Computational Strategies for the Design and Study of Molecularly

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COMPUTATIONAL STRATEGIES FOR THE DESIGN AND STUDY OF MOLECULARLY IMPRINTED MATERIALS Ian Alan Nicholls, Björn C.G. Karlsson, Gustaf D Olsson, and Annika M. Rosengren Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/ie3033119 • Publication Date (Web): 12 Mar 2013 Downloaded from http://pubs.acs.org on March 20, 2013

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Industrial & Engineering Chemistry Research

COMPUTATIONAL STRATEGIES FOR THE DESIGN AND STUDY OF MOLECULARLY IMPRINTED MATERIALS Ian A. Nicholls,*a,b Björn C. G. Karlsson,a Gustaf D. Olsson,a Annika M. Rosengrena a

Bioorganic and Biophysical Chemistry Laboratory, Linnæus University Centre for Biomaterials

Chemistry, Linnæus University, SE-391 82 Kalmar, Sweden. bDepartment of Chemistry - BMC, Uppsala University, Box 576, SE-751 23, Uppsala, Sweden. KEYWORDS. Molecular Mechanics, Quantum Mechanics, Molecular Dynamics, Multivariate Statistics, Chemometrics, Molecularly Imprinted Polymer, MIP. ABSTRACT. The need for materials with predetermined ligand-selectivities for use in sensing and separation technologies, e.g. membranes and chromatography, has driven the development of molecularly imprinted polymer science and technology. Over recent years, the need to develop robust predictive tools capable of handling the complexity of molecular imprinting systems has become apparent. The current status of the use of in silico techniques in molecular imprinting is here presented, and we highlight areas where new developments are contributing to improvements in the rational design of molecularly imprinted polymers. INTRODUCTION

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The development of materials with biomimetic, antibody-like recognition characteristics has been driven by the needs of analytical science and technology, in particular separation, e.g. membranes and chromatography, and sensor technologies. A strategy for producing synthetic materials highly selective for molecular structures, ranging from ions to biomacromolecules, is Molecular Imprinting, Fig. 11–4 In essence, the method involves the formation of cavities in a synthetic polymer matrix that are of complementary in functional and structural character to a preselected template molecule or ion. It is the ability to selectively recognize and bind the template structure in the presence of closely related chemical species that has driven the use of molecularly imprinted polymers (MIPs) in various biomedical and biotechnological applications. In contrast to the use of antibodies, MIP-production does not require the use of laboratory animals nor hapten conjugation protocols. Moreover, it is relatively cost efficient, and synthesis strategies are most often amenable to up-scaling for commercial purposes.5 Of particular importance for some applications, e.g. in some industrial settings, is that the materials are stable to extremes of pH, temperature and organic solvents, performance criteria very much in contrast with their biological counterparts.6 Although molecular imprinting science and technology and in particular its applications1,3–5,7,8 continues to expand, characterizing and understanding the physical mechanisms underlying MIPformation and MIP–ligand recognition has received relatively little focus. Accordingly, as “rational MIP design” necessitates knowledge of the molecular-level events occurring in prepolymerization mixtures, the polymerization reactions, and of the factors influencing polymerligand recognition, improved insights should ultimately lead to molecularly imprinted materials with better performance. Still today the molecular-level nature of MIP binding-site heterogeneity and the low yields of high-fidelity sites are challenges which may be addressed through better


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molecular-level understanding of the imprinting process.9 Earlier efforts to gain fundamental insights into the molecular-level events underlying the imprinting process using thermodynamic models10 and spectroscopic methods11–13 have over the past decade been complemented by in silico strategies. A factor that has contributed significantly to the increasing use of computational strategies is the development of new and improved software together with increases in computing power. Currently, the range of computational techniques in use span from statistical treatments to quantum mechanical simulations. The current status of the use of in silico techniques in molecular imprinting is here presented, and we highlight areas where new developments are contributing to improvements in the rational design of molecularly imprinted polymers.

Figure 1. Highly schematic representation of the molecular imprinting process: The formation of reversible interactions between the template and polymerizable functionality may involve one or more of the following interactions: [(A) reversible covalent bond(s), (B) covalently attached polymerizable binding groups that are activated for non-covalent interaction by template cleavage, (C) electrostatic interactions, (D) hydrophobic or van der Waals interactions or (E) coordination with a metal centre; each formed with complementary functional groups or structural elements of the template, (a-e) respectively]. A subsequent polymerization in the presence of crosslinker(s), a cross-linking reaction or other process, results in the formation of an insoluble matrix (which itself can contribute to recognition through steric, van der Waals and even


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electrostatic interactions) in which the template sites reside. Template is then removed from the polymer through disruption of polymer—template interactions, and extraction from the matrix. The template, or analogues thereof, may then be selectively rebound by the polymer in the sites vacated by template, the ‘imprints’. While the representation here is specific to vinyl polymerization, the same basic scheme can equally be applied to sol-gel, polycondensation etc. Adapted with permission from the publishers of reference 4.

QUANTUM- AND MOLECULAR MECHANICAL-BASED METHODS: THE PREPOLYMERIZATION STAGE

To date, reports of MIP-design protocols utilizing electronic structure methods, semi-empirical, ab inito or DFT, are becoming increasingly common in the design and evaluation of MIPs, in particular for studies of the MIP pre-polymerization stage.14–37 In general, the primary focus has been directed towards selection and evaluation of monomers and the stoichiometries of monomers and template. These studies have employed different levels of theory and/or different methods and basis sets, for correlating results to the behavior of final MIPs. Targets of significance in waste water treatment, and water purification in general have received particular focus: acetochlor,36 aniline,26 naproxen17 and theophylline,23,38,39 Chart 1.

Chart 1. Examples of targets for electronic structure based studies due to their potential adverse impact on the environment and/or negative health effects.


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Although quantum mechanical (QM)-based methods have proven successful in the development of MIPs, the use of these methods has however been rather limited. The demand placed upon computational resources by the complexity of the systems studied, such as full system molecular imprinted polymer pre-polymerization mixtures, depend upon the level of detail and numbers of atoms in the investigated systems, in particular in the case of QM-methods. Besides studies of isolated complexes, one widely implemented strategy to decrease the demand on resources needed has been the exclusion of explicit solvent. However, there are ways to compensate for these challenges and still investigate solvent effects on functional monomertemplate complexation at the pre-polymerization stage, while barely affecting the computational time40 through the implementation of a PCM representation of a solvent. Here, solvent effects are approximated by simulating a cavity with a surface that is polarizable according to the dielectric constant of the solvent. The PCM representation has been utilized in several studies.20,41–43 It has previously been demonstrated that as long as the solvent itself is aprotic, predictions regarding the influence of solvent on the characteristics of the final material appear to be valid.44 A major issue associated with the use of continuum model representations of solvent is that polar solvents often compete with monomers for template access through hydrogen bond interactions, interactions that need to be addressed in other ways.38,45 To date, electronic structure based methods have not only been employed for screening at the pre-polymerization stage but also in attempts to evaluate recognition and rebinding behavior of templates in models representative of the final polymeric materials.33,39,46–51 These studies did not, however, deal with a predicted polymer network but rather simplistic models of predicted recognition sites. To overcome limitations associated with the relatively low number of atoms that can be studied using electronic structure based methods (~50-100), classical molecular mechanics (MM) has typically


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been introduced to study MIP pre-polymerization mixtures at larger atomistic scales e.g. including explicit solvent molecules and multiple copies of functional- as well as crosslinking monomers. To face the need for a better representation of all possible interactions taking place at the prepolymerization stage, Piletsky and co-workers52 developed an molecular dynamics (MD)-based screening strategy, involving a virtual library of 20 different functional monomers for the molecular imprinting of an ephedrine enantiomer and later also for the development of MIPs for e.g. phenol,53 cholic acid,54 simazine,55 cocaine, methadone and morphine,56,57 creatinine,58 biotin59 and the cyanobacterial toxin microcystin-LR.60,61 Based on the protocol developed by Piletsky, Wei et al.62 used a virtual library-based MD-strategy to develop a MIP targeting 17β-estradiol. The authors simulated either one single monomer-template pair or one single template molecule, solvated with eight functional monomers. This was done in order to account for the effect of monomer dimerization on the degree of template complexation. To study the stability of a 1:1 stoichiometric complex between the template; 2,4-dichlorophenoxyacetic acid (2,4-D) and a functional monomer, Molinelli et al.63 reported a series of detailed studies involving the insertion of pre-minimized complexes into different explicit solvents (chloroform or water). By using different starting geometries in the different solvents and examining hydrogen bond interactions in chloroform and π-π stacking interaction in water, the authors proposed mechanisms to explain the nature of the interactions involved during the pre-polymerization stage, as well as during MIP rebinding in aqueous solution. In a follow-up paper, the influence of the porogen on the selectivity in 4-nitrophenol imprinted MIPs was evaluated by using predesigned single complexes of the template and a functional monomer.64 The higher stability of the simulated template complex in chloroform, as compared with the more polar porogen acetonitrile, provided further support for the use of a porogen of low polarity with


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a lower capacity to compete for the electrostatic interactions between the template and polymer building blocks in the pre-polymerization mixture. As previously mentioned, the use of electronic structure-based methods for elucidating monomer-template binding energies is often limited by the relative size of a typical multiple component MIP pre-polymerization mixture. Despite the challenges imposed by such systems, several examples have so far been presented in which these methods have been integrated into a MM-based functional monomer-template screening process. Dong et al.36 developed a MD approach based on a combination of MM and QM involved in the screening of the strongest binding functional monomer to be used in the imprinting of the herbicide acetochlor from a library of forty commonly used functional monomers. Initially, a series of MD steps for a single functional monomer-template pair, surrounded by explicit solvent molecules (either acetonitrile, chloroform or carbon tetrachloride) were performed. After equilibration, information regarding the stability of the studied complex was obtained through extraction of the energy of the complex through MM. In a second step, energy of the complex was calculated using an electronic structure method, including solvent effects through a PCM. Based on the results from these calculations, the three strongest binding functional monomers in each solvent system were selected in the imprinting of acetochlor. Later, a similar strategy has also been employed to screen for the best functional monomer to be used in the imprinting of rhodamine B65 and sulfadimidine.66 As a further development of the MD approach for the study of template complexation during the pre-polymerization step, studies on complex formation in other formats than the commonly used bulk polymerization mixture have recently also been performed. Yoshida et al.67 conducted MD simulations to study the stability of functional monomertemplate complexation during a surface imprinting pre-polymerization step. This MIP was prepared to demonstrate chiral recognition for tryptophan methyl ester using n-DDP as


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recognition site-members in a water-in-oil emulsion. The authors simulated the dynamics of a single complex consisting of one molecule of the template and two molecules of n-DDP, initially in vacuum or at the toluene-water interface. Obtained results from simulations revealed that the imprinting effect originated from the stability of the formed n-DDP-tryptophan methyl ester complexes at the pre-polymerization stage. To further explore the applicability of MD for the study of the nature and extent of template complexation in surface imprinting pre-polymerization mixtures, Toorisaka et al.68 performed an analysis of the stability of a complex formed at a water-toluene interface consisting of a cobalt ion, one molecule of alkyl imidazole and the substrate analogue, Nα-t-Boc-L-histidine, thus forming the basis for the active site in a MIP demonstrating enzymatic activity.

Chart 2. Snapshot from performed 2-phase MD-simulation of a bisphenol A MIP-pre-polymerization mixture, applied in the production of MIP-nanoparticles using the miniemulsion polymerization approach. In this picture, the template is presented using a van der Waal volume representation and all other pre-polymerization components are presented using a licorice representation.


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In a recent study by Olsson and co-workers (unpublished data), the first example of an allcomponent MD simulation of a two-phase miniemulsion MIP pre-polymerization system was described. Results from this study, using bisphenol A as a template, demonstrated the nature and extent of bisphenol complexation and the distribution of the template in the water-oil phases simulated. Moreover, this work demonstrated the potential use of MD for characterizing a wateroil phase separations in general, Chart 2. The diverse nature of non-covalent interactions present in a MIP pre-polymerization mixture is by no means limited to those formed between functional monomer and template. Accordingly, to provide a more accurate representation of the events that may influence monomer-template complexation, recent efforts have engaged all-component pre-polymerization mixtures containing multiple components present in stoichiometric ratios that correspond to those used in MIP preparation protocols. Simulating all non-covalent interactions in a MIP pre-polymerization mixture can potentially reveal the range of complexes that can underlie the site heterogeneity typically observed in MIPs. O’Mahony et al.69 simulated functional monomer-naproxen complexation during the pre-polymerization stage and the effect of template dimerization on MIP performance. Even though the initiator was excluded from the mixture, results obtained from the analyzed trajectories revealed a high degree of functional monomer-naproxen complexation as well as the contribution of the crosslinking agent to the high degree of selectivity obtained in the final MIP. Moreover, O’Mahony et al. also reported on the role of template dimerization on MIP performance by examining the molecular basis of the imprinting effect in a quercetin MIP.70 Interestingly, in this case, results from MD-simulations revealed the formation of sheet-like structures of quercetin-functional monomer complexes, where complex stabilities were unaffected by the concentration of the crosslinking agent used. Further advances in allcomponent MD simulations, were reported by Karlsson et al.71 in a study where all interactions


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taking place in a bupivacaine-MIP pre-polymerization mixture were examined. This study revealed a clear correlation between spectroscopic studies and simulated data, and highlighted the origin of the recognition-site heterogeneity typically observed in methacrylic acid – ethyleneglycol dimethacrylate (MAA-EGDMA) MIPs. In a recent study by Sellergren et al.72 efforts to develop polymers selective for the endocrine disrupter 17β-estradiol were supported through all component MD-studies. Theoretical and experimental screening strategies were combined in a study using a series of functional monomer combinations selected through experimental screening, and validated through MD. An allcomponent MD-treatment of MIP pre-polymerization mixture was later also used by Wiklander et al. for the design of a synthetic avidin mimic with recognition for biotin73 and by Henschel and co-workers74 for predicting the structure and stability of MIP-recognition sites designed to catalyze the Diels-Alder reaction. The importance of template and monomer dimerization during the pre-polymerization step on MIP performance has been implicated in the results of several experimental studies.75–77 MDstudies have recently been used to examine such events in more detail. The basis for the formation and extent of phenylalanine anilide dimerization, previously studied by Katz and Davies, was investigated by Olsson and co-workers.78 Contributions from these all-component simulations, using the same stoichiometries as in the actual pre-polymerization mixtures, gave further insights into the structure of functional monomer-template complexation and provided strong support to previous X-ray crystallographic and NMR spectroscopic data. Structural diversity of the template is another phenomenon that can pose challenges for analyzing the interactions taking place in a pre-polymerization mixture. This was the focus of a work on the preparation and analysis of MIPs with recognition for the anticoagulant drug warfarin which demonstrates structural diversity in non-polar and low-pH media.74,79–83 Here MD studies


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highlighted the impact of structural variation on the events present in the pre-polymerization mixture82

QUANTUM- AND MOLECULAR MECHANICAL-BASED METHODS: POLYMER NETWORKS To date, in silico studies of aspects of the molecular imprinting process have primarily focused on the pre-polymerization stage, prior to polymer gelation. Studies of polymer micro- and macrostructure have relied primarily upon spectroscopic methods. More recently, attempts have been made to simulate molecular imprints in the polymers by building highly simplistic polymer models that have been used to model MIP-template rebinding. Dourado et al.84 performed theoretical studies on both the pre-polymerization step and used a “frozen” pre-polymerization mixture to examine ligand adsorption. This was done using structure derived from the last step in the MD simulation of the mixture, which effectively served as a static model for a ‘real’ bulk polymer. Although the authors could demonstrate a molecular memory after imprinting of one of the templates used, pyrazine, a virtual polymer prepared using a structural analogue, pyrimidine, as template did not demonstrate a molecular memory for pyrimidine over pyrazine. The limitation of the theoretical model used was attributed to the influence of electrostatic interactions between the templates investigated and the pre-polymerization mixture components. The role of a growing polymer chain on the stability of functional monomer-template complexation has also been studied by MD simulations. An MD-based strategy was developed in which homo- and co-polymeric chains of functional monomers were built and used for screening in the search for an optimal monomer or monomer pair. Pavel and Lagowski85,86 described an approach involving calculations of potential energy differences for a series of functional monomer-template complexes using theophylline as template. To include the influence of the


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presence of the monomers in polymeric chains, a linear polymeric chain of these functional monomers was built and the energetic difference of the formed complex between the polymeric chain in the presence and absence of the template was investigated. In addition to these studies, Pavel et al.87 described a MD-based screening of a series of warfare agents, which provided detailed information on the intermolecular interactions underlying functional monomer-template complexation. Results from these investigations pointed at the importance of electrostatic interactions as well as the presence of –COOH and CH2=CH- groups on the functional monomers for obtaining a high degree of interaction with the template. In a further development of the strategy, Monti et al.88 demonstrated the potential of using a combination of MD, MM, docking and site mapping for obtaining the ‘best’ functional monomer in the imprinting of the same template, theophylline. In addition, Lv et al.89 used MD to examine the origin to the MIP molecular memory by performing studies on the selective adsorption properties of a dimethoateMIP after predicting the binding energies for a series of homo-polymers and the template. On the same theme, Srebnik et al.,90–92 carried out a series MD simulations to also highlight the importance of pores in the MIP matrix on template recognition properties. Later, Youngermann and Srebnik93 identified factors that contribute to binding-site imperfections that influence the recognition-site heterogeneity typically demonstrated by MIPs. After performing a series of topological analyses using a fictitious rigid dumbbell-like non-functional template and a modeled cross-linked polymer network, before and after template removal, they concluded that the typically low yield of imprints (10-15%) was a result of the quality of the generated pores. Furthermore they proposed that after considering both the size and shape of the template, the ‘best’ performing MIP should have a high degree of cross-linkage (90%) and that the low quality of the imprints induced by the templates resulted from the aggregation of template during the pre-polymerization stage.


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Zhao et al.94 used a cubic model of a MIP to represent a hydrogel network in order to investigate the physical properties of a MIP matrix. Simulations were also been conducted to study the role of the solvent during template rebinding, as well as the dynamics of the MIP network itself studying properties such as solvent uptake and conformational stability. This modeled polymer matrix was then studied in the presence of the template, cholesterol, and the dynamics of this hydrogel as well as its interactions with explicit solvent molecules were evaluated. Results obtained revealed a highly ordered structure of water solvating the hydrogel and the authors could conclude that by adjusting the amount of carboxyl groups incorporated in the polymer matrix they could control the water structure and thereby the diffusion of water through the polymer. As a further advance on this theme, Azenha et al.31 studied the outcome from a series of MD simulations of intermediate stages of the sol-gel process in the imprinting of damascenone in xerogels. The results underscored the importance of hydrophobic interactions during the imprinting process and presented one of the first examples and usefulness of MD simulations for predicting the recognition behavior of MIP xerogels. In summary, the increasing number of in silico studies of molecular-level aspects of MIP synthesis and performance are providing new insights moving us closer to true rational design.


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MULTIVARIATE STATISTICAL METHODS The analysis of complex chemical systems is preferably approached through the multivariate methods available within the discipline of chemometrics. When seeking optimal experimental parameters, experimental design is a key issue, where different factors are varied systematically and data are used in conjunction with multivariate statistics. Experimental design is used to extract as much relevant information with a minimum number of experiments through the systematic variation of relevant factors. Chemometric methods have been used in the field of molecular imprinting to examine polymer-rebinding data, and even polymer composition. To improve the recognition properties of MIPs different chemometric methods have been employed in the establishment of polymer compositions and subsequent rebinding analyses. Several chemometric techniques have been applied to rebinding data for the optimization of polymer composition95–104

as well as in the optimization of the MIP rebinding

environment.104–122 Chemometric methods have been employed for optimization and analysis of the composition of MIPs. For example, Navarro-Villoslada et al. optimised a MIP imprinted with bisphenol A, through the systematic and simultaneous alteration of the amount of template, monomer, cross-linker and initiator as well as different porogen in conjunction with different polymerization methods.95 The polymers were prepared as mini-MIPs and rebinding was evaluated. In this study they validated the prediction power of the model through the preparation and analysis of the rebinding to the MIP that had the composition that yielded the maximum specific binding. When using chemometric methods to generate a model from several factors it is important to validate this model through the preparation of a separate validation set of MIPs. This work, however, only validated the optimum part of the model. In another study, by NavarroVilloslada and Takeuchi, a library of small-scale piroxicam imprinted polymers was screened.96 The piroxicam MIP-library differed in composition through the amount of monomer, cross-


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linker, template, initiator and porogenic solvent, as well as through different polymerization methods. MIP systems with different cross-linkers were handled separately in the modeling process where the experimental binding data was fitted to a first order calibration curve and validated using a cross-validation process. Further studies employing the chemometric analysis of optimal MIP compositions was performed by Kempe and Kempe.98 They used multivariate regression to analyze and optimize the amount of monomer, cross-linker and porogenic solvent of propranolol imprinted polymer beads. The rebinding of propranolol was measured using a radioligand-binding assays and the derived quadratic model was validated. The difficulty in deciding upon MIP protocol when introducing a new template in molecular imprinting was further studied by Davies and coworkers.97 They designed a sulfamethazine imprinted polymer based on chemometric methods by considering two factors, amount of monomer and the amount of cross-linker. The rebinding of the template and structurally similar compounds were evaluated using a competition rebinding assay. The data was fitted to a quadratic regression model containing squared terms and the model was subsequently validated at the optimal composition. It is interesting to note that in many of the studies seeking to find the optimal polymer composition with regards to rebinding properties, the optimum is reached in the corner of the experimental region.95–97,100,101,110 These studies would benefit from extending the experimental region to make sure a local optimum is clearly identified so that more substantial conclusions regarding MIP design could be drawn. The parameters affecting the ligand rebinding to a MIP offer further opportunity for optimization of a molecularly imprinted material. In a study by Rosengren et al. rebinding of bupivacaine to a MIP was examined in different binding environments.114 The different binding environments were achieved through the variation of the rebinding media polarity as well as the


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temperature. It was demonstrated that the rebinding of bupivacaine to this MIP, described in terms of temperature and dielectric constant, could be fitted to a third degree equation with crossterms. The rebinding models derived in this work were validated with independent binding data obtained from a separate batch of polymers. Tarley et al. used a flow preconcentration system coupled to amperometric detection to measure the rebinding of chloroguaiacol to a MIP.105 They employed an experimental design to set up an experiment for the study of the influence of the mobile phase physical properties (pH, flow rate, KCl concentration, elution flow rate and eluent volume) on rebinding properties. The factors were analyzed and the most important with respect to rebinding of chloroguaiacol to the MIP were identified pH and KCl concentration). In the final optimization of the rebinding, a Doehlert design yielded a quadratic model with cross terms. In another study by Baggiani et al. chemometric methods were used to analyze molecular descriptors of pentachlorophenol and some structural analogues.113 The binding to the thermally polymerized pentachlorophenol-MIP was further analyzed by chromatography. Correlation of MIP selectivity with molecular descriptors was analyzed using principal component analysis and principal component regression. The pentachlorophenol-MIP demonstrates a significant pattern of selectivity towards several phenols, which could be explained in terms of steric and electronic molecular descriptors. In another study also employing molecular descriptors as variables in a rebinding study Kempe and Kempe correlate the imprinting factors from the analysis of a penicillin G-MIP with these molecular descriptors.120 Here principal component analysis and partial least squares regression analysis were applied to the chromatographic retention data from several β-lactam antibiotics. Descriptors associated with molecular topology, shape, size and volume could be correlated to the imprinting factor. Models that describe the relation between the imprinting factor and the molecular descriptors were generated and their ability to recognize an untested compound was discussed.


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Nantasenamat et al. analyzed literature data from several published high performance liquid chromatography studies correlating the imprinting factors with molecular descriptors and mobile phase composition through the use of artificial neural networks.115,116 The mobile phase was described through the use of both measured (pH and ionic strength) and literature (dielectric constant) data. The collected data-set was divided into two groups consisting of uniformly sized MIPs and irregularly sized MIPs and each analyzed separately. They concluded that the use of mobile phase descriptors alone was sufficient to predict the imprinting factor with good performance. In work by Wu et al., stochastic simulation was performed to examine the interaction between the components present in the pre-polymerization mixture.123 The stochastic algorithm was used to position the monomer and template units in a lattice matrix. Subsequently the crosslinker was added, the template removed and the binding sites in the modeled polymer matrix was analyzed for heterogeneity. This modeling resulted in simulated MIPs that display trends comparable to those of MIPs formed under the corresponding imprinting conditions. Recently, the combination of chemometrics and quantum chemistry was used to design and synthesize metolachlor deschloro imprinted polymers capable of extracting chloroacetamide herbicides from food samples.121 They evaluated the selectivities of the recognition materials by high performance liquid chromatography and thereafter retention properties were predicted for different chloroacetamide herbicides using cluster analysis. An optimization of the solid phase extraction conditions was also performed and the retention protocol was optimized with regard to the selectivity and sensitivity. Their resultant solid phase extraction column exhibited selective binding properties for chloroacetamide herbicides. Jia et al. also point at the opportunities of the use of a combination of quantum chemistry and chemometrics in the analysis of complex matrices.104 Since the selection of monomer is crucial in the preparation of a MIP, the interaction


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between the template, Tanshinone IIA, and two widely used functional monomers, methacrylic acid and acrylamide, was compared theoretically. Studied complexes between template and monomer had molar ratios of 1:1, 1:2 and 1:3 and the results from these molecular complex optimization studies implied MAA to be superior over acrylamide in the imprinting of Tanshinone IIA. The MIPs were synthesized using different molar ratios of MAA and crosslinker. The final optimal polymer composition, at which the response of binding amount was at a maximum, was determined and validated. In a recent study by Gholivand et al. a chemometric approach was used for the simultaneous determination of several compounds in environmental and food samples.122 An electrochemical approach based on a molecularly imprinted polymer modified carbon paste electrode and multivariate regression analysis. The MIP acts as a selective recognition element and a preconcentration agent for cyanazine and propazine. Experimental designs were used in the optimization of the variables affecting the cathodic stripping voltammetric currents for the analytes. Thereafter the proposed method was applied to the simultaneous determination of cyanazine and propazine in onions, tomatoes, rice and river water samples with satisfactory results.

CONCLUSIONS Efforts to employ computational strategies for describing, predicting and analyzing molecular imprinting systems is a rapidly expanding research area.9 We attribute this expansion to the increasing availability of powerful computers and improved software suitable for such tasks, and to the growing awareness of the importance of results derived from in silico studies of MIP


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systems on rational MIP design. Accordingly, we perceive that this increasing reliance on these methods in MIP science and technology shall continue to grow.

AUTHOR INFORMATION Corresponding Author * Ian A. [email protected]

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. ACKNOWLEDGMENT The financial support from the Swedish Research Council (VR), the Knowledge Foundation (KKS), Crafoord Foundation, Carl-Tryggers’ Foundation and the Linnæus University is most gratefully acknowledged. ABBREVIATIONS QM, quantum mechanics; MM, molecular mechanics, MD, molecular dynamics, MIP, molecularly imprinted polymers; DFT, density functional methods; PCM, polarizable continuum model; MAA, methacrylic acid; n-DPP, phenyl phosphonic acid monododecyl ester; NMR, nuclear magnetic resonance.


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TOC-GRAPHIC


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Computational Strategies for the Design and Study of Molecularly

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