Four non-small-cell lung cancer (NSCLC) registration trials were utilized to develop models linking survival to risk factors and changes in tumor size during treatment. The purpose was to leverage existing quantitative knowledge to facilitate future development of anti-NSCLC drugs. Eleven risk factors were screened using a Cox model. A mixed exponential decay and linear growth model was utilized for modeling tumor size. Survival times were described in a parametric model. Eastern Cooperative Oncology Group (ECOG) score and baseline tumor size were consistent prognostic factors of survival. Tumor size was well described by the mixed model. The parametric survival model includes ECOG score, baseline tumor size, and week 8 tumor size change as predictors of survival duration. The change in tumor size at week 8 allows early assessment of the activity of an experimental regimen. The survival model and the tumor model will be beneficial for early screening of candidate drugs, simulating NSCLC trials, and optimizing trial designs.

Nivolumab is a human monoclonal antibody that blocks the interaction between PD-1 programmed death-1 (PD-1) and its ligands, PD-L1 and PD-L2. Nivolumab demonstrated efficacy in clinical trials for various types of cancer. A time-varying clearance was identified for nivolumab. We show that the change of clearance over time is associated with the post-treatment effects: clearance decreases when disease status improves. This interaction between posttreatment effects and drug exposure may lead to a biased steep estimate of the exposure-response (E-R) relationship for efficacy. Under this scenario, simulations were performed to develop a proposed methodology to assess the causal effect of drug exposure upon clinical response. Data from nivolumab trials were subsequently used to verify the proposed methodology for E-R analysis. The results showed that E-R analysis results based on pharmacokinetic (PK) metrics derived from the first dose are more consistent with the true E-R or dose-response relationship than the steady-state PK metrics.

Exploratory analyses of data pertaining to pharmacokinetic, pharmacodynamic, and disease progression are often referred to as the pharmacometrics (PM) analyses. The objective of the current report is to assess the role of PM, at the Food and Drug Administration (FDA), in drug approval and labeling decisions. We surveyed the impact of PM analyses on New Drug Applications (NDAs) reviewed over 15 months in 2005-2006. The survey focused on both the approval and labeling decisions through four perspectives: clinical pharmacology primary reviewer, their team leader, the clinical team member, and the PM reviewer. A total of 31 NDAs included a PM review component. Review of NDAs involved independent quantitative evaluation by FDA pharmacometricians. PM analyses were ranked as important in regulatory decision making in over 85% of the 31 NDAs. Case studies are presented to demonstrate the applications of PM analysis.

Drug-induced liver injury (DILI) is a major concern in public health management, drug development, and regulatory implementation. The Liver Toxicity Knowledge Base (LTKB) was developed with the specific aim of enhancing our understanding of DILI. It seeks to achieve improvement in DILI prediction through integrated analysis of diverse sources of drug-elicited data. The project has also produced a centralized resource of data as well as predictive models that will be useful for research and drug regulation.