Principal Scientist Vantage Research Inc Natick, Massachusetts, United States
Disclosure(s):
Maithreye Rengaswamy: No financial relationships to disclose
Dinesh B. Bedathuru: No financial relationships to disclose
Objectives: In this poster, we have used a calibrated and validated QSP model of UC with a single all-comers Virtual Population calibrated to multiple approved therapies to predict the efficacy of novel therapeutic combinations of anti-TL1A in Phase 3 clinical trials of UC patients. Background and Motivation: UC is an autoimmune disease of the bowel (categorized as an Inflammatory Bowel Disease, or IBD) with an annual incidence rate of 7.3 to 30.2 per 100,000 in North America (1). IBD presents an unmet treatment need, with patients losing response to treatment (biologics) over time; one study reported that over half of UC patients were not in remission at 12 months, while a third showed inadequate response (2). Hence, it is imperative to find novel therapies and combinations that can provide alternatives to existing therapies. QSP modeling has been used successfully to model several diseases e.g., IBD, Asthma, RA etc. and has been used to support decision making throughout the drug development cycle (3-5).
Methods: We added clinically relevant outcomes for UC, i.e., the Mayo score to a published model of UC(6,7) and further developed a single Virtual Population of all-comer UC Virtual Patients that are a) Calibrated to multiple clinical trials (8-12)- Adalimumab (anti-TNF-α), Vedolizumab (anti- α4ꞵ7), Ustekinumab (anti-IL-12/23) and Mirizikumab(anti-IL-23), and anti-TL1A. b) Validated against the VEGA trial (13),a Ph 3 combination trial using anti-TNF-α (Golilumab) and anti-IL-23 (Guselkumab) monotherapies. We used this single Vpop to predict the impact of two different combinations of anti-TL1A with other therapies of clinical interest.
Results: We created and calibrated an all comers (anti-TNF-α responder and non responder mixed) Virtual population (Vpop) consistent with the baseline patient characteristics and response to therapy as reported in Phase 3 induction trials for Adalimumab, Vedolizumab, Ustekinumab and Mirizikumab and Phase 2 trials of anti-TL1A.
We predict the Phase 3 efficacy of two different combinations of anti-TL1A which are of potential clinical interest in this Virtual Population, i.e., anti-TL1A + Adalimumab, anti-TL1A + Vedolizumab. Conclusions and Next Steps: We demonstrate the utility of a calibrated QSP model of IBD in predicting the Phase 3 efficacy of novel combinations of therapies of interest. We propose to continue model development by a) Calibrating to other approved and failed therapies (e.g., Tofacitinib, Secukinumab) to refine the representation of disease and enable efficacy prediction of novel therapies and combinations. b) Including mechanisms of anti-TNF-a non-response to enable prediction of efficacy of novel therapies in the anti-TNF-a non-responder subpopulation.
Citations: 1) Caron B, et al, Epidemiology of Inflammatory Bowel Disease across the Ages in the Era of Advanced Therapies. J Crohns Colitis. 2024 Oct 30;18(Supplement_2):ii3-ii15. doi: 10.1093/ecco-jcc/jjae082. 2) Bokemeyer B, et al, . Rates of clinical remission and inadequate response to advanced therapies among patients with ulcerative colitis in Germany. Int J Colorectal Dis. 2023 May 8;38(1):116. doi: 10.1007/s00384-023-04397-7. 3) Whittaker DG, et al; Leveraging in vitro data from novel drug candidates to prioritize antibody combinations in autoimmune disease using a QSP model of IBD. Whittaker DG, Shaliban A, Roy M, Packrisamy P, Rengaswamy M, Damian V, Gupta A; Presented at PAGE 2023 4) Gadkar K, et al;. Integrated systems modeling of severe asthma: Exploration of IL-33/ST2 antagonism. CPT Pharmacometrics Syst Pharmacol. 2022 Sep;11(9):1268-1277. 5) Bedathuru D, et al; Multiscale, mechanistic model of Rheumatoid Arthritis to enable decision making in late stage drug development. NPJ Syst Biol Appl. 2024 Nov 4;10(1):126. 6) Rogers KV, et al;. A Dynamic Quantitative Systems Pharmacology Model of Inflammatory Bowel Disease: Part 1 - Model Framework. Clin Transl Sci. 2021 Jan;14(1):239-248. doi: 10.1111/cts.12849. Epub 2020 Aug 21. 7) Dey, S, et al; Extending a Quantitative Systems Pharmacology model of Ulcerative Colitis to a single all-comers Virtual Population for optimizing therapeutic combinations in Phase 3 trials. Accepted, PAGE 2025. 8) Sandborn WJ, et al;. Adalimumab induces and maintains clinical remission in patients with moderate-to-severe ulcerative colitis. Gastroenterology. 2012 Feb;142(2):257-65.e1-3. doi: 10.1053/j.gastro.2011.10.032. 9) Feagan BG, et al; GEMINI 1 Study Group. Vedolizumab as induction and maintenance therapy for ulcerative colitis. N Engl J Med. 2013 Aug 22;369(8):699-710. doi: 10.1056/NEJMoa1215734. 10) Sands BE, et al;. Ustekinumab as Induction and Maintenance Therapy for Ulcerative Colitis. N Engl J Med. 2019 Sep 26;381(13):1201-1214. doi: 10.1056/NEJMoa1900750. 11) D'Haens G, et al; Mirikizumab as Induction and Maintenance Therapy for Ulcerative Colitis. N Engl J Med. 2023 Jun 29;388(26):2444-2455. doi: 10.1056/NEJMoa2207940. Erratum in: N Engl J Med. 2023 Aug 24;389(8):772. doi: 10.1056/NEJMx230004. PMID: 37379135. 12) Danese S, et al; Anti-TL1A Antibody PF-06480605 Safety and Efficacy for Ulcerative Colitis: A Phase 2a Single-Arm Study. Clin Gastroenterol Hepatol. 2021 Nov;19(11):2324-2332.e6. 13) Feagan BG, et al; Guselkumab plus golimumab combination therapy versus guselkumab or golimumab monotherapy in patients with ulcerative colitis (VEGA): a randomised, double-blind, controlled, phase 2, proof-of-concept trial. Lancet Gastroenterol Hepatol. 2023 Apr;8(4):307-320.
