Lebrikizumab (Anti-IL13) and Periostin in Asthma

Needles in a haystack From disease heterogeneity to Dx in the clinic

Garret Hampton PhD on behalf of the Genentech Biomarker Groups AAPS Tuesday Nov 4 2014

• Introduction to our core biomarker strategies

• Disease heterogeneity

• Example: lebrikizumab (anti-IL13) and periostin in asthma

Clinical Disease heterogeneity Next-gen Technologies • Biomarker prevalence • Limited tissue • Prognostic significance • Real-time Dx • Subsets of unmet need • Monitoring • Pipeline opportunities • Dx hypotheses Disease Technology

Therapeutics

Exploratory biomarkers Diagnostic endpoints • Pharmacodynamic • Companion Dx endpoints • Predictive • Response surrogates • Response surrogate • Resistance (innate/acquired) “Standard” co-development of drug and CDx(s)

gRED

Research Pre-IND Phase I / II Phase III / LC

Co-CDx development Co-launch Dx assay Test clinical Dx Validate clinical Dx Hypothesis Drug & CDx generation development Correlation Correlation

CDx development and launch

Roche Dx IVD development process (DCC) Technical Validation & Prototype Development feasibility registration

DG DI DO LD

Prototype IVD development

Roche Dx BAs have different processes, timelines, costs for prototype, IVD development Roche Dx DCC process (Design Control and Commercialization): DG = Design Goal, DI = Design Input DO = Design Output, LD = Launch Decision Genentech / Roche oncology pipeline with CDx 5

Phase I Phase II Phase III Registration (26 NMEs) (8 NMEs+ 11 AIs) (3 NMEs+17 AIs) (2 NMEs+4 AIs) MDM2 ant solid & hem tumors Perjeta BC neoadjuvant Avastin HER2+ BC adj MabThera2 NHL sc HER3 MAb solid tumors Perjeta HER2+ mBC 2nd line Avastin HER2-neg. BC adj Avastin2 glioblastoma 1st line CSF-1R MAb solid tumors Perjeta HER2+ gastric cancer Avastin NSCLC adj Herceptin2 HER2+ BC sc MEK inh solid tumors Kadcyla HER2+ gastric cancer Avastin high risk carcinoid Tarceva4 NSCLC EGFR mut 1st line Tweak MAb oncology Erivedge operable BCC Avastin1 ovarian cancer 1st line Kadcyla3 HER2+ pretr. mBC Ang2-VEGF MAb oncology onartuzumab TNmBC, 1st/2nd line Avastin1 rel. ovarian ca. Pt-resistant Erivedge3 advanced BCC Raf & MEK dual inh solid tumors onartuzumab mCRC 1st line Avastin1 rel. ovarian ca. Pt-sensitive CD44 MAb solid tumors onartuzumab NSCLC non sq. 1st l Perjeta HER2+ early BC Compound Name CDx? MDM2 ant solid & hem tumors onartuzumab NSCLC sq. 1st line Tarceva NSCLC adj MEK inh solid tumors onartuzumab glioblastoma 2nd line Kadcyla HER2+ mBC 3rd line AKT inhibitor solid tumors Zelboraf papillary thyroid cancer Kadcyla HER2+ mBC 1st line RG105 Rituxan PD-L1 MAb solid tumors imgatuzumab (GA201) solid tumors Kadcyla HER2+ early BC Hercepti RG597 Steap 1 ADC prostate ca. pictilisib (PI3K inh) solid tumors onartuzumab gastric cancer n ✔ ADC ovarian ca. PI3K/mTOR inh solid & hem tum obinutuzumab iNHL relapsed RG435 Avastin ADC multiple myeloma parsatuzumab (EGFL7 Mab) solid tum obinutuzumab DLBCL ADC oncology CD22 ADC hem tumors obinutuzumab iNHL front-line RG1415 Tarceva ✔ ADC oncology CD79b ADC hem tumors Zelboraf m. melanoma adj RG7204 Zelboraf Bcl-2 inh CLL and NHL HER3/EGFR m. epithelial tumors onartuzumab NSCLC 2nd/3rd line ✔ ChK1 inh solid tum & lymphoma glypican-3 MAb liver cancer obinutuzumab CLL RG3616 Erivedge PI3K inh solid tumors cobimetinib (MEKinh) m. melanoma RG1273 Perjeta ✔ ADC metastatic melanoma PI3k inh glioblastoma 2L RG3502 Kadcyla ✔ ChK1 inh(2) solid tumors ALK inhibitor NSCLC New Molecular Entity (NME) PI3K inh solid tumors Additional Indication (AI) WT-1 peptide cancer vaccine Personalized Healthcare project 1 US only: ongoing evaluation for FDA submission 2 Submitted in EU Dan O’Day at Analyst Event ASCO 2013 3 Approved in US, submitted in EU 4 Approved in EU, submitted in US

Therapeutics

Pao & Girard Lancet Oncol 2011; 12: 175–80; Planchard ASCO 2013 Abs: 8009 Biomarker discovery in asthma 8

Definition – ASTHMA: a syndrome of loosely affiliated pathological processes resulting in reversible airway obstruction and hyper reactivity Standard of care guidelines Step 6 High-dose ICS – asthma is treated empirically according to + LABA + oral corticosteroid clinical severity and response to treatment, not Step 5 0.43 according to underlying biology High-dose ICS Cont. + LABA 0.54 – severity is defined by inhaled corticosteroid (ICS) Step 4 0.9 1.1 Uncont. responsiveness Medium-dose ICS Cont. Uncont. + LABA Step 3 Low-dose ICS + LABA, or Medium-dose ICS 2.8

Step 2 Low-dose ICS 4.1

Step 1 SABA PRN 3.7

14.7

Cumulative US & 5-EU Patient Population (M) High-Need NHLBI Guidelines for the Diagnosis and Management of Asthma, Oct 2007; GNE (severe) (~2M) Multiple mediators contribute to type 2 airway inflammation 10

Sources of IL13 Th2, Tc2, NKT, MΦ eos, basos, MCs ILC2s, T2Ms

Targets of IL13

epithelium, fibroblasts, ASM, B cells, VECs

• Mediated by Type 2 T-helper cell (TH2)-mediated eosinophilic inflammation • Substantial evidence that IL13 plays a major role in asthma pathology and inhaled corticosteroids reduce IL-13 levels • IL13 is a potential therapeutic target

Not all asthma patients have elevated IL13 expression 11

Potential anti-IL13 responders Challenges: IL13 levels in sputum (pg/g) IL13 is produced and consumed at the site of inflammation; difficult to detect systemically

Airway sampling is technically complex and not logistically feasible in large multi-center clinical studies

Key questions:

How do we identify patients with elevated levels of airway IL13 noninvasively?

How can we link targets, ICS ICS pathophysiology, and clinical no ICS responsive refractory outcomes? – Lung function (FEV1) Likely anti-IL13 – Exacerbations nonresponders

Saha SK et al, JACI 121:685-91 (2008) Two groups of asthmatics defined by bronchial gene signature 12

IL-13 responsive gene signature in bronchial epithelium Type 2 cytokine Asthmatics asthmatics & controls expression in bronchial biopsies

Airway eosinophilia

Type 2 “HIGH” and “LOW” asthmatics are distinguished by – eosinophilic airway inflammation – subepithelial fibrosis – mucus composition – response to ICS therapy

Woodruff PG et al, AJRCCM 180:388 (2009) Systemic Periostin levels differentiate mild-moderate asthmatics on 13 the basis of airway TH2 signature and eosinophilia

UCSF cohort 1

Owen Solberg Dave Choy

• Replicated previous finding of epithelial Th2 gene signature • Th2-HIGH asthmatics have higher blood periostin than Th2- LOW asthmatics • Eosinophilic asthmatics are Th2-HIGH and have elevated blood periostin levels Rationale for biomarker selection 14

• Desired biomarker characteristics – reflect airway inflammation – mechanistic link to target – noninvasive measurement – robust, reproducible assays Biomarkers in severe asthma: the BOBCAT study 15

• N = 67 completed study; 59 each evaluable for sputum, bronchoscopy • ICS dose: > 1000 μg/d FPI equivalent + LABA, LTRA (7 on OCS) • FEV1 = 60 + 11 percent predicted (entry requirement: 40-80%) • ACQ = 2.7 + 0.8 (entry requirement > 1.5)

Jeff Harris Jon Hidayat Serum periostin is elevated in severe asthmatics on ICS with residual 16 eosinophilic airway inflammation: BOBCAT

BOBCAT cohort Airway eosinophil cutoffs pre-specified from literature: Sputum 3% Haldar et al, NEJM 360:973 (2009); Green et al, Lancet 360:1715 (2002); many others Tissue (total biopsy area) 22/mm2 Miranda et al, JACI 113:101 (2004); Silkoff et al, JACI 116:1249 (2005) BOBCAT exploratory study: Periostin is the best single predictor of 17 airway eosinophilia among multiple biomarkers

Regression model of biomarkers and demographic features ROC analysis vs. vs. airway eosinophil status* in BOBCAT (N=59 evaluable) airway eosinophil status* other factors that were not significant included FEV1 and ACQ

Estimate Std. Error z-score p-value

Age -0.0396 0.039 -1.015 0.31

Sex (male) -0.2031 0.889 -0.229 0.82

Body mass index -0.1004 0.066 -1.527 0.13

Blood eos 1.7482 3.621 0.483 0.63

Serum IgE -0.0002 0.001 -0.100 0.92

FeNO 0.0476 0.038 1.238 0.22

Serum periostin 0.2491 0.092 2.719 0.007

*Eosinophil composite scores LOW: sputum < 3% AND tissue < 22/mm2 HIGH: sputum > 3% OR tissue > 22/mm2

Sofia Mosesova Jia et al, JACI 130:647 (2012) Measuring periostin: the Elecsys® immunoassay 18

Roche Diagnostics is developing a commercial immunoassay for the detection of serum periostin: the Elecsys® periostin assay Elecsys®(Perios- n(Assay( Assay still under development; not yet commercially available The$Elecsys$Perios; n$Assay:$ Results to date: • Automated$ElectroEclehcesmysil®u(mPeinreiossc-enc(Ae$s(sEaCyL()$Immunoassay$$ • high-throughput automated platform based on electrochemiluminescent (ECL) technology• TheB$Ealescesyd swith$$oPenri$ots h;highen$Assasna ysensitivity,d:$wich$princi pshortal$ turnaround times, wide measuring ranges and • high precisionAutomated$ Eoverlectroc htheemil uentireminescen crangee$(ECL)$Im munoassay$$ • • HBaigsehd$osne$nthsei$;savnidtwyi,c$hw$pirdinec$ipmale$asuring$range$and$precision$$ • highly• • sensitive,SHhigoh$rste$ntsui;rvnitay ,rmonoclonal$woiuden$md$e;asmureinsg$$ra nantibodyge$and$precis-ibasedon$$ assay that detects all known splice variants• Sh oofrt$ tuperiostinrnaround$; m es$ • Central$lab$ • both •intra Cent-r aandl$lab$ inter-assay coefficient of variation values are < 4%, indicating robust precision• • FFrooz zeen$nse$rsuemr$usamm$psleasm$ ples$

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Cobas e601 Stefan$Palme$ Elecsys$Perios; n$sandwich$assay$ ECL Measuring Unit ECL schematic(simplified$sche me)$ Stefan Palme

Stefan$Palme$ Lebrikizumab (anti-IL-13) Phase II POC Study Design

Corren et al. NEJM, 2011

Design : Randomized, double-blind, placebo-controlled, multicenter study (N=219) study Patients: Diagnosis of asthma inadequately controlled on inhaled glucocorticoids for >12 months Primary efficacy outcome: Relative change in prebronchodilator FEV1 from baseline to week 12 Change in FEV1 from Baseline in Adult Asthmatics with a-IL13

ALL EFFICACY EVALUABLE SUBJECTS Relative Mean FEV1 change at week 12 % Total ITT Periostin Periostin population High Low Change

FEV 1 Placebo 4.3% 5.8% 3.5% Time (Weeks) Lebrikizumab 9.8% 14.0% 5.1%

PERIOSTIN HIGH SUBJECTS ONLY Difference 5.5% 8.2% 1.6% % (p=0.02) (p=0.03) (p=0.61) Change FEV Corren et al. NEJM, 2011 1

Time (Weeks)

PERIOSTIN LOW SUBJECTS ONLY % Change

FEV 1

Time (Weeks) FeNO, eosinophils, periostin predict FEV1 benefit from lebrikizumab 21

FeNO HIGH SERUM PERIOSTIN HIGH BLOOD EOSINOPHILS HIGH

FeNO ³ 21 ppB SERUM PERIOSTIN ³ 50.2 ng/mL EOS ³ 210 PER mL

0 0 0

2 2

2 PLACEBO, N=55 LEBRIKIZUMAB, N=54 PLACEBO, N=59 LEBRIKIZUMAB, N=51 PLACEBO, N=59 LEBRIKIZUMAB, N=51

1 1 1

V V V

E E E

5 5 5

F F F

1 1 1

N N N

I I I

E E E

0 0 0

G G G

S S S

1 1 1

N N N

± ± ±

A A A

N N N

H H H

A A A

E E E

C C C

5 5 5

M M M

T T T

N N N

E E E

C C C

0 0 0

R R R

E E E

P P P

5 5 5

- - -

0 4 8 12 16 20 24 28 32 0 4 8 12 16 20 24 28 32 0 4 8 12 16 20 24 28 32

WEEKS WEEKS WEEKS

FeNO LOW SERUM PERIOSTIN LOW BLOOD EOSINOPHILS LOW

FeNO < 21 ppB SERUM PERIOSTIN < 50.2 ng/mL EOS < 210 PER mL

0 0 0

2 2

2 PLACEBO, N=55 LEBRIKIZUMAB, N=49 PLACEBO, N=50 LEBRIKIZUMAB, N=51 PLACEBO, N=53 LEBRIKIZUMAB, N=55

1 1 1

V V V

E E E

5 5 5

F F F

1 1 1

N N N

I I I

E E E

0 0 0

G G G

S S S

1 1 1

N N N

± ± ±

A A A

N N N

H H H

A A A

E E E

C C C

5 5 5

M M M

T T T

N N N

E E E

C C C

0 0 0

R R R

E E E

P P P

5 5 5

- - -

0 4 8 12 16 20 24 28 32 0 4 8 12 16 20 24 28 32 0 4 8 12 16 20 24 28 32

WEEKS WEEKS WEEKS

Median values for each biomarker in mITT population prespecified as cutoffs Consistent exacerbation reduction and lung function improvement in 22 periostin-HI patients in two independent ph2 studies

Exacerbation Reduction (vs. placebo) Mean FEV1 Improvement (vs. placebo)

100% 20.0%

90% 18.0%

80% 16.0% 67% 70% 60% 14.0% 60% 12.0%

50% 10.0% 9.1% 8.2% 40% 8.0% 29% 30% 6.0% 20% 5% 4.0% 2.6% 1.6% 10% 2.0%

0% 0.0% Milly Lute/Verse Milly Lute/Verse

High: 1.0%, 15.4% High: 1.1%, 17.6% 95% CI rate High: -15%, 90% High: 38%, 90% Low: -4.5%, 7.7% Low: -2.1%, 8.8% red vs. Pbo Low: -69%, 70% Low: -55%, 60%

Periostin High Periostin Low General considerations for measuring Type 2 biomarkers 23

• Desired biomarker characteristics – reflect airway inflammation – mechanistic link to target – noninvasive measurement – robust, reproducible assays

• Assay platforms – induced sputum: cytometry, analytes? – FeNO: NiOX MINA® (FDA approved) – blood eos (CBC): ~10 different platforms – serum periostin: Roche Dx Elecsys® (currently under development)

MILLY: screening (Day -7) vs. baseline (Day 0) Periostin FeNO Blood eos (N=201) (N=212) (N=210) Intra-patient variability Mean CV, % 5.0 19.8 21.3 95% CI (4.4, 5.6) (17.4, 22.2) (18.7, 24.0)

Corren et al, NEJM 365:1088 (2011) Conclusions 24

• Understanding disease heterogeneity is a basic part of our overall strategy across all therapeutic indications

• Expression profiling of mild/moderate asthmatics led to the molecular identification of a subset of patients with type 2/eosinophilic inflammation driven by IL-13 • Observational studies enable deeper understanding of biological characteristics of disease and associated biomarkers prior to clinical intervention • Serum periostin levels enable robust, non-invasive identification of patients likely to benefit or not benefit from anti-IL-13 therapy • Patients with ―high‖ periostin and other Type 2 biomarkers derive the most benefit from lebrikizumab shown in two Phase 2 studies • Type 2 biomarkers are both predictive and prognostic [not discussed here] • Many considerations of what biomarkers are appropriate to use

Arron - TAC Acknowledgments 25

Arron lab (past and present) Development Sciences UCSF Dave Choy, Jim Jia, Heleen Scheerens, Rich Erickson John Fahy Daryle DePianto Lindsay Brady, Mitch Denker Prescott Woodruff Deepti Nagarkar Asthma strategy team Owen Solberg Sanjay Chandriani Rhona O’Leary, Bruce Goldberg Margaret Solon Matt Yocum, Stacy Miller Francesca Lamb, Daisy Leung Christine Nguyen ITGR Biomarker Discovery Damien Springuel, Kristi Jones Kelly Wong McGrath John Monroe, Tim Behrens, Raj Thadani, David Leibowitz Kim Okamoto Mike Townsend, Mary Keir, Jenny Buchanan, Andrew McKee Marcel van der Brug, Joe Berryhill, Charlene Liao Rob Graham, Brian Yaspan, Beth Vasievich, Jennifer Davis University of Leicester Ward Ortmann, Katie Bradley, Roche Professional Diagnostics Peter Bradding Aarti Shikotra Katrina Morshead Stefan Palme, Bruce Jordan Richard Carter Immunology Discovery Chandra Ohri Lawren Wu, Flavius Martin, MILLY Investigators Rajita Pappu, Eric Brown, Beverley Hargadon Jonathan Corren, Robert Lemanske, Nicola Andy Chan Hanania, Philip Korenblat Bioinformatics Daniela Kopecka, Jiri Vytiska, Vladimir Zindr, Ildiko Breining, Queen’s University Belfast Valeria Csajbok, Edit Mohacsi, Judit Schlezak, Zsuzsanna Barmak Modrek, Alex Abbas, Salai, Veronica Urban, Adam Antczak Anna Bodzenta- Liam Heaney Robert Gentleman, Hilary Clark Lukasyk, Maciej Marczak, Robert Mroz, Ewa Springer, Oral Claire Butler Alpan, Raiqua Arastu, Samir Arora, James Baker, Gary Biostatistics Berman, David Bernstein, Edward Campbell, John Cantu, Emma Doran Mario Castro, Lee Clore, John Condemi, Dan Dalan, Carolyn Zheng Su, Sofia Mosesova, Daul, Joseph Diaz, Faisal Fakih, Bruce Finkel, Charles Hsin-Ju Hsieh Fogarty, John Given, David Grant, George Gwinn, Alan Halsey, Anthony Henry, Robert Jacobs, Rohit Katial, Mary BOBCAT Study Group BOBCAT team Lawrence, Jeffrey Leflein, S. David Miller, Kari Nadeau, Anjuli Qutayba Hamid, Séverine Audusseau, Jon Hidayat, Tricia Bauer Nayak, Michael Pacin, Tidence Prince, Emory Robinette, Jeffrey Rosch, Sudhir Sekhsaria, Deren Sinkowitz, Laura Steven Kelsen, Michel Laviolette, Clinical Development Somerville, Weily Soong, Sheldon Spector, Allan Stillerman, Ammar Hatab, Mario Castro, Gene Bleecker, John Sundy, Robert Townley, Suzanne Weakley, John Winder Jeff Harris, John Matthews Neil Thomson, Richard Leigh, Merdad Parsey, Karin Rosén EXTRA Investigators Ron Olivenstein, James Good, Cécile Holweg, Dana McClintock Nicola Hanania, Sally Wenzel, William Busse, Gail Gauvreau, Nizar Jarjour, Adel Mansur, Oral Alpan, Daniel Hamilos, John Condemi Pedro Avila, Joel Kline, Irvin Mayers, Kyle Hogarth, Liam Heaney

Appendix 26

Arron - TAC Type 2 biomarkers may be both prognostic and predictive 27 for exacerbations Agenda 28

• Introduction to our core biomarker strategies

• Disease heterogeneity

• Example: lebrikizumab (anti-IL13) and periostin in asthma