Spatiotemporal dynamics of local anesthetic diffusion in nerve revealed by a 2D computational model.

Abstract

Despite their extensive clinical use, the intra-neural pharmacokinetics of local anesthetics, including the mechanisms determining their onset and duration, remain incompletely understood, particularly under pathological conditions. We developed a detailed, spatially-compartmentalized computational model of human peripheral nerve fascicles to simulate the diffusion, accumulation, and clearance kinetics of lidocaine and bupivacaine under physiological and acidic conditions. The model integrates nerve fiber architecture, extracellular fluid compartments, and capillary-mediated clearance, parameterized using experimentally validated anatomical and physicochemical data. It was implemented in Python 3.12, employing a fourth-order Runge-Kutta integrator via the SciPy library. The results revealed that onset is limited primarily by extracellular diffusion rather than transmembrane transport. Lidocaine reached the predefined onset threshold (≥50% of nerve fibers with ≥50% external concentration) at 8.7 s under physiological pH and 3.2 s under acidic conditions. Bupivacaine exhibited longer onset times, 79.2 s and 16.1 s, respectively. Acidic conditions markedly reduced equilibrium concentrations within nerve fibers (by 3.6-fold for lidocaine, 3.5-fold for bupivacaine), significantly shortening their durations of action (lidocaine: 758 to 233 s; bupivacaine: 6980 to 2059 s). These findings mirror known clinical efficacy reductions in inflamed or acidotic tissues. In conclusion, local anesthetic onset is primarily governed by extracellular diffusion rather than membrane permeability, rendering it largely independent of drug pKa. Local tissue acidosis reduces nerve fiber accumulation and accelerates anesthetic washout, thereby diminishing both efficacy and duration of action. These results explain longstanding clinical observations and suggest that nerve fibers act as kinetic reservoirs modulated by tissue pH. This computational model offers a valuable framework for predicting anesthetic behavior and optimizing drug delivery strategies in regional anesthesia, particularly under pathological conditions such as inflammation.