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W-27: Identifying Gaps in Competitive Intelligence and Business Development Strategy: Opportunities in the PD-1/PD-L1 landscape

Poster Presenter

Matthew Eberle

Lead Developer, Analytics & Custom Solutions
BizInt Solutions, Inc.
United States

Objectives

Should companies operating in the IO space look to other targets? Or, might there still be valuable opportunities discoverable through analysis? We used analysis and visualization tools to leverage drug competitor and clinical trial data to identify gaps in a crowded target area (PD-1/PD-L1).

Method

We searched Citeline's Trialtrove and Pharmaprojects databases for all drugs and all industry-sponsored clinical trials for PD-1 or PD-L1 inhibitors. These were visualized and analyzed using BizInt Smart Charts tools to map areas of intensive trial activity and potential options for differentiation.

Results

At submission, Pharmaprojects identifies 207 PD-1 or PD-L1 antagonists with only 1 listed as Discontinued and 59 at No Development Reported. The focus is almost entirely on cancer: 68 indications out of 82 listed for the drugs are in oncology. Trialtrove showed 2,306 trials involving a PD-1/PDL-1 drug, with 1,776 listing at least one as a primary drug. While some indications for PD-1/PD-L1 drugs appeared highly competitive, there are also a number of less studied indications. Opportunities can be identified at the indication level by reviewing visualizations created with Pharmaprojects data. By viewing indications and phase of development as a heat map or bubble chart, we can find areas of saturation at every phase of development and also indications where development is all in early stages. Using Trialtrove data we can explore a selected area, such as CNS cancer in greater detail. Further, even a relatively saturated indication, such as NSCLC, reveals areas of opportunity. Once again a heat map or bubble chart is useful for identifying opportunities. By plotting drugs against type of combination we can quickly see which drugs have trials for specific types of combinations and which may so far only be tested as monotherapy. By linking a selected indication, such as NSCLC, with selected patient segments, we can see competitor development strategy as well as identify gaps that represent opportunity. The key trials identified in initial visualizations can be viewed in greater detail using tables and more focused visualizations such as trial timelines.

Conclusion

Appropriate data combined with analysis and visualizations tools facilitate better strategic decisions for drug developers operating in the IO space and beyond, including for PD-1 developers to maintain a competitive and differentiated development program, and for smaller companies seeking the optimum partner for their combination strategy. By beginning our analysis of the target landscape broadly at the drug level, we were able to identify potential opportunities in a landscape presumed to be already controlled by a few launched drugs. In fact, much of the ongoing development for this target does focus on NSCLC and Melanoma, indications for which drugs already have approval. Therefore, opportunities for differentiation lie in indications where our analysis shows fewer competitors. The tools also support detailed review of those competitor drugs that are being developed for these new indications. Further review at the trial level using Trialtrove data reveals additional opportunities not evident looking at the drug level alone. Trials are identified by the type of drug combination. Some companies are running or planning combination trials. Analysis of the Trialtrove data shows partners with PD-1 agents without combination trials. Trial details also include patient segment details. Visualization and analysis provides insight into underserved patient segments which can be pivotal to a competitive development strategy. The broad to narrow data-centric approach employed here should be equally applicable to other indications and therapeutic areas. Starting with the full landscape and including both drug and clinical trials intelligence allows opportunities to be identified that could be missed with a narrow focus on a few key competitor companies or drugs. After identifying potential opportunities the data for specific competitors can be reviewed in depth to confirm what analysis suggests.