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Progress in predicting human ADME parameters in silico.

S Ekins1, C L Waller, P W Swaan

  • 1Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Drop Code 0730, Indianapolis, IN 46285, USA. ekins_sean@lilly.com

Journal of Pharmacological and Toxicological Methods
|March 29, 2001
PubMed
Summary

Computational methods for drug absorption, distribution, metabolism, and excretion (ADME) have evolved significantly. Current in vitro data and in silico models accelerate drug discovery by predicting in vivo outcomes earlier in development.

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Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Computational Chemistry and Cheminformatics

Background:

  • The application of computational methods to Absorption, Distribution, Metabolism, and Excretion (ADME) has progressed through distinct historical phases.
  • Early models (1960s-1970s) relied on limited in vivo data, while the subsequent era (1980s-1990s) integrated in vitro methods as surrogates for in vivo studies.
  • The current era leverages extensive in vitro datasets for predicting human in vivo ADME parameters, driven by the need to reduce drug development costs and failure rates.

Purpose of the Study:

  • To review the historical evolution of computational approaches for predicting ADME properties.
  • To assess the impact of in vitro and in silico methods on accelerating drug discovery and development.
  • To explore the future trajectory of computational ADME modeling.

Main Methods:

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  • Analysis of historical trends in computational ADME modeling.
  • Review of the integration of in vitro and in silico techniques in drug discovery.
  • Examination of the use of high-throughput screening data for model development.

Main Results:

  • Computational ADME modeling has advanced from limited in vivo data to sophisticated in silico predictions based on extensive in vitro data.
  • The pharmaceutical industry has increasingly invested in early-stage ADME and safety assessments to mitigate late-stage failures.
  • Parallel utilization of in silico, in vitro, and in vivo approaches is emerging for complex ADME challenges like drug-drug interactions.

Conclusions:

  • Computational ADME modeling has become integral to modern drug discovery, enabling faster screening and earlier identification of viable drug candidates.
  • The trend is towards higher throughput data and validated in silico models for virtual screening of ADME parameters.
  • Future progress will likely involve further integration of diverse data types and advanced computational techniques to predict in vivo human pharmacokinetics and safety more accurately.