Toxicity Prediction of Perfluorobutanoic acid Using Different Web Applications: An In Silico Approach

Background: Perfluorobutanoic acid (PFBA) and its related salts are members of the group of perfluoroalkyl substances (PFASs). PFASs are synthetic compounds that have been used widely over the past several decades in industrial applications because of their resistance to heat, oil, stains, grease, and water. In silico analyses were employed to address gaps in current toxicity data and to predict PFBA’s potential molecular interactions and hazard profile. Aims: In the present study, the toxicity of PFBA was evaluated using different web applications. The use of web applications can minimize the number of animals, and it is cost-effective also. Materials and Methods: This was an in silico study designed to predict the toxic, pharmacokinetic, and biological properties of PFBA using different online computational tools. Various web applications were used for toxicity prediction. CarcinoPred-EL was used for carcinogenicity assessment, Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures for absorption, distribution, metabolism, excretion, and toxicity analysis, ProTox-II for estimating rodent oral toxicity, and Lazar for mutagenicity prediction. The Brain Or IntestinaL EstimateD permeation method plot model was applied to assess gastrointestinal absorption and blood–brain barrier permeability, whereas the prediction of activity spectra for substances prediction was used to identify potential biological activities. No statistical analysis was required, as the study relied on computational prediction tools and qualitative data interpretation. Results and Conclusions: The web-based tools provided valuable preliminary information regarding the potential toxicological and pharmacokinetic characteristics of PFBA. The use of in silico methods minimized animal use and proved to be a cost-effective and efficient approach for screening the toxicity of chemical compounds. PFBA has limited and incomplete in vitro and in vivo toxicity data; in silico predictions help fill significant mechanistic gaps.