In silico ADME prediction: data, models, facts and myths.
A critical review of a very recent work in the field of in silico ADME prediction is presented with emphasis on the work published during the period 2000-2002, and several other review articles are mentioned in order to offer a broader view of the field. We find that not much progress has been made in developing robust and predictive models, and that the lack of accurate data, together with the use of questionable modeling end-points, has greatly hindered the real progress in defining generally applicable models. Due to the largely empirical nature of QSAR/QSPR approaches, general and truly predictive models for complex phenomena, such as absorption and clearance, may still be chimeric. The development of local models for use within focused chemical series may be the most appropriate way of utilizing in silico ADME predictions, once experience and data have been gained on a given project and/or structural class.
Lombardo
Franco
F
Pfizer Global Research and Development, Groton Laboratories, Groton, CT 06340, USA. franco_lombardo@groton.pfizer.com
Gifford
Eric
E
Shalaeva
Marina Y
MY
D000818:Animals; D001682:Biological Availability; D001798:Blood Proteins / Q000378:metabolism; D001812:Blood-Brain Barrier / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D007408:Intestinal Absorption; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D010599:Pharmacokinetics; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D012995:Solubility
D001798:Blood Proteins; D004364:Pharmaceutical Preparations