MODELING MECHANISM BASED INACTIVATION USING PBPK JAN WAHLSTROM DIRECTOR, PRECLINICAL

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1 MODELING MECHANISM BASED INACTIVATION USING PBPK JAN WAHLSTROM DIRECTOR, PRECLINICAL

2 ABSTRACT Quantitative prediction of the magnitude of drug-drug interactions (DDI) is critical to underwriting patient safety in the clinical setting. Key mechanistic information can help to inform physiologically-based modeling and enable reasonable predictions of DDI magnitude, including complex scenarios such as inhibitory metabolites. This presentation will focus on integrating in vitro, preclinical and clinical data to develop quantitative predictions for DDIs due to mechanism-based inactivation. 2

3 OUTLINE Introduction Definitions Assay types Regulatory Guidance Static and dynamic modeling Case studies Delavirdine (determining mechanism) CTP-347 (isotope effects) Verapamil (complex DDI and pharmacogenomics) 3

4 DEFINITIONS Time-dependent inhibition (TDI): the apparent inhibitory potency of a new chemical entity (NCE) increases over time Slow binders (rare), formation of inhibitory metabolites, mechanism-based inactivation Metabolism-dependent inhibition (MDI): the apparent inhibitory potency of an NCE increases over time, requiring one or more metabolic conversions Formation of inhibitory metabolites, mechanism-based inactivation Mechanism-based inactivation (MBI): the apparent inhibition potency of an NCE increases over time due to a enzymatic product that irreversibly inactivates the enzyme and does not leave the active site Ogilvie, BW, et al, in Drug-Drug Interactions, Informa Health Care (28)

5 AUC Ratio OVERVIEW Two features dominate the impact of MBI on victim drugs Therapeutic Index Contribution of clearance mechanisms (f m ) fm =.98 fm =.95 fm =.8 fm =.7 fm = [Inhibitor, mm] Additionally, reactive metabolites may leave the DME active site and haptenize other proteins, potentially leading to immune responses Wahlstrom JL, et al. Expert Opinion on Drug Discovery 1(7) , 26 5

6 Ln % Activity Remaining Inactivation Rate (min -1 ) IN VITRO ASSAYS Application of CYP MDI assays across the drug discovery / development continuum is dependent upon: Content: Will this information impact decision making? How robust does the information need to be? Entry Point: Throughput of Assay - What is the compound pressure for the assay? Follow-up: When will a more rigorous assay be applied to further characterize the lead molecule? Structural Assessment (Structural Alerts?) Kinetics Screen (Single Conc) Kinetics Evaluation (IC 5 shift) Bioactivation Assessment (GSH, KCN) Kinetics Characterization (K I, k inact ) Mechanism Characteriztion (Heme, Apo, MIC) Preincubation Time (min) Tofisopam Concentration (mm) Grimm, SW, et al. Drug Metab Dispos , 29 Wahlstrom JL, et al. Expert Opinion on Drug Discovery 1(7) , 26 6

7 REGULATORY GUIDANCE Determining Whether an NME is a Time-Dependent Inhibitor FDA DDI Guidance, Figure 4 FDA: Any time-dependent loss of initial product formation rate may indicate time-dependent inhibition, and definitive in vitro studies to obtain TDI parameters (i.e., k inact and K I where k inact and K I are maximal inactivation rate constant and apparent inactivation constant, respectively) are recommended. If in vitro results suggest a TDI potential (e.g., R>1.1), an in vivo study is recommended. Alternatively, the sponsor can estimate the degree of drug-drug interactions using mechanistic models EMA: If the inhibition is enhanced by pre-incubations, timedependent inhibition (TDI) is present. The increased inhibition over time may either be due to formation of an inhibitory metabolite or due to mechanism-based inactivation (MBI). For mechanism based inactivators, k inact (maximum inactivation rate constant) and K I (the inhibitor concentration producing half the maximal rate of inactivation) should be determined

8 PREDICTING THE IN VIVO SITUATION: STATIC EQUATIONS Single Inactivator AUC AUC [ I ] ctr 1 fm kinact fu [ I] 1 ( K I fu [ I]) k deg (1 f m ) [1] Multiple Inactivators Definitions AUC: Area Under the Plasma Concentration Time Curve f m(cyp) : Fraction metabolized by a specific CYP K i : Measure of Inactivation Potency k inact : Rate of Inactivation [I] invivo : Inhibitor Concentration in vivo f u : Fraction Unbound in Plasma k deg : degredation rate of the P45 1 fm kinact fu [ I] ( K f [ I]) k (1 f The static approach assumes inactivator concentration is constant and maximal AUC AUC [ I ] ctr n 1 i1 I u deg m ) [2] Mayhew, BS, et al. Drug Metab Dispos , 2 8

9 ESTIMATING CYP HALF-LIFE Pulse-chase In vitro Enzyme level after induction CYP Half-life estimates In vivo Enzyme recovery after inactivation or induction Half-life estimates may vary depending upon the method used for characterization Yang, J, et al. Curr Drug Metab , 28 9

10 IMPACT OF CYP HALF-LIFE ON PREDICTIONS Azithromycin: weak TDI Mibefradil: potent TDI k deg of -- (1 day), (3 days) or (6 days) As f m increases, the impact of k deg increases Burt, HJ, et al. Xenobiotica 4(5) , 21 1

11 QUANTITATIVE PREDICTIONS: PBPK Physiologically-based pharmacokinetic (PBPK) modeling integrates in vitro, in vivo and in silico data to simulate outcomes Incorporates physiological and physicochemical properties Treats the body as compartments connected by a circulatory system Integrates in vitro and clinical observations to support M&S PBPK is suited for modeling applications where: Changes in physiology or populations may impact PK variability Changes in physicochemical properties or formulations may alter PK Dynamic simulations of drug interactions are desired Jamei, M et al. Expert Opin Drug Metab Toxicol 29;5:

12 WHY SHOULD WE CARE ABOUT PBPK? Regulatory Expectations Application of PBPK to FDA IND/NDA Submissions (28-213, 33 Submissions) Key regulatory questions in clinical pharmacology reviews: What intrinsic factors (age, gender, disease, polymorphism, etc) influence exposure? What extrinsic factors (drugs, herbal products, diet, etc) influence exposure? Based on exposure-response, what dosage regimen adjustments, if any, are recommended? Potential for improved decision making Study Timing (delay until proof of concept achieved) Improved/abbreviated study design Necessity of clinical studies PBPK enables the rational translation of in vitro or pre-clinical data to the clinical situation Zhao, P. et al. Best practice in the use of physiologically based pharmacokinetic modeling and simulation. 12 Clin Pharmacol Ther. 212; 92:17-2 Huang, SW. et al. The Utility of Modeling and Simulation in Drug Development and Regulatory Review. J Pharm Sci 213;12:

13 MECHANISMS OF CYP INACTIVATION Mechanisms of inactivation Heme alkylation Heme Destruction Apoprotein alkylation Metabolite Intermediate Complex (MIC) Formation X Diagnostic Loss of heme (~4 nm in HPLC assay) Baer, BR, et al. Chem Res Toxicol , 29 Hanson, KL, et al. Drug Metab Dispos , 21 Pearson, JT, et al. Chem Res Toxicol , 27 Ekroos, M, et al. PNAS , 26 Foti, RS, et al. Drug Metab Dispos , Alkylated protein UV Spectroscopy

14 A U C (m M * h ) C m a x (m M ) CASE STUDY: DELAVIRDINE Greater than dose proportional exposure was observed in single ascending dose studies Delavirdine Structure Nonlinearity (AUC) Nonlinearity (C max ) 2 A U C 1 C m a x D o s e (m g ) Possible mechanisms for greater than dose proportional exposure: Decrease in elimination Metabolite inhibition Dose-dependent tissue distribution Dose-dependent absorption D o s e (m g ) What additional experiments can provide insight to the mechanism of nonlinear PK? 14

15 % of C ontrol % of C ontrol % of C ontrol A b s o rb a n c e L n % A c tiv ity R e m a in in g In a c tiv a tio n R a te (m in -1 ) DELAVIRDINE: DETERMINING THE MECHANISM OF MBI It is critical to distinguish between inhibition and inactivation for accurate simulations Binding Spectrum Time-Dependent Inhibition of CYP3A4 TDI Kinetics K s = 16 µm K I = 13 µm K inact =.3 min Type I spectrum W a v e le n g th (n m ) D e la v ir d in e C o n c e n tra tio n (m M ) P re in c u b a tio n T im e (m in ) Heme Destruction Apoprotein Alkylation Delavirdine M ib e fr a d il R a lo x ife n e D e la v ir d in e 1 C O B in d in g % H e m e R e m a in in g 8 6 % A c tiv ity T im e (m in ) T im e (m in ) T im e (m in ) In vitro experiments indicate apoprotein alkylation is the mechanism of inactivation 15

16 P la s m a C o n c e n tra tio n (n g /m L ) % C Y P 3 A A c tiv ity R e m a in in g P la s m a c o n c e n tra tio n (u M ) % G u t C Y P 3 A A c tiv ity R e m a in in g % L iv e r C Y P 3 A A c tiv ity R e m a in in g DELAVIRDINE: TRANSLATION TO IN VIVO PREDICTIONS Marked differences in effects may be observed between the gut and liver Predicted Single Dose PK Predicted Effect: Gut CYP3A Predicted Effect: Liver CYP3A T im e (h ) 1 m g 2 m g 4 m g 8 m g 1 2 m g 1 5 m g m g 3 m g 4 m g T im e (h ) 1 m g 2 m g 4 m g 8 m g 1 2 m g 1 5 m g m g 3 m g T im e (h ) 1 m g 2 m g 4 m g 8 m g 1 2 m g 1 5 m g m g 3 m g Predicted Multiple Dose PK (6 mg bid) Predicted Effect: Gut and Liver CYP3A (6 mg bid) 4 3 A c tu a l P K S im u la te d P K 1 L iv e r C Y P 3 A G u t C Y P 3 A T im e (h ) 1 2 T im e (h ) Co-administration of delavirdine is contraindicated for drugs that are highly dependent on CYP3A for clearance and for which elevated plasma concentrations are associated with serious events 16

17 PAROXETINE: REDUCING MBI IN VITRO Predicting the effects of selective deuterium incorporation on metabolism can be difficult CTP-347 Binding Spectrum Type I spectrum Binding Constant P45 Cycle Potential for masking of isotope effect: Metabolic switching? Is CD bond-breaking rate limiting? Product release rate limiting? Early irreversible step? Intrinsic Clearance (HLM) TDI Experiment Spectral experiment (MIC) Paroxetine CTP-347 In vitro experiments indicate CTP-347 does not inactivate CYP2D6 Ener, ME. et al. PNAS 21; 17: Uttamsingh, V. et al. J Pharmacol Exp Ther 215; 354:

18 P la s m a C o n c e n tra tio n s (n g /m L ) Active Liver CYP2D6 (% ) PAROXETINE: REDUCING MBI IN VIVO PBPK reasonably simulates the effect of CYP2D6 inactivation on paroxetine PK PK for paroxetine and CPT-347 Predicted CYP2D6 liver activity Effect on urinary DM/DX 1 5 P a ro x e tin e D a y 1 P a ro x e tin e D a y P a ro x e tin e C P T C P T D a y 1 C P T D a y 1 4 P a ro x e tin e D a y 1 S IM 5 5 D a y 1 4 P a ro x e tin e S IM C P T D a y 1 S IM C P T D a y 1 4 S IM 1 2 T im e (h ) T im e (h ) f m paroxetine after multiple doses Median % fm and fe in absence of inhibitor(s) f m CPT-347 after multiple doses CYP1A2 Liver CYP2C19 Liver CYP2D6 Liver CYP3A4 Liver CYP3A5 Liver Additional HLM Renal PBPK modeling and simulation provide additional insight to the effects of MBI Uttamsingh, V. et al. J Pharmacol Exp Ther 215;354:

19 VERAPAMIL: COMPLEX DDI Verapamil and metabolites Complex DDI: multiple species or mechanisms of DDI Inactivation through MIC Verapamil enantiomers Norverapamil enantiomers Inactivation parameters (CYP3A4) PBPK modeling CYP3A activity in gut and liver Parameters S-VPM R-VPM S-NVPM R-NVPM K I (um) K inact (min -1 ) Inactivation by D617 is an order of magnitude less PBPK successfully simulated verapamil PK Wang, J. et al. Eur J Pharm Sci 213; 5:29-32 Wang, YH. et al. Drug Metab Dispos 24; 32:

20 % C Y P 3 A A c tiv ity R e m a in in g VERAPAMIL: COMPLEX DDI AND PHARMACOGENETICS CYP3A5 has been detected in 1-4% of Europeans, 33% of Japanese and 55% of African Americans Norverapamil MIC (CYP3A4 and CYP3A5) Inactivation parameters (CYP3A4/3A5 comparison) Parameters S-VPM (CYP3A4) S-VPM (CYP3A5) NVPM (CYP3A4) NVPM (CYP3A5) K I (um) K inact (min -1 ) Clinical PK CYP3A activity in gut and liver 1 C Y P 3 A 4 (g u t) C Y P 3 A 5 (g u t) C Y P 3 A 4 (liv e r) T im e (h ) PBPK successfully predicted the impact of CYP3A5 polymorphism on verapamil PK Jin, Y. et al. Clin Pharm Ther 27;82: Wang, YH. et al. Drug Metab Dispos 25;33: Isoherranen, N, YH. et al. Drug Metab Dispos 28;36:

21 CONCLUSIONS Early screens can be used to determine if compounds exhibit time-dependent inhibition (TDI) Hits in the early TDI screen may be followed up by characterization experiments Mechanisms of CYP inactivation (MBI) include heme alkylation, heme destruction and apoprotein alkylation Static or dynamic models may be used to estimate the magnitude of DDI in vivo Modeling is sensitive to CYP half-life estimates, particularly at high f m PBPK modeling may be used to perform dynamic simulations of drug-drug interactions PBPK is the preferred method for modeling and simulation of clinical situations involving complex DDI 21

22 ACKNOWLEDGEMENTS Gary Skiles, Dan Rock, Rob Foti, Brooke Rock, Larry Wienkers 22