Blogs / Analysis Blog

Palantir Pharma: mitigating R&D risk through data fusion

With development times of ten years or more and costs of over $1 billion per new medicine, pharmaceutical R&D is an expensive, lengthy, and risky process. Enterprises can minimize the risk associated with drug development programs by better understanding the universe of data relevant to particular diseases, targets, and drug candidates.

Unfortunately, achieving this level of understanding is technically challenging, as the data is typically scattered across public and private databases and stored in a wide range of formats. Furthermore, novel research data is noisy and can be fraught with contradictions. Serious data security and access control concerns only add to this complexity.

The Palantir Gotham data fusion platform helps pharmaceutical companies overcome these R&D challenges by integrating public and private data of nearly any type and empowering secure collaboration both within and beyond the enterprise.

In this three-part series of videos, you will see how the Palantir Gotham platform facilitates rapid, intuitive, and comprehensive exploration across a variety of data sets. We focus on just a handful of ways in which Palantir removes the friction between pharmaceutical researchers and their data, allowing them to (1) discover connections between assays performed by different teams, (2) evaluate evidence for a drug-protein interaction, and (3) capture the investigative process for future use.

For these demonstrations we have integrated data from public sources including ChEMBL, PubMedKEGG, PDB, and PubChem. From high-level literature reviews to the analysis of specific variations in a genetic sequence, Palantir Gotham increases efficiency in the drug development process and amplifies signal strength for key pipeline decisions.

Part 1: To start, we use Palantir Gotham to collaboratively model biological relationships and assess their certainty in order to better understand the risk associated with targeting a particular pathway.

Part 2: Next, we explore new experimental results that were produced by an external team. We investigate their underlying scientific rationale and apply these insights to my own drug development research.

Part 3: Finally, we investigate structured assay data and quickly drill down on a compound of interest. We use a variety of techniques to make sense of complex experimental results in both structured and unstructured formats.

Drug development is just one of many potential pharmaceutical applications of the Palantir Gotham platform. Our goal is to solve your organization’s most challenging data problems across the pharmaceutical pipeline. To learn more, read about our Pharma solution and contact our health team:

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