Computational Toxicology | 2021

A mechanistic model to study the kinetics and toxicity of salicylic acid in the kidney of four virtual individuals

 
 
 
 
 

Abstract


Abstract In comparison to liver toxicology, little is known about mechanisms of adverse effects in the kidney and only limited computational models exist to investigate nephrotoxicity. However, the kidney is a major target for toxicity by pharmaceuticals and environmental pollutants. Accumulation is known to play an important role in certain nephrotoxicity pathways. Therefore, physiologically-based kinetic (PBK) and mechanistic models are considered to offer valuable insights into mechanisms of nephrotoxicity. This study addresses the growing attention given to exposure-based and toxicokinetics-driven toxicity which has resulted in increasing recent application of PBK modelling. This research presents the development of a novel mechanistic kidney model embedded in a PBK model parameterised for aspirin and salicylic acid. It is set up to study a combination of young/healthy individuals as well as the elderly, with three exposure scenarios simulating real renal exposure. Key challenges in this endeavour revolve around limited data available in the public literature and uncertainties related to scaling in vitro data to an in vivo setting. Results show that at a low, chronic therapeutic dose the predicted proximal tubular cell concentrations in all considered population groups are below the hypothesised toxicity threshold of 0.09\xa0mM. Sensitivity analyses indicate that at both higher dose scenarios, active transporter activity has the most impact on predicted proximal tubular cell concentrations and therefore individual transporter expression may be key indicators of cellular toxicity. In addition, the fraction unbound of salicylic acid in plasma, the glomerular filtration rate and a decreased urinary pH have a high impact on predicted proximal tubular cell concentrations which largely exceed the toxicity threshold for all individuals at both higher dose scenarios.

Volume 19
Pages 100172
DOI 10.1016/J.COMTOX.2021.100172
Language English
Journal Computational Toxicology

Full Text