Extrapolation of Preclinical Pharmacokinetics and Molecular Feature Analysis of “Discovery-Like” Molecules to Predict Human Pharmacokinetics

Abstract

ABSTRACT:The prediction of human pharmacokinetics from preclinical species is an integral component of drug discovery. Recent studies with a 103-compound dataset suggested that scaling from monkey pharmacokinetic data tended to be the most accurate method for predicting human clearance. Additionally, interrogation of the twodimensional molecular properties of these molecules produced a set of associations which predict the likely extrapolative outcome (success or failure) of preclinical data to project human pharmacokinetics. However, a limitation of the previous analyses was the relative paucity of data for typical "discovery-like" molecules (molecular weight >300 and/or clogP >3). The objective of this investigation was to generate preclinical data required for extension of this dataset for additional discovery-like molecules and determine whether the aforementioned findings continue to apply for these molecules. In vivo nonrodent intravenous pharmacokinetic data were generated for 13 molecules, and data for 8 additional molecules were obtained from the literature. Additionally, the various scaling methodologies and molecular features analysis were applied to this new dataset to predict human pharmacokinetics. Whereas the predictive accuracies demonstrated across all of the various methodologies were lower for this higher clearance compound dataset, scaling from monkey liver blood flow continued to be an accurate methodology, and human volume of distribution was similarly well predicted regardless of scaling methodology. Lastly, application of the molecular feature associations, particularly data-dependent associations, afforded an improved predictivity compared with the liver blood flow scaling approaches, and provides insight into the extrapolation of high clearance compounds in the preclinical species to human.The use of in vivo preclinical pharmacokinetic data generated during lead optimization to predict human pharmacokinetic properties is commonplace in the pharmaceutical industry, with a variety of methodologies available for data predictivity (Ings, 1990;Pogessi, 2004). Recent investigations using a diverse set of 103 nonpeptide xenobiotics with rat, dog, monkey, and human intravenous pharmacokinetic data have yielded a number of interesting and useful observations, including the value of monkey pharmacokinetic data in successful prediction of human pharmacokinetics, and the relative lack of added value provided by dog pharmacokinetic data (Ward and Smith, 2004a,b). Additionally, when several quantitative methodologies, including liver blood flow-based scaling and body weight-based allometry, were applied to this dataset for predicting human clearance (CL), the greatest accuracy was achieved using the monkey liver blood flow approach, rather than allometry, even with the inclusion of mechanistic or empirical allometric correction factors (Nagilla and Ward, 2004). Finally, the availability of this comprehensive dataset has allowed the identification of a set of associations between preclinical pharmaco...

Pharmacological, Pharmacokinetic, and Primate Analgesic Efficacy Profile of the Novel Bradykinin B1 Receptor Antagonist ELN441958

“…There are often major differences in the pharmacology of neurotransmitter systems involved in pain transmission between humans and rodents, e.g., cholecystokinin (Hökfelt et al, 2001) and substance P (Hill, 2000). In addition, scaling from monkey pharmacokinetic data is generally the most accurate method to predict human clearance (Evans et al, 2006). In the bradykinin system, human and monkey B 1 receptors are functionally indistinguishable, whereas rodent receptors exhibit a different agonist and antagonist pharmacology (Regoli et al, 2001).…”

Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds

“…Previous reports have focused on prediction methods that utilize animal pharmacokinetic data (Caldwell et al, 2004;Ward and Smith, 2004a,b;Jolivette and Ward, 2005;Evans et al, 2006;Mahmood et al, 2006;Martinez et al, 2006;Tang and Mayersohn, 2006;Fagerholm, 2007;McGinnity et al, 2007) and in vitro data (Obach et al, 1997;Lombardo et al, 2002Lombardo et al, , 2004Nestorov et al, 2002;Riley et al, 2005;Grime and Riley, 2006). Recently, the availability of computational chemistry methodologies has increased, and these have been applied to the prediction of human pharmacokinetics and/or general absorption-distribution-metabolism-excretion-toxicology properties (Cruciani et al, 2005;Ghafourian et al, 2006;Gleeson et al, 2006;Lombardo et al, 2006;Gleeson, 2007;Gunturi and Narayanan, 2007;Norinder and Bergstroem, 2007).…”

Cross-Species Differences in the Preclinical Pharmacokinetics of CT7758, an α4β1/α4β7 Integrin Antagonist

“…Total blood clearance (CL b ) was estimated to be 132, 53, and 19 ml/min/kg in mice, rat, and cynomolgus monkey, respectively. These values represented, respectively, $ 100, 69, and 41% of hepatic blood flow in those species (Davies and Morris, 1993;Evans et al, 2006). After intravenous administration of CT7758 at 3 mg/kg to rodents and cynomolgus monkey, plasma levels tended to decrease in a multiexponential manner with a terminal half-life of 1 hour.…”