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RWD – Acceptance by Regulatory Authorities RWD – Acceptance by Regulatory Authorities HOW BIG DATA WILL DISRUPT BUSINESS MODELS Carl Janssen, MD Country Lead Oncology Pfizer GmbH [email protected] If your Doctors can’t cure your Cancer, maybe you can Hacking a Cure We can imagine that in the near future, one could biopsy a tumor, for example, get the genome report, and based on that, identify a drug or a therapy that could help https://medium.com/nf2-project/hacking-a- cure-video-63a77b37e5c Big data (1993) Big data (1996) CancerLinQ CancerLinQ Patients Providers Research/Public Health Improved outcomes Real-time “second Mining Big Data for Clinical trial matching opinions” correlations and new insights Safety monitoring Observational and Real-time side effect guideline-driven clinical Comparative effectiveness management decision support research Patient-reported Real-time access to Hypothesis-generating outcomes resources at the point of exploration of data care Evidence based care Identifying early signals for Quality reporting and adverse events and benchmarking effectiveness in “off label” use Real World Data – Examples of Use RWD – acceptance by Regulatory Authorities RWD – acceptance by Regulatory Authorities • There is major potential to increase the use of RWE to support lifecycle product development and monitoring and to improve decision-making for regulation and HTA. While the greatest potential is for authorised products, there is an important role in supporting innovative products and adaptive pathways • There are challenges to realising the full potential for RWE and these include: incomplete access to electronic healthcare data from different Member States and a lack of hospital in-patient data; variable data quality and a lack of harmonisation; the need to develop methods for efficacy and HTA outcomes; and delays to start studies. RWD as supportive data As part of the conditional approval, Pfizer is committed to generating additional efficacy and safety data for BOSULIF in patients with Ph+ CML previously treated with one or more TKIs not suitable for imatinib, dasatinib and nilotinib, and will submit the findings to the European Medicines Agency (EMA). Following review of the data by the EMA’s Committee for Medicinal Products for Human Use (CHMP), the EC will consider converting the conditional marketing authorization to a full marketing authorization. Electronic healthcare data in different Member States 2L NSCLC Product Development Snapshot Pembrolizumab Durvalumab Atezolizumab Avelumab – PD-L1+ Phase III Durvalumab +/- tremelimumab Durvalumab + AZD-9291 (T790M+) – Temp closed Nivolumab + bavituximab (P) LUNG-MAP Nivolumab + Pembrolizumab + SBRT Durvalumab – 3L EGF816 (EGFR+) Phase II Nivolumab + Pembrolizumab + acalabrutinib INC280 (cMET+) Nivolumab + aza Pembrolizumab + azacitidine Nivo + galunisertib Pembro + epacadostat Durvalumab + ibrutinib Atezolizumab + PDR001 + LAG-525 Nivo + ceritinib Pembrolizumab + Durvalumab + MEDI-6469 AtezolizumabCD40 agonist + (P) – 2L+ chemo or immunotherapy PDR001 Nivo + urelumab Pembrolizumab + RT Durvalumab + mogamulizumab radiosurgery (P) Atezolizumab + epacadostat Phase I-I/II Nivo + epacadostat Pembrolizumab + Durvalumab + epacadostat gemcitabine Atezolizumab + immunotherapy Nivo + varlilumab Pembrolizumab + Durvalumab + gefitinib Atezolizumab + erlotinib Nivo + antiLAG-3 entinostat Durvalumab + tremelimumab Atezolizumab + cobimetinib Pembrolizumab + necitumumab Durvalumab + Nivo + lirilumab Atezolizumab + NLG-919 – 2L+ Pembrolizumab + ramucirumab ramucirumab (P) Nivo + FPA008 Atezolizumab + varlilumab (P) – 2L+ Durvalumab + Pembrolizumab + lenvatinib Atezolizumab + beva/chemo– 2L+ Nivo + ALT-803 (P) Pembrolizumab + enoblituzumab selumetinib (P) Nivo + plinabulin (P) Pembrolizumab + GSK3174998 Nivo + HS-110 Pembrolizumab + MK-8353 – KRASmt Nivolumab Pembrolizumab Durvalumab Atezolizumab Avelumab Novartis Biomarkers in immuno-oncology Watson for Drug Discovery • 25 million abstracts • more than 1 million full-text journal articles • 4 million patents Watson for Drug Discovery • identification of new drug targets • combination therapies • patient selection strategies Hacking a Cure (cont’d) 1.
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