Biographics Laboratory 3R, CH-Basel and Department of Pharmaceutical Sciences, University of Basel, Switzerland.
ALTEX. 2009;26(3):167-76. doi: 10.14573/altex.2009.3.167.
The VirtualToxLab is an in silico tool for predicting the toxic (endocrine-disrupting) potential of drugs, chemicals and natural products. It is based on a fully automated protocol and calculates the binding affinity of any molecule of interest towards a series of 12 proteins, known or suspected to trigger adverse effects and estimates the resulting toxic potential. In contrast to other approaches in the field, the technology allows to rationalize a prediction at the molecular level by interactively analyzing the binding mode of the tested compound with any target protein in 3D. The technology is accessible over the Internet (via a secure SSH protocol) and available for any science-oriented organization. The toxic potential - a complex value derived from the individual binding affinities, their standard deviation and the quality of the underlying model (number and ratio of training and test compounds, activity range covered) - of existing and hypothetical compounds is estimated by simulating and quantifying their interactions towards a series of macromolecular targets at the molecular level using automated flexible docking combined with multidimensional QSAR (mQSAR). Currently, those targets comprise 12 proteins: the androgen, aryl hydrocarbon, estrogen alpha/beta, glucocorticoid, mineralocorticoid, thyroid alpha/beta liver X and the peroxisome proliferator-activated receptor gamma as well as the enzymes cytochrome P450 3A4 (CYP 3A4) and 2A13 (CYP 2A13). Up to date, the technology has been used to predict the toxic potential for more than 2,000 drugs, chemicals and natural compounds. All results are posted in the Internet - in this account, a few will be discussed in detail with reference to the molecular mechanisms triggering the adverse effect.
VirtualToxLab 是一种用于预测药物、化学品和天然产物的毒性(内分泌干扰)潜力的计算工具。它基于一个全自动的方案,计算任何感兴趣分子与一系列 12 种已知或怀疑会引发不良反应的蛋白质的结合亲和力,并估计由此产生的毒性潜力。与该领域的其他方法相比,该技术允许通过交互式分析测试化合物与任何目标蛋白质在 3D 中的结合模式,从分子水平上合理化预测。该技术可通过安全的 SSH 协议在互联网上访问,可供任何面向科学的组织使用。现有的和假设的化合物的毒性潜力——一个由单个结合亲和力、它们的标准偏差和基础模型的质量(训练和测试化合物的数量和比例、涵盖的活性范围)衍生的复杂值——通过模拟和量化它们在分子水平上与一系列大分子靶标的相互作用来估计使用自动灵活对接与多维定量构效关系 (mQSAR) 相结合。目前,这些靶标包括 12 种蛋白质:雄激素、芳烃受体、雌激素 α/β、糖皮质激素、盐皮质激素、甲状腺 α/β、肝 X 和过氧化物酶体增殖物激活受体 γ 以及酶细胞色素 P450 3A4 (CYP 3A4) 和 2A13 (CYP 2A13)。迄今为止,该技术已用于预测 2000 多种药物、化学品和天然化合物的毒性潜力。所有结果都在互联网上发布——在这个账户中,将详细讨论其中一些结果,并参考引发不良反应的分子机制。