An Automated Drug Concentration Screening and Quality Assurance Program for Clinical Trials

Abstract

The collection and analysis of drug concentration data collected during clinical trials is growing in popularity as a mechanism for explaining variability in patient outcomes. This paper describes an automated drug concentration screening and quality assurance program to monitor the acquisition of drug dosing information and concentration time data during clinical trials. This program serves to expedite the data cleaning process, allows for monitoring of possible concentration-related safety events, screens for drugdrug interactions during the execution of clinical trials, and provides the ability to produce interim, blinded reports to the sponsor and the data safety monitoring board. The goal of this paper is to describe the operation of the program as it has been used in practice and to highlight its benefits in the development program for delavirdine mesylate, a nonnucleoside reverse transcriptase inhibitor recently approved for the treatment of H N infection.

Pharmacometrics and the Transition to Model-Based Development

“…The necessary data can be difficult to locate, and often the key data required for modeling are not available until the traditional efficacy and safety analyses have been completed 16 . Data assembly and scrubbing are remarkably time‐consuming, can result in high discard rates, and may delay completion of modeling and simulation activities 17 . Furthermore, significant resistance to the use of modeling and simulation results in decision making is sometimes encountered because of unfamiliarity on the part of the development team with interpretation of the results and the urgent timelines of the development program 8 , 13 …”

Challenges in the transition to model-based development

“…Our experiences in developing a process for real-time data assembly during the delavirdine phase III clinical development program are illustrative of the issues that arise and the value provided by this strategy. 8 …”

“…During the design of the phase III development program, concerns over potential safety issues associated with saturable metabolism prompted the sponsor to initiate a program to monitor drug concentrations in all of the patients who enrolled in two double-blind, randomized, pivotal registration trials to allow dosing adjustments in patients who were experiencing elevated concentrations of delavirdine. 8 Maintaining the study blind was an important issue with respect to the data assembly and concentration monitoring program. The procedures for maintaining the study blind started with very strict conservative reporting standards.…”

The Process of Knowledge Discovery from Large Pharmacokinetic Data Sets

“…(1) creating a data set on which knowledge will be performed; (2) cleaning and processing the data (i.e., data quality analysis); 6,7 (3) data structure analysis-exploratory examination of raw data (concentrations and covariates) for hidden structure and the reduction of the dimensionality of the covariate vector; (4) determining the basic pharmacokinetic model that best describes the data and generating post hoc empiric individual Bayesian parameter estimates; (5) searching for patterns and relationships between parameters, and parameters and covariates through graphical displays and visualization; (6) using modern statistical modeling techniques such as multiple linear regression (MLR), generalized additive modeling (GAM), 8 and tree-based modeling (TBM) to reveal structure in the data and initially select explanatory covariates; (7) consolidating the discovered knowledge in (6) into irreducible form (i.e., developing a population pharmacokinetic model using the nonlinear mixed-effects modeling approach); (8) determining model robustness through sensitivity analysis, examination of parametric/nonparametric standard errors, or stability, determination, and predictive performance; and (9) communicating and integrating the discovered pharmacokinetic knowledge.…”