Computational Prediction of the Chromosome-Damaging Potential of Chemicals

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• 作者：Andreas Rothfuss ; Thomas Steger-Hartmann ; Nikolaus Heinrich ; Jö ; rg Wichard
• 刊名：Chemical Research in Toxicology
• 出版年：2006
• 出版时间：October 2006
• 年：2006
• 卷：19
• 期：10
• 页码：1313 - 1319
• 全文大小：83K
• 年卷期：v.19,no.10(October 2006)
• ISSN：1520-5010

文摘

We report on the generation of computer-based models for the prediction of the chromosome-damagingpotential of chemicals as assessed in the in vitro chromosome aberration (CA) test. On the basis ofpublicly available CA-test results of more than 650 chemical substances, half of which are drug-likecompounds, we generated two different computational models. The first model was realized using the(Q)SAR tool MCASE. Results obtained with this model indicate a limited performance (53%) for theassessment of a chromosome-damaging potential (sensitivity), whereas CA-test negative compounds werecorrectly predicted with a specificity of 75%. The low sensitivity of this model might be explained bythe fact that the underlying 2D-structural descriptors only describe part of the molecular mechanismleading to the induction of chromosome aberrations, that is, direct drug-DNA interactions. The secondmodel was constructed with a more sophisticated machine learning approach and generated a classificationmodel based on 14 molecular descriptors, which were obtained after feature selection. The performanceof this model was superior to the MCASE model, primarily because of an improved sensitivity, suggestingthat the more complex molecular descriptors in combination with statistical learning approaches are bettersuited to model the complex nature of mechanisms leading to a positive effect in the CA-test. An analysisof misclassified pharmaceuticals by this model showed that a large part of the false-negative predictedcompounds were uniquely positive in the CA-test but lacked a genotoxic potential in other mutagenicitytests of the regulatory testing battery, suggesting that biologically nonsignificant mechanisms could beresponsible for the observed positive CA-test result. Since such mechanisms are not amenable to modelingapproaches it is suggested that a positive prediction made by the model reflects a biologically significantgenotoxic potential. An integration of the machine-learning model as a screening tool in early discoveryphases of drug development is proposed.