Introduction: Why Crohn’s Disease Needs a Personalized Approach

Crohn’s disease (CD) presents a persistent clinical challenge, with up to 75% of patients experiencing treatment failure within the first year of therapy. Despite advances in biologic treatments—particularly anti-TNF agents—a substantial proportion of individuals either fail to respond initially or lose therapeutic efficacy over time. As a result, disease progression continues unchecked in many cases, with up to 80% of patients ultimately requiring surgery.^1,2

These statistics highlight the urgent need to move beyond the traditional one-size-fits-all approach to treatment. While randomized controlled trials (RCTs) remain the gold standard for evidence generation, their findings typically reflect average treatment effects, often masking meaningful variations in how individual patients respond to therapy. Personalized medicine seeks to fill this gap by tailoring interventions based on each patient’s biological, clinical, and molecular profile.³

A recently validated framework is helping translate RCT data into personalized care by identifying distinct patient subgroups with different responses to available drug classes. In Crohn’s disease, this framework has already uncovered subpopulations that respond preferentially to either anti-TNF or anti-IL-12/23 therapies. By bridging the divide between trial data and real-world clinical decision-making, this approach promises more effective, individualized treatment strategies that could reduce disease burden and improve long-term outcomes.⁴

Building the Framework: From Data to Decision

At the heart of this personalized approach is an integrative framework that unifies data from diverse sources—including RCTs, real-world evidence (RWE), multi-omics, and artificial intelligence (AI)—to support individualized treatment recommendations. By applying Bayesian inference and advanced statistical modeling, the framework identifies patterns of differential drug response across patient subgroups.⁵

In Crohn’s disease, data from 15 RCTs involving over 5,700 patients were harmonized to build a robust, generalizable model. AI-driven simulations predict likely treatment outcomes at the individual level, allowing clinicians to compare the expected effectiveness of therapies—such as anti-TNF versus anti-IL-23 agents—based on a patient’s unique clinical and biological profile. This stratification supports real-time clinical decision tools that enhance treatment selection and avoid trial-and-error prescribing.⁶

Crucially, the framework incorporates multi-omics inputs—including genomic, transcriptomic, proteomic, and metabolomic data—to account for biological variability that is often overlooked in conventional models. The result is a precision-guided workflow: clinical trial data → integrative modeling → patient stratification → personalized therapy—with the potential to be applied across a broad spectrum of immune-mediated and chronic diseases.^7,8

Application in Crohn’s Disease: From Classification to Clinical Impact

Crohn’s disease is particularly well-suited to this framework due to its inherent heterogeneity and high rates of treatment resistance. Patients often present with diverse phenotypes, disease courses, and immune profiles, making uniform treatment strategies ineffective for many. Molecular stratification has emerged as a powerful tool to resolve this complexity.

Recent studies using multi-omics and transcriptomic data have successfully classified CD into biologically distinct subtypes. These subtypes correlate with immune and metabolic activity and align with clinical features such as disease behavior, prognosis, and drug response. For instance, gene expression signatures can now predict whether a patient is more likely to benefit from anti-TNF or anti-IL-23 therapies, offering the potential to match patients with the most appropriate biologic from the outset.⁹

Furthermore, molecular activity scores derived from gene set variation analysis provide a quantitative way to track disease severity and therapeutic response. These biomarkers are proving useful not only for guiding treatment decisions but also for dynamically monitoring patients over time—ensuring therapies remain aligned with disease evolution.^10,11

Importantly, integrating these molecular insights with clinical parameters allows for even more refined patient stratification. Personalized treatment plans informed by this framework could reduce exposure to ineffective therapies, improve remission rates, and ultimately decrease the need for surgery and hospitalizations. The clinical and economic impact of such targeted strategies could be profound, particularly in a disease as costly and complex as Crohn’s.^12,13

Reference:

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2. Scheurlen KM, Parks MA, Macleod A, Galandiuk S. Unmet Challenges in Patients with Crohn's Disease. J Clin Med. 2023 Aug 27;12(17):5595. doi: 10.3390/jcm12175595. PMID: 37685662; PMCID: PMC10488639.

3. Clinton JW, Cross RK. Personalized Treatment for Crohn's Disease: Current Approaches and Future Directions. Clin Exp Gastroenterol. 2023 Dec 14;16:249-276. doi: 10.2147/CEG.S360248. PMID: 38111516; PMCID: PMC10726957.

4. Rudrapatna VA, Ravindranath VG, Arneson DV, Mosenia A, Butte AJ, Wang S. From trial data to personalized medicine: a validated framework with an application to Crohn's disease. NPJ Digit Med. 2025 May 31;8(1):327. doi: 10.1038/s41746-025-01627-w. PMID: 40450151; PMCID: PMC12126477.

5. Rudrapatna VA, Ravindranath VG, Arneson DV, Mosenia A, Butte AJ, Wang S. From trial data to personalized medicine: a validated framework with an application to Crohn's disease. NPJ Digit Med. 2025 May 31;8(1):327. doi: 10.1038/s41746-025-01627-w. PMID: 40450151; PMCID: PMC12126477.

6. Jagirdhar GSK, Perez JA, Perez AB, Surani S. Integration and implementation of precision medicine in the multifaceted inflammatory bowel disease. World J Gastroenterol. 2023 Sep 28;29(36):5211-5225. doi: 10.3748/wjg.v29.i36.5211. PMID: 37901450; PMCID: PMC10600960.

7. Lamb CA, Saifuddin A, Powell N, Rieder F. The Future of Precision Medicine to Predict Outcomes and Control Tissue Remodeling in Inflammatory Bowel Disease. Gastroenterology. 2022 Apr;162(5):1525-1542. doi: 10.1053/j.gastro.2021.09.077. Epub 2022 Jan 4. PMID: 34995532; PMCID: PMC8983496.

8. Clinton JW, Cross RK. Personalized Treatment for Crohn's Disease: Current Approaches and Future Directions. Clin Exp Gastroenterol. 2023 Dec 14;16:249-276. doi: 10.2147/CEG.S360248. PMID: 38111516; PMCID: PMC10726957.

9. Weiser M, Simon JM, Kochar B, Tovar A, Israel JW, Robinson A, Gipson GR, Schaner MS, Herfarth HH, Sartor RB, McGovern DPB, Rahbar R, Sadiq TS, Koruda MJ, Furey TS, Sheikh SZ. Molecular classification of Crohn's disease reveals two clinically relevant subtypes. Gut. 2018 Jan;67(1):36-42. doi: 10.1136/gutjnl-2016-312518. Epub 2016 Oct 14. PMID: 27742763; PMCID: PMC5426990.

10. S Cao, M Colonna, P Deepak, DOP19 High dimensional profiling of IL-23-responsive cells revealed molecular signatures that predict response to anti-IL-23 therapy in patients with Crohn’s Disease, Journal of Crohn's and Colitis, Volume 18, Issue Supplement_1, January 2024, Pages i106–i107, https://doi.org/10.1093/ecco-jcc/jjad212.0059

11. Andersen, V.; Bennike, T.B.; Bang, C.; Rioux, J.D.; Hébert-Milette, I.; Sato, T.; Hansen, A.K.; Nielsen, O.H. Investigating the Crime Scene—Molecular Signatures in Inflammatory Bowel Disease. Int. J. Mol. Sci. 2023, 24, 11217. https://doi.org/10.3390/ijms241311217

12.Pavlidis S, Monast C, Loza MJ, Branigan P, Chung KF, et al. (2019) I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment. PLOS Computational Biology 15(4): e1006951. https://doi.org/10.1371/journal.pcbi.1006951

13.Arsenescu R, Bruno ME, Rogier EW, Stefka AT, McMahan AE, Wright TB, Nasser MS, de Villiers WJ, Kaetzel CS. Signature biomarkers in Crohn's disease: toward a molecular classification. Mucosal Immunol. 2008 Sep;1(5):399-411. doi: 10.1038/mi.2008.32. Epub 2008 Jul 2. PMID: 19079204.