A Leading North American Biotech Company Advances Its Fight Against Cancer

Ontotext empowers the company to accelerate research and expedite the discovery of new insights for cancer immunotherapies up to 10 times faster by using data most effectively

  • Enabled more than 10x faster discovery of novel insights
  • Accelerated hypothesis generation and evaluation by over 1000%
  • Minimized risk in new research effort

The Goal

A leading North American biotech company wanted to bring their novel therapies to patients faster. Their research focused on developing innovative treatment options that harnessed the power of the body’s immune system to fight cancer.

The company wanted to be able to compare hundreds of therapeutic target candidates quickly and efficiently. They also needed to minimize the time their researchers spent on mining information from scientific literature as well as enrich their proprietary data with additional facts relevant to their research.

They needed a solution that would gather all known information about biomedical entities in a central and easily accessible system.

The Challenge

The main challenge the company faced was how to effectively use high-quality single-cell transcriptomics data and enrich it with known facts to accelerate the process of getting from product development bench to patient bedside. Prioritizing potential therapeutic candidates and making decisions about which targets to pursue required a lot of manual work, which made the job complex and time-consuming.

Researchers had to collect publicly available data from over 50 separate databases as well as other relevant information. Then they had to manually compare hundreds of potential candidates to assess their suitability. Each candidate required researchers to spend many hours, sometimes even days, checking for references in the relevant scientific literature, scanning patents and clinical trials, and extracting valuable information. This information often lacked the necessary metadata or evidence, leading to uncertainty in the target selection process, and slowing time to delivery.

The Solution: Ontotext’s AI-powered Target Discovery

The semantic knowledge graph based solution provided by Ontotext enabled the company to access information about biomedical entities (such as genes, proteins, compounds, drugs, etc.) easily and in one central place. It included 30 structured datasets and 5 unstructured databases as well as a common data model, establishing connections and building inferences between them.

The graph was further enhanced with natural language processing pipelines. Based on the company’s requirements, the pipelines automatically extracted data from over 80 million documents, which was then normalized and integrated back into the graph. The application also provided data visualization and analytics views as well as a ranking methodology, which the scientific team could leverage without needing any prior technical or coding knowledge.

The solution was tailored to the company’s needs and equipped it with the necessary tooling to efficiently analyze and extract insights from all public and proprietary data, whether structured or derived from text via Artificial Intelligence (AI).

Business Benefits

With Ontotext’s AI-based Target Discovery, the company is now able to:

  • accelerate their research and discover novel insights at least 10x faster by interlinking relevant knowledge from public and proprietary data, and enriching this knowledge with automatically extracted data from scientific publications
  • make data-driven decisions with high confidence because of the transparent provenance and confidence metrics connected to each fact in the knowledge graph
  • enhance the potential for innovation with the ability to uncover relationships that were previously hidden in the data thus significantly speeding up hypothesis generation and evaluation by 1000%
  • democratize data insights enabled by the ability to automatically prioritize targets based on predefined properties, specific to the therapeutic area (and without any coding!)
  • minimize risk in new research thanks to the automatic ranking of hundreds of candidates based on predefined criteria and custom algorithms

Why Choose Ontotext?

As a result of Ontotext’s significant expertise in knowledge graph technology and the challenges in drug discovery, the solution met and exceeded the company’s business needs. The solution uses  Ontotext’s GraphDB, LinkedLifeData Inventory, tailored NLP pipelines, and graph algorithms. It helps them enhance a strong pipeline of potential candidates that are currently in the advanced preclinical research stage, indicating they are preparing for clinical trials.

Do you think this case resembles your particular business needs?

New call-to-action

Contact Us Now