Applying machine learning techniques for ADME-Tox prediction: a review. (February 2015)

• Record Type: Journal Article
• Authors: Maltarollo, Vinícius Gonçalves; Gertrudes, Jadson Castro; Oliveira, Patrícia Rufino; Honorio, Kathia Maria
• Is Part Of: Expert opinion on drug metabolism and toxicology. Volume 11:Number 2(2015:Feb.)
• Page Start: 259
• Page End: 271
• Publication Date: 2015-02
• Subjects: Drugs -- Toxicology -- Periodicals; Drugs -- Metabolism -- Periodicals; 615.7
• DOI: 10.1517/17425255.2015.980814
• Languages: English
• ISSNs: 1742-5255

Abstract

<abstract> <title> <x xml:space="preserve">Abstract</x> </title> <p> <bold> <italic>Introduction:</italic> </bold> Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety were considered some of the major causes of clinical failures in the development of new chemical entities. In this context, machine learning (ML) techniques have been often used in ADME-Tox studies due to the existence of compounds with known pharmacokinetic properties available for generating predictive models.</p> <p> <bold> <italic>Areas covered:</italic> </bold> This review examines the growth in the use of some ML techniques in ADME-Tox studies, in particular supervised and unsupervised techniques. Also, some critical points (e.g., size of the data set and type of output variable) must be considered during the generation of models that relate ADME-Tox properties and biological activity.</p> <p> <bold> <italic>Expert opinion:</italic> </bold> ML techniques have been successfully employed in pharmacokinetic studies, helping the complex process of designing new drug candidates from the use of reliable ML models. An application of this procedure would be the prediction of ADME-Tox properties from studies of quantitative structure–activity relationships or the discovery of new compounds from a virtual screening using filters based on results obtained from ML techniques.</p> </abstract>