Biopharmaceutics Drug Disposition Classification System (BDDCS) and Its Application in Drug Discovery and Development

Transcription

1 Biopharmaceutics Drug Disposition Classification System (BDDCS) and Its Application in Drug Discovery and Development Leslie Z. Benet, Ph.D. Professor of Bioengineering and Therapeutic Sciences University of California San Francisco AAPS Physical Pharmacy and Biopharmaceutics Section Webinar September 6, 2012

2 Cellular and animal studies from our laboratory between examining transporter-enzyme interplay led us to make 22 predictions concerning drug absorption and disposition. These predictions served as the basis for the BDDCS as described in our January 2005 Pharmaceutical Research paper C-Y. Wu and L.Z. Benet. Pharm. Res. 22:11-23 (2005)

3 Wu and Benet recognized that for drugs exhibiting high intestinal permeability rates the major route of elimination in humans was via metabolism, while drugs exhibiting poor intestinal permeability rates were primarily eliminated in humans as unchanged drug in the urine and bile. Therefore they suggested that at least for drugs on the market, where extent of metabolism is always known, but extent of absorption may not have been quantitated, that metabolism may serve as a surrogate for permeability rate.

4 Low Permeability Rate High Permeability Rate Major Routes of Drug Elimination 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

5 Biopharmaceutics Drug Disposition Classification System Extensive Metabolism Poor Metabolism BDDCS High Solubility Class 1 High Solubility Extensive Metabolism (Rapid Dissolution and 70% Metabolism for Biowaiver) Low Solubility Class 2 Low Solubility Extensive Metabolism Class 3 High Solubility Poor Metabolism Class 4 Low Solubility Poor Metabolism Wu and Benet, Pharm. Res. 22: (2005)

6 Major Differences Between BDDCS and BCS Purpose: BCS Facilitate biowaivers of in vivo bioequivalence studies. BDDCS Prediction of drug disposition and potential DDIs in the intestine & liver. Criterion: BDDCS Predictions based on intestinal permeability rate BCS Biowaivers are based on extent of intestinal absorption (permeability), which in a number of cases does not correlate with jejunal permeability rates

7 It should be noted that for such simple four class categorizations, high permeability does a remarkable job in predicting both extensive absorption (BCS) and extensive metabolism (BDDCS). However, low permeability is better at predicting poor metabolism than in predicting poor absorption for a number of Class 3 and 4 drugs

8 The recognition of the correlation between intestinal permeability rate and extent of metabolism allows prediction of BDDCS class for an NME to be based on passive membrane permeability. Our recent work suggests that even measurements in nonviable membranes like PAMPA will give the correct prediction Evaluating the Link between Passive Transcellular Permeability and Extent of Drug Absorption and Metabolism in Humans. C.A. Larregieu and L.Z. Benet, AAPS Abstract, October 2011

9 The recognition of the correlation between intestinal permeability rate and extent of metabolism and our finding that a nonviable membrane can serve as surrogate marker preceded an explanation for these findings. We suspect that high permeability rate compounds are readily reabsorbed from the kidney lumen and from the bile facilitating multiple access to the metabolic enzymes. This would be particularly important for metabolically eliminated drugs with low hepatic clearance (e.g., diazepam).

10 The Use of BDDCS for Drugs on the Market Predict potential drug-drug interactions not tested in the drug approval process Predict the potential relevance of transporterenzyme interplay Assist the prediction of when and when not transporter and/or enzyme pharmacogenetic variants may be clinically relevant Predict when transporter inhibition of uremic toxins may change hepatic elimination Predict the brain disposition Increase the eligibility of drugs for BCS Class 1 biowaivers using measures of metabolism

11 Low Permeability/ Metabolism High Permeability/ Metabolism Oral Dosing Transporter Effects High Solubility Class 1 Transporter effects minimal in gut and liver 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 Low Permeability/ Metabolism High Permeability/ Metabolism Oral Dosing Transporter Effects High Solubility Class 1 Transporter effects minimal in gut and liver 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

13 Class 1 highly soluble, high permeability, extensively metabolized drugs Transporter effects will be minimal in the intestine and the liver Even compounds like verapamil that can be shown in certain cellular systems (MDR1-MDCK) to be a substrate of P-gp will exhibit no clinically significant P-gp substrate effects in the gut and liver

14 Class 1 Drugs A major proposition of BDDCS is that Class 1 drugs are not substrates of clinical relevance for transporters in the intestine and liver (but the BBB and the kidney are not the gut and liver) [pre 2011 limitation]

15 Low Permeability High Permeability Intestinal: Transporter Effects High Solubility Class 1 Transporter effects minimal Class 3 Absorptive transporter effects predominate (but can be modulated by efflux transporters) Low Solubility Class 2 Efflux transporter effects predominate Class 4 Absorptive and efflux transporter effects could be important

16 Efflux transporter effects will predominate for Class 2 compounds. The high permeability of these compounds will allow ready access into the gut membranes, but the low solubility will limit the concentrations coming into the enterocytes, thereby preventing saturation of the efflux transporters.

17 Transporter-enzyme interplay will be primarily important for Class 2 compounds that are substrates for CYP 3A and Phase 2 gut enzymes (e.g. glucuronosyltransferases) where efflux transporter effects can control the access of the drug to the gut enzymes. DDIs in the intestine can lead to changes in metabolism that result from efflux transporter inhibition.

18 Recent Transporter Interplay Reviews from the Benet Lab The Role of Transporters in the Pharmacokinetics of Orally Administered Drugs. S. Shugarts and L. Z. Benet. Pharm. Res. 26, (2009). The Drug Transporter-Metabolism Alliance: Uncovering and Defining the Interplay. L. Z. Benet. Mol. Pharmaceut. 6, (2009). Predicting Drug Disposition via Application of a Biopharmaceutics Drug Disposition Classification System. L. Z. Benet. Basic Clin. Pharmacol. Toxicol. 106, (2010).

19 Low Permeability/ Metabolism High Permeability/ Metabolism Oral Dosing Transporter Effects High Solubility Class 1 Transporter effects minimal in gut and liver 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

20 Class 2 poorly soluble, highly permeable, extensively metabolized drugs Efflux transporter effects will be important in the intestine and the liver In the intestine efflux transporter enzyme (CYP 3A4 and UGTs) interplay can markedly affect oral bioavailability In the liver the efflux transporter-enzyme interplay will yield counteractive effects to that seen in the intestine. Uptake transporters can be important for the liver but not the intestine.

21 Class 2 poorly soluble, highly permeable, extensively metabolized drugs ---DDIs are seen as changes in metabolism Efflux transporter effects will be important in the intestine and the liver Metabolism changes due to transporter DDIs In the intestine efflux transporter enzyme (CYP 3A4 and UGTs) interplay can markedly affect oral bioavailability The overlap of inhibitors for gut enzymes and gut efflux transporters can cause multiple DDI causation. In the liver the efflux transporter-enzyme interplay will yield counteractive effects to that seen in the intestine --- Inhibition of efflux transporters causes decreased metabolism in the intestine and increased metabolism in the liver. Uptake transporters can be important for the liver but not the intestine --- Inhibition of uptake transporters causes decreased metabolism in the liver

22 Elucidating Rifampin s Inducing and Inhibiting Effects on Glyburide Pharmacokinetics and Blood Glucose in Healthy Volunteers: Unmasking the Differential Effect of Enzyme Induction and Transporter Inhibition for a Drug and Its Primary Metabolite HongXia Zheng, Yong Huang, Lynda Frassetto, and Leslie Z. Benet Clinical Pharmacology & Therapeutics 85:78-85 (2009)

23 Study Design Effects of Single IV Rifampin (RIF) on Glyburide Ten Healthy Volunteers Visit 1 Day 1 Glyburide 1.25mg P.O. (PK Study) Visit 2 Day 8 Rifampin 600mg I.V. Glyburide 1.25mg P.O. (PK Study)

24 Study Design (Continued) Inhibition and Induction Effects of RIF on Glyburide ALL Healthy Volunteers Rifampin 600mg P.O. for 6 days Visit 3 Day 15 Rifampin 600mg I.V. Glyburide 1.25mg P.O. (PK study) Visit 4 Day 17 Glyburide 1.25mg P.O. (PK study)

25 Glyburide (ng/ml) Inhibition of Glyburide Uptake by IV RIF Glyburide Control RIF IV+Glyburide Time (h) Cmax 81% T1/2 31% * * AUC0-inf 125% CL/F 53% * * Vss/F 60% * P<0.05 *

26 Glyburide (ng/ml) CYP450 Induction Effect on Glyburide When No RIF Present in the Plasma Glyburide Control Glyburide After RIF Induction Time (h) * * 197% * Cmax 48% AUC0-inf 63% CL/F Vss/F 32% ns

27 Glyburide (ng/ml) Uptake Inhibition and CYP450 Induction Effects on Glyburide When RIF Present in the Plasma 600 Glyburide Control RIF IV+Glyburide After RIF Induction Time (h) * * Cmax 9% AUC0-inf 22% CL/F 37% Vss/F 43% *

28 Low Permeability/ Metabolism High Permeability/ Metabolism Oral Dosing Transporter Effects High Solubility Class 1 Transporter effects minimal in gut and liver 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

29 Class 3 highly soluble, low permeability, poorly metabolized drugs Uptake transporters will be important for intestinal absorption and liver entry for these poor permeability drugs However, once these poorly permeable drugs get into the enterocyte or the hepatocyte efflux transporter effects can occur.

30 Class 3 highly soluble, low permeability, poorly metabolized drugs Uptake transporters will be important for intestinal absorption and liver entry for these poor permeability drugs However, once these poorly permeable drugs get into the enterocyte or the hepatocyte efflux transporter effects can occur. DDIs will be transporter mediated and cause changes in biliary and renal excretion for Class 3 and 4 drugs

31 The Use of BDDCS for Drugs on the Market Predict potential drug-drug interactions not tested in the drug approval process Predict the potential relevance of transporter-enzyme interplay Assist the prediction of when and when not transporter and/or enzyme pharmacogenetic variants may be clinically relevant Predict when transporter inhibition of uremic toxins may change hepatic elimination Predict the brain disposition Increase the eligibility of drugs for BCS Class 1 biowaivers using measures of metabolism

32 Recently the FDA has recommended that studies in renal failure patients be carried out even for drugs where renal elimination of unchanged drug is minimal This recommendation comes about in part based on a finding that was related to the development and characterization of BDDCS

33 Studies of Drugs Primarily Eliminated by Metabolism in Renal Failure Patients Potential inhibition or downregulation of metabolic enzymes by uremic toxins could be tested in vitro. We began to recognize that previously unexplained effects of renal disease on hepatic metabolism can result from accumulation of substances (toxins) in renal failure that modify hepatic uptake and efflux transporters. Sun et al., Effect of Uremic Toxins on Hepatic Uptake and Metabolism of Erythromycin. Drug Metab. Dispos. 32: (2004). Sun et al., Hepatic Clearance, But Not Gut Availability, of Erythromycin is Altered in Patients with End-Stage Renal Disease. Clin. Pharmacol. Ther. 87: (2010)

34 Hepatic Clearance, but Not Gut Availability of Erythromycin Is Altered in Patients with End-Stage Renal Disease Hong Sun, Lynda A. Frassetto, Yong Huang and Leslie Z. Benet Clinical Pharmacology & Therapeutics 87: (2010) Erythromycin is a Class 3 drug that is primarily eliminated unchanged in the bile. It is a substrate for hepatic uptake transporters that we have shown can be inhibited by uremic toxins in end-stage renal disease patients.

35 Erythromycin PK Parameters (mean ± SD) in 12 ESRD patients and12 controls after 125 mg iv and 250 mg oral doses Parameter Healthy Controls ESRD Patients % Change (p value) CL H (L/h/kg) 0.51 ± ± % (p = 0.01) F % 15 ± 6 21 ± 7 36% (NS, p = 0.36) F H % 50 ± ± 15 28% (p = 0.015) F abs F G % 33 ± ± 28

36 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

37 Caution A simple 4 category system won t predict every interaction. BDDCS doesn t propose that every drug in the class will be substrates or not substrates for uptake and efflux transporters. Rather, BDDCS enumerates what interactions should and should not be investigated.

38 The Use of BDDCS for Drugs on the Market Predict potential drug-drug interactions not tested in the drug approval process Predict the potential relevance of transporter-enzyme interplay Assist the prediction of when and when not transporter and/or enzyme pharmacogenetic variants may be clinically relevant Predict when transporter inhibition of uremic toxins may change hepatic elimination Predict the brain disposition Increase the eligibility of drugs for BCS Class 1 biowaivers using measures of metabolism

39 Since extent of metabolism correctly predicts high vs low intestinal permeability for at least 33 of 35 drugs, where human permeability measurements exist, Benet and co-workers a propose the following: We recommend that regulatory agencies add the extent of drug metabolism (i.e., 90% metabolized) as an alternate method for the extent of drug absorption (i.e., 90% absorbed) in defining Class 1 drugs suitable for a waiver of in vivo studies of bioequivalence. a Benet, Amidon, Barends, Lennernäs, Polli, Shah, Stavchansky & Yu. The Use of BDDCS in Classifying the Permeability of Marketed Drugs, Pharm. Res., 25: (2008)

40 A major advantage of BDDCS is that for drugs on the market, we know the extent of metabolism and they can generally be correctly classified without expensive and time consuming human permeability studies. I am very pleased that the January 2010 EMA Guideline on the Investigation of Bioequivalence includes metabolism as a criteria for permeability and that EMA only accepts extent of absorption as a criteria for Class 1 biowaivers. Also, the FDA has said informally that they would accept metabolism information as a basis for biowaivers.

41 Additional Uses of BDDCS for New Molecular Entities and Its Role in Drug Development Predict the major route of elimination for an NME in humans (metabolisms vs excretion of unchanged drug in the urine and bile) Predict the relevance of transporters and transporter-enzyme interplay in drug disposition as described earlier for drugs on the market Predict central or lack of central effects Predict the effects of high fat meals on the extent of bioavailability

42 How can the concepts presented be used in predicting DMPK of an NME? In silico methodology, at present, is not sufficient (except possibly for V ss ). We cannot predict clearance & bioavailability Permeability measures in Caco-2, MDCK or PAMPA vs metoprolol or labetalol will predict Class 1 & 2 vs. 3 & 4 and thus major route of elimination in humans. Is solubility over the ph range more than 0.2 mg/ml ( i.e., 50 mg highest dose strength) as proposed by Pfizer scientists, defining Class 1 & 3 vs. 2 & 4?

43 Can in silico models predict BDDCS class? BDDCS Applied to Over 900 Drugs Leslie Z. Benet, Fabio Broccatelli, and Tudor I. Oprea AAPS Journal 13: (2011)

44 Here we compile the BDDCS classification for 927 drugs, which include 30 active metabolites. Of the 897 parent drugs, 78.8% (707) are administered orally. Where the lowest measured solubility is found this value is reported for 72.7% (513) of these orally administered drugs and a Dose Number is recorded. Measured values are reported for percent excreted unchanged in urine, Log P and Log D7.4 when available. For all 927 compounds, the in silico parameters for predicted Log solubility in water, calculated Log P, Polar Surface Area and the number of hydrogen bond acceptors and hydrogen bond donors for the active moiety are also provided, thereby allowing comparison analyses for both in silico and experimentally measured values. We discuss the potential use of BDDCS to estimate disposition characteristics of novel chemicals (new molecular entities, NMEs) in the early stages of drug discovery and development.

45 High Permeability Low Permeability [LZ Benet, F Broccatelli and TI Oprea, AAPS J. 13: (2011)] [Broccatelli et al., Mol. Pharmaceut. 9: (2012)] Distribution of Drugs on the Market (698 oral IR) vs. Small Molecule NMEs NME percentages from a data set of 28,912 medicinal chemistry compounds tested for at least one target and having affinities at μm or less concentrations. High Solubility Class 1 Marketed Drugs 40% NMEs: 18% Class 3 Marketed Drugs 21% NMEs: 22% Low Solubility Class 2 Marketed Drugs 33% NMEs: 54% Class 4 Marketed Drugs 6% NMEs: 6%

46 Low Permeability High Permeability Best in silico solubility vs measured VolSurf+ r 2 =0.33 (ALOGPS r 2 = 0.24) Distribution of Drugs on the Market (698 oral IR) vs. Small Molecule NMEs [LZ Benet, F Broccatelli and TI Oprea, AAPS J. 13: (2011)] High Solubility Class 1 Marketed Drugs 40% NMEs: 18% 79.3% (55.8%) Class 3 Marketed Drugs 21% NMEs: 22% 90.1% (74.8%) Low Solubility Class 2 Marketed Drugs 33% NMEs: 54% 78.1% (85.1%) Class 4 Marketed Drugs 6% NMEs: 6% 39.5% (82.7%)

47 Low Permeability High Permeability For either measured or calculated Log P >2, the probability of extensive metabolism is 79.9 and 81.0 %, respectively. For Log P values < 0 the probability of poor metabolism is 84.0 % for M Log P and 83.4 % for CLogP. Distribution of Drugs on the Market (698 oral IR) vs. Small Molecule NMEs [LZ Benet, F Broccatelli and TI Oprea, AAPS J. 13: (2011)] High Solubility Class 1 Marketed Drugs 40% NMEs: 18% Class 3 Marketed Drugs 21% NMEs: 22% Low Solubility Class 2 Marketed Drugs 33% NMEs: 54% Class 4 Marketed Drugs 6% NMEs: 6%

48 When Log P values range from 0-2 (31.4% of drugs for M Log P and 27.5% of drugs for CLogP)

49

50 Additional Uses of BDDCS for New Molecular Entities and Its Role in Drug Development Predict the major route of elimination for an NME in humans (metabolisms vs excretion of unchanged drug in the urine and bile) Predict the relevance of transporters and transporterenzyme interplay in drug disposition as described earlier for drugs on the market Predict central or lack of central effects Predict the effects of high fat meals on the extent of bioavailability

51 The Use of BDDCS in Predicting the Brain Disposition of Orally Administered Drugs Fabio Broccatelli, Caroline A. Larregieu, Gabriele Cruciani, Tudor I. Oprea and Leslie Z. Benet Advanced Drug Delivery Reviews 64, (2012)

52 From the literature we were able to identify 153 drugs that met three criteria: a) central or lack of central pharmacodynamic effects were known (BBB+ or BBB-) b) the BDDCS class was identified c) information was available as to whether the drug was or was not a substrate for P-glycoprotein

53 Use of BDDCS in CNS Drug Discovery F. Broccatelli, C. A. Larregieu, G. Cruciani, T. I. Oprea and L. Z. Benet, Advanced Drug Delivery Reviews 64: (2012)

54 Class 1 Drugs A major proposition of BDDCS is that Class 1, P450/UGT metabolized drugs are not substrates of clinical relevance for transporters in the intestine, liver, kidney and brain.

55 Another Implication Class 1 compounds will achieve brain concentrations whether this is desired or not for an NME, which could be the rationale for not always wanting Class 1 NMEs.

56 Low Permeability High Permeability DDI Food Effects (High Fat Meals) Fleisher et al., Clin Pharmacokinet.36(3): , 1999 High Solubility Class 1 F extent T peak Low Solubility Class 2 F extent T peak Class 3 F extent T peak Class 4 F extent T peak

57 The observed effects of high fat meals on the extent of bioavailability, F extent, is consistent with high fat meals inhibiting transporters in the BDDCS predictions. Even if this is not found to be true, the supposition allows predictions of food effects on drug bioavailability. However, many factors are related to food effects, and the predictions here on F are only 70% of the time.

58 The Use of BDDCS for Drugs on the Market Predict potential drug-drug interactions not tested in the drug approval process Predict the potential relevance of transporterenzyme interplay Assist the prediction of when and when not transporter and/or enzyme pharmacogenetic variants may be clinically relevant Predict when transporter inhibition of uremic toxins may change hepatic elimination Predict the brain disposition Increase the eligibility of drugs for BCS Class 1 biowaivers using measures of metabolism

59 Additional Uses of BDDCS for New Molecular Entities and Its Role in Drug Development Predict the major route of elimination for an NME in humans (metabolisms vs excretion of unchanged drug in the urine and bile) Predict the relevance of transporters and transporter-enzyme interplay in drug disposition as described earlier for drugs on the market Predict central or lack of central effects Predict the effects of high fat meals on the extent of bioavailability

60 Collaborators & Acknowledgements Fabio Broccatelli, PhD Gabriele Cruciani, PhD Carolyn Cummins, PhD Joseph Custodio, PhD Margarida Estudante, PhD Lynda Frassetto, MD Adam Frymoyer, MD Yong Huang, PhD Caroline Larregieu, BS Hideaki Okochi, PhD Tudor Oprea, MD, PhD Maribel Reyes, PhD Selma Sahin, PhD Sarah Shugarts, PhD Hong Sun, MD, PhD Chi-Yuan Wu, PhD HongXia Zheng, MD, PhD Funding NIH grants GM and GM for a copy of the slides: Leslie.Benet@ucsf.edu

61 The In Vitro Model that Led to These Hypotheses membrane -P-gp and CYP3A4 in same cell line 6-well plate -Appropriate intestinal model In Vitro Model

62 P app Values for Verapamil in Caco-2 and MDR1-MDCK Cells (Sahin, Custodio, Estudante and Benet, in preparation) Cell Line Concentration PERMEABILITY (nm/s) A-to-B B-to-A Caco-2 10 nm nM+0.5 M GG M M+0.5 M GG MDR1-MDCK 10 nm nM+0.5 M GG M M+0.5 M GG