Beyond the Dose: Introducing LOGOS for Dosage Regimen Design in Therapeutics
- 1National and Kapodistrian University of Athens
Abstract
The aim of this study was to develop a framework for assessing the incremental value of decisions on drug amount, timing, input structure, cessation and restart. The LOGOS (Longitudinal Optimization of Global Outcomes and Strategies) approach combines matched nested regimen classes, mechanism-specific benchmarks and evidence requirements in a single longitudinal comparison. Thus, the contribution of additional regimen freedom is assessed separately from improvements due to different resources, constraints or evaluation horizons. Nested feasible classes have nondecreasing objective suprema, whereas a strict gain requires a feasible witness above a valid bound for the smaller class. The approach is illustrated through analytical examples of periodic smoothing, matched desensitization, relaxed exposure allocation, finite-course loading and safety after cessation. In a normalized finite Emax model, integrated benefit increased from 0.629606 for three equal boluses to 0.672945 after optimization of amounts, 0.677043 for an amount-and-time candidate and 0.679553 for a globally certified loading-maintenance-coast solution. The adjacent observed gains were 6.88%, 0.61% and 0.37%, respectively, and all schedules satisfied the full-tail injury bound. In addition, a periodic factorial example separated response curvature from a joint recovery and repair process, including a null case and results dependent on feasibility constraints. The LOGOS framework allows assessment of whether additional regimen flexibility improves benefit, provides no added value or restores feasibility. These dimensionless examples support conditional mathematical and model-based conclusions; clinical superiority and safe restart in patients have not been established.
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BibTeX
@misc{karalis2026beyond,
title = {{Beyond the Dose: Introducing LOGOS for Dosage Regimen Design in Therapeutics}},
author = {V. Karalis},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.17.26365797},
url = {https://www.medrxiv.org/content/10.64898/2026.10.17.26365797v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Beyond the Dose: Introducing LOGOS for Dosage Regimen Design in Therapeutics
AU - V. Karalis
PY - 2026
DA - 2026/10/17
PB - medRxiv
DO - 10.64898/2026.10.17.26365797
UR - https://www.medrxiv.org/content/10.64898/2026.10.17.26365797v1
AB - The aim of this study was to develop a framework for assessing the incremental value of decisions on drug amount, timing, input structure, cessation and restart. The LOGOS (Longitudinal Optimization of Global Outcomes and Strategies) approach combines matched nested regimen classes, mechanism-specific benchmarks and evidence requirements in a single longitudinal comparison. Thus, the contribution of additional regimen freedom is assessed separately from improvements due to different resources, constraints or evaluation horizons. Nested feasible classes have nondecreasing objective suprema, whereas a strict gain requires a feasible witness above a valid bound for the smaller class. The approach is illustrated through analytical examples of periodic smoothing, matched desensitization, relaxed exposure allocation, finite-course loading and safety after cessation. In a normalized finite Emax model, integrated benefit increased from 0.629606 for three equal boluses to 0.672945 after optimization of amounts, 0.677043 for an amount-and-time candidate and 0.679553 for a globally certified loading-maintenance-coast solution. The adjacent observed gains were 6.88%, 0.61% and 0.37%, respectively, and all schedules satisfied the full-tail injury bound. In addition, a periodic factorial example separated response curvature from a joint recovery and repair process, including a null case and results dependent on feasibility constraints. The LOGOS framework allows assessment of whether additional regimen flexibility improves benefit, provides no added value or restores feasibility. These dimensionless examples support conditional mathematical and model-based conclusions; clinical superiority and safe restart in patients have not been established.
ER -