Prediction of DDIs Arising from CYP3A Induction Using a Physiologically-based Dynamic Model. Lisa Almond 22 nd June 2016

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1 Prediction of DDIs Arising from CYP3A Induction Using a Physiologically-based Dynamic Model Lisa Almond 22 nd June 2016

2 Growing impact of PBPK on drug labels Revatio (Sildenafil) Pulmonary Arterial Hypertension Xarelto (Rivaroxaban) Deep Vein Thrombosis and Pulmonary Embolism Edurant (Rilpivirine) HIV infection i Iclusig (Ponatinib) Chronic Myeloid Leukemia Olysio (Simerprevir) Hepatitis C Opsumit (Macitentan) Pulmonary Arterial Hypertension i Imbruvia (Ibrutinib) Mantle Cell Lymphoma and Chronic Lymphocytic Leukemia i Movantik (Naloxegol) Opioid Induced Constipation Cerdelga (Eliglustat) Gaucher Disease Jevtana (Cabazitaxel) Prostate Cancer i Zykadia (Ceritinbi) Metastatic Non-Small Cell Lung Cancer Bosulif (Bosutinib) Chronic Myelogenous Leukemia i Lynparza (Olaparib) Advanced Ovarian Cancer i Farydak (Panobinostat) Multiple myeloma Lenvima (Lenvatinib) Thyroid cancer i Odomzo (Sonidegib) Basal cell carcinoma 2

3 Semi-Mechanistic Modelling of Induction Transcription Translation Degradation DNA mrna Enzyme C t Rate of Synthesis Induction Factor Rate of Degradation de dt t kdeg E0 1 E EC max 50 [I] [I] t t k deg E t E max = Ind max (fold) -1 EC 50 = IndC 50

4 Additional rate of synthesis Et/E0 Concentration (μm) Effect (fold induction) Dynamic Modelling of Enzyme Induction Time (h) Concentration (μm) Time (h) Time (h)

5 Reference IVIVE Values: Rifampicin C (ug/ml) Urinary 6β-Hydroxycortisol/Cortisol ratio (vs baseline) 5 4 Ind max = 8.0 IndC 50 = 0.32 µm Baseline Rifampin Dosing Post Dosing Study Days Tran et al, mg bid mg qid mg qid Time (h) Time (h) Time (h) Acocella et al, 1971

6 Induction Calibrator Urinary 6β-Hydroxycortisol/Cortisol ratio (vs baseline) Test in vitro Step 1. test (in vitro) vs. reference (in vitro) REF in vitro e.g. Rifampicin Step 2. REF in vitro vs. REF in vivo e.g. Rifampicin in vitro vs. Rifampicin in vivo 5 REF in vivo TEST in vivo Baseline Rifampin Dosing Post Dosing Study Days

7 Issues Tendency to under predict the magnitude of interaction due to CYP3A induction Some studies reported good predictions High inter-individual and lab variability Issues can be masked by differences in the way data are reported

8 Strategy Evaluation of DDIs with rifampicin (29 rifampicin DDI studies with 7 victims CYP3A substrates) Generate in vitro induction data (mrna & activity) for other prototypical inducers using multiple human hepatocyte donors Evaluation sources of uncertainty (drug & system parameters) - sensitivity analyses Refinement of rifampicin file and re-evaluation Ultimately learn/improve model fit-for-purpose (NCEs) Apply the refined model to calibrate other independent inducers Almond et al., DMD 44(6):

9 Strategy Evaluation of DDIs with rifampicin (29 rifampicin DDI studies with 7 victims CYP3A substrates) Strategy Generate in vitro induction data (mrna & activity) for other prototypical inducers using multiple human hepatocyte donors Evaluation sources of uncertainty (drug & system parameters) - sensitivity analyses Refinement of rifampicin file and re-evaluation Ultimately learn/improve model fit-for-purpose (NCEs) Apply the refined model to calibrate other independent inducers Almond et al., DMD 44(6):

10 Midazolam +/- rifampicin - Obs vs. Pred Dosing Regimen 1/AUC ratios 1/Cmax ratios RIF MDZ (single dose) MDZ RoA n Dose Stagger (h) 600 mg q.d. 5d 1 mg IV mg q.d. 7d 0.05 mg/kg IV mg q.d. 5d 1 mg IV mg q.d. 7d 0.05 mg/kg IV 3 12* mg q.d. 6d 2 mg IV obs pred FE obs pred FE 600 mg q.d. 5d 15 mg oral mg q.d. 5d 15 mg oral mg q.d. 5d 3 mg oral mg q.d. 5d 7.5 mg oral mg q.d. 9d mg/kg oral mg b.d. 7d 8 mg oral 19 0* mg b.d. 7d 8 mg oral mg q.d. 6d 7.5 mg oral mg q.d. 28d 2 mg oral RoA: route of administration Dose Stagger: the time after RIF dosing when MDZ was given. A negative value indicates MDZ was given prior to RIF last dose # study was carried out in patients rather than healthy volunteers *Ambiguous Almond et al., DMD 44(6):

11 Prediction of DDIs with original model fold fold Acceptance limits proposed by (Guest et al., 2011) Different methods of visualisation (AUC ratio vs 1/AUC ratio vs % change) were compared Almond et al., DMD 44(6):

12 Victim drugs Simulated Observed IV and oral studies Qualified victim drug files

13 Possible reasons for under prediction of oral AUC ratio System Parameters K deg liver K deg gut liver gut gut Drug Parameters - Victim fm (UGT component inducible?) Displacement fm UGT 2% AUC Ratio = 5.9 fm UGT 0% AUC Ratio = 6.1 UGT1A4 CYP3A4 renal Drug Parameters - Perpetrator C-T profiles fu gut Ind max, IndC 50 (in vivo calibrator) Burger et al., 1971

14 Urinary 6β-Hydroxycortisol/Cortisol ratio (vs baseline) Uncertainty: in vivo reference values Error associated with Indmax = 8, IndC50 = 0.32? RIF-specific factor Indmax = 8.0 IndC 50 = 0.32 µm Baseline Rifampin Dosing Study Days Post Dosing Tran et al, 1999 Different studies PK rifampicin & marker of CYP3A4 changes Endogenous marker ~ IV administration Are there gut and liver differences in Ind max, IndC 50 MR from IV study has blood flow limitations (increase in CYP activity may not be proportional to MR)

15 Changing Ind max values Model A (base) (8) (8) B (8) x2 (16) C x2 (16) x2 (16)

16 Rifampicin Refinement of Indmax Indmax = 8 Indmax = 8 (liver) Indmax = 16 Indmax = 16 (gut) fold fold Acceptance limits proposed by (Guest et al., 2011) Indmax 16 also gave highest accuracy for Cmax ratio Almond et al., DMD 44(6):

17 Strategy Evaluation of DDIs with rifampicin (29 rifampicin DDI studies with 7 victims CYP3A substrates) Generate in vitro induction data (mrna & activity) for other prototypical inducers using multiple human hepatocyte donors Evaluation sources of uncertainty (drug & system parameters) - sensitivity analyses Refinement of rifampicin file and re-evaluation Apply the refined model to calibrate other independent inducers

18 In vitro experiments Emax & EC50 for range prototypical inducers mrna and activity Multiple human hepatocytes (n=4) 48h incubation Concentration range tailored based on literature data Generated in a single laboratory 18

19 In vitro data Ind max IndC 50 Ind max :IndC 50 Activity mrna Ind max (fold) IndC 50 (µm) Ind max (fold) IndC 50 (µm) Rifampicin Mean SD Carbamazepine Mean SD Phenobarbital Mean SD Phenytoin Mean SD Efavirenz Mean SD Nifedipine Mean SD Almond et al., DMD 44(6):

20 Predictions using in vitro data (uncalibrated) mrna Activity 20

21 Prediction of Rifampicin DDIs (model comparison) Activity mrna IVIVE from our dataset was good for both mrna and activity Would we expect that to be the same for other data sets knowing the extensive inter-lab/donor variability? Published Emax, EC50 data Human hepatocytes Different methods Some are the same donors (CP vs primary; mrna vs activity) 21

22 Strategy Evaluation of DDIs with rifampicin (29 rifampicin DDI studies with 7 victims CYP3A substrates) Generate in vitro induction data (mrna & activity) for other prototypical inducers using multiple human hepatocyte donors Evaluation sources of uncertainty (drug & system parameters) - sensitivity analyses Refinement of rifampicin file and re-evaluation Apply the refined model to calibrate other independent inducers Almond et al., DMD 44(6):

23 Calibration Uncalibrated Calibrated (8) Calibrated (16) Activity mrna Carbamazepine Phenobarbital Phenytoin 23

24 Summary Systematic evaluation of predictions of DDIs mediated by CYP3A induction Range of substrates with IV and oral administration to investigate sources of uncertainty and to correct the under prediction Generated comprehensive in vitro dataset (mrna & activity) for prototypical inducers. Refined model was then used to calibrate these data and predictions compared to uncalibrated data The calibrated data (refined model) AND in vitro data gave reasonably good predictions of DDI magnitude Calibration of activity data with the original model gave under prediction of the observed DDI magnitude 24

25 Recommendations Calibration is helpful to combat inter donor and lab variability and allows prospective prediction with less emphasis on full characterisation of the in vitro system and mrna vs activity. We found our in vitro data yielded reasonable predictions. Use of an uncalibrated approach requires full characterisation using prototypical inducers to give understanding of the IVIVE of that system. 25

26 Continuing Challenges Prediction of DDIs due to mixed inhibition and induction Prueksaritanont T, et al., AAPS J 2013 Jones et al., 2015 Yamazaki 2015 DMD 43: Induction of other CYPs - lacking system data (kdeg) - relationship between mrna v activity - calibration/ivive Ramsden (9): Chan 2016 ISSX Busan Almond 2015 NA ISSX IQ 2B6 working group Ke 2016 CPT PSP In Press (induction of 2B6 & 3A4 by efavirenz) Low fm 3A4 substrates (lack of specificity of inducers) Depletion of inducers in incubation 26

27 Ibrutinib prediction of DDIs Coadministration of ibrutinib with CYP3A4 inhibitors Simulations using physiologically based pharmacokinetic (PBPK) models suggested that a moderate CYP3A inhibitor (diltiazem and erythromycin) may increase the AUC of ibrutinib 6 to 9-fold. Coadministration of ibrutinib with CYP3A4 inducers Simulations using PBPK suggested that a moderate CYP3A inducer (efavrirenz) may decrease the AUC of ibrutinib by up to 3-fold. 27

28 Label (Prescribing Information) 28

29 Acknowledgements Jane Kenny Susan Wong Suzanne Tay Iain Gardner Karen Rowland-Yeo Alice Ke Sophie Mukadam 29