Unlocking new therapeutic horizons: How researchers accelerate drug repurposing with Causaly
Unlocking new therapeutic horizons: How researchers accelerate drug repurposing with Causaly
Challenges before Causaly
In Life Sciences R&D, few strategies offer more promise than drug repurposing. For scientists, finding new indications for existing assets means navigating vast and fragmented data, assessing mechanistic rationale, and making confident decisions under pressure. It’s a process that demands both speed and scientific rigor.
From old assets to new treatments
Drug repurposing reduces development timelines and costs, while addressing unmet medical needs. The researchers’ goal is clear: extend the therapeutic potential of approved or previously shelved compounds by identifying new diseases they might treat. But the path is rarely straightforward. It requires scanning the biomedical landscape for diseases with unmet need, identifying plausible target-disease links, and gathering strong, contextual evidence to justify the huge investment.
Causaly solutions
Uncovering opportunities
With Causaly Discover, scientists begin by uncovering the mechanism of action of a drug of interest, such as Ibrutinib in this example which is approved for B-cell malignancies.
They can use Causaly to understand the biological effects of BTK inhibition and quickly surface mechanistic evidence related to inflammation, immune regulation, and autoimmunity, clues that point toward possible relevance beyond oncology.
Connecting compounds, targets and diseases
Once the mechanism is mapped, they turn to Causaly BioGraph to explore which diseases are most strongly associated with the drug’s primary target, BTK.
The Bio Graph visualizes how BTK is implicated in a range of diseases, outside of oncology. In this case, Multiple Sclerosis (MS) emerges as a disease of interest, based on the target's known biological role.
They use filters to prioritize by strength of evidence, publication recency, and relationship type. This way, they are refining and validating hypotheses in minutes.
Identifying unmet needs
Despite recent advancements, significant unmet need remains in Multiple Sclerosis, where current therapies offer limited efficacy. Using Causaly, scientists can quickly identify patients with the highest disease burden and lowest treatment success, helping them pinpoint where a BTK inhibitor like Ibrutinib could deliver the greatest impact.
Understanding the competitive and clinical landscape
To build a compelling case for repurposing, researchers must understand what else is in development. With Causaly Pipeline Graph, they can instantly check which indications have active clinical programs, which targets are crowded, and where white space exists.
Scientists can position a compound strategically, avoiding saturated areas and finding opportunities others may have overlooked.
Faster, smarter, more confident decisions
Historically, this kind of work required weeks of manual review across disconnected data sources. But with Causaly, researchers now get a unified, real-time view, complete with citations and clear evidence trails. They are able to generate and validate repurposing hypotheses in hours, not weeks.
Their favorite features? One customer highlighted how Causaly Copilot helped to prioritize and summarize findings, while another loved saving searches to Workspaces, which led to greater collaboration.
From insight to impact
By accelerating hypothesis generation and reducing the risk of low-quality leads, scientists are helping their organization unlock untapped value from its existing portfolio faster, and with greater scientific confidence.
It would be possible to browse the literature manually, but you can imagine the number of products I am responsible for and the amount of papers I would need to review, either industry-sponsored studies or pilot studies conducted by physicians. All this data is retrieved by Causaly, and I can filter and sort based on quality of evidence.
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