Implementing preemptive pharmacogenomics: Six reasons a research protocol may be “best clinical practice”

According to the National Center for Biotechnology Information, medication errors impact more than 7 million patients at a cost of 7,000-9,000 lives and $40 billion each year in the United States alone.¹ Adverse reactions vary widely, as exemplified by the vast number and types of warnings and contraindications listed on medication bottles. Relatively little is known about the contribution of inherited genetic variants to drug response. Research results vary widely. Estimates of drug response variability that is caused by genetic variants range from 20-95%.²

Imagine a world in which genotyping is routine in clinical practice. Clinicians could predict how a patient might respond to a drug before prescribing, rather than having to react suddenly after an adverse reaction has occurred. They would be able to prescribe more confidently, effectively and safely. Individuals could be tested once and then have a lifetime of information to predict their potential response to any drug rather than repeat for each illness or prescription as needs emerge. Preemptive pharmacogenomics offers this potential.

Preemptive pharmacogenomics testing is already available for some gene-drug combinations at Clinical Laboratory Improvement Amendments (CLIA) labs in some hospitals including University of Pittsburg Medical Center and St. Jude Children’s Research Hospital. However, advancing genome data from research to routine clinical use faces significant challenges. Here are six reasons that a CLIA lab might benefit from developing preemptive pharmacogenomics as a research protocol rather than taking on the complexities of establishing a new clinical best practice.

Identifying gene-drug associations

Definitive association of genetic markers to drug responses is a fundamental requirement before a preemptive pharmacogenomics recommendation can be provided to clinicians. It is also an exceptionally complex research challenge. Known genetic variants that influence drug response may involve genes associated with any aspect of drug absorption, distribution, metabolism, and excretion (ADME), or immune response to foreign substances. Alterations in gene function can occur in both regulatory and coding regions. Resulting phenotypic responses might include toxicity reactions, drug levels in blood or drug effects on other genes.³ Adverse drug reactions may involve both genetic and non-genetic factors.

In research, inconclusive results are expected, and protocols can be revised and re-run to establish certainty. However, for a gene-drug pair to be introduced for routine preemptive testing, the genotype association must be clearly distinguishable. Solely associating genetic markers with drug responses does not always yield clinically useful predictors of adverse reactions.⁴ Genotyping microarrays that are designed specifically to encompass all known pharmacologically relevant genes and variants can help to decipher associations between genotype and drug response.

Demonstrating clinical utility

Maintaining a research-based predictive pharmacogenomics initiative enables clinical researchers to investigate the clinical utility of individual gene-drug pairs across a wide range of potential influencing factors. Even after a gene-drug association has been identified, clinicians must be confident that a genotype-guided therapy will have the intended impact. Influences of multiple genes, multiple drugs or drug dosage on genotype-phenotype associations should be confirmed for the indicated drug. Identifying potentially different responses across relevant patient populations may also be required.

Providing clinician support

Clinical implementation starts with clinician acceptance. Clinicians may be more eager to adopt pharmacogenomics testing if they understand the results and can make recommendations. Ready access to testing labs and educational support in their institution can minimize barriers to adoption.³ Results from population-based longitudinal studies may help define usage parameters for the clinical decision support (CDS) tools that are essential for clinician acceptance of a new clinical best practice.

Educating patients for consent

Patients may be concerned about providing permanent genetic data that may be used for their lifetime. Clinicians should be able to explain how genotyping differs from, for example, a standard blood toxicity screen of specific body fluids at a single moment in time for a specific purpose. Once armed with knowledge of the implications of a genetic record, patients must decide and consent to either withhold or release incidental findings now and discoveries of new genotype-phenotype associations in the future.

Supporting administrative decision-making

As with any new technology under consideration for clinical implementation, health care institutions face administrative challenges in implementing pharmacogenomics. Considerations include cost implications such as access to local infrastructure to conduct tests, minimizing impact of adverse drug reactions on hospital resources and minimizing time to achieve therapeutic solutions for patients. A research approach conducted in a CLIA lab may provide a more flexible and less risky platform to investigate the administrative implications of adopting preemptive pharmacogenomics for routine clinical use.

Conclusion

When preemptive pharmacogenomics is established as a wide-spread clinical best practice, each patient’s individual genomic variation will be considered essential in every decision to prescribe medication along with other conventional metrics such as age, blood chemistry and lifestyle. The ability to test an individual once and gain information for a lifetime offers significant potential to change the face of medication therapy in the future.

However, establishing preemptive pharmacogenomics as a clinical best practice requires extensive research, educational resources and administrative support. For now, a research protocol based on a pharmacogenomics microarray platform designed with SNP markers for ADME, critical drug response, and customized population coverage may provide the quickest and most cost-effective approach to gaining the knowledge required for current preemptive pharmacogenomics applications.

References

1. Tariq RA, et al. Medication Dispensing Errors and Prevention. [Updated 2021 Feb 16]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK519065/
2. Arwood, MJ, et al. Implementing pharmacogenomics at your institution: establishment and overcoming implementation challenges. Clin. Transl. Sci. 9(5):233-245. https://doi.org/10.1111/cts.12404 (2016).
3. Mukerjee, G, et al. User considerations in assessing pharmacogenomic tests and their clinical support tools. npj Genomic Med 3,26 (2018). https://doi.org/10.1038/s41525-018-0065-4.
4. Yogita A, et al., in Innovative Approaches in Drug Discovery Ethnopharmacology, Systems Biology and Holistic Targeting. 2017. Chapter 7. Pharmacogenomics 195-234 (cited in Science Direct Pharmacogenomics https://www.sciencedirect.com/book/9780128018149/innovative-approaches-in-drug-discovery)