Advertisement

Drug Transporters as Therapeutic Targets: Computational Models, Challenges, and Future Perspective

  • Deepak Singla
  • Ritika Bishnoi
  • Sandeep Kumar Dhanda
  • Shailendra Asthana
Chapter

Abstract

Tissue level expression, mutation, and substrate specificity of the transporter proteins have been widely accepted for their usefulness in drug disposition and efficacy. Many transporters play a significant role in normal human physiology as well as in disease conditions. Association of these properties, with systemic plasma concentration of the drug, is the leading reason for adverse drug reactions and drug resistance. The identification and validation of transporter proteins in experiments and their atomic resolution for characterization of structural-functional relationship is a costly, time-consuming, and more tedious process. However, predictive in silico tools claimed well for accurately accessing the pharmacokinetics, pharmacodynamics properties in early drug discovery stage. But the huge amount of data requires the development of reliable computational techniques and databases for the identification and/or prediction of membrane transport proteins as well as their ligands has become essential. Here, we review the available datasets and the computational methods, which put forth more insights for better understanding of human drug transporter proteins.

Keywords

Transporter Inhibitor Drug Database ADMET, ligand prediction 

Notes

Acknowledgments

The authors are thankful to the Indian Council of Medical Research (ICMR) and the Department of Biotechnology (DBT), Government of India, for financial assistance.

References

  1. Alexander SPH, Benson HE, Faccenda E, Pawson AJ, Sharman JL, McGrath JC, Catterall WA, Spedding M, Peters JA, Harmar AJ, Abul-Hasn N, Anderson CM, Anderson CMH, Araiksinen MS, Arita M, Arthofer E, Barker EL, Barratt C, Barnes NM, Bathgate R, Beart PM, Belelli D, Bennett AJ, Birdsall NJM, Boison D, Bonner TI, Brailsford L, Bröer S, Brown P, Calo G, Carter WG, Catterall WA, Chan SLF, Chao MV, Chiang N, Christopoulos A, Chun JJ, Cidlowski J, Clapham DE, Cockcroft S, Connor MA, Cox HM, Cuthbert A, Dautzenberg FM, Davenport AP, Dawson PA, Dent G, Dijksterhuis JP, Dollery CT, Dolphin AC, Donowitz M, Dubocovich ML, Eiden L, Eidne K, Evans BA, Fabbro D, Fahlke C, Farndale R, Fitzgerald GA, Fong TM, Fowler CJ, Fry JR, Funk CD, Futerman AH, Ganapathy V, Gaisnier B, Gershengorn MA, Goldin A, Goldman ID, Gundlach AL, Hagenbuch B, Hales TG, Hammond JR, Hamon M, Hancox JC, Hauger RL, Hay DL, Hobbs AJ, Hollenberg MD, Holliday ND, Hoyer D, Hynes NA, Inui K-I, Ishii S, Jacobson KA, Jarvis GE, Jarvis MF, Jensen R, Jones CE, Jones RL, Kaibuchi K, Kanai Y, Kennedy C, Kerr ID, Khan AA, Klienz MJ, Kukkonen JP, Lapoint JY, Leurs R, Lingueglia E, Lippiat J, Lolait SJ, Lummis SCR, Lynch JW, MacEwan D, Maguire JJ, Marshall IL, May JM, McArdle CA, McGrath JC, Michel MC, Millar NS, Miller LJ, Mitolo V, Monk PN, Moore PK, Moorhouse AJ, Mouillac B, Murphy PM, Neubig RR, Neumaier J, Niesler B, Obaidat A, Offermanns S, Ohlstein E, Panaro MA, Parsons S, Pwrtwee RG, Petersen J, Pin J-P, Poyner DR, Prigent S, Prossnitz ER, Pyne NJ, Pyne S, Quigley JG, Ramachandran R, Richelson EL, Roberts RE, Roskoski R, Ross RA, Roth M, Rudnick G, Ryan RM, Said SI, Schild L, Sanger GJ, Scholich K, Schousboe A, Schulte G, Schulz S, Serhan CN, Sexton PM, Sibley DR, Siegel JM, Singh G, Sitsapesan R, Smart TG, Smith DM, Soga T, Stahl A, Stewart G, Stoddart LA, Summers RJ, Thorens B, Thwaites DT, Toll L, Traynor JR, Usdin TB, Vandenberg RJ, Villalon C, Vore M, Waldman SA, Ward DT, Willars GB, Wonnacott SJ, Wright E, Ye RD, Yonezawa A, Zimmermann M (2013) The concise guide to PHARMACOLOGY 2013/14: overview. Br J Pharmacol 170:1449–1458.  https://doi.org/10.1111/bph.12444 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Ashraf T, Kis O, Banerjee N, Bendayan R (2012) Drug transporters at brain barriers: expression and regulation by neurological disorders. Adv Exp Med Biol 763:20–69PubMedGoogle Scholar
  3. Biegel A, Gebauer S, Hartrodt B, Brandsch M, Neubert K, Thondorf I (2005) Three-dimensional quantitative structure-activity relationship analyses of beta-lactam antibiotics and tripeptides as substrates of the mammalian H+/peptide cotransporter PEPT1. J Med Chem 48:4410–4419.  https://doi.org/10.1021/jm048982w CrossRefPubMedGoogle Scholar
  4. Broccatelli F, Carosati E, Neri A, Frosini M, Goracci L, Oprea TI, Cruciani G (2011) A novel approach for predicting P-glycoprotein (ABCB1) inhibition using molecular interaction fields. J Med Chem 54:1740–1751.  https://doi.org/10.1021/jm101421d CrossRefPubMedPubMedCentralGoogle Scholar
  5. Cabrera MA, González I, Fernández C, Navarro C, Bermejo M (2006) A topological substructural approach for the prediction of P-glycoprotein substrates. J Pharm Sci 95:589–606.  https://doi.org/10.1002/jps.20449 CrossRefPubMedGoogle Scholar
  6. Cascorbi I (2006) Role of pharmacogenetics of ATP-binding cassette transporters in the pharmacokinetics of drugs. Pharmacol Ther 112:457–473.  https://doi.org/10.1016/j.pharmthera.2006.04.009 CrossRefPubMedGoogle Scholar
  7. César-Razquin A, Snijder B, Frappier-Brinton T, Isserlin R, Gyimesi G, Bai X, Reithmeier RA, Hepworth D, Hediger MA, Edwards AM, Superti-Furga G (2015) A call for systematic research on solute carriers. Cell 162:478–487.  https://doi.org/10.1016/j.cell.2015.07.022 CrossRefPubMedGoogle Scholar
  8. Chen L, Li Y, Zhao Q, Peng H, Hou T (2011a) ADME evaluation in drug discovery. 10. Predictions of P-glycoprotein inhibitors using recursive partitioning and naive Bayesian classification techniques. Mol Pharm 8:889–900.  https://doi.org/10.1021/mp100465q CrossRefPubMedGoogle Scholar
  9. Chen S-A, Y-Y O, Lee T-Y, Gromiha MM (2011b) Prediction of transporter targets using efficient RBF networks with PSSM profiles and biochemical properties. Bioinformatics 27:2062–2067.  https://doi.org/10.1093/bioinformatics/btr340 CrossRefPubMedGoogle Scholar
  10. Chen L, Li Y, Yu H, Zhang L, Hou T (2012) Computational models for predicting substrates or inhibitors of P-glycoprotein. Drug Discov Today 17:343–351.  https://doi.org/10.1016/j.drudis.2011.11.003 CrossRefPubMedGoogle Scholar
  11. Cianchetta G, Singleton RW, Zhang M, Wildgoose M, Giesing D, Fravolini A, Cruciani G, Vaz RJ (2005) A pharmacophore hypothesis for P-glycoprotein substrate recognition using GRIND-based 3D-QSAR. J Med Chem 48:2927–2935.  https://doi.org/10.1021/jm0491851 CrossRefPubMedGoogle Scholar
  12. Crivori P, Reinach B, Pezzetta D, Poggesi I (2006) Computational models for identifying potential P-glycoprotein substrates and inhibitors. Mol Pharm 3:33–44.  https://doi.org/10.1021/mp050071a CrossRefPubMedGoogle Scholar
  13. De Cerqueira Lima P, Golbraikh A, Oloff S, Xiao Y, Tropsha A (2006) Combinatorial QSAR modeling of P-glycoprotein substrates. J Chem Inf Model 46:1245–1254.  https://doi.org/10.1021/ci0504317 CrossRefPubMedGoogle Scholar
  14. Dean M, Rzhetsky A, Allikmets R (2001) The human ATP-binding cassette (ABC) transporter superfamily. Genome Res 11:1156–1166.  https://doi.org/10.1101/gr.184901 CrossRefPubMedGoogle Scholar
  15. DeGorter MK, Xia CQ, Yang JJ, Kim RB (2012) Drug transporters in drug efficacy and toxicity. Annu Rev Pharmacol Toxicol 52:249–273.  https://doi.org/10.1146/annurev-pharmtox-010611-134529 CrossRefPubMedGoogle Scholar
  16. Demel MA, Kraemer O, Ettmayer P, Haaksma E, Ecker GF (2010) Ensemble rule-based classification of substrates of the human ABC-transporter ABCB1 using simple physicochemical descriptors. Mol Inform 29:233–242.  https://doi.org/10.1002/minf.200900079 CrossRefPubMedGoogle Scholar
  17. Ding Y-L, Shih Y-H, Tsai F-Y, Leong MK (2014) In silico prediction of inhibition of promiscuous breast cancer resistance protein (BCRP/ABCG2). PLoS One 9:e90689.  https://doi.org/10.1371/journal.pone.0090689 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Dong Z, Zhong Z, Yang L, Wang S, Gong Z (2014) MicroRNA-31 inhibits cisplatin-induced apoptosis in non-small cell lung cancer cells by regulating the drug transporter ABCB9. Cancer Lett 343:249–257.  https://doi.org/10.1016/j.canlet.2013.09.034 CrossRefPubMedGoogle Scholar
  19. Ekins S (2002) Application of three-dimensional quantitative structure-activity relationships of P-glycoprotein inhibitors and substrates. Mol Pharmacol 61:974–981.  https://doi.org/10.1124/mol.61.5.974 CrossRefPubMedGoogle Scholar
  20. Gantner ME, Emiliano M, Ianni D, Ruiz ME, Talevi A, Bruno-blanch LE (2013) Development of conformation independent computational models for the early recognition of breast cancer resistance protein substrates. Biomed Res Int.  https://doi.org/10.1155/2013/863592
  21. Garg P, Dhakne R, Belekar V (2014) Role of breast cancer resistance protein (BCRP) as active efflux transporter on blood-brain barrier (BBB) permeability. Mol Divers 19:163–172.  https://doi.org/10.1007/s11030-014-9562-2 CrossRefPubMedGoogle Scholar
  22. Gombar VK, Polli JW, Humphreys JE, Wring SA, Serabjit-Singh CS (2004) Predicting P-glycoprotein substrates by a quantitative structure-activity relationship model. J Pharm Sci 93:957–968.  https://doi.org/10.1002/jps.20035 CrossRefPubMedGoogle Scholar
  23. Gromiha MM, Yabuki Y, Suresh MX, Thangakani AM, Suwa M, Fukui K (2009) TMFunction: database for functional residues in membrane proteins. Nucleic Acids Res 37:D201–D204.  https://doi.org/10.1093/nar/gkn672 CrossRefPubMedGoogle Scholar
  24. Guo Y, Kotova E, Chen Z-S, Lee K, Hopper-Borge E, Belinsky MG, Kruh GD (2003) MRP8, ATP-binding cassette C11 (ABCC11), is a cyclic nucleotide efflux pump and a resistance factor for fluoropyrimidines 2′,3′-dideoxycytidine and 9′-(2′-phosphonylmethoxyethyl)adenine. J Biol Chem 278:29509–29514.  https://doi.org/10.1074/jbc.M304059200 CrossRefPubMedGoogle Scholar
  25. Hammann F, Gutmann H, Jecklin U, Maunz A, Helma C, Drewe J (2009) Development of decision tree models for substrates, inhibitors, and inducers of P-glycoprotein. Curr Drug Metab 10:339–346.  https://doi.org/10.2174/138920009788499021 CrossRefPubMedGoogle Scholar
  26. Hauswald S, Duque-Afonso J, Wagner MM, Schertl FM, Lübbert M, Peschel C, Keller U, Licht T (2009) Histone deacetylase inhibitors induce a very broad, pleiotropic anticancer drug resistance phenotype in acute myeloid leukemia cells by modulation of multiple ABC transporter genes. Clin Cancer Res 15:3705–3715.  https://doi.org/10.1158/1078-0432.CCR-08-2048 CrossRefPubMedGoogle Scholar
  27. Hazai E, Hazai I, Ragueneau-Majlessi I, Chung SP, Bikadi Z, Mao Q (2013) Predicting substrates of the human breast cancer resistance protein using a support vector machine method. BMC Bioinformatics 14:130.  https://doi.org/10.1186/1471-2105-14-130 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Hediger MA, Clémençon B, Burrier RE, Bruford EA (2013) The ABCs of membrane transporters in health and disease (SLC series): introduction. Mol Asp Med 34:95–107.  https://doi.org/10.1016/j.mam.2012.12.009 CrossRefGoogle Scholar
  29. Hee Choi Y, Yu A-M (2014) ABC transporters in multidrug resistance and pharmacokinetics, and strategies for drug development. Curr Pharm Des 20:793–807.  https://doi.org/10.2174/138161282005140214165212 CrossRefGoogle Scholar
  30. Honjo Y, Morisaki K, Mickley Huff L, Robey RW, Hung J, Dean M, Bates SE (2002) Single-Nucleotide Polymorphism (SNP) analysis in the ABC half-transporter ABCG2 (MXR/BCRP/ABCP1). Cancer Biol Ther 1:696–702.  https://doi.org/10.4161/cbt.322 CrossRefPubMedGoogle Scholar
  31. Huang Y, Sadée W (2006) Membrane transporters and channels in chemoresistance and -sensitivity of tumor cells. Cancer Lett 239:168–182.  https://doi.org/10.1016/j.canlet.2005.07.032 CrossRefPubMedGoogle Scholar
  32. Huang J, Ma G, Muhammad I, Cheng Y (2007) Identifying P-glycoprotein substrates using a support vector machine optimized by a particle swarm. J Chem Inf Model 47:1638–1647.  https://doi.org/10.1021/ci700083n CrossRefPubMedGoogle Scholar
  33. Huang H-L, Li M-C, Vasylenko T, Ho S-Y (2014) Computational prediction and analysis of human transporters using physicochemical properties of amino acids. Int J Eng Tech Res 2:180–187.  https://doi.org/10.1186/1741-7007-7-50 Google Scholar
  34. Kamphorst J, Cucurull-Sanchez L, Jones B (2007) A performance evaluation of multiple classification models of human PEPT1 inhibitors and non-inhibitors. QSAR Comb Sci 26:220–226.  https://doi.org/10.1002/qsar.200630025 CrossRefGoogle Scholar
  35. Karlgren M, Ahlin G, Bergström CAS, Svensson R, Palm J, Artursson P (2012a) In vitro and in silico strategies to identify OATP1B1 inhibitors and predict clinical drug-drug interactions. Pharm Res 29:411–426.  https://doi.org/10.1007/s11095-011-0564-9 CrossRefPubMedGoogle Scholar
  36. Karlgren M, Vildhede A, Norinder U, Wisniewski JR, Kimoto E, Lai Y, Haglund U, Artursson P (2012b) Classification of inhibitors of hepatic organic anion transporting polypeptides (OATPs): influence of protein expression on drug-drug interactions. J Med Chem 55:4740–4763.  https://doi.org/10.1021/jm300212s CrossRefPubMedPubMedCentralGoogle Scholar
  37. Kim M, Turnquist H, Jackson J, Sgagias M, Yan Y, Gong M, Dean M, Sharp JG, Cowan K (2002) The multidrug resistance transporter ABCG2 (Breast Cancer Resistance Protein 1) Effluxes Hoechst 33342 and is overexpressed in hematopoietic stem cells. Clin Cancer Res 8:22–28PubMedGoogle Scholar
  38. Klepsch F, Vasanthanathan P, Ecker GF (2014) Ligand and structure-based classification models for prediction of P-glycoprotein inhibitors. J Chem Inf Model 54:218–229.  https://doi.org/10.1021/ci400289j CrossRefPubMedGoogle Scholar
  39. Kruh GD, Guo Y, Hopper-Borge E, Belinsky MG, Chen Z-S (2007) ABCC10, ABCC11, and ABCC12. Pflugers Arch 453:675–684.  https://doi.org/10.1007/s00424-006-0114-1 CrossRefPubMedGoogle Scholar
  40. Langer T, Eder M, Hoffmann RD, Chiba P, Ecker GF (2004) Lead identification for modulators of multidrug resistance based on in silico screening with a pharmacophoric feature model. Arch Pharm (Weinheim) 337:317–327.  https://doi.org/10.1002/ardp.200300817 CrossRefGoogle Scholar
  41. Larsen SB, Jørgensen FS, Olsen L (2008) QSAR models for the human H(+)/peptide symporter, hPEPT1: affinity prediction using alignment-independent descriptors. J Chem Inf Model 48:233–241.  https://doi.org/10.1021/ci700346y CrossRefPubMedGoogle Scholar
  42. Lather V, Madan AK (2005) Topological model for the prediction of MRP1 inhibitory activity of pyrrolopyrimidines and templates derived from pyrrolopyrimidine. Bioorg Med Chem Lett 15:4967–4972.  https://doi.org/10.1016/j.bmcl.2005.08.011 CrossRefPubMedGoogle Scholar
  43. Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han B, Zhou Y, Wishart DS (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42:D1091–D1097.  https://doi.org/10.1093/nar/gkt1068 CrossRefPubMedGoogle Scholar
  44. Leong MK, Chen H-B, Shih Y-H (2012) Prediction of promiscuous p-glycoprotein inhibition using a novel machine learning scheme. PLoS One 7:e33829.  https://doi.org/10.1371/journal.pone.0033829 CrossRefPubMedPubMedCentralGoogle Scholar
  45. Li W-X, Li L, Eksterowicz J, Ling XB, Cardozo M (2007) Significance analysis and multiple pharmacophore models for differentiating P-glycoprotein substrates. J Chem Inf Model 47:2429–2438.  https://doi.org/10.1021/ci700284p CrossRefPubMedGoogle Scholar
  46. Li H, Dai X, Zhao X (2008) A nearest neighbor approach for automated transporter prediction and categorization from protein sequences. Bioinformatics 24:1129–1136.  https://doi.org/10.1093/bioinformatics/btn099 CrossRefPubMedGoogle Scholar
  47. Li H, Benedito VA, Udvardi MK, Zhao PX (2009) TransportTP: a two-phase classification approach for membrane transporter prediction and characterization. BMC Bioinformatics 10:418.  https://doi.org/10.1186/1471-2105-10-418 CrossRefPubMedPubMedCentralGoogle Scholar
  48. Li D, Chen L, Li Y, Tian S, Sun H, Hou T (2014) ADMET evaluation in drug discovery. 13. Development of in silico prediction models for P-glycoprotein substrates. Mol Pharm 11:716–726.  https://doi.org/10.1021/mp400450m CrossRefPubMedGoogle Scholar
  49. Lin L, Yee SW, Kim RB, Giacomini KM (2015) SLC transporters as therapeutic targets: emerging opportunities. Nat Rev Drug Discov 14:543–560.  https://doi.org/10.1038/nrd4626 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Matsson P, Englund G, Ahlin G, Bergström CAS, Norinder U, Artursson P (2007) A global drug inhibition pattern for the human ATP-binding cassette transporter breast cancer resistance protein (ABCG2). J Pharmacol Exp Ther 323:19–30. http://doi:10.1124/jpet.107.124768
  51. Matsson P, Artursson P (2013) Computational prospecting for drug–transporter interactions. Clin Pharmacol Ther 94:30–32. https://doi.org/10.1038/clpt.2013.67
  52. Mishra NK, Chang J, Zhao PX (2014) Prediction of membrane transport proteins and their substrate specificities using primary sequence information. PLoS One 9:e100278.  https://doi.org/10.1371/journal.pone.0100278 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Mizuarai S, Aozasa N, Kotani H (2004) Single nucleotide polymorphisms result in impaired membrane localization and reduced atpase activity in multidrug transporter ABCG2. Int J Cancer 109:238–246.  https://doi.org/10.1002/ijc.11669 CrossRefPubMedGoogle Scholar
  54. Montanari F, Ecker GF (2014) BCRP Inhibition: from data collection to ligand-based modeling. Mol Inform 33:322–331.  https://doi.org/10.1002/minf.201400012 CrossRefPubMedGoogle Scholar
  55. Montanari F, Ecker GF (2015) Prediction of drug-ABC-transporter interaction – recent advances and future challenges. Adv Drug Deliv Rev 86:17–26.  https://doi.org/10.1016/j.addr.2015.03.001 CrossRefPubMedGoogle Scholar
  56. Müller H, Pajeva IK, Globisch C, Wiese M (2008) Functional assay and structure-activity relationships of new third-generation P-glycoprotein inhibitors. Bioorg Med Chem 16:2448–2462.  https://doi.org/10.1016/j.bmc.2007.11.057 CrossRefPubMedGoogle Scholar
  57. Ng C, Xiao Y-D, Lum BL, Han Y-H (2005) Quantitative structure-activity relationships of methotrexate and methotrexate analogues transported by the rat multispecific resistance-associated protein 2 (rMrp2). Eur J Pharm Sci 26:405–413.  https://doi.org/10.1016/j.ejps.2005.07.008 CrossRefPubMedGoogle Scholar
  58. Nieth C, Lage H (2005) Induction of the ABC-transporters Mdr1/P-gp (Abcb1), mrpl (Abcc1), and bcrp (Abcg2) during establishment of multidrug resistance following exposure to mitoxantrone. J Chemother 17:215–223.  https://doi.org/10.1179/joc.2005.17.2.215 CrossRefPubMedGoogle Scholar
  59. O’Brien C, Cavet G, Pandita A, Hu X, Haydu L, Mohan S, Toy K, Rivers CS, Modrusan Z, Amler LC, Lackner MR (2008) Functional genomics identifies ABCC3 as a mediator of taxane resistance in HER2-amplified breast cancer. Cancer Res 68:5380–5389.  https://doi.org/10.1158/0008-5472.CAN-08-0234 CrossRefPubMedGoogle Scholar
  60. Okabe M, Szakács G, Reimers MA, Suzuki T, Hall MD, Abe T, Weinstein JN, Gottesman MM (2008) Profiling SLCO and SLC22 genes in the NCI-60 cancer cell lines to identify drug uptake transporters. Mol Cancer Ther 7:3081–3091.  https://doi.org/10.1158/1535-7163.MCT-08-0539 CrossRefPubMedPubMedCentralGoogle Scholar
  61. Ou Y-Y, Chen S-A, Gromiha MM (2010) Classification of transporters using efficient radial basis function networks with position-specific scoring matrices and biochemical properties. Proteins 78:1789–1797.  https://doi.org/10.1002/prot.22694 PubMedGoogle Scholar
  62. Pajeva IK, Wiese M (2002) Pharmacophore model of drugs involved in P-glycoprotein multidrug resistance: explanation of structural variety (hypothesis). J Med Chem 45:5671–5686.  https://doi.org/10.1021/jm020941h CrossRefPubMedGoogle Scholar
  63. Palmeira A, Rodrigues F, Sousa E, Pinto M, Vasconcelos MH, Fernandes MX (2011) New uses for old drugs: pharmacophore-based screening for the discovery of P-glycoprotein inhibitors. Chem Biol Drug Des 78:57–72.  https://doi.org/10.1111/j.1747-0285.2011.01089.x CrossRefPubMedGoogle Scholar
  64. Pan Y, Chothe PP, Swaan PW (2013) Identification of novel breast cancer resistance protein (BCRP) inhibitors by virtual screening. Mol Pharm 10:1236–1248.  https://doi.org/10.1021/mp300547h CrossRefPubMedGoogle Scholar
  65. Pedersen JM, Matsson P, Bergström CAS, Norinder U, Hoogstraate J, Artursson P (2008) Prediction and identification of drug interactions with the human ATP-binding cassette transporter multidrug-resistance associated protein 2 (MRP2; ABCC2). J Med Chem 51:3275–3287.  https://doi.org/10.1021/jm7015683 CrossRefPubMedGoogle Scholar
  66. Penzotti JE, Lamb ML, Evensen E, Grootenhuis PDJ (2002) A computational ensemble pharmacophore model for identifying substrates of P-glycoprotein. J Med Chem 45:1737–1740.  https://doi.org/10.1021/jm0255062 CrossRefPubMedGoogle Scholar
  67. Pick A, Müller H, Mayer R, Haenisch B, Pajeva IK, Weigt M, Bönisch H, Müller CE, Wiese M (2011) Structure-activity relationships of flavonoids as inhibitors of breast cancer resistance protein (BCRP). Bioorg Med Chem 19:2090–2102.  https://doi.org/10.1016/j.bmc.2010.12.043 CrossRefPubMedGoogle Scholar
  68. Pinto M, Trauner M, Ecker GF (2012) An in silico classification model for putative ABCC2 substrates. Mol Inform 31:547–553.  https://doi.org/10.1002/minf.201200049 CrossRefPubMedPubMedCentralGoogle Scholar
  69. Poongavanam V, Haider N, Ecker GF (2012) Fingerprint-based in silico models for the prediction of P-glycoprotein substrates and inhibitors. Bioorg Med Chem 20:5388–5395.  https://doi.org/10.1016/j.bmc.2012.03.045 CrossRefPubMedPubMedCentralGoogle Scholar
  70. Quentin Y, Fichant G (2000) ABCdb: an ABC transporter database. J Mol Microbiol Biotechnol 2:501–504PubMedGoogle Scholar
  71. Rais R, Acharya C, Tririya G, Mackerell AD, Polli JE (2010) Molecular switch controlling the binding of anionic bile acid conjugates to human apical sodium-dependent bile acid transporter. J Med Chem 53:4749–4760.  https://doi.org/10.1021/jm1003683 CrossRefPubMedPubMedCentralGoogle Scholar
  72. Ren Q, Paulsen IT (2005) Comparative analyses of fundamental differences in membrane transport capabilities in prokaryotes and eukaryotes. PLoS Comput Biol 1:e27.  https://doi.org/10.1371/journal.pcbi.0010027 CrossRefPubMedPubMedCentralGoogle Scholar
  73. Ren Q, Chen K, Paulsen IT (2007) TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. Nucleic Acids Res 35:D274–D279.  https://doi.org/10.1093/nar/gkl925 CrossRefPubMedGoogle Scholar
  74. Ritschel T, Hermans SMA, Schreurs M, van den Heuvel JJMW, Koenderink JB, Greupink R, Russel FGM (2014) In silico identification and in vitro validation of potential cholestatic compounds through 3D ligand-based pharmacophore modeling of BSEP inhibitors. Chem Res Toxicol 27:873–881.  https://doi.org/10.1021/tx5000393 CrossRefPubMedGoogle Scholar
  75. Saier MH (1998) Molecular phylogeny as a basis for the classification of transport proteins from bacteria, archaea and eukarya. Adv Microb Physiol 40:81–136.  https://doi.org/10.1016/S0065-2911(08)60130-7 CrossRefPubMedGoogle Scholar
  76. Saier MH (2000) A functional-phylogenetic classification system for transmembrane solute transporters. Microbiol Mol Biol Rev 64:354–411.  https://doi.org/10.1128/MMBR.64.2.354-411.2000 CrossRefPubMedPubMedCentralGoogle Scholar
  77. Saier MH, Reddy VS, Tamang DG, Västermark A (2014) The transporter classification database. Nucleic Acids Res 42:D251–D258.  https://doi.org/10.1093/nar/gkt1097 CrossRefPubMedGoogle Scholar
  78. Schlessinger A, Khuri N, Giacomini KM, Sali A (2013) Molecular modeling and ligand docking for solute carrier (SLC) transporters. Curr Top Med Chem 13:843–856.  https://doi.org/10.2174/1568026611313070007 CrossRefPubMedPubMedCentralGoogle Scholar
  79. Sedykh A, Fourches D, Duan J, Hucke O, Garneau M, Zhu H, Bonneau P, Tropsha A (2013) Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions. Pharm Res 30:996–1007.  https://doi.org/10.1007/s11095-012-0935-x CrossRefPubMedGoogle Scholar
  80. Shen J, Cue Y, Gy J, Li Y, Li L (2014) A genetic algorithm- back propagation artificial neural network model to quantify the affinity of flavonoids toward P-glycoprotein. Comb Chem High Throughput Screen 17:162–172.  https://doi.org/10.2174/1386207311301010002 CrossRefPubMedGoogle Scholar
  81. Sun H (2005) A naive bayes classifier for prediction of multidrug resistance reversal activity on the basis of atom typing. J Med Chem 48:4031–4039.  https://doi.org/10.1021/jm050180t CrossRefPubMedGoogle Scholar
  82. Szakács G, Váradi A, Ozvegy-Laczka C, Sarkadi B (2008) The role of ABC transporters in drug absorption, distribution, metabolism, excretion and toxicity (ADME-Tox). Drug Discov Today 13:379–393.  https://doi.org/10.1016/j.drudis.2007.12.010 CrossRefPubMedGoogle Scholar
  83. Tan W, Mei H, Chao L, Liu T, Pan X, Shu M, Yang L (2013) Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors. J Comput Aided Mol Des 27:1067–1073.  https://doi.org/10.1007/s10822-013-9697-8 CrossRefPubMedGoogle Scholar
  84. Tao L, Zhang P, Qin C, Chen SY, Zhang C, Chen Z, Zhu F, Yang SY, Wei YQ, Chen YZ (2015) Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools. Adv Drug Deliv Rev 86:83–100.  https://doi.org/10.1016/j.addr.2015.03.014 CrossRefPubMedGoogle Scholar
  85. Thai K-M, Huynh N-T, Ngo T-D, Mai T-T, Nguyen T-H, Tran T-D (2015) Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks. SAR QSAR Environ Res 26:139–163.  https://doi.org/10.1080/1062936X.2014.995701 CrossRefPubMedGoogle Scholar
  86. Van de Steeg E, Venhorst J, Jansen HT, Nooijen IHG, DeGroot J, Wortelboer HM, Vlaming MLH (2015) Generation of Bayesian prediction models for OATP-mediated drug–drug interactions based on inhibition screen of OATP1B1, OATP1B1∗15 and OATP1B3. Eur J Pharm Sci 70:29–36.  https://doi.org/10.1016/j.ejps.2015.01.004 CrossRefPubMedGoogle Scholar
  87. Viereck M, Gaulton A, Digles D, Ecker GF (2014) Transporter taxonomy: a comparison of different transport protein classification schemes. Drug Discov Today Technol 12:e37–e46.  https://doi.org/10.1016/j.ddtec.2014.03.004 CrossRefPubMedGoogle Scholar
  88. Wang Y-H, Li Y, Yang S-L, Yang L (2005) Classification of substrates and inhibitors of P-glycoprotein using unsupervised machine learning approach. J Chem Inf Model 45:750–757.  https://doi.org/10.1021/ci050041k CrossRefPubMedGoogle Scholar
  89. Wang Z, Chen Y, Liang H, Bender A, Glen RC, Yan A (2011) P-glycoprotein substrate models using support vector machines based on a comprehensive data set. J Chem Inf Model 51:1447–1456.  https://doi.org/10.1021/ci2001583 CrossRefPubMedGoogle Scholar
  90. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S, Shoemaker BA, Bolton E, Gindulyte A, Bryant SH (2012) PubChem’s BioAssay database. Nucleic Acids Res 40:D400–D412.  https://doi.org/10.1093/nar/gkr1132 CrossRefPubMedGoogle Scholar
  91. Warner DJ, Chen H, Cantin L-D, Kenna JG, Stahl S, Walker CL, Noeske T (2012) Mitigating the inhibition of human bile salt export pump by drugs: opportunities provided by physicochemical property modulation, in silico modeling, and structural modification. Drug Metab Dispos 40:2332–2341.  https://doi.org/10.1124/dmd.112.047068 CrossRefPubMedGoogle Scholar
  92. Welch MA, Köck K, Urban TJ, Brouwer KLR, Swaan PW (2015) Toward predicting drug-induced liver injury : parallel computational approaches to identify multidrug resistance protein 4 and bile salt export pump inhibitors. Drug Metab Dispos 43:725–734.  https://doi.org/10.1124/dmd.114.062539 CrossRefPubMedPubMedCentralGoogle Scholar
  93. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A (2013) HMDB 3.0--the human metabolome database in 2013. Nucleic Acids Res 41:D801–D807.  https://doi.org/10.1093/nar/gks1065 CrossRefPubMedGoogle Scholar
  94. Wu J, Li X, Cheng W, Xie Q, Liu Y, Zhao C (2009) Quantitative Structure Activity Relationship (QSAR) approach to Multiple Drug Resistance (MDR) Modulators based on combined hybrid system. QSAR Comb Sci 28:969–978.  https://doi.org/10.1002/qsar.200860134 CrossRefGoogle Scholar
  95. Xue Y, Yap CW, Sun LZ, Cao ZW, Wang JF, Chen YZ (2004) Prediction of P-glycoprotein substrates by a support vector machine approach. J Chem Inf Comput Sci 44:497–1505.  https://doi.org/10.1021/ci049971e Google Scholar
  96. Ye AY, Liu Q-R, Li C-Y, Zhao M, Qu H (2014) Human transporter database: comprehensive knowledge and discovery tools in the human transporter genes. PLoS One 9:e88883.  https://doi.org/10.1371/journal.pone.0088883 CrossRefPubMedPubMedCentralGoogle Scholar
  97. You H, Lee K, Lee S, Hwang SB, Kim K-Y, Cho K-H, No KT (2015) Computational classification models for predicting the interaction of compounds with hepatic organic ion importers. Drug Metab Pharmacokinet 30(5):347–351.  https://doi.org/10.1016/j.dmpk.2015.06.004 CrossRefPubMedGoogle Scholar
  98. Zhang H, Xiang M-L, Zhao Y-L, Wei Y-Q, Yang S-Y (2009) Support vector machine and pharmacophore-based prediction models of multidrug-resistance protein 2 (MRP2) inhibitors. Eur J Pharm Sci 36:451–457.  https://doi.org/10.1016/j.ejps.2008.11.014 CrossRefPubMedGoogle Scholar
  99. Zhao M, Chen Y, Qu D, Qu H (2011) TSdb: a database of transporter substrates linking metabolic pathways and transporter systems on a genome scale via their shared substrates. Sci China Life Sci 54:60–64.  https://doi.org/10.1007/s11427-010-4125-y CrossRefPubMedGoogle Scholar
  100. Zheng X, Ekins S, Raufman J-P, Polli JE (2009) Computational models for drug inhibition of the human apical sodium-dependent bile acid transporter. Mol Pharm 6:1591–1603.  https://doi.org/10.1021/mp900163d CrossRefPubMedPubMedCentralGoogle Scholar
  101. Zhong L, Ma C-Y, Zhang H, Yang L-J, Wan H-L, Xie Q-Q, Li L-L, Yang S-Y (2011) A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA-CG-SVM method. Comput Biol Med 41:1006–1013.  https://doi.org/10.1016/j.compbiomed.2011.08.009 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.ICMR-National Institute of PathologyNew DelhiIndia
  2. 2.CSIR-Institute of Microbial TechnologyChandigarhIndia
  3. 3.Drug Discovery Research CenterTranslational Health Science and Technology Institute (THSTI), NCR Biotech Science Cluster, 3rd MilestoneFaridabadIndia
  4. 4.Host-Parasite Interaction Biology groupICMR-National Institute of Malaria Research (NIMR)Sec-8 DwarkaIndia
  5. 5.La Jolla Institute for Allergy and ImmunologyLa JollaUSA

Personalised recommendations