Angiogenesis inhibitors drug pipeline landscape

Angiogenesis is a major focus of research, because it plays a central role in tumor growth, survival, and progression. Therefore, angiogenesis inhibition has been an attractive approach for cancer therapy. The discovery of the main molecular drivers of tumor angiogenesis, the vascular endothelial growth factor (VEGF) opened the avenue for pharmacological blockade. Since, the US Food and Drug Administration (FDA) approved the first angiogenesis inhibitor (AI), bevacizumab for the treatment of metastatic colorectal cancer in 2004. In following years, a large number of AI have been discovered and developed. Many of them are now entering clinical trial, or achieving approval for clinical use. Although promising in pre-clinical trials, AI proved to be problematic in the clinical context. Therefore, optimal AI therapy strategies become critical. In that context, pharma competitive intelligence can help academic scientists and physicians to get an understanding of industry drug research and development (R&D) trends, and could benefit research laboratories by identifying public-private partnership opportunities. We used global data from Pharmaprojects, a proprietary drug pipeline database (Informa Healthcare, UK). This is the most complete database of its kind: data from this source are consistent with FDA database. It includes drugs developed in 42 pharmaceutical markets worldwide from 1980 to the present and, it is widely used in pharmaceutical industry. Search queries were run for mechanisms of action and molecular targets identified through a review of the scientific literature. We used a dataset downloaded on July 1st, 2016. A total of 651 drug R&D projects have been identified. The dataset includes drug's chemical and brand names, diseases indications and therapeutic areas, development statuses (in clinical trials, registered, or launched), the company that developed it and the identity of licensees, patent and licensing availability, and chemical, manufacturing preclinical, clinical and marketing information. Data analysis techniques were used to explore the underlying patterns in this large data set and identify the systematic relationships between variables. We rely on an interactive data visualization tool, Tableau Desktop software, a server automation to filter and assemble data in dashboards, with customize annotations. As of the end of June 2016, the AI drug pipeline involved 466 firms, overlayed 222 different targets and 211 disease indications with 60 ongoing industry sponsored clinical trials. Detailed results will be presented regarding R&D trends, top targets by sponsor, drugs in R&D by therapeutic area, mechanisms of action in ongoing AI trials, drug partnership deals … We will discuss the outcomes in AI pharmaceutical R&D in in light of failure-to-success ratios, transition probabilities and phase lengths.