Guwahati (Assam) [India], February 3 (ANI): Researchers at the Indian Institute of Technology, Guwahati, in partnership with leading institutions around the world, have devised an innovative multi-stage clinical trial method that aims to revolutionise personalised medical care.
This cutting-edge approach customises treatment regimens in real time based on each patient’s individual responses during trials, resulting in highly customised and effective healthcare solutions, as per the press release.
The research aims to enhance patient-specific treatment plans, improve outcomes, reduce trial-and-error in medication, and foster a more efficient, personalised healthcare approach
The research, conducted in partnership with Duke-NUS Medical School, the National University of Singapore, Singapore, and the University of Michigan, USA, focuses on Dynamic Treatment Regimes (DTRs) designed through Sequential Multiple Assignment Randomised Trials (SMARTs).
Together, these frameworks tackle the critical challenge of optimising treatment strategies, a sequence of treatments, for patients with varying responses to therapies over time.
DTRs are advanced decision rules that adapt treatments dynamically as a patient’s condition evolves. For example, if a diabetes patient does not respond well to an initial medication, the DTR might recommend switching drugs or combining therapies. By incorporating intermediate outcomes, such as changes in blood sugar levels, DTRs move beyond the one-size-fits-all model, tailoring care to individual progress and needs.
Multi-stage clinical trials are essential for developing effective DTRs, and SMART methodology enables researchers to test various treatment sequences to find the best fit for each patient. Unlike traditional trials, SMART involves multiple stages of treatment, where patients are reassigned based on their responses to earlier interventions.
Traditional SMART trials assign patients to treatment arms in equal numbers, even when some treatments prove less effective, based on interim data. This often leads to unnecessary treatment failures. Dr Palash Ghosh and his team have developed an adaptive randomisation method that dynamically assigns patients to treatment arms based on real-time trial data by optimally changing the patient allocation ratios in favour of a better-performing treatment sequence at that point time of the trial.
This innovation ensures that more patients receive effective treatments while maintaining scientific rigour. By focusing on both short-term and long-term outcomes, the method improves the entire treatment process, reducing failures and enhancing patient care.
Speaking about the research Dr. Palash Ghosh, Assistant Professor, Department of Mathematics, IIT Guwahati, said in a statement, “Adaptive designs like this would encourage more patient participation in clinical trials like SMART. When patients see they are receiving treatments tailored to their needs, they are more likely to stay engaged. This approach also has vast potential for public health interventions, such as tailoring substance abuse recovery plans to individual needs as well as in other chronic diseases.”
The findings of this research have been published in the esteemed journal Biometrics in a paper coauthored by Dr Palash Ghosh, along with his research scholar Rik Ghosh from IIT Guwahati, Dr Bibhas Chakraborty from Duke-NUS Medical School, National University of Singapore, along with Dr Inbal Nahum-Shani and Dr Megan E. Patrick, from the University of Michigan.
This new adaptive multi-stage clinical trial method represents a major stride toward more effective, patient-centric healthcare, potentially transforming public health interventions and advancing the global pursuit of personalised medicine. As a next step, the research team is collaborating with Indian medical institutions to conduct SMART trials for the effective management of mental health issues using traditional Indian medicines, as per the press release. (ANI)
(The story has come from a syndicated feed and has not been edited by the Tribune Staff.)
