Hemodialysis biocompatibility mathematical models to predict the inflammatory biomarkers released in dialysis patients based on hemodialysis membrane characteristics and clinical practices

Abstract

AbstractChronic kidney disease affects millions of people around the globe and many patients rely on hemodialysis (HD) to survive. HD is associated with undesired life-threatening side effects that are linked to membrane biocompatibility and clinical operating conditions. The present study develops a mathematical model to predict the inflammatory biomarkers released in HD patients based on membrane morphology, chemistry, and interaction affinity. Based on the morphological characteristics of two clinical-grade HD membrane modules (CTA and PAES-PVP) commonly used in Canadian hospitals, a molecular docking study, and the release of inflammatory cytokines during HD and in vitro incubation experiments, we develop five sets of equations that describe the concentration of eight biomarkers (serpin/antithrombin-III, properdin, C5a, 1L-1α, 1L-1β, C5b-9, IL6, vWF). The equations developed are functions of membrane properties (pore size, roughness, chemical composition, affinity to fibrinogen, and surface charge) and HD operating conditions (blood flow rate, Qb, and treatment time, t). We expand our model based on available clinical data and increase its range of applicability in terms of flow rate and treatment time. We also modify the original equations to expand their range of applicability in terms of membrane materials, allowing the prediction and validation of the inflammatory response of several clinical and synthesized membrane materials. Our affinity-based model solely relies on theoretical values of molecular docking, which can significantly reduce the experimental load related to the development of more biocompatible materials. Our model predictions agree with experimental clinical data and can guide the development of novel materials and support evidence-based membrane synthesis of HD membranes, reducing the need for trial-and-error approaches.