Chemical Sensors Based on Molecularly Imprinted Polymers Can Determine Drug Release Kinetics from Nanocarriers without Filtration, Centrifugation, and Dialysis Steps

Enhancing the Predictive Performance of Molecularly Imprinted Polymer-Based Electrochemical Sensors Using a Stacking Regressor Ensemble of Machine Learning Models

The performance of electrochemical sensors is influenced by various factors. To enhance the effectiveness of these sensors, it is crucial to find the right balance among these factors. Researchers and engineers continually explore innovative approaches to enhance sensitivity, selectivity, and reliability. Machine learning (ML) techniques facilitate the analysis and predictive modeling of sensor performance by establishing quantitative relationships between parameters and their effects. This work presents a case study on developing a molecularly imprinted polymer (MIP)-based sensor for detecting doxorubicin (Dox), emphasizing the use of ML-based ensemble models to improve performance and reliability. Four ML models, including Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and K-Nearest Neighbors (KNN), are used to evaluate the effect of each parameter on prediction performance, using the SHapley Additive exPlanations (SHAP) method to determine feature importance. Based on the analysis, removing a less influential feature and introducing a new feature significantly improved the model's predictive capabilities. By applying the min-max scaling technique, it is ensured that all features contribute proportionally to the model learning process. Additionally, multiple ML models─Linear Regression (LR), KNN, DT, RF, Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Support Vector Regression (SVR), XGBoost, Bagging, Partial Least Squares (PLS), and Ridge Regression─are applied to the data set and their performance in predicting the sensor output current is compared. To further enhance prediction performance, a novel ensemble model is proposed that integrates DT, RF, GB, XGBoost, and Bagging regressors, leveraging their combined strengths to offset individual weaknesses. The main benefit of this work lies in its ability to enhance MIP-based sensor performance by developing a novel stacking regressor ensemble model, which improves prediction performance and reliability. This methodology is broadly applicable to the development of other sensors with different transducers and sensing elements. Through extensive simulation results, the proposed stacking regressor ensemble model demonstrated superior predictive performance compared to individual ML models. The model achieved an R-squared (R2) of 0.993, significantly reducing the root-mean-square error (RMSE) to 0.436 and the mean absolute error (MAE) to 0.244. These improvements enhanced sensitivity and reliability of the MIP-based electrochemical sensor, demonstrating a substantial performance gain over individual ML models.

Elimination of non-specific adsorption in the molecularly imprinted membrane: application for tetracycline detection

A vital challenge in using imprinted membranes for selective sensing is their non-specific adsorption (NSA). In this study, a novel, rapid, and green approach of NSA-free molecularly imprinted membrane (MIM) preparation was proposed. Sodium alginate was employed as a functional polymer (to interact with the template) and as a membrane matrix, then cross-linked with calcium before template removal to block the unreacted groups, followed by exposure to phosphate to chelate any remaining sites. Unlike the non-imprinted membrane (NIM), which is prepared similarly to MIM and lacks the template cavities, the MIM demonstrated exceptional imprinting factor (IF) (Q(NIM) ≈ 0 mg/g) compared to the initial IF of around 4 before NSA suppress, and a selectivity factor over 10 times greater than that of existing MIMs in the literature. The NSA-free MIM was used as a ready-to-use sensor for spectro-fluorescence and smartphone-based fluorescence detection of tetracycline (TC), achieving detection limits of 0.005 mg/L and 0.015 mg/L, respectively, which were below the maximal acceptable concentrations of TC in real samples. The detection of TC in milk and honey samples using the NSA-free MIM showed significant recoveries (86-101%) compared to those found by MIM before NSA supress (114-122%). The proposed methodology serves as an inspiration for extending NSA removal strategies to other MIMs based on various anionic polymers, including carboxylate, sulfonate, phosphonate, and phenolate anionic groups.

Molecular Imprinting Technology for Advanced Delivery of Essential Oils

Essential oils (EOs) hold therapeutic potential, but their conventional delivery systems have some limitations. This review focuses on the critical review and discussion of research related to EO delivery systems. The review also explores how molecular imprinting technologies (MIT) can advance EO delivery. MIT offer several techniques, namely covalent, non-covalent, and semi-covalent imprinting, creating targeted cavities that selectively bind and release EOs. These approaches promise significant advantages including increased selectivity, controlled release, and protection from environmental degradation. However, some challenges related to the stability and biocompatibility of MIPs remain unsolved. Integrating nanotechnology through methods like nanoparticle imprinting and some lithographic techniques seems promising to overcome these limitations. Some recently established models and systems used for EO-related research are paving the way for a more efficient and targeted EO delivery approach to harnessing the therapeutic power of EOs. Therefore, some recent and future research seems promising, and eventually it will increase the effectiveness of MIP-based EO delivery systems.

Modeling the Electric Double Layer at the Liposome Vesicle via Classical Density Functional Theory: Solution of Poisson's Equations for Curved Membranes

The electric double layer at the liposome vesicle membrane has been investigated by a modified fundamental-measure theory in the framework of the restricted primitive model. An analytical equation has been obtained for the mean electrostatic potential (MEP) by solving Poisson's equation for curved membranes. This study investigates the influence of vesicle size, membrane thickness, surface charges, and electrolyte concentration on the structure, composition, and width of electric double layers (EDLs) on the inner and outer membrane walls. Our findings indicate that a thin and denser layer of ions is formed at the concave wall of the membrane (inner wall) compared to that at the outer membrane. As expected, the width of the diffuse layer decreases with the concentration and surface charge. Also, when the surface charges on both concave and convex walls are the same, the absolute value of MEPs on the inner membrane, concave wall, is greater than that on the convex wall. We have also investigated the diffuse potential, which decreases with concentration, membrane thickness, and cavity size, whereas it increases with surface charges. As we expect, the contact density of counterions at the inner concave wall of the vesicle cavity is always greater than the corresponding value at the convex wall, whereas this trend reverses for co-ions. Also, the contact density of counterions (co-ions) at the inner wall decreases (increases) with cavity size, whereas it increases at the outer wall (decreases). Finally, depletion of co-ions occurs at the membrane walls with enhancement in surface charges.

Designing Molecularly Imprinted Polymer-Modified Boron-Doped Diamond Electrodes for Highly Selective Electrochemical Drug Sensors

Drug detection in biological solutions is essential in studying the pharmacokinetics of the body. Electrochemical detection is an accurate and rapid method, but measuring multiple drugs that react at similar potentials is challenging. Herein, we developed an electrochemical sensor using a boron-doped diamond (BDD) electrode modified with a molecularly imprinted polymer (MIP) to provide specificity in drug sensing. The MIP is a polymer material designed to recognize and capture template molecules, enabling the selective detection of target molecules. In this study, we selected the anticancer drug doxorubicin (DOX) as the template molecule. In the electrochemical measurements using an unmodified BDD, the DOX reduction was observed at approximately -0.5 V (vs Ag/AgCl). Other drugs, i.e., mitomycin C or clonazepam (CZP), also underwent a reduction reaction at a similar potential to that of DOX, when using the unmodified BDD, which rendered the accurate quantification of DOX in a mixture challenging. Similar measurements conducted in PBS using the MIP-BDD only resulted in a DOX reduction current, with no reduction reaction observed in the presence of mitomycin C and CZP. These results suggest that the MIP, whose template molecule is DOX, inhibits the reduction of other drugs on the electrode surface. Selective DOX measurement using the MIP-BDD was also possible in human plasma, and the respective limits of detection of DOX in PBS and human plasma were 32.10 and 16.61 nM. The MIP-BDD was durable for use in six repeated measurements, and MIP-BDD may be applicable as an electrochemical sensor for application in therapeutic drug monitoring.