Targeting the glabellar frown lines with OnabotulinumtoxinA: In-silico evidence supporting concentrated, low-volume...

Botulinum Neurotoxin Type A (BoNT-A) remains the cornerstone of glabellar frown line treatment, yet conventional low-dose, high-volume protocols often result in limited durability and imprecise diffusion. This study presents multiscale, in silico framework specifically designed to evaluate high-dose (60-80 Units), low-volume (≤0.045 mL/site) BoNT-A glabellar injection strategies across anatomically realistic conditions. Using a synthetic cohort of 20,000 virtual patients, we integrated finite element modelling of anisotropic tissue diffusion, receptor-specific pharmacokinetics/pharmacodynamics (PK/PD), and machine learning-based off-target risk prediction. Our findings demonstrate that reduced-volume, concentrated dosing significantly enhances SV2 receptor occupancy (up to 93.7 %), confines toxin diffusion within target musculature, and prolongs clinical duration to 124.7 days-surpassing conventional benchmarks. To our knowledge, this is the first study to mechanistically simulate dose-volume interactions of BoNT-A in 3D facial anatomy using integrated PK/PD and Artificial Intelligence models. Our model revealed a 30-70 % variance in BoNT-A exposure and efficacy when comparing fixed-dose protocols (as per FDA labels) to weight-adjusted regimens across simulated populations. These results establish a data-driven rationale for transitioning toward precision-targeted toxin administration and lay the foundation for prospective clinical trials and personalized dosing algorithms. While robust, the study relies on synthetic data and model-driven assumptions; clinical validation is required to confirm translatability.