Quantitative structure-imprinting factor relationship of molecularly imprinted polymers

Ion-imprinted membranes for lithium recovery: A review

This review critically examines the effectiveness of ion-imprinted membranes (IIMs) in selectively recovering lithium (Li) from challenging sources such as seawater and brine. These membranes feature customized binding sites that specifically target Li ions, enabling selective separation from other ions, thanks to cavities shaped with crown ether or calixarene for improved selectivity. The review thoroughly investigates the application of IIMs in Li extraction, covering extensive sections on 12-crown-4 ether (a fundamental crown ether for Li), its modifications, calixarenes, and other materials for creating imprinting sites. It evaluates these systems against several criteria, including the source solution's complexity, Li+ concentration, operational pH, selectivity, and membrane's ability for regeneration and repeated use. This evaluation places IIMs as a leading-edge technology for Li extraction, surpassing traditional methods like ion-sieves, particularly in high Mg2+/Li+ ratio brines. It also highlights the developmental challenges of IIMs, focusing on optimizing adsorption, maintaining selectivity across varied ionic solutions, and enhancing permselectivity. The review reveals that while the bulk of research is still exploratory, only a limited portion has progressed to detailed lab verification, indicating that the application of IIMs in Li+ recovery is still at an embryonic stage, with no instances of pilot-scale trials reported. This thorough review elucidates the potential of IIMs in Li recovery, cataloging advancements, pinpointing challenges, and suggesting directions for forthcoming research endeavors. This informative synthesis serves as a valuable resource for both the scientific community and industry professionals navigating this evolving field.

Modification of Cotton Fabric with Molecularly Imprinted Polymer-Coated Carbon Dots as a Sensor for 17 α-methyltestosterone

Molecularly imprinted polymers@ethylenediamine-modified carbon dots grafted on cotton fabrics (MIPs@EDA-CDs/CF) and smartphone-based fluorescence image analysis were proposed and used for the first time for the detection of 17 α-methyltestosterone (MT). The EDA-CDs were synthesized and grafted on cotton fabric before coating with the MIPs. The MIPs were synthesized using the MT as a template molecule, methacrylic acid (MAA) as a functional monomer, ethylene glycol dimethacrylate (EGDMA) as a cross-linker, and azobisisobutyronitrile (AIBN) as an initiator. The MIPs@EDA-CDs/CF were characterized using FTIR, SEM-EDS, and RGB fluorescence imaging. The fluorescence images were also taken using a smartphone and the ImageJ program was used for RGB measurement. The Δ red intensity was linearly proportional to MT concentration in the range of 100 to 1000 μg/L (R2 = 0.999) with a detection limit of 44.4 μg/L and quantification limit of 134 μg/L. The MIPs@EDA-CDs/CF could be stored at 4 °C for a few weeks and could be reused twice. The proposed method could apply for the specific determination of MT in water and sediment samples along with satisfactory recoveries of 96–104% and an acceptable relative standard deviation of 1–6% at the ppb level.

Development and critical evaluation of a novel fluorescent nanosensor based on a molecularly imprinted polymer for the rapid detection of procymidone in ginseng

Effective methods are required to quantify the organochlorine pesticide procymidone due to its potentially harmful effects toward human health and the environment.

Optimization of Cortisol-Selective Molecularly Imprinted Polymers Enabled by Molecular Dynamics Simulations

Today, we heavily rely on technology and increasingly utilize it to monitor our own health. The identification of sensitive, accurate biosensors that are capable of real-time cortisol analysis is one important potential feature for these technologies to aid us in the maintenance of our physical and mental wellbeing. Detection and quantification of cortisol, a well-known stress biomarker present in sweat, offers a noninvasive and potentially real-time method for monitoring anxiety. Molecularly imprinted polymers are attractive candidates for cortisol recognition elements in such devices as they can selectively rebind a targeted template molecule. However, mechanisms of imprinting and subsequent rebinding depend on the choice and composition of the prepolymerization mixture where the molecular interactions between the template, functional monomer, cross-linker, and solvent molecules are not fully understood. Here, we report the synthesis and evaluation of a molecularly imprinted polymer selective for cortisol detection. Molecular dynamics simulations were used to investigate the interactions between all components in the prepolymerization mixture of the as-synthesized molecularly imprinted polymer. Varying the component ratio of the prepolymerization mixture indicates that the number of cross-linker molecules relative to the template impacts the quality of imprinting. It was determined that a component ratio of 1:6:30 of cortisol, methacrylic acid, and ethylene glycol dimethacrylate, respectively, yields the optimal theoretical complexation of cortisol for the polymeric systems investigated. Experimental synthesis and rebinding results demonstrate an imprinting factor of up to 6.45. The trends in cortisol affinity predicted by molecular dynamics simulations of the prepolymerization mixture were also corroborated through experimental analysis of those modeled molecularly imprinted compositions, demonstrating the predictive capabilities of these simulations.

The Use of Computational Methods for the Development of Molecularly Imprinted Polymers

Recent years have witnessed a dramatic increase in the use of theoretical and computational approaches in the study and development of molecular imprinting systems. These tools are being used to either improve understanding of the mechanisms underlying the function of molecular imprinting systems or for the design of new systems. Here, we present an overview of the literature describing the application of theoretical and computational techniques to the different stages of the molecular imprinting process (pre-polymerization mixture, polymerization process and ligand-molecularly imprinted polymer rebinding), along with an analysis of trends within and the current status of this aspect of the molecular imprinting field.

Selective binding of antibiotics using magnetic molecular imprint polymer (MMIP) networks prepared from vinyl-functionalized magnetic nanoparticles

Adverse effects of pharmaceutical emerging contaminants (PECs), including antibiotics, in water supplies has been a global concern in recent years as they threaten fresh water security and lead to serious health problems to human, wildlife and the environment. However, detection of these contaminants in water sources, as well as food products, is difficult due to their low concentration. Here, we prepared a new family of magnetic molecular imprinted polymer (MMIP) networks for binding antibiotics via a microemulsion polymerization technique using vinyl silane modified Fe₃O₄ magnetic nanoparticles. The cross-linked polymer backbone successfully integrated with 20-30 nm magnetic nanoparticles and generated a novel porous polymeric network structure. These networks showed a high binding capacity for both templates, erythromycin and ciprofloxacin at 70 and 32 mg/g. Both MMIPs were also recyclable, retaining 75 % and 68 % of the binding capacity after 4 cycles. These MMIPs have showed a clear preference for binding the template molecules, with a binding capacity 4- to 7-fold higher than the other antibiotics in the same matrix. These results demonstrate our MMIP networks, which offered high binding capacity and selectivity as well as recyclability, can be used for both removal and monitoring hazardous antibiotic pollutants in different sources/samples and food products.

The first in vivo application of synthetic polymers based on methacrylic acid as an aflatoxin sorbent in an animal model

This study attempts to evaluate the potential aflatoxin binder activity of a molecularly imprinted polymer (TMU95) synthesized to target the aflatoxin B₁ (AFB₁) analog molecule in comparison to a commercial toxin binder (CTB). Adsorption experiments were carried out to assess the ability to bind to AFB₁ at various pH values. The strength of binding was investigated by the chemisorption index. The isothermal analysis was used to determine the maximum adsorption capacity values. The ability of TMU95 and CTB to adsorb essential minerals was evaluated and the obtained data suggested that CTB would significantly reduce availability of them compared to TMU95. The in vivo efficacy of TMU95 as an aflatoxin (AF) binder in duckling exposed to aflatoxin-contaminated feed from 4 to 18 days of age in comparison to the CTB was also assessed. TMU95 and CTB were effective in reducing the adverse effects caused by AFs on feed conversion ratio of duckling (p ≤ 0.01), and also showed a minor reduction of injuries caused by AFs on visceral organs enlargement (p ≤ 0.01). It was concluded that TMU95 could absorb AFB₁ in vitro efficiently and had beneficial health effects that could alleviate some of the toxic effects of AFs on growing duckling performance similar to CTB.

Green Strategies for Molecularly Imprinted Polymer Development

Molecular imprinting is a powerful technology to create artificial receptors within polymeric matrices. Although it was reported for the first time by Polyakov, eighty-four years ago, it remains, nowadays, a very challenging research area. Molecularly imprinted polymers (MIPs) have been successfully used in several applications where selective binding is a requirement, such as immunoassays, affinity separation, sensors, and catalysis. Conventional methods used on MIP production still use large amounts of organic solvents which, allied with stricter legislation on the use and release of chemicals to the environment and the presence of impurities on final materials, will boost, in our opinion, the use of new cleaner synthetic strategies, in particular, with the application of the principles of green chemistry and engineering. Supercritical carbon dioxide, microwave, ionic liquids, and ultrasound technology are some of the green strategies which have already been applied in MIP production. These strategies can improve MIP properties, such as controlled morphology, homogeneity of the binding sites, and the absence of organic solvents. This review intends to give examples reported in literature on green approaches to MIP development, from nano- to micron-scale applications.

The effective and selective separation of (-)-epigallocatechin gallate by molecularly imprinted chitosan beads

The (-)-epigallocatechin gallate (EGCG) imprinted chitosan beads (EICBs) were fabricated for the effective and selective separation of EGCG. The EGCG molecules interacted with the amino groups of chitosan in the imprinting process, resulting in a highly porous structure of EICBs and more adsorption sites. Consequently, EICBs exhibited better adsorption performance than non-imprinted chitosan beads. The maximum adsorption capacity of EGCG onto EICBs reached 135.50 mg/g at 313 K. The imprinting factor of EICBs was 4.22, indicating that EICBs possess good recognition ability and selectivity for EGCG. After five cycles of reuse, only a slight decrease (7.77%) in the adsorption capacity was observed, demonstrating the satisfactory reusability of EICBs. Furthermore, the adsorption of EGCG onto EICBs is deduced to be the monolayer adsorption on an energetically homogeneous surface; the hydrogen bonding between EGCG and EICBs is the main driving force for the adsorption. Our studies suggest that EICBs have a great potential for the effective and selective separation of EGCG.

AutoWeka: toward an automated data mining software for QSAR and QSPR studies

In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predictive modeling. The latter is based on the concept of quantitative structure-activity/property relationship (QSAR/QSPR) when applied in modeling the biological activity and chemical properties, respectively, of compounds. Data mining is a powerful technology underlying QSAR/QSPR as it harnesses knowledge from large volumes of high-dimensional data via multivariate analysis. Although extremely useful, the technicalities of data mining may overwhelm potential users, especially those in the life sciences. Herein, we aim to lower the barriers to access and utilization of data mining software for QSAR/QSPR studies. AutoWeka is an automated data mining software tool that is powered by the widely used machine learning package Weka. The software provides a user-friendly graphical interface along with an automated parameter search capability. It employs two robust and popular machine learning methods: artificial neural networks and support vector machines. This chapter describes the practical usage of AutoWeka and relevant tools in the development of predictive QSAR/QSPR models.

AVAILABILITY

The software is freely available at http://www.mt.mahidol.ac.th/autoweka.

Molecular imprinting science and technology: a survey of the literature for the years 2004-2011

Herein, we present a survey of the literature covering the development of molecular imprinting science and technology over the years 2004-2011. In total, 3779 references to the original papers, reviews, edited volumes and monographs from this period are included, along with recently identified uncited materials from prior to 2004, which were omitted in the first instalment of this series covering the years 1930-2003. In the presentation of the assembled references, a section presenting reviews and monographs covering the area is followed by sections describing fundamental aspects of molecular imprinting including the development of novel polymer formats. Thereafter, literature describing efforts to apply these polymeric materials to a range of application areas is presented. Current trends and areas of rapid development are discussed.

A functional monomer is not enough: principal component analysis of the influence of template complexation in pre-polymerization mixtures on imprinted polymer recognition and morphology

In this report, principal component analysis (PCA) has been used to explore the influence of template complexation in the pre-polymerization phase on template molecularly imprinted polymer (MIP) recognition and polymer morphology. A series of 16 bupivacaine MIPs were studied. The ethylene glycol dimethacrylate (EGDMA)-crosslinked polymers had either methacrylic acid (MAA) or methyl methacrylate (MMA) as the functional monomer, and the stoichiometry between template, functional monomer and crosslinker was varied. The polymers were characterized using radioligand equilibrium binding experiments, gas sorption measurements, swelling studies and data extracted from molecular dynamics (MD) simulations of all-component pre-polymerization mixtures. The molar fraction of the functional monomer in the MAA-polymers contributed to describing both the binding, surface area and pore volume. Interestingly, weak positive correlations between the swelling behavior and the rebinding characteristics of the MAA-MIPs were exposed. Polymers prepared with MMA as a functional monomer and a polymer prepared with only EGDMA were found to share the same characteristics, such as poor rebinding capacities, as well as similar surface area and pore volume, independent of the molar fraction MMA used in synthesis. The use of PCA for interpreting relationships between MD-derived descriptions of events in the pre-polymerization mixture, recognition properties and morphologies of the corresponding polymers illustrates the potential of PCA as a tool for better understanding these complex materials and for their rational design.

Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection

This study explores the chemical space and quantitative structure-activity relationship (QSAR) of a set of 60 sulfonylpyridazinones with aldose reductase inhibitory activity. The physicochemical properties of the investigated compounds were described by a total of 3230 descriptors comprising of 6 quantum chemical descriptors and 3224 molecular descriptors. A subset of 5 descriptors was selected from the aforementioned pool by means of Monte Carlo (MC) feature selection coupled to multiple linear regression (MLR). Predictive QSAR models were then constructed by MLR, support vector machine and artificial neural network, which afforded good predictive performance as deduced from internal and external validation. The investigated models are capable of accounting for the origins of aldose reductase inhibitory activity and could be utilized in predicting this property in screening for novel and robust compounds.

Exploring the chemical space of aromatase inhibitors

Aromatase, a rate-limiting enzyme catalyzing the conversion of androgen to estrogen, is overexpressed in human breast cancer tissue. Aromatase inhibitors (AIs) have been used for the treatment of estrogen-dependent breast cancer in post-menopausal women by blocking the biosynthesis of estrogen. The undesirable side effects in current AIs have called for continued pursuit for novel candidates with aromatase inhibitory properties. This study explores the chemical space of all known AIs as a function of their physicochemical properties by means of univariate (i.e., statistical and histogram analysis) and multivariate (i.e., decision tree and principal component analysis) approaches in order to understand the origins of aromatase inhibitory activity. Such a non-redundant set of AIs spans a total of 973 compounds encompassing both steroidal and non-steroidal inhibitors. Substructure analysis of the molecular fragments provided pertinent information on the structural features important for ligands providing high and low aromatase inhibition. Analyses were performed on data sets stratified according to their structural scaffolds (i.e., steroids and non-steroids) and bioactivities (i.e., actives and inactives). These analyses have uncover a set of rules characteristic to active and inactive AIs as well as revealing the constituents giving rise to potent aromatase inhibition.

QSAR MODEL OF THE QUORUM-QUENCHING N-ACYL-HOMOSERINE LACTONE LACTONASE ACTIVITY

A quantitative structure-activity relationship (QSAR) study was performed to model the lactonolysis activity of N-acyl-homoserine lactone lactonase. A data set comprising of 20 homoserine lactones and related compounds was taken from the work of Wang et al. Quantum chemical descriptors were calculated using the semiempirical AM1 method. Partial least squares regression was utilized to construct a predictive model. This computational approach reliably reproduced the lactonolysis activity with high accuracy as illustrated by the correlation coefficient in excess of 0.9. It is demonstrated that the combined use of quantum chemical descriptors with partial least squares regression are suitable for modeling the AHL lactonolysis activity.

Molecular Docking of Aromatase Inhibitors

Aromatase is an enzyme that plays a critical role in the development of estrogen receptor positive breast cancer. As aromatase catalyzes the aromatization of androstenedione to estrone, a naturally occurring estrogen, it is a promising drug target for therapeutic management. The undesirable effects found in aromatase inhibitors (AIs) that are in clinical use necessitate the discovery of novel AIs with higher selectivity, less toxicity and improving potency. In this study, we elucidate the binding mode of all three generations of AI drugs to the crystal structure of aromatase by means of molecular docking. It was demonstrated that the docking protocol could reliably reproduce the interaction of aromatase with its substrate with an RMSD of 1.350 Å. The docking study revealed that polar (D309, T310, S478 and M374), aromatic (F134, F221 and W224) and non-polar (A306, A307, V370, L372 and L477) residues were important for interacting with the AIs. The insights gained from the study herein have great potential for the design of novel AIs.

Elucidating the structure-activity relationships of the vasorelaxation and antioxidation properties of thionicotinic acid derivatives

Nicotinic acid, known as vitamin B₃, is an effective lipid lowering drug and intense cutaneous vasodilator. This study reports the effect of 2-(1-adamantylthio)nicotinic acid (6) and its amide 7 and nitrile analog 8 on phenylephrine-induced contraction of rat thoracic aorta as well as antioxidative activity. It was found that the tested thionicotinic acid analogs 6-8 exerted maximal vasorelaxation in a dose-dependent manner, but their effects were less than acetylcholine (ACh)-induced nitric oxide (NO) vasorelaxation. The vasorelaxations were reduced, apparently, in both N^G-nitro-L-arginine methyl ester (L-NAME) and indomethacin (INDO). Synergistic effects were observed in the presence of L-NAME plus INDO, leading to loss of vasorelaxation of both the ACh and the tested nicotinic acids. Complete loss of the vasorelaxation was noted under removal of endothelial cells. This infers that the vasorelaxations are mediated partially by endothelium-induced NO and prostacyclin. The thionicotinic acid analogs all exhibited antioxidant properties in both 2,2-diphenyl-1-picrylhydrazyl (DPPH) and superoxide dismutase (SOD) assays. Significantly, the thionicotinic acid 6 is the most potent vasorelaxant with ED₅₀ of 21.3 nM and is the most potent antioxidant (as discerned from DPPH assay). Molecular modeling was also used to provide mechanistic insights into the vasorelaxant and antioxidative activities. The findings reveal that the thionicotinic acid analogs are a novel class of vasorelaxant and antioxidant compounds which have potential to be further developed as promising therapeutics.

Synthesis and theoretical study of molecularly imprinted nanospheres for recognition of tocopherols

Molecular imprinting is a technology that facilitates the production of artificial receptors toward compounds of interest. The molecularly imprinted polymers act as artificial antibodies, artificial receptors, or artificial enzymes with the added benefit over their biological counterparts of being highly durable. In this study, we prepared molecularly imprinted polymers for the purpose of binding specifically to tocopherol (vitamin E) and its derivative, tocopherol acetate. Binding of the imprinted polymers to the template was found to be two times greater than that of the control, non-imprinted polymers, when using only 10 mg of polymers. Optimization of the rebinding solvent indicated that ethanol-water at a molar ratio of 6:4 (v/v) was the best solvent system as it enhanced the rebinding performance of the imprinted polymers toward both tocopherol and tocopherol acetate with a binding capacity of approximately 2 mg/g of polymer. Furthermore, imprinted nanospheres against tocopherol was successfully prepared by precipitation polymerization with ethanol-water at a molar ratio of 8:2 (v/v) as the optimal rebinding solvent. Computer simulation was also performed to provide mechanistic insights on the binding mode of template-monomer complexes. Such polymers show high potential for industrial and medical applications, particularly for selective separation of tocopherol and derivatives.

Modeling the LPS neutralization activity of anti-endotoxins

Bacterial lipopolysaccharides (LPS), also known as endotoxins, are major structural components of the outer membrane of Gram-negative bacteria that serve as a barrier and protective shield between them and their surrounding environment. LPS is considered to be a major virulence factor as it strongly stimulates the secretion of pro-inflammatory cytokines which mediate the host immune response and culminating in septic shock. Quantitative structure-activity relationship studies of the LPS neutralization activities of anti-endotoxins were performed using charge and quantum chemical descriptors. Artificial neural network implementing the back-propagation algorithm was selected for the multivariate analysis. The predicted activities from leave-one-out cross-validation were well correlated with the experimental values as observed from the correlation coefficient and root mean square error of 0.930 and 0.162, respectively. Similarly, the external testing set also yielded good predictivity with correlation coefficient and root mean square error of 0.983 and 0.130. The model holds great potential for the rational design of novel and robust compounds with enhanced neutralization activity.

Theoretical and computational strategies for rational molecularly imprinted polymer design

The further evolution of molecularly imprinted polymer science and technology necessitates the development of robust predictive tools capable of handling the complexity of molecular imprinting systems. A combination of the rapid growth in computer power over the past decade and significant software developments have opened new possibilities for simulating aspects of the complex molecular imprinting process. We present here a survey of the current status of the use of in silico-based approaches to aspects of molecular imprinting. Finally, we highlight areas where ongoing and future efforts should yield information critical to our understanding of the underlying mechanisms sufficient to permit the rational design of molecularly imprinted polymers.

Computational insights on sulfonamide imprinted polymers

Molecular imprinting is one of the most efficient methods for preparing synthetic receptors that possess user defined recognition properties. Despite general success of non-covalent imprinting for a large variety of templates, some groups of compounds remain difficult to tackle due to their structural complexity. In this study we investigate preparation of molecularly imprinted polymers that can bind sulfonamide compounds, which represent important drug candidates. Compared to the biological system that utilizes metal coordinated interaction, the imprinted polymer provided pronounced selectivity when hydrogen bond interaction was employed in an organic solvent. Computer simulation of the interaction between the sulfonamide template and functional monomers pointed out that although methacrylic acid had strong interaction energy with the template, it also possessed high non-specific interaction with the solvent molecules of tetrahydrofuran as well as being prone to self-complexation. On the other hand, 1-vinyl-imidazole was suitable for imprinting sulfonamides as it did not cross-react with the solvent molecules or engage in self-complexation structures.