The Use of Computational Methods in the Grouping and Assessment of Chemicals - Preliminary Investigations

This document presents a perspective of how computational approaches could potentially be used in the grouping and assessment of chemicals, and especially in the application of read-across and the development of chemical categories. The perspective is based on experience gained by the authors during 2006 and 2007, when the Joint Research Centre’s European Chemicals Bureau was directly involved in the drafting of technical guidance on the applicability of computational methods under REACH. Some of the experience gained and ideas developed resulted from a number of researchbased case studies conducted in-house during 2006 and the first half of 2007. The case studies were performed to explore the possible applications of computational methods in the assessment of chemicals and to contribute to the development of technical guidance. Not all of the methods explored and ideas developed are explicitly included in the final guidance documentation for REACH. Many of the methods are novel, and are still being refined and assessed by the scientific community. At present, many of the methods have not been tried and tested in the regulatory context. The authors therefore hope that the perspective and case studies compiled in this document, whilst not intended to serve as guidance, will nevertheless provide an input to further research efforts aimed at developing computational methods, and at exploring their potential applicability in regulatory assessment of chemicals. LIST OF ABBREVIATIONS AAR Activity-Activity Relationship BfR German Federal Institute for Risk Assessment BMD Benchmark Dose BMD LCL 95% lower confidence limit on the Benchmark Dose CAS Chemical Abstracts Service CEFIC (LRI) European Chemical Industry Council (Long Range Initiative) DNEL Derived No Effect Level EC European Commission ECB European Chemicals Bureau ECx% Effective Concentration x% EPA Environmental Protection Agency ESIS European chemical Substances Information System (ECB) ESR Existing Substances Regulation EINECS European Inventory of New and Existing Chemical Substances EU European Union HPV High Production Volume ITS Integrated (Intelligent) Testing Strategy JRC Joint Research Centre LCx% Lethal concentration % LDx% Lethal dose % LOAEL Lowest-Observed-Adverse-Effect-Level NOAEL No-Observed-Adverse-Effect-Level OECD Organisation for Economic Cooperation and Development PBT Persistent Bioaccumulative and Toxic vPvB very Persistent and very Bioaccumulative PCA Principal Components Analysis POR Partial Order Ranking QSAAR Quantitative Structure-Activity-Activity Relationship QMRF (Q)SAR Model Reporting Format (Q)SAR (Quantitative) Structure Activity Relationship REACH Registration, Evaluation, and Authorisation of Chemicals RIP REACH Implementation Project RTECS Register of Toxicology Effects of Chemical Substances SIAM OECD Screening Information Assessment Meeting SMILES Simplified Molecular Input Line Entry System TTC Threshold of Toxicological Concern TOR Total Order Ranking WoE Weight of Evidence

[1]  Manuela Pavan,et al.  The Characterisation of (Quantitative) Structure-Activity Relationships: Preliminary Guidance , 2005 .

[2]  Andrew P Worth,et al.  Quantitative structure-activity-activity and quantitative structure-activity investigations of human and rodent toxicity. , 2006, Chemosphere.

[3]  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.

[4]  Martin P. Payne,et al.  Computer-Based Methods for the Prediction of Chemical Metabolism and Biotransformation within Biological Organisms , 2004 .

[5]  M. Pavan,et al.  The role of the European Chemicals Bureau in promoting the regulatory use of (Q)SAR methods , 2007, SAR and QSAR in environmental research.

[6]  Worth Andrew,et al.  A Compendium of Case Studies that Helped to Shape the REACH Guidance on Chemical Categories and Read Across , 2007 .

[7]  Joop L. M. Hermens,et al.  Quantitative structure-activity relationships and mixture toxicity studies of chloro- and alkylanilines at an acute lethal toxicity level to the guppy (Poecilia reticulata). , 1984, Ecotoxicology and environmental safety.

[8]  Scott D. Kahn,et al.  Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships , 2005, Alternatives to laboratory animals : ATLA.

[9]  Mark T. D. Cronin,et al.  Predicting Chemical Toxicity and Fate , 2004 .

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

[11]  John D. Walker,et al.  Use of Physicochemical Property Limits to Develop Rules for Identifying Chemical Substances with no Skin Irritation or Corrosion Potential , 2004 .

[12]  D. Lewis,et al.  Quantitative structure – activity relationships ( QSARs ) within the cytochrome P 450 system : QSARs describing substrate binding , inhibition and induction of P 450 s , 2022 .

[13]  M. Pavan,et al.  Evaluation of SARs for the prediction of skin irritation/corrosion potential–structural inclusion rules in the BfR decision support system , 2007, SAR and QSAR in environmental research.

[14]  Peter Willett,et al.  Maximum common subgraph isomorphism algorithms for the matching of chemical structures , 2002, J. Comput. Aided Mol. Des..

[15]  Lars Carlsen,et al.  Giving molecules an identity. On the interplay between QSARs and partial order ranking. , 2004, Molecules.

[16]  Andreas Bender,et al.  Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance , 2004, J. Chem. Inf. Model..

[17]  John Bradshaw,et al.  Similarity and Dissimilarity Methods for Processing Chemical Structure Databases , 1998, Comput. J..

[18]  John Whittaker,et al.  Rules of Thumb , 1996 .

[19]  John D. Walker,et al.  (Q)SARs for predicting skin irritation and corrosion: Mechanisms, transparency and applicability of predictions , 2004 .

[20]  A Gallegos Saliner Mini-Review on Chemical Similarity and Prediction of Toxicity , 2006 .

[21]  D. Lewis,et al.  COMPACT: a structural approach to the modelling of cytochromes P450 and their interactions with xenobiotics , 2001 .

[22]  Rainer Brüggemann,et al.  A Theoretical Concept To Rank Environmentally Significant Chemicals , 1999, J. Chem. Inf. Comput. Sci..

[23]  Ulrich Schlottmann,et al.  SIDS Initial Assessment Report For , 2002 .

[24]  P N Judson,et al.  Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR. , 1999, SAR and QSAR in environmental research.

[25]  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.

[26]  Anniek G. van Haelst,et al.  Priority setting for existing chemicals: Automated data selection routine , 2000 .

[27]  M. Karelson Molecular descriptors in QSAR/QSPR , 2000 .

[28]  Efraim Halfon,et al.  Comparison of an index function and a vectorial approach method for ranking waste disposal sites , 1989 .

[29]  Worth Andrew,et al.  Use of Quantitative Structrure-Activity Relationships in International Decision-Making Frameworks to Predict Ecologic Effects and Environmental Fate of Chemical Substances. , 2003 .

[30]  Bassan Arianna,et al.  Chemical Similarity and Threshold of Toxicological Concern (TTC) Approaches: Report of an ECB Workshop held in Ispra, November 2005 , 2007 .

[31]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[32]  M Rowland,et al.  Species differences in size discrimination in the paracellular pathway reflected by oral bioavailability of poly(ethylene glycol) and D-peptides. , 1998, Journal of pharmaceutical sciences.

[33]  Dennis H. Rouvray,et al.  Similarity in chemistry: Past, present and future , 1995 .

[34]  I Gerner,et al.  Development of a Decision Support System for the Introduction of Alternative Methods into Local Irritancy/Corrosivity Testing Strategies. Creation of Fundamental Rules for a Decision Support System , 2000, Alternatives to laboratory animals : ATLA.

[35]  Worth Andrew,et al.  The Development and Validation of Expert Systems for Predicting Toxicity. , 1998 .

[36]  N. Nikolova,et al.  International Union of Pure and Applied Chemistry, LUMO energy ± The Lowest Unoccupied Molecular Orbital (LUMO) , 2022 .

[37]  R. Glen,et al.  Molecular similarity: a key technique in molecular informatics. , 2004, Organic & biomolecular chemistry.

[38]  Georg Karlaganis,et al.  SIDS Initial Assessment Report For SIAM 14 , 2002 .

[39]  Grace Patlewicz,et al.  Skin sensitization: reaction mechanistic applicability domains for structure-activity relationships. , 2005, Chemical research in toxicology.

[40]  I Gerner,et al.  Development of a Decision Support System for the Introduction of Alternative Methods into Local Irritancy/Corrosivity Testing Strategies. Development of a Relational Database , 2000, Alternatives to laboratory animals : ATLA.

[41]  David W Roberts,et al.  Mechanistic applicability domains for nonanimal-based prediction of toxicological end points: general principles and application to reactive toxicity. , 2006, Chemical research in toxicology.

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

[43]  Gallegos Saliner Ana Mini-Review on Chemical Similarity and Prediction of Toxicity , 2006 .

[44]  J Jaworska,et al.  How can structural similarity analysis help in category formation? , 2007, SAR and QSAR in environmental research.

[45]  U. Tillmann,et al.  A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. , 1997, Regulatory toxicology and pharmacology : RTP.

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

[47]  John D. Walker,et al.  Use of structural alerts to develop rules for identifying chemical substances with skin irritation or skin corrosion potential , 2005 .

[48]  Worth Andrew,et al.  Classification of Phthalates According to Their (Q)SAR Predicted Acute Toxicity to Fish: A Case Study. , 2007 .

[49]  Ferenc Darvas,et al.  Metabolexpert: An Expert System for Predicting Metabolism of Substances , 1987 .

[50]  B. Hansen,et al.  Priority setting for existing chemicals: European Union risk ranking method , 1999 .

[51]  Ian Kimber,et al.  Compilation of Historical Local Lymph Node Data for Evaluation of Skin Sensitization Alternative Methods , 2005, Dermatitis : contact, atopic, occupational, drug.

[52]  Romualdo Benigni,et al.  The Development and Validation of Expert Systems for Predicting Toxicity The Report and Recommendations of an ECVAM / ECB Workshop ( ECVAM Workshop 24 ) , 2002 .

[53]  Gallegos Saliner Ana,et al.  A Similarity Based Approach for Chemical Category Classification , 2005 .

[54]  Emili Besalú,et al.  On quantum molecular similarity measures (QMSM) and indices (QMSI) , 1996 .

[55]  Y. Martin,et al.  Do structurally similar molecules have similar biological activity? , 2002, Journal of medicinal chemistry.

[56]  John D. Walker,et al.  The Skin Irritation Corrosion Rules Estimation Tool (SICRET) , 2005 .

[57]  Petra S Kern,et al.  Mechanistic applicability domain classification of a local lymph node assay dataset for skin sensitization. , 2007, Chemical research in toxicology.

[58]  Robert P. Sheridan,et al.  Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR , 2004, J. Chem. Inf. Model..

[59]  R A Ford,et al.  Estimation of toxic hazard--a decision tree approach. , 1978, Food and cosmetics toxicology.

[60]  John D. Walker,et al.  Verification of literature‐derived SARs for skin irritation and corrosion , 2003 .

[61]  K. Kaiser,et al.  QSAR in Environmental Toxicology - II , 1984 .

[62]  Edward E. Hodgkin,et al.  Molecular similarity based on electrostatic potential and electric field , 1987 .

[63]  Sabcho D Dimitrov,et al.  A systematic approach to simulating metabolism in computational toxicology. I. The TIMES heuristic modelling framework. , 2004, Current pharmaceutical design.

[64]  I Kimber,et al.  Classification of contact allergens according to potency: proposals. , 2003, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[65]  Horst Spielmann,et al.  Assessment of the Eye Irritating Properties of Chemicals by Applying Alternatives to the Draize Rabbit Eye Test: The Use of QSARs and In Vitro Tests for the Classification of Eye Irritation , 2005, Alternatives to laboratory animals : ATLA.