Scientists discovered a previously unknown mechanism that allows cancer cells to flourish, according to research published Jan. 5, 2026, by Technische Universität Dresden. The study revealed that the protein MCL1, previously understood to primarily prevent cancer cells from undergoing apoptosis, or programmed cell death, actively stimulates cancer metabolism.

Researchers found that MCL1 controls the mammalian target of rapamycin (mTOR) growth pathway, effectively linking cell survival and energy utilization. This connection explains the observed effectiveness of MCL1-targeting drugs in cancer treatment, but also elucidates why these drugs sometimes cause damage to the heart.

"We were surprised to find that MCL1 has this dual role," said Dr. Elena Schmidt, lead researcher on the project at Technische Universität Dresden. "It's not just about keeping cancer cells alive; it's also about fueling their growth."

The team identified a method to potentially mitigate the risk of heart damage associated with MCL1-targeting drugs. By selectively modulating MCL1's activity on the mTOR pathway, researchers believe they can preserve the anti-cancer effects while minimizing harm to healthy tissues. This discovery could pave the way for safer and more effective cancer therapies.

The findings highlight the complex interplay between cell survival and metabolism in cancer. Cancer cells often rewire their metabolic processes to support rapid growth and proliferation, a phenomenon that has been a focus of intense research in recent years. Understanding the molecular mechanisms that drive this metabolic reprogramming is crucial for developing targeted therapies.

The study's implications extend to the field of artificial intelligence (AI) in drug discovery. AI algorithms are increasingly used to identify potential drug targets and predict drug efficacy. The discovery of MCL1's dual role underscores the importance of considering multiple cellular processes when developing AI-driven drug discovery pipelines. AI models need to account for the interconnectedness of cellular pathways to accurately predict drug effects and potential side effects.

"This research emphasizes the need for more sophisticated AI models that can capture the complexity of cancer biology," explained Dr. Marcus Klein, a computational biologist not involved in the study. "We need AI that can not only identify potential drug targets but also predict how those targets interact with other cellular processes."

The researchers are currently working on developing more selective MCL1 inhibitors that specifically target the protein's metabolic function while sparing its anti-apoptotic function in healthy cells. They are also exploring the use of AI to optimize the design of these inhibitors and predict their efficacy in different cancer types. The next phase of research will involve preclinical studies to validate the safety and efficacy of the new MCL1 inhibitors in animal models.