1. Biotechnology in Fighting Fatal Disease – Cancer National Biotechnology Symposium 2012 Innovations in Biotechnology: From Education to Industry Sep 1, 2012, AMA, Ahmedabad Dr. Chirag Desai, MD, DM (Oncology) Hemato-oncology Clinic, Vedanta, Ahmedabad Chiragdesai.oncology@gmail.com

2. Metastatic colon cancer (unresectable) 1970s – Only 5-FU – survival of 6 mths 1980s – Leucovorin/5-FU – survival of 9 mths 1990s – Only 5-FU – Addition of oxaliplatin/ Irinotecan/capecitabine - – survival of 18 mths 2000s – Avastin - – survival of 21 mths Now – Erbitux – survival of 25 mths Survival Increased 4 folds In 30 years

3. Breast Cancer – stage II 1960s – Only surgery - 40% cured 1970s – CMF - 50% cured 1980s – CMF and Tamoxifen - 60% cured 1990s – Anthracyclines and taxanes - 67% cured 2000s – Aromatase inhibitors - 73% cured Now – Herceptin - 80% cured ?? Cure Rate Doubled In 40 years

4. Biotechnology in Cancer: Translational Research Bench to bedside

5. The biological revolution of 20 th century totally reshaped all fields of biomedical study -- cancer research being only one of them.

6. Biotechnology helps in elucidating the normal cellular functioning And the derangements thereof resulting in disease Including cancer…….and The ways to tackle these derangements

7. Chronic Myeloid Leukemia One cancer One gene One Treatment One chromosome

8. Most Cancers Each cancer Multiple genes Multiple Treatments Multiple chromosomes

9. Initiation Normal Cell Pre-Cancerous Cell Cancer Cell Invasion Angiogenesis Metastases Signal transduction Migration Seed/Soil Immune Surveillance Promotion

10. Prevention/early detection Diagnosis/ prognosis Treatment

11. Biotechnology techniques and processes (Evans, P. R. Biotechnology and Biological Preparations in Encyclopaedia of PT vol. 1, 3 rd edn.)

12. Growth Factor Growth Factor receptor Signal Transduction

13. Research and development network

14. Examples of Biologicals: Growth Factors: EGFVEGFFGFIGFPDGFReceptors:EGFRHer-2VEGFRPDGFRER/PRSTIs:TKIsmTORICDKIFTIsOthersMabs:RituximabAvastinHerceptinErbituxBiomab

15. clinicaloptions.com/oncology Providing Personalized Care in an Era of Molecular Medicine Evolution From Empiric to Personalized Therapy in NSCLC FactorsAgent Affected Clinical Asian, never-smoker, femaleErlotinib, gefitinib Untreated CNS metastases, no hemoptysis, uncontrolled hypertension Bevacizumab Histologic AdenocarcinomaErlotinib, gefitinib NonsquamousBevacizumab, pemetrexed Thymidylate synthasePemetrexed Molecular EGFR mutationErlotinib, gefitinib ERCC1/RRM1Platinum RRM1Gemcitabine KRAS mutationErlotinib, gefitinib ↑ EGFR by FISHErlotinib? Gefitinib? ↑ EGFR by IHCCetuximab? EML4-ALK fusionCrizotinib Adapted from Gandara DR, et al. Clin Lung Cancer. 2009;10:148-150.

16. Patient Selection Improves Treatment Results in NSCLC Median survival (months) 1970s1980s1990–2005 BSC: 2–4 months Cisplatin- based regimens: 6–8 months Platinum- based doublets (3rd gen): 8–10 months 2005 Bevacizumab + platinum- based doublet: >12 months 28 24 20 16 12 8 4 0 2008 Pemetrexed + platinum: >12 months Bevacizumab + platinum: >14 months Non-squamousAdeno-onlyAdeno-only Molecular selection…. EGFR-mut+ Erlotinib alone >27 months 2009/10 No selectionClinical selection

17. VEGF in clinic Antibody – Bevacezumab (Avastin) Lung Cancer, Colon Cancer, Ovarian Cancer, Renal Cell Carcinoma, Brain Tumors, Breast Cancer Lung Cancer, Colon Cancer, Ovarian Cancer, Renal Cell Carcinoma, Brain Tumors, Breast Cancer Tyrosine kinase Inhibitors: Sunitinib, Sorafenib, Pazopinib, Axitinib, Dovitinib, others Renal Cell Cancer, Neuro-endocrine tumors, Liver cancers, GIST, Sarcoma

21. Biologicals are effective in: Lung cancer Colon cancer Breast cancer Head and neck cancer Leukemias/Lymphomas Renal/Liver cancers Others Biologicals help in the treatment of >80% of cancers either curatively or in advanced cancers

22. Challenges in the development of biologicals

23. RBF Symposium Feb 2011 A Nobel Prize by Chance

24. Start at the top 1.Formulate testable hypothesis 2.Make the plan / design the study Generate Hypothesis Design study Collect information Analyse and interprete finding Develop new theory

25. clinicaloptions.com/oncology Providing Personalized Care in an Era of Molecular Medicine Phase I (~ 18 mos) Phase II (~ 18 mos) Phase III (~ 36 mos) Preclinical (~ 18 mos) Total Time ~ 90 mos or 7.5 yrs BiomarkerIntegration N = 30 N = 300 N = 1600 Drug Approval Confirm target Assay development Integrate biomarker Assay performance Phases of Development of New Biomarker linked to New Drug Biomarker informative? Assay performance Clinical validation Coprimary endpoint Clinical application of biomarker Gandara D, et al. NCI CAPR Workshop. 2011. Printed with permission. New Therapeutic Agent: Development Phases

26. A large number of biologically/molecularly acting drugs are under development Traditional end points are less relevant New end points required OS still is a gold standard end point Surrogate end points need to be re-defined Even though response rate is less important, exact definition of response is critical Ongoing analysis of tissue/blood based biomarkers is critical

27. Surrogate End points with targeted Therapies: Traditional PFS PFS QoL QoL OS OS Pharmacoeconomics Pharmacoeconomics Others Others Exploratory Target inhibition Tissue level Blood level Pharmacogenetic Tissue based Blood based

28. clinicaloptions.com/oncology Providing Personalized Care in an Era of Molecular Medicine Primary endpoint: 8-wk disease control rate; 30% assumed Kim ES, et al. AACR 2010. Abstract LBA1. Reprinted with permission. BATTLE: Phase II NSCLC Biomarker Study Umbrella protocol Core biopsy EGFRKRAS/BRAF VEGFRXR/CyclinD1 Biomarker profile Randomization: Equal  Adaptive Erlotinib Equal (n = 25) Adaptive (n = 33) Vandetanib Equal (n = 23) Adaptive (n = 29) Erlotinib + Bexarotene Equal (n = 21) Adaptive (n = 15) Sorafenib Equal (n = 26) Adaptive (n = 72)

29. clinicaloptions.com/oncology Providing Personalized Care in an Era of Molecular Medicine Kim ES, et al. AACR 2010. Abstract LBA1. Reprinted with permission. BATTLE: Phase II NSCLC Biomarker Study—Discovery Platform

30. Who should do this? Generate Hypothesis Design study Collect information Analyse and interprete finding Develop new theory Academic institutions Corporate hospitals Individual practitioners Medical associations Collaborative effort

31. Who should do this? Generate Hypothesis Design study Collect information Analyse and interprete finding Develop new theory

32. Innovations chiragdesai.oncology@gmail.com

33. Future: Tests like Oncotype Dx21 in breast cancer Drugs like Imatinib in CML Outcome like sequential use of chemo and targeted drugs in myeloma Making cancer a chronic Disease