A MULTIVARIATE INTERPOLATION APPROACH FOR PREDICTING DRUG LD50 VALUE

Abstract

Objective: The present study aimed to develop a multivariate interpolation based on the quantitative structure-toxicity relationship (QSTR) that can accurately predict the oral median lethal dose (LD50) values of drugs in mice by considering five different toxicologic endpoints. Material and Method: A mathematical model was created using a comprehensive dataset comprising LD50 values from 319 pharmaceuticals belonging to various pharmacological classes. We developed a polynomial model that can predict the range of LD50 values for pharmaceuticals. We employed a technique called two-variable polynomial interpolation. This method allowed us to estimate the approximate values of a function at any point within a two-dimensional (2D) space by utilizing a polynomial equation. Result and Discussion: The resulting model demonstrated the ability to predict LD50 values for new or untested drugs, rendering it a valuable tool in the early stages of drug development. The Ghose-Crippen-Viswanadhan octanol-water partition coefficient (ALogP) and Molecular Weight (MW) were selected as suitable descriptors for building the best QSAR model. Based on our evaluation, the model achieved an overall success rate of 86.73%. Compared to traditional experimental methods for LD50 determination, this innovative approach offers time and cost efficiency while reducing animal testing requirements. Our model can improve drug safety, optimize dosage regimens, and assist decision-making processes during preclinical studies and drug development. This approach provided a reliable and efficient method for preliminary acute toxicity assessments.