Table 2:

Issues, obstacles, and potential solutions in pharmacogenomic implementation

Category	Issue	Example	Perceived Obstacle	Potential Solution(s)
Pharmacogenes	Majority of individuals in most populations are “wild type”	Less than 1% of individuals are TPMT poor metabolizers111	Very large numbers needed to test for successful prospective trials and for clinical benefit	• Prespecify plan to analyze subset with variant • Conduct trials of across multiple drugs and genes, which inform panel-based testing
	Rare variants with uncertain effect	46 of 64 haplotypes for CYP2C9 have unknown function83	Insufficient data to ascertain phenotype with absolute certainty	• Assay only variants with known function • Include uncertainty on clinical reports • Functional studies
	Spectrum of effects due to variants within one gene	Distinct variants in CYP2C19 confer complete loss of function, partial loss of function, or gain of function	Need to express genetic effect as quasi-continuous trait	• Use activity scores to annotate variant effect
	Complexity of gene assays	Different assay technologies required for CYP2C19, CYP2D6 and HLA	Lack of comprehensive local infrastructure for multiple laboratory developed tests	• Development of off-the-shelf assays for pharmacogenes • Reliance on send-out laboratories for some or all pharmacogenomic testing
Drug Effects	Hard endpoints are rare	There were no deaths in the 1650 randomized patients treated with warfarin in the GIFT trial62	Robust methods to prove impact of genotype-guided therapy on hard endpoints not well-developed.	• Use surrogate, but clinically relevant, endpoints such as major bleeding, length of hospitalization, symptom control, or healthcare cost • Perform large retrospective analyses of hard endpoints using EHR-linked biobank data
	Efficacy endpoints poorly defined outside of clinical trials	Serial assessment of depression symptoms inconsistently documented in EHR data	Cannot perform retrospective analyses on efficacy	• Prospective data collection with oversampling of participants with pharmacogenetic variants
Healthcare institutions / Local health information technology	Results for each gene require interpretation to discrete clinical guidance	Clinical decision support for warfarin provides dosing calculation, not genetic test results	Lack of technological infrastructure for interpretation from gene test results to functional effect to dosing guidance	• Widespread sharing of technical solutions and clinical decision support across institutions
	Functional predictions and clinical guidance evolve with new evidence	New evidence for the role of NUDT15 variants in thiopurine toxicity23	Need to continually assess evidence, which is consistently expanding to include more drugs and more genes	• Continued support for development of guidelines to guide appropriate testing
	Provider resistance to receiving or using pharmacogenomic information	No agreement among healthcare providers about who should take responsibility for results85	Limited ordering of pharmacogenomic testing and/or lack of use of pharmacogenomic guidance	• Identification and recruitment of clinical champions for specific drug-gene interactions • Increased provider education • Interruptive prescriber alerts making the pharmacogenomic-informed choices the default
	Evolving EHR systems	EHR system changes or upgrades may cause loss of reporting or decision support functionality	Large ongoing costs of system maintenance	• Commitment from EHR vendors for continual support of pharmacogenomic implementation • Computable guidelines for pharmacogenomics
Healthcare systems	Patient movement across EHR systems	A patient’s pharmacogenomic results do not follow them when they receive care in another system	Loss of potential benefit of test and/or potential for repeat testing	• Provision of pharmacogenetic results to patients • Portability of results for transfer to other EHR systems
	Diversity of pharmacogenomic assays	Depending on TPMT genotype interpretation, a patient may be labeled as poor or intermediate metabolizer	Lack of consistency of results across CLIA-approved tests	• Standardization of minimal test requirements • Standardization of interpretation of variant effects
	Reimbursement challenges	Pharmacogenomic testing is variably reimbursed across clinical scenarios, states, genes/drugs, and payors	Pharmacogenomic testing is not cost-effective	• Increase data available on cost benefit and improve and standardize analyses to promote reimbursement • Develop comprehensive cost-effectiveness model as opposed to models for individual drug-gene pairs.