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Research ArticleArticle

EXTRAPOLATION OF PRECLINICAL PHARMACOKINETICS AND MOLECULAR FEATURE ANALYSIS OF “DISCOVERY-LIKE” MOLECULES TO PREDICT HUMAN PHARMACOKINETICS

Christopher A. Evans, Larry J. Jolivette, Rakesh Nagilla and Keith W. Ward
Drug Metabolism and Disposition July 2006, 34 (7) 1255-1265; DOI: https://doi.org/10.1124/dmd.105.006619
Christopher A. Evans
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Larry J. Jolivette
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Rakesh Nagilla
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Keith W. Ward
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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 two-dimensional 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.

Footnotes

  • Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.

  • doi:10.1124/dmd.105.006619.

  • ABBREVIATIONS: CL, clearance (ml/min/kg); BrW, brain weight (g); clogP, calculated logarithm of the octanol-water partition coefficient; CMR, calculated molar refractivity (ml/mol); HBA, number of hydrogen bond acceptors; HBD, number of hydrogen bond donors; LBF, liver blood flow; MAE, mean absolute error; MLP, maximum life span potential (years); MRT, mean residence time (minutes); nrot, number of rotatable bonds; PSA, polar surface area (Å2); Vd, volume of distribution (l/kg); PK, pharmacokinetic(s).

    • Received July 20, 2005.
    • Accepted April 12, 2006.
  • The American Society for Pharmacology and Experimental Therapeutics
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Drug Metabolism and Disposition: 34 (7)
Drug Metabolism and Disposition
Vol. 34, Issue 7
1 Jul 2006
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Research ArticleArticle

EXTRAPOLATION OF PRECLINICAL PHARMACOKINETICS AND MOLECULAR FEATURE ANALYSIS OF “DISCOVERY-LIKE” MOLECULES TO PREDICT HUMAN PHARMACOKINETICS

Christopher A. Evans, Larry J. Jolivette, Rakesh Nagilla and Keith W. Ward
Drug Metabolism and Disposition July 1, 2006, 34 (7) 1255-1265; DOI: https://doi.org/10.1124/dmd.105.006619

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Research ArticleArticle

EXTRAPOLATION OF PRECLINICAL PHARMACOKINETICS AND MOLECULAR FEATURE ANALYSIS OF “DISCOVERY-LIKE” MOLECULES TO PREDICT HUMAN PHARMACOKINETICS

Christopher A. Evans, Larry J. Jolivette, Rakesh Nagilla and Keith W. Ward
Drug Metabolism and Disposition July 1, 2006, 34 (7) 1255-1265; DOI: https://doi.org/10.1124/dmd.105.006619
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