Computational prediction of genotoxicity: room for improvement

• Published in: Drug discovery today Vol. 10; no. 16; pp. 1119 - 1124
• Main Authors: Snyder, Ronald D., Smith, Mark D.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.08.2005; Elsevier Science
• Subjects: Animals; Biological and medical sciences; DEREK; General pharmacology; Genotoxicity; Humans; MCASE; Medical sciences; Mutagenicity Tests - methods; Pharmaceutical technology. Pharmaceutical industry; Pharmacology. Drug treatments; QSAR; Quantitative Structure-Activity Relationship; Sensitivity and Specificity; Software; TOPKAT; MCASE; Pharmaceutical Science; QSAR; Genotoxicity; DEREK; TOPKAT; Drug Discovery; Bioinformatics; Pharmaceutical technology; Toxicity; Prediction
• ISSN: 1359-6446; 1878-5832
• DOI: 10.1016/S1359-6446(05)03505-1

The ability to predict genotoxicity of novel chemical entities computationally, thus precluding the necessity for conducting biological assays, might ultimately be achievable but we're not there yet. Decades of mutagenesis and clastogenesis studies have yielded enough structure-activity-relationship (SAR) information to make feasible the construction of computational models for prediction of endpoints based on molecular structure and reactivity. Although there is cause for optimism that these approaches might someday reduce or eliminate the need for actual genotoxicity testing, we are in fact a long way from this. We provide an overview of the state of the art of such approaches, dissecting out how these models are suboptimal. It is clear that current programs still have limited predictive capabilities. We propose that one of the major contributing factors for the inherent lack of sensitivity (typically 50-60%) is inadequate coverage of non-covalent DNA interactions. Suboptimal specificity can be partly attributed to chemical space considerations with associated non-causal activity correlations.