In silico ADME-Tox modeling: progress and prospects

References

• Hughes JP, Rees S, Kalindjian SB, et al. Principles of early drug discovery. Br J Pharmacol. 2011;162:1239–1249.
   PubMed Web of Science ®Google Scholar
• Venkatesh S, Lipper RA. Role of the development scientist in compound lead selection and optimization. J Pharm Sci. 2000;89:145–154.
   PubMed Web of Science ®Google Scholar
• Wardell WM, May MS, Trimble AG. New drug development by United States pharmaceutical firms with analyses of trends in the acquisition and origin of drug candidates, 1963-1979. Clin Pharmacol Ther. 1982;32:407–417.
   PubMed Web of Science ®Google Scholar
• Hall AH. Computer modeling and computational toxicology in new chemical and pharmaceutical product development. Toxicol Lett. 1998;102-103:623–626.
   PubMed Web of Science ®Google Scholar
• Valerio LG Jr. Application of advanced in silico methods for predictive modeling and information integration. Expert Opin Drug Metab Toxicol. 2012;8:395–398.
   PubMed Web of Science ®Google Scholar
• Yang C, Valerio LG Jr., Arvidson KB. Computational toxicology approaches at the US Food and Drug Administration. Altern Lab Anim. 2009;37:523–531.
   PubMed Web of Science ®Google Scholar
• Valerio LG Jr. In silico toxicology for the pharmaceutical sciences. Toxicol Appl Pharmacol. 2009;241:356–370.
   PubMed Web of Science ®Google Scholar
• Jones HM, Chen Y, Gibson C, et al. Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin Pharmacol Ther. 2015;97:247–262.
   PubMed Web of Science ®Google Scholar
• Lamanna C, Bellini M, Padova A, et al. Straightforward recursive partitioning model for discarding insoluble compounds in the drug discovery process. J Med Chem. 2008;51:2891–2897.
   PubMed Web of Science ®Google Scholar
• Faller B, Ertl P. Computational approaches to determine drug solubility. Adv Drug Deliv Rev. 2007;59:533–545.
   PubMed Web of Science ®Google Scholar
• Huuskonen J. Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology. J Chem Inf Comput Sci. 2000;40:773–777.
   PubMedGoogle Scholar
• Klopman G, Zhu H. Estimation of the aqueous solubility of organic molecules by the group contribution approach. J Chem Inf Comput Sci. 2001;41:439–445.
   PubMedGoogle Scholar
• Tetko IV, Tanchuk VY, Kasheva TN, et al. Estimation of aqueous solubility of chemical compounds using E-state indices. J Chem Inf Comput Sci. 2001;41:1488–1493.
   PubMedGoogle Scholar
• Ali J, Camilleri P, Brown MB, et al. Revisiting the general solubility equation: in silico prediction of aqueous solubility incorporating the effect of topographical polar surface area. J Chem Inf Model. 2012;52:420–428.
   PubMed Web of Science ®Google Scholar
• Chevillard F, Lagorce D, Reynes C, et al. In silico prediction of aqueous solubility: a multimodel protocol based on chemical similarity. Mol Pharm. 2012;9:3127–3135.
   PubMed Web of Science ®Google Scholar
• Raevsky OA, Polianczyk DE, Grigorev VY, et al. In silico prediction of aqueous solubility: a comparative study of local and global predictive models. Mol Inform. 2015;34:417–430.
   PubMed Web of Science ®Google Scholar
• Jelfs S, Ertl P, Selzer P. Estimation of pKa for drug like compounds using semiempirical and information-based descriptors. J Chem Inf Model. 2007;47:450–459.
   PubMed Web of Science ®Google Scholar
• Milletti F, Storchi L, Sforna G, et al. New and original pKa prediction method using grid molecular interaction fields. J Chem Inf Model. 2007;47:2172–2181.
   PubMed Web of Science ®Google Scholar
• Cruciani G, Milletti F, Storchi L, et al. In silico pKa prediction and ADME profiling. Chem Biodivers. 2009;6:1812–1821.
   PubMed Web of Science ®Google Scholar
• Lee PH, Ayyampalayam SN, Carreira LA, et al. In silico prediction of ionization constants of drugs. Mol Pharm. 2007;4:498–512.
   PubMed Web of Science ®Google Scholar
• Wang NN, Dong J, Deng YH, et al. ADME properties evaluation in drug discovery: prediction of caco-2 cell permeability using a combination of NSGA-II and boosting. J Chem Inf Model. 2016;56:763–773.
   PubMed Web of Science ®Google Scholar
• Kokate A, Li X, Williams PJ, et al. In silico prediction of drug permeability across buccal mucosa. Pharm Res. 2009;26:1130–1139.
   PubMed Web of Science ®Google Scholar
• Yan A, Liang H, Chong Y, et al. In-silico prediction of blood-brain barrier permeability. SAR QSAR Environ Res. 2013;24:61–74.
   PubMed Web of Science ®Google Scholar
• Hatanaka T, Yoshida S, Kadhum WR, et al. In silico estimation of skin concentration following the dermal exposure to chemicals. Pharm Res. 2015;32:3965–3974.
   PubMed Web of Science ®Google Scholar
• Kaitin KI. Obstacles and opportunities in new drug development. Clin Pharmacol Ther. 2008;83:210–212.
   PubMed Web of Science ®Google Scholar
• Ghosh J, Lawless MS, Waldman M, et al. Modeling ADMET. Methods Mol Biol. 2016;1425:63–83.
   PubMedGoogle Scholar
• Kuentz M, Nick S, Parrott N, et al. A strategy for preclinical formulation development using GastroPlus as pharmacokinetic simulation tool and a statistical screening design applied to a dog study. Eur J Pharm Sci. 2006;27:91–99.
   PubMed Web of Science ®Google Scholar
• Parrott N, Lave T. Prediction of intestinal absorption: comparative assessment of GASTROPLUS and IDEA. Eur J Pharm Sci. 2002;17:51–61.
   PubMed Web of Science ®Google Scholar
• Sjogren E, Westergren J, Grant I, et al. In silico predictions of gastrointestinal drug absorption in pharmaceutical product development: application of the mechanistic absorption model GI-Sim. Eur J Pharm Sci. 2013;49:679–698.
   PubMed Web of Science ®Google Scholar
• Tubic M, Wagner D, Spahn-Langguth H, et al. In silico modeling of non-linear drug absorption for the P-gp substrate talinolol and of consequences for the resulting pharmacodynamic effect. Pharm Res. 2006;23:1712–1720.
   PubMed Web of Science ®Google Scholar
• Huang W, Lee SL, Yu LX. Mechanistic approaches to predicting oral drug absorption. AAPS J. 2009;11:217–224.
   PubMed Web of Science ®Google Scholar
• Yu LX, Lipka E, Crison JR, et al. Transport approaches to the biopharmaceutical design of oral drug delivery systems: prediction of intestinal absorption. Adv Drug Deliv Rev. 1996;19:359–376.
   PubMed Web of Science ®Google Scholar
• Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev. 2001;50(Suppl 1):S41–S67.
   PubMed Web of Science ®Google Scholar
• Alqahtani S, Kaddoumi A. Development of a physiologically based pharmacokinetic/pharmacodynamic model to identify mechanisms contributing to entacapone low bioavailability. Biopharm Drug Dispos. 2015;36:587–602.
   PubMed Web of Science ®Google Scholar
• Xia B, Yang Z, Zhou H, et al. Development of a novel oral cavity compartmental absorption and transit model for sublingual administration: illustration with Zolpidem. AAPS J. 2015;17:631–642.
   PubMed Web of Science ®Google Scholar
• Ando H, Hisaka A, Suzuki H. A new physiologically based pharmacokinetic model for the prediction of gastrointestinal drug absorption: translocation model. Drug Metab Dispos. 2015;43:590–602.
   PubMed Web of Science ®Google Scholar
• Jamei M, Turner D, Yang J, et al. Population-based mechanistic prediction of oral drug absorption. AAPS J. 2009;11:225–237.
   PubMed Web of Science ®Google Scholar
• Ahmed SS, Ramakrishnan V. Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction. PLoS One. 2012;7:e40654.
   PubMed Web of Science ®Google Scholar
• Xu X, Zhang W, Huang C, et al. A novel chemometric method for the prediction of human oral bioavailability. Int J Mol Sci. 2012;13:6964–6982.
   PubMed Web of Science ®Google Scholar
• Tian S, Li Y, Wang J, et al. ADME evaluation in drug discovery. 9. Prediction of oral bioavailability in humans based on molecular properties and structural fingerprints. Mol Pharm. 2011;8:841–851.
   PubMed Web of Science ®Google Scholar
• Paixao P, Gouveia LF, Morais JA. Prediction of the human oral bioavailability by using in vitro and in silico drug related parameters in a physiologically based absorption model. Int J Pharm. 2012;429:84–98.
   PubMed Web of Science ®Google Scholar
• Olivares-Morales A, Hatley OJ, Turner D, et al. The use of ROC analysis for the qualitative prediction of human oral bioavailability from animal data. Pharm Res. 2014;31:720–730.
   PubMed Web of Science ®Google Scholar
• Del Amo EM, Ghemtio L, Xhaard H, et al. Applying linear and non-linear methods for parallel prediction of volume of distribution and fraction of unbound drug. PLoS One. 2013;8:e74758.
   PubMed Web of Science ®Google Scholar
• Paixao P, Aniceto N, Gouveia LF, et al. Prediction of drug distribution in rat and humans using an artificial neural networks ensemble and a PBPK model. Pharm Res. 2014;31:3313–3322.
   PubMed Web of Science ®Google Scholar
• Lombardo F, Jing Y. In silico prediction of volume of distribution in humans. Extensive data set and the exploration of linear and nonlinear methods coupled with molecular interaction fields descriptors. J Chem Inf Model. 2016;56:2042–2052.
   PubMed Web of Science ®Google Scholar
• Li H, Chen Z, Xu X, et al. Predicting human plasma protein binding of drugs using plasma protein interaction QSAR analysis (PPI-QSAR). Biopharm Drug Dispos. 2011;32:333–342.
   PubMed Web of Science ®Google Scholar
• Ghafourian T, Amin Z. QSAR models for the prediction of plasma protein binding. Bioimpacts. 2013;3:21–27.
   PubMedGoogle Scholar
• Zhivkova Z, Doytchinova I. Quantitative structure–plasma protein binding relationships of acidic drugs. J Pharm Sci. 2012;101:4627–4641.
   PubMed Web of Science ®Google Scholar
• Zhu XW, Sedykh A, Zhu H, et al. The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein binding. Pharm Res. 2013;30:1790–1798.
   PubMed Web of Science ®Google Scholar
• Ayrton A, Morgan P. Role of transport proteins in drug absorption, distribution and excretion. Xenobiotica. 2001;31:469–497.
   PubMed Web of Science ®Google Scholar
• Van De Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov. 2003;2:192–204.
   PubMed Web of Science ®Google Scholar
• Broccatelli F, Carosati E, Neri A, et al. A 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
• Lee CA, O’Connor MA, Ritchie TK, et al. Breast cancer resistance protein (ABCG2) in clinical pharmacokinetics and drug interactions: practical recommendations for clinical victim and perpetrator drug-drug interaction study design. Drug Metab Dispos. 2015;43:490–509.
   PubMed Web of Science ®Google Scholar
• Montanari F, Ecker GF. BCRp inhibition: from data collection to ligand-based modeling. Mol Inform. 2014;33:322–331.
   PubMed Web of Science ®Google Scholar
• Montanari F, Cseke A, Wlcek K, et al. Virtual screening of drug bank reveals two drugs as new BCRP inhibitors. SLAS Discov. 2017;22:86–93.
   PubMed Web of Science ®Google Scholar
• Montanari F, Pinto M, Khunweeraphong N, et al. Flagging drugs that inhibit the bile salt export pump. Mol Pharm. 2016;13:163–171.
   PubMed Web of Science ®Google Scholar
• Kotsampasakou E, Brenner S, Jager W, et al. Identification of novel inhibitors of organic anion transporting polypeptides 1B1 and 1B3 (OATP1B1 and OATP1B3) using a consensus vote of six classification models. Mol Pharm. 2015;12:4395–4404.
   PubMed Web of Science ®Google Scholar
• Aniceto N, Freitas AA, Bender A, et al. Simultaneous prediction of four ATP-binding cassette transporters’ substrates using multi-label QSAR. Mol Inform. 2016;35:514–528.
   PubMed Web of Science ®Google Scholar
• Graham H, Walker M, Jones O, et al. Comparison of in-vivo and in-silico methods used for prediction of tissue: plasma partition coefficients in rat. J Pharm Pharmacol. 2012;64:383–396.
   PubMed Web of Science ®Google Scholar
• Poulin P, Schoenlein K, Theil FP. Prediction of adipose tissue: plasma partition coefficients for structurally unrelated drugs. J Pharm Sci. 2001;90:436–447.
   PubMed Web of Science ®Google Scholar
• Poulin P, Theil FP. A priori prediction of tissue: plasmapartition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery. J Pharm Sci. 2000;89:16–35.
   PubMed Web of Science ®Google Scholar
• Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J Pharm Sci. 2002;91:129–156.
   PubMed Web of Science ®Google Scholar
• Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. II. Generic physiologically based pharmacokinetic models of drug disposition. J Pharm Sci. 2002;91:1358–1370.
   PubMed Web of Science ®Google Scholar
• Berezhkovskiy LM. Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination. J Pharm Sci. 2004;93:1628–1640.
   PubMed Web of Science ®Google Scholar
• Rodgers T, Leahy D, Rowland M. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. J Pharm Sci. 2005;94:1259–1276.
   PubMed Web of Science ®Google Scholar
• Rodgers T, Rowland M. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci. 2006;95:1238–1257.
   PubMed Web of Science ®Google Scholar
• Kell DB, Goodacre R. Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery. Drug Discov Today. 2014;19:171–182.
   PubMed Web of Science ®Google Scholar
• Kirchmair J, Williamson MJ, Afzal AM, et al. FAst MEtabolizer (FAME): a rapid and accurate predictor of sites of metabolism in multiple species by endogenous enzymes. J Chem Inf Model. 2013;53:2896–2907.
   PubMed Web of Science ®Google Scholar
• Kingsley LJ, Wilson GL, Essex ME, et al. Combining structure- and ligand-based approaches to improve site of metabolism prediction in CYP2C9 substrates. Pharm Res. 2015;32:986–1001.
   PubMed Web of Science ®Google Scholar
• He SB, Li MM, Zhang BX, et al. Construction of metabolism prediction models for CYP450 3A4, 2D6, and 2C9 based on microsomal metabolic reaction system. Int J Mol Sci. 2016;17
   Web of Science ®Google Scholar
• Nembri S, Grisoni F, Consonni V, et al. In silico prediction of cytochrome p450-drug interaction: qSARs for CYP3A4 and CYP2C9. Int J Mol Sci. 2016;17.
   PubMed Web of Science ®Google Scholar
• Kusama M, Toshimoto K, Maeda K, et al. In silico classification of major clearance pathways of drugs with their physiochemical parameters. Drug Metab Dispos. 2010;38:1362–1370.
   PubMed Web of Science ®Google Scholar
• Toshimoto K, Wakayama N, Kusama M, et al. In silico prediction of major drug clearance pathways by support vector machines with feature-selected descriptors. Drug Metab Dispos. 2014;42:1811–1819.
   PubMed Web of Science ®Google Scholar
• Berellini G, Waters NJ, Lombardo F. In silico prediction of total human plasma clearance. J Chem Inf Model. 2012;52:2069–2078.
   PubMed Web of Science ®Google Scholar
• Golbamaki A, Benfenati E. In Silico Methods for Carcinogenicity Assessment. Methods Mol Biol. 2016;1425:107–119.
   PubMedGoogle Scholar
• Lu J, Zhang P, Zou X, et al. In silico prediction of chemical toxicity profile using local lazy learning. Comb Chem High Throughput Screen. 2017;20:346–353.
   Web of Science ®Google Scholar
• Zhang H, Ren JX, Kang YL, et al. Development of novel in silico model for developmental toxicity assessment by using naive Bayes classifier method. Reprod Toxicol. 2017;71:8–15.
   PubMed Web of Science ®Google Scholar
• Hewitt M, Przybylak K. In silico models for hepatotoxicity. Methods Mol Biol. 2016;1425:201–236.
   PubMedGoogle Scholar
• Kim E, Nam H. Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints. BMC Bioinformatics. 2017;18:227.
   PubMed Web of Science ®Google Scholar
• Zhong M, Nie X, Yan A, et al. Carcinogenicity prediction of noncongeneric chemicals by a support vector machine. Chem Res Toxicol. 2013;26:741–749.
   PubMed Web of Science ®Google Scholar
• Perez-Nueno VI, Souchet M, Karaboga AS, et al. GESSE: predicting drug side effects from drug-target relationships. J Chem Inf Model. 2015;55:1804–1823.
   PubMed Web of Science ®Google Scholar
• Zhang W, Liu F, Luo L, et al. Predicting drug side effects by multi-label learning and ensemble learning. BMC Bioinformatics. 2015;16:365.
   PubMed Web of Science ®Google Scholar
• Dickins M, Modi S. The importance of predictive ADME simulation. Drug Discov Today. 2002;7:755–756.
   PubMed Web of Science ®Google Scholar
• Modi S. Positioning ADMET in silico tools in drug discovery. Drug Discov Today. 2004;9:14–15.
   PubMed Web of Science ®Google Scholar
• Vedani A, Smiesko M. In silico toxicology in drug discovery - concepts based on three-dimensional models. Altern Lab Anim. 2009;37:477–496.
   PubMed Web of Science ®Google Scholar
• Clark DE, Grootenhuis PD. Progress in computational methods for the prediction of ADMET properties. Curr Opin Drug Discov Devel. 2002;5:382–390.
   PubMed Web of Science ®Google Scholar