Predicting the toxic potential of drugs and chemicals in silico.

Based on the 3D structure of the target protein (ERalphabeta, AR, PPARgamma, TRalphabeta, GR; CYP3A4) or a surrogate thereof (AhR), the Biographics Laboratory 3R has generated a series of virtual test kits and validated them against 693 compounds. In a pilot project (ToxDataBase), both existing and new drugs or environmental chemicals can be screened for their endocrine-disrupting potential or the probability to trigger drug-drug interactions in silico. After peer testing (2007-8), it is planned to make the database available on the Internet.

[1]  A. Vedani,et al.  Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor. , 2005, Journal of medicinal chemistry.

[2]  R. Evans,et al.  PPARδ: a dagger in the heart of the metabolic syndrome , 2006 .

[3]  Markus A. Lill,et al.  Simulating α/β Selectivity at the Human Thyroid Hormone Receptor: Consensus Scoring Using Multidimensional QSAR , 2007 .

[4]  T. Willson,et al.  The PPARs: from orphan receptors to drug discovery. , 2000, Journal of medicinal chemistry.

[5]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[6]  A. Burdick,et al.  The toxicology of ligands for peroxisome proliferator-activated receptors (PPAR). , 2006, Toxicological sciences : an official journal of the Society of Toxicology.

[7]  G. Chang,et al.  Macromodel—an integrated software system for modeling organic and bioorganic molecules using molecular mechanics , 1990 .

[8]  U. Singh,et al.  A NEW FORCE FIELD FOR MOLECULAR MECHANICAL SIMULATION OF NUCLEIC ACIDS AND PROTEINS , 1984 .

[9]  Angelo Vedani,et al.  A new force field for modeling metalloproteins , 1990 .

[10]  Angelo Vedani,et al.  Algorithm for the systematic solvation of proteins based on the directionality of hydrogen bonds , 1991 .

[11]  Gerd Folkers,et al.  PrGen: Pseudoreceptor Modeling Using Receptor‐mediated Ligand Alignment and Pharmacophore Equilibration , 1998 .

[12]  A. Vedani,et al.  In silico prediction of harmful effects triggered by drugs and chemicals. , 2005, Toxicology and applied pharmacology.

[13]  James J. P. Stewart,et al.  MOPAC: A semiempirical molecular orbital program , 1990, J. Comput. Aided Mol. Des..

[14]  Markus A Lill,et al.  The challenge of predicting drug toxicity in silico. , 2006, Basic & clinical pharmacology & toxicology.

[15]  H. Rosenkranz,et al.  Use of artificial intelligence in structure-affinity correlations of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) receptor ligands. , 1991, Carcinogenesis.

[16]  Max Dobler,et al.  5D-QSAR: the key for simulating induced fit? , 2002, Journal of medicinal chemistry.

[17]  W. Wahli,et al.  Peroxisome proliferator-activated receptors: nuclear control of metabolism. , 1999, Endocrine reviews.

[18]  A. Hopfinger,et al.  Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism , 1997 .

[19]  Fulvio Loiodice,et al.  Synthesis, biological evaluation, and molecular modeling investigation of new chiral fibrates with PPARalpha and PPARgamma agonist activity. , 2005, Journal of medicinal chemistry.

[20]  Markus A Lill,et al.  Impact of induced fit on ligand binding to the androgen receptor: a multidimensional QSAR study to predict endocrine-disrupting effects of environmental chemicals. , 2005, Journal of medicinal chemistry.

[21]  Markus A Lill,et al.  Novel ligands for the chemokine receptor-3 (CCR3): a receptor-modeling study based on 5D-QSAR. , 2005, Journal of medicinal chemistry.

[22]  Yong Li,et al.  Structural and biochemical basis for selective repression of the orphan nuclear receptor liver receptor homolog 1 by small heterodimer partner. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Roberto Todeschini,et al.  A new algorithm for optimal, distance based, experimental design , 1992 .

[24]  Leming Shi,et al.  3D QSAR studies on peroxisome proliferator-activated receptor γ agonists using CoMFA and CoMSIA , 2004 .

[25]  Peter Zbinden,et al.  Quasi-Atomistic Receptor Surface Models: A Bridge between 3-D QSAR and Receptor Modeling , 1998 .

[26]  Markus A Lill,et al.  Prediction of Small‐Molecule Binding to Cytochrome P450 3A4: Flexible Docking Combined with Multidimensional QSAR , 2006, ChemMedChem.

[27]  A. Vedani,et al.  Computational Modeling of Receptor‐Mediated Toxicity , 2006 .

[28]  S. Ekins,et al.  Three- and four-dimensional quantitative structure activity relationship analyses of cytochrome P-450 3A4 inhibitors. , 1999, The Journal of pharmacology and experimental therapeutics.

[29]  H Briem,et al.  Multiple-conformation and protonation-state representation in 4D-QSAR: the neurokinin-1 receptor system. , 2000, Journal of medicinal chemistry.