From Uniformity to Individuality: Computational Modelling Provides a Mechanistic Rationale for Weight-Adjusted Botulinum Neurotoxin A Dosing.

Fixed-dose Botulinum neurotoxin type A regimens ignore patient-specific variables, such as body weight, despite allometric principles governing the distribution of biologic drugs. This computational study developed a weight-adjusted dosing framework for onabotulinumtoxinA (ONA) across nine therapeutic and aesthetic indications. Mechanistic pharmacokinetic/pharmacodynamic models incorporating allometric scaling were integrated with machine learning and Bayesian hierarchical analysis to simulate ONA distribution, efficacy, and toxicity in synthetic patient cohorts (N=10,000). Body weight was the dominant determinant of ONA bioavailability and therapeutic outcome for systemic indications (chronic migraine, cervical dystonia, adult spasticity, overactive bladder), with weight-scaling exponents of 0.55-0.72 and predicted dose requirements showing 30-70% variance from fixed regimens. Weight-adjusted dosing improved the simulated duration of efficacy by up to 50% and reduced adverse event rates by 20-60% in systemic indications, while low-weight populations exhibited a heightened risk of toxicity under fixed dosing. For aesthetic indications and hyperhidrosis, weight-scaling exponents approached zero (0.02-0.18); muscle thickness and skin elasticity superseded weight as determinants of dosing. Machine learning models achieved high predictive accuracy (mean absolute error <10 units; toxicity AUC >0.85). All predictions apply exclusively to ONA and are not interchangeable with other botulinum toxin formulations. All predictions are derived from in silico simulations and have not been validated in prospective clinical trials. These findings require confirmation in controlled human studies before clinical adoption.