Relating molecular properties and in vitro assay results to in vivo drug disposition and toxicity outcomes.

A primary goal of lead optimization is to identify compounds with improved absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. A number of reports have linked computed molecular properties to desirable in vivo ADMET outcomes, but a significant limitation of these analyses is the failure to control statistically for possible covariates. We examine the relationship between molecular properties and in vitro surrogate assays vs in vivo properties within 173 chemical series from a database of 3773 compounds with rodent pharmacokinetic and toxicology data. This approach identifies the following pairs of surrogates as most predictive among those examined: rat primary hepatocyte (RPH) cytolethality/volume of distribution (V(d)) for in vivo toxicology outcomes, scaled microsome metabolism/calculated logP for in vivo unbound clearance, and calculated logD/kinetic aqueous solubility for thermodynamic solubility. The impact of common functional group substitutions is examined and provides insights for compound design.

[1]  Michal Vieth,et al.  Structure-guided expansion of kinase fragment libraries driven by support vector machine models. , 2010, Biochimica et biophysica acta.

[2]  M. Gleeson.  Generation of a set of simple, interpretable ADMET rules of thumb. , 2008, Journal of medicinal chemistry.

[3]  Michal Vieth,et al.  Dependence of molecular properties on proteomic family for marketed oral drugs. , 2006, Journal of medicinal chemistry.

[4]  Tudor I. Oprea,et al.  Is There a Difference between Leads and Drugs? A Historical Perspective , 2001, J. Chem. Inf. Comput. Sci..

[5]  T. Iga,et al.  Effect of chlorpromazine on isolated rat hepatocytes. , 1982, Biochemical pharmacology.

[6]  James L Stevens,et al.  The future of drug safety testing: expanding the view and narrowing the focus. , 2009, Drug discovery today.

[7]  Stephen R. Johnson,et al.  Molecular properties that influence the oral bioavailability of drug candidates. , 2002, Journal of medicinal chemistry.

[8]  Paul D Leeson,et al.  Time-related differences in the physical property profiles of oral drugs. , 2004, Journal of medicinal chemistry.

[9]  P. Selzer,et al.  Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. , 2000, Journal of medicinal chemistry.

[10]  Ian A. Watson,et al.  Rationalizing Lead Optimization by Associating Quantitative Relevance with Molecular Structure Modification , 2009, J. Chem. Inf. Model..

[11]  B. Ring,et al.  Predictions of the In Vivo Clearance of Drugs from Rate of Loss Using Human Liver Microsomes for Phase I and Phase II Biotransformations , 2006, Pharmaceutical Research.

[12]  Andrew G. Leach,et al.  Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure. , 2006, Journal of medicinal chemistry.

[13]  M. Congreve,et al.  A 'rule of three' for fragment-based lead discovery? , 2003, Drug discovery today.

[14]  P. Leeson,et al.  A comparison of physiochemical property profiles of development and marketed oral drugs. , 2003, Journal of medicinal chemistry.

[15]  Ian A. Watson,et al.  Characteristic physical properties and structural fragments of marketed oral drugs. , 2004, Journal of medicinal chemistry.

[16]  Edward H. Kerns,et al.  The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery , 2010, Nature Reviews Drug Discovery.

[17]  M. Waring.  Defining optimum lipophilicity and molecular weight ranges for drug candidates-Molecular weight dependent lower logD limits based on permeability. , 2009, Bioorganic & medicinal chemistry letters.

[18]  M. Edwards,et al.  Using the Golden Triangle to optimize clearance and oral absorption. , 2009, Bioorganic & medicinal chemistry letters.

[19]  Kiyomi Ito,et al.  Comparison of the Use of Liver Models for Predicting Drug Clearance Using in Vitro Kinetic Data from Hepatic Microsomes and Isolated Hepatocytes , 2004, Pharmaceutical Research.

[20]  R. Barro,et al.  A New Data Set of Educational Attainment in the World, 1950-2010 , 2010 .

[21]  Dawn G Goodman,et al.  Best Practices Guideline: Toxicologic Histopathology , 2004, Toxicologic pathology.

[22]  J. Hughes,et al.  Physiochemical drug properties associated with in vivo toxicological outcomes. , 2008, Bioorganic & medicinal chemistry letters.

[23]  7 – Morphological Evaluation of Gap Junctional Intercellular Communication , 1994 .