title: A flexible approach for optimising in silico ADME/Tox characterisation of lead candidates.
abstract: Over the years, multiple in silico solutions have been developed for the early characterisation of lead candidates at early stages of the drug development process. Despite the nascent promise this technology holds for the pharmaceutical and biotech industries, in many cases, inherent limitations in many of these computational technologies still hinders the prediction performance of absorption, distribution, metabolism and excretion (ADME), and toxicological (Tox) properties. However, as the result of recent developments in this arena and key technology collaborations, Bio-Rad Laboratories, Inc. has made some breakthroughs with their in silico ADME/Tox prediction and lead optimisation solutions. The company's KnowItA11 ADME/Tox system, when used in conjunction with Equbits' Foresight support vector machine platform and other best-of-breed partnering technologies, provides an intelligent and flexible approach to in silico modelling that helps to overcome these difficulties. The system ultimately does this by offering various approaches and technologies that can lead researchers toward improvement in results and overall greater confidence in the in silico approach as a whole. In this technology evaluation, several examples and case studies on mutagenicity and hERG-channel blocking illustrate how researchers can take advantage of this system from compound characterisation to knowledge extraction to achieve better and faster results in their research process.
authors.affiliation: Micropolis, Isatis 02, F-05000 Gap, France. yann@ybsolution.com
authors.forename: Yann
authors.initials: Y
authors.lastname: Bidault
mesh_terms: D000818:Animals; D002460:Cell Line; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods* / Q000639:trends; D064420:Drug-Related Side Effects and Adverse Reactions / Q000209:etiology / Q000378:metabolism* / Q000517:prevention & control; D006801:Humans; D008956:Models, Chemical; D011237:Predictive Value of Tests