Biopharmaceutics Drug Disposition Classification System (BDDCS) and Drug Interactions

1. Biopharmaceutics Drug Disposition Classification System (BDDCS) and Drug Interactions Leslie Z. Benet, PhD Professor of Bioengineering and Therapeutic Sciences Schools of Pharmacy and Medicine University of California San Francisco DDI-2017 20th Anniversary: International Conference on Drug-Drug Interactions Seattle June 19, 2017 Ann Arbor June 16, 2017

2. In 1983 I spent a sabbatical with Prof. Herbert Remmer at the University of Tübingen, Germany PrProf. Remmer was one of the very early experts in Cytochrome P-450 chemistry, which began to be recognized as the major enzyme for drug metabolism. A great deal of our scientific discussions in 1983 focused around whether there was one P-450 or two P-450s. Prof. Remmer was a toxicologist. He, and every other pharmacological scientist, believed that free drug concentrations were the driving force for toxic responses and that free concentrations at peripheral (nonsystemic) toxicity sites were the same as the free concentration measured in the systemic circulation.

3. Yet, transporters were recognized as important for endogenous mediators such as glucose in the early 1960s. And in 1976, Juliano and Ling recognized that a transporter, identified as P-glycoprotein, was constitutive and being upregulated in cancer tumors as a tumor protective mechanism to efflux drugs out of the tumor. In 1992, Ishikawa proposed that drugmetabolites were being eliminated from the body by an active phase 3 “metabolic” process via ABC efflux pumps. It was called phase 3, since Ishikawa’s original observation was that this was the mechanism for eliminating phase 2 metabolites into the bile or urine.

4. Transporters and the Blood-Brain Barrier In 1994 Schinkel and coworkers from the Netherlands Cancer Institute generated mice homozygous for a disruption of the gene encoding P-glycoprotein. The mice were viable and fertile and appeared phenotypically normal, but they displayed an increased sensitivity to the centrally neurotoxic pesticide ivermectin (100-fold) and to the carcinostatic drug vinblastine (3-fold). By comparing wild-type and knockout mice, they proposed that P-glycoprotein was a major component of the blood-brain barrier and that the absence of active P-glycoprotein transport resulted in elevated drug levels in many tissues (especially in brain) and in decreased drug elimination.

5. One year later we proposed the potential for transporter-enzyme interplay

6. (Clin Pharmacol Ther 1995;58:492-7) And suggested that this CYP3A and P-gp interplay could also be important for the gut, in addition to the liver (Clin Pharmacol Ther 1992; 52:453-7)

7. In the early 1990s our group carried out interaction studies in humans with cyclosporine, tacrolimus and sirolimus with and without ketoconazole, an inhibitor of CYP3A and P-gp, as well as with and without rifampin, an inducer of CYP3A and P-gp. These studies suggested that the major effect of the drug-drug interaction is on bioavailability, as opposed to clearance, and that this interaction occurs primarily in the intestine.

8. Ten years later we made a very simple discovery based on the FDABiopharmaceutics Classification System High Solubility Low Solubility Class 1 High Solubility High Permeability Rapid Dissolution Class 2 Low Solubility High Permeability High Permeability Class 3 High Solubility Low Permeability Class 4 Low Solubility Low Permeability Low Permeability Amidon et al., Pharm Res 12: 413-420, 1995

9. The Discovery is Very Obvious by Looking at Sample Drugs in Each BCS Class Biopharmaceutics Classification System High Solubility Low Solubility 1 2 Acetaminophen Propranolol Metoprolol Valproic acid Carbamazepine Cyclosporine Ketoconazole Tacrolimus High Permeability 3 4 Acyclovir Cimetidine Ranitidine Chlorothiazide Furosemide Methotrexate Low Permeability Amidon et al., Pharm Res 12: 413-420, 1995

10. Major Routes of Drug Elimination(the very simple discovery)and proposed the Biopharmaceutics Drug Disposition Classification System (BDDCS) High Solubility Low Solubility Class 1 Metabolism Class 2 Metabolism High Permeability Rate Class 3 Renal & Biliary Elimination of Unchanged Drug Class 4 Renal & Biliary Elimination of Unchanged Drug Low Permeability Rate Wu and Benet, Pharm. Res. 22: 11-23 (2005)

11. But there was more to the discovery Wu and Benet (Pharm. Res. 2005; 22: 11-23) recognized that one could make a number of predictions about drug disposition and drug-drug interactions based on their Biopharmaceutics Drug Disposition Classification System (BDDCS), as modified from BCS, that incorporated uptake and efflux transporters, as well as the potential for transporter-enzyme interplay.

12. Prediction of Oral Dosing Transporter Effects Based on BDDCS Class High Solubility Low Solubility Class 1 Transporter effects minimal in gut and liver and clinically insignificant Class 2 Efflux transporter effects predominate in gut, but both uptake & efflux transporters can affect liver High Permeability/ Metabolism Class 3 Absorptive transporter effects predominate (but can be modulated by efflux transporters) Class 4 Absorptive and efflux transporter effects could be important Low Permeability/ Metabolism

13. Major Differences BetweenBDDCSand BCS • Purpose: BCS – Biowaivers of in vivo bioequivalence studies. BDDCS – Prediction of drug disposition and potential DDIs in the intestine & liver.

14. Major Differences BetweenBDDCSand BCS • Permeability Criterion:BDDCS –Predictions based on intestinal permeability rate. • BCS – Biowaivers based on extent of absorption, which in a number of cases does not correlate with jejunal permeability rates.

15. We now suspect that high permeability rate compounds are readily reabsorbed from the kidney lumen and from the bile facilitating multiple access to the metabolic enzymes. In essence the only way the body can eliminate these compounds is via metabolism. This would explain why drugs with quite low hepatic clearance are still completely eliminated by metabolism (e.g., diazepam). aa What is the Basis for the Discovery? The recognition of the correlation between intestinal permeability rate and extent of metabolism preceded an explanation for these findings. That is, why should intestinal permeability rate predict the extent of metabolism?

16. What are the Implications for New Molecular Entities and DDIs? • For an NME, measuring a surrogate of human intestinal absorption, such as Caco-2 permeability or even PAMPA, allows prediction of the major route of elimination in humans prior to dosing either to animals or man. • Furthermore, one knows whether DDIs relating to metabolism will be a major factor or not.

17. Major Differences BetweenBDDCSand BCS • Solubility Criterion BCS – Highest approved dose strength is soluble in 250 ml of water at 37˚C over the pH range 1.0-6.8. (However, carboxylic acids could not be soluble at pH 1.0, yet still function as Class 1 in BCS). • BDDCS-Solubility is a characteristic of a drug substance that subsumes a number of individual characteristics that we and others have not yet been able to identify or quantify that appear to be determinants of drug disposition. For an NME, a solubility cut-off of 0.3 mg/ml (Dave & Morris, 2016) over the pH range 1.0-6.8 works best for the initial evaluation.

18. So predictions of potential drug disposition routes, affects of transporters and DDIs can be made before an NME is dosed to either an animal or man High Solubility Low Solubility Class 1 Transporter effects minimal in gut and liver and clinically insignificant Class 2 Efflux transporter effects predominate in gut, but both uptake & efflux transporters can affect liver High Permeability/ Metabolism Class 3 Absorptive transporter effects predominate (but can be modulated by efflux transporters) Class 4 Absorptive and efflux transporter effects could be important Low Permeability/ Metabolism S. Shugarts and L. Z. Benet. Pharm. Res. 26, 2039-2054 (2009).

19. Potential DDIs Predicted by BDDCS • Class 1: Only metabolic in the intestine and liver • Class 2: Metabolic, efflux transporter and efflux transporter-enzyme interplay in the intestine. Metabolic, uptake transporter, efflux transporter and transporter-enzyme interplay in the liver. • Class 3 and 4: Uptake transporter, efflux transporter and uptake-efflux transporter interplay

20. Now, let me go back to a statement in one of my earlier slides to a belief in 1967 and still not recognized generally today Prof. Remmer was a toxicologist. He, and every other pharmacological scientist, believed that free drug concentrations were the driving force for toxic responses and that free concentrations at peripheral (nonsystemic) toxicity sites were the same as the free concentration measured in the systemic circulation.

21. Now, let me go back to a statement in one of my earlier slides to a belief far earlier than 1983 and still not recognized generally today Prof. Remmer was a toxicologist. He, and every other pharmacological scientist, believed that free drug concentrations were the driving force for toxic responses and that free concentrations at peripheral (nonsystemic) toxicity sites were the same as the free concentration measured in the systemic circulation. But this is not a true condition, what transporters do is cause unbound concentrations of substrate drugs to be different at different sites in the body, and this will be the case for all BDDCS Class 2, 3 and 4 drugs that are transporter substrates. But the condition of equal unbound concentrations will hold for BDDCS Class 1 drugs.

22. However, scientists are very poor at predicting solubility. We recently showed that the correlation between measured and predicted minimum solubility yielded an r2 of no more than 33%, even when the predictions included pH BDDCS Applied to Over 900 Drugs. L.Z. Benet, F. Broccatelli and T.I. Oprea. AAPS J. 13, 519-547 (2011)That is, we don’t understand the physics of solubility. Last year, we proposed that for highly soluble drugs, where concentrations are not limited by solubility, active processes may occur but they are overwhelmed by passive permeability.Reliability of In Vitro and In Vivo Methods for Predicting the Effect of P-Glycoprotein on the Delivery of Antidepressants to the Brain. Y. Zheng, X. Chen and L. Z. Benet. Clin. Pharmacokinet. 55, 143-167 (2016).aa Why Should Solubility Affect Disposition? US FDA solubility is a property of the drug in a formulation and is not an intrinsic property of the actual pharmaceutical ingredient itself.Some suggest that solubility is a fundamental principal for oral absorption since only drug in solution has the ability to permeate across enterocytes, but it is not directly relevant to drug clearance.

23. Our latest thinking on solubility The work of Dave & Morris (Int. J. Pharm. 511:111-126, 2016) suggests that a 0.3 mg/ml cut-off over the pH range 1-6.8 adequately predicts BDDCS class, independent of highest approved dose strength. This pH range is important, so we would not reclassify acids that only fail the solubility criteria at pH 1, or suggest that a drug may be a different BDDCS class at a lower dosage, since solubility appears to predict disposition parameters. Solubility is a useful differentiator for BDDCS Class 1 and 2 drugs, but provides little additional predictability for BDDCS Class 3 and 4 drugs.

24. Later in this symposium you will hear from Drs. El-Kattan and Varma of the very useful development of the Extended Clearance Classification System (ECCS) that predicts transporter mediated PK and DDIs independent of solubility considerations. Although I believe that the outliers from ECCS are more than for BDDCS (since BDDCS is less prescriptive) and that the two systems are complimentary, here I present some other attributes of BDDCS.

25. More recently we have used BDDCS to make predictions concerning drug toxicity Use of the Biopharmaceutics Drug Disposition Classification System (BDDCS) to Predict the Occurrence of Idiosyncratic Cutaneous Adverse Drug Reactions Associated with Antiepileptic Drug Usage. R Chan, C-y Wei, Y-t Chen & LZ Benet, AAPS J. 2016, 18:357-366. And our most recently published paper: Evaluation of DILI Predictive Hypotheses in Early Drug Development. R. Chan & LZ Benet, Chem. Res. Toxicol. 2017, 30:1017-1029.

26. Relationship Between FDA Drug Label Section, and BDDCS Classification Chan and Benet (2017) Chemical Research in Toxicology

27. I cannot present the details of our April 2017 paper, and will only say here that we show that none of the DILI predictive metrics, except keeping daily dose < 50 mg, provides any better prediction of DILI than just avoiding Class 2 drugs. However, I will note that our paper supports the presentation to be given by Dr. Ken Brouwer tomorrow afternoon that BSEP inhibition by itself does not adequately predict DILI. Now, I return to BDDCS predictions:

28. Hepatic Clearance Predictions fromIn Vitro-In Vivo Extrapolation and the Biopharmaceutical Drug Disposition Classification SystemChristine M. Bowman and Leslie Z. BenetDrug Metab. Dispos. 44:1731-1735 (2016)Hypothesis: Transporter effects for Class 2 drugs would make IVIVE predictions based on microsomal/hepatocyte incubations less accurate than those for Class 1 drugs where transporter effects should be negligible.

29. Our Hypothesis was CorrectUsing less than a 2-fold difference between predicted and measured clearance as a success criterion 81.9 % of Class 2 drugs were poorly predicted, while 62.3% of Class 1 drugs were poorly predicted But why are IVIVE predictions so poor? (Leading to our yet unpublished proposal)

30. CH,u QH∙Cout QH∙Cin Fig. 1 Homogeneous Liver Model Fig. 2 Heterologous Liver Model CLH,u We believe the poor predictability is due to the incorrect assumption that the liver is a homogenous system and that the unbound steady-state drug concentration in direct contact with the metabolic enzymes within the hepatocytes is equal to the average steady-state concentration in the liver driving elimination, i.e. that Chep,u in Fig. 2 equals CH,u in Fig. 1.

32. I cannot present the derivation here, which is based on mass balance, but the following is the equation that we believe should be used to predict in vivo clearance using the well-stirred model from an in vitro measurement of metabolism of total drug characterized by the rate constant ke,mic, the volume of the microsomal mixture Vmic and fraction of unbound drug in the microsomal mixture, fu,mic.

33. Rss,uu Hypothesis Assumptions • Rss,uu will vary from drug to drug; no universal IVIVE scaling factor will give successful predictions of hepatic clearance. • The distributionbetween drug concentration in contact with enzymes and the average organ steady state driving force concentration will be the same across mammalian species. Similarly pH differences within the liver that have been incorporated in some IVIVE predictive equations are contained within Rss,uu. Therefore, a drug’s Rss,uu is expected to be the similar across mammals. • We are not assuming thatthe metabolic enzymes in animal models are the same or have the same activities as in humans • Not suggesting that the animal drug clearance will predict human drug clearance • The difference in the metabolic capacity is defined by the in vitro drug elimination characteristics (i.e., ke,mic) in the animal versus human

34. Are there data in the literature consistent with the Rss,uu hypothesis? Rss,uu-based IVIVE scaling factor in animal model is predictive for humans • Marked difference between observed and predicted human CLint,in vivo • Human CLint,in vivo values corrected with rat IVIVE scaling factors yield better predictions of human CLint,in vivo Using Rat Scaling Factor to Correct Human Hepatocyte IVIVE Predictions* *Naritomi et al. 2003. DMD 31: 580-588

35. Drug Cocktails to Predict Clearance of an NME A further implication of the Rss,uu concept is that drug cocktails (or endogenous metabolism of cortisol) will not predict the clearance of an NME, even if the NME and a drug in the cocktail are metabolized by exactly the same enzyme(s). That is because Rss,uu is drug specific depending on the distribution characteristics of each particular drug. Thus, the values of Rss,uu of two drugs would not be expected to be the same just because they are both metabolized by the same enzyme, even if the two drugs are metabolized to a similar type of metabolic product (e.g., the clearance of one benzodiazepine in a patient will not predict the clearance of other benzodiazepines). However, using a drug of interest to predict a potential drug interaction would probably be expected to give a correct estimate of the in vivo interaction since this is equivalent to changing the reaction rate in the microsome/hepatocyte incubation.

36. Thus Far •We propose that BDDCS can help in predicting disposition characteristics and a number of other drug features as well as potential DDIs of an NME prior to ever dosing the drug to animals or man. BDDCS is complementary, and less prescriptive, to the more recent Extended Clearance Concept and ECCS. • We have presented a theoretical basis as to why an IVIVE animal scaling factor may provide a useful prediction of the IVIVE relationship in humans and showed some data from the literature supporting this. • We have presented a theoretical basis as to why drug cocktails have not been successful in predicting clearance of an NME quantitatively, but why in vitro studies could be predictive of drug interaction extent.

37. But • We have only addressed predictions of hepatic metabolism (trying to understand initially why we have been so unsuccessful in past IVIVE attempts) • And even for the hepatic metabolism predictions we need experimental data to confirm the theoretical hypotheses (a major effort of our lab now). • We have not yet addressed transporters in our presentations, nor transporter-enzyme interplay, or oral drug administration predictions. •Thus, I am leaving topics open to be able to participate in the 25th Anniversary of the International Conference on Drug-Drug Interactions

38. Collaborators & Acknowledgements • Christine Bowman, MS • Fabio Broccatelli, PhD • Rosa Chan, BS • Lynda A. Frassetto, MD • Chelsea Hosea, PhD • Shufang Liu, BS • Hideaki Okochi, PhD • Tudor I. Oprea, MD, PhD • Sarah Shugarts, PhD • Jasleen Sodhi, BS • Alan R. Wolfe, BS • Chi-Yuan Wu, PhD • Yi Zheng, PhD Funding NIH grants GM 61390 and GM 75900 Slides available from Leslie.Benet@ucsf.edu