Over the last decades, there has been considerable growth in the use of pharmacogenomic (PGx) testing due to increased awareness of patients developing moderate to serious adverse drug reactions (ADRs) attributed to individual genetic variation. The US Food and Drug Administration (FDA) “Table of Pharmacogenomic Biomarkers in Drug Labeling” contains 457 entries (status March 2021) relating to dosage and administration, warnings, precautions, drug interactions, adverse reactions, or clinical pharmacology [1]. For example, codeine, a frequently prescribed opiate present in Tylenol #3 (acetaminophen with codeine), contains the boxed warning:

A survey involving clinicians from academic medical centers showed 99% agreed that PGx variants would influence a patients’ response to drug therapy and should be acted upon when a clinically significant drug-genome interaction was present (92%) [2]. Previous studies have shown that over 80% of patients can carry at least one functional gene variant influencing one of the 100 most prescribed medications in the United States, and the rate of rehospitalization can be significantly reduced by implementation of PGx test recommendations [3-7].

Recommendations for actionable prescribing decisions are routinely based on clearly defined, peer-reviewed guidelines with different evidence levels (levels 1-4) issued by international pharmacogenetic consortia and professional societies such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) and maintained in high-quality public and expert-curated databases, including PharmGKB [8-11]. Currently, most laboratories conducting PGx testing use targeted genotyping technologies to screen for specific variants to determine ADRs. Examples of these technologies include single or multiplexed polymerase chain reaction (PCR) assays combined with Taqman hydrolysis probe chemistry, microarrays (ThermoFisher Scientific), mass spectrometry (Agena Biosciences), bead-based molecular assays (Luminex), or next-generation sequencing (NGS) assays (Illumina) [12-14]. In 2018, Fabbri et al [15] described 38 commercially available PGx test panels offering personalized medication prescription guidance in clinical settings. The only genes included in all of these panels were CYP2D6 and CYP2C19. Of the 38 panels, 31 (82%) included 8 genes or less [15]. PGx testing as described in this study encompasses deep sequencing (>1000X) of 141 single-nucleotide polymorphisms (SNPs) or indels across 23 genes by NGS.

The aim of this study is to evaluate the overall use and to describe how PGx report recommendations, including genetic-based dosing guidance (PGx), drug-gene interaction (DGI)–based guidance, and drug-drug interaction (DDI)–based guidance, were applied to optimize drug dosing in a clinical setting that had not previously relied on pharmacogenetic test reports. Changes in prescription, patient compliance, and drug use were monitored based on updated medication lists and data in associated quantitative urine drug toxicology (UDT) reports, with limited access to patient progress reports. UDT reports were evaluated in a pain management setting before and after application of PGx panels to prevent ADR events (Figure 1).

Analytical sensitivity (call rate) was determined at >97.1% by positive agreement of all 141 variants including sex determination through 2 SRY probes and CNVs/indels. Genomic DNA ranging from 0.64 to 26 ng/µL (5-195 ng input gDNA) was sequenced across three validation plate runs with 68 positive control samples showing unambiguous genotypes. Buccal swabs were stored for up to 14 days at 4 °C prior to gDNA preparation; gDNA storage stability at 4 °C was confirmed for up to 6 days and up to 6.5 months for storage at –20 °C with up to 10 freeze/thaw cycles to yield high quality (>99.3%) genotyping results Multimedia Appendix 5.

All alleles covered per gene target or targets and resulting phenotypes were routinely described in the test details section in each PGx report (Table 1) following the results for PGx dosing and DGI or DDI (Figure 2). Of the 171 patients studied, drug adherence data was not available for 69 patients for which PGx report data was summarized. PGx report implementation could only be studied on the remaining 102 patients. A total of 26 PGx reports showed no medication list provided by the clinic, 8 of which medication lists were made available and added onto the PGx report retroactively. Medication lists provided showed that patients were prescribed an average of 5 different medications (ranging from 0 to 25 medications), resulting on average in 1 moderate pharmacogenetic guidance and 3 moderate DDI observations per patient. Among patient PGx reports with medication lists provided (n=146), 57.5% (n=84) showed one or more moderate and 5.5% (n=8) at least one serious PGx (ie, purely gene-based) interaction. A total of 96 (66%) patients showed at least one moderate and 15% (n=22) one or more serious DGIs or DDIs (Figure 3 and Multimedia Appendix 6).

Phenotypes and associated genotypes are summarized in Table 2 with an overview of population frequencies compared to the SDC. As shown in Figure 3, 5.5% (n=8) of 146 patients showed serious ADRs based on changes in either CYP2C19 (poor, intermediate to rapid metabolizers) or CYP2D6 (poor or ultra-rapid metabolizers) and one SLCO1B1 reduced function genotype. CYP2C19 genotype frequencies for 3 metabolizer types causing serious ADRs were spread across all 5 super populations ranging from 0.9% to 47.4% frequency (Table 2, CYP2C19 section). CYP2D6 genotype frequencies for intermediate to ultra-rapid metabolizers ranged from 1.2% to 57.1% frequency and SLCO1B1 poor function genotypes from 1.8% to 37% (Table 2, CYP2D6 and SLCO1B1 section). While SAS population frequencies for CYP2C19 ultra-rapid metabolizers and CYP2D6 poor metabolizers are determined as nonexistent in the 1000 Genome Database data, more recent studies show frequencies of 0.24% [24] and 0.84% [25], indicating possible occurrence within the SAS super population.

Medications affecting patients most severely based on their individual genotype in this cohort were amitriptyline for decreased exposure among 2 CYP2C19 rapid metabolizers and increased exposure for 1 CYP2C19 poor metabolizer, citalopram (insufficient response, CYP2C19 rapid metabolizer), clopidogrel (reduced response, CYP2C19 intermediate metabolizer), metoprolol with significantly increased sensitivity for a CYP2D6 poor metabolizer, paroxetine (reduced response in CYP2D6 ultra-rapid metabolizer), simvastatin (poor function of SLCO1B1 inducing high myopathy risk), and tramadol (CYP2D6 poor metabolizer with risk for no response). The top 15 medications affecting patients based on a DGI or DDI were identified (Figure 3). The most frequently occurring moderate DDI involved opioids observed in combination with central nervous system depressants such as muscle relaxants, benzodiazepines, sleep drugs, or the nerve pain medications gabapentin and pregabalin (Multimedia Appendix 6).

Prescription regimens were determined for 102 patients based on drug adherence report data before and after the PGx report was made available. Remaining patients either showed no drug adherence data or limited drug adherence data before the PGx report but no further information afterward. An active change in prescriptions based on the PGx report was observed for 85 (83%) patients for which a specific drug was either discontinued or switched within the defined drug classes of the report, or a new drug added. A total of 17 (17%) patient reports showed no predictive evidence of ADRs even when prescribed up to 11 medications (on average 2.5 medications per patient). Appropriately, no action was taken by the provider in these cases to deviate from the original prescription regimen. All adjustments made to patient prescriptions were studied for potential contraindications or possible new ADRs based on the PGx report.

Of the 85 patients whose medication lists were adjusted, only 3 showed that recommendations in the PGx report were not being followed for unknown reasons. “Patient A” was shown to be administered 5 medications (Keflex, Pennsaid, Skelaxin, MS Contin, and Lidocaine CV). PGx reporting indicated a normal PGx response and one moderate DDI to MS Contin (morphine) and Skelaxin (metaxalone), and a moderate PGx interaction for Pennsaid (diclofenac). Cessation of Skelaxin and Pennsaid removed all moderate ADRs; however, the addition of Percocet (oxycodone and acetaminophen) was not recommended:

The decreased response was alleviated with morphine prescriptions, for which there were no contraindications. Progress notes showed patient A:

Coreg (carvedilol) was added to the prescription regimen causing a moderate PGx warning:

The addition of Silenor (doxepin) was also contraindicated by the PGx report:

In this case the prescribed doxepin dosage was minimal (10 mg/day) according to progress notes. For the treatment of major depression or anxiety, adult oral dosages are initially 75 mg per day. The addition of Wellbutrin (bupropion), Soma (carisoprodol), Topamax (topiramate), and Prilosec (omeprazole) showed no contraindication except a moderate DDI between carisoprodol and morphine. The dose reduction for doxepin and the remaining moderate interaction for carvedilol were acceptable, as carvedilol was discontinued and the appropriate monitoring practices were carried out for patient A.

Similarly, for “Patient B,” 7 medications were listed, which showed a switch from codeine to morphine although no warnings against codeine were indicated (patient CYP2D6 normal metabolizer status). Instead, a switch to morphine warned:

Additionally, quetiapine and citalopram could cause a serious DDI (“concurrent use with agents known to prolong the QT interval should be avoided”), and the combinations of opioids with gabapentin prompted to “monitor patients for gabapentinoid-related side effects.” Further investigation into progress notes for patient B showed a suspected allergy or ADR to hydrocodone and oxycodone resulting in “nausea,” possibly explaining the emphasis on morphine and the patient avoiding exposure to other opioids such as codeine, hydrocodone, or oxycodone. An increase in morphine 15 mg immediate release formulation tablets (MSir) was initiated from 3 to 4 times daily, eventually 15 mg MSir 3 times per day with an additional 15 mg MS Contin (extended release) 2 times per day. Patient B:

Monthly reviews of the patient’s condition show:

Patient C was maintained on 10 of 11 initial medications with the appropriate removal of Plavix (clopidogrel) after 2 serious PGx warnings:

An additional serious DDI for Zocor (simvastatin) and Norvasc (amlodipine) warned:

After PGx reporting, clopidogrel was no longer observed in medication lists for drug adherence reports, but simvastatin was continued with amlodipine, and 9 moderate DDIs remained, cautioning to “limit the dosages and duration of each drug to the minimum possible while achieving the desired clinical effect.” The only alternative statin without adverse interactions recommended was fluvastatin. Progress notes for patient C showed simvastatin was prescribed less than 80 mg per day as recommended by the FDA in the PGx report at 40 mg per day. Patient C “denies muscle cramp, muscle twitches, muscle wasting, muscle weakness, neck pain, joint swelling. Denies fever, fatigue”; however, patient C eventually reported “muscle pain or tenderness” in the latter part of the 2-year treatment window. Monthly urinalysis screens and blood testing showed no discoloration in urine or abnormal glomerular filtration rates, but the reported muscle pain/tenderness and the combination of reduced SLCO1B1 gene function with concurrent daily 40 mg simvastatin and 5 mg amlodipine possibly indicated a statin-induced myopathy [31].

Serious ADRs can occur based on incidences of poor, intermediate, rapid, and ultra-rapid metabolizer types in all 5 super populations for prescriptions such as amitriptyline, citalopram or clopidogrel, metoprolol, paroxetine, simvastatin, and tramadol. While PGx cannot predict all ADRs (eg, allergies cannot be detected), dosing guidance and the additional DGI and DDI algorithm provided valuable insight to optimize prescription regimens. Limitations within this retrospective study include the lack of detailed patient demographics associated with UDT and PGx reports, and limited access to progress notes and long-term treatment outcomes. Rather than resorting to 1000 Genome Database population frequencies to characterize the SDC, specific demographics and additional case studies as the three previously presented would allow more comprehensive insights as to the combinatorial effect of prescription drugs among polypharmacy pain management patients.

In summary, the effect of PGx reports newly made available to medical staff in this context seems quite significant as observed by the individual PGx dosing/metabolizer status and DGI and DDI recommendations showing a corresponding modification of the medication regimen for each patient. Preventative action was observed for all serious interactions, and only moderate interactions were tolerated where there may not have been other alternatives. This study demonstrates the predictive value of PGx testing combined with a customized informational report to help improve clinical outcomes, which resulted in a successful application for patients in a pain management setting.