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The Impact of Genetic Variation on Drug Response in Adult IBD: A Systematic Review
Masomeh Askari1, Shaghayegh Baradaran Ghavami1, Nayeralsadat Fatemi1
1Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences Tehran Iran.
Background:
A major characterization of inflammatory bowel disease (IBD) is significant heterogeneity in treatment responses. Despite the increase in therapeutic agents, discovering an optimal patient-level treatment is still a major milestone. This study aims to provide a systematic review of the existing literature on the predictive biomarkers of the response to IBD treatment.
Methods:
On April 15, 2025, a literature search was conducted using the PubMed and Scopus databases, as well as a manual search. Controlled trials, case-control studies, cross-sectional studies, and cohort studies addressing predictive biomarkers for treatment response in adults with IBD were eligible for inclusion. The CASP tool was used to assess the quality of the methodologies employed in the included research. Due to a lack of information and expected heterogeneity, a qualitative study was planned instead of a quantitative one.
Results:
Of the 7570 identified articles, 31 met the inclusion criteria. DNA markers were assessed as predictive biomarkers. The majority of studies attempted to predict the response to anti-TNF drugs. There is significant variability across studies in both the definition of response and the considered biomarkers.
Conclusions:
At the moment, no biomarker provides sufficient predictive ability for clinical practice. Thus, we are still in the early stages of the quest for predictive biomarkers, and existing literature is lacking. Future studies should address the large heterogeneity among patients within prospective trials by conducting objective response evaluations. Prediction models are likely to be developed by combining multiple molecular markers from integrated omics levels and clinical characteristics.
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