RP-HPLC method development and validation for the quantification of prednisolone and salbutamol with their simultaneous removal from water using modified clay–activated carbon adsorbents

Abstract

Salbutamol sulfate (SAL) and prednisolone (PRD) are commonly used for treating respiratory and inflammatory conditions, yet they are frequently detected in aquatic ecosystems, posing significant risks to aquatic life and biodiversity. Despite the growing concern over pharmaceutical pollution, there is a lack of reliable and sustainable methods for quantifying these drugs in both pharmaceutical and environmental samples, as well as effective adsorbents for their removal from contaminated water. This study aims to fill this gap by developing a reliable reversed-phase high-performance liquid chromatography (RP-HPLC) method for quantifying SAL and PRD, while also creating an organoclay–activated carbon composite adsorbent for removing these drugs from water. The HPLC method was validated for linearity, precision, accuracy, robustness, and specificity, with detection limits of 1.06 μg mL⁻¹ for SAL and 0.95 μg mL⁻¹ for PRD. The adsorbent demonstrated high efficiency in removing both drugs, achieving maximum adsorption capacities of 731.64 mg g⁻¹ for SAL and 888.75 mg g⁻¹ for PRD at pH 7, with an adsorbent dose of 0.4 g and a temperature of 45 °C. Thermodynamic analysis revealed that the adsorption process is both endothermic and spontaneous. Characterization of the adsorbent using FTIR, SEM, XRD, and BET confirmed its effective structure. Adsorption followed the Langmuir model for SAL and the Sips model for PRD, with equilibrium reached within 240 minutes and the process following pseudo-second-order kinetics. Ethanol proved more effective than acetone and acetic acid for desorbing SAL, while acetone was more effective for PRD. The organoclay–activated carbon adsorbent was found to be cost-effective, offering a practical solution for large-scale water treatment. Sustainability assessments using the ComplexGAPI, BAGI, and RGB 12 algorithms highlighted its strong environmental friendliness. This research provides valuable insights for pharmaceutical quality control and the environmental remediation of pharmaceutical pollutants.