How AI Empowers Biomarker-Driven Clinical Trials

by Andrii Buvailo, PhD          Biopharma insight | Sponsored by Lantern Pharma

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Clinical trials are the lifeline of modern medicine, bridging the gap between lab-borne scientific discoveries and the availability of life-saving treatments for patients. Unfortunately, the success rate of clinical trials, particularly in the field of oncology, is not as robust as one might hope. In fact, a 2019 study of 7,455 interventional phase trials in oncology conducted from 2000 to 2015 suggests an estimate that only 3.4% of cancer drugs that enter Phase I clinical trials ultimately receive FDA approval. Many of these failures can be attributed to obstacles such as poor trial design and inefficient patient selection, often resulting from a lack of biomarker-driven insights. As a result, drug development can be a laborious and expensive process, with the cost of bringing a new drug to market estimated to be around $2.6 billion. As we grapple with these challenges, there is an urgent call for more efficient and targeted methods of drug development, and the answer may lie in the utilization of novel actionable biomarkers and the power of artificial intelligence (AI) to plan and execute clinical trials. For instance, IQVIA's artificial intelligence-based platform, using their Real World Data assets, have enabled precise patient and healthcare professional (HCP) targeting, doubling identified eligible patients, finding 30% more with uncontrolled symptoms, predicting 81% likely to quit treatment early, pinpointing events linked to early discontinuation, and boosting treatment transition success by 500% compared to prior methods.

 

What is a clinical trial?

Clinical trials form an integral part of the drug development process. They are carefully designed studies involving human participants, undertaken to determine the safety and efficacy of a new drug or medical device. This process is usually divided into four stages. Phase I trials primarily assess safety and dosage in a small group of healthy volunteers or patients. Phase II trials involve a larger group of patients and aim to assess the drug's efficacy and side effects. Phase III trials test the drug in an even larger group of patients over a more extended period to confirm its effectiveness, monitor side effects, and compare it to commonly used treatments. 

Designing clinical trials for central nervous system (CNS) drug evaluation, for example, for brain tumors, represents an additional challenge due to unique properties of the blood-brain barrier (BBB). The BBB's unique and stringent selectivity often necessitates the implementation of specialized Phase 0 trials in CNS oncology programs. Because of BBB, these trials require higher systemic drug concentrations to achieve detectable levels within the CNS, as compared to other therapeutic areas, increasing the complexity and risk of the process. 

RELATED: AI Breaches the Barrier Towards Better CNS Drug Discovery

The arena of clinical studies encompasses various approaches, including double-blind studies and more transparent strategies like open-label trials. With high failure rates in drug development, biomarkers are emerging as pivotal components of clinical trial experiment design, refining patient selection for more accurate outcomes. The trend towards using master protocols indicates a push for efficiency, as this model allows simultaneous examination of various treatments. More specialized frameworks, like basket and umbrella trials, further incorporate factors such as genetic biomarkers, reflecting the industry's shift towards more nuanced methodologies.
 

The Evolution of Clinical Biomarkers

Advancements in Next-Generation Sequencing (NGS) and our understanding of the human genome have revolutionized the field of biomarker discovery, particularly in oncology. Traditional biomarkers often utilized circulating markers in blood, plasma, and serum, or those detectable through imaging techniques. However, with the advent of genomics, we have been able to delve deeper, identifying individual genetic variations that play pivotal roles in disease pathology, specifically in cancers where genetic mutations often take center stage.

One of the defining moments in this biomarker-led therapeutic revolution was the approval of Herceptin (trastuzumab), a monoclonal antibody, by the FDA in 1998. Designed to target the HER2 protein, it was used to treat HER2-positive metastatic breast cancer, either as a first-line therapy in combination with other drugs, or as a standalone second-line treatment. This represented a significant stride forward in targeted cancer therapy, and opened the floodgates for similar advances. Today, we have a plethora of HER2-targeting drugs, including Tyrosine Kinase Inhibitors (TKIs) such as neratinib, lapatinib, and tucatinib, which have broadened the therapeutic scope beyond breast cancer to other cancers that overexpress the HER2 protein or exhibit HER2 gene amplification.

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