Table 1.

10 identified challenges that currently limit the widespread clinical implementation of pharmacogenomics.

Challengea	Goals for Improvement
#1: There is no global network of experts to help drive basic pharmacogenomics research and clinical implementation	• Create a global pharmacogenomics network comprising researchers, clinicians, patient representatives, and other professionals from academia, government, and industry • The goal of the network is to advance pharmacogenomics research and implementation in both developed and resource limited countries • Provide network members access to existing and new consortia, datasets, and regular meetings • Create a list of standards for data quality and implementation to improve the adoption of clinical pharmacogenomic testing • Consider sponsorship by or partnership with industry, national guideline organizations, regulatory bodies, and/or scientific societies to provide the infrastructure needed for society activities while ensuring arms-length involvement • Increase the visibility of pharmacogenomics within human genomics circles
#2: Mechanistic understanding of pharmacogenomic phenotypes is hindered by the lack of large datasets and available bio-samples	• Increase the availability of publicly available datasets that include drug adherence, doses, and concomitant medications along with before drug and on-drug phenotypic information from patients from multiple ethnic groups, and particularly non-Europeans • Accumulate large samples of individuals with DNA, RNA (including miRNA), endogenous metabolites, and kinetic assessments to allow for comprehensive -omics research • Assemble a pharmacogenomics sample bank, which includes appropriately banked samples such as blood and urine from individuals from multiple ethnic groups on drugs, and control individuals • Consider collection of real-world data from patients to supplement pharmacogenomics research
#3: Compared to common genetic variation, less is known regarding the impact and clinical actionability of rare genetic variation	• Conduct studies in founder populations and populations with high rates of consanguinity that are enriched for homozygous rare variation • Acquire sufficiently large samples (e.g. blood, urine, tissues) and optimize methods to examine the functional and clinical impact of rare and common variation together • Use multi-omics approaches to better assess in vivo functional consequences • Use machine learning approaches to improve functional prediction for rare variants, beginning with important pharmacogenes • Use innovative experimental approaches to examine mechanistic consequences of rare variants in vitro
#4: Models are underutilized to understand pharmacogenomic variation	• Use knock-out, knock-in, and humanized rodent models to understand functional variation, including identifying the physiologic and pharmacological roles of transporters and enzymes • Consider humanized mouse models as a tool to improve pharmacological studies, especially when ligand specificity of the encoded protein differs by species • Investigate organ and cell-specific impacts of genetic variants using animal models • Use patient-derived tumor xenograft models to elucidate pharmacogenomic variation in various cancer types
#5: Validated biomarkers are an untapped resource to improve pharmacogenomic discovery and implementation	• Recruit drug-naïve populations to study and validate specific metabolic biomarkers as surrogates for genotypes, especially in instances where genetic testing is not available • Perform genome-wide association studies of drug and metabolite levels to identify predictors of treatment response • Study endogenous metabolites to improve understanding of enzyme and transporter function • Create a set of criteria to determine which biomarkers are specific and valid for which genes • Determine the relative utility of pharmacogenomic testing versus biomarker assessments, while taking into consideration the disease, clinical setting, treatment selection, dosing, and medication adherence • Through measured biomarkers, determine the relative contribution of genetics and environment to functional variation
#6: Special and diverse populations are understudied	• Investigate genomic variation in multiple world populations to increase the power for genetic discovery, increase clinical relevance, and ensure democratization and accessibility of pharmacogenomics • Develop tailored pharmacogenomic algorithms that consider population differences in allele frequencies and functional variation • Support local pharmacogenomic research capacity in developing countries through Western training initiatives • Ensure diverse and special populations derive benefit from conducted research and avoid invoking further inequalities • Harness machine learning to improve functional variant prediction to reduce reliance on clinical studies • Consider special populations (e.g. children, elderly) to elucidate the contribution of genetic variation and non-genetic factors (e.g. development, comorbid illness) to interindividual variability of expression and function of pharmacogenes
#7: Many pharmacogenomic tests are not standardized, reimbursed, or regulated, limiting their clinical utility	• Collaborate closely with the medical technology industry to drive the creation of reliable and affordable pharmacogenomic tests, with universally accepted standards • Educate test manufacturers regarding the complexity of pharmacogene variant calling due to the presence of pseudogenes, copy number variation, and structural variation • For pharmacogenes, optimize the use of whole gene sequence versus precise calling in regions containing actionable variants • Identify scenarios where strand-specific haplotyping would be useful • Create laboratory standards for the source and quality of DNA • Administer pharmacogenomic tests pre-emptively or with rapid turnaround time to promote utility in hospital-based medicine • Create a set of clinical decision support guidelines and train health care practitioners to both administer and interpret test results • Develop infrastructure to link one-time genetic test results to longitudinally available electronic health records and ensure data protection • Increase adoption and accessibility by ensuring costs are reimbursed by ministries of health or insurance companies • Develop a uniform set of regulatory standards for testing to ensure universal acceptance
#8: Successful widespread pharmacogenomic implementation is limited by a lack of evidence of clinical utility and cost-effectiveness studies	• Create multidisciplinary teams of medical leads, scientists, laboratory technicians, and pharmacists • Promote learning health systems through prospective empirically-based implementation trials • Encourage health systems to become early adopters of pharmacogenomics • Develop health economic models to show cost-effectiveness of implementation • Improve the usability of electronic health recordso  º Introduce standardized phenotypes and harmonized data reportingo  º Include relevant follow-up datao  º Consider creation and adoption of alert-based system searchable by drug or gene name which may be improved by machine learning approacheso  º Update clinical decision support as more information becomes available regarding functional consequences of variantso  º Provide support, e.g. via a clinical research coordinator, to health care providers to reduce the time burden of entering information
#9: Education and advocacy initiatives are needed to increase the adoption of pharmacogenomics	• Develop educational materials, fact sheets, and training programs concerning the health and economic benefits of implementing genomics-guided medicine • Educate all relevant stakeholders (e.g. patients, providers, ministries of health, healthcare insurance companies, etc.) regarding the benefits of pharmacogenomic implementation, using N-of-1 to Phase IV studies and post-utilization evidence • Educate stakeholders regarding the difficulty of proving that a pharmacogenomic intervention has improved care: treatment has generally improved over time (historic controls may not be appropriate), withholding pharmacogenomic testing from a control group is not ethical, and it is impossible to track the prevention of poor outcomes • Highlight the unmet need by emphasizing the high prevalence of actionable pharmacogenomic variation in the context of current prescribing and drug use patterns
#10: Additional Challenge: The threshold for clinical actionability based on cell-free DNA testing is unknown	• Investigate the promise of cell-free DNA testing, including regarding epigenetic mechanisms (i.e. DNA methylation), as a complement to germline DNA testing • Determine the threshold of mutational burden in cell-free DNA reads to consider clinical actionability • Consider tumor antigen load together with mutation load to optimize immune therapy in cancer treatment • Determine how to differentiate normal mosaicism from tumor DNA • Optimize the detection and functional prediction of minor clones

A PowerPoint slide containing this information in figure form is available in the supplementary information.

^aThe relative importance of each of these 10 challenges depends on many factors including the specific drug, disease/disorder, and population, thus they are not necessarily listed in any ranked order.