Boehringer Ingelheim tapped the drug discovery company Healx to help identify new indications for molecules within its pipeline and to leverage its artificial intelligence platforms to explore their potential against rare neurological conditions.
Healx’s AI-based Healnet program draws from an array of data sources focused on rare diseases and pharmacology—including targets, symptoms, sequencing results and scientific literature—to help predict how well a treatment will match groups of patients. The goal is to help the German drugmaker prioritize its indications for further research.
The Cambridge, U.K.-based company also has experience in rare disorders such as Pitt-Hopkins and Fragile X syndrome, characterized by developmental delays and intellectual disabilities. Its Healnet platform previously identified eight repurposed drug candidates for the latter, including one currently progressing through mid-phase clinical trials, according to Healx.
“We believe that sophisticated technologies, such as AI and machine learning methods, will be fundamental to any drug discovery and development project in the future,” Healx CEO Tim Guilliams said in a statement.
“The project with Boehringer Ingelheim presents an opportunity for further innovation in identifying rare disease candidates for patients whose medical needs are not currently met,” Guilliams said. “Harnessing BI’s expertise for drug development, especially in rare neurological conditions, in combination with our AI-powered drug discovery platform, will enable faster identification of potential new treatments.”
This past October, Healx raised $56 million to launch an ambitious slate of 40 new rare disease programs, as well as double its headcount and advance some of its own assets further through clinical testing.
That’s a big leap from its previous stable of 10 programs, managed by a staff of about 41 employees who have been more focused in tech than biotech—though the latest funding will help to expand its drug R&D and clinical staff.
“We know those are big numbers and it's ambitious. But from a preclinical perspective and a data and machine learning perspective, our approach is actually scalable,” Guilliams told FierceBiotech at the time. “Making the predictions, selecting the rare diseases, selecting the drugs—we can do this at a larger scale.”