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A mathematical modelling framework for the "stop when you feel better" approach to antibiotic prescribing.

Ledda, A.; Evans, S.; Ferretti, L.; Roope, L.; Pople, D.; Pollard, J.; Borek, A. J.; Pouwels, K. B.; Tonkin-Crine, S.; Walker, A. S.; Robotham, J. V.

2026-08-05 infectious diseases
10.64898/2026.08.03.26359575 medRxiv
Show abstract

To prevent antimicrobial resistance (AMR), antibiotic courses are traditionally prescribed to completion, overlooking their collateral impact on the commensal microbiota. We developed a deterministic within-host model linking pathogen growth, immune responses, antibiotic action, and commensal dynamics to explore how treatment duration influences resistance development. Simulations across community-acquired infection parameters showed that 71% of cases successfully treated with a standard 7-day regimen could also be cured with shorter "Stop When Better" (SWB) courses, where treatment cessation was triggered by a predefined threshold in pathogen abundance, and 97% by SWB plus 1 day. Compared with standard treatment, SWB+1 approaches reduced overall antibiotic exposure and resistance emergence in commensal niches while preserving efficacy. While this threshold-based definition of "feeling better" offers a clear theoretical framework, it would require translation into clinical cues. Therefore, although these findings suggest that response-based antibiotic stopping rules could inform future adaptive prescribing strategies, further empirical validation is essential.

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