Biomedical subjects

Kenneth G Kowalski

Publications and source records attributed to Kenneth G Kowalski.

3 recordsLinked to original sources

PubMed2005

How modeling and simulation have enhanced decision making in new drug development.

The idea of model-based drug development championed by Lewis Sheiner, in which pharmacostatistical models of drug efficacy and safety are developed from preclinical and available clinical data, offers a quantitative approach to improving drug development and development decision-making. Examples are presented that support this paradigm. The first example describes a preclinical model of behavioral activity to predict potency and time-course of response in humans and assess the potential for differentiation between compounds. This example illustrates how modeling procedures expounded by Lewis Sheiner provided the means to differentiate potency and the lag time between drug exposure and response and allow for rapid decision making and dose selection. The second example involves planning a Phase 2a dose-ranging and proof of concept trial in Alzheimer's disease (AD). The issue was how to proceed with the study and what criteria to use for a go/no go decision. The combined knowledge of AD disease progression, and preclinical and clinical information about the drug were used to simulate various clinical trial scenarios to identify an efficient and effective Phase 2 study. A design was selected and carried out resulting in a number of important learning experiences as well as extensive financial savings. The motivation for this case in point was the "Learn-Confirm" paradigm described by Lewis Sheiner. The final example describes the use of Pharmacokinetic and Pharmacodynamic (PK/PD) modeling and simulation to confirm efficacy across doses. In the New Drug Application for gabapentin, data from two adequate and well-controlled clinical trials was submitted to the Food and Drug Administration (FDA) in support of the approval of the indication for the treatment of post-herpetic neuralgia. The clinical trial data was not replicated for each of the sought dose levels in the drug application presenting a regulatory dilemma. Exposure response analysis submitted in the New Drug Application was applied to confirm the evidence of efficacy across these dose levels. Modeling and simulation analyses showed that the two studies corroborate each other with respect to the pain relief profiles. The use of PK/PD information confirmed evidence of efficacy across the three studied doses, eliminating the need for additional clinical trials and thus supporting the approval of the product. It can be speculated that the work by Lewis Sheiner reflected in the FDA document titled "Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products" made this scientific approach to the drug approval process possible.

Alzheimer Disease↗

PubMed2005

Collapsing mechanistic models: an application to dose selection for proof of concept of a selective irreversible antagonist.

When data fail to support fully mechanistic models, alternative modeling strategies must be pursued. Simpler, more empirical models or the fixing of various rate constants are necessary to avoid over-parameterization. Fitting empirical models can dilute information, limit interpretation, and cloud inference. Fixing rate constants requires external, relevant, and reliable information on the mechanism and can introduce subjectivity as well as complicate determining the validity of model extrapolation. Furthermore, both these methods ignore the possibility that failure of the data to support the mechanistic model could contain information about the pharmacodynamic process. If the pathway has processes with "fast" dynamics, these steps could collapse yielding parametrically simpler classes of models. The collapsed models would retain the mechanistic interpretation of the full model, which is crucial for performing substantive inference, while reducing the number of parameters to be estimated. These concepts are illustrated through their manifestations on the dose-effect relationship and ensuing dose selection for a proof of concept study. Specifically, a mechanistic model for a selective irreversible antagonist was posited and candidate classes of models were derived utilizing "fast dynamics" assumptions. Model assessment determined the rate-limiting step facilitating pertinent inference with respect to the mechanism. For comparison, inference using a more empirical modeling strategy is also presented. A general solution for the collapse of the typical PK-PD model differential equations is provided in Appendix A.

Administration, Oral↗

PubMed2003

A two-part mixture model for longitudinal adverse event severity data.

We fit a mixed effects logistic regression model to longitudinal adverse event (AE) severity data (four-point ordered categorical response) to describe the dose-AE severity response for an investigational drug. The distribution of the predicted interindividual random effects (Bayes predictions) was extremely bimodal. This extreme bimodality indicated that biased parameter estimates and poor predictive performance were likely. The distribution's primary mode was composed of patients that did not experience an AE. Moreover, the Bayes predictions of these non-AE patients were nearly degenerative, i.e., the predictions were nearly identical. To resolve this extreme bimodality we propose using a two-part mixture modeling approach. The first part models the incidence of AE's, and the second part models the severity grade given the patient had an AE. Unconditional probability predictions are calculated by mixing the incidence and severity model probability predictions. We also report results of simulation studies, which assess the predictive and statistical (bias and precision) performance of our approach.

Bayes Theorem↗