Predictive Toxicology and Toxicogenomics of Potassium Sorbate-Gene-Diseases Association

Subject Areas : Journal of Chemical Health Risks

K. Shanmuga Priya ¹ , V. Pushpa Rani ² , A. Anitha Nancy ³

1 - Centre for Environmental and Medical Sciences, PG & Research, Department of Advanced Zoology & Biotechnology, Loyola Institute of Frontier Energy (LIFE), Loyola College, University of Madras, Chennai, India-600034
2 - Centre for Environmental and Medical Sciences, PG & Research, Department of Advanced Zoology & Biotechnology, Loyola Institute of Frontier Energy (LIFE), Loyola College, University of Madras, Chennai, India-600034
3 - Centre for Environmental and Medical Sciences, PG & Research, Department of Advanced Zoology & Biotechnology, Loyola Institute of Frontier Energy (LIFE), Loyola College, University of Madras, Chennai, India-600034

Received: 2021-07-13 Accepted : 2021-11-08 Published : 2021-10-01

Keywords: Potassium sorbate, PreADMET, Predictive Toxicology, Comparative Toxicogenomics Database, Endocrine Disruptor Chemical,

Abstract :

In this century, exposure to numerous chemical from different sources became common in human life. Conversely, the toxicological data for a large portion of chemicals for its risk assessment are unknown. Potassium sorbate (PS) is preservative used in wide variety of food, cosmetic and pharmaceutical products and there many authors reported about the effect of PS. This investigation is to integrate computational TGx and predictive toxicology and first report of potassium sorbate on this aspect. It was aimed in order to understand the potential adverse health effects of PS by ADMET prediction and their curated interactions between PS-gene–disease relationships. PreADMET and Comparative Toxicogenomics Database were used for the computational study. PreADMET revealed prediction data for ADME via physic-chemical parameter along with Caco-2 cell, MDCK cell and BBB (blood-brain barrier), HIA (human intestinal absorption), skin permeability and plasma protein binding and toxicological prediction using chemical structures, such as mutagenicity and carcinogenicity. CTD results established curated and inferred interactions between PS-gene–disease relationships. The CTD outcomes exposed that PS may possess endocrine disruption potency and have impact on endocrine system diseases etiology. It is concluded, that computational prediction approach offers both a better understanding of the potential risks of chemical exposure to humans and a direction for future toxicological investigation.

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