Challenging Interchangeability: Dynamic Dose-Response Modelling of Botulinum Toxin A Products

Botulinum Neurotoxin type A (BoNT-A) formulations are used worldwide for both aesthetic and therapeutic indications, yet dose conversion between products remains based on empirical ratios that ignore pharmacodynamic variability. This study aimed to generate dynamic, time-resolved dose equivalence estimates for six FDA-approved BoNT-A formulations, onabotulinumtoxinA (ONA), abobotulinumtoxinA (ABO), incobotulinumtoxinA (INCO), daxibotulinumtoxinA (DAXI), prabotulinumtoxinA (PRABO), and letibotulinumtoxinA (LETI) and to assess how their clinical performance differs over time.

A hybrid, time-resolved in silico pharmacodynamic model was developed to simulate onset, peak effect, and decay kinetics using data aggregated from 49 clinical trials and regulatory submissions. Each formulation was modelled in 10,000 virtual patients to capture real-world variability in EC₅₀, maximal efficacy, and decay rate. Dose equivalence relative to 20 U of ONA was determined using two independent metrics: the area under the response curve (AURC) and time above a standardized efficacy threshold. External validation was performed against independent clinical datasets.

Simulated durations ranged from 10.6 weeks (ABO) to 14.5 weeks (PRABO), matching published clinical means within ±1.2 weeks. Time-resolved conversion ratios revealed significant temporal drift: ABO increased from 2.0 to 2.7 (≈35% drift), LETI from 1.1 to 1.4 (≈27 %), while DAXI and PRABO demonstrated decreasing ratios (≈15% drift), reflecting superior persistence. Efficiency analysis ranked PRABO and DAXI highest (AURC/unit = 0.059 and 0.057), followed by ONA (0.050), INCO (0.040), LETI (0.035), and ABO (0.031). Global sensitivity analysis identified decay rate (koff) as the dominant determinant of efficacy duration (Sobol index = 0.52).

Static dose conversion ratios fail to represent the dynamic pharmacodynamic behaviour of BoNT-A formulations. This simulation-based, data-driven approach reproduces clinical outcomes with high fidelity and provides a robust framework for effect-based dose equivalence, improving precision in product substitution and individualized treatment planning.

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