Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity.

The description of quantitative structure-activity relationship (QSAR) models has been a topic for scientific research for more than 40 years and a topic within the regulatory framework for more than 20 years. At present, efforts on QSAR development are increasing because of their promise for supporting reduction, refinement, and/or replacement of animal toxicity experiments. However, their acceptance in risk assessment seems to require a more standardized and scientific underpinning of QSAR technology to avoid possible pitfalls. For this reason, guidelines for QSAR model development recently proposed by the Organization for Economic Cooperation and Development (OECD) [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. OECD Environment Health and Safety Publications: Series on Testing and Assessment No. 69, Paris] are expected to help increase the acceptability of QSAR models for regulatory purposes. The guidelines recommend that QSAR models should be associated with (i) a defined end point, (ii) an unambiguous algorithm, (iii) a defined domain of applicability, (iv) appropriate measures of goodness-of-fit, robustness, and predictivity, and (v) a mechanistic interpretation, if possible [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. The present perspective provides an overview of these guidelines for QSAR model development and their rationale, as well as the promises and pitfalls of using QSAR approaches and these guidelines for predicting metabolism and toxicity of new and existing chemicals.

[1]  Manuela Pavan,et al.  Review of (Quantitative) Structure–Activity Relationships for Acute Aquatic Toxicity , 2008 .

[2]  M D Barratt,et al.  A quantitative structure-activity relationship for the eye irritation potential of neutral organic chemicals. , 1995, Toxicology letters.

[3]  Mark T. D. Cronin,et al.  The Role of Hydrophobicity in Toxicity Prediction , 2006 .

[4]  J C Dearden,et al.  Creation of predictive models of aquatic toxicity of environmental pollutants with different mechanisms of action on the basis of molecular similarity and HYBOT descriptors , 2004, SAR and QSAR in environmental research.

[5]  R. Benigni.  Structure-activity relationship studies of chemical mutagens and carcinogens: mechanistic investigations and prediction approaches. , 2005, Chemical reviews.

[6]  P. Khadikar,et al.  Use of the PI index in predicting toxicity of nitrobenzene derivatives. , 2002, Bioorganic & medicinal chemistry.

[7]  J. Hermens,et al.  Acute toxicity of polar narcotics to three aquatic species (Daphnia magna, poecilia reticulata and Lymnaea stagnalis) and its relation to hydrophobicity , 1998 .

[8]  J. Vervoort,et al.  Quantum chemistry based quantitative structure‐activity relationships for modeling the (sub)acute toxicity of substituted mononitrobenzenes in aquatic systems , 2006, Environmental toxicology and chemistry.

[9]  Marco Vighi,et al.  QSARs for toxicity of organophosphorous pesticides to Daphnia and honeybees , 1991 .

[10]  Paola Gramatica,et al.  Introduction General Considerations , 2022 .

[11]  Alexander Golbraikh,et al.  Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection , 2002, J. Comput. Aided Mol. Des..

[12]  Paola Gramatica,et al.  Validated QSAR Prediction of OH Tropospheric Degradation of VOCs: Splitting into Training-Test Sets and Consensus Modeling , 2004, J. Chem. Inf. Model..

[13]  M Pavan,et al.  Validation of a QSAR model for acute toxicity , 2006, SAR and QSAR in environmental research.

[14]  R. Sheridan,et al.  Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9. , 2007, Journal of medicinal chemistry.

[15]  Svante Wold,et al.  Multivariate quantitative structure-activity relationships (QSAR): conditions for their applicability , 1983, J. Chem. Inf. Comput. Sci..

[16]  J. Hermens,et al.  Classifying environmental pollutants , 1992 .

[17]  N. A. Diamantidis,et al.  Unsupervised stratification of cross-validation for accuracy estimation , 2000, Artif. Intell..

[18]  Z. Dang,et al.  Quantitative Structure–Activity Relationships for Enzymatic Activity of Chloroperoxidase on Metabolizing Organophosphorus Pesticides , 2007 .

[19]  Ovanes Mekenyan,et al.  Interspecies quantitative structure‐activity relationship model for aldehydes: Aquatic toxicity , 2004, Environmental toxicology and chemistry.

[20]  Andrew P Worth,et al.  Report of the Workshop on the Validation of QSARs and Other Computational Prediction Models , 2004, Alternatives to laboratory animals : ATLA.

[21]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

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

[23]  M. Newman,et al.  Steric Effects In Organic Chemistry , 1956 .

[24]  R. Lipnick,et al.  A QSAR study of the acute toxicity of some industrial organic chemicals to goldfish. Narcosis, electrophile and proelectrophile mechanisms. , 1987, Xenobiotica; the fate of foreign compounds in biological systems.

[25]  Xiang-Qun Xie,et al.  Fast approaches for molecular polarizability calculations. , 2007, The journal of physical chemistry. A.

[26]  T W Schultz,et al.  Quantitative structure-activity analyses of nitrobenzene toxicity to Tetrahymena pyriformis. , 1998, Chemical research in toxicology.

[27]  A. Petrauskas,et al.  ACD/Log P method description , 2000 .

[28]  Andreas P. Freidig,et al.  Narcosis and chemical reactivity QSARs for acute fish toxicity , 2000 .

[29]  M. Eisen,et al.  Octahedral Complexes in the Polymerization of α‐Olefins , 2007 .

[30]  R. Galeazzi,et al.  Stereoelectronic properties and activity of some imidazolinone herbicides: a computational approach☆ , 2003 .

[31]  John D. Walker,et al.  Quantitative structure–activity relationships (QSARs) in toxicology: a historical perspective , 2003 .

[32]  Nigel Greene,et al.  Computer systems for the prediction of toxicity: an update. , 2002, Advanced drug delivery reviews.

[33]  M. Ellersieck,et al.  Manual of acute toxicity: interpretation and data base for 410 chemicals and 66 species of freshwater animals , 1986 .

[34]  Andrew P Worth,et al.  Comparison of the applicability domain of a quantitative structure‐activity relationship for estrogenicity with a large chemical inventory , 2006, Environmental toxicology and chemistry.

[35]  T. Wobbes,et al.  Regioselectivity and quantitative structure-activity relationships for the conjugation of a series of fluoronitrobenzenes by purified glutathione S-transferase enzymes from rat and man. , 1996, Chemical research in toxicology.

[37]  J. Vervoort,et al.  Regioselectivity of cytochrome P-450 catalyzed hydroxylation of fluorobenzenes predicted by calculated frontier orbital substrate characteristics. , 1993, Biochemistry.

[38]  Gergana Dimitrova,et al.  A Stepwise Approach for Defining the Applicability Domain of SAR and QSAR Models , 2005, J. Chem. Inf. Model..

[39]  Mark T. D. Cronin,et al.  Evaluation of a novel short-term algal toxicity assay by the development of QSARs and inter-species relationships for narcotic chemicals , 2003 .

[40]  Yongjun Wang,et al.  Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction , 2008, J. Comput. Aided Mol. Des..

[41]  C. Russom,et al.  Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas) , 1997 .

[42]  J. Vervoort,et al.  Molecular orbital-based quantitative structure-activity relationship for the cytochrome P450-catalyzed 4-hydroxylation of halogenated anilines. , 1994, Chemical research in toxicology.

[43]  H. Kamaya,et al.  Local anesthetics destabilize lipid membranes by breaking hydration shell: infrared and calorimetry studies. , 1994, Biochimica et biophysica acta.

[44]  C. Hansch,et al.  On the role of polarizability in QSAR. , 2005, Bioorganic & medicinal chemistry.

[45]  A. Debnath,et al.  Mutagenicity of quinolines in Salmonella typhimurium TA100. A QSAR study based on hydrophobicity and molecular orbital determinants. , 1992, Mutation research.

[46]  Kunal Roy,et al.  Exploring predictive QSAR models for hepatocyte toxicity of phenols using QTMS descriptors. , 2008, Bioorganic & medicinal chemistry letters.

[47]  G. Veith,et al.  Rules for distinguishing toxicants that cause type I and type II narcosis syndromes. , 1990, Environmental health perspectives.

[48]  Tingjun Hou,et al.  ADME evaluation in drug discovery , 2002, Journal of molecular modeling.

[49]  Stewart B Kirton,et al.  In silico methods for predicting ligand binding determinants of cytochromes P450. , 2004, Current topics in medicinal chemistry.

[50]  Sean Ekins,et al.  Pharmacophore modeling of cytochromes P450. , 2002, Advanced drug delivery reviews.

[51]  H. Könemann.  Quantitative structure-activity relationships in fish toxicity studies. Part 1: relationship for 50 industrial pollutants. , 1981, Toxicology.

[52]  T W Schultz,et al.  QSARs for monosubstituted phenols and the polar narcosis mechanism of toxicity. , 1992, Quality assurance.

[53]  J. Devillers,et al.  A General QSAR Model for Predicting the Acute Toxicity of Pesticides to Oncorhynchus mykiss , 2000, SAR and QSAR in environmental research.

[54]  G. Hooiveld,et al.  Quantitative structure-activity relationships based on computer calculated parameters for the overall rate of glutathione S-transferase catalyzed conjugation of a series of fluoronitrobenzenes. , 1995, Chemical research in toxicology.

[55]  Tomas Öberg,et al.  A QSAR for Baseline Toxicity: Validation, Domain of Application, and Prediction , 2004 .

[56]  J.C. Dearden,et al.  Improved prediction of fish bioconcentration factor of Hydrophobic Chemicals , 2004, SAR and QSAR in environmental research.

[57]  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.

[58]  J. Dearden,et al.  QSAR studies of comparative toxicity in aquatic organisms. , 1991, The Science of the total environment.

[59]  Tom Aldenberg,et al.  Application of QSARs, extrapolation and equilibrium partitioning in aquatic effects assessment. I. Narcotic industrial pollutants , 1992 .

[60]  Svante Wold,et al.  A strategy for ranking environmentally occurring chemicals. Part III: Multivariate quantitative structure‐activity relationships for halogenated aliphatics , 1990 .

[61]  David F. V. Lewis,et al.  Quantitative structure–activity relationships (QSARs) within the cytochrome P450 system: QSARs describing substrate binding, inhibition and induction of P450s , 2004, InflammoPharmacology.

[62]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[63]  J. Matysiak.  Evaluation of electronic, lipophilic and membrane affinity effects on antiproliferative activity of 5-substituted-2-(2,4-dihydroxyphenyl)-1,3,4-thiadiazoles against various human cancer cells. , 2007, European journal of medicinal chemistry.

[64]  N. P. Franks,et al.  Molecular mechanisms of general anaesthesia , 1982, Nature.

[65]  N. P. Franks,et al.  Do general anaesthetics act by competitive binding to specific receptors? , 1984, Nature.

[66]  Gilman D. Veith,et al.  Structure–Toxicity Relationships for the Fathead Minnow, Pimephales promelas: Narcotic Industrial Chemicals , 1983 .

[67]  G M Singer,et al.  Quantitative structure-activity relationship of the mutagenicity of substituted N-nitroso-N-benzylmethylamines: possible implications for carcinogenicity. , 1986, Journal of medicinal chemistry.

[68]  T. Wayne Schultz,et al.  Response-Surface Analyses for Toxicity to Tetrahymena pyriformis: Reactive Carbonyl-Containing Aliphatic Chemicals , 1999, J. Chem. Inf. Comput. Sci..

[69]  T W Schultz,et al.  Structure-toxicity relationships for selected benzyl alcohols and the polar narcosis mechanism of toxicity. , 1988, Ecotoxicology and environmental safety.

[70]  Alexander Golbraikh,et al.  Rational selection of training and test sets for the development of validated QSAR models , 2003, J. Comput. Aided Mol. Des..

[71]  I. Juranić,et al.  A QSAR study of acute toxicity of N-substituted fluoroacetamides to rats. , 2006, Chemosphere.

[72]  C. Hansch,et al.  QSAR of apoptosis induction in various cancer cells. , 2003, Bioorganic & medicinal chemistry.

[73]  Joop L. M. Hermens,et al.  The acute toxicity of aldehydes to the guppy , 1988 .

[74]  Eñaut Urrestarazu Ramos,et al.  Quantitative Structure−Activity Relationships for the Aquatic Toxicity of Polar and Nonpolar Narcotic Pollutants , 1998 .

[75]  M. Cronin,et al.  Pitfalls in QSAR , 2003 .

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

[77]  Yiannis N. Kaznessis,et al.  Prediction of blood-brain partitioning using Monte Carlo simulations of molecules in water , 2001, J. Comput. Aided Mol. Des..

[78]  Anders Berglund,et al.  PCA and PLS with very large data sets , 2005, Comput. Stat. Data Anal..

[79]  J. Topliss,et al.  Chance factors in studies of quantitative structure-activity relationships. , 1979, Journal of medicinal chemistry.

[80]  Katie Chan,et al.  Structure-activity relationships for halobenzene induced cytotoxicity in rat and human hepatoctyes , 2007 .

[81]  S. Wold,et al.  Toxicity modeling and prediction with pattern recognition. , 1985, Environmental health perspectives.

[82]  J. Hermens,et al.  Understanding and estimating membrane/water partition coefficients: approaches to derive quantitative structure property relationships. , 1998, Chemical research in toxicology.

[83]  M T D Cronin,et al.  The importance of hydrophobicity and electrophilicity descriptors in mechanistically-based QSARs for toxicological endpoints , 2002, SAR and QSAR in environmental research.

[84]  Erik Johansson,et al.  Multivariate analysis of aquatic toxicity data with PLS , 1995, Aquatic Sciences.

[85]  R. M. Muir,et al.  Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients , 1962, Nature.

[86]  J. Vervoort,et al.  QSAR models for predicting in vivo aquatic toxicity of chlorinated alkanes to fish. , 2008, Chemical research in toxicology.

[87]  Joop L. M. Hermens,et al.  A Multivariate Approach to Quantitative Structure-Activity and Structure-Property Relationships , 1995 .

[88]  W. Russell,et al.  Ethical and Scientific Considerations Regarding Animal Testing and Research , 2011, PloS one.

[89]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[90]  David F. V. Lewis,et al.  Molecular modeling of human cytochrome P450–substrate interactions , 2002, Drug metabolism reviews.

[91]  Matthew D. Segall,et al.  Automatic QSAR modeling of ADME properties: blood–brain barrier penetration and aqueous solubility , 2008, J. Comput. Aided Mol. Des..

[92]  J. Devillers,et al.  Practical applications of quantitative structure-activity relationships (QSAR) in environmental chemistry and toxicology , 1990 .

[93]  A M Richard.  Commercial toxicology prediction systems: a regulatory perspective. , 1998, Toxicology letters.

[94]  M T D Cronin,et al.  Structure-Based Methods for the Prediction of the Dominant P450 Enzyme in Human Drug Biotransformation: Consideration of CYP3A4, CYP2C9, CYP2D6 , 2005, SAR and QSAR in environmental research.

[95]  Eñaut Urrestarazu Ramos,et al.  Algal growth inhibition of Chlorella pyrenoidosa by polar narcotic pollutants: toxic cell concentrations and QSAR modeling , 1999 .

[96]  Romualdo Benigni,et al.  Computational prediction of drug toxicity: the case of mutagenicity and carcinogenicity. , 2004, Drug discovery today. Technologies.

[97]  I. Pajeva,et al.  Quantitative structure-activity relationship (QSAR) and three-dimensional QSAR analysis of a series of xanthates as inhibitors and inactivators of cytochrome P450 2B1 , 2002, Xenobiotica; the fate of foreign compounds in biological systems.

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

[99]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors , 2003, J. Chem. Inf. Comput. Sci..