Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties

References

• Madden J. Toxicokinetic considerations in predicting toxicity. In: Cronin MTD, Madden JC, editors. In silico toxicology: principles and applications. Cambridge, UK: RSC; 2010. p. 531–557.
   Google Scholar
• Hou T, Wang J. Structure-ADME relationship: still a long way to go? Expert Opin Drug Metab Toxicol. 2008;4:759–770.
   PubMed Web of Science ®Google Scholar
• Creton S, Billington R, Davies W, et al. Application of toxicokinetics to improve chemical risk assessment: implications for the use of animals. Reg Toxicol Pharmacol. 2009;55:291–299.
   PubMed Web of Science ®Google Scholar
• European Food Safety Authority (EFSA). EFSA Panel on food additives and nutrient sources added to food (ANS); Guidance for submission for food additive evaluations. EFSA Journal. 2012;10(7):2760. doi: 10.2903/j.efsa.2012.2760. Available online: www.efsa.europa.eu/efsajournal
   Google Scholar
• Schultz TW, Amcoff P, Berggren E, et al. A strategy for structuring and reporting a read-across prediction of toxicity. Reg Toxicol Pharmacol. 2015;72:586–601.
   PubMed Web of Science ®Google Scholar
• Hou TJ, Wang JM, Zhang W, et al. Recent advances in computational prediction of drug absorption and permeability in drug discovery. Curr Med Chem. 2006;13:2653–2667.
   PubMed Web of Science ®Google Scholar
• Kharkar PS. Two-dimensional (2D) in silico models for absorption, distribution, metabolism, excretion and toxicity (ADME/T) in drug discovery. Curr Top Med Chem. 2010;10:116–126.
   PubMed Web of Science ®Google Scholar
• Mostrag-Szlichtyng A, Worth A Review of QSAR models and software tools for predicting biokinetic properties. JRC Technical Report EUR 24377 EN, Pubilcations Office of the European Union, Luxemburg 2010.
   Google Scholar
• Cheng FX, Li WH, Liu GX, et al. In silico ADMET prediction: recent advances, current challenges and future trends. Curr Top Med Chem. 2023;13:1273–1289.
   Google Scholar
• Moroy G, Martiny VY, Vayer P, et al. Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov Today. 2012;17:44–55.
   PubMed Web of Science ®Google Scholar
• Wang Y, Xing J, Xu Y, et al. In silico ADME/T modelling for rational drug design. Q Rev Biophys. 2015;48:488–515.
   PubMed Web of Science ®Google Scholar
• Gola J, Obrezanova O, Champness E, et al. ADMET Property prediction: the state of the art and current challenges. QSAR Comb Sci. 2006;25:1172–1180.
   Google Scholar
• Wang Y, Xiao J, Suzek TO, et al. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37:623–633.
   PubMed Web of Science ®Google Scholar
• Knox C, Law V, Jewison T, et al. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 2011;39:1035–1041.
   PubMed Web of Science ®Google Scholar
• Gaulton A, Bellis LJ, Bento AP, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40:1100–1107.
   PubMed Web of Science ®Google Scholar
• Judson R, Richard A, Dix D, et al. ACToR-aggregated computational toxicology resource. Toxicol Appl Pharmaco.L. 2008;233(1):7–13.
   PubMed Web of Science ®Google Scholar
• Fourches D, Barnes JC, Day NC, et al. Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. Chem Res Toxicol. 2010;23:171–183.
   PubMed Web of Science ®Google Scholar
• Davies M, Dedman N, Hersey A, et al. ADME SARfari: comparative genomics of drug metabolizing systems. Bioinformatics. 2015;31(10):1695–1697.
   PubMed Web of Science ®Google Scholar
• ADME SARfari. Available from:https://www.ebi.ac.uk/chembl/admesarfari
   Google Scholar
• Cao D, Wang J, Zhou R, et al. ADMET evaluation in drug discovery. 11. PharmacoKinetics knowledge base (PKKB): a comprehensive database of pharmacokinetic and toxic properties for drugs. J Chem Inf Model. 2012;52(5):1132–1137.
   PubMed Web of Science ®Google Scholar
• Orchard S, Al-Lazikani B, Bryant S, et al. Minimum information about a bioactive entity (MIABE). Nat Rev Drug Discov. 2011;10:661–669.
   PubMed Web of Science ®Google Scholar
• Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf Model. 2010;50:1189–1204.
   PubMed Web of Science ®Google Scholar
• Kilkenny N, Browne C, Cuthill WJ, et al. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. Plos Biol. 2010;8(6):e1000412.
   PubMed Web of Science ®Google Scholar
• Madden JC, Hewitt M, Przybylak K, et al. Strategies for the optimisation of in vivo experiments in accordance with the 3Rs philosophy. Reg Toxicol Pharmacol. 2012;63:140–154.
   PubMed Web of Science ®Google Scholar
• Fu X, Wojak A, Neagu D, et al. Data governance in predictive toxicology: A review. J Cheminform. 2011;3:24.
   PubMed Web of Science ®Google Scholar
• Young D, Martin T, Venkatapathy R, et al. Are the chemical structures in your QSAR correct? QSAR Comb Sci. 2010;27:1337–1345.
   Google Scholar
• Akhondi SA, Kors JA, Muresan S. Consistency of systematic chemical identifiers within and between small-molecule databases. J Cheminform. 2012;4(1):35.
   PubMed Web of Science ®Google Scholar
• Akhondi SA, Muresan S, Williams AJ, et al. Ambiguity of non‑systematic chemical identifiers within and between small‑molecule databases. J Cheminform. 2015;7:54.
   PubMed Web of Science ®Google Scholar
• Tropsha A. BestPractices for QSAR model development, validation, and exploitation. Mol Inform. 2010;29:476−488.
   PubMed Web of Science ®Google Scholar
• Jowsey IR, Clapp CJ, Safford B, et al. The impact of vehicle on the relative potency of skin-sensitizing chemicals in the local lymph node assay. Cutan Ocular Toxicol. 2008;27:67–75.
   PubMed Web of Science ®Google Scholar
• Mistry P, Neagu D, Trundle PR, et al. Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology. Soft Comput. 2016;20:2967–2979.
   Web of Science ®Google Scholar
• Ghafourian T, Samarasa EG, Brooks JD, et al. Validated models for predicting skin penetration from different vehicles. Eur J Pharm Sci. 2010;41:612–616.
   PubMed Web of Science ®Google Scholar
• Rowe PH. Essential statistics for the pharmaceutical sciences. Chichester, UK: JohnWiley & Sons; 2007. p. 13–20.
   Google Scholar
• MacDonald DD, Ingersoll CG, Berger TA. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Arch Environ Contam Toxicol. 2000;39:20–31.
   PubMed Web of Science ®Google Scholar
• Malazizi L, Neagu D, Chaudhry Q A data quality assessment algorithm with applications in predictive toxicology. Proceedings of the International Multiconference on Computer Science and Information Technology (IPCSIT). 2006:131–140.
   Google Scholar
• Gleeson MP, Modi S, Bender A, et al. The challenges involved in modelling toxicity data in silico: a review. Curr Pharm Des. 2012;18(9):1266–1291.
   PubMed Web of Science ®Google Scholar
• Steinmetz FP, Enoch SJ, Madden JC, et al. Methods for assigning confidence to toxicity data with multiple values − identifying experimental outliers. Sci Total Environ. 2014;482:358−65.
   PubMed Web of Science ®Google Scholar
• Roncaglioni A, Benfenati E, Boriani E, et al. A protocol to select high quality datasets of ecotoxicity values for pesticides. J Environ Sci Health B. 2004;39(4):641–652.
   PubMed Web of Science ®Google Scholar
• Steinmetz FP, Madden JC, Cronin MTD. Data quality in the human and environmental health sciences: using statistical confidence scoring to improve QSAR/QSPR modeling. J Chem Inf Model. 2015;55:1739−46.
   PubMed Web of Science ®Google Scholar
• Madden J. Sources of chemical information, toxicity data and assessment of their quality. In: Cronin MTD, Madden JC, Enoch SJ, et al., editors. Chemical toxicity prediction: category formation and read-across. Cambridge, UK: RSC; 2013. p. 98–126.
   Google Scholar
• Dearden JC. In silico prediction of ADMET properties: how far have we come? Expert Opin Drug Metab Toxicol. 2007;3(5):635–639.
   PubMed Web of Science ®Google Scholar
• Schneider K, Schwarza M, Burkholderb I, et al. “ToxRTool”, a new tool to assess the reliability of toxicological data. Toxicol Lett. 2009;189:138–144.
   PubMed Web of Science ®Google Scholar
• Klimisch HJ, Andreae M, Tillmann U. A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regul Toxicol Pharmacol. 1997;25:1–5.
   PubMed Web of Science ®Google Scholar
• Nendza M, Aldenberg T, Benfenati E, et al. Data quality assessment for in silico methods: a survey of approaches and needs. In: Cronin MTD, Madden JC, editors. In silico toxicology: principles and applications. Cambridge, UK: RSC; 2010. p. 59–118.
   Google Scholar
• Przybylak KR, Madden JC, Cronin MTD, et al. Assessing toxicological data quality: basic principles, existing schemes and current limitations. SAR QSAR Environ Res. 2012;23:435–459.
   PubMed Web of Science ®Google Scholar
• Frey JG, Bird CL. Cheminformatics and the semantic web: adding value with linked data and enhanced provenance. Wires Comput Mol Sci. 2013;3:465–481.
   Google Scholar
• Raies AB, Bajic VB. In silico toxicology: computational methods for the prediction of chemical toxicity. Wires Comput Mol Sci. 2016;6:147–172.
   Google Scholar
• Gleeson MP, Hersey A, Hannongbua S. In-silico ADME models: a general assessment of their utility in drug discovery applications. Curr Topic Med Chem. 2011;11:358–381.
   PubMed Web of Science ®Google Scholar
• Golbraikh A, Muratov E, Fourches D, et al. Data Set Modelability by QSAR. J Chem Inf Model. 2014;54:1–4.
   PubMed Web of Science ®Google Scholar
• Wang J, Hou T. Advances in computationally modeling human oral bioavailability. Adv Drug Deliver Rev. 2015;86:11–16.
   PubMed Web of Science ®Google Scholar
• Davis AM, Riley RJ. Predictive ADMET studies, the challenges and the opportunities. Curr Opin Chem Biol. 2004;8:378–386.
   PubMed Web of Science ®Google Scholar
• Cronin MTD, Schultz TW. Pitfalls in QSAR. J Mol Struc THEOCHEM. 2003;622:39–51.
   Web of Science ®Google Scholar
• Dalby A, Nourse JG, Hounshell WD, et al. Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited. J Chem Inf Model. 1992;32(3):244–255.
   Web of Science ®Google Scholar
• Chemical Information Systems, Inc. Available from: http://www.daylight.com/dayhtml/doc/theory/theory.smiles.html
   Google Scholar
• The International Union of Pure and Applied Chemistry (IUPAC). Available from: https://iupac.org/who-we-are/divisions/division-details/inchi/
   Google Scholar
• O’Boyle NM. Towards a universal SMILES representation - a standard method to generate canonical SMILES based on the InChI. J Cheminf. 2012;4:22.
   PubMed Web of Science ®Google Scholar
• Guasch L, Peach ML, Nicklaus MC. Tautomerism of warfarin: combined chemoinformatics, quantum chemical, and NMR investigation. J Org Chem. 2015;80:9900−09.
   PubMed Web of Science ®Google Scholar
• Guasch L, Yapamudiyansel W, Peach ML, et al. Experimental and chemoinformatics study of tautomerism in a database of commercially available screening samples. J Chem Inf Model. 2016;56:2149−61.
   PubMed Web of Science ®Google Scholar
• Yu H, Kühne R, Ebert R-U, et al. Comparative Analysis of QSAR models for predicting pKa of organic oxygen acids and nitrogen bases from molecular structure. J Chem Inf Model. 2010;50(11):1949–1960.
   PubMed Web of Science ®Google Scholar
• Lee AC, Yu J, Crippen GM. pKa prediction of monoprotic small molecules the SMARTS way. J Chem Inf Model. 2008;48:2042–2053.
   PubMed Web of Science ®Google Scholar
• Hansch C, Leo A, Hoekman D. Exploring QSAR: hydrophobic, electronic and steric constants. Washington, DC: ACS; 1995.
   Google Scholar
• Hou T, Xia K, Zhang W, et al. ADME evaluation in drug discovery. 4. Prediction of aqueous solubility based on atom contribution approach. J Chem Inf Comput Sci. 2004;44:266–275.
   PubMedGoogle Scholar
• Wang J, Krudy G, Hou T, et al. Development of reliable aqueous solubility models and their application in drug-like analysis. J Chem Inf Model. 2007;47:1395–1404.
   PubMed Web of Science ®Google Scholar
• Moda TL, Torres LG, Carrara AE, et al. PK/DB: database for pharmacokinetic properties and predictive in silico ADME models. Bioinformatics. 2008;4(19):2270–2271.
   Web of Science ®Google Scholar
• Newby D, Freitas AA, Ghafourian T. Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption. Eur J Med Chem. 2015;90:751–765.
   PubMed Web of Science ®Google Scholar
• Pham-The H, González-Álvarez I, Bermejo M, et al. The use of rule-based and QSPR approaches in ADME profiling: a case study on caco-2 permeability. Mol Inf. 2013;32:459–479.
   PubMed Web of Science ®Google Scholar
• Avdeef A. Absorption and drug development: solubility, permeability, and charge state. 2nd ed. Hoboken, New Jersey:John Wiley and Sons, Inc.; 2012.
   Google Scholar
• Khajeh A, Modarress H. Linear and nonlinear quantitative structure-property relationship modelling of skin permeability. SAR QSAR Environ Res. 2014;25(1):35–50.
   PubMed Web of Science ®Google Scholar
• Magnusson BM, Anissimov YG, Cross SE, et al. Molecular size as the main determinant of solute maximum flux across the skin. J Invest Dermatol. 2004;122:993–999.
   PubMed Web of Science ®Google Scholar
• Votano J, Parham M, Hall LM, et al. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure−information representation. J Med Chem. 2006;49:7169–7181.
   PubMed Web of Science ®Google Scholar
• Obach RS, Lombardo F, Waters NJ. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab Dispos. 2008;36(7):1385–1405.
   PubMed Web of Science ®Google Scholar
• Paixao P, Aniceto N, Gouveia LF, et al. Tissue-to-blood distribution coefficients in the rat: utility for estimation of the volume of distribution in man. Eur J Pharm Sci. 2013;50(3–4):526–543.
   PubMed Web of Science ®Google Scholar
• Martins IF, Teixeira AL, Pinheiro L, et al. A bayesian approach to in silico blood-brain barrier penetration modeling. J Chem Inf Model. 2012;52:1686−97.
   PubMed Web of Science ®Google Scholar
• Vasios G, Kosmidi A, Kalantzi OJ, et al. Simple physicochemical properties related with lipophilicity, polarity, molecular size and ionization status exert significant impact on the transfer of drugs and chemicals into human breast milk. Expert Opin Drug Metab Toxicol. 2016;12(11):1273–1278.
   PubMed Web of Science ®Google Scholar
• Okumura K, Lee IP, Dixon RL. Permeability of selected drugs and chemicals across the blood-testis barrier of the rat. J Pharmacol Exp Ther. 1975;194:89–95.
   PubMed Web of Science ®Google Scholar
• Lien EJ. Structure, properties and disposition of drugs. Prog Drug Res. 1985;29:67–95.
   PubMedGoogle Scholar
• Sakiyama R, Pardridge WM, Musto NA. Influx of testosterone-binding globulin (Tebg) and tebg-bound sex steroid hormones into the rat testis and prostate. J Clin Endocr Metab. 1988;67:98–103.
   PubMed Web of Science ®Google Scholar
• Hewitt M, Madden JC, Rowe PH, et al. Structure-based modelling in reproductivetoxicology: (Q)SARs for the placental barrier. SAR QSAR Environ Res. 2007;18(1–2):57–76.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Li CSW, Ye J, et al. Porcine brain microvessel endothelial cells as an in vitro model to predict in vivo blood-brain barrier permeability. Drug Metab Dispos. 2006;34(11):1935–1943.
   PubMed Web of Science ®Google Scholar
• Zhao YH, Le J, Abraham MH, et al. Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. J Pharm Sci. 2001;90:749–784.
   PubMed Web of Science ®Google Scholar
• Manga N, Duffy JC, Rowe PH, et al. Structure-based methods for the prediction of the dominant P450 enzyme in human drug biotransformation: consideration of CYP3A4, CYP2C9, CYP2D6. SAR QSAR Environ Res. 2005;16(1–2):43–61.
   PubMed Web of Science ®Google Scholar
• Yap CW, Chen YZ. Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines. J Chem Inf Model. 2005;45:982–992.
   PubMed Web of Science ®Google Scholar
• Chohan KK, Paine SW, Mistry J, et al. A rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries. J Med Chem. 2005;48:5154–5161.
   PubMed Web of Science ®Google Scholar
• Paixão P, Gouveia LF, Morais JA. Prediction of the in vitro intrinsic clearancedetermined in suspensions of human hepatocytes by using artificial neural networks. Eur Jpharm Sci. 2010;39:310–321.
   PubMed Web of Science ®Google Scholar
• Yap CW, Li ZR, Chen YZ. Quantitative structure–pharmacokinetic relationships for drug clearance by using statistical learning methods. J Mol Graph. 2006;24(5):383–395.
   Google Scholar
• Human oral bioavailability database. Available from: http://modem.ucsd.edu/adme/databases/databases_bioavailability.htm
   Google Scholar
• Kim M, Sedykh A, Chakravarti SK, et al. Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches. Pharm Res. 2014;31:1002–1014.
   PubMed Web of Science ®Google Scholar
• Sedykh A, Fourches D, Duan J, et al. Human Intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions. Pharm Res. 2013;30:996–1007.
   PubMed Web of Science ®Google Scholar
• P-gp inhibitor database.  Available from: http://modem.ucsd.edu/adme/databases/databases_Pgp_inhibitor.htm
   Google Scholar
• Broccatelli F, Carosati E, Neri A, et al. Novel approach for predicting P-glycoprotein (ABCB1) inhibition using Molecular Interaction Fields. J Med Chem. 2011;54:1740–1751.
   PubMed Web of Science ®Google Scholar
• Karlgren M, Vildhede A, Norinder U, et al. Classification of inhibitors of hepatic organic anion transporting polypeptides (OATPs): influence of protein expression on drug-drug interactions. J Med Chem. 2012;55:4740–4763.
   PubMed Web of Science ®Google Scholar
• Ng H W, Doughty SW, Luo H, et al. Development and validation of decision forest model for estrogen receptor binding prediction of chemicals using large data sets. Chem Res Toxicol. 2015;28:2343–2351.
   PubMed Web of Science ®Google Scholar
• Ajiboye AR, Abdullah-Arshah R, Qin H, et al. Evaluating the effect of dataset size on predictive model using supervised learning technique. Int J Softw Eng Comp Sci (IJSECS). 2015;1:75–84.
   Google Scholar
• Acrobat Adobe. Available from: https://acrobat.adobe.com/uk/en/acrobat/acrobat-pro.html
   Google Scholar
• Heller SR, McNaught A, Stein S, et al. InChI - the worldwide chemical structure identifier standard. J Cheminform. 2013;5:7.
   PubMed Web of Science ®Google Scholar
• Pence HE, Williams AJ. ChemSpider: an online chemical information resource. J Chem Educ. 2010;87:1123–1124.
   Web of Science ®Google Scholar
• Open Babel. Available from: http://openbabel.org.
   Google Scholar
• KNIME. Available from: http://www.knime.org.
   Google Scholar
• Heller SR, McNaught A, Pletnev I, et al. InChI, the IUPAC International Chemical Identifier. J Cheminform. 2015;7:23.
   PubMed Web of Science ®Google Scholar
• Warr WA. Representation of chemical structures. Wiley Interdiscip Rev Comput Mol Sci. 2011;1:557–579.
   Web of Science ®Google Scholar
• Cronin MTD. Finding the data to develop and evaluate (Q)SARs and populate categories for toxicity prediction. In: Cronin MTD, Madden JC, eds. In Silico Toxicology: principles and Applications. Cambridge, UK: The Royal Society of Chemistry; 2010. p. 31–58.
   Google Scholar
• QsarDB.  Available from: http://www.qsardb.org/.
   Google Scholar
• European Chemicals Agency, Helsinki, Finland.European Chemicals Agency (ECHA). Read-Across Assessment Framework (RAAF), ECHA-15-R-07-EN. 2015.Available from: https://echa.europa.eu/documents/10162/13628/raaf_en.pdf
   Google Scholar
• Patlewicz G, Kuseva C, Kesova A, et al. Towards AOP Application – Implementation of an Integrated Approach to Testing and Assessment (IATA) into a Pipeline Tool for Skin Sensitisation. Regul Toxicol Pharmacol. 2014;69:529–545.
   PubMed Web of Science ®Google Scholar