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Computational Models Supporting Lead Optimization in Drug Discovery

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Optimizing the “Drug-Like” Properties of Leads in Drug Discovery

Part of the book series: Biotechnology: Pharmaceutical Aspects ((PHARMASP,volume IV))

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Abstract

Design of successful drug development candidates requires balancing a number of different characteristics simultaneously including intrinsic activity, biopharmaceutical properties, synthesis, stability, and many others. Independently, each of these can be considered a barrier to drug performance or development success (Kerns and Di, 2003). Among the most important of these processes determining in vivo performance are absorption, distribution, metabolism and excretion, collectively referred to as ADME. The structure and physicochemical characteristics of the drug are important determinants of these processes, as are the characteristics of the physiological mechanisms. Further complicating the issue is the interrelationship of many of these processes, frequently in antagonistic ways. Increasing solute lipophilicity, for example, can decrease aqueous solubility, which frequently compromises oral absorption. Also, it can increase metabolic clearance, thus making difficult sustaining the pharmacologically relevant systemic exposure. In contrast, permeability, in many cases, increases with increasing lipophilicity, favoring absorption (Conradi et al., 1996; Hansch et al., 2004). The actual result will be determined by the relative contributions of these two competing phenomena.

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Authors and Affiliations

  1. ADMETRx, Inc., 1477 Campus Drive, Suite 600, Kalamazoo, MI, 49008, USA

    Philip S. Burton & Jay T. Goodwin

  2. Pharmacokinetics, Dynamics and Metabolism Nerviano Medical Sciences, Milan, Italy

    Italo Poggesi & Massimiliano Germani

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  1. Philip S. Burton
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  2. Italo Poggesi
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  3. Massimiliano Germani
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  4. Jay T. Goodwin
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Editor information

Editors and Affiliations

  1. University of Kansas, Lawrence, KS, 66045, USA

    Ronald T. Borchardt

  2. Wyeth Research, Monmouth Junction, NJ, 08852, USA

    Edward H. Kerns

  3. Pfizer, Inc., Kalamazoo, MI, 49007, USA

    Michael J. Hageman

  4. University of North Carolina, Chapel Hill, NC, 27599, USA

    Dhiren R. Thakker

  5. Eli Lilly Research Laboratory, Greenfield, IN, 46285, USA

    James L. Stevens

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© 2006 American Association of Pharmaceutical Scientists

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Burton, P.S., Poggesi, I., Germani, M., Goodwin, J.T. (2006). Computational Models Supporting Lead Optimization in Drug Discovery. In: Borchardt, R.T., Kerns, E.H., Hageman, M.J., Thakker, D.R., Stevens, J.L. (eds) Optimizing the “Drug-Like” Properties of Leads in Drug Discovery. Biotechnology: Pharmaceutical Aspects, vol IV. Springer, New York, NY. https://doi.org/10.1007/978-0-387-44961-6_9

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