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 20 th Anniversary: International Conference on Drug-Drug Interactions Seattle June 19, 2017 21
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. 2
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 drug metabolites 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. 3
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. 4
One year later we proposed the potential for transporter-enzyme interplay 5
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) 6 (Clin Pharmacol Ther 1995;58:492-7)
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. 7
Low Permeability High Permeability Ten years later we made a very simple discovery based on the FDA Biopharmaceutics Classification System High Solubility Class 1 High Solubility High Permeability Rapid Dissolution Low Solubility Class 2 Low Solubility High Permeability Class 3 High Solubility Low Permeability Class 4 Low Solubility Low Permeability Amidon et al., Pharm Res 12: 413-420, 1995 8
Low Permeability High Permeability The Discovery is Very Obvious by Looking at Sample Drugs in Each BCS Class Biopharmaceutics Classification System High Solubility 1 Acetaminophen 2 Propranolol Metoprolol Valproic acid Low Solubility Carbamazepine Cyclosporine Ketoconazole Tacrolimus 3 4 Acyclovir Cimetidine Ranitidine Chlorothiazide Furosemide Methotrexate Amidon et al., Pharm Res 12: 413-420, 1995 9
Major Routes of Drug Elimination (the very simple discovery) and proposed the Biopharmaceutics Drug Disposition Classification System (BDDCS) High Permeability Rate Low Permeability Rate High Solubility Class 1 Metabolism Low Solubility Class 2 Metabolism Class 3 Renal & Biliary Elimination of Unchanged Drug Class 4 Renal & Biliary Elimination of Unchanged Drug Wu and Benet, Pharm. Res. 22: 11-23 (2005) 10
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.
Low Permeability/ Metabolism High Permeability/ Metabolism Prediction of Oral Dosing Transporter Effects Based on BDDCS Class High Solubility Class 1 Transporter effects minimal in gut and liver and clinically insignificant Class 3 Absorptive transporter effects predominate (but can be modulated by efflux transporters) Low Solubility Class 2 Efflux transporter effects predominate in gut, but both uptake & efflux transporters can affect liver Class 4 Absorptive and efflux transporter effects could be important 12
Major Differences Between BDDCS and BCS Purpose: BCS Biowaivers of in vivo bioequivalence studies. BDDCS Prediction of drug disposition and potential DDIs in the intestine & liver. 13
Major Differences Between BDDCS and 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. 14
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? 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 15 eliminated by metabolism (e.g., diazepam).
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.
Major Differences Between BDDCS and 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. 17
Low Permeability/ Metabolism High Permeability/ Metabolism 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 Class 1 Transporter effects minimal in gut and liver and clinically insignificant Class 3 Absorptive transporter effects predominate (but can be modulated by efflux transporters) Low Solubility Class 2 Efflux transporter effects predominate in gut, but both uptake & efflux transporters can affect liver Class 4 Absorptive and efflux transporter effects could be important S. Shugarts and L. Z. Benet. Pharm. Res. 26, 2039-2054 (2009).
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 19
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. 20
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. 21
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. However, scientists are very poor at predicting solubility. We recently showed that the correlation between measured and predicted minimum solubility yielded an r 2 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).
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.
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.
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.
BDDCS Class Assessment (%) 100 Relationship Between FDA Drug Label Section, and BDDCS Classification ibution A. Distribution of FDA A. Distribution Hepatic of FDA A. Liability Distribution Hepatic of FDA A. with Liability Distribution Hepatic BDDCS of FDA A. with Liability Distribution Hepatic Class of BDDCS FDA with (n=264) Liability Hepatic Class of BDDCS FDA (n=264) with Liability Hepatic Class BDDCS with (n=264) Liability Class BDDCS with (n=264) Class BDDCS (n=264 Cla BDDCS Class BDDCS 1 BDDCS Class BDDCS Class 1 BDDCS 2Class BDDCS Class 1 BDDCS Class 2 Class 3BDDCS Class 1 BDDCS 2Class BDDCS Class 3 1Class 4BDDCS 2Class BDDCS 3Class 1 BDDCS 4 2Class BDDCS 3Class BDDCS 4Class 2 BDDCS 3Class BDDCS 4Class Class 3 BDDCS 4 A. Distribution of FDA Hepatic Liability with BDDCS Class (n=264) A. Distribution of FDA Hepatic Liability with BDDCS Class (n=264) BDDCS Class 1 BDDCS Class 2 BDDCS Class 3 BDDCS Class 4 BDDCS Class 1 BDDCS Class 2 BDDCS Class 3 BDDCS Class 4 83.3 % 83.3 % 83.3 % 62.5 % 62.5 % 62.5 % 62.5 % 62 75 56.7 % 62.5 % 56.7 % 44.2 % 45.6 % 47.2 %.2 % 45.6 % 47.2 % 44.2 45.6 % 45.6 % 47.2 % 45.6 % 47.2 % 47.2 % 44.2 % 45.6 % 47.2 % 50 44.2 % 45.6 % 47.2 % 32.9 % 32.9 % 32.9 % 32.9 % 32.9 % 30 % 30 % 30.6 % 30.6 % 32.9 % 30.6 % 30.6 % 27.9 % 30.6 % 27.9 % 27.9 % 30.6 % 27.9 % 27.9 % 27.5 % 27.5 % 27.5 % 27.5 % 32.9 % 27.5 % 23.3 % 23.3 30 % % 23.3 % 30.6 % 27.9 % 23.3 % 27.5 % 25 17.7 % 17.7 23.3 % 17.7 % 19.4 % 19.4 % 17.7 % 19.4 % 19.4 % 17.7 % 17.7 % 19.4 % 19.4 % 16.7 % 16.7 % 16.7 17.7 % 19.4 % 16.7 % 10 % 10 % 10 % 10 % 10 % 10 % 10 % 10 % % 3.3 % 4.7 % 4.7 % 4.7 % 3.3 % 3.8 3.3 % 3.8 % 4.7 % 4.7 62.5 % 3.3 % 4.7 % 3.8 % 2.8 3.8 % 3.8 % 3.8 % 3.8 % 2.8 % 2.8 % 2.8 % 2.8 % 2.8 % 2.8 0 % 0 % 0 % 0 % 0 % 0 % 0 % 0 0 s o mention dverse Reactions 3.3 % 27.9 % No mention No mention Adverse Reactions arnings and Precautions Adverse Reactions 32.9 % Adverse Reactions Warnings and Precautions 45.6 % Adverse Reactions Warnings and Precautions Boxed Warning Warnings and Precautions Warnings and Precautions 17.7 % Chan and Benet (2017) Chemical Research in Toxicology Boxed Warning 30.6 % Warnings and Precautions Boxed Warning Boxed Warning 47.2 % Withdrawn Warnings and Precautions Boxed Warning Withdrawn Withdrawn Boxed Warning Withdrawn Discontinued Discontinued Boxed Warning Withdrawn W Discontinued 27.5 % 19.4 % 10 %
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:
Hepatic Clearance Predictions from In Vitro-In Vivo Extrapolation and the Biopharmaceutical Drug Disposition Classification System Christine M. Bowman and Leslie Z. Benet Drug 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.
Our Hypothesis was Correct Using 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)
Fig. 1 Homogeneous Liver Model Fig. 2 Heterologous Liver Model Q H C in C H,u Q H C out CL H,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 C hep,u in Fig. 2 equals C H,u in Fig. 1.
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 k e,mic, the volume of the microsomal mixture V mic and fraction of unbound drug in the microsomal mixture, f u,mic.
R ss,uu Hypothesis Assumptions The difference in the metabolic capacity is defined by the in vitro drug elimination characteristics (i.e., k e,mic ) in the animal versus human R ss,uu will vary from drug to drug; no universal IVIVE scaling factor will give successful predictions of hepatic clearance. The distribution between 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 R ss,uu. Therefore, a drug s R ss,uu is expected to be the similar across mammals. We are not assuming that the 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
Are there data in the literature consistent with the R ss,uu hypothesis? R ss,uu -based IVIVE scaling factor in animal model is predictive for humans Marked difference between observed and predicted human CL int,in vivo Human CL int,in vivo values corrected with rat IVIVE scaling factors yield better predictions of human CL int,in vivo Using Rat Scaling Factor to Correct Human Hepatocyte IVIVE Predictions* Drug Human CL int In Vitro In Vivo Human Fold Difference Ratio Rat Fold Difference Ratio Corrected Human Ratio Acetaminophen 2.39 6.71 2.8 0.70 4.0 Diazepam 4.14 21.9 5.3 2.2 2.4 Diltiazem 72.4 292 4.0 0.49 8.1 Zidovudine 9.87 42.1 4.3 1.4 3.1 Troglitazone 67.4 10,000 148 73.1 2.0 FK079 40.2 636 15.8 6.1 2.6 FK480 9.98 336 33.7 25.1 1.3 FK1052 7.89 1570 199 43.6 4.6 *Naritomi et al. 2003. DMD 31: 580-588
Drug Cocktails to Predict Clearance of an NME A further implication of the R ss,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 R ss,uu is drug specific depending on the distribution characteristics of each particular drug. Thus, the values of R ss,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.
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.
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 25 th Anniversary of the International Conference on Drug-Drug Interactions
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