Exploiting BDDCS and the Role of Transporters

Exploiting BDDCS and the Role of Transporters (Therapeutic benefit of scientific knowledge of biological transporters, understanding the clinical relevant effects of active transport on oral drug absorption) Leslie Z. Benet, Ph.D. Department of Bioengineering and Therapeutics Sciences University of California, San Francisco SARI 2014 Pharmaceutical Science, Medicine Registration and Control Pretoria March 11, 2014

Now following Dr. Shah s excellent presentation, all delegates are well aware of BCS, the criteria involved and its use in obtaining biowaivers. In the early 2000s, I listened attentively to many presentations such as that given by Dr. Shah and began to recognize that certain previously unrecognized drug disposition characteristics may be inherent in the BCS system.

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, and as an additional parameter for supporting a biowaiver of in vivo bioequivalence studies.

Major Routes of Drug Elimination High Solubility Low Solubility High Permeability Rate Low Permeability Rate Class 1 Metabolism Class 3 Renal & Biliary Elimination of Unchanged Drug Class 2 Metabolism Class 4 Renal & Biliary Elimination of Unchanged Drug

Biopharmaceutics Drug Disposition Classification System BDDCS High Solubility Low Solubility Extensive Metabolism Class 1 High Solubility Extensive Metabolism (Rapid Dissolution and 70% Metabolism for Biowaiver) Class 2 Low Solubility Extensive Metabolism Poor Metabolism Class 3 High Solubility Poor Metabolism Class 4 Low Solubility Poor Metabolism Wu and Benet, Pharm. Res. 22: 11-23 (2005)

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. 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

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.

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.

2013 publications from the Benet Lab concerning permeability measurement correlations and intestinal transporters The Role of BCS (Biopharmaceutics Classification System) and BDDCS (Biopharmaceutics Drug Disposition Classification System) in Drug Development. L. Z. Benet. J. Pharm. Sci. 102, 34-42 (2013) Drug Discovery and Regulatory Considerations for Improving In Silico and In Vitro Predictions that Use Caco-2 as a Surrogate for Human Intestinal Permeability Measurements. C. A. Larregieu and L. Z. Benet. AAPS J. 15, 483-497 (2013). Intestinal Drug Transporters: An Overview. M. Estudante, J. G. Morais, G. Soveral and L. Z. Benet. Adv. Drug Deliv. Rev. 65, 1340-1356 (2013).

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).

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. BDDCS gives scientists and clinicians a roadmap for predicting drug disposition and drug-drug interaction characteristics very early, with little additional expense. Let s see further predictions

Prediction of Oral Dosing Transporter Effects Based on BDDCS Class High Permeability/ Metabolism Low Permeability/ Metabolism 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).

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

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.

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

P-gp and CYP3A4 Interplay in the Enterocytes Drug not metabolized or transported in the gut Drug metabolized on first entrance Drug cycled 4 times before metabolized Drug metabolites LUMEN Pgp Pgp Pgp Pgp Pgp Pgp 3A4 3A4 3A4 P-gp and CYP3A4 Interplay BLOOD

Studies to Characterize Transporter-Enzyme Interplay in CYP3A4 Transfected Caco-2 Cellular Systems Cummins et al., Pharm. Res., 18: 1102-1109 (2001) Benet et al., Adv. Drug Deliv. Rev., 50: S3-S11 (2001) Cummins et al., J. Pharmacol. Exp. Ther., 300: 1036-1045 (2002) Cummins et al., J. Pharmacol. Exp. Ther., 308: 143-155 (2004)

Oral Dosing Transporter Effects High Permeability/ Metabolism Low Permeability/ Metabolism 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

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)

Hepatocytes OATP 1B1 = Glyburide M = Glyburide Metabolites Bile 2C9 & 3A Pgp M Bile 2C9 & 3A Bile M M Blood

Hepatocytes OATP 1B1 When rifampin is present in the blood it can inhibit OATPs = Glyburide M = Glyburide Metabolites Bile 2C9 & 3A Pgp M Bile 2C9 & 3A Bile M Upon multiple dosing rifampin can induce CYP 2C9 and 3A4 M Blood

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)

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)

Inhibition of Glyburide Uptake by IV RIF Glyburide (ng/ml) 1000 800 600 400 200 Glyburide Control RIF IV+Glyburide 0 0 6 12 18 24 Time (h) Cmax 81% T1/2 31% * * AUC0-inf 125% CL/F 53% * * Vss/F 60% * P<0.05 *

CYP450 Induction Effect on Glyburide When No RIF Present in the Plasma Glyburide (ng/ml) 600 400 200 Glyburide Control Glyburide After RIF Induction 0 0 6 12 18 24 Time (h) * * 197% * Cmax 48% AUC0-inf 63% CL/F Vss/F 32% ns

Uptake Inhibition and CYP450 Induction Effects on Glyburide When RIF Present in the Plasma Glyburide (ng/ml) 600 400 200 Glyburide Control RIF IV+Glyburide After RIF Induction 0 0 6 12 18 24 Time (h) * * Cmax 9% AUC0-inf 22% CL/F 37% Vss/F 43% *

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.

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.

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

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: 1239-1246 (2004). Sun et al., Hepatic Clearance, But Not Gut Availability, of Erythromycin is Altered in Patients with End-Stage Renal Disease. Clin. Pharmacol. Ther. 87:465-472 (2010) Reyes and Benet, Effects of Uremic Toxins on Transport and Metabolism of Different Biopharmaceutics Drug Disposition Classification Systems Xenobiotics. J. Pharm. Sci. 100:3831-3842 (2011)

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 87:465-472 (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.

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 ± 0.13 0.35 ± 0.14 31% (p = 0.01) F % 15 ± 6 21 ± 7 36% (NS, p = 0.36) F H % 50 ± 10 64 ± 15 28% (p = 0.015) F abs F G % 33 ± 30 35 ± 28

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, 2039-2054 (2009). The Drug Transporter-Metabolism Alliance: Uncovering and Defining the Interplay. L. Z. Benet. Mol. Pharmaceut. 6, 1631-1643 (2009). Predicting Drug Disposition via Application of a Biopharmaceutics Drug Disposition Classification System. L. Z. Benet. Basic Clin. Pharmacol. Toxicol. 106, 162-167 (2010). The Role of BCS (Biopharmaceutics Classification System) and BDDCS (Biopharmaceutics Drug Disposition Classification System) in Drug Development. L.Z. Benet. J. Pharm. Sci. 102, 34-42 (2013)

BDDCS Applied to Over 900 Drugs Leslie Z. Benet, Fabio Broccatelli, and Tudor I. Oprea AAPS Journal 13: 519-547 (2011)

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.

Distribution of Drugs on the Market (698 oral IR) vs. Small Molecule NMEs High Solubility Low Solubility 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 Permeability Low Permeability Class 1 Marketed Drugs 40% NMEs: 18% Class 3 Marketed Drugs 21% NMEs: 22% Class 2 Marketed Drugs 33% NMEs: 54% Class 4 Marketed Drugs 6% NMEs: 6% [L.Z. Benet, J. Pharm. Sci. 102: 34-42 (2013)] [F. Broccatelli et al., Mol. Pharmaceut. 9: 570-580 (2012)]

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: 519-547 (2011)] High Permeability Low Permeability 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%)

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: 519-547 (2011)] High Permeability Low Permeability 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%

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

Improving the Prediction of the Brain Disposition of Orally Administered Drugs Using BDDCS F. Broccatelli, C.A. Larregieu, G. Cruciani, T.I. Oprea and L.Z. Benet Advanced Drug Delivery Reviews 64: 95-109 (2012)

From the literature we were able to identify 153 drugs that met three criteria: a) central or lack of central human pharmacodynamic effects were known b) the drug s permeability/metabolism and BDDCS class were identified c) information was available as to whether the drug was or was not a substrate for P-glycoprotein (since it is generally believed that P-gp substrates do not yield central effects)

In the analysis we found 18 of the 153 drugs were high permeability BDDCS Class 1 compounds that were also substrates of P-glycoprotein. But of those 18 BDDCS Class 1 drugs, 17 (94.4%) exhibited central pharmacodynamic effects in humans.

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.

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.

Kola and Landis, 2004

But the decrease in attrition due to PK/Bioavailability doesn t mean that we don t have problems to solve, it just means that we have recognized that these problems must be solved. That is, we must optimize the pharmacokinetics and bioavailability of the NME.

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 will predict Class 1 & 2 vs. 3 & 4 and thus major route of elimination in humans. Is solubility over the ph range 1-7.5 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.

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

Food Effects (High Fat Meals) Fleisher et al., Clin Pharmacokinet.36(3):233-254, 1999 High Permeability High Solubility Class 1 F extent T peak Low Solubility Class 2 F extent T peak Low Permeability Class 3 F extent T peak Class 4 F extent T peak

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 correct @ 70% of the time.

Conclusions Understanding transporter-enzyme interactions in terms of the permeability and solubility of drug compounds offers the potential for predicting: a. Major routes of elimination b. Transporter effects of drug absorption c. Food (High Fat Meal) effects d. Transporter effects on post absorption systemic levels and after i.v. dosing e. Enzyme transporter interplay f. Drug-drug interaction potential and its relationship to enzyme-transporter interplay

Conclusions g. Uremic toxins can inhibit hepatic uptake of Class 2 metabolized drugs and Class 3 and 4 biliarily excreted drugs causing decreased clearance. h. The translation of pharmacogenetic differences in metabolic enzymes (genetic polymorphisms) that do not always result in the expected differences in vivo. Phenotype-genotype discordance (as well as changes in the relationship as a function of disease states) may be explained by the effects of transporters on metabolic clearance. i. Obtaining a realistic perspective of new techniques (e.g., predictive ADME, QSAR, microdosing, systems biology). If validation of the technique primarily uses Class 1 drugs, there is no assurance that the technique will work for NMEs.