exiting the military (terminal leave), traveling extreme distances to receive care, or who may be deployed. The definition of days’ supply was changed if there was a clear reason elucidated by the dataset, i.e., a pediatric patient. Treatment episodes were constructed by defining any subsequent Rx ordered within the days’ supply from the previous order date, plus a 30 day gap allowance as part of the same treatment episode; overlaps were ignored.15 Drug exposure for a treatment episode was defined by the number of days from the earliest order date of the drug to the latest order date plus the estimated days’ supply of that final order. Total exposure for a given drug was defined as the sum total days of exposure over all treatment episodes for a given drug. Cumulative exposure for a drug class was defined as the sum total exposure of all drugs within that class. Active Rx’s prior to PGx testing were defined as any Rx order within the median days’ supply of the PGx test plus a 30 day gap allowance. Statistical Analysis Descriptive statistics and plotting were performed using R (version 3.6.1) and R studio (version 1.2.5019). This study is the first of its kind within the MHS and is descriptive in nature. Thus, hypothesis testing was used only to support clinically significant trends, with no corrections for multiple comparisons. Results of hypothesis testing were considered statistically significant at a two-sided significance level (α = 0.05). Based on results of the exploratory data analysis, a binomial regression model was developed using a forward addition process to explore which patient and provider characteristics were most strongly associated with a patient receiving a PGx test within 30 days of any index Rx. This analysis was limited to patients with at least one Rx–PGx match and only the higher utilizing PGx services (endocrinology, family medicine, internal medicine, other primary care, pain management and psychiatry, N = 713 patients) as previously described.12 Predictor variables were included in the model provided they were statistically significant (α = 0.05) in each step. The Akaike Information Criterion and clinical significance of effect size determined by ORs were also considered when choosing to retain predictor variables in the model. RESULTS Population Description The final analytic dataset was a subset originally described in Por et al.12 There were a total of 850 patients with pseudo-ids both in the PGx and Rx datasets. There were five patients who received a PGx test but not prescribed a single CPIC drug and were excluded from further analysis. The final combined Rx–PGx dataset included 845 unique patients that received 1,471 PGx tests and 7,725 index CPIC Rxs. Of the 845 patients, 797 had at least one Rx–PGx match where the remaining 48 did not have a single Rx–PGx match. Patient demographics were generally similar in both subgroups as previously described in detail (within 5–10%).12 The mean follow up time for each patient was 1,976 days (median 2,213, range 1–2,529). General CPIC Prescription Patterns Over the 5 year study period, patients had a median of 9 index CPIC Rxs (range 1–26) (Fig. 1). Pain medications were most commonly prescribed comprising 39.3% of all index Rxs (N = 3,035). The majority of patients had at least one pain Rx (94%, N = 794), with a median of 5 unique index pain Rxs (range 1–10). However, only 21.7% of these patients (N = 172) had an active pain Rx prior to the PGx test. NSAIDS accounted for 54.4% (N = 1,650) of all index pain Rxs, where opioids including tramadol accounted for 45.1% (N = 1,370). Median exposure to a single pain medication was 30 days (range 1–2,493), where median cumulative exposure to all pain medications was 266 days (range 2–5,043). FIGURE 1. Open in new tabDownload slide Most commonly prescribed CPIC actionable medications by drug class and PGx match in the MHS (2015–2020). Blue bar represents drugs that match to a CYP test, where the gray bar represents drugs that did not match to the CYP test. Antidepressants were the second most commonly prescribed, accounting for 23.5% of all index Rxs (N = 1,814). The majority of patients had at least one antidepressant Rx (79.1%, N = 668) with a median of 3 unique index antidepressant Rxs (range 1–9). Of these patients, 54.6% (N = 365) had an active antidepressant Rx prior to the PGx test. Selective serotonin reuptake inhibitors and serotonin–norepinephrine reuptake inhibitors accounted for 78.1% of all antidepressant Rxs, with the remaining split amongst TCAs and mirtazapine. Median exposure to a single antidepressant medication was 139 days (range 1–2,835), where median cumulative exposure to all antidepressants was 740 days (range 2–3,329). A sizable portion of patients had at least one stimulant Rx (32.1%, N = 271), at least one benzodiazepine Rx (25%, N = 211) or at least one antipsychotic Rx (19.3%, N = 163). Patients prescribed stimulants had a median of 2 index Rxs (range 1–3), where patients prescribed benzodiazepines or antipsychotics had a median of 1 Rx (range 1–2). Of particular interest, psychoactive medications from multiple drug classes were commonly prescribed with 28.3% (N = 239) of patients having at least one antidepressant and one stimulant Rx, 17.6% (N = 149) of patients having at least one antidepressant and one antipsychotic Rx, 8.4% (N = 71) of patients having at least one Rx, one antipsychotic and one benzodiazepine Rx and 2.6% (N = 22) of patients having at least one Rx from each of these four psychoactive drug classes. General Rx patterns and drug exposure are summarized in Table I. Clinical service level Rx patterns are summarized in Figure S1a–b. TABLE I. General Prescription Patterns and Drug Exposure (Data Presented as Median and Range) Drug class . Percent patients with at least one prescription (%) . Number of index prescriptions per patient . Total exposure per drug (days) . Cumulative exposure of all drugs (days) . Antidepressant 79.1 3 (1–9) 139 (1–2,835) 740 (2–3,329) Antiemetic 57.3 1 (1–3) 11 (1–1,178) 15 (1–1,221) Antipsychotic 19.3 1 (1–3) 94 (1–2,288) 134 (1–2,288) Benzodiazepine 25 1 (1) 5 (1–608) 5 (1–608) Cardiovascular 45.4 2 (1–5) 261 (1–2,524) 760 (1–4,669) Other 22.8 1 (1–4) 90 (1–2,373) 132 (1–3,845) Pain 94 5 (1–10) 30 (1–2,493) 266 (2–5,043) PPI 48.8 2 (1–4) 180 (6–2,551) 425 (6–3,103) Stimulant 32.1 2 (1–3) 185 (1–2,570) 348 (4–2,600) Drug class . Percent patients with at least one prescription (%) . Number of index prescriptions per patient . Total exposure per drug (days) . Cumulative exposure of all drugs (days) . Antidepressant 79.1 3 (1–9) 139 (1–2,835) 740 (2–3,329) Antiemetic 57.3 1 (1–3) 11 (1–1,178) 15 (1–1,221) Antipsychotic 19.3 1 (1–3) 94 (1–2,288) 134 (1–2,288) Benzodiazepine 25 1 (1) 5 (1–608) 5 (1–608) Cardiovascular 45.4 2 (1–5) 261 (1–2,524) 760 (1–4,669) Other 22.8 1 (1–4) 90 (1–2,373) 132 (1–3,845) Pain 94 5 (1–10) 30 (1–2,493) 266 (2–5,043) PPI 48.8 2 (1–4) 180 (6–2,551) 425 (6–3,103) Stimulant 32.1 2 (1–3) 185 (1–2,570) 348 (4–2,600) Open in new tab TABLE I. General Prescription Patterns and Drug Exposure (Data Presented as Median and Range) Drug class . Percent patients with at least one prescription (%) . Number of index prescriptions per patient . Total exposure per drug (days) . Cumulative exposure of all drugs (days) . Antidepressant 79.1 3 (1–9) 139 (1–2,835) 740 (2–3,329) Antiemetic 57.3 1 (1–3) 11 (1–1,178) 15 (1–1,221) Antipsychotic 19.3 1 (1–3) 94 (1–2,288) 134 (1–2,288) Benzodiazepine 25 1 (1) 5 (1–608) 5 (1–608) Cardiovascular 45.4 2 (1–5) 261 (1–2,524) 760 (1–4,669) Other 22.8 1 (1–4) 90 (1–2,373) 132 (1–3,845) Pain 94 5 (1–10) 30 (1–2,493) 266 (2–5,043) PPI 48.8 2 (1–4) 180 (6–2,551) 425 (6–3,103) Stimulant 32.1 2 (1–3) 185 (1–2,570) 348 (4–2,600) Drug class . Percent patients with at least one prescription (%) . Number of index prescriptions per patient . Total exposure per drug (days) . Cumulative exposure of all drugs (days) . Antidepressant 79.1 3 (1–9) 139 (1–2,835) 740 (2–3,329) Antiemetic 57.3 1 (1–3) 11 (1–1,178) 15 (1–1,221) Antipsychotic 19.3 1 (1–3) 94 (1–2,288) 134 (1–2,288) Benzodiazepine 25 1 (1) 5 (1–608) 5 (1–608) Cardiovascular 45.4 2 (1–5) 261 (1–2,524) 760 (1–4,669) Other 22.8 1 (1–4) 90 (1–2,373) 132 (1–3,845) Pain 94 5 (1–10) 30 (1–2,493) 266 (2–5,043) PPI 48.8 2 (1–4) 180 (6–2,551) 425 (6–3,103) Stimulant 32.1 2 (1–3) 185 (1–2,570) 348 (4–2,600) Open in new tab General and Clinical Service Level Patterns of Rx–PGx Matches There were 4,792 (63.8%) index Rx’s matching to 5,148 PGx tests (some drugs matched to >1 gene) (Table II). Patients had a median of 6 CPIC Rx’s (range 1–25) matching to a CYP enzyme. On average, patients had 63.2% of their individual index Rx’s match to a PGx test (median 69.2%, range 9.1%–100%). Over the 5 year period, the majority of patients had index Rx’s matching to CYP2D6 (74.9%, N = 597), CYP2C19 (73.3%, N = 584) or both (51.1%, N = 407). A smaller portion of patients (9.9%%, N = 79) had drugs matched to CYP2C9, while only two patients matched to CYP3A (tacrolimus). Individual drugs matching to these CYP enzymes are summarized in Figure S2a–c. Antidepressants (32.8%, N = 1,573), pain medications (24.8%, N = 1,190), and PPI’s (10.8%, N = 518) were the most common Rx to match a PGx test. All other categories accounted for <10% of matches, respectively. Pain medications (63.7%, N = 1,728), accounted for the largest portion of drugs that did not match a PGx test. The majority of these pain medications were NSAIDS (80%, N =1,383), which are metabolized by CYP2C9. TABLE II. CPIC Actionable Drugs Prescribed in the MHS with PGX CYP Testing (2015–2020) CYP . CPIC Level . Prescribed Drug . 5 years . ±90 days . ±30 days . N = 5,148 . N (% total) . N (% total) . CYP2C9 A Celecoxib 30 3 (10.0) – Ibuprofen 63 2 (3.2) 1 (1.6) Meloxicam 24 1 (4.2) – B/C Dronabinol 1 – – C Aspirin 13 1 (7.7) 1 (7.7) Diclofenac 12 3 (25.0) 2 (16.7) Indomethacin 6 1 (16.7) 1 (16.7) Nabumetone 2 – – Naproxen 44 3 (6.8) 1 (2.3) CYP2C19 A Amitriptylinea 109 19 (17.4) 7 (6.4) Citalopram 93 10 (10.8) 7 (7.5) Clopidogrel 96 31 (32.3) 23 (24.0) Escitalopram 170 45 (26.5) 21 (12.4) Lansoprazole 2 – – Voriconazole 1 – – B Clomipraminea 3 – – Doxepina 41 9 (22.0) 3 (7.3) Esomeprazole 157 8 (5.1) 3 (1.9) Imipraminea 5 1 (20.0) – Omeprazole 189 19 (10.1) 7 (3.7) Pantoprazole 161 20 (12.4) 5 (3.1) Rabeprazole 9 – – Sertralinea 266 65 (24.4) 26 (10.9) B/C Carisoprodol 6 1 (16.7) – C Diazepam 173 33 (19.1) 18 (10.4) CYP2D6 A Amitriptylinea 112 19 (17.0) 7 (6.2) Codeine 164 14 (8.5) 8 (4.9) Fluvoxamine 10 – – Nortriptyline 54 5 (9.3) 3 (5.6) Ondansetron 341 22 (6.5) 5 (1.5) Paroxetine 47 14 (29.8) 7 (14.9) Tamoxifen 2 1 (50.0) 1 (50.0) Tramadol 196 9 (4.6) 5 (2.6) A/B Tetrabenazine 2 – – B Aripiprazole 109 28 (25.7) 17 (5.6) Clomipraminea 3 – – Doxepina 41 9 (22.0) 3 (7.3) Hydrocodone 313 29 (9.3) 8 (2.6) Imipraminea 3 1 (33.3) – Risperidone 38 10 (26.3) 8 (21.1) Venlafaxine 212 79 (37.3) 55 (25.9) B/C Amphetamine 156 42 (26.9) 30 (19.2) Carvedilol 14 1 (7.1) 1 (7.1) Clozapine 2 – – Flecainide 1 – – Haloperidol 11 4 (36.4) 3 (27.3) Labetalol 3 – – Meclizine 36 7 (19.4) 2 (5.6) Metoclopramide 45 9 (20.0) 3 (6.7) Metoprolol 70 9 (12.9) 4 (5.7) Mirtazapine 114 33 (28.9) 15 (13.2) Perphenazine 2 1 (50.0) – Propafenone 1 – – Propranolol 122 27 (22.1) 13 (10.7) Tamsulosin 49 7 (14.3) 3 (6.1) C Duloxetine 200 61 (30.5) 30 (15.0) Fluoxetine 175 30 (17.1) 15 (8.6) Methylphenidate 119 28 (23.5) 16 (13.4) Modafinil 48 6 (12.5) 3 (6.2) Oxycodone 330 27 (8.2) 12 (3.6) Sertralinea 269 65 (24.2) 29 (10.8) Terbinafine 35 3 (8.6) 2 (5.7) Tolterodine 19 – – CYP3A4/5 A Tacrolimus 4 – – CYP . CPIC Level . Prescribed Drug . 5 years . ±90 days . ±30 days . N = 5,148 . N (% total) . N (% total) . CYP2C9 A Celecoxib 30 3 (10.0) – Ibuprofen 63 2 (3.2) 1 (1.6) Meloxicam 24 1 (4.2) – B/C Dronabinol 1 – – C Aspirin 13 1 (7.7) 1 (7.7) Diclofenac 12 3 (25.0) 2 (16.7) Indomethacin 6 1 (16.7) 1 (16.7) Nabumetone 2 – – Naproxen 44 3 (6.8) 1 (2.3) CYP2C19 A Amitriptylinea 109 19 (17.4) 7 (6.4) Citalopram 93 10 (10.8) 7 (7.5) Clopidogrel 96 31 (32.3) 23 (24.0) Escitalopram 170 45 (26.5) 21 (12.4) Lansoprazole 2 – – Voriconazole 1 – – B Clomipraminea 3 – – Doxepina 41 9 (22.0) 3 (7.3) Esomeprazole 157 8 (5.1) 3 (1.9) Imipraminea 5 1 (20.0) – Omeprazole 189 19 (10.1) 7 (3.7) Pantoprazole 161 20 (12.4) 5 (3.1) Rabeprazole 9 – – Sertralinea 266 65 (24.4) 26 (10.9) B/C Carisoprodol 6 1 (16.7) – C Diazepam 173 33 (19.1) 18 (10.4) CYP2D6 A Amitriptylinea 112 19 (17.0) 7 (6.2) Codeine 164 14 (8.5) 8 (4.9) Fluvoxamine 10 – – Nortriptyline 54 5 (9.3) 3 (5.6) Ondansetron 341 22 (6.5) 5 (1.5) Paroxetine 47 14 (29.8) 7 (14.9) Tamoxifen 2 1 (50.0) 1 (50.0) Tramadol 196 9 (4.6) 5 (2.6) A/B Tetrabenazine 2 – – B Aripiprazole 109 28 (25.7) 17 (5.6) Clomipraminea 3 – – Doxepina 41 9 (22.0) 3 (7.3) Hydrocodone 313 29 (9.3) 8 (2.6) Imipraminea 3 1 (33.3) – Risperidone 38 10 (26.3) 8 (21.1) Venlafaxine 212 79 (37.3) 55 (25.9) B/C Amphetamine 156 42 (26.9) 30 (19.2) Carvedilol 14 1 (7.1) 1 (7.1) Clozapine 2 – – Flecainide 1 – – Haloperidol 11 4 (36.4) 3 (27.3) Labetalol 3 – – Meclizine 36 7 (19.4) 2 (5.6) Metoclopramide 45 9 (20.0) 3 (6.7) Metoprolol 70 9 (12.9) 4 (5.7) Mirtazapine 114 33 (28.9) 15 (13.2) Perphenazine 2 1 (50.0) – Propafenone 1 – – Propranolol 122 27 (22.1) 13 (10.7) Tamsulosin 49 7 (14.3) 3 (6.1) C Duloxetine 200 61 (30.5) 30 (15.0) Fluoxetine 175 30 (17.1) 15 (8.6) Methylphenidate 119 28 (23.5) 16 (13.4) Modafinil 48 6 (12.5) 3 (6.2) Oxycodone 330 27 (8.2) 12 (3.6) Sertralinea 269 65 (24.2) 29 (10.8) Terbinafine 35 3 (8.6) 2 (5.7) Tolterodine 19 – – CYP3A4/5 A Tacrolimus 4 – – a Drugs metabolized by both CYP2C19 and CYP2D6, matched to specific CYP for PGx testing. Open in new tab TABLE II. CPIC Actionable Drugs Prescribed in the MHS with PGX CYP Testing (2015–2020) CYP . CPIC Level . Prescribed Drug . 5 years . ±90 days . ±30 days . N = 5,148 . N (% total) . N (% total) . CYP2C9 A Celecoxib 30 3 (10.0) – Ibuprofen 63 2 (3.2) 1 (1.6) Meloxicam 24 1 (4.2) – B/C Dronabinol 1 – – C Aspirin 13 1 (7.7) 1 (7.7) Diclofenac 12 3 (25.0) 2 (16.7) Indomethacin 6 1 (16.7) 1 (16.7) Nabumetone 2 – – Naproxen 44 3 (6.8) 1 (2.3) CYP2C19 A Amitriptylinea 109 19 (17.4) 7 (6.4) Citalopram 93 10 (10.8) 7 (7.5) Clopidogrel 96 31 (32.3) 23 (24.0) Escitalopram 170 45 (26.5) 21 (12.4) Lansoprazole 2 – – Voriconazole 1 – – B Clomipraminea 3 – – Doxepina 41 9 (22.0) 3 (7.3) Esomeprazole 157 8 (5.1) 3 (1.9) Imipraminea 5 1 (20.0) – Omeprazole 189 19 (10.1) 7 (3.7) Pantoprazole 161 20 (12.4) 5 (3.1) Rabeprazole 9 – – Sertralinea 266 65 (24.4) 26 (10.9) B/C Carisoprodol 6 1 (16.7) – C Diazepam 173 33 (19.1) 18 (10.4) CYP2D6 A Amitriptylinea 112 19 (17.0) 7 (6.2) Codeine 164 14 (8.5) 8 (4.9) Fluvoxamine 10 – – Nortriptyline 54 5 (9.3) 3 (5.6) Ondansetron 341 22 (6.5) 5 (1.5) Paroxetine 47 14 (29.8) 7 (14.9) Tamoxifen 2 1 (50.0) 1 (50.0) Tramadol 196 9 (4.6) 5 (2.6) A/B Tetrabenazine 2 – – B Aripiprazole 109 28 (25.7) 17 (5.6) Clomipraminea 3 – – Doxepina 41 9 (22.0) 3 (7.3) Hydrocodone 313 29 (9.3) 8 (2.6) Imipraminea 3 1 (33.3) – Risperidone 38 10 (26.3) 8 (21.1) Venlafaxine 212 79 (37.3) 55 (25.9) B/C Amphetamine 156 42 (26.9) 30 (19.2) Carvedilol 14 1 (7.1) 1 (7.1) Clozapine 2 – – Flecainide 1 – – Haloperidol 11 4 (36.4) 3 (27.3) Labetalol 3 – – Meclizine 36 7 (19.4) 2 (5.6) Metoclopramide 45 9 (20.0) 3 (6.7) Metoprolol 70 9 (12.9) 4 (5.7) Mirtazapine 114 33 (28.9) 15 (13.2) Perphenazine 2 1 (50.0) – Propafenone 1 – – Propranolol 122 27 (22.1) 13 (10.7) Tamsulosin 49 7 (14.3) 3 (6.1) C Duloxetine 200 61 (30.5) 30 (15.0) Fluoxetine 175 30 (17.1) 15 (8.6) Methylphenidate 119 28 (23.5) 16 (13.4) Modafinil 48 6 (12.5) 3 (6.2) Oxycodone 330 27 (8.2) 12 (3.6) Sertralinea 269 65 (24.2) 29 (10.8) Terbinafine 35 3 (8.6) 2 (5.7) Tolterodine 19 – – CYP3A4/5 A Tacrolimus 4 – – CYP . CPIC Level . Prescribed Drug . 5 years . ±90 days . ±30 days . N = 5,148 . N (% total) . N (% total) . CYP2C9 A Celecoxib 30 3 (10.0) – Ibuprofen 63 2 (3.2) 1 (1.6) Meloxicam 24 1 (4.2) – B/C Dronabinol 1 – – C Aspirin 13 1 (7.7) 1 (7.7) Diclofenac 12 3 (25.0) 2 (16.7) Indomethacin 6 1 (16.7) 1 (16.7) Nabumetone 2 – – Naproxen 44 3 (6.8) 1 (2.3) CYP2C19 A Amitriptylinea 109 19 (17.4) 7 (6.4) Citalopram 93 10 (10.8) 7 (7.5) Clopidogrel 96 31 (32.3) 23 (24.0) Escitalopram 170 45 (26.5) 21 (12.4) Lansoprazole 2 – – Voriconazole 1 – – B Clomipraminea 3 – – Doxepina 41 9 (22.0) 3 (7.3) Esomeprazole 157 8 (5.1) 3 (1.9) Imipraminea 5 1 (20.0) – Omeprazole 189 19 (10.1) 7 (3.7) Pantoprazole 161 20 (12.4) 5 (3.1) Rabeprazole 9 – – Sertralinea 266 65 (24.4) 26 (10.9) B/C Carisoprodol 6 1 (16.7) – C Diazepam 173 33 (19.1) 18 (10.4) CYP2D6 A Amitriptylinea 112 19 (17.0) 7 (6.2) Codeine 164 14 (8.5) 8 (4.9) Fluvoxamine 10 – – Nortriptyline 54 5 (9.3) 3 (5.6) Ondansetron 341 22 (6.5) 5 (1.5) Paroxetine 47 14 (29.8) 7 (14.9) Tamoxifen 2 1 (50.0) 1 (50.0) Tramadol 196 9 (4.6) 5 (2.6) A/B Tetrabenazine 2 – – B Aripiprazole 109 28 (25.7) 17 (5.6) Clomipraminea 3 – – Doxepina 41 9 (22.0) 3 (7.3) Hydrocodone 313 29 (9.3) 8 (2.6) Imipraminea 3 1 (33.3) – Risperidone 38 10 (26.3) 8 (21.1) Venlafaxine 212 79 (37.3) 55 (25.9) B/C Amphetamine 156 42 (26.9) 30 (19.2) Carvedilol 14 1 (7.1) 1 (7.1) Clozapine 2 – – Flecainide 1 – – Haloperidol 11 4 (36.4) 3 (27.3) Labetalol 3 – – Meclizine 36 7 (19.4) 2 (5.6) Metoclopramide 45 9 (20.0) 3 (6.7) Metoprolol 70 9 (12.9) 4 (5.7) Mirtazapine 114 33 (28.9) 15 (13.2) Perphenazine 2 1 (50.0) – Propafenone 1 – – Propranolol 122 27 (22.1) 13 (10.7) Tamsulosin 49 7 (14.3) 3 (6.1) C Duloxetine 200 61 (30.5) 30 (15.0) Fluoxetine 175 30 (17.1) 15 (8.6) Methylphenidate 119 28 (23.5) 16 (13.4) Modafinil 48 6 (12.5) 3 (6.2) Oxycodone 330 27 (8.2) 12 (3.6) Sertralinea 269 65 (24.2) 29 (10.8) Terbinafine 35 3 (8.6) 2 (5.7) Tolterodine 19 – – CYP3A4/5 A Tacrolimus 4 – – a Drugs metabolized by both CYP2C19 and CYP2D6, matched to specific CYP for PGx testing. Open in new tab The top utilizers of PGx tests (psychiatry, family medicine, other primary care, internal medicine, pain management, and endocrinology) accounted for 94.5% (N = 4,530) of all Rx–PGx matches, with psychiatry accounting for 73.4% (N = 3,323). Over the 5 year period, PGx tests matched to drug classes in similar proportion regardless of ordering service (Fig. 2A). However, when limiting to Rx matches within 30 days, PGx tests ordered by psychiatry, family medicine, and other care primary care predominantly matched to antidepressants (56.6%, 56.2%, and 40%, respectively). In contrast, within 30 days, 67.9% of internal medicine PGx tests matched to a cardiovascular drug, while 42.4% of pain medicine tests matched to a stimulant (Fig. 2B). Over the analyzed timeframe, there was a relatively low frequency of the same clinical service ordering both the PGx test and Rx (mean 30.9%, median 29.8%, range 20.3%–47.6%); however, when limited to 30 days matches increased significantly (mean 73.2%, median 74.6%, range 66.7%–76.2%, chi-square, P-value < 0.001, df = 1). FIGURE 2. Open in new tabDownload slide (A) Rx–PGx matching patterns over 5 years by Clinical Service. (B) Rx–PGx matching patterns within 30 days by Clinical Service. Provider Level Rx–PGx Patterns When limiting to high utilizers of PGx tests and services that prescribed >50 index Rx’s, there were 4,343 Rx–PGx matches in which the ordering provider was known for both the drug and PGx test. Of this subgroup, there were 1,245 unique providers (excluding providers ordering a refill). Only 20% (N = 249) of these providers ordered a PGx test, combining for a total of 1,266 PGx test orders (86% of all PGx tests in the study). Comparatively, a significantly higher proportion of internal medicine providers (43.5%, N = 73) and psychiatry providers (38%, N = 116) ordered a PGx test. In contrast, only 33 of 455 family medicine providers (6.8%) and 22 of 240 other primary care providers (8.4%) ordered a PGx test (multiple chi-square tests, P < 0.001 for all, df = 1 for all). Of providers that ordered a PGx test, 45.4% (N = 113) ordered both the Rx and PGx test in a match, which accounted for 15.4% (N = 667) of all Rx–PGx matches. Psychiatry had the highest proportion of providers to order both the matching Rx and PGx test (N = 84, 74.3%), followed by family medicine (N = 13, 11.5%). The remaining high utilizing services had <10% of their respective providers order both the Rx and PGx test. Of interest, some individual providers accounted for a significant proportion of such Rx–PGx matches in their respective service. For example, 1 of 84 psychiatry providers accounted for 25% (N = 141) of matches within psychiatry and 2 of 13 family medicine providers accounted 48.7% (N = 19) of matches within family medicine. When limiting to Rx–PGx matches within 30 days the proportion of providers ordering both the matching Rx and PGx test increased (60.4%, N = 64, chi-square, P = 0.014, df = 1) and the proportion of matches also increased (61.5%, N = 232, chi-square, P < 0 0.001, df = 1). Temporal Relationship of Index Rx to PGx Test The majority of index matching Rxs existed prior to a PGx test (69.5%, N = 3,285) compared to 1,418 Rxs (29.5%) prescribed after. Of the existing Rxs, only 30.4% (N = 1,000) were continued after the PGx test. The median time to discontinue any drug after PGx testing was 358 days (range 1–2,012), whereas the median time to discontinue the first drug after PGx testing was 164 days (range 1–1,981). While a low percentage of drugs were discontinued within 30 days after PGx test (7.7%, N = 77), approximately 13% (N = 38) of antidepressants were discontinued within 30 days. In comparison, only 4% (N = 11) of pain medications and 4.8% (N = 4) of cardiovascular medications were discontinued within 30 days of PGx testing. PGx tests were ordered on average 739 days (median 567, range 1–2,506) after any existing matching Rx, but limiting to the most recent matching RX this decreased to an average of 318 days (median 112, range 1–2,318). In contrast any new matching Rx was started on average 481 days after the PGx test (median 313, range 1–2,012), where the first new matching Rx was started on average after 123 days (median 42, range 1–1,719) (Fig. 3A and B). Although only 8.3% of PGx tests (N = 398) matched to an existing or new Rx within 30 days, 39.8% (N = 317) of patients had at least one Rx match within 30 days of the PGx test. Compared to the mean, the proportion of patients having at least one Rx match within 30 days was significantly higher when psychiatry (47.5%, N = 230, chi-square, P = 0.008, df = 1) or pain medicine (59.5%, N = 22, chi-square, P = 0.027, df = 1) tested. In comparison, the other high utilizing PGx services averaged a significantly lower proportion of patients having a match within 30 days (24.1%, N = 51, chi-square, P < 0.001, df = 1). FIGURE 3. Open in new tabDownload slide (A) Histograms of time from the PGx test to any Rx (background) and to most recent Rx (foreground). (B) Histograms of minimum time from the PGX test to prescription by individual CPIC drug. By drug class, 17.3% (N = 28) of index antipsychotic Rx’s matched to a PGx test within 30 days, while stimulants had a 15.2% (N = 49) match rate within 30 days. Benzodiazepines, antidepressant and cardiovascular medications had 30 day PGx match rates ranging from 10% to 13%. Medications classified as other, pain medications, PPI’s and anti-emetics had 30 day PGx test match rates at 5% or less. Specific drugs including venlafaxine (25.9%, N = 55), clopidogrel (24%, N = 23), amphetamine (19.2%, N = 30), aripiprazole (15.6%, N = 17), and duloxetine (15%, N = 30) had the highest match rates within 30 days (Table II). All other drugs matched <15% within 30 days. Binomial Regression The strongest predictor of whether a patient had a PGx test ordered within 30 days of an index Rx was if the test was ordered by specific combined psychiatry/pain management provider (OR 3.73, 95% CI 2.13–6.54), followed by whether the PGx test was ordered by a different individual psychiatry provider (OR 2.03, 95% CI 1.34–3.08). Male gender (OR 1.48 95% CI 1.085–2.01), having received an antipsychotic Rx (OR 1.88, 95% CI 1.26–2.81), and psychiatry as a clinical service ordering the PGx test Rx (OR 1.58, 95% CI 1.09–2.29) also remained statistically significant (Table III). CPIC evidence level, FDA actionable or informative labeling, age, race, inpatient status, beneficiary category, number of total Rx’s prior to PGx test (all and by drug class), and other clinical ordering services were not statistically significant predictors. Follow-up time and total cumulative drug exposure prior to the PGx test (total and by drug class) were statistically significant, but not clinically meaningful (OR 0.999 or 0.998, respectively). Thus, these predictor variables were excluded. TABLE III. Statistically Significant Predictors of Which Patient Received a PGX Test within 30 Days of Any Index RX Predictor . Odds ratio . 95% CI . P-value . PGX Ordered by Provider 1 (Psychiatry & Pain Management Provider) 3.73 2.13–6.54 <0.001 PGX Ordered by Provider 2 (Psychiatry Provider) 2.03 1.34–3.08 <0.001 Patient Received Antipsychotic 1.88 1.26–2.81 0.0022 Psychiatry Orders PGX as Clinical Service 1.58 1.09–2.29 0.016 Patient is Male 1.48 1.085–2.01 0.022 Predictor . Odds ratio . 95% CI . P-value . PGX Ordered by Provider 1 (Psychiatry & Pain Management Provider) 3.73 2.13–6.54 <0.001 PGX Ordered by Provider 2 (Psychiatry Provider) 2.03 1.34–3.08 <0.001 Patient Received Antipsychotic 1.88 1.26–2.81 0.0022 Psychiatry Orders PGX as Clinical Service 1.58 1.09–2.29 0.016 Patient is Male 1.48 1.085–2.01 0.022 Open in new tab TABLE III. Statistically Significant Predictors of Which Patient Received a PGX Test within 30 Days of Any Index RX Predictor . Odds ratio . 95% CI . P-value . PGX Ordered by Provider 1 (Psychiatry & Pain Management Provider) 3.73 2.13–6.54 <0.001 PGX Ordered by Provider 2 (Psychiatry Provider) 2.03 1.34–3.08 <0.001 Patient Received Antipsychotic 1.88 1.26–2.81 0.0022 Psychiatry Orders PGX as Clinical Service 1.58 1.09–2.29 0.016 Patient is Male 1.48 1.085–2.01 0.022 Predictor . Odds ratio . 95% CI . P-value . PGX Ordered by Provider 1 (Psychiatry & Pain Management Provider) 3.73 2.13–6.54 <0.001 PGX Ordered by Provider 2 (Psychiatry Provider) 2.03 1.34–3.08 <0.001 Patient Received Antipsychotic 1.88 1.26–2.81 0.0022 Psychiatry Orders PGX as Clinical Service 1.58 1.09–2.29 0.016 Patient is Male 1.48 1.085–2.01 0.022 Open in new tab DISCUSSION Our previous study was the first to describe the overall landscape of PGx testing in the U.S. Military population.12 We have extended this work by examining CPIC Rx patterns in this PGx tested population along with temporal PGx–Rx relationships. Our analysis is one of the first to quantify Rx patterns and describe in depth estimates of drug exposure in a PGx-tested cohort. This analysis was essential to elucidate the decision-making processes of MHS clinicians in regards to ordering a PGx test. Importantly, Chanfreau-Coffinier et al. estimated that 99% of patients receiving care in the Veterans Health Administration have at least one actionable pharmacogenetic variant.2 Further, Chanfreau-Coffinier et al. reported a high prevalence (54.8%, N = 4,259,193) of patients receiving at least 1 CPIC level A drug in the 8 year study period. Similarly, Hicks et al. estimated approximately 15.8% of patients (reported as 15,791 per 100,000 patients) were exposed to at least 1 CPIC level A drug per year in a large cohort study, including data from 6 academic medical centers.16 Given the high estimated prevalence of pharmacogenetic variants in the Veterans Health Administration population, the high estimated prevalence of exposure to CPIC level A drugs and that the MHS cares for 9.6 million beneficiaries whose demographics closely mirrors the demographics of the U.S. population, there is also a high likelihood for gene–drug interactions to occur in the MHS beneficiary population.17 However, although direct comparison is not currently available, the MHS appears to implement PGx testing far less than civilian counterparts. Several civilian academic centers have implemented PGx testing protocols,18–20 but such protocols are lacking even amongst the largest military treatment facilities, as such, PGx testing in the MHS is generally not comparable to those civilian academic medical centers. In a more comparable civilian cohort of a U.S. managed care population, Anderson et al reported 5,721 of 11 million patients (0.052%) received at least 1 PGx test from 2013 to 2017.21 In contrast, 929 out of 9.6 million MHS beneficiaries (0.0097%)17 received a PGx test from 2015 to 2020.12 Although Anderson et al included patients receiving six types of single-gene PGx tests (CYP2C19, CYP2D6, CYP2C9, VKORC1, HLA class I, and UGT1A1), compared to our inclusion criteria of five CYP tests, they reported a log-fold increase in prevalence of PGx testing compared to our data. Reasons for this are unclear, especially considering that insurance reimbursements and testing costs are not typically a consideration for MHS providers. To summarize, the MHS population is likely to be at similar risk for gene–drug interactions to the civilian population. Large academic centers are implementing institutional protocols of PGx testing, but PGx testing in the MHS appears to be a rare occurrence even compared to a U.S. civilian managed care population. There is generally no barrier to PGx testing by cost or limitation of insurance reimbursement in the MHS. What then drives the minority of clinicians in the MHS to utilize PGx testing? The Rx and Rx exposure patterns shed significant light on this question. Patients in our PGx tested cohort were high drug utilizers with a median of 9 CPIC Rx’s over the 5 year study period. Patients were also frequently prescribed multiple index pain medications (median 9) and antidepressants (median of 3), often being prescribed multiple psychoactive medications (at least 28.3%, N = 239). Providing further context, the raw estimated prevalence of PGx testing in the MHS over the study period (0.0097%) is orders of magnitude smaller than estimates of prevalence of depression amongst U.S. Military personnel and veterans (5%–13.5% depending on prior deployment status).22,23 Therefore, our dataset suggests that MHS clinicians appear to utilize PGx testing on average in only the most high risk patients, who have likely failed or experienced adverse effects from multiple drugs. In regards to preemptive or reactive PGx testing strategies, no definitive conclusions can be made from our dataset. The low frequency of PGx testing and the fact that the majority of index Rxs existed prior to the PGx test (69.5%, N = 3,285 Rx’s) strongly suggest a reactive strategy. Of note, our binomial regression model further elucidates the behavioral aspect of MHS clinicians’ choice to implement PGx testing. Importantly, neither drug exposure and CPIC level nor FDA actionable/informative labels had a statistically significant influence on whether or not providers ordered a PGx test within 30 days of an index Rx. In fact, venlafaxine, a CPIC level B drug, was one of the most commonly linked drugs to a PGx test within 30 days. Furthermore, only psychiatry as a service, and two individual psychiatry (one was a combined psychiatry/pain medicine provider) providers were statistically significant more likely to implement a PGx test within 30 days of an index Rx. In combination, these findings strongly suggest that PGx testing in the MHS is utilized for considerations other than as FDA labeled or classically described by CPIC, or that there is a need for enhanced PGx education. The need for enhanced PGx education is supported by DeLuca et al., in which on 37% of survey respondents correctly answered a PGx knowledge question and 18% of respondents strongly affirmed PGx education was provided in their graduate medical education.10 In addition, improving personalized care of depression, improving pain control and preventing adverse effects of NSAIDs is a priority for active duty Service Members. Musculoskeletal injuries are among the most common reasons for active duty Service Members to seek medical care, lose duty days, and require medical evacuation from the battlefield. Furthermore, in the years 2006, 2011, and 2014, approximately 70%–80% of all active duty Army Soldiers received at least one NSAID Rx.24 Meloxicam, ibuprofen, and celecoxib are CPIC level A drugs and the CPIC guideline on NSAIDS recommends significant dosing adjustments based on CYP2C9 metabolism, with suggestions to start pain control with another drug in the case of meloxicam and a CYP2C9 poor metabolizer.13,25 In our dataset, pain medications, to include NSAIDS and opioids, were the most commonly prescribed CPIC actionable drugs. However, pain medications were least likely to match a CYP test (Fig. 1) with only an approximate 40% match rate. CYP2C9 was one of the least tested CYPs, especially in comparison to CYP2C19 and CYP2D6.12 When limiting to meloxicam, celecoxib and ibuprofen (N = 1,038 Rx’s) only 117 (12.7%) matched to a CYP2C9 test. One possible reason for this finding may be that surgical services and emergency medicine commonly prescribed pain medicines in our cohort (514 index pain Rx’s, 134 index Rx’s of either meloxicam, celecoxib, or ibuprofen). Typically emergency medicine and surgical services may not be as concerned about dose personalization considering their fast paced and acute clinical practice environment. This is supported by the fact that emergency medical and surgical services only combined for 15 total PGx tests (approximately 1%) despite prescribing 31.8% (N = 514) of CPIC pain Rx’s (when the ordering service was known). Nevertheless, primary care services accounted for 67.4% (N = 1,089) of all CPIC pain Rx’s and 40.3% (N = 418) of meloxicam, celecoxib and ibuprofen Rx’s. However primary care had a Rx–PGx match rate of 31.7% (N = 345) to any pain medicine and when limiting to meloxicam, celecoxib, and ibuprofen the Rx–PGx match rate to CYP2C9 was 10.3% (N = 43). Therefore, although the low proportion of pain Rx–PGx matches (in particular NSAIDS metabolized by CYP2C9) is partially explained by practice patterns in different clinical settings, these low pain Rx–PGx match rates are better explained by the lack of standardized protocols and limited focus on PGx education within the MHS. The main limitation of this study was lack of access to PGx test results. The M2 database does not allow for PGx result extraction, which must be done by chart review. For this study, we also decided to include all CPIC drugs in the analysis, regardless of the CPIC drug level. Although many consider CPIC B or C to not be actionable, this is the first exploration of clinical utilization of PGx tests in the MHS and providers did not appear to follow CPIC guidelines or FDA labels. Therefore, we felt it important to demonstrate the entire spectrum of possible clinical utilization of PGx tests. Follow on studies include chart reviews to better determine clinical reasoning and knowledge gaps of providers as well as pilot studies to implement PGx testing protocols within high yield subspecialties. Long-term goals are to improve PGx education and embed standardized PGx testing protocols within the EHR best suited to the needs of the MHS patient population. In conclusion, clinical utilization of PGx testing in the MHS was heavily dependent on clinical service. It was largely provider driven but did not appear to follow CPIC guidelines. PGx testing was generally limited to high Rx-drug users and found to be an under-utilized resource. Implementing PGx testing protocols, simplifying PGx test-ordering by incorporating at minimum CYP2D6, CYP2C19, and CYP2C9 into PGx-testing panels, and unifying providers’ PGx knowledgebase in the MHS are feasible and would improve the clinical utilization of PGx tests. ACKNOWLEDGMENT The authors would like to thank Ms. Zanete Wright for her dedication and hard work to the WRAIR/USU Clinical Pharmacology Fellowship. SUPPLEMENTARY MATERIAL SUPPLEMENTARY MATERIAL is available at Military Medicine online. FUNDING This research was supported by the Clinical Pharmacology Fellowship Funds. CONFLICT OF INTEREST STATEMENT No author has any conflicts of interest to report. The identification of scientific products or scientific instrumentation is considered an integral part of the scientific endeavor and does not constitute endorsement or implied endorsement on the part of the authors, DoD, or any component agency. REFERENCES 1. Bank PCD , Swen JJ, Schaap RD, Klootwijk DB, Baak-Pablo R, Guchelaar HJ: A pilot study of the implementation of pharmacogenomic pharmacist initiated pre-emptive testing in primary care . Eur J Hum Genet 2019 ; 27 ( 10 ): 1532 – 41 . Google Scholar Crossref Search ADS PubMed WorldCat 2. 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Google Scholar Crossref Search ADS PubMed WorldCat 22. Gadermann AM , Engel CC, Naifeh JA, et al. : Prevalence of DSM-IV major depression among U.S. military personnel: meta-analysis and simulation . Mil Med 2012 ; 177 ( Suppl 8 ): 47 – 59 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 23. Trivedi RB , Post EP, Sun H, et al. : Prevalence, comorbidity, and prognosis of mental health among US veterans . Am J Public Health 2015 ; 105 ( 12 ): 2564 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 24. Walker LA , Zambraski EJ, Williams RF: Widespread use of prescription nonsteroidal anti-inflammatory drugs among U.S. Army active duty soldiers . Mil Med 2017 ; 182 ( 3 ): e1709 – 12 . Google Scholar Crossref Search ADS PubMed WorldCat 25. Theken KN , Lee CR, Gong L, et al. : Clinical Pharmacogenetics Implementation Consortium guideline (CPIC) for CYP2C9 and nonsteroidal anti-inflammatory drugs . Clin Pharmacol Ther 2020 ; 108 ( 2 ): 191 – 200 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes The views expressed in this manuscript are those of the authors and do not necessarily reflect the official policy of the DoD or the U.S. Government. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US. This work is written by (a) US Government employee(s) and is in the public domain in the US. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US.

DeLuca, Jesse; Oliver, Thomas; Hulsopple, Chad; Selig, Daniel; Por, Elaine; Turner, Clesson; Hellwig, Lydia; Livezey, Jeffrey

doi: 10.1093/milmed/usab333pmid: 34967401

ABSTRACT Pharmacogenomics is a pillar of personalized medicine that has the potential to deliver optimized treatment in many medical settings. Military medicine in the deployed setting is unique and therefore warrants separate assessment pertaining to its potential capabilities and impact. Pharmacogenomics for United States Active Duty Service Members medical care in the deployed setting has not, to our knowledge, been previously reviewed. We present potential applications of pharmacogenomics to forward medical care through two comprehensive references for deployed medical care, the Tactical Combat Casualty Care Guidelines (TCCC) and Emergency War Surgery (EWS) fifth edition. All drugs within the deployment manuals, TCCC guidelines and EWS book, were identified and the list was cross-referenced to the Clinical Pharmacogenetics Implementation Consortium guidelines and genes–drugs interactions list as well as the Food and Drug Administration Table of Pharmacogenomics Biomarkers in Drug Labeling. Ten pharmacologic categories were identified, consisting of 15 drugs, along with the classes, aminogylcosides, beta-blockers, and volatile anesthetics. Drugs and pharmacogenomics liabilities were tabulated. Eight specific drugs or classes are expounded upon given the belief of the authors of their potential for impacting future treatment on the battlefield in the setting of prolonged field care. This review outlines several genes with liabilities in the prolonged field care setting and areas that may produce improved care with further study. INTRODUCTION Combat readiness is the ability to perform with the right force, at the right time, to get the right result while preserving capabilities. The precision medicine corollary is that pharmacogenomics attempts to deliver the right drug at the right dose to the right patient by utilizing the patient’s genetic information to ensure that the drug selection and dose will produce the most efficacious result with the least likelihood of adverse events or side effects. Whether the battle is linear or nonlinear, forward capabilities will need optimization to meet a designated threshold while minimizing the medical footprint through utilization of very specific resuscitation and combat care equipment and training.1 Pharmacogenomics is a pillar of personalized medicine that has the potential to deliver optimized treatment in order to cure, care for, and therefore preserve mission readiness. Pharmacogenomics for United States Active Duty Service Members (ADSM) medical care in the deployed setting has not to our knowledge been previously reviewed. Pharmacogenomic testing may be performed either preemptively or reactively. There are data to suggest that preemptive point of care testing can be beneficial; this is decidedly unlikely in a forward setting, as is reactive testing. The question then becomes would preemptive testing of deploying ADSM or at least those at high risk of combat and combat stress–related injuries yield actionable information for prescribers in theater? Testing for glucose-6-phosphate dehydrogenase deficiency in the military to inform prescribing of antimalarial therapy for those deploying to endemic areas is an example of how preemptive testing can help guide medication prescribing in the deployed setting. The Food and Drug Administration (FDA) and Clinical Pharmacogenetics Implementation Consortium (CPIC) are regulatory and advisory institutions charged with, among other tasks, promulgating drug safety and offer guidance and guidelines on the metabolic capabilities of identified phenotypes and their impact on FDA-approved medications. They identify gene–drug pairs which clinical evidence has identified as potentially problematic depending on the expressed phenotype. We present potential applications of these resources as related to forward medical care through two comprehensive references for deployed medical care, the Tactical Combat Casualty Care Guidelines (TCCC) and Emergency War Surgery (EWS) fifth edition. Selected drugs mentioned in these resources which have recommendations identified by the FDA and/or CPIC could have significant impact in the prolonged field care setting. These gene–drug pairs have been identified and select potential clinical manifestations are elaborated on. All drugs from forward medical care manuals, EWS, and TCCC with any mention in the FDA or CPIC guidance are in Table I. TABLE I. List of Tactical Combat Casualty Care Guidelines (TCCC) and/or Emergency War Surgery (EWS) Drugs with Food and Drug Administration (FDA) and/or Clinical Pharmacogenetics Implementation Consortium (CPIC) Drug–Gene Interactions Pharmacologic category . Drug . Gene variation–drug interaction . Altitude Adaptation Sildenafil Provisional findings in GNB3a Analgesia NSAIDs CYP2C9 poor metabolizer may have higher plasma levels resulting in increased side effectsa,b,8 Fentanyl Provisional findings for ABCB1, COMT, and OPRM1a Morphine Provisional findings for COMT and OPRM1a Anesthesia Midazolam Provisional findings in CYP3A5a Succinylcholine RYR1 and CACNA1S may increase malignant hyperthermia risk; BCHE may result in increased plasma levelsa,b,38 Volatile anesthetics RYR1 and CACNA1S may increase malignant hyperthermia riska,b,38 Anticonvulsant Phenytoin HLA-B, CYP2C9, and CYP2C19 may alter plasma levels and toxicity risk; provisional SCN1A altered responsea,b,39,40 Diazepam Provisional findings in CYP2C19a,b Antidepressant Amitriptyline CYP2C19 and CYP2D6 may alter plasma levels resulting in decreased efficacy or increased side effectsa,b,32 Antiemetic Ondansetron CYP2D6 ultra-rapid metabolizers may have low plasma levels resulting in lower efficacy. Provisional findings for ABCB1a,b,18–22 Anti-infective Aminoglycosides Provisional findings for MT-RNR1a Voriconazole CYP2C19 actionable information on FDA labela,b Antiplatelet Clopidogrel CYP2C19 poor metabolizers may have lower antiplatelet efficacy. Provisional findings for CES1a,b,23,27 Cardiovascular Beta-blockers CYP2D6 actionable information on FDA label. Provisional findings in GRK5 and ADRB1a,b Diuretics Hydrochlorothiazide Provisional findings in YEATS4, NEDD4L, and PRKCAa Furosemide Provisional findings in ADD1a Pharmacologic category . Drug . Gene variation–drug interaction . Altitude Adaptation Sildenafil Provisional findings in GNB3a Analgesia NSAIDs CYP2C9 poor metabolizer may have higher plasma levels resulting in increased side effectsa,b,8 Fentanyl Provisional findings for ABCB1, COMT, and OPRM1a Morphine Provisional findings for COMT and OPRM1a Anesthesia Midazolam Provisional findings in CYP3A5a Succinylcholine RYR1 and CACNA1S may increase malignant hyperthermia risk; BCHE may result in increased plasma levelsa,b,38 Volatile anesthetics RYR1 and CACNA1S may increase malignant hyperthermia riska,b,38 Anticonvulsant Phenytoin HLA-B, CYP2C9, and CYP2C19 may alter plasma levels and toxicity risk; provisional SCN1A altered responsea,b,39,40 Diazepam Provisional findings in CYP2C19a,b Antidepressant Amitriptyline CYP2C19 and CYP2D6 may alter plasma levels resulting in decreased efficacy or increased side effectsa,b,32 Antiemetic Ondansetron CYP2D6 ultra-rapid metabolizers may have low plasma levels resulting in lower efficacy. Provisional findings for ABCB1a,b,18–22 Anti-infective Aminoglycosides Provisional findings for MT-RNR1a Voriconazole CYP2C19 actionable information on FDA labela,b Antiplatelet Clopidogrel CYP2C19 poor metabolizers may have lower antiplatelet efficacy. Provisional findings for CES1a,b,23,27 Cardiovascular Beta-blockers CYP2D6 actionable information on FDA label. Provisional findings in GRK5 and ADRB1a,b Diuretics Hydrochlorothiazide Provisional findings in YEATS4, NEDD4L, and PRKCAa Furosemide Provisional findings in ADD1a Abbreviations: ABCB1 = Adenosine Triphosphate-binding cassette subfamily B member 1, ADD1 = alpha adducin 1, ADRB1 = beta-1 adrenergic receptor, BCHE = butyrylcholinesterase, CACNA15 = calcium channel subunit alpha 1 S; CES1 = carboxylesterase; COMT = catechol-o-methyltransferase; GRK5 = G protein coupled receptor kinase 5; MT-RNR1 = mitochondrial encoded 12s ribonucleic acid 1; NEDD4L = neural precursor cell expressed developmentally downregulated 4 like; NSAIDs = Non-Steroidal Anti-inflammatory drugs (Ibuprofen and Meloxicam specifically); OPRM1 = opioid mu1-receptor; PRKCA = protein kinase C alpha; RYR1 = ryanodine receptor 1; SCN1A = sodium channel alpha subunit 1; YEATS4 = YEATS domain containing protein 4. a CPIC Genes–Drugs from https://cpicpgx.org/genes-drugs/ last modified September 18, 2020, and accessed February 10, 2021. Subject to updates and modifications. Users should refer to cpicpgx.org for most current content. b Pharmacogenomic information on FDA label. Open in new tab TABLE I. List of Tactical Combat Casualty Care Guidelines (TCCC) and/or Emergency War Surgery (EWS) Drugs with Food and Drug Administration (FDA) and/or Clinical Pharmacogenetics Implementation Consortium (CPIC) Drug–Gene Interactions Pharmacologic category . Drug . Gene variation–drug interaction . Altitude Adaptation Sildenafil Provisional findings in GNB3a Analgesia NSAIDs CYP2C9 poor metabolizer may have higher plasma levels resulting in increased side effectsa,b,8 Fentanyl Provisional findings for ABCB1, COMT, and OPRM1a Morphine Provisional findings for COMT and OPRM1a Anesthesia Midazolam Provisional findings in CYP3A5a Succinylcholine RYR1 and CACNA1S may increase malignant hyperthermia risk; BCHE may result in increased plasma levelsa,b,38 Volatile anesthetics RYR1 and CACNA1S may increase malignant hyperthermia riska,b,38 Anticonvulsant Phenytoin HLA-B, CYP2C9, and CYP2C19 may alter plasma levels and toxicity risk; provisional SCN1A altered responsea,b,39,40 Diazepam Provisional findings in CYP2C19a,b Antidepressant Amitriptyline CYP2C19 and CYP2D6 may alter plasma levels resulting in decreased efficacy or increased side effectsa,b,32 Antiemetic Ondansetron CYP2D6 ultra-rapid metabolizers may have low plasma levels resulting in lower efficacy. Provisional findings for ABCB1a,b,18–22 Anti-infective Aminoglycosides Provisional findings for MT-RNR1a Voriconazole CYP2C19 actionable information on FDA labela,b Antiplatelet Clopidogrel CYP2C19 poor metabolizers may have lower antiplatelet efficacy. Provisional findings for CES1a,b,23,27 Cardiovascular Beta-blockers CYP2D6 actionable information on FDA label. Provisional findings in GRK5 and ADRB1a,b Diuretics Hydrochlorothiazide Provisional findings in YEATS4, NEDD4L, and PRKCAa Furosemide Provisional findings in ADD1a Pharmacologic category . Drug . Gene variation–drug interaction . Altitude Adaptation Sildenafil Provisional findings in GNB3a Analgesia NSAIDs CYP2C9 poor metabolizer may have higher plasma levels resulting in increased side effectsa,b,8 Fentanyl Provisional findings for ABCB1, COMT, and OPRM1a Morphine Provisional findings for COMT and OPRM1a Anesthesia Midazolam Provisional findings in CYP3A5a Succinylcholine RYR1 and CACNA1S may increase malignant hyperthermia risk; BCHE may result in increased plasma levelsa,b,38 Volatile anesthetics RYR1 and CACNA1S may increase malignant hyperthermia riska,b,38 Anticonvulsant Phenytoin HLA-B, CYP2C9, and CYP2C19 may alter plasma levels and toxicity risk; provisional SCN1A altered responsea,b,39,40 Diazepam Provisional findings in CYP2C19a,b Antidepressant Amitriptyline CYP2C19 and CYP2D6 may alter plasma levels resulting in decreased efficacy or increased side effectsa,b,32 Antiemetic Ondansetron CYP2D6 ultra-rapid metabolizers may have low plasma levels resulting in lower efficacy. Provisional findings for ABCB1a,b,18–22 Anti-infective Aminoglycosides Provisional findings for MT-RNR1a Voriconazole CYP2C19 actionable information on FDA labela,b Antiplatelet Clopidogrel CYP2C19 poor metabolizers may have lower antiplatelet efficacy. Provisional findings for CES1a,b,23,27 Cardiovascular Beta-blockers CYP2D6 actionable information on FDA label. Provisional findings in GRK5 and ADRB1a,b Diuretics Hydrochlorothiazide Provisional findings in YEATS4, NEDD4L, and PRKCAa Furosemide Provisional findings in ADD1a Abbreviations: ABCB1 = Adenosine Triphosphate-binding cassette subfamily B member 1, ADD1 = alpha adducin 1, ADRB1 = beta-1 adrenergic receptor, BCHE = butyrylcholinesterase, CACNA15 = calcium channel subunit alpha 1 S; CES1 = carboxylesterase; COMT = catechol-o-methyltransferase; GRK5 = G protein coupled receptor kinase 5; MT-RNR1 = mitochondrial encoded 12s ribonucleic acid 1; NEDD4L = neural precursor cell expressed developmentally downregulated 4 like; NSAIDs = Non-Steroidal Anti-inflammatory drugs (Ibuprofen and Meloxicam specifically); OPRM1 = opioid mu1-receptor; PRKCA = protein kinase C alpha; RYR1 = ryanodine receptor 1; SCN1A = sodium channel alpha subunit 1; YEATS4 = YEATS domain containing protein 4. a CPIC Genes–Drugs from https://cpicpgx.org/genes-drugs/ last modified September 18, 2020, and accessed February 10, 2021. Subject to updates and modifications. Users should refer to cpicpgx.org for most current content. b Pharmacogenomic information on FDA label. Open in new tab METHODS All drugs within the deployment manuals, TCCC guidelines, and EWS book were identified and the list was cross-referenced to the CPIC guidelines and genes–drugs interactions list as well as the FDA Table of Pharmacogenomics Biomarkers in Drug Labeling. Table I lists all drug–gene interactions, except those with only glucose-6-phosphate dehydrogenase deficiency liability or used only topically, with interactions or provisional or possible interactions including those without enough data currently to recommend changes. Voriconazole was included because of its well-recognized impact on the pharmacokinetics of many medications, its use in invasive fungal infections, and the importance for invasive fungal infections coverage, as noted in EWS and seen in Afghanistan.2 Emergency War Surgery In 2018 the fifth edition of the EWS was published to provide a comprehensive how-to manual on all aspects of surgical care and critical care in combat as well as some common military relevant aspects of care in the austere environment. The EWS, first published in 1967, has contributions from all U.S. military medical services and has been translated into 20 different languages. The first sentence of its preface underscores the need for readiness because it is what we can control.3 The application of pharmacogenomics to the medications in EWS may lend to even further readiness of ADSM. Tactical Combat Casualty Care Guidelines The TCCC Guidelines provide the backbone for deployed healthcare providers on how to treat combat injuries. These evidence-based guidelines create standards for pre-hospital and Role 1 trauma management for all levels of providers, including self-care, buddy-aid, medics, paramedics, nurses, and physicians. Within the TCCC Guidelines, several sections recommend various pharmacotherapies as part of clinical care.4 The TCCC has created a Triple-Option Analgesia plan for treating ADSM on the battlefield with pain, which was developed as an effort to better streamline and simplify decision-making for pharmacotherapy analgesia options. In addition, this plan aimed to reduce the use of morphine in the field, especially in those with hemorrhagic shock or respiratory distress, two conditions which can be exacerbated by treatment with opioids. This three-tiered system calls for the use of acetaminophen and meloxicam for those with mild pain but still able to function, fentanyl for those with moderate or severe pain but without hemorrhagic shock or respiratory distress, and ketamine for moderate or severe pain and either hemorrhagic shock or respiratory distress.5 Acetaminophen, meloxicam, and moxifloxacin are part of the Combat Wound Medication Pack (CWMP) utilized to treat all ADSM.6 These packs allow for the field administration of pain relief for those that are still capable of combat despite their wounds and provide the opportunity for early antibiotic administration for wounds. Meloxicam is the only component of the CWMP identified by CPIC/FDA guidelines as having a potential metabolic liability. Reviewed Drugs Meloxicam Meloxicam is a nonsteroidal anti-inflammatory drug (NSAID) that inhibits cyclo-oxygenase-2, which prevents the formation of several prostaglandin products and intermediates. It is primarily metabolized by CYP2C9. In addition to its analgesic properties, it also has anti-inflammatory and antipyretic properties making it a valuable analgesic in the deployed setting. Meloxicam has found its place in the table of authorities (Triple-Option Analgesia) for the deployed ADSM for its use in mild pain.6 Meloxicam use is not unique to the deployed setting as over 90,000 prescriptions for meloxicam were prescribed for ADSM in 2014, making it the third most commonly prescribed NSAID, comprising 10% of all NSAID prescriptions.7 The CPIC Guidelines to aid providers in prescribing meloxicam recommend a lower starting dose or alternative medication for those that are CYP2C9 intermediate metabolizers and an alternative NSAID not metabolized by CYP2C9 for those that are poor metabolizers.8 Several small clinical trials examined genetic variants of CYP2C9 known to exhibit decreased enzyme activity and these variant phenotypes showed significantly higher areas under the curve and lower clearance levels. One of these studies demonstrated a half-life of over 100 hours, five times longer than normally observed. Some also showed a pharmacodynamic correlate as these subjects also had increased inhibition of thromboxane formation.9–12 The clinical implications of this prolonged half-life with regard to bleeding risk or surgical complications is unknown and warrants further investigation to evaluate the specific risk associated with use in the prolonged field care setting. Given that NSAID’s adverse drug reactions are typically dose dependent, these pharmacokinetic and pharmacodynamic changes could portend increased rates of side effects in patients with less functional phenotypes taking meloxicam. However, clinical trials demonstrating these risks have not been conducted. Several studies have shown an increased prevalence of certain CYP2C9 genotypes in patients taking NSAIDS with acute gastrointestinal bleeding.13–15 Two CYP2C9 alleles (*2 and *3) that demonstrate diminished or absent metabolic activity are found in at least 30% of European and Caucasian populations but are virtually non-existent in Asian or African American populations.16 One of the non-functional alleles (*13) is only found in East Asian populations and at low rates (∼1.0%).17 Ondansetron Ondansetron is a 5-hydroxytryptamine type 3 receptor antagonist used for the prevention of nausea and emesis, either postoperatively or due to cancer chemo- or radiation therapy. Ondansetron is also used routinely off-label in all hospital settings for other conditions that cause nausea or emesis such as gastroenteritis or as a common reaction to medications, including most of the recommended analgesic options in the TCCC Guidelines. Given the broad therapeutic utility, ondansetron, which has multiple routes of administration, including an oral dissolvable tablet, intravenous infusion, or intramuscular injection, is recommended for use in the injured soldier receiving analgesic pharmacotherapy. Ondansetron is a substrate for several cytochrome P450 enzymes but is primarily metabolized by CYP3A4. The CYP2D6 contribution to the metabolism, although minor, has been well studied with regard to the impact of different phenotypes on the efficacy and safety of ondansetron. Clinical trials have shown an association between the decreased efficacy of ondansetron in patients who are CYP2D6 Ultra Rapid metabolizers (UMs).18,19 These findings are further supported by pharmacokinetic studies showing a decrease in plasma levels of ondansetron in patients with UM CYP2D6 phenotype compared to other variants.20 These studies have only been performed in postoperative or cancer patients, but extrapolation to other patient populations would not be unreasonable, pending further data availability. Ondansetron is known to promote an induced QT interval prolongation in a dose-dependent fashion. The risk of torsades de pointes could be exaggerated in the trauma patient with potential electrolyte abnormalities or dysrhythmias. However, the association between elevated levels of ondansetron and increased side effect risk, including QT prolongation, in poor CYP2D6 metabolizers has yet to be demonstrated. The CPIC has issued a guideline on the use of CYP2D6 genetic variant results when treating a patient with ondansetron. They recommend selecting an alternative antiemetic for those that have been identified as having the UM phenotype due to potentially reduced effectiveness, with failure rates as high as 45% shown in trials.18,21 Studies have shown, in an ethnically diverse population such as the U.S. military, around 2% of patients could expect to be UM CYP2D6 metabolizers.22 Clopidogrel Clopidogrel is a pro-drug that undergoes two activation steps before becoming the final active moiety and CYP2C19 is involved in the formation of both intermediate products. This has led the FDA to issue a box warning in 2010 defining the risk associated with CYP2C19 poor metabolizers and ineffective platelet anti-aggregation although an alternate dosing regimen has not been established. Following issuance of the box warning from the FDA, the American College of Cardiology Foundation and the American Heart Association reviewed efficacy and adverse events from a multitude of studies. Notably, five of seven cohort studies showed an increase in cardiovascular events for those patients with CYP2C19*2 polymorphisms treated with clopidogrel ranging from a 53% increase to 5-fold increase in adverse outcomes. The CYP2C19*2 variant protein product has no activity and depending on ethnicity can range in presence from 19% to 50% of the population for at least one allele carriage.23 The variable efficacy of clopidogrel is of concern because in 2001-2007 there were 93 acute cardiac syndrome (ACS) cases among deployed service members, 81.7% of which were acute myocardial infarction.24 Active Duty Military developing ACS, including ST-segment elevation myocardial infarction, receive dual antiplatelet therapy as per treatment guidelines.25 According to EWS, Plavix (Clopidogrel) 300 mg load and 75 mg by mouth daily is the listed platelet inhibitor for ACS.3 In the prolonged field care setting, options to mitigate the risk of CYP2C19–Clopidogrel mismatch in the short term are worthy of consideration. These could include pushing forward a monitoring capability and/or developing a modeled knowledge base product to better inform dosing or to consider other antiplatelet drugs on the market. All come with costs but the last of these options is more readily cost evaluable. Ticagrelor 90 mg cost $410 for 60 tablets and Prasugrel 10 mg is around $22 for 30 tablets, while in comparison Clopidogrel 75 mg is $8 for 30 tablets.26 For the indication, cost per pill is amplified by the BID dosing of Ticagrelor and the loading dose of Prasugrel. Alternately, employing pharmacogenomic testing for those at some stratified risk and testing them for CYP2C19 variants could enhance efficacy with trivial cost increases. The cost of CYP2C19 testing has been supported for cost reduction to guide antiplatelet therapy in a previous review.27 Amitriptyline Amitriptyline has been used on and off label for military relevant illnesses including major depressive disorder, chronic fatigue syndrome, and posttraumatic stress disorder.28–30 Additionally, it is described in EWS as the only effective approach to pharmacologic pain management for trench foot.3 Since effects on warfighting are not completely reliable due to variability in diagnosis, it is unclear as to the extent of a role it may play on the future battlefield with incomplete domain dominance and resupply challenges along with more potential for cases in the subterranean environment.31 A neuropathic pain generator such as trench foot could be a real hindrance to mobility in the fighting wounded. Amitriptyline is metabolized into a less active form through CYP2D6 and active metabolites through CYP2C19. Both the FDA and CPIC have guidelines suggesting either alternative therapy or alteration in dosing with serum drug level monitoring to minimize the potential for adverse interactions.32 Increased serum concentration could result in a greater likelihood or greater severity of the off-target effects associated with tricyclic antidepressants including anticholinergic effects and sodium channel blockade and manifested by hyperthermia, mydriasis, confusion, and arrhythmias, among others. Additionally, amitriptyline holds the box warning for suicidality in the military relevant population aged 18-24 years, potentially exacerbated by upward excursions from the target range due to compromised drug metabolism.30 Poor clinical effectiveness is also a concern for ultra-rapid CYP2D6 or CYP2C19 metabolizers. From 2014 to 2019 there were 302 immersion foot injuries in the U.S. Military at a rate of 13 per 100,000 person-years and cold weather injuries have been shown to more likely affect black, non-hispanics.33 African Americans are a population with a higher average frequency of CYP2C19 ultra-rapid and poor metabolism alleles than the American population as a whole.34 Optimization of drug utilization can be best realized when the incidence of both injury and predisposition to altered metabolism are at their highest. Opioids Morphine and its derivatives have several advantages and liabilities. Morphine is a potent pain reliever yet has a multitude of side effects along with risk of dependency, misuse, and overdose. Short-term use in the case of point-of-care pain relief in the deployed setting could hold all of these risks but the most likely immediate risk is that of overdose. Opioid overdose classically presents as pinpoint pupils, respiratory depression, and decreased cognition. Respiratory depression can lead to neurologic damage and death. Additionally, in the deployed setting, an opioid-naïve ADSM could be overwhelmed with the common side effects of euphoria, itching, nausea, and vomiting and/or flushing.35 Morphine and hydromorphone are the only listed opioids in both TCCC and EWS. Although opioid mu1-receptor (OPRM1) variants may alter response to morphine, their pharmacokinetics are more predictable than the prodrug forms.36 It is fortuitous that morphine and hydromorphone are used over prodrug forms such as tramadol and codeine due to the necessity of these prodrugs to being metabolized by CYP2D6 and the variations that occur within the population. In this regard, use of morphine and hydromorphone should be preferentially continued due to the additive effects that CYP2D6 metabolism could have in the prolonged field care environment. Volatile Anesthetics and Succinylcholine General anesthesia has been shown, in some studies, to be associated with an unpredictable adverse drug event noted in up to 1 in 20 perioperative medication administrations, an eventuality potentially mitigated by pharmacogenomic information, rarely available to the anesthesiologist in a forward setting.37 Malignant hyperthermia (MH), a rare but life-threatening event, can be precipitated by volatile anesthetics and succinylcholine, hence the concern for limiting their usage in the deployed environment. The MH susceptibility is attributed to variations in the ryanodine receptor 1 (RYR1) and the voltage-gated calcium channel subunit alpha 1 S (CACNA1S) with an unknown prevalence in the general population. However, phenotypic expression of MH likely relies on genetics and nongenetic influences so pharmacogenomic testing, while offering some guidance, will not eliminate all concerns.38 The risk is uncommon but would likely be unmanageable when occurring in the prolonged field care setting and demand dantrolene for treatment. Preemptive testing may be more beneficial and less costly than supplying dantrolene in theater. Phenytoin Phenytoin is an anticonvulsant utilized for the reduction of seizure after traumatic brain injury (TBI). Levetiracetam is considered an adequate alternative but there are conflicting data.3 Each offers a unique side effect profile while addressing seizures. Phenytoin has CPIC recommended dosing or medication changes to minimize the risk of adverse drug reactions in patients who are CYP2C9 intermediate or poor metabolizers or are major histocompatibility complex class I, B (HLA-B)*15:02 carrier although corresponding clinical data are lacking.39,40 The potential identification of these patients could simplify the treatment plan for those suffering post-TBI seizures by finalizing, through clinical trial, the equality of levetiracetam for this indication or preemptive pharmacogenomic testing in this population. DISCUSSION The analysis of benefit of pharmacogenomic application at large is still in its infancy likely due to the ever-broadening nuances in genetic expression, yet long strides have been made to identify and implement testing recommendations. The easiest implementation seems to be when there is a severe outcome such as resulting in extreme organ failure with visualized deformities such as Stevens–Johnson syndrome. An example may be the Veterans Health Administration creation of the Molecular Diagnostics Working Group, including a pharmacogenetic subcommittee, that has recommended testing of 16 drug–gene pairs with a quarter of them noted due to their severity.41 Complete success in implementation will not only rely on the reduction of these gut-wrenching events that pepper the literature but will also incorporate identifying lack of efficacy. Inadequate response due to pharmacogenomic liability has been identified in a case series within the Canadian military literature as well as describes a pilot implementation program on a population level that was well received.42 Both of these examples showcase similar in-roads relative to pharmacogenomic testing implementation within the Military Health System (MHS). Pharmacogenomic testing buy-in still has several roadblocks such as the lack of consistent cost benefits in other populations and healthcare delivery systems, which have far less variables including the cost to train a replacement service member, evacuation, and mission failure. Therefore, implementation under the unique confines of U.S. military trauma, critical care, and the austere environment is even more so a challenge. We know that pharmacogenomics plays a significant role in the pharmacokinetics and pharmacodynamics of many medications. However, numerous other factors affect their metabolism, distribution, and clearance in trauma patients. The heterogeneity of the injuries, variability in blood flow and function of the liver and kidneys, the hypermetabolic state of trauma patients, and the levels of drug-binding proteins are some of the rapidly evolving factors that can affect whether a pharmacotherapy will be effective or safe. If and where pharmacogenomics can play a significant role in the battlefield management of ADSM is still unclear and therefore begs further exploration. Specifically, the application of pharmacogenomics to trauma patients is rare for several reasons. Foremost is the lack of pharmacogenomic testing results available on patients that need the rapid administration of life-saving or symptom-alleviating treatment regimens. In order to have effectively implemented pharmacogenomics in trauma cases, preemptive testing for patients, improved provider education in pharmacogenomic application, as well as access to testing results would all have to be in place. This is a formidable task likely to come at significant cost; however, given the time and cost already invested in training the elite combat service member, further investigation seems justified. The reliance on analgesia, anesthetic, antibiotic, and anti-nausea medications within trauma management, all areas where a solid pharmacogenomics knowledgebase exists, argues for the incorporation of pharmacogenomics discrimination. The Joint Trauma System (JTS)’s emphasis on the 4R’s, right patient, right place, right time, and right care parallels the goals of the precision care initiative.3 The JTS has been very successful in developing the right care for U.S. military engagement. However, a lack of dominance in one domain of the Multi-Domain Operation or Large Scale Combat Operation may prove that the right place and the right time no longer exist. Similarly, pharmacogenomics facilitates delivery of the right patient, right drug, and the right dose by identifying metabolic capabilities. Our review brings to light several medications specific to military care with identified liabilities. These liabilities may not have enough significant effect to support widespread implementation. However, a focused panel of tests such as CYP2D6, CYP2C9, and CYP2C19 is valid for a number of medications and may justify performance. Additionally, results follow the service member with only a one-time testing need. Pharmacogenomics may be an opportunity for performance improvement in this setting. CONCLUSION Pharmacogenomics offers the ability to deliver care more precisely, but as reviewed above, there are several burdens to consider, such as cost of testing, implementation, and further population and circumstance specific research. The pharmacogenomic risk of the military relevant medications argues the benefit of pharmacogenomic enhanced decisions to the ADSM in the setting of readiness and prolonged field care. Given the limited choice of available medicines and corresponding guidelines for those therapies, a focused, preemptive testing capability might reduce the potential for adverse drug events, medical footprint reduction, and cost savings. This review outlines several genes with liabilities in the prolonged field care setting and areas that may produce improved care with further study. ACKNOWLEDGMENTS Special thanks to Walter Reed Army Institute of Research, the Uniformed Services University, and the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. FUNDING None declared. CONFLICT OF INTEREST STATEMENT None declared. REFERENCES 1. Knight RM , Moore CH, Silverman MB: Time to update army medical doctrine . Mil Med 2020 ; 185 ( 9–10 ): e1343 – 6 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 2. Akers KS , Rowan MP, Niece KL, et al. : Antifungal wound penetration of amphotericin and voriconazole in combat-related injuries: case report . BMC Infect Dis 2015 ; 15 : 184. Google Scholar OpenURL Placeholder Text WorldCat 3. Cubano MA , Butler FK, Borden Institute , US Army Medical Department Center and School, Health Readiness Center of Excellence : U.S. Emergency War Surgery . 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Gonsalves SG , Dirksen RT, Sangkuhl K, et al. : Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for the use of potent volatile anesthetic agents and succinylcholine in the context of RYR1 or CACNA1S genotypes . Clin Pharmacol Ther 2019 ; 105 ( 6 ): 1338 – 44 . Google Scholar Crossref Search ADS PubMed WorldCat 39. Caudle KE , Rettie AE, Whirl-Carrillo M, et al. : Clinical pharmacogenetics implementation consortium guidelines for CYP2C9 and HLA-B genotypes and phenytoin dosing . Clin Pharmacol Ther 2014 ; 96 ( 5 ): 542 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 40. Calderon-Ospina CA , Galvez JM, López-Cabra C, et al. : Possible genetic determinants of response to phenytoin in a group of Colombian patients with epilepsy . Front Pharmacol 2020 ; 11 : 555. Google Scholar OpenURL Placeholder Text WorldCat 41. Vassy JL , Stone A, Callaghan JT, et al. : Pharmacogenetic testing in the Veterans Health Administration (VHA): policy recommendations from the VHA Clinical Pharmacogenetics Subcommittee . Genet Med 2019 ; 21 ( 2 ): 382 – 90 . Google Scholar Crossref Search ADS PubMed WorldCat 42. Wynn G , Jetly R, Vermetten E, et al. : Pharmacogenomics: a primer for the military mental health provider . J Mil Veteran Fam Health 2020 ; 6 ( S1 ): 44 – 50 . Google Scholar Crossref Search ADS WorldCat Author notes The views expressed are solely those of the authors and do not reflect the official policy or position of the Henry M. Jackson Foundation, Walter Reed Army Institute of Research, Uniformed Services University, the U.S. Army, the Department of Defense, or the U.S. Government. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US. This work is written by (a) US Government employee(s) and is in the public domain in the US. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Poon, Lucas; Por, Elaine D; Cho, Hyun Joon; Oliver, Thomas G

doi: 10.1093/milmed/usab243pmid: 34967400

ABSTRACT Introduction Providing patient-specific clinical care is an expanding focus for medical professionals and researchers, more commonly referred to as personalized or precision medicine. The goal of using a patient-centric approach is to provide safer care while also increasing the probability of therapeutic success through careful consideration of the influence of certain extrinsic and intrinsic human factors in developing the patient care plan. Of increasing influence on patient care is the phenotype and genotype information gathered from employing various next-generation sequencing methods. Guided by and partnered with our civilian colleagues, clinical components within the DoD are embracing and advancing genomic medicine in many facets—from the bench to the bedside—and in many therapeutic areas, from Psychiatry to Oncology. In this PubMed-based review, we describe published clinical research and interventions within the DoD using genome-informed data and emphasize precision medicine efforts in earlier stages of development with the potential to revolutionize the approach to therapeutics. Materials and Methods The new PubMed database was searched for articles published between 2015 and 2020 with the following key search terms: precision medicine, genomic, pharmacogenetic, pharmacogenomic, US military, and Department of Defense. Results Eighty-one articles were retrieved in our initial search. After screening the abstracts for studies that only involved direct testing of (or clinical interaction with) active duty, Reserve, National Guard, or civilian personnel working within the DoD and excluding any epidemiological or microbial isolation studies, seven were included in this review. Conclusion There are several programs and studies within the DoD, which investigate or use gene-based biomarkers or gene variants to deliver more precise clinical assessment and treatment. These genome-based precision medicine efforts aim to optimize the clinical care of DoD beneficiaries, particularly service members in the operational environment. INTRODUCTION Precision medicine has been defined as an attempt to match the correct diagnosis to the correct patient and offer the most appropriate therapy to maximize efficacy while minimizing toxicity or adverse effects. Historically, this has involved some combination of trial and error and evidenced-based therapy informed, at times, by available family history. Within the past half century, the U.S. Military began phenotypic analysis of drug metabolic capability to help inform the therapeutic regimens of service members. Specifically, glucose-6-phosphate dehydrogenase (G6PD) assays were used to predict which service member might experience hemolysis when prescribed certain antimalarial therapy and which medications might be more beneficial. This early effort to assess phenotypic metabolism has presaged the burgeoning genetic sequencing and analytic capabilities incorporated into current diagnostic and therapeutic systems and clinical practice. Genomic Medicine and Pharmacogenomics A subset of precision medicine, genomic medicine, is an emerging medical discipline that incorporates individual genetic information into clinical care and is becoming increasingly more prevalent as the knowledge base increases exponentially and clinical experience grows.1 Specifically, DNA sequencing has helped to identify genetic mutations to screen for when trying to determine an individual’s risk of developing certain health disorders, such as cystic fibrosis or cardiovascular disease (CVD), while also helping to determine which treatments (e.g., chemotherapy) may best target specific mutations, such as those found in tumors.2 Accordingly, molecular and genetic markers are increasingly used in medical practice to develop unique treatment plans and improve patient outcomes. Embedded within the scope of genomic medicine is the field of pharmacogenomics, often referred to as pharmacogenetics, which may be defined as the discipline studying the effect of gene(s) on an individual’s response to a specific drug, to include differences in drug metabolism as a consequence of liver enzyme genotype. The application of pharmacogenomic testing in clinical settings directly aligns with precision medicine concepts, since the results help clinicians in determining the most safe and efficacious treatment on an individual patient level. Ever-expanding advances in biotechnology, diagnostic platforms, and data analytics offer tremendous potential to enable pharmacogenomics and precision medicine in the care of a diverse, military beneficiary population. As such, the DoD, in partnership with other governmental and civilian agencies, has actively engaged in pharmacogenomics research and implementation to achieve the overall goal of improving patient care. Modern Precision Medicine Approaches Within the DoD Modern DoD precision medicine efforts began at some Air Force and Navy bases that started using the Patient-Centered Medical Home model in 2007.3 Implemented across the Military Health System (MHS) in 2009, the model relied heavily on the primary care physician to provide treatment based on a patient’s distinct needs, while other clinicians on the healthcare team developed individualized care plans.3,4 More recently, innovations in the analysis and interpretation of genetic variation have enabled more precise diagnostic and therapeutic capabilities in clinical practice and research facilities worldwide to include those of the DoD. In 2009, the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) was initiated. Its genesis was attributed to the significant rise of soldier suicides since 2002, prompting the Army to partner with the NIH’s National Institute of Mental Health to implement a large research study to address the topic of suicide prevention.5 Data from Army STARRS led researchers to investigate predictors of soldier suicides as well as possible genomic markers associated with resilience toward stress.6,7 In 2010, the Air Force Medical Service (AFMS) Patient-Centered Precision Care (PC2-Z) program was launched to gather clinical knowledge and provide recommendations for translating genome-informed medicine into precision health care for all Air Force healthcare beneficiaries.8 The following year, the Military Veteran Program, an observational cohort and biobank within the Department of Veteran’s Affairs (VA) healthcare system, was developed to improve the understanding of how disease is influenced by genetics, behavior, and environmental factors within the veteran population.9 In 2015, President Barack Obama announced the launch of the Precision Medicine Initiative (PMI). Edified to “enable a new era of medicine,” the PMI desired to offer individualized medical care by incorporating genetic makeup and environmental and lifestyle factors to improve health and treat disease.10 A primary objective of the PMI involved advancing tailored treatments for cancer by expanding genetically based clinical cancer trials.11 The Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network, a collaboration between the VA, DoD, and National Cancer Institute, was one component of that effort.12 DoD educational opportunities and programs to prepare the next generation of clinician-researchers for the genomics field are also expanding. Medical students attending the Uniformed Services University of the Health Sciences (USUHS), in their preclinical training period, are introduced to basic pharmacogenomics learning modules, while fourth-year medical students who opt for the Clinical Pharmacology elective rotation are exposed to readings, lectures, and case discussions on advanced pharmacogenomics topics administered by both the USUHS and WRAIR faculty. In addition, the joint USUHS/WRAIR Clinical Pharmacology fellowship for select doctoral-level professionals has pharmacogenomics modules built into their standardized curriculum to introduce and advance precision medicine education. The host of active academic, research, and clinical activities within the DoD which incorporate genetic information, and its increasing role in the practice of medicine, has attracted the interest of the military leadership.13 However, the literature lacks a distillation of these genome-based activities. Thus, the purpose of our investigation was to report on the areas of military medicine affected by genomic testing by providing a narrative review of published literature describing personalized medicine efforts within the DoD, guided by either genome-wide or gene-specific testing. METHODS To identify articles to be included in our review, we searched the new PubMed database for articles published between 2015 and 2020 with the following key search criteria and terms: “precision medicine,” “genomic,” “pharmacogenetic,” “pharmacogenomic,” with the “or” operator between them, and joined this with the “and” operator to either “US military” or “Department of Defense.” Eighty-one articles were retrieved in our initial search. We screened the abstracts for content that only involved direct testing of (or clinical interaction with) active duty, Reserve, National Guard, or civilian personnel working within the DoD and excluded any microbial isolation studies. Epidemiological studies that principally reported on the prevalence or incidence of diseases, diagnosed using genomic sequencing, were also excluded from our review. RESULTS A total of seven articles fulfilled our criteria and are presented in Table I. TABLE I. Title and Description of Journal Articles Fulfilling Search Criteria Title of publication . Description of publication . Authors . Collaboration to accelerate proteogenomics cancer care: The Department of Veterans Affairs, Department of Defense, and the National Cancer Institute’s Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network An overview of the APOLLO network stakeholders and their functions Fiore et al.12 Precision Military Medicine: Conducting a multi-site clinical utility study of genomic and lifestyle risk factors in the United States Air Force An overview of the successes and challenges of conducting civilian led multi-site research within the military Delaney et al.8 The Uniformed Services University’s Surgical Critical Care Initiative (SC2i): Bringing Precision Medicine to the critically ill A description of the SC2i experience to foster advancement of precision medicine Belard et al.18 Genomewide association studies of suicide attempts in US soldiers A search for genome-wide associations with previous suicide attempts Stein et al.19 Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) A description of a precision medicine model to predict suicides Kessler et al.6 Genome-wide analyses of psychological resilience in U.S. Army soldiers An investigation for genetic variants associated with Soldier resilience Stein et al.7 Determination of cytochrome P450 isoenzyme 2D6 (CYP2D6) genotypes and pharmacogenomic impact on primaquine metabolism in an active-duty US Military population An investigation in the role of CYP2D6 isoenzyme genotypes in primaquine metabolism Spring et al.24 Title of publication . Description of publication . Authors . Collaboration to accelerate proteogenomics cancer care: The Department of Veterans Affairs, Department of Defense, and the National Cancer Institute’s Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network An overview of the APOLLO network stakeholders and their functions Fiore et al.12 Precision Military Medicine: Conducting a multi-site clinical utility study of genomic and lifestyle risk factors in the United States Air Force An overview of the successes and challenges of conducting civilian led multi-site research within the military Delaney et al.8 The Uniformed Services University’s Surgical Critical Care Initiative (SC2i): Bringing Precision Medicine to the critically ill A description of the SC2i experience to foster advancement of precision medicine Belard et al.18 Genomewide association studies of suicide attempts in US soldiers A search for genome-wide associations with previous suicide attempts Stein et al.19 Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) A description of a precision medicine model to predict suicides Kessler et al.6 Genome-wide analyses of psychological resilience in U.S. Army soldiers An investigation for genetic variants associated with Soldier resilience Stein et al.7 Determination of cytochrome P450 isoenzyme 2D6 (CYP2D6) genotypes and pharmacogenomic impact on primaquine metabolism in an active-duty US Military population An investigation in the role of CYP2D6 isoenzyme genotypes in primaquine metabolism Spring et al.24 Open in new tab TABLE I. Title and Description of Journal Articles Fulfilling Search Criteria Title of publication . Description of publication . Authors . Collaboration to accelerate proteogenomics cancer care: The Department of Veterans Affairs, Department of Defense, and the National Cancer Institute’s Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network An overview of the APOLLO network stakeholders and their functions Fiore et al.12 Precision Military Medicine: Conducting a multi-site clinical utility study of genomic and lifestyle risk factors in the United States Air Force An overview of the successes and challenges of conducting civilian led multi-site research within the military Delaney et al.8 The Uniformed Services University’s Surgical Critical Care Initiative (SC2i): Bringing Precision Medicine to the critically ill A description of the SC2i experience to foster advancement of precision medicine Belard et al.18 Genomewide association studies of suicide attempts in US soldiers A search for genome-wide associations with previous suicide attempts Stein et al.19 Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) A description of a precision medicine model to predict suicides Kessler et al.6 Genome-wide analyses of psychological resilience in U.S. Army soldiers An investigation for genetic variants associated with Soldier resilience Stein et al.7 Determination of cytochrome P450 isoenzyme 2D6 (CYP2D6) genotypes and pharmacogenomic impact on primaquine metabolism in an active-duty US Military population An investigation in the role of CYP2D6 isoenzyme genotypes in primaquine metabolism Spring et al.24 Title of publication . Description of publication . Authors . Collaboration to accelerate proteogenomics cancer care: The Department of Veterans Affairs, Department of Defense, and the National Cancer Institute’s Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network An overview of the APOLLO network stakeholders and their functions Fiore et al.12 Precision Military Medicine: Conducting a multi-site clinical utility study of genomic and lifestyle risk factors in the United States Air Force An overview of the successes and challenges of conducting civilian led multi-site research within the military Delaney et al.8 The Uniformed Services University’s Surgical Critical Care Initiative (SC2i): Bringing Precision Medicine to the critically ill A description of the SC2i experience to foster advancement of precision medicine Belard et al.18 Genomewide association studies of suicide attempts in US soldiers A search for genome-wide associations with previous suicide attempts Stein et al.19 Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) A description of a precision medicine model to predict suicides Kessler et al.6 Genome-wide analyses of psychological resilience in U.S. Army soldiers An investigation for genetic variants associated with Soldier resilience Stein et al.7 Determination of cytochrome P450 isoenzyme 2D6 (CYP2D6) genotypes and pharmacogenomic impact on primaquine metabolism in an active-duty US Military population An investigation in the role of CYP2D6 isoenzyme genotypes in primaquine metabolism Spring et al.24 Open in new tab The APOLLO Network The APOLLO network, formed in 2016, is a tri-partite entity, which leverages the institutional talents of the VA, DoD, and NIH. Through the various resources offered by each institution (e.g., cancer treatment expertise, high-throughput sequencing, and tissue biobank samples from service members), APOLLO aims to screen cancer patients for both genomic and proteomic anomalies (known as proteogenomics) to match their tumor types to targeted therapies.12 The PC2-Z Program The PC2-Z program included a clinical utility study component performed with a civilian collaborator (Coriell) that analyzed genetic data from over 2,000 active duty and civilian volunteers working within the AFMS component.14 Phase I of the study provided personalized reports to the volunteers with the goal of disseminating the concept of genomic medicine to their healthcare providers, thus preparing the latter for a widespread introduction of genomics into clinical care, while Phase II extended enrollment to all Air Force active duty members and their spouses, as well as Air Force retirees and their spouses.8,15 This investigation was modeled on the Coriell Personalized Medicine Collaborative study where participants provided detailed medical histories, DNA was tested, and personalized risk reports were then provided for potentially actionable health conditions.16 The critical aspect of this study was underscored by Coriell in their assessment that “what a participant opts to do after receiving their results—be it consulting with a genetic counselor or physician, or affecting a lifestyle change to mitigate disease risk—is a significant part of the effort that will shape the future of precision medicine.”14 The Surgical Critical Care Initiative At USUHS, similar efforts to individualize care for those who may become critically ill have begun. In 2013, USUHS unrolled the Surgical Critical Care Initiative (SC2i) in conjunction with the Walter Reed National Military Medical Center and several other civilian healthcare systems.17 Biomarker samples were collected from over 1,000 (military and civilian) critically ill patients to develop clinical decision support systems with the original intent of assisting military surgeons in managing devastating injuries, to include those that are combat-related.18 Biospecimens collected from collaborating institutions were employed to build biological response profiles (based on cytokines, chemokines, RNA, and other biomarkers) in order to produce predictive algorithms that would indicate the biological response to injury and disease. The application of these predictive models has helped to support acute care (setting) decision-making such as the identification and treatment of invasive fungal infections as well as the activation of massive transfusion protocols.18 Genomic Studies and Suicide Risk Assessments Within Army STARRS In their research of an Army STARRS subcomponent, which was dedicated to examining new soldiers in basic training (i.e., New Soldier Study), Stein et al. attempted to look for genome-wide associations with previous suicide attempts.19 The study cohort, which previously completed a self-assessment questionnaire evaluating for a history of recent and lifetime mental disorders, also submitted blood samples for genotyping. The investigators reported that a genome-wide significant region on chromosome 6, located within the melanocortin 2 receptor accessory protein 2 gene, was associated with suicide attempts in soldiers of European ancestry and proposed this as a plausible susceptibility gene for suicidality.19 However, this association was not seen in soldiers of African or Latino ancestry in the study cohort. Kessler et al. explored the possibility that a precision medicine model, built on administrative patient data gathered from outpatient mental health visits, had the ability to predict suicides among outpatients.6 Nested within Army STARRS, a deidentified patient data set named the Historical Administrative Data System (HADS), which contains clinical and nonclinical data from nearly 1 million Army soldiers serving between 2004 and 2009, was used to construct the regression-based statistical predictive model.6,20 The HADS-based model, using a multitude of variables (operationalized as indicators) nested within multiple domains (e.g., sociodemographics, Army career, clinical factors, and prior crime factors), sought to predict suicidality within a 26-week time frame after the index mental health specialist outpatient visit. Validation of the model was performed with the actual suicides that took place between 2008 and 2012 among the data set cohort, where the investigators noted that crime perpetration, measured mental disorders, and hospitalization for any physical health problem were important predictors in their model.6 Utilizing genome-wide association methods, Stein et al. sought to identify genetic variants associated with “resilience” phenotypes among soldiers within the Army STARRS platform.7 The investigators genotyped over 10,000 new Army soldiers and assessed their ability to handle stress using a self-administered questionnaire, which ultimately yielded a total resilience score. The statistical analysis revealed a genome-wide significant locus located upstream from the doublecortin-like kinase 2 gene on chromosome 4, while the kelch-like family member 36 gene on chromosome 16 was also detected as significant (using genome-wide gene-association methodology) in those soldiers with self-assessed resilience.7,21,22 Although these findings were only pertinent to the study subjects of European ancestry, the investigators were able to facilitate a proof-of-feasibility study, highlighting a genetic basis for resilience-associated variants with potentially actionable targets for more precise psychiatric care.7 Pharmacogenetic Phenotype Determination in Malaria Treatment Malaria infection remains the most significant infectious disease threat to deployed U.S. service members.23 In addition to having developed several novel antimalarial drugs, DoD researchers have also studied the impact of pharmacogenetic phenotypes on antimalarial drug pharmacokinetics and efficacy. Spring et al. investigated the pharmacokinetics of primaquine, an 8-aminoquinolone antimalarial drug. Forty-five active duty healthy volunteers were administered a single oral dose of primaquine, and the investigators scaffolded the pharmacokinetic findings according to their hepatic CYP2D6 isoenzyme phenotype.24 The primaquine metabolite 5,6-ortho-quinone, produced via CYP2D6-mediated drug metabolism, serves as a surrogate marker for the presence of 5-hydroxyprimaquine, which is linked to therapeutic efficacy (of primaquine) and believed to be necessary for the radical cure of Plasmodium vivax infections.24,25 The investigators demonstrated that 5,6-ortho-quinone was found in 100%, 75%, and 19% of the urine samples in volunteers with the normal, intermediate, and poor (CYP2D6) metabolizer phenotype, respectively, as predicted by genotype.24 These findings underscore the importance of determining CYP2D6 genotype status when pursuing radical cure with primaquine for individuals with P. vivax infections. DISCUSSION The goal of precision-themed efforts across the military is to optimize the care and health of all DoD beneficiaries. Increasingly, research and clinical entities within the DoD incorporate genome-based activities to advance clinical care, aided by rapid technologic and methodologic advancements in the research domain. At the military treatment facility level, there has been even a steady increase in the number of referrals for genetic services.13 As the DoD continues to transition military hospitals and clinics from the various branches of service to the Defense Health Agency (DHA), maintaining medical readiness is a top priority. Indeed, primary objectives include the optimization of medical care of service members in the operational environment and the management of deployment and combat-related injuries expeditiously and efficiently. The research studies described above underscore these objectives, and some continue to progress and evolve. One prime example of this is the SC2i’s development of an additional clinical decision support tool that helps to guide the timing of wound closure in traumatic wounds.26 Additionally, the DoD has funded the Study to Assess Risk and Resilience in Servicemembers Longitudinal Study, which expands on the data from Army STARRS to produce actionable information to address mental and behavioral health issues in the military.27 Our review highlights precision medicine efforts across the DoD enterprise; however, it has a few notable limitations. First, our search net was limited as it did not include terms such as “Army,” “Navy,” “Service Member,” or “Diagnostic Testing,” and including these might have returned a more comprehensive list of genomic studies. Second, this investigation is a narrative review and not a systematic review, which focuses on a few key studies that met our search criteria. Of note, there are other official DoD activities not captured by our initial criteria but worth highlighting below as they provide a more complete review on this topic. These efforts focus on advancing precision medicine through genomics, with the shared goal of improving the health of our beneficiaries. Monitoring Environmental Exposures The current strategy for environmental and occupational medicine in the military relies on environmental surveillance and military intelligence of toxic chemicals, but to effectively protect service members, a personalized approach is necessary to offer treatment options based on an individual’s biological factors (to include their genomics) and their susceptibility and response to exposures.28 In particular, warfighters in deployed operations can be exposed to markedly elevated concentrations of toxicants, potentially leading to health consequences not readily discernible by the patient nor provider in the short term. Genetic variants (or polymorphisms) can be inherited or acquired over the life span of an individual (i.e., somatic mutations) and are associated with morbidities such as cancer, and somatic mutations can be the result of environmental exposures such as cigarette smoke or radiation.29,30 Therefore, health risk mitigation involves monitoring one’s external exposures to enhance the understanding of possible factors in disease development and targeted prevention or treatment measures as outlined in the PMI.10 One such approach is the Air Force’s Total Exposure Health initiative. This emerging development, which acknowledges the utility of genomics, aims to expand the scope of exposure assessment, to include exposures from deployment or workplace settings and one’s environment and lifestyle.31 In addition, the DHA’s Defense Occupational & Environmental Health Readiness System is a system of record for reporting and assessing occupational and environmental exposures that can provide a time-weighted average exposure concentration for a certain location and links the warfighter to that geography, thus serving as a database that offers the potential to improve treatment modalities (via exposure targeting) and quantify disease risk when bridged with one’s genetic factors.28 Precision Medicine and Chronic Conditions The MHS is a multibillion-dollar enterprise that provides health care to approximately 9.6 million service members, retired personnel, and their dependents.32 Additionally, the DoD spends about $1.5 billion a year in obesity-related healthcare costs for current and former service members and their families, as well as costs to replace unfit personnel.33 For service members, chronic medical conditions can directly impact readiness as they degrade the ability to support physically demanding mission requirements or deploy to remote locations where healthcare resources may be limited.34 Thus, effective clinical management of chronic conditions, to include diabetes, CVD, and cancer, is essential to ensure the health of the fighting force and help reduce healthcare costs. The fiscal impact of diabetes in the USA in 2017, calculated from direct and indirect costs of diagnosed diabetes, was estimated at $327 billion.35 For the military, the costs of providing diabetes care for dependents and retirees are becoming a serious economic concern.36 In fact, an adult diabetes study conducted by Chao et al. examined the MHS population from FY2006 to FY2010 and demonstrated increasing annual prevalence rates in all non-active duty age group cohorts, except for the 18- to 44-year-old cohort, using one of their specialized algorithms.37 Thus, optimal management of diabetes mellitus in military medicine must align with PMI, which includes the practice of prescribing the most appropriate patient-specific drug. In some cases, clinicians should consider a genetics-based approach to drug prescribing, and one such resource is the guidelines offered by the Clinical Pharmacogenetics Implementation Consortium (CPIC), which considers G6PD gene status as an actionable pharmacogenomic event for some antidiabetic drugs (i.e., glimepiride and glipizide).38 The recently chartered DHA Pharmacogenomics Work Group, a collaboration of several DoD healthcare professionals formed to help implement pharmacogenomics testing and implementation across the enterprise, currently incorporates the use of the CPIC guidelines in their educational activities. Cardiovascular disease, the most common chronic medical condition afflicting the active duty component of the U.S. Army, has increased with prevalence rates over the past decade rising from 6.8% in 2007 to 9.4% in 2014.39 In 2011, 18% of active duty soldiers reported being diagnosed with high blood pressure, a prominent risk factor associated with stroke and CVD, while 15% of soldiers reported being diagnosed with high cholesterol, also a key risk factor for heart disease.39–41 Research in the expression of PCSK9, a gene that encodes proteins used to regulate low-density lipoproteins (LDLs) in the bloodstream, helped to recognize the association of those individuals with PCSK9 “gain-of-function” mutations and high blood cholesterol levels.42 As such, the DHA designated select PCSK9 inhibitor drugs (e.g., evolocumab) as uniform formulary agents, in recognition that these drugs do offer an additional LDL-lowering option for those patients who meet clinical criteria.43 Interestingly, it should be noted that in addition to genetic predisposition, epigenetic dysregulation has also emerged as a hallmark of complex diseases such as CVD and hypertension, resulting in epigenetic variants that can be transmitted for several generations.44,45 CONCLUSION We herein have described several programs and studies within the DoD that utilize gene-based biomarkers or gene variants as indicators to deliver more precise clinical assessment and treatment. Moreover, interagency collaborations and environmental exposure assessments, which have the capacity to directly benefit the health and operational readiness of our warfighters, were also discussed. The Army STARRS, which attempted to evaluate for genetic markers associated with resilience and suicidality, was also highlighted since this is an area of growing concern for the DoD, especially as officials have reported that a total of 541 service members died by suicide in 2018 at rates of 24.8, 22.9, and 30.6 per 100,000 individuals in the active component, Reserves, and National Guard, respectively.46 In the current healthcare environment where the prevalence of chronic conditions is associated with high operational costs, precision medicine activities are essential in helping to manage the medical needs of these patients while also aiming to reduce healthcare delivery costs. As technology continues to advance, precision-themed efforts will undoubtedly maintain pace to provide optimized health care for the management of a variety of diseases and conditions while also helping to sustain the health and readiness of the fighting force. ACKNOWLEDGMENTS None declared. FUNDING There are no funding sources. CONFLICT OF INTEREST STATEMENT Material has been reviewed by the WRAIR. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official, or as reflecting true views or official policy, of the Department of the Army, DoD, or U.S. Government. REFERENCES 1. NIH National Human Genome Research Institute : Genomics and medicine . Available at https://www.genome.gov/health/Genomics-and-Medicine, published 2020 ; accessed May 24, 2020 . 2. Orchard C : Genomic medicine in the real world: “hope” and “hype” . Pharmacogenetics: from discovery to patient care . Available at https://www.hsph.harvard.edu/ecpe/genomic-medicine-in-the-real-world-hope-and-hype/; accessed May 16, 2021 . 3. Deloitte Center for Government Insights : Innovation in the Military Health System . Available at https://www2.deloitte.com/content/dam/insights/us/articles/4818_Innovations_military_health/DI_Innovations-military-health.pdf; accessed November 8, 2020 . 4. U.S. Army Medical Command : ARMY PCMH operations manual: leaders guide to Army Patient Centered Medical Home . Available at http://www.usafp.org/wp-content/uploads/2013/12/ARMY-PCMH-Operations-Manual-Final-24-Jan-14.pdf, published 2014 ; accessed November 8, 2020 . 5. NIH National Institute of Mental Health : The making of Army STARRS: an overview . Available at https://www.nimh.nih.gov/health/topics/suicide-prevention/suicide-prevention-studies/the-making-of-army-starrs-an-overview.shtml; accessed May 22, 2020 . 6. Kessler RC , Stein MB, Petukhova MV, et al. : Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) . Mol Psychiatry 2017 ; 22 ( 4 ): 544 – 51 . Google Scholar Crossref Search ADS PubMed WorldCat 7. Stein MB , Choi KW, Jain S, et al. : Genome-wide analyses of psychological resilience in U.S. Army soldiers . Am J Med Genet B Neuropsychiatr Genet 2019 ; 180 ( 5 ): 310 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 8. Delaney SK , Brenner R, Schmidlen TJ, et al. : Precision Military Medicine: conducting a multi-site clinical utility study of genomic and lifestyle risk factors in the United States Air Force . NPJ Genom Med 2017 ; 2 : 2. Google Scholar OpenURL Placeholder Text WorldCat 9. Gaziano JM , Concato J, Brophy M, et al. : Million veteran program: a mega-biobank to study genetic influences on health and disease . J Clin Epidemiol 2016 ; 70 : 214 – 23 . Google Scholar Crossref Search ADS PubMed WorldCat 10. The White House : The Precision Medicine Initiative . 2015 . Available at https://obamawhitehouse.archives.gov/precision-medicine; accessed May 18, 2020 . 11. The White House : FACT SHEET: President Obama’s Precision Medicine Initiative . Available at https://obamawhitehouse.archives.gov/the-press-office/2015/01/30/fact-sheet-president-obama-s-precision-medicine-initiative; accessed May 29, 2020 . 12. Fiore LD , Rodriguez H, Shriver CD: Collaboration to accelerate proteogenomics cancer care: the Department of Veterans Affairs, Department of Defense, and the National Cancer Institute’s Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) Network . Clin Pharmacol Ther 2017 ; 101 ( 5 ): 619 – 21 . Google Scholar Crossref Search ADS PubMed WorldCat 13. De Castro MJ , Turner CE: Military genomics: a perspective on the successes and challenges of genomic medicine in the Armed Services . Mol Genet Genomic Med 2017 ; 5 ( 6 ): 617 – 20 . Google Scholar Crossref Search ADS PubMed WorldCat 14. Coriell Institute for Medical Research : Coriell expands personalized medicine partnership with air force . Available at https://cpmc.coriell.org/component/k2/item/231-coriell-expands-personalized-medicine-partnership-with-air-force, published 2015 ; accessed May 24, 2020 . 15. Levite J : Utility of genetics clinical study seeks volunteers across AF . United States Air Force . Available at https://www.af.mil/News/Article-Display/Article/1051141/utility-of-genetics-clinical-study-seeks-volunteers-across-af/, published 2017 ; accessed May 24, 2020 . 16. Keller MA , Gordon ES, Stack CB, et al. : Coriell personalized medicine collaborative: a prospective study of the utility of personalized medicine . Per Med 2010 ; 7 ( 3 ): 301 – 17 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 17. SC2i.org : Surgical Critical Care Initiative newsletter . Available at https://stage-sc2i.hjf.org/site/assets/files/1213/sc2inewsletterissue1-final.pdf; accessed May 31, 2020 . 18. Belard A , Buchman T, Dente CJ, Potter BK, Kirk A, Elster E: The Uniformed Services University’s Surgical Critical Care Initiative (SC2i): bringing precision medicine to the critically ill . Mil Med 2018 ; 183 ( Suppl 1 ): 487 – 95 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 19. Stein MB , Ware EB, Mitchell C, et al. : Genomewide association studies of suicide attempts in US soldiers . Am J Med Genet B Neuropsychiatr Genet 2017 ; 174 ( 8 ): 786 – 97 . Google Scholar Crossref Search ADS PubMed WorldCat 20. NIH National Institute of Mental Health : Army STARRS Historical Administrative Data Study (HADS): looking at the past . Available at https://www.nimh.nih.gov/health/topics/suicide-prevention/suicide-prevention-studies/army-starrs-historical-administrative-data-study-hads-looking-at-the-past.shtml; accessed May 31, 2020 . 21. U.S. National Library of Medicine : DCLK2 doublecortin like kinase 2 . Available at https://www.ncbi.nlm.nih.gov/gene/166614; accessed May 31, 2020 . 22. U.S. National Library of Medicine : KLHL36 kelch like family member 36 . Available at https://www.ncbi.nlm.nih.gov/gene/79786; accessed May 31, 2020 . 23. Walter Reed Army Institute of Research : New antimalarial drug, tafenoquine, approved for malaria prevention . Available at https://www.wrair.army.mil/node/67; accessed May 31, 2020 . 24. Spring MD , Sousa JC, Li Q, et al. : Determination of cytochrome P450 isoenzyme 2D6 (CYP2D6) genotypes and pharmacogenomic impact on primaquine metabolism in an active-duty US military population . J Infect Dis 2019 ; 220 ( 11 ): 1761 – 70 . Google Scholar Crossref Search ADS PubMed WorldCat 25. Avula B , Tekwani BL, Chaurasiya ND, et al. : Metabolism of primaquine in normal human volunteers: investigation of phase I and phase II metabolites from plasma and urine using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry . Malar J 2018 ; 17 ( 1 ): 294. Google Scholar OpenURL Placeholder Text WorldCat 26. Uniformed Services University : Surgical Critical Care Initiative research . Available at https://www.usuhs.edu/sc2i/research; accessed May 23, 2020 . 27. STARRS-LS : Study to assess risk & resilience in servicemembers – longitudinal study . Available at https://starrs-ls.org/#/; accessed November 8, 2020 . 28. Bradburne C , Lewis JA: Personalizing environmental health: at the intersection of precision medicine and occupational health in the military . J Occup Environ Med 2017 ; 59 ( 11 ): e209 – 14 . Google Scholar Crossref Search ADS PubMed WorldCat 29. Karki R , Pandya D, Elston RC, Ferlini C: Defining “mutation” and “polymorphism” in the era of personal genomics . BMC Med Genomics 2015 ; 8 : 37. Google Scholar OpenURL Placeholder Text WorldCat 30. American Cancer Society : Changes in genes . 2014 . Available at https://www.cancer.org/cancer/cancer-causes/genetics/genes-and-cancer/gene-changes.html; accessed May 24, 2020 . 31. Whitney PF , Jacobson JR: US Air Force occupational and environmental health program . Available at https://ckapfwstor001.blob.core.usgovcloudapi.net/pfw-images/dbimages/OH%20ch%202.pdf, published 2019 ; accessed May 19, 2021 . 32. Military Health System : About the Military Health System . Available at http://www.health.mil/About-MHS, published 2016 ; accessed May 4, 2020 . 33. National Center for Chronic Disease Prevention and Health Promotion : Chronic diseases and military readiness . Available at https://www.cdc.gov/chronicdisease/resources/publications/factsheets/military-readiness.htm, updated 2019 ; accessed May 31, 2020 . 34. U.S. Army Public Health Center : Health of the force . Available at https://phc.amedd.army.mil/PHC%20Resource%20Library/2016HealthoftheForcereport.pdf, published 2016 ; accessed May 5, 2020 . 35. Centers for Disease Control and Prevention : National diabetes statistics report . 2020 . Available at https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf, published 2020 ; accessed July 30, 2020 . 36. Klonoff DC : The personalized medicine for diabetes meeting summary report . J Diabetes Sci Technol 2009 ; 3 ( 4 ): 677 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 37. Chao SY , Zarzabal LA, Walker SM, et al. : Estimating diabetes prevalence in the Military Health System population from 2006 to 2010 . Mil Med 2013 ; 178 ( 9 ): 986 – 93 . Google Scholar Crossref Search ADS PubMed WorldCat 38. Clinical Pharmacogenetics Implementation Consortium (CPIC) : Genes-drugs . Available at https://cpicpgx.org/genes-drugs/, updated 2019 ; accessed May 23, 2020 . 39. Shrestha A , Ho TE, Vie LL, et al. : Comparison of cardiovascular health between US Army and civilians . J Am Heart Assoc 2019 ; 8 ( 12 ): e009056. Google Scholar OpenURL Placeholder Text WorldCat 40. Go AS , Bauman MA, Coleman King SM, et al. : An effective approach to high blood pressure control: a science advisory from the American Heart Association, the American College of Cardiology, and the Centers for Disease Control and Prevention . J Am Coll Cardiol 2014 ; 63 ( 12 ): 1230 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 41. National Center for Chronic Disease Prevention and Health Promotion : Heart disease . Available at https://www.cdc.gov/heartdisease/risk_factors.htm; accessed July 29, 2020 . 42. U.S. National Library of Medicine : PCSK9 gene . Available at https://ghr.nlm.nih.gov/gene/PCSK9#conditions, published 2020 ; accessed July 29, 2020 . 43. Military Health System : MTF formulary management for PCSK9 inhibitor agent subclass . Available at https://www.health.mil/Reference-Center/Fact-Sheets/2017/02/21/Formulary-Management-for-PCSK9-Inhibitors, published 2017 ; accessed July 29, 2020 . 44. Kotchen TA , Cowley AW Jr., Liang M: Ushering hypertension into a new era of precision medicine . JAMA 2016 ; 315 ( 4 ): 343 – 4 . Google Scholar Crossref Search ADS PubMed WorldCat 45. Cowley AW Jr., Nadeau JH, Baccarelli A, et al. : Report of the National Heart, Lung, and Blood Institute working group on epigenetics and hypertension . Hypertension 2012 ; 59 ( 5 ): 899 – 905 . Google Scholar Crossref Search ADS PubMed WorldCat 46. Lopez CT : DOD releases report on suicide among troops, military family members . U.S. Department of Defense . Available at https://www.defense.gov/Explore/News/Article/Article/1972793/dod-releases-report-on-suicide-among-troops-military-family-members/, published 2019 ; accessed May 24, 2020 . Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US. This work is written by (a) US Government employee(s) and is in the public domain in the US. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US.

DiEuliis, Diane; Giordano, James

doi: 10.1093/milmed/usab017pmid: 34967406

ABSTRACTDevelopments in genetics, pharmacology, biomarker identification, imaging, and interventional biotechnology are enabling medicine to become increasingly more precise in “personalized” approaches to assessing and treating individual patients. Here we describe current scientific and technological developments in precision medicine and elucidate the dual-use risks of employing these tools and capabilities to exert disruptive influence upon human health, economics, social structure, military capabilities, and global dimensions of power. We advocate continued enterprise toward more completely addressing nuances in the ethical systems and approaches that can—and should—be implemented (and communicated) to more effectively inform policy to guide and govern the biosecurity and use of current and emerging bioscience and technology on the rapidly shifting global stage.

Hellwig, Lydia D; Krokosky, Alyson; Vargason, Ashlee; Turner, Clesson

doi: 10.1093/milmed/usab007pmid: 34967403

ABSTRACTGenetic counseling for military beneficiaries poses unique challenges and counseling opportunities. In order to fully meet the needs of this population, genetic counseling involves critical ethical and psychosocial considerations. This article reviews some elements of genetic counseling that must be considered when working with beneficiaries in the military health system.

Doll, Bruce; De Castro, Mauricio J; Fries, Melissa H; Pock, Arnyce R; Seibert, Diane; Yang, Wendy

doi: 10.1093/milmed/usab406pmid: 34967402

ABSTRACT Pressed by the accumulating knowledge in genomics and the proven success of the translation of cancer genomics to clinical practice in oncology, the Obama administration unveiled a $215 million commitment for the Precision Medicine Initiative (PMI) in 2016, a pioneering research effort to improve health and treat disease using a new model of patient-powered research. The objectives of the initiative include more effective treatments for cancer and other diseases, creation of a voluntary national research cohort, adherence to privacy protections for maintaining data sharing and use, modernization of the regulatory framework, and forging public–private partnerships to facilitate these objectives. Specifically, the DoD Military Health System joined other agencies to execute a comprehensive effort for PMI. Of the many challenges to consider that may contribute to the implementation of genomics—lack of familiarity and understanding, poor access to genomic medicine expertise, needs for extensive informatics and infrastructure to integrate genomic results, privacy and security, and policy development to address the unique requirements of military medical practice—we will focus on the need to establish education in genomics appropriate to the provider’s responsibilities. Our hypothesis is that there is a growing urgency for the development of educational experiences, formal and informal, to enable clinicians to acquire competency in genomics commensurate with their level of practice. Several educational approaches, both in practice and in development, are presented to inform decision-makers and empower military providers to pursue courses of action that respond to this need. INTRODUCTION AND BACKGROUND Pressed by the accumulating knowledge in genomics and the proven success of incorporating cancer genomics into the clinical practice of oncology, the Obama administration unveiled a $215 million commitment for the Precision Medicine Initiative (PMI)1 in 2016. This pioneering research effort will improve health and treat disease using a new model of patient-powered research. The initiative’s short-term goals focus on cancer research, whereas the long-term goals will bring precision medicine to all areas of health. Genomics, a relatively recent yet rapidly accumulating repository of advances in gene-based knowledge, is a central tenant of precision medicine. The origins of precision medicine may never be clearly identified. A century ago, the ABO blood type groups were identified, followed 50 years later by the elucidation of the structure and function of DNA and RNA. However, the completion of the Human Genome Project2 significantly advanced the prospects for therapies targeted to individual patient characteristics. Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.1 The objectives of the PMI include more effective treatments for cancer and other diseases, creation of a voluntary national research cohort, adherence to privacy protections for maintaining data sharing and use, modernization of the regulatory framework, and forging public–private partnerships to facilitate these objectives.1 Agencies across the Federal government are leveraging their unique expertise and history to carry forward the President’s vision of individualized treatments for every American. Specifically, the DoD Military Health System (MHS) joined other agencies to execute a comprehensive effort for PMI. Concurrently, the DoD is working with the Department of Veterans Affairs to expand the Million Veteran Program that seeks to define the links between genes and disease with a voluntary research cohort of veteran enrollees. The maturation of a health information exchange based on the MHS health data from both active duty and veteran electronic health records will facilitate the integration of genomics into clinical practice. Along with the NIH’s All of Us program, these complementary efforts will advance genomics knowledge. Our hypothesis is that there is a growing urgency for the development of educational experiences, formal and informal, to enable clinicians to acquire competency in genomics commensurate with their level of practice. Several educational approaches, both in practice and in development, are presented to inform decision-makers and empower military providers to pursue courses of action that respond to this need.3 CURRENT ISSUES AND GAPS The ever-increasing integration of genomics into MHS routine clinical practice continues to have substantial ramifications to the practice of military medicine and operational readiness. With the increasing availability of highly specialized tests, such as large panels, exome sequencing, pharmacogenetics, and universal non-invasive prenatal testing,4 the provider community must possess the required knowledge to correctly order, interpret, and act on such testing. With over 1.4 million men and women on active duty, 1.1 million serving in the National Guard and Reserve forces and 718,000 civilian personnel, the DoD provides military forces to deter war and protect the nation’s security. Importantly, mission readiness hinges on the health of the force. Health is increasingly considerate of advances in genomics. However, there are complex issues faced by MHS, including limited access to genomic medicine expertise, the perceived relevance of genetic testing and interpretation, needs for extensive informatics and infrastructure to integrate genomic results, privacy, and security, and policy development to address the unique requirements of military medical practice. Available research to provide guidance on the means for integration has generally developed along specific lines of clinical care, most notably cancer. Thus, the relative absence of overarching national standards is a significant limitation for the integration of efforts to define a unified set of standards to which clinicians can be trained. Some experts advocate that genetic testing be performed only in a clinical setting with professional counseling.5 However, commercial genetic screening products have emerged that patients could choose to pursue without any input from their physicians. Commercial direct–to-consumer (DTC) tests became available in 2007. However, over time, the cost of commercial genetic testing has decreased substantially. In 2007, the price was $1,000; by 2012, it had fallen to $99 and has not changed since then. During the same period, membership in 23andMe, for example, increased from 100,000 to more than 10 million persons. Recently, 23&Me has offered discounted fees for military members who submit samples for sequencing.5 With the increasing affordability of DTC DNA sequencing, the incorporation of patient genotype data into the practice of medicine is increased as a result of patient intentions rather than clinician election. A perception that the provider has become the second opinion regarding a patient’s health is a consequence of such patient-initiated inquiries. DTC companies have empowered individuals to independently obtain their personal genomic profiles, which provide them with a view of their genetic risks for hundreds of diseases and atypical drug responses. 23andMe now includes FDA-authorized reports for the following categories of disease risk results: variants associated with selected classical autosomal dominant conditions, such as BRCA1/2-associated hereditary breast, ovarian, and low-density lipoprotein receptor (LDLR)-associated familial hypercholesterolemia; risk alleles associated with a several-fold increased risk for certain diseases, such as Alzheimer disease and factor V Leiden hereditary thrombophilia; and carrier testing for autosomal recessive diseases, including cystic fibrosis and sickle cell anemia. The company also offers a polygenic risk score for genetic susceptibility to type 2 diabetes mellitus, which is derived from its own database of consumer genetic and health history data.5 Subsequent to receipt, the patient presents this inexpensive, accessible genetic information to the provider community. Preparing health professionals to use genetic (individual genes) and genomics (combination of genes, the environment, psychosocial, and/or cultural factors) concepts gained interest for educators during the past two decades although the basic principles of inheritance were described in 1866 by Gregor Mendel,6 the human genome was first described in 1953,7 and basic genetic concepts began to be taught in undergraduate education in the 1960s. Genomics emerged at the confluence of genetics, statistics, and large-scale data sets, necessitating a broad-based understanding of the interrelated disciplines in order to develop curricula. While the knowledge from genomics is increasing, the gap between its availability and utility for the provider has also increased. A concurrent lag exists in educational formats to broadly prepare healthcare providers with the understanding to apply genomics for precision medicine approaches to patient care. Advances in genomics are not occurring in isolation and compete with other learning demands placed upon medical specialists. Board examinations vary in regard to an emphasis on genomic knowledge. Furthermore, the receptivity to genomic medicine varies across and within the specialties.8 Perceptions of genomics, which largely emanate from oncology, are proving to be mixed, with perceived benefits often tempered by a host of concerns. Concerns have centered on questions of utility with few treatments available and genomic information yet to be fully deciphered. Furthermore, genetic test access, cost, privacy, ethical guidelines, and the potential for genetic tests to cause psychological harm, impeded insurance access, multidisciplinary implementation, and distracting the patient from a primary health issue may undercut the assimilation of genomics.9 Given these challenges, the military medical environment has reacted purposefully. Addressing this education gap is most important to ensure the currency of genomics integration into military medical care for the readiness of the force. Furthermore, the disposition of the patient to receive and incorporate this information into their lifestyle decisions figures prominently in their individual readiness to accomplish a mission, let alone remain in the military. Not only is the provider confronted with the decision on how to incorporate genomics into the practice of precision medicine but also an appreciation for the consequences regarding this information’s impact on the service member. Earnest DoD efforts to align policy on privacy, patient rights and disabilities, have sought to stay in register with the rapid accumulation of genomic knowledge. Three actual case studies involving genetic-based diseases illustrate the significant import not only for the patient’s health but their aspirations for a military career. The studies illustrate both the routine and unique relevance of clinician currency in genomics when evaluating a service member. Consequences for service members may include limitations on duty assignment, security clearance and operational readiness, and medical discharge. CASE STUDIES Case 1 A 29-year-old Army helicopter pilot, combat-proven, experienced a left-leg deep venous thrombosis at 30 weeks of pregnancy while reassigned to a 13-hour/day desk job. After pregnancy, while running, she had evidence of chronic left leg thrombophlebitis and was found to carry a Factor V Leiden mutation predisposing to thrombosis. She was not considered fit to return to flying and left the Army without any medical benefits because her Factor V Leiden mutation was considered a pre-existent condition. Case 2 A 32-year-old active duty USN member was diagnosed with Stage 2 infiltrating ductal carcinoma of the left breast. She was treated surgically and with radiation/chemotherapy but experienced a recurrence in the right breast. She was tested for BRCA1/2 mutation and was found to have a pathologic BRCA1 mutation. Her degree of the disease led to medical discharge, but she did not receive any disability for her disease because it was considered the result of a pre-existent condition. Case 3 A 21-year-old female service member presented at the primary care clinic. She was asymptomatic when she presented. She came because her mother was diagnosed with arrythmogenic right ventricular cardiomyopathy and found to have a PKP2 pathogenic mutation. The variant is associated with an increased risk of sudden cardiac death (SCD). Further pedigree analysis demonstrated an extensive family history of SCD. The service member wanted to discuss the option of her getting tested or not. All of the health and military implications were discussed. She elected to get tested and the test came back positive for the same mutation as her mother. The current guidance for a person with a pathogenic PKP2 variant is that they do not engage in physical activity. A cardiologist recommended she does not engage in physical activity. The clinical restriction on exercise prompted her release from active duty. Increased knowledge of genetics and genomics impacts both the patient profile and the policies intended to manage the information. These cases illustrate the complex challenges confronting the clinician with interpreting available, relevant genomic data for patients, educating the patient to understand the information, and complying with privacy policies on genomic data. Genomic data enable the application of predictive analytics to a patient’s clinical presentation. Ultimately, military healthcare practices are perfected when prognostic and diagnostic accuracy improves. Prior to the availability of this information, attempts to correlate events with clinical outcomes in all three cases would have led to inaccurate conclusions about the underlying cause. Data substantiate genomics knowledge and mandate policy oversight for its utility. Enhanced genomic patient profiles are subject to military policies intended to provide protection in coverage that is similar to the Genetic Information Nondiscrimination Act of 2008 (GINA).10 GINA prohibits genetic information discrimination in employment. Privacy of health information (which includes genetic information) is addressed in numerous overarching and DoD policies11 as well as service-specific guidance. Given the fast-paced acquisition of genomic knowledge, all clinicians must deliberately assess the currency of their knowledge and understanding of this powerful diagnostic/prognostic tool. Particularly pertinent to the military clinician, force readiness depends upon sustained world-class care to which service members are entitled throughout their career. Genomics is a mainstay of world-class care. A literature review covering 2015 to 2018 focused upon genomic medicine education and training for providers from medical students to established physicians. In some sectors of care, the value of the genomic information was undermined by the absence of a plan for care. Medical providers’ perceived or actual comprehension of genetic concepts, conditions, and/or testing has been shown to be highly variable and frequently poor.12 Clinicians felt unprepared to order genomic tests,13 integrate patient data into clinical practice,14 and explain test results.15 Additional considerations are the relatively small community of clinical geneticists and the supporting staff of counselors available for consultation in the MHS. In the current environment, a range of factors—formal education, years in practice, confidence, supposed utility, and consequences—are likely to influence a clinician’s embrace of genomics in practice. GENOMICS CURRICULUM INITIATIVES The American Association of Medical Colleges’ Medical School Objectives Project provided objectives in genetics education,16 and the Association of Professors of Human and Medical Genetics provided a more expansive set of guidelines.17 These guidelines were further updated in 2013,18 accounting for some of the remarkable advances attributed to the completion of the Human Genome Project as well as the increased availability of genomic assays and direct-to-consumer genomic testing. The future genomics-based medical care mandates that all next-generation medical practitioners will achieve basic genomic literacy during their undergraduate medical education by obtaining the basic knowledge and skills to practice precision medicine. As early as 2015, genomics-based education within the USUHS School of Medicine and the USUHS Graduate School of Nursing along with other medical schools across the country19 had advanced to include expanded instruction on these same topics, along with educational sessions specifically focusing on molecular technologies disease and gene identification, hereditary genomics, cancer genomics, interpreting genetically based test results, and the emerging field of precision medicine. The curriculum was designed to provide not only training in the basic scientific aspect of precision medicine in terms of genetic foundations for human diseases but also the practical aspect of the clinical practice of precision medicine. Genomic research has been moving at an accelerating pace, which demands genomic education curriculum updates in parallel with the advances.3,12,20 During the current academic year (2020-2021), the USUHS medical student and advanced practice nursing curricula have been further modified to include a greater emphasis on clinical applications of genomics in terms of specific consideration of the military risks, benefits, clinical utility, and ethical concerns of genetically based information and testing. Small group discussion sessions are being developed to not only familiarize students with the GINA but to heighten their understanding of the potential ramifications of testing for certain genetic traits (e.g. carriers for the RyR1 gene mutation associated with increased risk for exertional rhabdomyolysis, or sickle cell trait) on military operational readiness to include deployment considerations, particularly when involving austere and/or extreme climatic environments.21–23 The clinical specialty fields where the precision medicine approach already plays a dominant or significant role are given in-depth coverage, including not only medical genetics (hereditary diseases) and oncology (cancer) but also infectious diseases. Interactive sessions focusing on interpreting genetic data and applying it to clinical situations are being expanded. With the curriculum’s balanced focus on both basic sciences and clinical applications of genomic medicine, the students are well prepared to go into clinical years and residencies with a solid foundation and a basic understanding of genomic medicine practice. To respond to the increased demand for genetic information, multiple medical centers across the country have introduced genetic and genome-wide analysis to make genetic testing available to patients.24 Expanding genomics education, several institutions have offered students the opportunity to undergo testing themselves as part of a medical school genomics curriculum. Stanford University has instituted an elective course on genomics and personalized medicine, where students learn principles of genetics and genomics through a combination of interactive lectures and hands-on analysis of genomic data, using either their personal genotypic or publicly available data.25 The use of personal genome testing could enhance genetics education for at least some medical and graduate students. Students were more likely to elect to undergo testing if they felt that they understood the risks and benefits of the test and enough about genetics to understand the results. The data also suggested that students who experienced anxiety during the decision-making process for undergoing genetic testing were more likely to decide against testing than students who did not. The study highlighted the importance of obtaining rigorous informed consent before offering genome testing in the classroom or elsewhere.26 These results are consistent with another study, in which primary care physicians currently offering genomic testing services as part of their practice were more likely to order the test for their patients if they felt well-informed about genomics and if they had undergone testing.27 Anticipating the challenges of genomics integration into precision medicine, three key academic themes emerge. First is a need to address many of the common misconceptions related to what modern genetics entails and how it is related to the practice of everyday medicine. All too common is the perception that geneticists in particular only focus on the esoteric, whereas the reality is that medical genetics can influence the management of a wide range of relatively common disorders such as familial hypercholesterolemia, hereditary breast and ovarian cancer syndrome, and hereditary hemochromatosis. Second is the need to quicken the emphasis on clinical applications of genomics while not negating an understanding of the associated basic science. Not only is DTC and pharmacogenetic testing becoming more commonplace, but as was pointed out in a recent article, it is entirely possible that genetic markers could soon be used to predict which patients are most likely to experience optimal treatment responses following certain procedures, such as bariatric surgery.28 Third is the importance of teaching students how to correctly interpret and convey genetically based risk estimations. Experience to date has indicated that most clinical providers have difficulty explaining relative vs. absolute risk to patients.29 An alternative to all clinicians integrating genomics into their practice would be for the clinical geneticists and genetic counselors to assume the responsibilities for presenting genomic information to patients. However, their relative small numbers practicing nationally, let alone in the MHS, preclude this possibility. According to the National Society of Genetic Counselors (NSGC), only 5,000 genetic counselors are certified to practice in the USA.30 Comprehensive requirements for professional counseling include preparation of a third- to fourth-generation pedigree, determination of the pretest risk for particular diseases, discussion of the risks and benefits of testing, a 10-step informed consent process, consideration of the potential for disclosure of results, and performance of a psychological assessment.5 The American College of Medical Geneticists (ACMG) guidelines support patients being informed by a clinician that their test results might not rule out the possibility of a disease or its risk and that they may receive incidental results that are unrelated to their initial reason for having the test. The ACMG concurs with the NSGC that patients must be made aware that their genetic information could be disclosed to others, potentially affecting their eligibility for long-term care or life insurance.5 A decade ago, the American Nurses Association (ANA) published Essentials of Genetic and Genomic Nursing: Competencies, Curricula Guidelines, and Outcome Indicators, which recommended that academic faculty collaborate with other disciplines to incorporate genetic and genomic information in courses and participate in both National Council Licensure Examination (NCLEX) and certification test development to ensure that test items assess knowledge of pertinent aspects of genetics and genomics.31 Practicing nurses are encouraged to pursue this information through continuing education. Key NCLEX competencies for nursing practice include the following: advocating for patients’ access to genetic and genomic resources, services, and support groups, such as condition-specific groups, genetic support groups, and rare diseases groups; incorporating technologies specific to genetics and genomics into nursing practice; and presenting information to patients in a way that is appropriate to a diverse population that appreciates their culture, religion, knowledge, and literacy level and in their preferred language, Nurses may need to expand or acquire skills in the following areas (Consensus Panel): identifying relevant genetic, environmental, and genomic information in patients’ health histories; generating and documenting a pedigree based on available family history; identifying patients who may benefit from genetic and genomic information based on assessment data; and understanding the ethical, legal, fiscal, and societal issues related to genetic and genomic information. Incorporating knowledge gained from genomics encourages nurses to build on skills they already possess, such as collaboration, patient advocacy, patient and family education, patient assessment, and documentation. Applications of genetic testing have expanded beyond the diagnosis of complex medical conditions to include pre-conception genetic screening,32 selection of embryos for in vitro fertilization,33 non-invasive screening for fetal abnormalities,34 and CRISPR-Cas9-based gene therapy35,36 to list a few applications. Patients may need help interpreting and understanding genetic and genomic information, particularly as it impacts the active duty. Nurses need to possess a good understanding of these topics in order to provide appropriate support. For nurses in advanced practice, the ANA and the International Society of Nurses in Genetics published the Essential Genetic and Genomic Competencies for Nurses with Graduate Degrees, which identifies 38 competency areas, ranging from genetic risk assessment and clinical management to leadership and research.31 Developing a strong knowledge base in these sciences and technologies will ensure that nurses can collaborate effectively with patients and other healthcare providers to advance the goal of precision medicine for the benefit of all patients. FUTURE DIRECTIONS Genomic medicine will be one of the cornerstones in efforts to have the healthiest, fittest force in the world. There is mounting evidence for the actual impact it can have on conditions directly affecting military readiness such as treating mental health disorders faster and more efficiently37,38 or enhancing population screening of potentially deadly, but silent, disorders.39 To adequately provide for the genomics needs of our patient population, the DoD will need a multi-tiered approach that includes not only increasing the number of genetics professionals but also teaching the next generations of providers as well those currently in practice in the MHS. The MilSeq Project,40,41 Enabling Personalized Medicine through Exome Sequencing in the U.S. Air Force, has highlighted the need and the feasibility to better train an ever-broadening array of physicians and advanced nurse practitioners in the interpretation of exome sequencing results and in the communication of salient results to their patients. The MilSeq study included whole exome sequencing (WES) in 93 ostensibly healthy active duty individuals with three objectives: (1) demonstrating the clinical applicability/utility of genomic screening in military individuals; (2) exploring the integration of genomic information into the military electronic health record system; and (3) devising alternative genomic medicine delivery models that better leverage our limited and geographically restricted, genomic resources. To that end, 12 clinical providers were trained with an intensive 3-hour course focusing on important pre- and post-test counseling issues in addition to basic genetic concepts. The healthcare providers then delivered and explained the results of the WES, including variants related to monogenic disorders (e.g., familial hypercholesterolemia), carrier status, pharmacogenomics, and moderate-risk variants (e.g., APOE4 and Alzheimer’s disease risk). All the encounters were recorded and analyzed by a group of geneticists and genetic counselors to determine patients were receiving appropriate care and at all times, and the provider had access to a genomic resource center staffed by genetic counselors and clinical geneticists should they have questions. Along those lines, studies in the military looking at a hybrid model of genomics care have yielded positive results.21 Teaching an intensive crash-course of basic pre- and post-test counseling concepts to primary care providers and pairing this approach with available, remote genetics feedback and support proved to be a successful pilot. These providers were able to appropriately convey the results of exome sequencing to a small cohort of healthy individuals within the Air Force. Although this small pilot was successful, further ongoing studies will be needed to evaluate the efficacy, suitability, and safety of this approach on a large scale. If nothing else, this study highlighted recurrent educational shortcomings primary care providers have when approaching genomics, such as considering cascade testing and family members, or how to best convey relative/absolute risks and uncertainty about a genetic result to a patient. Although the study was carried on Air Force personnel, by Air Force providers, the study as a whole was reviewed by Army genetic professionals, and the results widely circulated among the Tri-Service genomics community with positive feedback. The MilSeq study represents a potential path for the Services to implement as an alternative mode of delivery for genomic medicine. CONCLUSION The present military health care environment pressures all providers to allocate limited time to provision of care, maintenance of competencies, and operational commitments. Considering the MHS structure and shortage of military clinical geneticists, the initial encounters with patients and the integration of genomics will fall to initial encounters in the primary care environment. The preparedness of the healthcare community without specific competencies in genomics to play a role in this workforce has been highlighted, prompting widespread calls for education across the spectrum of training from students through experienced clinicians. Looking to the future, though, it is becoming increasingly evident that a fundamental understanding of genomics in general and military-relevant genetics in particular is needed by medical personnel at all levels. Developing a competent workforce will be crucial to realizing the potential of genomics. Because there is not a one-size-fits-all solution, studies will have to look at a combination of strategies focused upon training experiences for healthcare providers working synergistically with remote genetic counselors, via tele-health and/or tele-mentoring along with traditional methods to convey new knowledge. Leading educational approaches will include immersive and experiential learning; interdisciplinary and inter-professional education; and electronic- and web-based approaches. The paper illustrates the diversity of approaches currently employed to address the growing genomics knowledge. This diversity presents as a strength by offering the variety of training methods to match the varied ways knowledge is acquired. However, the educational diversity may limit the identification of the most efficient approach to ensure a rapid acquisition of knowledge by the largest number of those who need the training. Thus, how genomic information could or should be utilized remains a topic of significant debate. Genomic knowledge offers the potential to make diagnosis of disease a more efficient and cost-effective process, thereby facilitating the implementation of preventive measures that mitigate impacts of disease on readiness. With the next wave of scientific breakthroughs imminent, there is a growing imperative to proceed with an aggressive planning strategy for the development and implementation of genomics education now. ACKNOWLEDGMENTS We would like to thank Dr. Scott McLean for his constructive comments that significantly improved the manuscript. FUNDING None declared. CONFLICT OF INTEREST STATEMENT None declared. REFERENCES 1. The White House, Office of the Press Secretary : FACT SHEET: President Obama’s Precision Medicine Initiative . 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Google Scholar Crossref Search ADS PubMed WorldCat 40. Green RC , Killian J, Gardner CL, de Castro M: The MilSeq project: enabling personalized medicine through exome sequencing in the U.S. Air force . Available at https://www.genomes2people.org/research/milseq/; Accessed September 10, 2020 . 41. Maxwell MD , Hsu R, Islam R, et al. : Educating military primary health-care providers in genomic medicine: lessons learned from the MilSeq Project . Genet Med 2020 ; 22 ( 10 ): 1710 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes Material has been reviewed by Uniformed Services University of the Health Sciences (USUHS). There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official, or as reflecting true views of the Uniformed Services University of the Health Sciences or the Department of Department of Defense. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US. This work is written by (a) US Government employee(s) and is in the public domain in the US. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US.