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Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System

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Abstract

Purpose

To assess the utility of Extended Clearance Classification System (ECCS) in understanding absorption, distribution, metabolism, and elimination (ADME) attributes and enabling victim drug-drug interaction (DDI) predictions.

Methods

A database of 368 drugs with relevant ADME parameters, main metabolizing enzymes, uptake transporters, efflux transporters, and highest change in exposure (%AUC) in presence of inhibitors was developed using published literature. Drugs were characterized according to ECCS using ionization, molecular weight and estimated permeability.

Results

Analyses suggested that ECCS class 1A drugs are well absorbed and systemic clearance is determined by metabolism mediated by CYP2C, esterases, and UGTs. For class 1B drugs, oral absorption is high and the predominant clearance mechanism is hepatic uptake mediated by OATP transporters. High permeability neutral/basic drugs (class 2) showed high oral absorption, with metabolism mediated generally by CYP3A, CYP2D6 and UGTs as the predominant clearance mechanism. Class 3A/4 drugs showed moderate absorption with dominant renal clearance involving OAT/OCT2 transporters. Class 3B drugs showed low to moderate absorption with hepatic uptake (OATPs) and/or renal clearance as primary clearance mechanisms. The highest DDI risk is typically seen with class 2/1B/3B compounds manifested by inhibition of either CYP metabolism or active hepatic uptake. Class 2 showed a wider range in AUC change likely due to a variety of enzymes involved. DDI risk for class 3A/4 is small and associated with inhibition of renal transporters.

Conclusions

ECCS provides a framework to project ADME profiles and further enables prediction of victim DDI liabilities in drug discovery and development.

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Abbreviations

BCRP:

Breast cancer resistant protein

CYP:

Cytochrome P-450

DDIs:

Drug-drug interactions

ECCS:

Extended clearance classification system

EMA:

European Medicines Agency

FDA:

Food and Drug Administration

NME:

New molecular entity

OAT:

Organic anion transporter

OATP:

Organic anion transporting polypeptide

OCT:

Organic cation transporter

P-gp:

P-glycoprotein

PK:

Pharmacokinetics

UGT:

Uridine 5’-diphospho-glucuronosyltransferase

References

  1. Beaumont K, Smith DA. Does human pharmacokinetic prediction add significant value to compound selection in drug discovery research? Curr Opin Drug Discov Devel. 2009;12(1):61–71.

    CAS  PubMed  Google Scholar 

  2. Mager DE. Quantitative structure-pharmacokinetic/pharmacodynamic relationships. Adv Drug Deliv Rev. 2006;58(12–13):1326–56.

    Article  CAS  PubMed  Google Scholar 

  3. Rostami-Hodjegan A, Tucker GT. Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev. 2007;6(2):140–8.

    CAS  Google Scholar 

  4. van de Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev. 2003;2(3):192–204.

    Google Scholar 

  5. van De Waterbeemd H, Smith DA, Beaumont K, Walker DK. Property-based design: optimization of drug absorption and pharmacokinetics. J Med Chem. 2001;44(9):1313–33.

    Article  Google Scholar 

  6. Varma MV, Khandavilli S, Ashokraj Y, Jain A, Dhanikula A, Sood A, et al. Biopharmaceutic classification system: a scientific framework for pharmacokinetic optimization in drug research. Curr Drug Metab. 2004;5(5):375–88.

    Article  CAS  PubMed  Google Scholar 

  7. Varma MV, Steyn SJ, Allerton C, El-Kattan AF. Predicting clearance mechanism in drug discovery: extended clearance classification system (ECCS). Pharm Res. 2015;32(12):3785–802.

    Article  CAS  PubMed  Google Scholar 

  8. Mullins ME, Horowitz BZ, Linden DH, Smith GW, Norton RL, Stump J. Life-threatening interaction of mibefradil and beta-blockers with dihydropyridine calcium channel blockers. JAMA. 1998;280(2):157–8.

    Article  CAS  PubMed  Google Scholar 

  9. Tamraz B, Fukushima H, Wolfe AR, Kaspera R, Totah RA, Floyd JS, et al. OATP1B1-related drug-drug and drug-gene interactions as potential risk factors for cerivastatin-induced rhabdomyolysis. Pharmacogenet Genomics. 2013;23(7):355–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bjornsson TD, Callaghan JT, Einolf HJ, Fischer V, Gan L, Grimm S, et al. The conduct of in vitro and in vivo drug-drug interaction studies: a Pharmaceutical Research and Manufacturers of America (PhRMA) perspective. Drug Metab Dispos Biol Fate Chem. 2003;31(7):815–32.

    Article  CAS  PubMed  Google Scholar 

  11. Zhang H, Cui D, Wang B, Han YH, Balimane P, Yang Z, et al. Pharmacokinetic drug interactions involving 17alpha-ethinylestradiol: a new look at an old drug. Clin Pharmacokinet. 2007;46(2):133–57.

    Article  CAS  PubMed  Google Scholar 

  12. Bloomer J, Derimanov G, Dumont E, Ellens H, Matheny C. Optimizing the in vitro and clinical assessment of drug interaction risk by understanding co-medications in patient populations. Expert Opin Drug Metab Toxicol. 2013;9(6):737–51.

    Article  CAS  PubMed  Google Scholar 

  13. FDA. Drug interaction studies - study design, data analysis, implications for dosing, and labeling recommendations. Center for Drug Evaluation and Research (CDER); 2012.

  14. EMA. Guideline on the investigation of drug interactions. Committee for Human Medicinal Products (CHMP); 2012.

  15. Williams JA, Hyland R, Jones BC, Smith DA, Hurst S, Goosen TC, et al. Drug-drug interactions for UDP-glucuronosyltransferase substrates: a pharmacokinetic explanation for typically observed low exposure (AUCi/AUC) ratios. Drug Metab Dispos. 2004;32(11):1201–8.

    Article  CAS  PubMed  Google Scholar 

  16. Kiang TK, Ensom MH, Chang TK. UDP-glucuronosyltransferases and clinical drug-drug interactions. Pharmacol Ther. 2005;106(1):97–132.

    Article  CAS  PubMed  Google Scholar 

  17. International Transporter C, Giacomini KM, Huang SM, Tweedie DJ, Benet LZ, Brouwer KL, et al. Membrane transporters in drug development. Nat Rev Drug Discov. 2010;9(3):215–36.

    Article  Google Scholar 

  18. Watanabe T, Kusuhara H, Sugiyama Y. Application of physiologically based pharmacokinetic modeling and clearance concept to drugs showing transporter-mediated distribution and clearance in humans. J Pharmacokinet Pharmacodyn. 2010;37(6):575–90.

    Article  CAS  PubMed  Google Scholar 

  19. Maeda K, Ikeda Y, Fujita T, Yoshida K, Azuma Y, Haruyama Y, et al. Identification of the rate-determining process in the hepatic clearance of atorvastatin in a clinical cassette microdosing study. Clin Pharmacol Ther. 2011;90(4):575–81.

    Article  CAS  PubMed  Google Scholar 

  20. Varma MV, Steyn SJ, Allerton C, El-Kattan AF. Predicting clearance mechanism in drug discovery: extended clearance classification system (ECCS). Pharma Res. 2015.

  21. Varma MV, Obach RS, Rotter C, Miller HR, Chang G, Steyn SJ, et al. Physicochemical space for optimum oral bioavailability: contribution of human intestinal absorption and first-pass elimination. J Med Chem. 2010;53(3):1098–108.

    Article  CAS  PubMed  Google Scholar 

  22. Lombardo F, Obach RS, Varma MV, Stringer R, Berellini G. Clearance mechanism assignment and total clearance prediction in human based upon in silico models. J Med Chem. 2014;57(10):4397–405.

    Article  CAS  PubMed  Google Scholar 

  23. Amidon GL, Lennernas H, Shah VP, Crison JR. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm Res. 1995;12(3):413–20.

    Article  CAS  PubMed  Google Scholar 

  24. Thomas VH, Bhattachar S, Hitchingham L, Zocharski P, Naath M, Surendran N, et al. The road map to oral bioavailability: an industrial perspective. Expert Opin Drug Metab Toxicol. 2006;2(4):591–608.

    Article  CAS  PubMed  Google Scholar 

  25. Smith DA, Beaumont K, Maurer TS, Di L. Volume of distribution in drug design. J Med Chem. 2015;58(15):5691–8.

    Article  CAS  PubMed  Google Scholar 

  26. Varma MV, Gardner I, Steyn SJ, Nkansah P, Rotter CJ, Whitney-Pickett C, et al. pH-Dependent solubility and permeability criteria for provisional biopharmaceutics classification (BCS and BDDCS) in early drug discovery. Mol Pharm. 2012;9(5):1199–212.

    Article  CAS  PubMed  Google Scholar 

  27. Lewis DF. Modelling human cytochromes P450 for evaluating drug metabolism: an update. Drug Metabol Drug Interact. 2000;16(4):307–24.

    Article  CAS  PubMed  Google Scholar 

  28. Feng B, Hurst S, Lu Y, Varma MV, Rotter CJ, El-Kattan A, et al. Quantitative prediction of renal transporter-mediated clinical drug-drug interactions. Mol Pharm. 2013;10(11):4207–15.

    Article  CAS  PubMed  Google Scholar 

  29. Feng B, LaPerle JL, Chang G, Varma MV. Renal clearance in drug discovery and development: molecular descriptors, drug transporters and disease state. Expert Opin Drug Metab Toxicol. 2010;6(8):939–52.

    Article  CAS  PubMed  Google Scholar 

  30. Varma MV, Feng B, Obach RS, Troutman MD, Chupka J, Miller HR, et al. Physicochemical determinants of human renal clearance. J Med Chem. 2009;52(15):4844–52.

    Article  CAS  PubMed  Google Scholar 

  31. Varma MV, Pang KS, Isoherranen N, Zhao P. Dealing with the complex drug-drug interactions: towards mechanistic models. Biopharm Drug Dispos. 2015;36(2):71–92.

    Article  CAS  PubMed  Google Scholar 

  32. Varma MV, Bi YA, Kimoto E, Lin J. Quantitative prediction of transporter- and enzyme-mediated clinical drug-drug interactions of organic anion-transporting polypeptide 1B1 substrates using a mechanistic net-effect model. J Pharmacol Exp Ther. 2014;351(1):214–23.

    Article  PubMed  Google Scholar 

  33. Di L, Feng B, Goosen TC, Lai Y, Steyn SJ, Varma MV, et al. A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development. Drug Metab Dispos Biol Fate Chem. 2013;41(12):1975–93.

    Article  CAS  PubMed  Google Scholar 

  34. Granfors MT, Backman JT, Neuvonen M, Ahonen J, Neuvonen PJ. Fluvoxamine drastically increases concentrations and effects of tizanidine: a potentially hazardous interaction. Clin Pharmacol Ther. 2004;75(4):331–41.

    Article  CAS  PubMed  Google Scholar 

  35. Neuvonen PJ, Jalava KM. Itraconazole drastically increases plasma concentrations of lovastatin and lovastatin acid. Clin Pharmacol Ther. 1996;60(1):54–61.

    Article  CAS  PubMed  Google Scholar 

  36. Lilja JJ, Kivisto KT, Neuvonen PJ. Grapefruit juice-simvastatin interaction: effect on serum concentrations of simvastatin, simvastatin acid, and HMG-CoA reductase inhibitors. Clin Pharmacol Ther. 1998;64(5):477–83.

    Article  CAS  PubMed  Google Scholar 

  37. Lindamood C, Ortiz S, Shaw A, Rackley R, Gorski JC. Effects of commonly administered agents and genetics on nebivolol pharmacokinetics: drug-drug interaction studies. J Clin Pharmacol. 2011;51(4):575–85.

    Article  CAS  PubMed  Google Scholar 

  38. Bosilkovska M, Samer CF, Deglon J, Rebsamen M, Staub C, Dayer P, et al. Geneva cocktail for cytochrome p450 and P-glycoprotein activity assessment using dried blood spots. Clin Pharmacol Ther. 2014;96(3):349–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Klatt S, Fromm MF, Konig J. The influence of oral antidiabetic drugs on cellular drug uptake mediated by hepatic OATP family members. Basic Clin Pharmacol Toxicol. 2013;112(4):244–50.

    Article  CAS  PubMed  Google Scholar 

  40. VandenBrink BM, Foti RS, Rock DA, Wienkers LC, Wahlstrom JL. Evaluation of CYP2C8 inhibition in vitro: utility of montelukast as a selective CYP2C8 probe substrate. Drug Metab Dispos Biol Fate Chem. 2011;39(9):1546–54.

    Article  CAS  PubMed  Google Scholar 

  41. Aquilante CL, Kosmiski LA, Bourne DW, Bushman LR, Daily EB, Hammond KP, et al. Impact of the CYP2C8 *3 polymorphism on the drug-drug interaction between gemfibrozil and pioglitazone. Br J Clin Pharmacol. 2013;75(1):217–26.

    Article  CAS  PubMed  Google Scholar 

  42. Regazzi MB, Iacona I, Campana C, Raddato V, Lesi C, Perani G, et al. Altered disposition of pravastatin following concomitant drug therapy with cyclosporin A in transplant recipients. Transplant Proc. 1993;25(4):2732–4.

    CAS  PubMed  Google Scholar 

  43. Benet LZ, Broccatelli F, Oprea TI. BDDCS applied to over 900 drugs. AAPS J. 2011;13(4):519–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tapaninen T, Backman JT, Kurkinen KJ, Neuvonen PJ, Niemi M. Itraconazole, a P-glycoprotein and CYP3A4 inhibitor, markedly raises the plasma concentrations and enhances the renin-inhibiting effect of aliskiren. J Clin Pharmacol. 2011;51(3):359–67.

    Article  CAS  PubMed  Google Scholar 

  45. Igel S, Drescher S, Murdter T, Hofmann U, Heinkele G, Tegude H, et al. Increased absorption of digoxin from the human jejunum due to inhibition of intestinal transporter-mediated efflux. Clin Pharmacokinet. 2007;46(9):777–85.

    Article  CAS  PubMed  Google Scholar 

  46. Hartter S, Sennewald R, Nehmiz G, Reilly P. Oral bioavailability of dabigatran etexilate (Pradaxa((R))) after co-medication with verapamil in healthy subjects. Br J Clin Pharmacol. 2013;75(4):1053–62.

    Article  PubMed  Google Scholar 

  47. Tachibana T, Kato M, Takano J, Sugiyama Y. Predicting drug-drug interactions involving the inhibition of intestinal CYP3A4 and P-glycoprotein. Curr Drug Metab. 2010;11(9):762–77.

    Article  CAS  PubMed  Google Scholar 

  48. Shugarts S, Benet LZ. The role of transporters in the pharmacokinetics of orally administered drugs. Pharm Res. 2009;26(9):2039–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cho HJ, Kim JE, Kim DD, Yoon IS. In vitro-in vivo extrapolation (IVIVE) for predicting human intestinal absorption and first-pass elimination of drugs: principles and applications. Drug Dev Ind Pharm. 2014;40(8):989–98.

    Article  CAS  PubMed  Google Scholar 

  50. Hisaka A, Nakamura M, Tsukihashi A, Koh S, Suzuki H. Assessment of intestinal availability (FG) of substrate drugs of cytochrome p450s by analyzing changes in pharmacokinetic properties caused by drug-drug interactions. Drug Metab Dispos Biol Fate Chem. 2014;42(10):1640–5.

    Article  PubMed  Google Scholar 

  51. Yamada A, Maeda K, Kamiyama E, Sugiyama D, Kondo T, Shiroyanagi Y, et al. Multiple human isoforms of drug transporters contribute to the hepatic and renal transport of olmesartan, a selective antagonist of the angiotensin II AT1-receptor. Drug Metab Dispos Biol Fate Chem. 2007;35(12):2166–76.

    Article  CAS  PubMed  Google Scholar 

  52. Zhou L, Chen X, Gu Y, Liang J. Transport characteristics of candesartan in human intestinal Caco-2 cell line. Biopharm Drug Dispos. 2009;30(5):259–64.

    Article  CAS  PubMed  Google Scholar 

  53. MacFadyen RJ, Meredith PA, Elliott HL. Enalapril clinical pharmacokinetics and pharmacokinetic-pharmacodynamic relationships. An overview. Clin Pharmacokinet. 1993;25(4):274–82.

    Article  CAS  PubMed  Google Scholar 

  54. Shionoiri H, Naruse M, Minamisawa K, Ueda S, Himeno H, Hiroto S, et al. Fosinopril. Clinical pharmacokinetics and clinical potential. Clin Pharmacokinet. 1997;32(6):460–80.

    Article  CAS  PubMed  Google Scholar 

  55. Tachibana T, Kato M, Sugiyama Y. Prediction of nonlinear intestinal absorption of CYP3A4 and P-glycoprotein substrates from their in vitro Km values. Pharm Res. 2012;29(3):651–68.

    Article  CAS  PubMed  Google Scholar 

  56. Varma MV, Ambler CM, Ullah M, Rotter CJ, Sun H, Litchfield J, et al. Targeting intestinal transporters for optimizing oral drug absorption. Curr Drug Metab. 2010;11(9):730–42.

    Article  CAS  PubMed  Google Scholar 

  57. Morgan P, Van Der Graaf PH, Arrowsmith J, Feltner DE, Drummond KS, Wegner CD, et al. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discov Today. 2012;17(9–10):419–24.

    Article  CAS  PubMed  Google Scholar 

  58. Varma MV, Lin J, Bi YA, Kimoto E, Rodrigues AD. Quantitative rationalization of gemfibrozil drug interactions: consideration of transporters-enzyme interplay and the role of circulating metabolite gemfibrozil 1-O-beta-glucuronide. Drug Metab Dispos Biol Fate Chem. 2015;43(7):1108–18.

    Article  CAS  PubMed  Google Scholar 

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ACKNOWLEDGMENTS AND DISCLOSURES

The authors thank Charlotte Allerton for valuable input during this work.

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Authors and Affiliations

  1. Pharmacokinetcis, Dynamics and Metabolism, Pfizer Inc., Cambridge, Massachusetts, USA

    Ayman F. El-Kattan, Stefan J. Steyn, Dennis O. Scott & Tristan S. Maurer

  2. Pharmacokinetcis, Dynamics and Metabolism, Pfizer Inc., Groton, Connecticut, USA

    Manthena V. Varma

  3. Clinical Pharmacology, Pfizer Inc., Groton, Connecticut, USA

    Arthur Bergman

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  1. Ayman F. El-Kattan
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  2. Manthena V. Varma
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  3. Stefan J. Steyn
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  4. Dennis O. Scott
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  5. Tristan S. Maurer
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Correspondence to Ayman F. El-Kattan.

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El-Kattan, A.F., Varma, M.V., Steyn, S.J. et al. Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System. Pharm Res 33, 3021–3030 (2016). https://doi.org/10.1007/s11095-016-2024-z

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