Optimal Experimental Design for Assessment of Enzyme Kinetics in a Drug Discovery Screening Environment

Abstract

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 0.01 and 100 μM. The optimization was performed by finding the sample times and C(0) returning the lowest uncertainty (S.E.) of the model parameter estimates. Individual optimal designs, one general optimal design and one, for laboratory practice suitable, pragmatic optimal design (OD) were obtained. In addition, a standard design (STD-D), representing a commonly applied approach for metabolic stability investigations, was constructed. Simulations were performed for OD and STD-D by using the Michaelis-Menten (MM) equation, and enzyme kinetic parameters were estimated with both MM and a monoexponential decay. OD generated a better result (relative standard error) for 99% of the compounds and an equal or better result [(root mean square error (RMSE)] for 78% of the compounds in estimation of metabolic intrinsic clearance. Furthermore, high-quality estimates (RMSE < 30%) of both V(max) and K(m) could be obtained for a considerable number (26%) of the investigated compounds by using the suggested OD. The results presented in this study demonstrate that the output could generally be improved compared with that obtained from the standard approaches used today.