Early prediction of drug metabolism and toxicity: systems biology approach and modeling

• Published in: Drug Discovery Today Vol. 9; no. 3; pp. 127 - 135
• Main Authors: Bugrim, Andrej, Nikolskaya, Tatiana, Nikolsky, Yuri
• Format: Book Review Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.02.2004; Elsevier Science
• Subjects: ADME/Tox; Animals; Biological and medical sciences; Computer Simulation; Databases, Factual - trends; Drug Design; Drug Evaluation, Preclinical - methods; Drug-Related Side Effects and Adverse Reactions; General pharmacology; Medical sciences; Models, Biological; Pharmaceutical Preparations - metabolism; Pharmacokinetics; Pharmacokinetics. Pharmacogenetics. Drug-receptor interactions; Pharmacology. Drug treatments; pre-clinical prediction; Quantitative Structure-Activity Relationship; Systems biology; Techniques & Methods; Pharmaceutical Science; Systems biology; ADME/Tox; Biochemistry; Drug Discovery; pre-clinical prediction; Drug; Structure activity relation; Toxicity; Prediction; Preclinical trial; Metabolism; Pharmacokinetics; Modeling
• ISSN: 1359-6446; 1878-5832
• DOI: 10.1016/S1359-6446(03)02971-4

Many of the drug candidates that fail in clinical trials are withdrawn because of unforeseen effects of human metabolism, such as toxicity and unfavorable pharmacokinetic profiles. Early pre-clinical elimination of such compounds is important but not yet possible. An ideal system would enable researchers to make a confident elimination decision based purely on the structure of a new compound, and incorporate and use multiple pre-clinical experimental data to support such a decision. Currently available resources can be split into three categories: (i) structure–activity relationships (SAR) computational models based on compound structure; (ii) ‘pattern’ databases of tissue or organ response to drugs, compiled from high-throughput experiments; and (iii) ‘systems biology’ databases of metabolic pathways, genes and regulatory networks. In this review, we outline the advantages and drawbacks of each of these systems and suggest directions for their integration. Pre-clinical elimination of ADME/Tox averse drug candidates is important, but not possible yet. The ideal system should allow elimination decision based on compound's structure, and utilize all pre-clinical data. Available in silico resources should be integrated in a systems biology framework.