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The Power of Biomarkers in Clinical Trials: How Computing Technology is Shaping the Future

Detailed view of a computer interface used by geneticists showing a complex genome mapping software with colorful DNA strands without displaying any personal identity in a modern research lab

Insight by: Debbie Lin

In clinical trials, finding the right patients and predicting treatment success can be challenging. Biomarkers—measurable indicators of disease or response to treatment—offer a powerful solution, especially when combined with advanced computing. Here’s how some pioneering companies are harnessing biomarkers and computing tech to improve clinical trials.

Biomarkers act as personalized guides, giving researchers insights into which patients are likely to respond best to certain drugs. Companies like QuantHealth and Unlearn.AI are using AI to simulate patient responses and refine trial design. QuantHealth’s AI-driven clinical simulator forecasts patient responses based on biomarkers, optimizing trials to save time and resources. Unlearn.AI’s digital twin technology reduces control group sizes by creating virtual patient models, enabling trials to be faster and more focused on relevant participants.

For complex disease areas, BullFrog AI and biotx.ai offer advanced biomarker analysis tools. BullFrog AI’s bfLEAP™ platform identifies patient subgroups that respond best to certain treatments, supporting smaller, targeted trials. Similarly, biotx.ai has partnered with Simbec-Orion to use causal AI models for oncology and rare diseases, predicting outcomes and refining patient selection.

Predictive Oncology uses machine learning to predict responses to cancer therapies, focusing on precision oncology by enhancing patient stratification in trials. Meanwhile, Tulane’s iPredict study integrates mobile tech and AI, using wearables to monitor patient biometrics in real time. This approach boosts patient engagement and adherence in cardiovascular trials.

Each of these organizations demonstrates the power of combining biomarkers with computing to enhance trial efficiency and precision. These technologies not only accelerate trial timelines and reduce costs but also ensure that life-saving therapies reach patients faster. As the landscape evolves, AI and biomarkers are transforming trials to be more effective and patient-centered, delivering better health outcomes.

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