Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation

ChemoinformaticsmethodologiessuchasQSAR/QSPRhavebeenusedfordecadesindrugdiscovery projects,especiallyforthefindingofnewcompoundswiththerapeuticpropertiesandtheoptimization ofADMEpropertiesonchemicalseries.Theapplicationofcomputationaltechniquesinpredictive toxicologyismuchmorerecent,andtheyareexperiencinganincreasinglyinterestbecauseofthenew legalrequirementsimposedbynationalandinternationalregulations.Inthepharmaceuticalfield, theUSFoodandDrugAdministration(FDA)supporttheuseofpredictivemodelsforregulatory decision-makingwhenassessing thegenotoxic andcarcinogenicpotentialofdrug impurities. In Europe,theREACHlegislationpromotestheuseofQSARinordertoreducethehugeamountof animaltestingneededtodemonstratethesafetyofnewchemicalentitiessubjectedtoregistration, providedtheymeetspecificconditionstoensuretheirqualityandpredictivepower.Inthisreview, theauthorssummarizethestateofartofinsilicomethodsforregulatorypurposes,withespecial emphasisonQSARmodels. KEywoRdS Computational Toxicology, Docking, QSAR, REACH, Read-Across, Virtual Screening

[1]  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.

[2]  J. Contrera,et al.  A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. , 1998, Regulatory toxicology and pharmacology : RTP.

[3]  Jianzhong Liu,et al.  Identification of possible sources of nanotoxicity from carbon nanotubes inserted into membrane bilayers using membrane interaction quantitative structure--activity relationship analysis. , 2008, Chemical research in toxicology.

[4]  Alexander Tropsha,et al.  Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. , 2010, Chemical research in toxicology.

[5]  J. Leszczynski,et al.  Introduction of simplex-informational descriptors for QSPR analysis of fullerene derivatives , 2016, Journal of Mathematical Chemistry.

[6]  Edwin J Matthews,et al.  In silico approaches to explore toxicity end points: issues and concerns for estimating human health effects , 2007, Expert opinion on drug metabolism & toxicology.

[7]  Patricia L Bishop,et al.  Bridging the gap between regulatory acceptance and industry use of non-animal methods. , 2016, ALTEX.

[8]  Tian Xia,et al.  Physicochemical properties determine nanomaterial cellular uptake, transport, and fate. , 2013, Accounts of chemical research.

[9]  J M Blaney,et al.  A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.

[10]  D S Goodsell,et al.  Automated docking of flexible ligands: Applications of autodock , 1996, Journal of molecular recognition : JMR.

[11]  P. Ertl,et al.  Computational analysis of structure-activity relationships. , 2010, Progress in medicinal chemistry.

[12]  María Pilar Vinardell Martínez-Hidalgo.  ALTERNATIVAS A LA EXPERIMENTACIÓN ANIMAL EN TOXICOLOGÍA: SITUACIÓN ACTUAL , 2007 .

[13]  Luis G Valerio,et al.  Predictive computational toxicology to support drug safety assessment. , 2013, Methods in molecular biology.

[14]  Kannan Krishnan,et al.  Cutting Edge PBPK Models and Analyses: Providing the Basis for Future Modeling Efforts and Bridges to Emerging Toxicology Paradigms , 2012, Journal of toxicology.

[15]  Uko Maran,et al.  QSAR DataBank repository: open and linked qualitative and quantitative structure–activity relationship models , 2015, Journal of Cheminformatics.

[16]  J. Dearden,et al.  QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.

[17]  H. van de Waterbeemd,et al.  ADMET in silico modelling: towards prediction paradise? , 2003, Nature reviews. Drug discovery.

[18]  Jerzy Leszczynski,et al.  Improved model for fullerene C60 solubility in organic solvents based on quantum-chemical and topological descriptors , 2011 .

[19]  María Pilar Vinardell Martínez-Hidalgo.  Alternatives to animal experimentation in toxicology: present situation , 2007 .

[20]  Martin Kuentz,et al.  A strategy for preclinical formulation development using GastroPlus as pharmacokinetic simulation tool and a statistical screening design applied to a dog study. , 2006, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[21]  Alexander Tropsha,et al.  Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research , 2010, J. Chem. Inf. Model..

[22]  Paola Gramatica,et al.  Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity. , 2016, Environment international.

[23]  M. J. Duart,et al.  Use of molecular topology for the prediction of physico-chemical, pharmacokinetic and toxicological properties of a group of antihistaminic drugs. , 2002, International journal of pharmaceutics.

[24]  N. Kruhlak,et al.  In silico screening of chemicals for bacterial mutagenicity using electrotopological E-state indices and MDL QSAR software. , 2005, Regulatory toxicology and pharmacology : RTP.

[25]  Naomi L Kruhlak,et al.  Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: Part B. Use of (Q)SAR systems for early detection of drug-induced hepatobiliary and urinary tract toxicities. , 2009, Regulatory toxicology and pharmacology : RTP.

[26]  Cédric Merlot,et al.  In silico methods for early toxicity assessment. , 2008, Current opinion in drug discovery & development.

[27]  Kunal Roy,et al.  Predictive toxicology using QSAR: A perspective , 2010 .

[28]  Melvin E. Andersen,et al.  Applying Mode-of-Action and Pharmacokinetic Considerations in Contemporary Cancer Risk Assessments: An Example with Trichloroethylene , 2004 .

[29]  Alvin I. Chen,et al.  Physiologically based pharmacokinetic models: integration of in silico approaches with micro cell culture analogues. , 2012, Current drug metabolism.

[30]  Paola Gramatica,et al.  On the development and validation of QSAR models. , 2013, Methods in molecular biology.

[31]  Michael S Victoroff,et al.  Evidence-based toxicology: a comprehensive framework for causation , 2005, Human & experimental toxicology.

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

[33]  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.

[34]  Rafael Gozalbes,et al.  QSAR-based permeability model for drug-like compounds. , 2011, Bioorganic & medicinal chemistry.

[35]  Kunal Roy,et al.  In Silico Models for Ecotoxicity of Pharmaceuticals , 2016, Methods in molecular biology.

[36]  Philip Judson.  Using Computer Reasoning about Qualitative and Quantitative Information to Predict Metabolism and Toxicity , 2007 .

[37]  Robert J Kavlock,et al.  Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. , 2012, Toxicological sciences : an official journal of the Society of Toxicology.

[38]  D MacDonald,et al.  Uses and limitations of quantitative structure-activity relationships (QSARs) to categorize substances on the Canadian domestic substance list as persistent and/or bioaccumulative, and inherently toxic to non-human organisms , 2002, SAR and QSAR in environmental research.

[39]  Kunal Roy,et al.  Development and validation of a robust QSAR model for prediction of carcinogenicity of drugs. , 2011, Indian journal of biochemistry & biophysics.

[40]  Ann M Richard,et al.  Chemical structure indexing of toxicity data on the internet: moving toward a flat world. , 2006, Current opinion in drug discovery & development.

[41]  E. Matthews,et al.  Prediction of drug-related cardiac adverse effects in humans--B: use of QSAR programs for early detection of drug-induced cardiac toxicities. , 2010, Regulatory toxicology and pharmacology : RTP.

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

[43]  Luis G ValerioJr.  In silicotoxicology models and databases as FDA Critical Path Initiative toolkits , 2011 .

[44]  Andrew Worth,et al.  Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential. , 2013, Toxicology.

[45]  A. Vedani,et al.  VirtualToxLab - in silico prediction of the toxic (endocrine-disrupting) potential of drugs, chemicals and natural products. Two years and 2,000 compounds of experience: a progress report. , 2009, ALTEX.

[46]  Jerzy Leszczynski,et al.  Review of Current and Emerging Approaches for Quantitative Nanostructure-Activity Relationship Modeling: The Case of Inorganic Nanoparticles , 2016 .

[47]  David M. Reif,et al.  Environmental Impact on Vascular Development Predicted by High-Throughput Screening , 2011, Environmental health perspectives.

[48]  Johann Gasteiger,et al.  Chemoinformatics: Achievements and Challenges, a Personal View , 2016, Molecules.

[49]  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.

[50]  Jerzy Leszczynski,et al.  Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides , 2014, Environmental Science and Pollution Research.

[51]  Emilio Benfenati,et al.  REACH and in silico methods: an attractive opportunity for medicinal chemists. , 2014, Drug discovery today.

[52]  A. Tropsha,et al.  Quantitative nanostructure-activity relationship modeling. , 2010, ACS nano.

[53]  V. Poroikov,et al.  Directions in QSAR Modeling for Regulatory Uses in OECD Member Countries, EU and in Russia , 2008, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

[54]  Christie M Sayes,et al.  A framework for grouping nanoparticles based on their measurable characteristics , 2013, International journal of nanomedicine.

[55]  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.

[56]  Anton J Hopfinger,et al.  Affinity of drugs and small biologically active molecules to carbon nanotubes: a pharmacodynamics and nanotoxicity factor? , 2009, Molecular pharmaceutics.

[57]  Hilda Witters,et al.  A European perspective on alternatives to animal testing for environmental hazard identification and risk assessment. , 2013, Regulatory toxicology and pharmacology : RTP.

[58]  J. Wheeler,et al.  The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: A retrospective validation approach. , 2016, Regulatory toxicology and pharmacology : RTP.

[59]  Martin Paparella,et al.  International regulatory needs for development of an IATA for non-genotoxic carcinogenic chemical substances. , 2016, ALTEX.

[60]  Chihae Yang,et al.  Computational Toxicology Approaches at the US Food and Drug Administration a , 2009, Alternatives to laboratory animals : ATLA.

[61]  Chartchalerm Isarankura-Na-Ayudhya,et al.  Advances in computational methods to predict the biological activity of compounds , 2010, Expert opinion on drug discovery.

[62]  David J. Livingstone,et al.  The Characterization of Chemical Structures Using Molecular Properties. A Survey , 2000, J. Chem. Inf. Comput. Sci..

[63]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[64]  L Mädler,et al.  Parametrization of nanoparticles: development of full-particle nanodescriptors. , 2016, Nanoscale.

[65]  Deepak Singla,et al.  Open source software and web services for designing therapeutic molecules. , 2013, Current topics in medicinal chemistry.

[66]  Luis G Valerio,et al.  In silico toxicology models and databases as FDA Critical Path Initiative toolkits , 2011, Human Genomics.

[67]  Jerzy Leszczynski,et al.  Comprehension of drug toxicity: Software and databases , 2014, Comput. Biol. Medicine.

[68]  Maurice Dickins,et al.  The importance of predictive ADME simulation. , 2002, Drug discovery today.

[69]  Cédric Merlot,et al.  Computational toxicology--a tool for early safety evaluation. , 2010, Drug discovery today.

[70]  T. Puzyn,et al.  Toward the development of "nano-QSARs": advances and challenges. , 2009, Small.

[71]  Naomi L Kruhlak,et al.  Combined Use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows Software to Achieve High-Performance, High-Confidence, Mode of Action–Based Predictions of Chemical Carcinogenesis in Rodents , 2008, Toxicology mechanisms and methods.

[72]  Thomas Hartung,et al.  Re-evaluation of animal numbers and costs for in vivo tests to accomplish REACH legislation requirements for chemicals - a report by the transatlantic think tank for toxicology (t(4)). , 2009, ALTEX.

[73]  H Marquart,et al.  Use of read-across and tiered exposure assessment in risk assessment under REACH--a case study on a phase-in substance. , 2010, Regulatory toxicology and pharmacology : RTP.

[74]  J. Contrera,et al.  Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices. , 2003, Regulatory toxicology and pharmacology : RTP.

[75]  George Loizou,et al.  Development of PK- and PBPK-based modeling tools for derivation of biomonitoring guidance values , 2010, Comput. Methods Programs Biomed..

[76]  J P Doucet,et al.  Application of topological descriptors in QSAR and drug design: history and new trends. , 2002, Current drug targets. Infectious disorders.

[77]  Richard Woodward,et al.  The Organisation for Economic Co-operation and Development (OECD) , 2009 .

[78]  J. Kazius,et al.  Derivation and validation of toxicophores for mutagenicity prediction. , 2005, Journal of medicinal chemistry.

[79]  M. J. Duart,et al.  Search of a Topological Pattern to Evaluate Toxicity of Heterogeneous Compounds , 2001, SAR and QSAR in environmental research.

[80]  Scott Boyer.  The Use of Computer Models in Pharmaceutical Safety Evaluation , 2009, Alternatives to laboratory animals : ATLA.

[81]  Werner Treptow,et al.  Affinity of C60 neat fullerenes with membrane proteins: a computational study on potassium channels. , 2010, ACS nano.

[82]  Rahul Kumar,et al.  In Silico Approach for Predicting Toxicity of Peptides and Proteins , 2013, PloS one.

[83]  D. Young,et al.  Are the Chemical Structures in Your QSAR Correct , 2008 .

[84]  Luis G Valerio,et al.  The in silico prediction of human-specific metabolites from hepatotoxic drugs. , 2010, Current drug discovery technologies.

[85]  Romualdo Benigni.  Predictive toxicology today: the transition from biological knowledge to practicable models , 2016, Expert opinion on drug metabolism & toxicology.

[86]  K. Taylor,et al.  EU statistics on animal experiments for 2014. , 2016, ALTEX.

[87]  Rafael Gozalbes,et al.  QSAR-based solubility model for drug-like compounds. , 2010, Bioorganic & medicinal chemistry.

[88]  Mark T D Cronin,et al.  The Current Status and Future Applicability of Quantitative Structure–activity Relationships (QSARs) in Predicting Toxicity , 2002, Alternatives to laboratory animals : ATLA.