Computational toxicology, friend or foe?

There is increasing public pressure to reduce animal testing and yet maintain public safety from exposure to chemicals either in the environment we live in, the food that we eat or the drugs that we take to treat illnesses. Computational approaches offer the attraction of being both fast and cheap to run being able to process thousands of chemical structures in a few minutes. As a result these approaches have seen an increase in interest and effort over the last decade most notably in the pharmaceutical industry where costs for new drug development is soaring and the failure rate for safety reasons is high. Many applications and approaches have been published covering a wide variety of different human and environmental health issues. As with all new technology, there is a tendency for these approaches to be hyped up and claims of reliability and performance may be exaggerated. So just how good are these computational methods? This review is intended to provide an overview of the state of the art in computational toxicology and to illustrate where some of the limitations of these approaches exist so that these valuable tools are applied and interpreted correctly.

[1]  Gabriele Cruciani,et al.  Modeling Phospholipidosis Induction: Reliability and Warnings , 2013, J. Chem. Inf. Model..

[2]  David M. Reif,et al.  Test driving ToxCast: endocrine profiling for 1858 chemicals included in phase II. , 2014, Current opinion in pharmacology.

[3]  Dominic P. Williams,et al.  Toxicophores: investigations in drug safety. , 2006, Toxicology.

[4]  Robert J Kavlock,et al.  Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms , 2014, Nature Biotechnology.

[5]  K. Houck,et al.  An evaluation of 25 selected ToxCast chemicals in medium‐throughput assays to detect genotoxicity , 2015, Environmental and molecular mutagenesis.

[6]  Russell O. Potts,et al.  Predicting Skin Permeability , 1992, Pharmaceutical Research.

[7]  H. Yamada,et al.  The Japanese toxicogenomics project: application of toxicogenomics. , 2010, Molecular nutrition & food research.

[8]  Naomi L Kruhlak,et al.  A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals. , 2007, Regulatory toxicology and pharmacology : RTP.

[9]  Weida Tong,et al.  Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs. , 2013, Toxicological sciences : an official journal of the Society of Toxicology.

[10]  J. Hughes,et al.  Physiochemical drug properties associated with in vivo toxicological outcomes. , 2008, Bioorganic & medicinal chemistry letters.

[11]  Daniela Schuster,et al.  In silico methods in the discovery of endocrine disrupting chemicals , 2013, The Journal of Steroid Biochemistry and Molecular Biology.

[12]  Errol Zeiger,et al.  Comparison of the Ames II and traditional Ames test responses with respect to mutagenicity, strain specificities, need for metabolism and correlation with rodent carcinogenicity. , 2009, Mutagenesis.

[13]  Kunal Roy,et al.  Development of classification and regression based QSAR models to predict rodent carcinogenic potency using oral slope factor. , 2012, Ecotoxicology and environmental safety.

[14]  Irini A. Doytchinova,et al.  Quantitative structure--clearance relationships of acidic drugs. , 2013, Molecular pharmaceutics.

[15]  C. Elangbam,et al.  Drug-induced Valvulopathy: An Update , 2010, Toxicologic pathology.

[16]  Gilles Klopman,et al.  Benchmark Performance of MultiCASE Inc. Software in Ames Mutagenicity Set , 2010, J. Chem. Inf. Model..

[17]  Melvin E. Andersen,et al.  Modeling Drug- and Chemical-Induced Hepatotoxicity with Systems Biology Approaches , 2012, Front. Physio..

[18]  Anne Hersey,et al.  Estimation of volume of distribution in humans from high throughput HPLC-based measurements of human serum albumin binding and immobilized artificial membrane partitioning. , 2006, Journal of medicinal chemistry.

[19]  Nigel Greene,et al.  Using an in vitro cytotoxicity assay to aid in compound selection for in vivo safety studies. , 2010, Bioorganic & medicinal chemistry letters.

[20]  Michael P Holt,et al.  Mechanisms of drug-induced liver injury , 2006, The AAPS Journal.

[21]  Paola Gramatica,et al.  The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .

[22]  Charles C. Persinger,et al.  How to improve R&D productivity: the pharmaceutical industry's grand challenge , 2010, Nature Reviews Drug Discovery.

[23]  R. Elton,et al.  Relationship between chemical structure and the occupational asthma hazard of low molecular weight organic compounds , 2005, Occupational and Environmental Medicine.

[24]  Ovanes Mekenyan,et al.  A mechanistic approach to modeling respiratory sensitization. , 2014, Chemical research in toxicology.

[25]  E. Zeiger,et al.  Salmonella mutagenicity tests: IV. Results from the testing of 300 chemicals , 1988, Environmental and molecular mutagenesis.

[26]  Jeffrey J Sutherland,et al.  Relating molecular properties and in vitro assay results to in vivo drug disposition and toxicity outcomes. , 2012, Journal of medicinal chemistry.

[27]  Nigel Greene,et al.  Latest advances in computational genotoxicity prediction , 2012, Expert opinion on drug metabolism & toxicology.

[28]  M T D Cronin,et al.  Development of mechanism-based structural alerts for respiratory sensitization hazard identification. , 2012, Chemical research in toxicology.

[29]  Andrew G. Garrow,et al.  The value of in silico chemistry in the safety assessment of chemicals in the consumer goods and pharmaceutical industries. , 2012, Drug discovery today.

[30]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[31]  George Daston,et al.  Framework for identifying chemicals with structural features associated with the potential to act as developmental or reproductive toxicants. , 2013, Chemical research in toxicology.

[32]  Luis G Valerio,et al.  Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities. , 2012, Toxicology and applied pharmacology.

[33]  Mark T D Cronin,et al.  The Use of a Chemistry-based Profiler for Covalent DNA Binding in the Development of Chemical Categories for Read-across for Genotoxicity , 2011, Alternatives to laboratory animals : ATLA.

[34]  R. Elton,et al.  Occupational asthma and the chemical properties of low molecular weight organic substances. , 1994, Occupational medicine.

[35]  J. Talwalkar,et al.  Drug-induced liver injury. , 2014, Mayo Clinic proceedings.

[36]  H. Zimmerman,et al.  Hepatotoxicity: The adverse effects of drugs and other chemicals on the liver , 1978 .

[37]  Ulf Norinder,et al.  In silico categorization of in vivo intrinsic clearance using machine learning. , 2013, Molecular pharmaceutics.

[38]  David M. Reif,et al.  In Vitro Screening of Environmental Chemicals for Targeted Testing Prioritization: The ToxCast Project , 2009, Environmental health perspectives.

[39]  A. Robertson,et al.  Structure activity hypotheses in occupational asthma caused by low molecular weight substances. , 1991, The Annals of occupational hygiene.

[40]  E. Zeiger,et al.  Salmonella mutagenicity tests: II. Results from the testing of 270 chemicals. , 1986, Environmental mutagenesis.

[41]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[42]  Ralph Kühne,et al.  Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses , 2010, Molecular Diversity.

[43]  H. Rosenkranz,et al.  Structure-activity model of chemicals that cause human respiratory sensitization. , 1997, Regulatory toxicology and pharmacology : RTP.

[44]  Alessandro Giuliani,et al.  Putting the Predictive Toxicology Challenge Into Perspective: Reflections on the Results , 2003, Bioinform..

[45]  Edward H. Kerns,et al.  The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery , 2010, Nature Reviews Drug Discovery.

[46]  T. Singer,et al.  Comparative evaluation of in silico systems for ames test mutagenicity prediction: scope and limitations. , 2011, Chemical research in toxicology.

[47]  Neil Kaplowitz,et al.  Idiosyncratic drug hepatotoxicity , 2005, Nature Reviews Drug Discovery.

[48]  Nigel Greene,et al.  In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: an industry survey. , 2012, Regulatory toxicology and pharmacology : RTP.

[49]  Tudor I. Oprea,et al.  The significance of acid/base properties in drug discovery. , 2013, Chemical Society reviews.

[50]  Nigel Greene,et al.  Physicochemical drug properties associated with in vivo toxicological outcomes: a review , 2009, Expert opinion on drug metabolism & toxicology.

[51]  Carol A Marchant,et al.  In Silico Tools for Sharing Data and Knowledge on Toxicity and Metabolism: Derek for Windows, Meteor, and Vitic , 2008, Toxicology mechanisms and methods.

[52]  J C Lindon,et al.  A QSAR investigation of dermal and respiratory chemical sensitizers based on computational chemistry properties , 2009, SAR and QSAR in environmental research.

[53]  Kellyn S. Betts,et al.  Tox21 to Date: Steps toward Modernizing Human Hazard Characterization , 2013, Environmental health perspectives.

[54]  Nigel Greene,et al.  The development of structure-activity relationships for mitochondrial dysfunction: uncoupling of oxidative phosphorylation. , 2013, Toxicological sciences : an official journal of the Society of Toxicology.

[55]  Judith C. Madden,et al.  Assessment of Methods To Define the Applicability Domain of Structural Alert Models , 2011, J. Chem. Inf. Model..

[56]  Andreas Hartmann,et al.  Towards the creation of an international toxicology information centre. , 2005, Toxicology.

[57]  Lutz Müller,et al.  Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens I. Sensitivity, specificity and relative predictivity. , 2005, Mutation research.

[58]  Jiri Aubrecht,et al.  Predicting safety toleration of pharmaceutical chemical leads: cytotoxicity correlations to exploratory toxicity studies. , 2010, Toxicology letters.

[59]  G. Williams,et al.  The hepatocyte primary culture/DNA repair assay using mouse or hamster hepatocytes. , 1983, Environmental mutagenesis.

[60]  O. Engkvist,et al.  Beyond size, ionization state, and lipophilicity: influence of molecular topology on absorption, distribution, metabolism, excretion, and toxicity for druglike compounds. , 2012, Journal of medicinal chemistry.

[61]  T. Schroeter,et al.  Target promiscuity and physicochemical properties contribute to pharmacologically induced ER-stress. , 2013, Toxicology in vitro : an international journal published in association with BIBRA.

[62]  C. Lipinski Drug-like properties and the causes of poor solubility and poor permeability. , 2000, Journal of pharmacological and toxicological methods.

[63]  R. Tennant,et al.  Definitive relationships among chemical structure, carcinogenicity and mutagenicity for 301 chemicals tested by the U.S. NTP. , 1991, Mutation research.

[64]  Horvath Dragos,et al.  Predicting the predictability: a unified approach to the applicability domain problem of QSAR models. , 2009, Journal of chemical information and modeling.

[65]  I. Kola,et al.  Can the pharmaceutical industry reduce attrition rates? , 2004, Nature Reviews Drug Discovery.

[66]  Ferran Sanz,et al.  The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction , 2014, International journal of molecular sciences.

[67]  A. Worth,et al.  Q)SARs for Predicting Effects Relating to Reproductive Toxicity , 2008 .

[68]  Ronald D Snyder,et al.  Assessment of atypical DNA intercalating agents in biological and in silico systems. , 2007, Mutation research.

[69]  Franco Lombardo,et al.  Prediction of volume of distribution values in humans for neutral and basic drugs using physicochemical measurements and plasma protein binding data. , 2002, Journal of medicinal chemistry.

[70]  C. Elangbam,et al.  5-hydroxytryptamine (5HT)-induced valvulopathy: compositional valvular alterations are associated with 5HT2B receptor and 5HT transporter transcript changes in Sprague-Dawley rats. , 2008, Experimental and toxicologic pathology : official journal of the Gesellschaft fur Toxikologische Pathologie.

[71]  E. Zeiger,et al.  Salmonella mutagenicity tests: III. Results from the testing of 255 chemicals. , 1987, Environmental mutagenesis.

[72]  Scott Boyer,et al.  Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities. , 2013, Regulatory toxicology and pharmacology : RTP.

[73]  Nigel Greene,et al.  Developing structure-activity relationships for the prediction of hepatotoxicity. , 2010, Chemical research in toxicology.

[74]  Vijay K. Gombar,et al.  Quantitative Structure − Activity Relationship Models of Clinical Pharmacokinetics : Clearance and Volume of Distribution , 2013 .

[75]  Nigel Greene,et al.  The computational prediction of genotoxicity , 2010, Expert opinion on drug metabolism & toxicology.

[76]  Nigel Greene,et al.  Comparing Measures of Promiscuity and Exploring Their Relationship to Toxicity , 2012, Molecular informatics.

[77]  A. Stepan,et al.  Structural alert/reactive metabolite concept as applied in medicinal chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical examination of trends in the top 200 drugs marketed in the United States. , 2011, Chemical research in toxicology.

[78]  Alessandro Giuliani,et al.  Alternatives to the carcinogenicity bioassay: in silico methods, and the in vitro and in vivo mutagenicity assays , 2010, Expert opinion on drug metabolism & toxicology.

[79]  M. Hewitt,et al.  Chapter 12:Developing the Applicability Domain of In Silico Models: Relevance, Importance and Methods , 2010 .

[80]  Jiri Aubrecht,et al.  New and emerging technologies for genetic toxicity testing , 2011, Environmental and molecular mutagenesis.

[81]  K R Przybylak,et al.  Hepatotoxicity: A scheme for generating chemical categories for read-across, structural alerts and insights into mechanism(s) of action , 2013, Critical reviews in toxicology.

[82]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[83]  D. Basketter,et al.  In vitro approaches to the identification and characterization of skin sensitizers. , 2013, Cutaneous and ocular toxicology.

[84]  Ralph Kühne,et al.  Chemical Domain of QSAR Models from Atom-Centered Fragments , 2009, J. Chem. Inf. Model..

[85]  Nina Nikolova-Jeliazkova,et al.  QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review , 2005, Alternatives to laboratory animals : ATLA.

[86]  E. Zeiger,et al.  Salmonella mutagenicity test results for 250 chemicals. , 1983, Environmental mutagenesis.

[87]  Nicholas Ball,et al.  Use of category approaches, read-across and (Q)SAR: general considerations. , 2013, Regulatory toxicology and pharmacology : RTP.