Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models.

The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project "eTOX" (electronic toxicity) and its application to the in silico models developed within the frame of this project.

[1]  Uko Maran,et al.  QSAR DataBank - an approach for the digital organization and archiving of QSAR model information , 2014, Journal of Cheminformatics.

[2]  Yi Li,et al.  In silico ADME/Tox: why models fail , 2003, J. Comput. Aided Mol. Des..

[3]  Valerie J. Gillet,et al.  Comparison of Conformational Analysis Techniques To Generate Pharmacophore Hypotheses Using Catalyst , 2005, J. Chem. Inf. Model..

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

[5]  Thomas Hartung,et al.  "ToxRTool", a new tool to assess the reliability of toxicological data. , 2009, Toxicology letters.

[6]  Igor V. Tetko,et al.  Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information , 2011, J. Cheminformatics.

[7]  J. Dearden,et al.  How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR) , 2009, SAR and QSAR in environmental research.

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

[9]  Junmei Wang,et al.  Structure – ADME relationship : still a long way to go ? , 2008 .

[10]  M. Cases,et al.  Inroads to Predict in Vivo Toxicology—An Introduction to the eTOX Project , 2012, International journal of molecular sciences.

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

[12]  L. Toledo-Pereyra.  Trust , 2006, Mediation Behaviour.

[13]  Helmut Segner,et al.  Data quality assessment for in silico methods: A survey of approaches and needs , 2010 .

[14]  Ferran Sanz,et al.  Applicability Domain Analysis (ADAN): A Robust Method for Assessing the Reliability of Drug Property Predictions , 2014, J. Chem. Inf. Model..

[15]  J. C. Madden.  Chapter 5:Sources of Chemical Information, Toxicity Data and Assessment of Their Quality , 2013 .

[16]  Andrew P. Worth,et al.  The Role of Qsar Methodology in the Regulatory Assessment of Chemicals , 2010 .

[17]  Tingjun Hou,et al.  Recent developments of in silico predictions of intestinal absorption and oral bioavailability. , 2009, Combinatorial chemistry & high throughput screening.

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

[19]  K R Przybylak,et al.  Assessing toxicological data quality: basic principles, existing schemes and current limitations , 2012, SAR and QSAR in environmental research.

[20]  Paola Gramatica,et al.  The QSPR-THESAURUS: The Online Platform of the CADASTER Project , 2014, Alternatives to laboratory animals : ATLA.

[21]  Wei Zhang,et al.  Recent advances in computational prediction of drug absorption and permeability in drug discovery. , 2006, Current medicinal chemistry.

[22]  Pantelis Sopasakis,et al.  Collaborative development of predictive toxicology applications , 2010, J. Cheminformatics.

[23]  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..

[24]  Philip Neville Judson.  Towards establishing good practice in the use of computer prediction , 2009 .

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

[26]  Bernd Beck,et al.  Industrial applications of in silico ADMET , 2014, Journal of Molecular Modeling.

[27]  Huidong Yu,et al.  Recent developments of in silico predictions of oral bioavailability. , 2011, Combinatorial chemistry & high throughput screening.