Do drug metabolism and pharmacokinetic departments make any contribution to drug discovery?

Analytical strategies for identifying drug metabolites

With the dramatic increase in the number of new chemical entities (NCEs) arising from combinatorial chemistry and modern high-throughput bioassays, novel bioanalytical techniques are required for the rapid determination of the metabolic stability and metabolites of these NCEs. Knowledge of the metabolic site(s) of the NCEs in early drug discovery is essential for selecting compounds with favorable pharmacokinetic credentials and aiding medicinal chemists in modifying metabolic "soft spots". In development, elucidation of biotransformation pathways of a drug candidate by identifying its circulatory and excretory metabolites is vitally important to understand its physiological effects. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have played an invaluable role in the structural characterization and quantification of drug metabolites. Indeed, liquid chromatography (LC) coupled with atmospheric pressure ionization (API) MS has now become the most powerful tool for the rapid detection, structure elucidation, and quantification of drug-derived material within various biological fluids. Often, however, MS alone is insufficient to identify the exact position of oxidation, to differentiate isomers, or to provide the precise structure of unusual and/or unstable metabolites. In addition, an excess of endogenous material in biological samples often suppress the ionization of drug-related material complicating metabolite identification by MS. In these cases, multiple analytical and wet chemistry techniques, such as LC-NMR, enzymatic hydrolysis, chemical derivatization, and hydrogen/deuterium-exchange (H/D-exchange) combined with MS are used to characterize the novel and isomeric metabolites of drug candidates. This review describes sample preparation and introduction strategies to minimize ion suppression by biological matrices for metabolite identification studies, the application of various LC-tandem MS (LC-MS/MS) techniques for the rapid quantification and identification of drug metabolites, and future trends in this field.

Recent advances in pharmacokinetic extrapolation from preclinical data to humans

The early characterisation of drug metabolism and pharmacokinetic (DMPK) properties of new chemical entities plays a key role in the pharmaceutical industry's effort to reduce attrition. Specifically, a major goal of early DMPK studies is to accurately predict the behaviour of new chemical entities in humans, thus allowing likely failures to be terminated rapidly and resource to be placed on molecules most likely to succeed. The present review summarises progress over the past several years in the key technologies used in the pharmaceutical industry to achieve these goals: namely, in vivo, in vitro and in silico/computational tools. The limitations of the various assays are discussed, with attention also given to likely future directions in this field.

Fromin vivotoin vitro/in silicoADME: progress and challenges

High-throughput screening technologies in biological sciences of large libraries of compounds obtained via combinatorial or parallel chemistry approaches, as well as the application of design rules for drug-likeness, have resulted in more hits to be evaluated with respect to their ADME or drug metabolism and pharmacokinetic properties. The traditional in vivo methods using preclinical species, such as rat, dog or monkey, are no longer sufficient to cope with this demand. This editorial discusses the changes towards medium- to high-throughput in vitro and in silico ADME screening. In addition, much more attention is now put on early safety and risk assessment of promising lead series and potential clinical candidates.

Allometric scaling of pharmacokinetic parameters in drug discovery: can human CL, Vss and t1/2 be predicted from in-vivo rat data?

In a drug discovery environment, reasonable go/no-go human in-vivo pharmacokinetic (PK) decisions must be made in a timely manner with a minimum amount of animal in-vivo or in-vitro data. We have investigated the accuracy of the in-vivo correlation between rat and human for the prediction of the total systemic clearance (CL), the volume of distribution at steady state (Vss), and the half-life (t1/2) using simple allometric scaling techniques. We have shown, using a large diverse set of drugs, that a fixed exponent allometric scaling approach can be used to predict human in-vivo PK parameters CL, Vss and t(1/2) solely from rat in-vivo PK data with acceptable accuracy for making go/no-go decisions in drug discovery. Human in-vivo PK predictions can be obtained using the simple allometric scaling relationships CL(Human) approximately = 40 CL(Rat) (L/hr), Vss(Human) approximately = 200 Vss(Rat) (L), and t1/2(Human) approximately = 4 t1/2(Rat) (hr). The average fold error for human CL predictions for N = 176 drugs was 2.25 with 79% of the drugs having a fold error less than 3. The average fold error for human Vss predictions for N = 144 drugs was 1.85 with 84% of the drugs having a fold error less than 3. The average fold error for human t1/2 predictions for N = 145 drugs was 2.05 with 76% of the drugs having a fold error less than 3. Using these simple allometric relationships, the sorting of drug candidates into a low/medium/high/very high human classification scheme was also possible from rat data. Since these simple allometric relationships between rat and human CL, Vss, and t1/2 are reasonably accurate, easy to remember and simple to calculate, these equations should be useful for making early go/no-go in-vivo human PK decisions for drug discovery candidates.