Background. The CYP4503A5*1 genotype is associated with lower tacrolimus concentrations. Although its effect is important, it incompletely explains the variability in tacrolimus concentrations and has a relatively low minor allele frequency in whites relative to African Americans (AA). Methods. We studied clinical and recipient genetic correlates of dose-normalized tacrolimus troughs (n=12,277) in the first 6 months posttransplant using a customized single-nucleotide polymorphism chip with 2722 variants in a large, ethnically diverse (144 AA and 551 non-AA) adult kidney transplant population through a seven-center consortium. Results. During the 6-month study, AAs had consistently lower median (interquartile range) troughs than non-AAs, 6.2 (4.4–8.4) ng/mL vs. 8.3 (6.4–10.4) ng/mL (P<0.0001), despite 60% higher daily doses, 8 (5–10) mg vs. 5 (4–7) mg (P<0.0001). The median tacrolimus trough concentration in week 1 posttransplant was particularly low in AAs (2.1 [1.2–3.5] ng/mL) compared with non-AAs (5.0 [3.1–8.2] ng/mL) (P<0.0001), despite similar initial doses. In single-variant analysis, CYP3A5*3 (rs776746) was the top variant (P=2.4×10⁻³³) associated with troughs. After adjustment for CYP3A5*3, clinical factors and race, 35 additional variants were identified (P<0.01, not significant at false discovery rate 20%). In the final multivariant, regression models beginning with these variants and clinical factors, seven variants were identified in the non-AA and seven variants in the AA group towards the first trough concentrations. Rs776746 (CYP3A5), rs2239393 (COMT) and diabetes were the only factors common in both populations. Conclusion. We identified variants beyond CYP3A5*3, which may further explain pharmacokinetic variability of tacrolimus and demonstrated that important variants differ by race.

The number of older adults undergoing kidney transplantation has increased, yet little is known about calcineurin inhibitor (CNI) metabolism in this group. We studied CNI troughs and doses to determine if there were age‐related differences in metabolism and dose requirements. We studied 348 young (18–34 years), 1831 middle (35–64 years) and 374 older (65–84 years) adult kidney transplant recipients enrolled in a seven‐center prospective study. Troughs were obtained from each patient 2×/week in weeks 1–8 and 2×/month in months 3–6. A multivariable linear‐mixed model examined the effect of age on log dose and weight normalized troughs. Older recipients had higher normalized tacrolimus troughs than middle or young age adults despite receiving doses a median of 1–2 mg/day lower. Age and CYP3A5*1 genotype had the largest effect on tacrolimus troughs. Older recipients also had higher normalized cyclosporine troughs than middle or young adults despite receiving median doses 100 mg/day lower. After normalization for dose and weight, CNI troughs were more than 50% higher in older adults than young adults. These data support age‐related changes in CNI metabolism. Further studies are needed to determine optimal dosing of CNIs in the elderly.

Tacrolimus is dependent on CYP3A5 enzyme for metabolism. Expression of the CYP3A5 enzyme is controlled by several alleles including CYP3A5*1, CYP3A5*3, CYP3A5*6 and CYP3A5*7. African Americans (AAs) have on average higher tacrolimus dose requirements than Caucasians; however, some have requirements similar to Caucasians. Studies in AAs have primarily evaluated the CYP3A5*3 variant; however, there are other common nonfunctional variants in AAs (CYP3A5*6 and CYP3A5*7) that do not occur in Caucasians. These variants are associated with lower dose requirements and may explain why some AAs are metabolically similar to Caucasians. We created a tacrolimus clearance model in 354 AAs using a development and validation cohort. Time after transplant, steroid and antiviral use, age and CYP3A5*1, *3, *6 and *7 alleles were significant toward clearance. This study is the first to develop an AA-specific genotype-guided tacrolimus dosing model to personalize therapy.

A new global pandemic of coronavirus disease 2019 (COVID-19) has resulted in high mortality and morbidity. Currently numerous drugs are under expedited investigations without well-established safety or efficacy data. Pharmacogenomics may allow individualization of these drugs thereby improving efficacy and safety. In this review, we summarized the pharmacogenomic literature available for COVID-19 drug therapies including hydroxychloroquine, chloroquine, azithromycin, remdesivir, favipiravir, ribavirin, lopinavir/ritonavir, darunavir/cobicistat, interferon beta-1b, tocilizumab, ruxolitinib, baricitinib, and corticosteroids. We searched PubMed, reviewed the Pharmacogenomics Knowledgebase (PharmGKB^®) website, Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines, the U.S. Food and Drug Administration (FDA) pharmacogenomics information in the product labeling, and the FDA pharmacogenomics association table. We found several drug-gene variant pairs that may alter the pharmacokinetics of hydroxychloroquine/chloroquine (CYP2C8, CYP2D6, SLCO1A2, and SLCO1B1); azithromycin (ABCB1); ribavirin (SLC29A1, SLC28A2, and SLC28A3); and lopinavir/ritonavir (SLCO1B1, ABCC2, CYP3A). We also identified other variants, that are associated with adverse effects, most notable in hydroxychloroquine/chloroquine (G6PD; hemolysis), ribavirin (ITPA; hemolysis), and interferon β -1b (IRF6; liver toxicity). We also describe the complexity of the risk for QT prolongation in this setting because of additive effects of combining more than one QT-prolonging drug (i.e., hydroxychloroquine/chloroquine and azithromycin), increased concentrations of the drugs due to genetic variants, along with the risk of also combining therapy with potent inhibitors. In conclusion, although direct evidence in COVID-19 patients is lacking, we identified potential actionable genetic markers in COVID-19 therapies. Clinical studies in COVID-19 patients are deemed warranted to assess potential roles of these markers.