Strategies for Developing and Validating PBPK Models for Extrapolation to Unstudied Population

Transcription

1 Strategies for Developing and Validating PBPK Models for Extrapolation to Unstudied Population Nina Isoherranen Department of Pharmaceutics University of Washington

2 Processes driving drug disposition are altered in special populations in population specific manner Transporter abundance Gastric emptying time Renal function Organ volume Organ blood flows Hematocrit Enzyme abundance Genotype Tissue composition Plasma binding proteins

3 PBPK modeling has great potential to predict drug disposition in special populations via well-designed population models System Model Drug Model Organ blood flows and organ size can be altered Basic physicochemical parameters, B/P, K p tissues GFR, kidney blood flow and transporter expression can change Enzyme expression can be modified in special populations CL h, F h V max and K m for HLMs hepatocytes, CYPS or other enzymes F a k a, F g dissolution parameters, dosing 3

4 Potential applications of PBPK model Predict Pharmacokinetics of a drug prior to human dosing from in vitro and animal data Predict susceptibility to DDIs (and PGX) and magnitude of DDIs with coadministered drugs Hypothesis testing and mechanistic interpretation of data Predict disposition in sensitive populations such as organ impairment populations, pediatrics and pregnant women Is PBPK modeling expected to replace experimental studies, provide a mathematical method to explain obtained data or aid in study design?

5 When is a PBPK model fit for purpose? What is the purpose/intended use of PBPK models in published literature and how is model performance established? Does model development approach match the intended use? How is model quality assessed? Can we expect a specific acceptance criteria to be applicable for all models? Should model acceptance criteria be driven by intended use or by characteristics of the drug? Evaluation of objective statistical criteria for model validation: Examples PBPK model development and validation for atomoxetine PBPK model development and validation for all-trans-retinoic acid

6 Use of PBPK models has increased significantly with diverse uses and model development strategies A total of 366 papers were identified; 74% original research and 26% reviews commentaries and prediction method descriptions Inclusion criteria: original model enough information for replication, results compared to in vivo data excluded library files and publications without model input parameters Possibly under-representation of literature due to stringent criteria Sager Sager et et al DMD al DMD

7 Use of PBPK has increased significantly but majority of models are still used for DDI and basic PK simulation The use of PBPK models in publications is different from that in regulatory submissions In 2013 FDA regulatory filings PBPK was used for: 60% DDI 21% pediatric 6% absorption 13% other The use of PBPK models somewhat reflects the generally accepted confidence on PBPK model performance Pan et al 2014; Sager et al DMD 2015

8 Published models are predominantly full-pbpk and use a combination of top-down and bottom up approaches 72% of published models used full -PBPK; Requires some prediction or analysis of K p values Mechanistically more powerful and allow simulation of tissue concentrations, 13/271 original papers reported simulated tissue concentrations Pregnancy and pediatric modeling exclusively used full PBPK 27% were minimal PBPK Minimal PBPK will almost always require some level of in vivo data. Bottom-up approach for CL parameters is most commonly used: Bottom-up in 35% of cases vs top down back-calculation in 21% Use of in vivo CL as such in 18% Scaling factors used in 17% of the models, mainly for transporter substrates Parameter estimation and sensitivity analysis only in 9% of the papers Sager et al DMD 2015

9 General issues identified in publications 97% of the models were compared to independent dataset (independent population): BUT. Minority (32%) of the papers matched simulated population (age, gender and genotype) to observed Simulated genotypes were only reported for 21% of the models Since physiological parameters in the model depend on the population, model attention should be devoted to population selection Sager et al DMD 2015

10 Model acceptance criteria are inconsistent and often not well defined Majority did not specify an a priori acceptance criterion for model acceptance Reported model acceptance criteria did not match the drug characteristics We found no papers in which a priori model acceptance criteria were driven by knowledge of the variability in PK parameters of the drug of interest Sager et al DMD 2015

11 Attention to inter-study variability in observed data; often single studies are used for model verification/validation Does one clinical study predict another within fit for purpose The analysis shows that in some cases the twofold criteria method is an unreasonable expectation when the observed data are obtained from studies with small sample size. Proposed prediction criteria that considers sample size and variance

12 Based on variability and the sample size in clinical studies one can calculate the confidence interval for prediction σ is the calculated variability of a given PK parameter in the population CV% represents the observed mean of coefficient of variation (CV) of PK parameter from all the identified studies, x is the observed mean parameter N is the mean number of subjects in the observed studies. A and B are the calculated upper and lower boundaries for % confidence interval fold error (due to critical value 4.26) Note this criteria eliminates subjective decisions on fit-for-purpose Abduljalil et al DMD 2014

13 From statistical perspective 2-fold criteria is too stringent for some drugs but too lenient for others n=10 subjects per study Statistically, if a critical value of % CI is used every study should be simulated within the acceptance criteria If sufficient number of studies exist one can use a 95% CI and expect 5% of studies be outside acceptance range Abduljalil et al DMD 2014

14 Huang et al DMD 2017 PBPK modeling for Atomoxetine to test the objective criterion and model validation Atomoxetine is a CYP2D6 probe with extensive clinical PK data: ideal model compound Goal: Test whether rigorously validated model could be extrapolated to different populations Can use statistical criteria to delineate reasons of model failures

15 Huang et al DMD in press 2017 Determination of a priori model acceptance criteria and model development acceptance criterion ranges : For AUC fold in EM fold in PM, for C max fold in EM fold in PM Based on observed variability in ATM disposition model performance expectance is much higher than just 2-fold even with % CI! The model is expected to fail in 9.7% of studies if bioequivalence (1.25-fold, critical value 1.658)

16 PBPK modeling for Atomoxetine to test the objective criterion and model validation Huang et al DMD 2017 Verification refers to initial model development with in vivo data, model parameters can be changes based on these datasets

17 For model verification all simulated studies meet the acceptance criteria Trial Dose (mg) observed AUC (μg.hr/ml) AUC acceptance range (μg.hr/ml) simulated AUC (μg.hr/ml) observed C max (ng/ml) C max acceptance range (ng/ml) simulated C max (ng/ml) EM population po dosing B4L-LC-HFBJ 10 a B4L-LC-HFBJ 90 a B4Z-LC-LYAE 30 b PM population po dosing B4L-LC-HFBJ 10 a B4L-LC-HFBJ 90 a B4Z-LC-LYAE 30 b EM population iv dosing B4Z-LC-LYAM 20 a Huang et al DMD 2017

18 PBPK model validation for Atomoxetine Validation uses independent datasets from healthy volunteers with defined genotypes, model parameters cannot be changed based on these datasets or during validation Huang et al DMD 2017

19 Validation of the atomoxetine PBPK model- what % is right Trial Dose (mg) observed AUC (μg.hr/ml) AUC acceptance range (μg.hr/ml) simulated AUC (μg.hr/ml) observed C max (ng/ml) C max acceptance range (ng/ml) simulated C max (ng/ml) EM population FDA approved dosing regimen B4Z-LC-LYAM 40 a B4Z-LC-LYAL 40 a B4Z-LC-LYAZ 60 a B4L-LC-HFBH 20 b B4Z-LC-LYAE 45 b PM population FDA approved dosing regimen B4Z-LC-LYAK 40 a B4L-LC-HFBH 20 b B4Z-LC-LYAE 45 b EM population off label dosing regimen B4L-LC-HFBJ 120 a B4Z-LC-LYAE 60 b B4Z-LC-LYAE 75 b PM population off label dosing regimen B4L-LC-HFBJ 120 a B4Z-LC-LYAE 60 b B4Z-LC-LYAE 75 b Huang et al DMD 2017

20 PBPK model Extrapolation: Can the validated model be used for special populations? Huang et al DMD 2017

21 Trial Extrapolation of the model to DDI studies Dose (mg) observed AUC (μg.hr/ml) AUC acceptance range (μg.hr/ml) simulated AUC (μg.hr/ml) observed C max (ng/ml) C max acceptance range (ng/ml) simulated C max (ng/ml) ATM+FLX ATM ATM+PRX ATM ATM+PRX ATM ATM+DES ATM+MDZ (PMs) Huang et al DMD 2017

22 Extrapolation of the model to special populations: Population observed AUC (μg.hr/ml) Pediatrics AUC acceptance range (μg.hr/ml) simulated AUC (μg.hr/ml) observed C max (ng/ml) C max acceptance range (ng/ml) simulated C max (ng/ml) Pediatric Population (CDER, 2002; Brown et al., 2016) EM EM EM PM Huang et al DMD 2017

23 Extrapolation of the model to special populations: renal impairment Population observed AUC (μg.hr/ml) AUC acceptance range (μg.hr/ml) simulated AUC (μg.hr/ml) observed C max (ng/ml) C max acceptance range (ng/ml) simulated C max (ng/ml) End Stage Renal Disease Patient Population Trial B4Z-LC-HFBM (CDER, 2002) healthy (H) ESRD ratio of E-H Atomoxetine is essentially all metabolically cleared, Why 2-fold increase in AUC is predicted? Alternative AUC acceptance criteria based on the observed variability and sample size in ESRD is fold, still fail Observed healthy population is not within expected healthy population PK Atomoxetine label states no change in PK in renal disease; body weight based CL is unchanged, not reflected in simple AUC comparison Huang et al DMD 2017

24 Extrapolation of the model to special populations: renal impairment Healthy ESRD Note different scales on y-axis The observed AUC in ESRD is well predicted using healthy population! Huang et al DMD 2017

25 Extrapolation of the model to special populations: hepatic impairment Population observed AUC (μg.hr/ml) AUC acceptance range (μg.hr/ml) simulated AUC (μg.hr/ml) observed C max (ng/ml) C max acceptance range (ng/ml) simulated C max (ng/ml) Hepatic Impairment Patient Population Trial B4Z-LC-HFBN (CDER, 2002) healthy subjects (H) CP-B CP-C ratio of CP-B/H ratio of CP-C/H Why did predictions fail? Alternative AUC acceptance criteria based on the observed variability and sample size are fold for Child-Pugh B and fold for Child-Pugh C, still fail In SimCYP, CYP2D6 decreased by 68% -90%, and liver volume by 29%-39% in the population model Based on atomoxetine model and PK: CYP2D6 expression decreases 0-25% if liver volume changes CYP2D6 expression decreases 32-89% if liver volume stays the same Huang et al DMD 2017

26 atra ( M) atra pharmacokinetics is complex and time and concentration dependent (K m = 10 nm) CYP26A1 atra 4-OH-atRA 1 Day 1 Day 7 atra Target gene transcription Time (minutes) 40% - 60% reduction of atra AUC upon chronic dosing iss observed in patients atra used exclusively in cancer (APL) patients 26

27 atra PBPK model development Model development challenges: No iv data available for RA in humans atra disposition is highly variable Minimal PK data available from healthy individuals, arbitrary model acceptance criteria in healthy population Saturation kinetics and in vivo induction not systematically studied in humans Jing et al (2017), J Pharmacol Exp Ther 27

28 atra PBPK model predicted dose-dependent kinetics in healthy humans using a PBPK model built based on in vitro data 10 mg atra (n=54) 20 mg atra (n=29) Jing et al (2017), Experimental data from Thudi et al (2011), Peng et al (2014) 28

29 atra PBPK model predicted time-dependent kinetics in healthy humans 22.5 mg/m 2 b.i.d atra for 15 days (n=11) ing et al (2017), J Pharmacol Exp Ther zpolat et al (2003), J Pharm Pharm Sci Day 9 Observed 42% AUC decrease Predicted 64% AUC decrease Day 15 Observed 37% AUC decrease Predicted 65% AUC decrease

30 atra disposition and autoinduction were successfully simulated in adult cancer patients with refined cancer population model 57% 62% Chronic myeloid leukemia patients dosed with 40 mg/m 2 atra b.i.d. for 7 days Jing et al (2017) In 9 out of 11 studies, simulated AUCs were within 2-fold of the observed The simulated mean oral clearance decreased as the dose increased 30

31 Activity Remaining (%) PBPK model can predict the complex DDIs with atra CYP3A4 CYP26A1 CYP2C8 atra metabolism inhibitors Ketoconazole (nm) (K m = 10 nm) 45 mg/m 2 atra 45 mg/m 2 atra b.i.d. Observed 2.2-fold increase Predicted 2.6-fold increase Day mg/m 2 atra KTZ 400 mg KTZ 400 mg Jing et al (2017) 31

32 Conclusions Increased rigor is needed to define guidance for publication of PBPK models including attention to population models and predefined acceptance criteria Model performance does depend on the drug- is fit-for-purpose possible for all drugs? Clinical data can be mined with statistical methods to define the expectations for a model. If acceptance criteria is broader than % CI acceptance criteria likely does not have sufficient rigor to differentiate good model from a bad one Rigorous model validation in healthy populations will provide critical information of population parameters in special populations such as organ impairment

33 Acknowledgements Post docs and graduate students: Jenny Sager Weize Huang Jing Jing Mariko Nakano Collaborators: UW DIDB Isabelle Ragueneau-Majlessi Funding (NIH) T32 GM R01 GM R01 GM P01 DA TL1 TR FDA CDER/ORISE (MN) Photo by Mike Riffle, UW