OBJECTIVES: To evaluate treatment sequences for immunological diseases and to quantify and compare the variability in efficacy.

METHODS: A 3-year state transition model was developed to assess efficacy across treatment sequences for 7 immunological diseases: Crohn’s disease (CD), ulcerative colitis (UC), plaque psoriasis (PsO), psoriatic arthritis (PsA), rheumatoid arthritis (RA), ankylosing spondylitis (AS), and non-radiographical axial spondylarthritis (NR-AxSpA). Therapies were included based on indications listed in their European Medicines Agency label at the end of 2020. Each treatment sequence included ≤3 and 1 blended biological line, followed by best supportive care. Given that true sequencing data are not yet available for these diseases, assumptions based on clinical plausibility were applied when needed. Response measures were disease-specific and selected on quality and most frequent availability. Efficacy was differentiated between biologic-naïve and biologic-experienced populations. Induction treatment efficacy was based on percentage of responders, informed by published network meta-analyses. Maintenance treatment persistency was informed by published real-world data. The efficacy variability was calculated as the difference in average number of failures per patient between the best treatment sequence versus the worst.

RESULTS: In PsO, 1284 treatment sequences were possible, and the estimated average number of failures ranged from 0.58 to 2.44, translating to an efficacy variability of 1.86 failures per patient. The number of possible sequences and efficacy variability for the other diseases were: UC- 54 and 1.73; PsA- 660 and 0.56; RA- 660 and 0.37; CD- 24 and 0.27; AS- 120 and 0.26; NR-AxSpA- 24 and 0.25.

CONCLUSIONS: PsO, UC, and PsA treatment sequence options exhibited the greatest variability in efficacy, whereas RA, CD, AS and NR-AxSpA treatment sequence demonstrated the least variability. Results suggest that the ability to choose the most efficacious treatment sequence would provide the greatest opportunity to reduce treatment failures and therefore, maximise patient outcomes.