PFF Teal = MAIN COLORS PFF Green = Light Green = Red = HIGHLIGHT COLORS Light Grey = Dark Grey = Black = INTEGRATING BIOMARKERS IN CLINICAL STUDIES TO AFFECT PATIENT CARE FERNANDO J. MARTINEZ, MD, MS TRANSLATIONAL SCIENCE: PROGRESS TOWARDS PERSONALIZED MEDICINE FOR IPF - BIG DATA MEETS PATIENT CARE NOVEMBER 13, 2015

This slide has been removed at the request of the presenter because it contains unpublished data.

How can biomarkers be used in drug development? UseDrug Development Target validationDemonstrate potential target plays biological role Early compound screeningIdentifies most promising compounds Pharmacodynamics assaysDetermines drug activity/dose selection Patient selectionDefines inclusion/exclusion criteria Surrogate endpointAllows short-term outcome measures in place of long-term primary endpoint Michele C & Ball J. Evaluation of biomarkers and surrogate endpoints In chronic disease; 2010 Biomarkers used to confirm the MOA ideally need to be available at the initiation of clinical development (Phase I) in order to be most useful* *Groves et al, Quintiles

This slide has been removed at the request of the presenter because it contains unpublished data.

UseDrug Development Target validationDemonstrate potential target plays biological role Early compound screeningIdentifies most promising compounds Pharmacodynamics assaysDetermines drug activity/dose selection Patient selectionDefines inclusion/exclusion criteria Surrogate endpointAllows short-term outcome measures in place of long-term primary endpoint Michele C & Ball J. Evaluation of biomarkers and surrogate endpoints In chronic disease; 2010 Can include general prognostic markers Can include pathway specific enrichment markers Biomarkers for patient selection can be developed prospectively with drug and biomarker development occurring in parallel, or they can be developed by effectively playing “catch-up” at a later stage * *Groves et al, Quintiles How can biomarkers be used in drug development?

 Treatment targeting molecular pathways may benefit only a subset of patients  Treatment heterogeneity requires biomarker development  Multiple Phase III designs are available in this setting  The choice of trial design should be guided by the strength of the biomarker’s credentials Freidlin & Korn Nat Rev Clin Oncol 2014; 11:81-90 Biomarker Enrichment Strategies: Borrowing from Oncology

Trial Options for Biomarkers with Varying Strength Freidlin & Korn Nat Rev Clin Oncol 2014; 11:81-90

‘Fall-back’ Design for Biomarkers With Weak Credentials Freidlin & Korn Nat Rev Clin Oncol 2014; 11:81-90

Disease Progression in IPF is Associated with Altered Lung Microbiome VariableParameter EstimateSEHR (95%CI)P-value Age (per 10 yrs) (0.33,1.36)0.28 Male gender (0.391,2.27)0.89 Smoking Status (0.6,2.36)0.62 FVC 10% (0.97,1.83)0.076 DLCO 10% increase (0.59,1.58)0.89 Desaturation <88% (2.41,23.9)<0.001* GERD (1.21,8.29)0.019* Shannon Diversity Index (0.33,0.85)0.0087* Streptococcus > 4% (3.15,36.68)<0.001* Staphylococcus > 2% (1.67,14.27)0.0037* Concordance Index Han et al; Lancet Respir Med 2014; 2:

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Co-trimoxazole decreases all cause mortality in per protocol analysis in 181 fibrotic IIP (89% IPF) Shulgina et al; Thorax 2013; 68:

CLEAN UP IPF Fernando J. Martinez, M.D., M.S. (contact PI) Weill Cornell Medical Center University of Michigan Health System Kevin Anstrom, PhD (co-PI) Michael Durheim, MD (co-I) Duke University Imre Noth, MD (co-PI) University of Chicago Robert Kaner, MD (co-I) Xiaoping Wu, MD (co-I) Weill Cornell Medical Center Kevin Flaherty, MD, MS (co-I) University of Michigan Health System Ganesh Raghu, MD (co-I) University of Washington

What is CLEAN UP IPF hypothesis? 17 Our principal hypothesis is that antimicrobial therapy in IPF patients will improve clinical outcomes

inclusion/exclusion CLEAN UP IPF inclusion/exclusion InclusionExclusion Age > 40 Local IPF diagnosis Able to provide informed consent Currently on regular antibiotic therapy Antibiotic therapy contraindication Pregnancy or planning pregnancy

CLEAN UP IPF additional components Included in current study  Baseline genotyping  Baseline PBMC genomic signature in subset Proposed  Bronchoscopic substudy  Microbiome analysis of bronch, oral, poop samples  Circulating inflammatory cell subtypes

Response segregated by pre-alert and post-alert enrollment status Martinez et al NEJM 2014; 370:

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NAC effectiveness by TOLLIP genotype sets up another opportunity for biomarker driven trial Oldham et al AJRCCM Articles in press Published on 02-September-2015 as /rccm )C

UseDrug Development Target validationDemonstrate potential target plays biological role Early compound screeningIdentifies most promising compounds Pharmacodynamics assaysDetermines drug activity/dose selection Patient selectionDefines inclusion/exclusion criteria Surrogate endpointAllows short-term outcome measures in place of long-term primary endpoint Michele C & Ball J. Evaluation of biomarkers and surrogate endpoints In chronic disease; 2010 How can biomarkers be used in drug development?

This slide has been removed at the request of the presenter because it contains unpublished data.

Biomarker Strategy for Therapeutics in IPF Development TargetTherapeuticBiomarker strategy αvβ6STX-100 mAb TGF-β activity (pSMAD2 and gene changes) in BAL IL-2 SirolimusChange in peripheral blood CXCR4+ fibrocytes IL-4/IL-13SAR IL-4/IL-13 bispecific mAb (Sanofi) Peripheral blood biomarkers IL-13QAX576 mAb TBD IL-13Tralokinumab mAb Peripheral blood biomarkers LPA1BMS mAbTBD CTGF (antagonist)FG-3019 mAbTBD LOXL2 (antagonist)GS6624 Prognostic value of baseline level of LOXL-2in peripheral blood PDGF/FGF-2/VEGFBIBF-1120TBD PI3K/mTorGSK pAKT in plasma and BAL cells Serum amyloid proteinPRM-151 Fibrocytes, IL-6, other peripheral biomarkers Carbon monoxideSerum MMP7 Anti-inflammatory/anti-fibroticPirfenidone-

One final thought from the PFF Summit….   In the first study, company researchers presented data suggesting that Veracyte's molecular classifier, which is in development, has the potential to accurately distinguish IPF from other ILDs without the need for surgery… The classifier is being developed using whole-genome, deep RNA sequencing and with training by histopathology “truth.” These results reinforce previous findings*. * Kim, SY. (June 2015). Classification of usual interstitial pneumonia in patients with interstitial lung disease: assessment of a machine learning approach using high-dimensional transcriptional data. Lancet Resp. Med., 2015; 3(6): 473–482. Veracyte press release

Integrating biomarkers in clinical studies to affect patient care: Conclusions  Biomarkers have the potential to – limit treatment heterogeneity – identify new and highly promising therapeutic targets – enhance efficiency of future therapeutic development  Biological samples should be routinely collected in all therapeutic trials  Linking to molecular diagnostics may be optimal  Companion diagnostic strategies should be considered in future development