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New Clinical Trial Approach at IIT Guwahati Revolutionizes Personalized Medicine

The study, published in the journal Biometrics, was co-authored by Palash Ghosh and Rik Ghosh from IIT Guwahati, Bibhas Chakraborty from Duke-NUS Medical School at the National University of Singapore

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Researchers from IIT Guwahati, the National University of Singapore, and the University of Michigan have developed a multi-stage clinical trial method that could transform personalized medical care. This new approach dynamically adapts treatment plans in real-time based on a patient’s individual response, leading to more effective and tailored healthcare solutions.

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The study, published in the journal Biometrics, was co-authored by Palash Ghosh and Rik Ghosh from IIT Guwahati, Bibhas Chakraborty from Duke-NUS Medical School at the National University of Singapore, and Inbal Nahum-Shani and Megan E. Patrick from the University of Michigan, USA. Their research focuses on Dynamic Treatment Regimes (DTRs), a model developed through Sequential Multiple Assignment Randomized Trials (SMARTs) to optimize treatment strategies for patients whose responses to therapies change over time.

DTRs provide a structured yet flexible decision-making framework, adjusting treatments as a patient’s condition evolves. For instance, if a diabetes patient does not respond effectively to an initial medication, the system may recommend switching drugs or combining therapies. By considering intermediate outcomes such as blood sugar levels, this model moves beyond the traditional one-size-fits-all treatment approach, ensuring that care is tailored to individual progress.

Palash Ghosh, Assistant Professor at IIT Guwahati’s Department of Mathematics, emphasized the importance of multi-stage clinical trials in developing effective DTRs. He explained that SMART methodology enables researchers to test various treatment sequences to find the best possible fit for each patient. Unlike traditional trials, which assign patients to treatment groups in equal numbers, SMART trials involve multiple stages, with patients reassigned based on their responses to earlier interventions.

The research team has introduced an adaptive randomization method that dynamically allocates patients to treatment groups based on real-time trial data. Ghosh pointed out that in conventional SMART trials, patients are equally distributed across treatment arms even when interim data suggests some treatments are less effective, leading to unnecessary treatment failures. The new method optimizes patient allocation, prioritizing better-performing treatment sequences as the trial progresses, ensuring that more patients receive effective therapies while maintaining scientific integrity.

By focusing on both short-term and long-term outcomes, this innovation is expected to enhance the overall treatment process, reduce failures, and improve patient care. Ghosh also highlighted that adaptive designs like this could encourage greater patient participation in clinical trials, as individuals are more likely to remain engaged when they see their treatments being tailored to their needs.

The researchers believe this approach holds significant potential for public health interventions, including personalized recovery plans for substance abuse and the management of chronic diseases. They are currently working with Indian medical institutions to conduct SMART trials aimed at improving mental health treatment using traditional Indian medicines.

Also Read: ADYPU, IIT Guwahati Partner To Set Up India’s First Blast And Impact Research Centre

IIT Guwahati