From trial data to personalized medicine: a validated framework with an application to Crohn’s disease
Abstract Clinical practice is currently guided by studies that average over patient outcomes. This may not be the best approach, as different patients may have different treatment responses. Here we extend a method for simulating clinical trials to identify optimal treatments for each patient, and w...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01627-w |
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| author | Vivek A. Rudrapatna Vignesh G. Ravindranath Douglas V. Arneson Arman Mosenia Atul J. Butte Shan Wang |
| author_facet | Vivek A. Rudrapatna Vignesh G. Ravindranath Douglas V. Arneson Arman Mosenia Atul J. Butte Shan Wang |
| author_sort | Vivek A. Rudrapatna |
| collection | DOAJ |
| description | Abstract Clinical practice is currently guided by studies that average over patient outcomes. This may not be the best approach, as different patients may have different treatment responses. Here we extend a method for simulating clinical trials to identify optimal treatments for each patient, and we illustrate this approach in the context of Crohn’s disease. Using the data from 15 randomized trials (N = 5703), we used statistical hypothesis testing to identify seven subgroups with distinct responses to three different drug classes. The largest subgroup consisted of patients with equivocal responses to all drug classes, whereas the second largest showed superiority with anti-TNFs. We also identified a subgroup of women over 50 with superior responses to anti-IL-12/23s. Interestingly, this group appeared under-represented in the trials (2%) compared to patients at the University of California (25%). Overall, these results underscore the importance of studying personalized medicine, demonstrate the value of clinical trial data, and provide a roadmap for applying this method broadly across diseases. These results also highlight the importance of diverse and representative recruitment into clinical trials. |
| format | Article |
| id | doaj-art-eacacc4478e849778b5c15d7954c5d7c |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-eacacc4478e849778b5c15d7954c5d7c2025-08-20T03:22:11ZengNature Portfolionpj Digital Medicine2398-63522025-05-018111110.1038/s41746-025-01627-wFrom trial data to personalized medicine: a validated framework with an application to Crohn’s diseaseVivek A. Rudrapatna0Vignesh G. Ravindranath1Douglas V. Arneson2Arman Mosenia3Atul J. Butte4Shan Wang5Division of Gastroenterology, Department of Medicine, University of CaliforniaBakar Computational Health Sciences Institute, University of CaliforniaBakar Computational Health Sciences Institute, University of CaliforniaSchool of Medicine, University of CaliforniaBakar Computational Health Sciences Institute, University of CaliforniaDepartment of Mathematics and Statistics, University of San FranciscoAbstract Clinical practice is currently guided by studies that average over patient outcomes. This may not be the best approach, as different patients may have different treatment responses. Here we extend a method for simulating clinical trials to identify optimal treatments for each patient, and we illustrate this approach in the context of Crohn’s disease. Using the data from 15 randomized trials (N = 5703), we used statistical hypothesis testing to identify seven subgroups with distinct responses to three different drug classes. The largest subgroup consisted of patients with equivocal responses to all drug classes, whereas the second largest showed superiority with anti-TNFs. We also identified a subgroup of women over 50 with superior responses to anti-IL-12/23s. Interestingly, this group appeared under-represented in the trials (2%) compared to patients at the University of California (25%). Overall, these results underscore the importance of studying personalized medicine, demonstrate the value of clinical trial data, and provide a roadmap for applying this method broadly across diseases. These results also highlight the importance of diverse and representative recruitment into clinical trials.https://doi.org/10.1038/s41746-025-01627-w |
| spellingShingle | Vivek A. Rudrapatna Vignesh G. Ravindranath Douglas V. Arneson Arman Mosenia Atul J. Butte Shan Wang From trial data to personalized medicine: a validated framework with an application to Crohn’s disease npj Digital Medicine |
| title | From trial data to personalized medicine: a validated framework with an application to Crohn’s disease |
| title_full | From trial data to personalized medicine: a validated framework with an application to Crohn’s disease |
| title_fullStr | From trial data to personalized medicine: a validated framework with an application to Crohn’s disease |
| title_full_unstemmed | From trial data to personalized medicine: a validated framework with an application to Crohn’s disease |
| title_short | From trial data to personalized medicine: a validated framework with an application to Crohn’s disease |
| title_sort | from trial data to personalized medicine a validated framework with an application to crohn s disease |
| url | https://doi.org/10.1038/s41746-025-01627-w |
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