Computational Systems Pharmacology of Antibody-Drug Conjugates: A - PowerPoint PPT Presentation

Computational Systems Pharmacology of Antibody-Drug Conjugates: A Joint Academia-Industry Experience Inez Lam IMAG-AND Futures Meeting March 17, 2020

Mac Gabhann Lab at Johns Hopkins University C COMPUTATIONAL DESIGN of THERAPEUTICS LAB of

Vascular HIV Diseases Diverse Biological Contexts for Multiscale Modeling Cancer Endometriosis

Diverse Research Contexts for Multiscale Modeling Johns Hopkins-AstraZeneca Scholars Program: A Joint Academia-Industry Experience

Applying Q uantitative S ystems P harmacology at the intersection of academia and industry Data Da In Industry Ac Academia ia Co Computational Lots of data Long-term projects Ex Experiments Mo Models ls & Drug development Big picture questions Simul Si ulations ons expertise Modeling expertise Real-world application Clinical expertise Pr Prediction ons

Antibody-Drug Conjugates: The Best of Both Worlds? Antibody targets cancer cells Small molecule drug kills cancer cells (also known as the warhead ) Linker joins antibody and drug + = Selective killing of tumor cells High Cytotoxicity High Specificity

Why Model Antibody-Drug Conjugates? Need to optimize 3 different entities to determine collective properties of the ADC: Antibody – Mu Multiple design levers can be controlled Warhead – Ph Pharmacokinetics and pharmacodynamics of Why model individual components and overall ADC ADCs? Linker Therapeutic Index : balance between safety & toxicity – Th at multiple scales Drug to Antibody Ratio Can be applied at any stage of drug development process (DAR): average number of drug molecules attached to the antibody Discovery Preclinical Clinical

Existing models have explored various aspects of ADCs Groups Gr ps No Notabl ble M Mode dels Sel Selec ected ted Insi Insights hts Un Univers rsity ty Preclinical, multiscale model of T-DM1 (Ciliers 2016) Co-administration of unconjugated of of Model of payload distribution (Khera 2017) Ab with ADC may help improve Mi Michigan Agent-Based Model of T-DM1 (Menezes 2020) distribution of ADC in the tissue

Existing models have explored various aspects of ADCs Groups Gr ps No Notabl ble M Mode dels Sel Selec ected ted Insi Insights hts Un Univers rsity ty Preclinical, multiscale model of T-DM1 (Ciliers 2016) Co-administration of unconjugated of of Model of payload distribution (Khera 2017) Ab with ADC may help improve Michigan Mi Agent-Based Model of T-DM1 (Menezes 2020) distribution of ADC in the tissue PKPD Model of brentuximab-vedotin (Shah 2012) Model suggested fractioned dosing SU SUNY NY PKPD model of inotuzumab ozogamicin (Betts 2016) regimen is superior to a conventional Bu Buffal alo Tumor Disposition Model for T-DM1 (Singh 2016) dosing regimen for acute lymphocytic Dual Cell-Level Systems PKPD Model (Singh 2019) leukemia (ALL)

Existing models have explored various aspects of ADCs Groups Gr ps Notabl No ble M Mode dels Sel Selec ected ted Insi Insights hts Un Univers rsity ty Preclinical, multiscale model of T-DM1 (Ciliers 2016) Co-administration of unconjugated of of Model of payload distribution (Khera 2017) Ab with ADC may help improve Michigan Mi Agent-Based Model of T-DM1 (Menezes 2020) distribution of ADC in the tissue PKPD Model of brentuximab-vedotin (Shah 2012) Model suggested fractioned dosing SUNY SU NY PKPD model of inotuzumab ozogamicin (Betts 2016) regimen is superior to a conventional Bu Buffal alo Tumor Disposition Model for T-DM1 (Singh 2016) dosing regimen for acute lymphocytic Dual Cell-Level Systems PKPD Model (Singh 2019) leukemia (ALL) Found undesirable tumor properties Novartis ADC Modeling Framework (Vasalou 2015) that can impair ADC tissue Indus Industry Genentech ADC PK Model (Sukumaran 2017) homogeneity and explored ADC design scenarios to counteract them

Goal : Build a multiscale, integrated computational model of AstraZeneca pyrrolobenzodiazepine ( PBD PBD ) ADCs using differential equations with “bench to bedside” translation Our Systems Use model to improve prediction of therapeutic index (TI) Pharmacology and to understand : – Properties of the ADC to build the optimal therapy Model of – Kinetics and mechanisms of ADC action in tumor PBD ADCs – Off-target and bystander effects – Factors that have the biggest impact on safety and efficacy Framework can be adapted to other types of ADCs

In In Vivo Model In In Vitro Model Clinical Model Cl In In vitro da data In vivo da In data Clinical data Cl Computational Models Co Cellular & Intracellular Tumor Properties and Virtual Patients with Mechanisms of ADCs Systemic Distribution Specific Attributes Simul Si ulations ons Predict Bystander Effects Run Virtual Clinical Trials Modify ADC Design and Tumor and Test Different Characteristics Growth/Inhibition Treatment Scenarios Li Link to Therapeutic Index of ADCs

In Vitro Model Schematic In Bindi Bi ding g & Unbi Un bindi ding + – Ag ADC AD ADC AD Ag Ag Ag W ex extracel ellul ular Cell Surface In Internalization Cytosol & & Recycling Tr Trafficking & Release of Warhead – Ag + ADC AD Ag Ag Ag ADC AD – Ag x DAR AD ADC Ag W in intra trace cellu llula lar Ag Ag Endosome Lysosome + W nuc DNA DN nuclea ear Ce Cell Death Formation Fo on of of – Effector Ef or Com omplex W nuc DN DNA nuclea ear Nucleus

In Vitro Model Schematic In + – Ag ADC AD ADC AD Ag Ag Ag W ex extracel ellul ular Cell Surface Cytosol – Ag + AD ADC Ag Ag Ag ADC AD – Ag x DAR AD ADC Ag W in intra trace cellu llula lar Ag Ag Endosome Lysosome + W nuc DN DNA nuclea ear Drug to Antibody Ratio Use In Vitro Mode Us del to Cell Death Ce (DAR): average number Ex Explor ore Ke Key ADC of drug molecules De Design Properties attached to the antibody – W nuc DNA DN nuclea ear Nucleus

Ex Explor oring Ke Key ADC Desi sign Prop operties: s: Varying DAR R between 1 and 10 As As DAR AR increases: Warhead-DN Wa DNA complex increases linearly Extracellular Warhead increases linearly Ex Ce Cell Population decreases exponentially Bi Bigge ggest ga gain in cell killing g from fir first fe few warhead mole lecule les: Sug Sugges ests op optimal DAR for thi his syste system may y be betw tween 2-4

Usin Us ing the In Vit itro Model l to Explo lore Key AD ADC C Desig ign Pr Propertie ies 10 10 10 2 10 k ki kill = = 10 3 1/ 1/nM nM/hr hr 10 4 10 10 10 5 10 7 10 10 6 10 Li Linker Design Warhead Potency Wa Sensitivity Ana Sens nalysis

In Vivo Model Connects Pharmacokinetics to In Vitro Model Intravenous Dosing Blood Rest of Body ADC Ab + W Clearance

In In Vivo Model In In Vitro Model Clinical Model Cl In In vitro da data In vivo da In data Clinical data Cl Computational Models Co Cellular & Intracellular Tumor Properties and Virtual Patients with Mechanisms of ADCs Systemic Distribution Specific Attributes Simul Si ulations ons Predict Bystander Effects Run Virtual Clinical Trials Modify ADC Design and Tumor and Test Different Characteristics Growth/Inhibition Treatment Scenarios Li Link to Therapeutic Index of ADCs

Developing long-term partnerships with both academic and industry researchers enables di diversity Diverse of of expertise se and resou sources for multiscale modeling contexts enrich the Harnessing the ad advan antag ages of each research environment research experience Understanding both research environments will str strea eamline ne futur future e col ollabora orati tions ons

Thanks for listening! Questions? Special Thanks To: Feilim Mac Gabhann Rosalin Arends Kathryn Ball Phin Chooi Thaïs Callieau Balakumar Vijayakrishnan Peter Tyrer Conor Barry Sarvenaz Sarabipour Christy Pickering Wangui Mbuguiro Adriana Gonzalez Johns Hopkins-AstraZeneca Scholars Program