Save Time, Reduce Bias, Make Better Decisions

With almost half a million papers on obesity in PubMed, and 2 papers added per minute, identifying promising therapeutic targets is time-consuming, subject to bias and is not always comprehensive. Causaly extracts insights rather than papers, uncovering over 5,000 targets for obesity in seconds.

Obesity is projected to impact 17.5% of the global population by 2030.¹ The economic impact of obesity is profound, costing the U.S. $261 billion in 2016 alone.² Although treatments are available, long-term management remains challenging. There is an urgent need for developing novel treatments to alleviate the global impact of obesity.

With 40% of efficacy-related failures in clinical trials attributed to poor target-disease linkage,³ selecting promising can have a big impact on drug success. By machine-reading the entire volume of biomedical literature, Causaly can expedite target identification, enabling the exploration of more novel avenues.

Causaly machine-read the literature and extracted over 5,000 targets for obesity, supported by almost 45,000 documents.

To increase confidence in a target’s viability, results can then be refined to those investigated in preclinical models, uncovering 3,500+ targets. Around 450 of these targets were reported in primary data in 2023.

Causaly’s advanced filtering capabilities allow the prioritization of targets by novelty, the number of publications, and the strength of the evidence for the role of the target in disease pathophysiology:

By leveraging AI, drug discovery teams can expedite the identification and prioritization of credible targets, paving the way for the exploration of novel therapeutic avenues, enabling drug discovery teams to transition projects into clinical programs with more confidence.