Quantifying and Communicating Uncertainty in Preclinical Human Dose-Prediction.

Human dose-prediction is fundamental for ranking lead-optimization compounds in drug discovery and to inform design of early clinical trials. This tutorial describes how uncertainty in such predictions can be quantified and efficiently communicated to facilitate decision-making. Using three drug-discovery case studies, we show how several uncertain pieces of input information can be integrated into one single uncomplicated plot with key predictions, including their uncertainties, for many compounds or for many scenarios, or both.

Model-based drug discovery: implementation and impact.

Model-based drug discovery (MBDDx) aims to build and continuously improve the quantitative understanding of the relation between drug exposure (target engagement) efficacy and safety, to support target validation; to define compound property criteria for lead optimization and safety margins; to set the starting dose; and to predict human dose and scheduling for clinical candidates alone, or in combination with other medicines. AstraZeneca has systematically implemented MBDDx within all drug discovery programs, with a focused investment to build a preclinical modeling and simulation capability and an in vivo information platform and architecture, the implementation, impact and learning of which are discussed here.

The pharmacokinetics and hepatic disposition of repaglinide in pigs: mechanistic modeling of metabolism and transport.

The predictive power of using in vitro systems in combination with physiologically based pharmacokinetic (PBPK) modeling to elucidate the relative importance of metabolism and carrier-mediated transport for the pharmacokinetics was evaluated using repaglinide as a model compound and pig as the test system. Repaglinide was chosen as model drug as previous studies in humans have shown that repaglinide is subject to both carrier-mediated influx to the liver cells and extensive hepatic metabolism. A multiple sampling site model in pig was chosen since it provides detailed in vivo information about the liver disposition.

Optimal experimental design for assessment of enzyme kinetics in a drug discovery screening environment.

A penalized expectation of determinant (ED)-optimal design with a discrete parameter distribution was used to find an optimal experimental design for assessment of enzyme kinetics in a screening environment. A data set for enzyme kinetic data (V(max) and K(m)) was collected from previously reported studies, and every V(max)/K(m) pair (n = 76) was taken to represent a unique drug compound. The design was restricted to 15 samples, an incubation time of up to 40 min, and starting concentrations (C(0)) for the incubation between 0.

Measurement of unbound drug exposure in brain: modeling of pH partitioning explains diverging results between the brain slice and brain homogenate methods.

Currently used methodology for determining unbound drug exposure in brain combines measurement of the total drug concentration in the whole brain in vivo with estimation of brain tissue binding from one of two available in vitro methods: equilibrium dialysis of brain homogenate and the brain slice uptake method. This study of 56 compounds compares the fraction of unbound drug in brain (f(u,brain)), determined using the brain homogenate method, with the unbound volume of distribution in brain (V(u,brain)), determined using the brain slice method. Discrepancies were frequent and were primarily related to drug pH partitioning, attributable to the preservation of cellular structures in the slice that are absent in the homogenate.