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Diabetes mellitus is a worldwide problem with an immense pharmacoeconomic burden. The multifactorial and complex nature of the disease lends itself to personalized pharmacotherapeutic approaches to treatment. Variability in individual risk and subsequent development of diabetes has been reported in addition to differences in response to the many oral glucose lowering therapies currently available for diabetes pharmacotherapy. Pharmacogenomic studies have attempted to uncover the heritable components of individual variability in risk susceptibility and response to pharmacotherapy. We review the current pharmacogenomics evidence as it relates to common oral glucose lowering therapies and how they can be utilized in the management of polygenic and monogenic forms of diabetes. Evidence supports the use of genetic testing and personalized approaches to the treatment of monogenic diabetes of the young. The data are not as robust for the current application of pharmacogenetic approaches to the treatment of polygenic type 2 diabetes mellitus, but there are suggestions as to future applications in this regard. We reviewed pertinent primary literature sources as well as current evidence-based guidelines on diabetes management.

Genomics is increasingly becoming an important part of health care, and pharmacists are well-positioned to be practice-based leaders in pharmacogenomics and precision medicine. Competencies available through the Genetics/Genomics Competency Center provide a framework for pharmacogenomics instruction in both pharmacy school curricula and continuing education programs. Given the significant advancements in pharmacogenomics over the past decade, the 2019-2020 American Association of Colleges of Pharmacy Pharmacogenomics Special Interest Group updated the pharmacist competencies. The process used a systematic approach which included mapping pharmacogenomics-specific competencies to the Entrustable Professional Activities for pharmacists and seeking consensus from key stakeholders. The final result is an expansion to 30 competencies that reflect the contemporary roles pharmacists play in the application of pharmacogenomics in clinical practice. When implemented into curricula, these competencies will ensure that learners are "practice-ready" to integrate pharmacogenomics into patient care. Additional post-graduate training is needed for advanced roles in pharmacogenomics implementation, education, and research.

The antiretroviral protease inhibitor atazanavir inhibits hepatic uridine diphosphate glucuronosyltransferase (UGT) 1A1, thereby preventing the glucuronidation and elimination of bilirubin. Resultant indirect hyperbilirubinemia with jaundice can cause premature discontinuation of atazanavir. Risk for bilirubin-related discontinuation is highest among individuals who carry two UGT1A1 decreased function alleles (UGT1A1*28 or *37). We summarize published literature that supports this association and provide recommendations for atazanavir prescribing when UGT1A1 genotype is known (updates at www.pharmgkb.org).

Background— A barrier to statin therapy is myopathy associated with elevated systemic drug exposure. Our objective was to examine the association between clinical and pharmacogenetic variables and statin concentrations in patients. Methods and Results— In total, 299 patients taking atorvastatin or rosuvastatin were prospectively recruited at an outpatient referral center. The contribution of clinical variables and transporter gene polymorphisms to statin concentration was assessed using multiple linear regression. We observed 45-fold variation in statin concentration among patients taking the same dose. After adjustment for sex, age, body mass index, ethnicity, dose, and time from last dose, SLCO1B1 c.521T>C ( P ABCG2 c.421C>A ( P R 2 =0.56 for the final model). Atorvastatin concentration was associated with SLCO1B1 c.388A>G ( P C ( P R 2 =0.47). A second cohort of 579 patients from primary and specialty care databases were retrospectively genotyped. In this cohort, genotypes associated with statin concentration were not differently distributed among dosing groups, implying providers had not yet optimized each patient’s risk–benefit ratio. Nearly 50% of patients in routine practice taking the highest doses were predicted to have statin concentrations greater than the 90th percentile. Conclusions— Interindividual variability in statin exposure in patients is associated with uptake and efflux transporter polymorphisms. An algorithm incorporating genomic and clinical variables to avoid high atorvastatin and rosuvastatin levels is described; further study will determine whether this approach reduces incidence of statin myopathy.