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By reading this brochure, you will learn:
  • The limitations of relying just on PubMed for knowledge reviews and what insights will be missed
  • How advanced data curation connects together causal insights from multiple sources and uncovers cause-and-effect relationships not previously described in the literature
  • What return-on-investment can be delivered by leveraging advanced data curation technology
  • What others in the biopharma industry have to say about advanced data curation
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Please kindly fill out the form below and a member of our team will be touching base with you shortly.

By submitting this form, you submit your information to Biorelate, who may use it to communicate with you regarding your request via the form and their other services. You may unsubscribe from these communications at any time by clicking the Unsubscribe button at the bottom of our marketing emails or contacting us at info@biorelate.com.

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By reading this case study, you will discover:
  • Why leveraging cause-and-effect analysis is imperative for advancing drug target selection
  • How a robust proof of mechanism enhances the likelihood of drug discovery projects advancing from clinical development to market launch
  • Two detailed examples of applying groundbreaking Natural Language Processing-fueled technology to understanding complex biological mechanisms:

    a. Investigating the application of roflumilast, a drug approved for treating chronic obstructive pulmonary disease (COPD), in treating Non-Cystic Fibrosis Bronchiectasis.

    b. Examining the observed correlation between obesity and increased expression of the protein LCN2, a relationship currently lacking a direct cause-and-effect rationale.
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Galactic AI™ use cases

Biomarker
Selection

Galactic AI™ uncovers previously undiscovered connections between potential targets, biomarkers, and diseases, providing robust evidence to substantiate biomarker hypotheses.

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Galactic AI™ harnesses in-depth knowledge of biomarkers, elucidating their mechanisms and proposing innovative hypotheses for highly specific candidates

Effective drug programs depend on reliable biomarkers. Strong pharmacodynamic biomarkers facilitate strategic decisions about advancing assets to subsequent phases. By presenting the mechanisms underlying disease pathophysiology, Galactic AI™ reveals both established and emerging biomarkers. Researchers gain insights into not only the recognised candidates for a condition but also their potential efficacy and how they compare to more innovative alternatives.

Evidence
Cause-and-effect
Mechanism understanding
Evidence
How much is the absence of optimal evidence hurting your biomarker selection process?

Galactic AI™ leverages sophisticated data curation techniques to uncover potential pharmacodynamic (PD) biomarkers for your specific targets and disease areas, paving the way for successful drug discovery.

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Cause-and-effect paths example
Cause-and-effect
Contextualise your biomarkers within biological frameworks by investigating the cause-and-effect dynamics among targets, biomarkers, and diseases.

Galactic AI™ meticulously curates directional relationships, delineating credible pathways from targets to biomarkers and onto diseases.

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Galactic AI Scheme
Mechanism understanding
Unlock extensive pharmacodynamic biomarker evidence to substantiate your mechanistic hypotheses.

Through Galactic AI™'s curated cause-and-effect relationships, you can pinpoint biomarkers that confirm your target-disease mechanisms. Schedule a demo to discover how we can assist in validating your mechanistic target hypotheses.

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Discover potential biomarkers through directional relationships using Galactic AI™

Select pharmacodynamic biomarkers with stronger evidence to assess target engagement accurately.

Galactic AI™ enables you to formulate hypotheses about target-disease interactions and pinpoint biomarkers to corroborate these theories.

40 Million +

Documents Processed

Galactic AI™ processes journals, patents, clinical trials and many more sources (and can process in-house text).

60 Million +

Cause-and-Effect Relationships

Relationships between concepts, such as cause-and-effect, are classified with directionality. All in context.

2.7 Billion +

Cause-and-Effect Interaction Mentions in Context

Explainability and greater flexibility to refine your data with more contextual data points.

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Testimonials

Customer testimonials

Our customers span globally recognised companies like Pfizer and AstraZeneca as well as innovative drug discovery biotechs.

“The contextual data from Biorelate is critical to our efforts to embed knowledge graphs into the drug discovery pipeline.”

Dr. Ben Sidders

Director, Oncology Informatics - AstraZeneca

“Biorelate has been a game changer. The cutting-edge platform can make novel connections between bioactive molecules and disease to help biologists find hidden insights.”

Christopher J Nicholson

Head of Biology - Pepper Bio

“Biorelate has managed to provide a user-friendly tool to Idorsia scientists to cope with the massive amount of biomedical literature and more efficiently discover relevant aspects for our projects to speed up processes.”

Peter Groenen

Head of Translational Biomarkers - Idorsia Pharmaceuticals

“Biorelate’s NLP-derived contextual interaction data, combined with ETX’s proprietary databases, enables e-therapeutics’ approach to the modelling of complex biology and the use of those models for target identification and deconvolution."

Dr. Jonny Wray

Chief Technology Officer - e-Therapeutics

"The collaboration between Biorelate has been extremely productive and we look forward to working together on future developments."

Professor Ruth Roberts

‍Co-Founder and Director- Apconix

Cause-and-effect paths example
Hypotheses

Support mechanistic hypotheses

Galactic AI™ enables you to:

Identify

Generate hypotheses for new biomarkers

Discover

Discover biomarkers backed by existing literature

Enhance

Enhance your mechanistic insights

Relationships

Directional relationships

Isolate potential biomarkers from directional relationships

Comprehensive cause-and-effect relationships reveal novel insights to point you in new directions for indication expansion

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Galactic AI Scheme

How Galactic AI™ works

Galactic AI™ is engineered to meticulously parse and process vast quantities of biomedical literature, extracting and curating valuable data from a multitude of textual sources.

Cause-and-effect paths example

The most advanced data curation in biopharma

Galactic Al™ uncovers previously undiscovered connections between potential targets, biomarkers, and diseases, providing robust evidence to substantiate biomarker hypotheses.

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