Optimal Control Strategies for Reducing Toxic Formation in Acetaminophen Metabolism

Authors

DOI:

https://doi.org/10.12150/jnma.2025.1830

Abstract

Acetaminophen (N-acetyl-para-aminophenol) is the most widely used painkiller in the world. Consuming acetaminophen involves a complex metabolic system, since it is converted into non-toxic and toxic metabolites called N-acetyl-p-benzoquinone imine (NAPQI). Acetaminophen is metabolized through a series of complex metabolic processes in the liver that involve enzymes as catalysts. This study presents a metabolic analysis of acetaminophen metabolism and its optimal regulation to reduce toxin formation. The metabolic study uses kinetic modeling combined with metabolic control analysis to identify key enzymes that can be modified to reduce hepatotoxicity due to excessive drug consumption. The sensitivity result shows that cytochrome (CYP) and uridine 5’-diphosphate-glucuronosyltransferase (UGT) are the two key enzymes that can be considered as internal control parameters. By inhibiting the reaction rate of CYP and UGT, the formation of N-acetyl-p-benzoquinone imine (NAPQI) can be reduced up to 69.9%, and the formation of Acetaminophen-Glucuronide (APAP-G) can be increased by approximately 0.49% such that Acetaminophen in the liver (APAP-L) can be excreted directly into the urine. Increasing the concentration of antioxidant GSH can also prevent hepatotoxicity by forming the complex NAPQI-GSH so that hepatotoxicity due to overconsumption of acetaminophen can be reduced.

Author Biographies

  • Naurah Zahwa

    Applied Mathematics Laboratory, Department of Mathematics, Hasanuddin University, Indonesia

  • Kasbawati

    Applied Mathematics Laboratory, Department of Mathematics, Hasanuddin University, Indonesia

  • Syamsuddin Toaha

    Applied Mathematics Laboratory, Department of Mathematics, Hasanuddin University, Indonesia

Published

2025-09-15

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How to Cite

Optimal Control Strategies for Reducing Toxic Formation in Acetaminophen Metabolism. (2025). Journal of Nonlinear Modeling and Analysis, 7(5), 1830-1858. https://doi.org/10.12150/jnma.2025.1830