Chemometric-Assisted Spectrophotometric Methods for Simultaneous Drug Determination in New Helicobacter pylori Treatment Regimens - Environmental Sustainability Assessment

Abstract

The recent FDA approval of VOQUEZNA™ TRIPLE PAK™ 7-day therapy, which includes vonoprazan (VON), amoxicillin (AMO), and clarithromycin (CLA), marks a significant advancement in the treatment of Helicobacter pylori (H. pylori) infections. Accurate quantification of these active pharmaceutical ingredients (APIs) is critical for ensuring therapeutic efficacy and safety. However, conventional analytical methods often require extensive sample pretreatment and separation, which can be time-consuming and environmentally burdensome.This study presents the first simultaneous quantification of VON, AMO, and CLA using innovative chemometric-assisted spectrophotometric methods aligned with Green Analytical Chemistry (GAC) principles. Our methods eliminate the need for pretreatment or separation, thereby enhancing both analytical efficiency and environmental sustainability. We developed orthogonal partial least squares (OPLS), principal component regression (PCR), and Artificial Neural Network (ANN) models, utilizing the Design of Experiment (DoE) approach to minimize solvent use and waste.Model validation was achieved through Orthogonal Array-based Latin Hypercube Sampling (OALHS), ensuring robust performance evaluation. The models demonstrated high precision, with recovery percentages ranging from 98.00% to 102.00%. The calibration set model fitting was assessed using the determination coefficient (R2), and the cross-validation coefficient (Q2), all model's R2 and Q2 values were close to 1.0, indicating the calibration samples' high capacity for explanation and prediction, while the root mean square error of calibration (RMSEC) values were found to be less than 0.1. The prediction of the validation set was employed by the root mean square error of prediction (RMSEP) and relative root mean square errors of prediction (RRMSEP), the values were found (0.0335-0.0613) and (0.7207-0.5287) for RMSEP and RRMSEP, respectively, while the bias-corrected mean square error of prediction (BCMSEP) wasfound to be between 0.0014 and 0.0001. To evaluate and enhance the sustainability of the methods, comprehensive tools were utilized: SPIDER Solvent Tool, RGB12 Algorithm, AGREE, and the Need Quality Sustainability (NQS) Index. This work supports the Sustainable Development Goals (SDGs) by demonstrating advancements in environmentally sustainable analytical methods.