Ponente
Descripción
Anesthesiology is the medical specialty that studies the absence of sensation in the human body when surgeries or certain examinations need to be performed. This field faces the challenge of personalizing drug administration to ensure patient safety and adequate depth of hypnosis. Due to the complexity of surgical and anesthetic procedures and interpatient variability, mathematical modeling serves as an important tool to predict agent distribution and concentration in the body. A relevant phenomenon in this process is hysteresis, which represents the time delay between plasma concentration of the anesthetic and its clinical effect on the central nervous system. This work aimed to implement and numerically analyze a compartmental pharmacokinetic model for anesthetics in order to investigate concentration dynamics in the central and peripheral (slow and fast absorption) compartments, integrating an effect site to characterize the actual disposition of the medicine in the body. The research is embedded in the field of interdisciplinary biomathematics, as it involves a system of ordinary differential equations (ODEs) and the estimation of coefficients related to the biological phenomenon. To solve the equations, a numerical discretization using Euler's method was implemented in the Python programming language. The study is based on the classic concepts of compartmental pharmacokinetics (Shargel, L. & Yu, A.B.C., Applied Biopharmaceutics and Pharmacokinetics, McGraw-Hill Education, 2016). The specific approach to anesthetic depth and interpatient variability analysis takes into account previous findings regarding the behavior of an anesthetic agent (Gonzalez-Cava, J.M. et al., B.S.P.C. 59, 101931, 2020) and the influence of different parameters such as age, weight, height, and gender on its dynamics in the organism (Schnider, T.W. et al., Anesthesiology 90, 1502-1516, 1999). Results from computer simulations demonstrate that the model satisfactorily reproduces the delay in effect-site concentration relative to the plasma, consistent with the expected clinical behavior. It is expected that the analysis of different scenarios and parameters will contribute to the understanding of individualized drug titration and its effects. Thus, it can be concluded that the integration of applied mathematics and computing is a viable alternative for developing medical decision-support technologies, promoting greater precision and safety in surgical procedures.