title: A forecasting approach to accelerate drug development.
abstract: The clinical phase of drug development should be concluded sooner and at a lower cost if primarily only the pivotal and supportive studies were to be conducted. Such improved efficiency requires development of a decision support system that delivers five new capabilities: (i) it enables one to predict a result of a clinical study and to identify those studies that are expected to have an acceptable probability of success; (ii) it will allow one to optimally utilize available pharmacokinetic and pharmacodynamic (PK/PD) data and improve its predictive capability as more data become available; (iii) it will enable one to project useful population results, not just mean results; (iv) predictions will be accompanied by a measure of reliability; and (v) expected initial clinical results will be predictable from animal and related drug class data. With such a tool population targets could be specified very early in the drug development programme, challenged, and then rationally revised at each step during the development process. This report describes progress in developing and testing a clinical trials Forecaster, a prototype for such a system. The Forecaster generates estimates of the joint density for a population of combined PK/PD parameters. That population then serves as a surrogate for the population of individuals. When the resulting joint density is sampled, the obtained sets of parameters may be used to generate data that is statistically indistinguishable from the original experimental data. Such simulated data can be used to validate assumptions, and make inferences on specified population targets that are accompanied by a measure of prediction reliability. We demonstrate use of the forecaster by employing N = 22 PK/PD parameter sets for an orally administered analgesic.
authors.affiliation: University of California, San Francisco 94143-0446, USA.
authors.forename: C A
authors.initials: CA
authors.lastname: Hunt
authors.forename: S
authors.initials: S
authors.lastname: Guzy
authors.forename: D L
authors.initials: DL
authors.lastname: Weiner
mesh_terms: D015982:Bias; D002986:Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D004341:Drug Evaluation; D005544:Forecasting; D006801:Humans; D015999:Multivariate Analysis; D010599:Pharmacokinetics; D010600:Pharmacology; D012044:Regression Analysis; D015203:Reproducibility of Results; D013997:Time Factors