Model-Based Assessments of CYP-Mediated Drug-Drug Interaction Risk of Alectinib: Physiologically Based Pharmacokinetic Modeling Supported Clinical Development.

Alectinib is a selective anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive non-small cell lung cancer. Alectinib and its major active metabolite M4 exhibited drug-drug interaction (DDI) potential through cytochrome P450 (CYP) enzymes CYP3A4 and CYP2C8 in vitro. Clinical relevance of the DDI risk was investigated as part of a rapid development program to fulfill the breakthrough therapy designation.

In vitro metabolism of alectinib, a novel potent ALK inhibitor, in human: contribution of CYP3A enzymes.

1. The in vitro metabolism of alectinib, a potent and highly selective oral anaplastic lymphoma kinase inhibitor, was investigated. 2.

Optimum designs for non-linear mixed effects models in the presence of covariates.

In this article, we present a new method for optimizing designs of experiments for non-linear mixed effects models, where a categorical factor with covariate information is a design variable combined with another design factor. The work is motivated by the need to efficiently design preclinical experiments in enzyme kinetics for a set of Human Liver Microsomes. However, the results are general and can be applied to other experimental situations where the variation in the response due to a categorical factor can be partially accounted for by a covariate.

Minimizing DILI risk in drug discovery - A screening tool for drug candidates.

Drug-induced liver injury (DILI) is a leading cause of acute hepatic failure and a major reason for market withdrawal of drugs. Idiosyncratic DILI is multifactorial, with unclear dose-dependency and poor predictability since the underlying patient-related susceptibilities are not sufficiently understood. Because of these limitations, a pharmaceutical research option would be to reduce the compound-related risk factors in the drug-discovery process.

Reaction phenotyping: advances in the experimental strategies used to characterize the contribution of drug-metabolizing enzymes.

During the process of drug discovery, the pharmaceutical industry is faced with numerous challenges. One challenge is the successful prediction of the major routes of human clearance of new medications. For compounds cleared by metabolism, accurate predictions help provide an early risk assessment of their potential to exhibit significant interpatient differences in pharmacokinetics via routes of metabolism catalyzed by functionally polymorphic enzymes and/or clinically significant metabolic drug-drug interactions.