ADMET Introduction and Links

ADMET stands for absorption, distribution, metabolism, elimination (or excretion), and toxicity. These are key processes and phenomena occurring when chemical substances are transported and transformed inside living organisms. ADMET modeling and calculations are critical in developing new drugs and evaluating the risks and side effects of chemical substances such as food additives, pesticides and environmental pollutants, which may contact or enter the body of humans or other life forms. The term ADMET is frequently used in context with molecular modeling and in-silico prediction of pharmacokinetic, metabolic and related endpoints [1-7]. Sometimes, the word “toxicity” is dropped from the list of pharmacological-activity criteria, and the acronym ADME is used instead. Also, the notation ADME-Tox is employed, textually highlighting the importance and combination of both pharmacokinetic and toxicological research.

Various open-source and commercial software tools are available for ADMET modeling [8-11]. These tools can be applied to the virtual screening of chemical compound libraries and databases. The typical goal is to identify candidate compounds for further investigations, including the synthesis and characterization of new compounds and the structural refinement of existing ones. If ADMET-based estimations reveals unfavorable properties, compounds will be eliminated from candidate pools. ADMET profiling depends on molecular structure information as modeling input. The selection of a suitable set of molecular descriptors, derived from molecular graphs or other molecular representations, is critical for the successful estimation of compound properties and, finally, the distinction between candidates and non-candidates. ADMET profiling is integrated into early stages of drug design to speed up drug discovery and to minimize the number of compounds dropping out during later stages.

Keywords: pharmacology, pharmacokinetics, lead optimization, virtual screening, high-throughput screening, drug design, de novo design, pharmacopohore modeling, molecular modeling, QSAR modeling.

References and Links

[1]	S. K. Balani, G. T. Miwa, L. S. Gan, J. T. Wu and F. W. Lee: Strategy of utilizing in vitro and in vivo ADME tools for lead optimization and drug candidate selection. Curr. Top. Med. Chem. 2005, 5 ( 11), 1033-1038. doi: 10.2174/156802605774297038.
[2]	N. E. Campillo and J. A. Páez: In Silico ADME Approaches. Frontiers in Drug Design & Discovery 2012, 4, 291-332. doi: 10.2174/97816080520281090401.
[3]	D. Cao, J. Wang, R. Zhou; Y. Li, H. Yu and T. Hou: ADMET evaluation in drug discovery. 11. PharmacoKinetics Knowledge Base (PKKP): a comprehensive database of of pharmacokinetic and toxic properties for drugs. J. Chem. Inf. Model. 2012, 52 ( 5), 1132-1137. doi: 10.1021/ci300112j.
[4]	I. V. Tetko, P. Bruneau, H.-W. Mewes, D. C. Rohrer and G. I. Poda: Can we estimate the accuracy of ADME-Tox? Drug Discovery Today 2006, 11 ( 15-16), 700-707. doi: 10.1016/j.drudis.2006.06.013 (Preprint).
[5]	H. van de Waterbeemd and E. Gifford: ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discovery 2003, 2, 192-204. doi: 10.1038/nrd1032.
[6]	A. White and S. Modi: In silico ADMET models: is the future really bright? March 22, 2012 [www.drugdiscoverytoday.com/view/24705/in-silico-admet-models-is-the-future-really-bright/].
[7]	Scott Wildman Lab: ADMET. [biochem.wustl.edu/wildmans/Methods/ADMET.html].
[8]	Accylres®: ADMET and Predictive Toxicology [accelrys.com/products/discovery-studio/admet.html].
[9]	Your Cloe® Gateway: Search our Open Access ADME/PK Database. [www.cloegateway.com/services/cloe_knowledge/pages/service_frontpage.php].
[10]	eADMET: Compuatational models designed to predict the ADMET of molecules. [www.eadmet.com/en/index.php]. Note: eADMET created the OCHEM platform for in-silico ADME-Tox modeling and prediction.
[11]	Simulationsplus, Inc.: What is ADMET Predictor™? [www.simulations-plus.com/Products.aspx?pID=13].