In silico prediction of harmful effects triggered by drugs and chemicalsAuthors: Angelo Vedani, Max Dobler and Markus A. Lill
Journal: Toxicology and Applied Pharmacology
Biographics Laboratory 3R, Friedensgasse 35, 4056 Basel, Switzerland.
While the computer-assisted discovery and optimization of drug candidates based on the known three-dimensional structure of the macromolecular target (structure-based design) or a binding-site surrogate (receptor modeling) is doubtless one of the more potent approaches in rational drug design, the simulation and quantification of side effects triggered by drugs and chemicals is still in its infancy. Major obstacles include the often not available 3D structure of the molecular target, the low specificity of the involved bioregulators and the identification of the controlling metabolic pathways. In the recent past, our laboratory has explored concepts allowing to simulate receptor-mediated toxic phenomena by developing algorithms allowing to construct realistic 3D binding-site surrogates of receptors known or assumed triggering adverse effects and validating them against large batches of molecular data. The underlying technology (software Quasar and Raptor, respectively) specifically allows for induced fit, solvation phenomena and entropic effects. It has been applied to various systems both of pharmacological and toxicological interest including the neurokinin-1, chemokine-3, bradykinin B2, steroid, 5HT2A, aryl hydrocarbon, estrogen and androgen receptor, respectively. In this account we describe the design of a virtual laboratory allowing for a reliable estimation of harmful effects triggered by drugs, chemicals and their metabolites in silico.
In the recent past, the Biographics Laboratory 3R has compiled a 3D database including the surrogates of three major receptor systems known to mediate adverse effects (the aryl hydrocarbon, the estrogen and the androgen receptor, respectively) and validated them against a total of 345 compounds (drugs, chemicals, toxins) using multidimensional QSAR technologies. Within this pilot project we could demonstrate that our virtual laboratory is able to both recognize toxic compounds substantially different from those used in the training set as well as to classify harmless compounds as being nontoxic. This suggests that our approach may be used for the prediction of adverse effects of drug molecules and chemicals. It is the aim to provide cost-covering access to this technology — particularly to universities, hospitals and regulatory bodies as it bears a significant potential to recognize hazardous compounds early in the development process and hence improve resource and waste management as well as reduce animal testing.