Preclinical to Clinical Translation of Antibody Drug Conjugates Robert Lutz, PhD Crescendo Biopharma Consulting World ADC Summit Berlin 2016 1
Bio ImmunoGen 23 years Researcher in cell death and survival pathways Identified BH3 domain with T. Chittenden Led ADC research Internal programs IMGN Kadcyla (T-DM1) research lead Alliance JRDC member Led ADC early development Project lead for IMGN development compounds Functional leadership for development teams Pharm/Tox; Clin Pharm; Biomarkers; Project mgmt. Independent consultant Helping emerging companies with research and development efforts 2
Attendee introductions 1-2 minutes Who you are scientific and professional networking is important to success Connection to the ADC field Your specialty 3
Translational R&D objectives Leverage information and experience from development to incorporate into research decision making Understand clinical development goals and challenges during research phase Use research approaches to identify successful development strategies 4
Agenda PK considerations Target considerations/ patient selection Biomarkers Metabolism Discussion topics Workshop concept: Build slides as we go along 5
Pharmacokinetic considerations Preclinical interests: Antigen-mediated clearance Cross-reactive model similar expression profile and levels to human tissue? Similar MAb affinity across species? Impact on dose linearity important for dose escalation paradigms Metabolic clearance Similar metabolic clearance across species (eg. Linker cleavage)? Initial metabolism studies are helpful for prediction In vitro stability studies Required by regulatory agencies, but do they provide any clinically relevant information for ADCs? Exposure relationship to efficacy and safety What drives outcome? Peak concentrations - Cmax? Duration of exposure - AUC? Threshold concentrations Ctrough? Excretion pathway Understanding elimination pathways can identify safety risks 6
Workshop input Preclinical PK considerations Difference in animal species and strains Which predicts human outcome? Modeling to backfill concepts of metabolism Understanding metabolic fates 7
Pharmacokinetic considerations Clinical interests: Exposure relationship to efficacy and safety Cmax/AUC/Ctrough - use modeling to identify options to optimize important exposure parameters Patient to patient variability Dose proportionality important real time information for dose escalation decisions Drug accumulation dose schedule optimization PK sampling strategy - capturing the right balance between objectives for PK analysis and patient impact Population PK assessments What factors impact dose/plasma concentration relationship? Shed antigen Can cause patient to patient variability; impact on dose escalation predictions; impact on safety Often not represented in preclinical models It s all about hitting plasma concentration targets consistently 8
Case study: IMGN853 (mirvetuximab soravtansine) Ab-Sulfo-SPDB-DM4 3.4 average Folate receptor α-targeting ADC for treatment of platinum-resistant ovarian cancer Significant patient to patient variability in exposure observed during dose escalation in phase 1 Dosing was Q3W based on total body weight (mg/kg). Higher exposures were predictive of dose-limiting reversible ocular toxicity independent of administered dose. Initial PK analysis suggested body weight was not a predictive factor of exposure variability 9
Observed PK Variability A U C 0-2 4 (h r *u g /m l) 2 5 0 5 0 0 0 2 0 0 4 0 0 0 C M a x ( g /m l) 1 5 0 1 0 0 5 0 3 0 0 0 2 0 0 0 1 0 0 0 0 0 5 0 1 0 0 1 5 0 0 0 5 0 1 0 0 1 5 0 W e ig h t (K g ) W e ig h t (K g )
Important factor identified Not absolute body weight rather why the patient was that body weight Plasma volume is not proportional to body weight in obese patients Obese patients were effectively being over-dosed and defining the maximally tolerated dose. Correlate: lean patients were being under-dosed Important factor in disease indications where obesity is a known risk factor Dosing by total body weight leads to higher exposure variability Using clinical data set: dosing by body surface area decreased variability; dosing by adjusted ideal body weight least variability AIBW = IBW + 0.4x(actual BW-IBW); Male: IBW= 50 + 2.3x(height over 60 inches); Female: IBW=45.5 + 2.3(height over 60 inches). Body mass index? 11
C M a x ( g /m l) Alternate Dosing Approaches Decrease Weight Dependence 2 5 0 5 m g /k g A IB W 2 0 0 1 5 0 1 0 0 5 0 0 0 5 0 1 0 0 1 5 0 W e ig h t (K g )
A U C 0-2 4 (h r*u g /m l) IM G N 8 5 3 A U C 0-2 4 (h r*u g /m l) IM G N 8 5 3 No Ocular Toxicity in 5 mg/kg AIBW Cohort 5 m g /k g T B W 5 m g /k g A IB W 4 0 0 0 4 0 0 0 3 0 0 0 3 0 0 0 2 0 0 0 2 0 0 0 1 0 0 0 1 0 0 0 0 Y e s N o 0 Y e s N o O c u la r T o x ic ity O c u la r T o x ic ity No corneal toxicity observed through multiple cycles Decreased variance observed in early exposure levels with AIBW dosing
Critical to clinical outcome ASCO 2015 data Escalation phase TBW RP2D ~3.3 4 mg/kg nearly all responses at doses > RP2D AIBW RP2D 6 mg/kg Expansion phase - 53% ORR with additional 29% SD 14
Impact on T-DM1 outcome? Dosing based on total body weight Quartile analysis of T-DM1 exposure data (C min ) from EMILIA trial suggests room for additional improvement Would AIBW dosing give less variable PK and more consistent benefit? From FDA review 15
Workshop input Clinical PK considerations RP2D Better exposures if characterize TMDD/other factors more fully Even lower doses may be better High need for PK/PD modeling to control impact of dose interruptions, dose reductions etc. 16
Dose schedule considerations Differing growth characteristics between xenograft tumor models and human tumors Recovery time from subclinical and clinical toxicities Dose limiting toxicities and treatment limiting toxicities Required number of patient visits for treatment and assessments 17
Target considerations Understanding the relationship between target expression and efficacy How to evaluate preclinically? In vitro potency In vivo efficacy CDX models; PDX models Assessing target expression levels Need an IHC method with an appropriate dynamic range to differentiate various levels of target expression Cell pellets as calibrators expression level determined by quantitative FACS validate method with orthogonal method (ie. Radiolabeled MAb). Need human tumor expression data set to identify clinically relevant range of target expression 18
Target expression in human tumor samples Scoring approach What is the right one? H-score versions incorporates multiple expression levels and measures of homogeneity 1-3 plus, focal/hetero/homo versions A balance is needed between resolution of scoring method and practical aspects: Sample quality eg. archived samples Reproducibility of scoring by pathologists 19
Assessing dynamic range Zhoa et al AACR 2015 20
Target expression in human tumor samples What sample should be used? What is available? Archived tissues Primary tumor sample Metastatic tumor sample Matched primary and metastatic samples (from same surgery) Matched primary and metastatic samples (collected over time relapse) Similarity of expression over sample categories de-risks sample bias differences increases risk, pushes sampling challenge Fresh biopsies Growing support Still difficult 21
Variability of expression Zhoa et al AACR 2015 22
Target expression Research considerations Disease indication prioritization generate data base on expression profile Normal tissue expression Identify potential targeted toxicities Sensitivity of tissue to payload MOA; expression level; tissue accessibility 23
Prioritize tumor indications with high expression Zhoa et al AACR 2015 24
Clinical strategy Target expression for patient selection/ stratification or retrospective analysis When to select? Phase 1 escalation Phase 1 expansion Phase 2 With target dependent efficacy, selection is critical for late stage trials Incremental increase in end point responses, huge decreases in late stage enrollment needs Prototype CDX assay is needed for patient selection Ethical considerations changing landscape How selective? Dependent on complexity of expression profile how precise can it be? Selection feasibility % of patients above cut-off; reproducibility of cut-off determination; nature of sample available 25
Workshop input Target considerations Soluble antigen how do we deal with this? Stoichiometry Impact on clearance efficacy and safety Cancer cell stem targets 26
Biomarker strategy Two major categories why? when? MOA markers Sensitivity/ resistance markers Sensitivity/resistance markers Target internalization factors Payload MOA factors Drug resistance protein Cell cycle factors Immunogenic factors 27
Biomarker strategy Approaches to identify sensitivity/ resistance markers Genomics and proteomics PDX models patient samples Cell biology studies Look shallow and look deep 1 factor can make an enormous difference in outcome Patient selection/ stratification Samples: Archived tissue; Fresh biopsies; Blood-based biomarkers CTCs, cdna, others Start retrospective; stratify in phase 2; selection for pivotal Enrichment of patients likely to respond can be a major factor in drug development 28
Clinical safety signals Preclinical considerations Role of target in pathological conditions Sensitivity of target positive normal tissue to payload MOA In pathological conditions In therapy-damaged tissue Availability of models to identify risks Integrate as part of preclinical toxicology strategy Clinical considerations Inclusion/exclusion criteria Selection/ stratification Prototype CDX assay needed for patient selection 29
Workshop input Biomarker considerations 30
Linker Stability - What is Real? Start with this consideration: Only a small amount of administered antibody (or ADC) becomes localized in tumor: (~ 0.01% injected dose/g tumor) "Antibodies as Carriers of Cytotoxicity" in Contributions to Oncology 43, 1-145, H. Huber, W. Queisser eds, Karger, Basel 1992 The rest of the administered antibody (or ADC) is catabolized via: Non-specific uptake by normal tissue Specific uptake by antigen-positive normal tissue It all goes somewhere The nature of the metabolite/catabolite is likely the major factor 31
Kadcyla Avoids Toxicity Associated with Maytansine Kadcyla maytansine Clinical development of Maytansine discontinued due to lack of therapeutic window severe GI toxicity and neuropathy But both molecules cleared through hepatobiliary excretion Issell and Crooke 1978 Cancer Treat Rev. 5(4):199-207 32
Potential Importance of Metabolism and Elimination T-DM1 maytansine IC50: 0.03-0.09 nm Lysine-SMCC-DM1 IC50: 8-17 nm Lack of GI toxicity for T-DM1 likely due to the low cytotoxic potency of the catabolite lysine-smcc-dm1 Shen et al. 2012 Curr Drug Metab. 2012 13(7):901-910 Sun et al. Bioconjug Chem. 2011 Apr 20;22(4):728-35 33
Disulfide-linked ADCs Dramatically Enhance the Efficacy for some Targets Mean tumor volume (mm 3 ) Anti-Tumor Activity in Mice Bearing OVCAR3 Xenografts 2000 1500 1000 500 PBS Thioether-linked ADC (10 mg/kg q3w) Control ADC (10 mg/kg q3w) Ab-SPDB-DM4 (5 mg/kg) Control ADC (5 mg/kg) 0 20 40 60 80 100 120 140 160 Days Post Inoculation 5 mg/kg 10 mg/kg q3w OVCAR3 (~300,000 antigens/cell) 34
Disulfide-Linked Maytansinoid ADCs Are Well Tolerated a Kadcyla T 1/2 MTD* DLT* Uncleavable (4.43 d) 3.6 mg/kg Reversible thrombocytopenia b SAR3419 c IMGN853 SO 3 d Cantuzumab mertansine Disulfide (7.93 d) Disulfide (~5 d) Disulfide (2.0 d) 4.3 mg/kg Reversible ocular toxicity ~6.0 mg/kg Reversible ocular toxicity 6.4 mg/kg Reversible elevation of liver enzymes a Krop et al, Journal Clinical Oncology 2011 29 (4)398 ; Krop et al, Journal Clinical Oncology 2010 28 (16)2698 b Younes et al. Journal of Clinical Oncology 2012 30(22) 2776-82. c K. Moore et al. ASCO 2014 (MTD not yet established) d A. Tolcher et al., J Clin Oncol. 2003; 21:211 35
Catabolites of Disulfide ADCs Are Safely Eliminated Disulfide-linked Maytansinoid Conjugates relative potency (in vitro) 200-500 S-oxidation in the liver 1-25 Sun et al. Bioconjug Chem. 2011 Apr 20;22(4):728-35. Catabolites are oxidized in the liver to less cytotoxic species and excreted via hepatobiliary elimination 36
Discussion topics Linker role Delivery mechanism Payload retention/ bystander effect Metabolism/ detoxification In silico safety modeling Site-directed conjugation Alternate binding moieties Immunogenicity What can be done preclinically Much of an issue for ADCs in oncology? 37
Helping patients 38