On some "disadvantages" of the population approach

Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA)

Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.

Approaches to handling missing or "problematic" pharmacology data: Pharmacokinetics

Missing or erroneous information is a common problem in the analysis of pharmacokinetic (PK) data. This may present as missing or inaccurate dose level or dose time, drug concentrations below the analytical limit of quantification, missing sample times, or missing or incorrect covariate information. Several methods to handle problematic data have been evaluated, although no single, broad set of recommendations for commonly occurring errors has been published. In this tutorial, we review the existing literature and present the results of our simulation studies that evaluated common methods to handle known data errors to bridge the remaining gaps and expand on the existing knowledge. This tutorial is intended for any scientist analyzing a PK data set with missing or apparently erroneous data. The approaches described herein may also be useful for the analysis of nonclinical PK data.

A Systematic Evaluation of Effect of Adherence Patterns on the Sample Size and Power of a Clinical Study

The objective of our study was to evaluate the effect of adherence patterns on the sample size and power of a clinical trial. Simulations from a population pharmacokinetic/pharmacodynamic (PK/PD) model linked to an adherence model were used. Four types of drug characteristics, such as long (~35 hours) and short (~12 hours) half-life in combination with earlier or delayed time to reach steady-state PD end points were studied. Adherence patterns were simulated using Markov chains. Our results clearly demonstrate the significant impact of varying levels and patterns of nonadherence on the sample size and power of a study. For drugs with short half-lives the evidence to support efficacy could be diluted by various patterns of nonadherence that would make its efficacy indistinguishable from the response to placebo. Prospectively utilizing clinical trial simulations with thorough incorporation of various adherence patterns would provide valuable information when designing a trial.

Determining causal exposure-response relationships with randomized concentration-controlled trials

Determining causal effects in exposure-response relationships is an important but also a challenging task since confounding factors that affect both drug exposure and response often exist and lead to confounding biases in causal effect estimation. Randomized concentration control (RCC) trials are designed to eliminate or to reduce the confounding bias. However, statistical issues in the design and analysis of these trials have not been examined closely in the literature. Analysis of dose-exposure relationship may also be affected by confounding factors if they affect dose adjustments. We examined these issues and suggest methodological and practical solutions. In particular, we proposed using instrumental variables (IV) for the estimation of causal effects in both exposure-response and dose-exposure relationships. We also examined the impacts of confounded treatment heterogeneity on the IV estimate for RCC trials. We illustrated these approaches with a trial design scenario showing the importance of considering multiple practical factors that may alter the performance of the IV estimate. The performance of the IV estimates was examined by simulations for a wide range of scenarios. The results showed clear advantages for the IV estimates over routine estimates. Some situations in which the IV estimates may fail were also identified.

Influence of body weight, ethnicity, oral contraceptives, and pregnancy on the pharmacokinetics of azithromycin in women of childbearing age

Women of childbearing age commonly receive azithromycin for the treatment of community-acquired infections, including during pregnancy. This study determined azithromycin pharmacokinetics in pregnant and nonpregnant women and identified covariates contributing to pharmacokinetic variability. Plasma samples were collected by using a sparse-sampling strategy from pregnant women at a gestational age of 12 to 40 weeks and from nonpregnant women of childbearing age receiving oral azithromycin for the treatment of an infection. Pharmacokinetic data from extensive sampling conducted on 12 healthy women were also included. Plasma samples were assayed for azithromycin by high-performance liquid chromatography. Population data were analyzed by nonlinear mixed-effects modeling. The population analysis included 53 pregnant and 25 nonpregnant women. A three-compartment model with first-order absorption and a lag time provided the best fit of the data. Lean body weight, pregnancy, ethnicity, and the coadministration of oral contraceptives were covariates identified as significantly influencing the oral clearance of azithromycin and, except for oral contraceptive use, intercompartmental clearance between the central and second peripheral compartments. No other covariate relationships were identified. Compared to nonpregnant women not receiving oral contraceptives, a 21% to 42% higher dose-adjusted azithromycin area under the plasma concentration-time curve (AUC) occurred in non-African American women who were pregnant or receiving oral contraceptives. Conversely, azithromycin AUCs were similar between pregnant African American women and nonpregnant women not receiving oral contraceptives. Although higher levels of maternal and fetal azithromycin exposure suggest that lower doses be administered to non-African American women during pregnancy, the consideration of azithromycin pharmacodynamics during pregnancy should guide any dose adjustments.