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Genialis raises $13m in Series A

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Computational precision medicine company Genialis has raised more than $13 million (£10.5 million) in Series A financing.

The company is developing next-generation patient classifiers using machine learning and high-throughput omics data to capture underlying disease biology and predict how patients will likely respond to targeted therapies.

Genialis will use the funds from the Series A to expand its proprietary ResponderID platform and build out its collection of clinically validated biomarker models to provide pinpoint diagnoses for cancer patients.

To date, the company has used ResponderID in collaboration with biopharma to analyse clinical trial data and inform future trial designs for numerous investigational drugs.

Genialis also supports the commercialisation of next-gen biomarker assays with several leading diagnostic firms.

Genialis co-founder and CEO, Rafael Rosengarten, Ph.D, said:

“With ResponderID, we sought to disrupt the historical linear progression of drug discovery and development, rather aiming to close the loop between drug development, patient care and new drug discovery.

“We chose to focus initially on biomarkers that improve the efficiency of drug development, that ensure the right patient gets the right medicine, and make an impact on real people’s lives in a shorter period of time.”

ResponderID is a machine learning platform for clinical and translational research, built alongside partners across the industry and advanced internal R&D.

ResponderID yields new biomarkers for drug development and discovery programmes, as well as diagnostic tests.

Hamzeh Abdul-Hadi, Investment Director at co-lead investor, Debiopharm Innovation Fund, said:

“ResponderID, Genialis’ predictive biomarker platform, enables precision medicine by identifying patients that are most likely to respond to treatments.

“Its use in drug development will optimise study designs and improve chances of clinical trials success, driving much-needed productivity gains for pharma R&D and accelerating the time to market for promising new drugs.”

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