Predicting human hepatic clearance and DDI effects from in vitro metabolism and transport data

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Predicting uman epatic clearance and DDI effects from in vitro abolism and transport data Kenici Umeara, Birk Poller, Annett Kunze and Gian Camenisc Novartis Parma AG, DMPK DDI-2015,

clearance models (istory) Advanced IVIVE odology incl.

1 Predicting uman epatic clearance and DDI effects from in vitro abolism and transport data Kenici Umeara, Birk Poller, Annett Kunze and Gian Camenisc Novartis Parma AG, DMPK DDI-2015, International Conference on Drug-Drug Interactions June 30, 2015 Objectives Demonstrate te role of transporters in epatic elimination and epatic clearance prediction Different liver clearance models (istory) Advanced IVIVE odology incl. transporters (extended clearance model) Estimate contribution of epatic transporter(s) to Drug-Drug Interaction (DDI) potential as victim drug(s) Static and dynamic approaces Propose te extended clearance classification od to facilitate/optimize Drug Development process 2 DDI-2015 Kenici Umeara June 30,

2 Assumptions and Limitations is assumed to be te major (only) elimination patway were DDI can occur Well-stirred model CL = Q E Q f = Q + f Only unbound (free) concentration is subject to epatic elimination Inibition type for evaluating DDI potential: competitive u,b u,b CL int CL int 3 DDI-2015 Kenici Umeara June 30, 2015 Basic Clearance Models f u,b Q CL int CL int = CL or CL int = inf abolism is rate-limiting clearance process epatocyte uptake (influx) is ratelimiting clearance process (Iwatsubo et al., 1997) (Watanabe et al., 2010) 4 DDI-2015 Kenici Umeara June 30,

Predicting Clearance R 2 =0.781 Rat clearance prediction wit: CLint = CL Under-predictive mainly for class 3 and 4 compounds Classes of uptake transporter substrates!

3 Predicting Clearance R 2 =0.781 Rat clearance prediction wit: CLint = CL Under-predictive mainly for class 3 and 4 compounds Classes of uptake transporter substrates! Rat clearance prediction wit: CLint = CLinf Over-predictive mainly for (low-medium clearance) class 2 and 4 compounds R 2 =0.679 Classes of efflux transporter substrates! Transporters are indeed playing a significant role 5 DDI-2015 Kenici Umeara June 30, 2015 Hepatic Mecanistic Clearance Model f u,b Q inf CL Blood eff CL CL int,all CL int,all = inf eff (CL + (CL ) ( = ) ( inf,act eff,act + + inf,pas eff,pas ) (CL ) + (CL ) ) Sirianni and Pang, 1997 Sitara et al., DDI-2015 Kenici Umeara June 30,

4 Hepatic clearance mecanisms Wat in vitro assays are available? + inf, act inf,pas CL CL + eff,act eff,pas (Niemi et al., 2011) Mecanism: In vitro assay: Active processes: Factors to consider: Influx (uptake) Suspended epatocyte SLC transporters inf,pas Metabolism microsomes, S9 (Pase I) enzymes fu,mic Biliary retion Sandwic-cultured epatocytes ABC transporters fu,ep, abolism Efflux (back-flux) Overall clearance in sandwic-cultured epatocytes ABC (SLC) transporters Differential approac or eff = inf,pas (Umeara and Camenisc, 2012) 7 DDI-2015 Kenici Umeara June 30, 2015 Predicting Clearance R 2 =0.781 Rat clearance prediction wit CLint = CLint,all R 2 =0.928 R 2 =0.928 CLint = CL R 2 =0.679 CLint = CLinf 8 DDI-2015 Kenici Umeara June 30,

5 Interim Conclusions (1) Te new extended clearance model is valid Main reason for basic models not being predictive are indeed transporter-related clearance processes Neverteless, basic models are predictive for some compounds wereas tey are under-/over-predictive for oters 9 DDI-2015 Kenici Umeara June 30, 2015 Static DDI prediction as a Victim Drug in 1 inf, act 1+ pv/ inf Inibition 1 CL 1+ Inibition Inibition 1 inf, 1+ Extended epatic organ model: f u,b Q inf,i CL,i CL Blood CL, int, alli eff, i Inibition 1 eff, act 1+ eff CL int, all, i ( = ( inf, act eff, act = inf, i eff, i ( CL + ( CL, i, i, i pv/ inf) + eff) + ), i ) inf, pas eff, pas ) ( CL ) + ( CL ) ) )) )) 10 DDI-2015 Kenici Umeara June 30,

6 Static Worst-case DDI prediction - Atorvastatin Atorvastatin: mainly eliminated from liver, a substrate of epatic uptake transporters OATP1B1 and oxidative enzyme CYP3A Predicted AUC ratio Predicted AUC ratio inf,c inibition (%) CL inibition (%) 11 DDI-2015 Kenici Umeara June 30, 2015 Static prediction on te epatic uptake clearance (1) RAF and REF concepts (Kunze et al., 2014) 12 DDI-2015 Kenici Umeara June 30,

7 Static prediction on te epatic uptake clearance (2) OATP1B1 and OATP1B3-mediated uptake of statins ato: atorvastatin, cer: cerivastatin, flu: fluvastatin, lov: lovastatin, pit: pitavastatin, pra: pravastatin, ros: rosuvastatin, sim: simvastatin (Kunze et al., 2014) 13 DDI-2015 Kenici Umeara June 30, 2015 Dynamic DDI prediction as a Victim Drug in Victim drug (transporter substrates as e.g. atorvastatin): Perpetrator drug: Periperal Q Xp k21 k12 Central Cb, V1 Periperal Q Xpi k21i k12i Central Cbi, V1i CLr CLoters Inibition CLr(i) CLoters(i ) p.o. (1 st order absorption) Dose kafafg exp(-ka t) inf CL Blood Q C1, V1 eff C2, V2 CL Inibition Ci, V1+V2 CL(i) CL(i) Q p.o. (1 st order absorption) Dosei kaifaifgi exp(-kai t) Modification of a model proposed in (Watanabe et al., 2009) 14 DDI-2015 Kenici Umeara June 30,

8 Dynamic Worst-case DDI prediction - Atorvastatin Influx Metabolism Influx+ abolism Plasma Atorvastatin plasma concentration (ng/ml) Rinf=1.0 (control) Rinf=3.0 Rinf=1 inf, c=0 Atorvastatin plasma concentration (ng/ml) R=1.0 (control) R=3.0 R=1 Atorvastatin plasma concentration (ng/ml) Control inf,c=0 and R= Time () Time () Time () Atorvastatin liver concentration (ng/g liver) Rinf=1.0 (control) Rinf=3.0 Rinf=1 inf,c=0 Atorvastatin liver concentration (ng/g liver) R=1.0 (control) R=3.0 R=1 Atorvastatin liver concentration (ng/g liver) Control inf,c=0 and R= Time () 15 DDI-2015 Kenici Umeara June 30, Time () Dose: 40 mg p.o., single dose (PBPK model was validated to compare wit clinical observations) R (competitive inibition factor)=1, 3 and Time () Static vs. Dynamic DDI prediction - Atorvastatin Influx: inf,c = 0 Hepatic process inibited: worst case Metabolism: R = 10 Influx+abolism: inf,c=0 and R=10 Model Static Dynamic Static Dynamic Static Dynamic AUCratio (plasma) AUCratio (liver) ND 0.6 ND 1.1 ND 0.6 ND: not determined All values are expressed as relative canges compared to control values witout any DDI effects. Static DDI prediction results: Camenisc and Umeara, DDI-2015 Kenici Umeara June 30,

9 Interim Conclusions (2) For te static DDI potential prediction of victim drugs were te epatic uptake is te rate-determining step, RAF and REF concepts provide useful information on te contribution ratio of te uptake transporters Te effects of clearance process inibition on exposure of victim drug(s) migt be differently estimated between static and dynamic predictions, indicating importance of dynamic modeling approac Te IVIVE approac followed by DDI potential prediction using static and dynamic models is working but very work intensive i.e. cannot be used as a standard procedure Wit elp of compound/clearance classification system, te drug development strategy can be facilitated and optimized (e.g. BDDCS: CL works for class 1 and class 2 compounds, wereas inf works for classes 1 and 3) 17 DDI-2015 Kenici Umeara June 30, 2015 BDDCS Solubility D/250 ml Solubility < D/250 ml Metabolism 70% Hig solubility Extensive abolism Transporter effects minimal Class 1 Class 2 Low solubility Extensive abolism Efflux transporter effects can occur Metabolism < 70% Hig solubility Poor abolism Uptake transporter effects predominant (may be modulated by efflux transporters) Class 3 Class 4 Low solubility Poor abolism Uptake and efflux transporter effects may be important (Wu and Benet, 2005) 18 DDI-2015 Kenici Umeara June 30,

10 Extended Model vs BDDCS Metabolism is major epatic elimination patway Metabolism 70% Metabolism < 70% eff inf = pas Solubility D/250 ml Hig solubility Extensive abolism Transporter effects minimal Hig solubility Poor abolism Uptake transporter effects predominant (may be modulated by efflux transporters) Class 3 Class 1 eff << CL pas eff eff >> CL CL ( CL+ CL) inf + ( CL inf eff inf ( CL+ CL) ) Class 2 Class 4 Soluility < D/250 ml Low solubility Extensive abolism Efflux transporter effects can occur Low solubility Poor abolism Uptake and efflux transporter effects may be important Drug transport and abolism may contribute to epatic elimination 19 DDI-2015 Kenici Umeara June 30, 2015 Compound-(sub)Class Dependent Drug Development Key information from extended model: If inf eff pas compound is a class 1 or 2 compound Well-stirred liver model: igly permeable if CLint,all = pas >> Q (i.e. > 3-times Q) BDDCS (in vivo observationbased) Wu and Benet, 2005 ID-ECCCM (in vitro data-based extended clearance concept classification od) Camenisc and Umeara, 2012 Camenisc et al., 2015 Propranolol Quinidine Verapamil CsA Ketoconazole Atorvastatin - Propranolol Quinidine Verapamil Ketoconazole CL predictive for epatic elimination Metabolism-based DDI Aliskiren Pravastatin Valsartan Ciidine Digoxin Furosemide Ciprofloxacin Ciidine Ciprofloxacin CsA Atorvastatin Digoxin Furosemide Aliskiren Pravastatin Valsartan CL not predictive for epatic elimination Metabolism- and transporter-based DDI 20 DDI-2015 Kenici Umeara June 30,

11 Final Conclusions Wit elp of above relationsips compounds can be assigned to (sub)classes according to te in vitro data-based extended clearance concept classification od (ECCCM) Correct ECCCM class assignment is te prerequisite for a compounddependent, tailor-made pre-clinical and clinical follow-up program in Drug Development. e.g. knowing te ECCCM compound (sub)classes allows a compound-class dependent static and dynamic prediction of te DDI potential of drug substances Te igly predictive clearance estimates based on te extended organ model can be used for te elaboration of more complex and predictive PK/PD models, after confirming te ECCCM class 3 and 4 assignment 21 DDI-2015 Kenici Umeara June 30,

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