Computational prediction of pharmacokinetic parameters as an <i>in vitro</i> approach for assessing paracetamol tablets for IVIVC; a strategy in COVID-19 disruptive times.

Abstract

In vitro-in vivo correlation (IVIVC) is a desirable attribute for any drug dissolution test to establish relevance and confidence in evaluating the quality and safety of products. The pharmacokinetic parameters of paracetamol have been studied extensively but information about the IVIVC is scanty and mostly controversial; this justifies its choice as a model drug for this study. This work is aimed to evaluate and compare the IVIVC dissolution profile of different brands of paracetamol tablets using authentic pharmacokinetic parameters such as; maximum observed dug concentration (Cmax), time to reach Cmax (Tmax) and Area under Curve concentration-time curve (AUC) obtained from literature. In vitro release data were obtained for each brand (n=12) using the USP II apparatus at 50 rpm in 900 ml phosphate buffer of pH 5.8, maintained at 37±0.5 °C and the results were mathematically extrapolated to predict in vivo data. The percent predicted error (% PE) for Cmax ranges from 1.70 to 6.52 % across the brands, while those for Tmax and AUC were < 0 % and > 20 % respectively. The observed low prediction error for Cmax and Tmax (<10 %) demonstrated that the paracetamol IVIVC model was valid based on FDA guidelines. While a satisfactory result could not be achieved for AUC, promising results were obtained exploiting the convolution based IVIVC model.