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In Silico ADME Techniques Used in Early-Phase Drug Discovery

  • Matthew L. Danielson
  • Bingjie Hu
  • Jie Shen
  • Prashant V. Desai
Chapter
Part of the AAPS Advances in the Pharmaceutical Sciences Series book series (AAPS, volume 25)

Abstract

The process of drug discovery and development is time consuming and expensive. In silico tools, in combination with in vitro and in vivo models, provide a valuable resource to improve the efficiency of this process. In this chapter, we provide an overview of various in silico tools and models used to identify and resolve absorption, distribution, metabolism, and excretion (ADME) challenges in drug discovery. In general, structure-based in silico techniques such as docking and molecular dynamics simulations have limited applicability in the ADME space due to the promiscuity of many ADME targets and the limited availability of high-resolution 3-D structures. Pharmacophore models, a ligand-based in silico method, can be used to identify key structural features responsible for the interaction with the target of interest. However, due to broad ligand specificity and the probability of multiple binding sites in many ADME targets, pharmacophore models have limited prospective applicability across structurally diverse chemical scaffolds. Conversely, quantitative structure-property relationship (QSPR) models are capable of extracting knowledge from a wide variety of chemical scaffolds and have prospectively shown utility as predictive models for many ADME endpoints measured in the pharmaceutical industry. QSPR models, especially those based on machine learning techniques, are known to have limited interpretability. To address this challenge, the use of QSPR models is typically coupled with information derived from trends between ADME endpoints and physicochemical properties (e.g., lipophilicity, polar surface area, number of hydrogen bond donors, etc.) during drug discovery. Furthermore, knowledge extracted by the matched molecular pair analysis (MMPA) of ADME data provides insight that is used to identify fragment replacements to improve the ADME characteristics of compounds. In conclusion, an effective amalgamation of in silico tools is necessary to influence the design of compounds that will possess favorable ADME properties. Finally, in silico tools should never be used in isolation; they make up one arm of the integrated and iterative learning cycle that is comprised of in silico, in vitro, and in vivo models that we recommend using to effectively drive a drug discovery project.

Keywords

In silico ADME Quantitative structure-property relationship models Matched-molecular pair analysis Predictive models Physico-chemical properties 

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Copyright information

© American Association of Pharmaceutical Scientists 2017

Authors and Affiliations

  • Matthew L. Danielson
    • 1
  • Bingjie Hu
    • 1
  • Jie Shen
    • 1
  • Prashant V. Desai
    • 1
  1. 1.Computational ADME, Drug DispositionLilly Research Laboratories, Eli Lilly and CompanyIndianapolisUSA

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