Bench-to-Bedside Translation of ADCs using PK/PD M&S. Dhaval K. Shah, Ph.D.

Transcription

1 Bench-to-Bedside Translation of ADCs using PK/PD M&S Dhaval K. Shah, Ph.D. 8/23/2016

2 Outline Overview: ADCs Prediction of Clinical Efficacy using a Multi-Scale Mechanistic PK/PD Model Prediction of Clinical PK using a Platform PBPK Model for ADCs Summary

3 Antibody-Drug Conjugate (ADCs) >60 in the Clinic Deliver cytotoxic agents to the tumor via tumor specific, over expressed, cell surface antigens Improved efficacy Improved selectivity Minimizes normal tissue exposure to the cytotoxic agent Decreased toxicity Improved therapeutic index (TI)

4 Preclinical-to-Clinical Translation: Oncology Multi-Scale System for Oncology Cell Human Body Seconds Years One should not take the lack of IVIVC and the lack of preclinical-to-clinical translation for granted, but try to understand the mechanistic reasons behind it

5 Outline Overview: ADCs Prediction of Clinical Efficacy using a Multi-Scale Mechanistic PK/PD Model Prediction of Clinical PK using a Platform PBPK Model for ADCs Summary

6 Strategy: Translation of ADC Efficacy (1) In Vitro: Characterization of ADC & drug PK at cellular level (2) Drug PK in Mouse: Characterization of drug PK in plasma & tumor tissue, after administration of the drug alone (3) Characterization of ADC PK in Mouse Plasma (4) Predicting tumor ADC and drug concentrations after ADC administration in xenograft mouse (5) Estimation of Drug Efficacy in Mouse: Characterization of ADC induced preclinical TGI data using the PK/PD model (6) Prediction of ADC and drug plasma PK in the clinical (7) Preclinical-to-clinical translation of system and efficacy parameters, and clinical trial simulations Brentuximab vedotin (SGN-35, Adcetris ) anti-cd30 vc-mmae (DAR ~4) JPKPD Dec;39(6):

7 Extracellular MMAE (nm) Intracellular MMAE (nm) pmole MME/10^6 Karpas 299 cells Model Predicted (Total) Observed (Intracellular) Model Predicted (Intracellular) (1) In vitro PK of ADC & drug Time (h) Observed Model Predicted Time (h) Observed Model Predicted Time (h) Adcetris Case Study CD30 Receptor# / cell (Okeley et al.) Binding affinities (Nagata et al.) Internalization rate (Sutherland et al.) Payload efflux (Okeley et al.) kon ADC The model predicted concentration vs. time profiles of intracellular and extracellular MMAE, reasonably well. Consistent with experimental results, the model predicted that intracellular MMAE concentrations would be more than 100 times MMAE concentration in media. ADC Free ADC Bound kint koff Ag ADC Step-2: Characterization of MMAE PK in plasma & tumor tissue of PL xenograft Free mouse, after IV administration of MMAE PL Free kout PL (2) Drug PK in mouse after administration of the drug alone Bolus Dose MMAE X2 PL V2 PL CLD PL X1 PL V1 PL Vascular Exchange Payload Surface Exchange PL Free kout PL PL Free kon PL koff PL PLBound CL PL kin PL The model was able to characterize both the profiles well with reasonable confidence in the parameter estimates. Incorporation of intracellular tubulin binding was necessary to characterize tumor MMAE concentrations.

8 Adcetris Case Study Conc. (nm) (3) Characterization of ADC PK in Mouse Plasma X2 ADC V2 ADC Bolus Dose ADC CLD ADC CL ADC X1 ADC V1 ADC k dis SGN-35 plasma PK in mouse, characterized well with a two compartment model Exponential decay well characterized the average DAR vs. time profile in mouse Dissociation half life of MMAE was ~6 days (4) Predicting tumor ADC and drug concentrations PK X2 ADC V2 ADC Bolus Dose ADC CLD ADC X1 ADC V1 ADC Surface Exchange ADC Vascular Exchange Tumor kon ADC koff ADC kint Ag ADC Free ADC Bound X2 PL V2 PL CL ADC CLD PL CL PL DAR k dis X1 PL V1 PL Vascular Exchange Payload Surface Exchange PL Free kout PL PL Free kon PL koff PL The model did a very good job in predicting all the profiles using a PD predefined set of parameters, kkill Max without PL Tumor estimating any parameter. kc PL 50 Tumor TV kgthis 1 increases V1 confidence in the ability of the novel ADC tumor Ex V Max disposition 1 model to predict payload concentration Tau at Tau site-of-action. Tau Cell ψ ψ k dis kin PL V1 V2 V3 Cell V4 PL Bound Plasma Conjugated MMAE Tumor MMAE Plasma Free MMAE Time (Day)

9 Adcetris Case Study (5) Estimation of Drug Efficacy in Mouse (6) Prediction of ADC and drug plasma PK in clinical L540cy Karpas299 PK from two different trials with different regimens PK X2 ADC V2 ADC Bolus Dose ADC CLD ADC X1ADC V1 ADC Surface Exchange ADC Vascular Exchange Tumor kon ADC koff ADC kint Ag ADC Free ADC Bound CL ADC DAR k dis PL Free kon PL koff PL PL Bound PD X2 PL V2 PL CLD PL CL PL TV kg 1 V1 Ex V Max 1 ψ ψ kg 1 Ex TV kg L X1 PL V1 PL Vascular Exchange Payload Surface Exchange PL Free kout PL Tau V1 V2 V3 Tau V4 TGI data from two different xenograft (L540cy and Karpas299), treated with various dosing regimens. Modeled using the mechanistic population PK/PD model, where MMAE conc. in tumor was driving the efficacy. k dis kkill PL Max Tumor kc PL 50 Tumor kin PL Tau Cell Cell Death SGN-35 MMAE Two compartment model was able to characterize the multiple dose clinical PK of SGN-35 and MMAE reasonably well. The parameter estimates for SGN-35 and MMAE clinical PK were utilized for clinical trial simulations. Payload dissociation half-life ~9 days, monkey.

10 w Adcetris Case Study (7) Translation of parameters and clinical trial simulations (a) The growth rate of the tumor was set to match clinically observed values (doubling time days) (b) The initial tumor burden and maximum possible tumor burden were set to clinically relevant values (c) The number of CD30 receptors on cancer cells were changed to the value obtained from a cancer patient (more than 5 times less than xenograft cell line) 9/17/08 - Baseline Top image: L hilar LN, 1.9 x 1.6 cm Bottom image: subq scalp nodule, 2.5 x 2 cm Complete resolution of nodal and subcutaneous involvement TIME

11 Application to Other ADCs: Kadcyla Trastuzumab Emtansine, T-DM1

12 Application to Other ADCs: Mylotarg Inotuzumab Ozogamicin

13 Application for MID3: Adcetris Dose vs. Antigen Conc. Dose vs. MAb Affinity Antigen Conc. vs. MAb Affinity (1.5 mpk) Dose vs. Payload Efflux Rate Dose vs. Tumor Growth Rate

14 Outline Overview: ADCs Prediction of Clinical Efficacy using a Multi-Scale Mechanistic PK/PD Model Prediction of Clinical PK using a Platform PBPK Model for ADCs Summary

15 Platform PBPK Model for ADCs: Motivation Prediction of Clinical PK ad DDI Understanding Differential Target Expression Develop Better Exposure-Response Relationships

16 Platform PBPK Model for ADCs: Approach JPKPD 2014, 41:1, 55-69

17 Platform PBPK Model for ADCs: Approach

18 ADC PBPK Model: Kadcyla Case Study Strategy (1) Characterization of unconjugated/released drug PK (2) Characterization of ADC stability (DAR vs. Time) (3) Prediction of preclinical PK: ADC and components (4) Prediction of human PK (Scale-up)

19 ADC PBPK Model: Kadcyla Case Study (1) Characterization of unconjugated/released drug PK

20 ADC PBPK Model: Kadcyla Case Study (2) Characterization of ADC stability (DAR vs. Time) Conc, nm T-DM1 rat TT data TT model T-DM1 data T-DM1 model Time, hours

21 ADC PBPK Model: Kadcyla Case Study (3) Prediction of preclinical PK: ADC Note: Observed ADC concentrations were measured as total tissue radioactivity

22 ADC PBPK Model: Kadcyla Case Study (3) Prediction of preclinical PK: components

23 ADC PBPK Model: Kadcyla Case Study (4) Prediction of human PK (Scale-up) Scale-up Strategy

24 ADC PBPK Model: Kadcyla Case Study (4) Prediction of human PK (Scale-up)

25 Application to Other ADCs: SGN-75

26 Application to Other ADCs: A1mcMMAF

27 Application to Other ADCs: HuC242-DM1

28 Application to Other ADCs: Anti-STEAP1-vcMMAE Measurement error due to the use of residualizing isotope.

29 Application to Other ADCs: Anti-TENB2-vc-MMAE

30 Outline Overview: ADCs Prediction of Clinical Efficacy using a Multi-Scale Mechanistic PK/PD Model Prediction of Clinical PK using a Platform PBPK Model for ADCs Summary

31 Summary Quantitative characterization and integration of preclinical PK & PD data is essential for successful preclinical-to-clinical translation of ADCs PK/PD M&S is a very useful tool to aid rational discovery and development (MID3) of ADCs There is a need to conduct novel experiments, to better understand cellular and whole body disposition of ADCs and their components Understanding preclinical and clinical PK behavior of the released drug is equally important

32 Acknowledgements Alison, Nahor (Pfizer) Roche NIGMS (NIH) Center for Protein Therapeutics 1R01GM