Background Computationally-designed molecular imprinted polymers (MIP) incorporation into electrochemical sensors has many advantages to the performance of the designed sensors. The innovative self-validated ensemble modeling (SVEM) approach is a smart machine learning-based technique that enabled the design of more accurate predictive models utilizing smaller data sets. Objective The novel SVEM experimental design methodology is exploited here exclusively to optimize the composition of four eco-friendly PVC membranes augmented by a computationally designed magnetic molecularly imprinted polymer to quantitatively determine drotaverine hydrochloride in its combined dosage form and human plasma. Besides, the application of hybrid computational simulations such as molecular dynamics and quantum mechanical calculations (MD/QM) is a time-saving and eco-friendly provider for the tailored design of the MIP particles. Methods Here, for the first time, the predictive power of machine learning is assembled with computational simulations to develop four PVC-based sensors decorated by computationally designed MIP particles utilizing four different experimental designs known as central composite, SVEM-LASSO, SVEM-FWD, and SVEM-PFWD. The pioneering Agree approach further assessed the greenness of the analytical methods, proving their eco-friendliness. Results The proposed sensors showed decent Nernstian responses towards drotaverine hydrochloride in the range of (58.60—59.09 mV/decade) with a linear quantitative range of (1 x 10−7—1 x 10−2 M) and limits of detection in the range of (9.55 x 10−8 – 7.08 x 10−8 M). Moreover, the proposed sensors showed ultimate eco-friendliness and selectivity for their target in its combined dosage form and spiked human plasma. Conclusion The proposed sensors were validated as per IUPAC recommendations proving their sensitivity and selectivity for drotaverine determination in dosage form and human plasma. Highlights This work presents the first ever application of both the innovative SVEM designs and MD/QM simulations in the optimization and fabrication of drotaverine-sensitive and selective MIP-decorated PVC sensors.

Greenness-by-design (GbD) is an approach that integrates green chemistry principles into the method development stage of analytical processes, aiming to reduce their environmental impact. In this work, we applied GbD to a novel univariate double divisor corrected amplitude (DDCA) method that can resolve a quaternary pharmaceutical mixture in a fixed-dose polypill product. We also used a genetic algorithm as a chemometric modeling technique to select the informative variables for the analysis of the overlapping mixture. This resulted in more accurate and efficient predictive models. We used a computational approach to study the effect of solvents on the spectral resolution of the mixture and to minimize the spectral interferences caused by the solvent, thus achieving spectral resolution with minimal analytical effort and ecological footprint. The validated methods showed wide linear concentration ranges for the four components (1-30 µg/mL for losartan, 2.5-30 µg/mL for atorvastatin and aspirin, and 2.5-35 µg/mL for atenolol) and achieved high scores on the hexagon and spider charts, demonstrating their eco-friendliness.

A method was optimized and validated for simultaneous estimation of some antibiotics such as CTC, DOX, FF, FLU, NAL, SDI, STZ and TMP in fish muscle and water samples.

Electrochemical sensors are situated as effective tools for the sensitive and selective determination of several heavy metal traces, pesticides, and a vast diversity of pharmaceuticals in different matrices. The development of advanced electrochemical sensors requires the collaboration of all scientific knowledge especially; computational chemistry, mathematics, and classical and quantum physics. This interdisciplinary in analytical chemistry made it possible to get benefits from molecular modeling, and simulations to develop more selective and sensitive electro-analytical platforms with lowered cost, time, and effort. Recently, the optimization of sensor design was more practical and robust in the light of computational simulation techniques such as molecular docking, dynamics simulation, and quantum calculations. Molecular modeling approaches (MMA) enabled the analyst to explore unrelenting molecular systems ranging from small chemical systems to massive biological molecules and material assemblies in the fields of computational chemistry. Furthermore, MAA has been recently used in the optimization of the design of different electrochemical sensors. Thus, in this review, we went over the different ap-plications of MMA and demonstrate these techniques on both the molecular and quantum levels. Moreover, we focused on the benefits of bringing such innovative techniques to the field of electro-analytical chemistry and highlighted some of the recently reported electrochemical sensors.

A facile green microwave-assisted method was developed for the production of highly fluorescent nitrogen doped carbon quantum dots (N-CQDs) using sucrose and urea as starting materials. The fluorescent N-CQDs were utilized as nano-sensors for the spectrofluorimetric estimation of furosemide after subjecting to extensive spectroscopic characterization. The quantum yield of the obtained N-CQDs was found to be 0.57. After excitation of the produced N-CQDs at 216 nm, a strong emission band appeared at 376 nm. The fluorescence emission of N-CQDs was quantitatively quenched by adding increased concentrations of the drug. A linear relationship was obtained over the concentration range of 0.1–1.0 μg/mL. The developed method was successfully applied for the estimation of furosemide in its pharmaceutical preparations and biological samples. The mechanism of the quenching was studied and explained. Interference likely to be introduced from co-administered drugs was also studied.