dOFV distributions : A New Diagnostic For The Adequacy Of Parameter Uncertainty In Nonlinear Mixed-Effects Models Applied To The Bootstrap

Knowledge of the uncertainty in model parameters is essential for decision-making in drug development. Contrarily to other aspects of nonlinear mixed effects models (NLMEM), scrutiny towards assumptions around parameter uncertainty is low, and no diagnostic exists to judge whether the estimated uncertainty is appropriate. This work aims at introducing a diagnostic capable of assessing the appropriateness of a given parameter uncertainty distribution. The new diagnostic was applied to case bootstrap examples in order to investigate for which dataset sizes case bootstrap is appropriate for NLMEM. The proposed diagnostic is a plot comparing the distribution of differences in objective function values (dOFV) of the proposed uncertainty distribution to a theoretical Chi square distribution with degrees of freedom equal to the number of estimated model parameters. The uncertainty distribution was deemed appropriate if its dOFV distribution was overlaid with or below the theoretical distribution. The diagnostic was applied to the bootstrap of two real data and two simulated data examples, featuring pharmacokinetic and pharmacodynamic models and datasets of 20-200 individuals with between 2 and 5 observations on average per individual. In the real data examples, the diagnostic indicated that case bootstrap was unsuitable for NLMEM analyses with around 70 individuals. A measure of parameter-specific "effective" sample size was proposed as a potentially better indicator of bootstrap adequacy than overall sample size. In the simulation examples, bootstrap confidence intervals were shown to underestimate inter-individual variability at low sample sizes. The proposed diagnostic proved a relevant tool for assessing the appropriateness of a given parameter uncertainty distribution and as such it should be routinely used

Modeling Exposure-Driven Adverse Event Time Courses in Oncology Exemplified by Afatinib

Models were developed to characterize the relationship between afatinib exposure and diarrhea and rash/acne adverse event (AE) trajectories, and their predictive ability was assessed. Based on pooled data from seven phase II/III clinical studies including 998 patients, mixed-effects models for ordered categorical data were applied to describe daily AE severity. Clinical trial simulation aided by trial execution models was used for internal and external model evaluation. The final exposure-safety model consisted of longitudinal logistic regression models with first-order Markov elements for both AEs. Drug exposure was included as daily area under the concentration-time curve (AUC), and drug effects on the AEs were correlated. Clinical trial simulation allowed adequate prediction of maximum AE grades and AE severity time courses but overestimated the proportion of AE-dependent dose reductions and discontinuations. Both diarrhea and rash/acne were correlated with afatinib exposure. The developed modeling framework allows a prospective comparison of dosing strategies and study designs with respect to safety.Matthias Freiwald and Mats O. Karlsson contributed equally</p

Population pharmacokinetic drug–drug interaction pooled analysis of existing data for rifabutin and HIV PIs

Extensive but fragmented data from existing studies were used to describe the drug-drug interaction between rifabutin and HIV PIs and predict doses achieving recommended therapeutic exposure for rifabutin in patients with HIV-associated TB, with concurrently administered PIs