Integrating Full Bayesian Inference and Student's t-Distribution Method for Enhanced Outlier Handling in Caffeine Population Pharmacokinetics: Assessing Drug-Drug Interactions with Enasidenib in Relapsed or Refractory AML and MDS Patients

As the first-in-class, selective, and potent inhibitor of the isocitrate dehydrogenase-2 (IDH2) mutant protein, enasidenib was approved by the US Food and Drug Administration (FDA) in 2017 for the treatment of adult patients with relapsed or refractory acute myeloid leukemia (AML) with an IDH2 mutation. Known for its interactions with various cytochrome P450 (CYP) enzymes and transporters in vitro, a clinical pharmacokinetics (PK) trial was initiated to assess the impact of multiple doses of enasidenib on the single-dose PK of sensitive probe substrates of several cytochrome P450 enzymes and transporters. In this study, a population pharmacokinetic analysis approach was employed to address challenges posed by high, nonzero baseline caffeine concentrations. Moreover, we integrated full Bayesian inference into this approach innovatively for a more detailed understanding of parameter uncertainty and greater modeling flexibility, alongside Student's t-distribution for robust error modeling in handling the abnormal outlier caffeine concentration data observed in this trial. Our analyses demonstrated that multiple doses of enasidenib altered caffeine clearance to a clinically meaningful extent, as evidenced by an approximate 8-fold decrease. This finding led to a specific recommendation in the package insert to avoid the concurrent use of certain CYP1A2 substrates with enasidenib, unless directed otherwise in the prescribing information. Furthermore, this research underlines the technical benefits of integrating full Bayesian inference and incorporating Student's t-distribution for residual error modeling in the PK field.