Bench-to-Bedside Translation of ADCs using PK/PD M&S

Dhaval K. Shah, Ph.D.dshah4@buffalo.edu

8/23/2016

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

Antibody-Drug Conjugate (ADCs)

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)

>60 in the Clinic

Preclinical-to-Clinical Translation: Oncology

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

Cell Human BodySeconds Years

Multi-Scale System for Oncology

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

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 afterADC 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. 2012 Dec;39(6):643-59.

Adcetris® Case Study0

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• 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.

CD30 Receptor# / cell (Okeley et al.)Binding affinities (Nagata et al.)Internalization rate (Sutherland et al.)Payload efflux (Okeley et al.)

• 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.

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konADCADCFree ADCBound kintAg

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(1) In vitro PK of ADC & drug

(2) Drug PK in mouse after administration of the drug alone

Step-2: Characterization of MMAE PK in plasma & tumor tissue of xenograft mouse, after IV administration of MMAE

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• 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

• The model did a very good job in predicting all the profiles using a predefined set of parameters, without estimating any parameter.

• This increases confidence in the ability of the novel ADC tumor disposition model to predict payload concentration at site-of-action.

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Adcetris® Case Study(3) Characterization of ADC PK in Mouse Plasma

(4) Predicting tumor ADC and drug concentrations

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• 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.

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L540cy Karpas299

SGN-35

MMAE

PK from two different trials with different regimens

• 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.

Adcetris® Case Study(5) Estimation of Drug Efficacy in Mouse (6) Prediction of ADC and drug plasma PK in clinical

(a) The growth rate of the tumor was set to match clinically observed values (doubling time 20-140 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)

Adcetris® Case Study(7) Translation of parameters and clinical trial simulations

9/17/08 - Baseline

Complete resolution of nodal and subcutaneous involvement

Top image: L hilar LN, 1.9 x 1.6 cm

Bottom image: subQ scalp nodule, 2.5 x 2 cm

TIME

w

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

Application to Other ADCs: Mylotarg®Inotuzumab Ozogamicin

Application for MID3: Adcetris®Dose vs. Antigen Conc. Dose vs. MAb Affinity

Dose vs. Payload Efflux Rate Dose vs. Tumor Growth Rate

Antigen Conc. vs. MAb Affinity (1.5 mpk)

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

Platform PBPK Model for ADCs: Motivation

Understanding Differential

Target Expression

Develop BetterExposure-Response

Relationships

Prediction of Clinical PK ad DDI

Platform PBPK Model for ADCs: Approach

JPKPD 2014, 41:1, 55-69

Platform PBPK Model for ADCs: Approach

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)

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

ADC PBPK Model: Kadcyla® Case Study(2) Characterization of ADC stability (DAR vs. Time)

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

Note: Observed ADC concentrations were measured as total tissue radioactivity

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

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

Scale-up Strategy

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

Application to Other ADCs: SGN-75

Application to Other ADCs: A1mcMMAF

Application to Other ADCs: HuC242-DM1

Application to Other ADCs: Anti-STEAP1-vcMMAE

Measurement error due to the use of residualizing isotope.

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

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

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

Alison, Nahor (Pfizer)

Roche

NIGMS (NIH)

Acknowledgements

1R01GM114179-01

Center for Protein Therapeutics