The Pharmacogenetics of OATP1B1 Variants and their impact on the Pharmacokinetics and Efficacy of Elbasvir/Grazoprevir

Abstract

Aim: To evaluate the effect of SLCO1B1 genetic variants on grazoprevir pharmacokinetics and efficacy. Methods: A retrospective analysis of 1578 hepatitis C virus-infected participants from ten Phase II/III clinical trials. Results: Relative to noncarriers of the risk allele, geometric mean ratios (95% CI) of grazoprevir area under curve (AUC)0–24 were: rs4149056 (risk allele C), one copy, 1.13 (1.06–1.21), two copies, 1.43 (1.16–1.77); and rs11045819 (risk allele A), one copy, 0.93 (0.87–1.00); two copies, 0.78 (0.61–1.00). The rs2306283 variant was not associated with grazoprevir exposure. None of the SLCO1B1 variants were associated with sustained virologic response. Conclusion: Genetic variants in SLCO1B1 were associated with modest changes in grazoprevir pharmacokinetics, but not with meaningful differences in efficacy.

Current challenges and opportunities for pharmacogenomics: perspective of the Industry Pharmacogenomics Working Group (I-PWG)

“…Rather, studies are designed primarily with a therapeutic hypothesis and are powered to detect differences in safety and efficacy, with PGx or genetic objectives as either a tertiary or exploratory objective. Phase I studies are generally underpowered for even candidate variant analyses unless data from multiple studies are pooled (Guo et al 2019 ; Kobie et al 2019 ). While phase II and phase III studies are larger, even these studies are often underpowered when conducting genome-wide association studies.…”

“…For this reason, it is often important to pool as many early phase clinical studies with pharmacokinetic (PK) data as possible to boost statistical power to assess a broader set of ADME genes. In addition, larger phase II/III studies can also be used where estimates of PK parameters can be derived using population PK modeling to assess potential impact of variants in ADME genes (Guo et al 2019 ; Kobie et al 2019 ). Even so, the statistical power to detect genetic associations in these data sets may not be sufficient.…”