MEDiC Life Sciences Secures $5M Investment from LG and Hanmi Pharmaceutical

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MEDiC Life Sciences has received a $5 million investment from LG and Hanmi Pharmaceutical. The funding comes as the company prepares for a larger Series-A funding round later this year and follows an earlier $8 million Seed round backed by investors like Illumina Accelerator and Wing Venture Capital.

MEDiC, which specializes in cancer research, plans to use the new funding to accelerate its work in discovering cancer biomarkers—biological indicators that can help predict how well a patient will respond to certain treatments. The company collaborates with major pharmaceutical companies, including Bristol Myers Squibb and Hanmi, to improve cancer drug development using its innovative technology.

At the center of MEDiC’s research is its MCAT™ platform, which can analyze millions of gene-to-drug interactions to identify specific genetic mutations that respond well to cancer therapies. This approach, known as synthetic lethality, aims to make existing cancer treatments more effective by targeting the right genetic markers in patients.

Both LG and Hanmi expressed strong support for MEDiC’s approach. A representative from LG highlighted the company’s ability to combine AI and biotechnology to tackle some of the biggest challenges in cancer drug development. Hanmi’s representative added that MEDiC’s technology has the potential to improve the success of clinical trials and provide better treatment options for cancer patients.

MEDiC’s co-founder and CEO, Kyuho Han, Ph.D., said that the investment will help the company strengthen its partnerships with pharmaceutical companies and advance the clinical testing of its biomarkers. “This funding is an important step as we continue to validate our technology and work on discovering new drug targets,” Han said.

Founded in 2020 by Stanford alumni Kyuho Han and Hong-Pyo Lee, MEDiC focuses on using advanced functional genomics to find new ways to treat solid tumors. The company’s technology relies on 3D tumor models, which offer a more accurate representation of how cancer behaves compared to traditional methods.

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