Computer-Aided Design of Eco-Friendly Imprinted Polymer Decorated Sensors Augmented by Self-Validated Ensemble Modeling Designs for the Quantitation of Drotaverine Hydrochloride in Dosage Form and Human Plasma

Author:

E. Mostafa Aziza1ORCID,Eissa Maya S2ORCID,Elsonbaty Ahmed2ORCID,Attala Khaled2ORCID,A. Abdel Salam Randa1ORCID,M. Hadad Ghada1ORCID,Abdelshakour Mohamed A3ORCID

Affiliation:

1. Suez Canal University, Faculty of Pharmacy, Department of Pharmaceutical Analytical Chemistry , Ismailia 41522, Egypt

2. Egyptian Russian University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry , Badr City, Cairo 11829, Egypt

3. Sohag University, Faculty of Pharmacy, Department of Pharmaceutical Analytical Chemistry , Sohag 82524, Egypt

Abstract

Abstract Background Computationally designed molecular imprinted polymer (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 (ML) technique that enables the design of more accurate predictive models using 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 (DVN) in its combined dosage form and human plasma. Furthermore, 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. Method Here, for the first time, the predictive power of ML is assembled with computational simulations to develop four PVC-based sensors decorated by computationally designed MIP particles using 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 toward DVN in the range of 58.60–59.09 mV/decade with a linear quantitative range of 1 × 10−7 – 1 × 10−2 M and limits of detection in the range of 9.55 × 10−8 to 7.08 × 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. Conclusions The proposed sensors were validated in accordance with International Union of Pure and Applied Chemistry (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.

Publisher

Oxford University Press (OUP)

Subject

Pharmacology,Agronomy and Crop Science,Environmental Chemistry,Food Science,Analytical Chemistry

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