Analysis and prediction of defects in UV photo-initiated polymer microarrays

Biodegradability analysis of Dioxins through in silico methods: Model construction and mechanism analysis

The biodegradation treatment of dioxins has long been of interest due to its good ecological and economic effects. In this study, the biodegradability of polychlorinated dibenzo-p-dioxins (PCDDs) were investigated by constructing machine learning and multiple linear regression models. The maximum chlorine atomic charge (qHirshfeldCl+), which characterizes the biodegradation ability of PCDDs, was used as the response value. The random forest model was used to rank the importance on the 1471 descriptors of PCDDs, and the BCUTp-1 h, QXZ, JGI4, ATSC8c, VE3_Dt, topoShape, and maxwHBa were screened as the important descriptors by Pearson's correlation coefficient method. A quantitative structure-activity relationship (QSAR) model was constructed to predict the biodegradability of PCDDs. In addition, the extreme gradient boosting (XGBoost) and random forest model were also constructed and proved the good predictability of QSAR model. The biodegradability of polychlorinated dibenzofurans (PCDFs) can also be predicted by the constructed three models from a certain level after adjusting some model parameters, which further proved the versatility of the models. Besides, the sensitivity analysis of the QSAR model and a 3D-QSAR model was developed to investigate the biodegradability mechanisms of PCDDs. Results showed that the descriptors BCUTp-1 h, JGI4, and maxwHBa were the key descriptors in the biodegradability effect by the sensitivity analysis of the QSAR model. Coupled with the results of PCDDs biodegradability 3D-QSAR model, BCUTp-1 h, JGI4, and maxwHBa were confirmed as the main descriptors that affect the biodegradability of dioxins. This study provides a novel theoretical perspective for the research of the biodegradation of both PCDDs and PCDFs dioxins.

Discovery of a polymer resistant to bacterial biofilm, swarming, and encrustation

Innovative approaches to prevent catheter-associated urinary tract infections (CAUTIs) are urgently required. Here, we describe the discovery of an acrylate copolymer capable of resisting single- and multispecies bacterial biofilm formation, swarming, encrustation, and host protein deposition, which are major challenges associated with preventing CAUTIs. After screening ~400 acrylate polymers, poly(tert-butyl cyclohexyl acrylate) was selected for its biofilm- and encrustation-resistant properties. When combined with the swarming inhibitory poly(2-hydroxy-3-phenoxypropyl acrylate), the copolymer retained the bioinstructive properties of the respective homopolymers when challenged with Proteus mirabilis, Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli. Urinary tract catheterization causes the release of host proteins that are exploited by pathogens to colonize catheters. After preconditioning the copolymer with urine collected from patients before and after catheterization, reduced host fibrinogen deposition was observed, and resistance to diverse uropathogens was maintained. These data highlight the potential of the copolymer as a urinary catheter coating for preventing CAUTIs.

High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology

The complex interaction of cells with biomaterials (i.e., materiobiology) plays an increasingly pivotal role in the development of novel implants, biomedical devices, and tissue engineering scaffolds to treat diseases, aid in the restoration of bodily functions, construct healthy tissues, or regenerate diseased ones. However, the conventional approaches are incapable of screening the huge amount of potential material parameter combinations to identify the optimal cell responses and involve a combination of serendipity and many series of trial-and-error experiments. For advanced tissue engineering and regenerative medicine, highly efficient and complex bioanalysis platforms are expected to explore the complex interaction of cells with biomaterials using combinatorial approaches that offer desired complex microenvironments during healing, development, and homeostasis. In this review, we first introduce materiobiology and its high-throughput screening (HTS). Then we present an in-depth of the recent progress of 2D/3D HTS platforms (i.e., gradient and microarray) in the principle, preparation, screening for materiobiology, and combination with other advanced technologies. The Compendium for Biomaterial Transcriptomics and high content imaging, computational simulations, and their translation toward commercial and clinical uses are highlighted. In the final section, current challenges and future perspectives are discussed. High-throughput experimentation within the field of materiobiology enables the elucidation of the relationships between biomaterial properties and biological behavior and thereby serves as a potential tool for accelerating the development of high-performance biomaterials.

Theoretical design of novel antimalarial agents against P. falciparum strain, Dd2 through the QSAR modeling of synthesized 2'-substituted triclosan derivatives

In an attempt to design compounds with higher antimalarial activities, quantitative structure-activity relationship (QSAR) technique was utilized in the development of a molecular model for some synthesized 2′-substituted triclosan derivatives through a hybrid of the GA-MLR method. The model was found to have excellent statistical parameters (R² = 0.8919, R²_Adj = 0.8728, LOF = 0.2563). The descriptors mean effect (MF) revealed BCUTw-1l, which increases with an increase in molecular weight, to be the most contributive to the antimalarial activity. Consequently, compound 3, with the highest activities (pEC₅₀ = 6.9586) was deployed as the design template. The molecular weight of the template was increasing through substitutions of its atoms at several positions with heavier atoms/groups to increases the descriptor (BCUTw-1l) value. Twelves (12) theoretical derivatives of the template were designed where six of the designed derivatives have better activity than the design template. The most active designed compound, 3L was found to have the highest antimalarial activity (pEC₅₀ = 7.930) than that of the design template.

Validating a Predictive Structure-Property Relationship by Discovery of Novel Polymers which Reduce Bacterial Biofilm Formation

Synthetic materials are an everyday component of modern healthcare yet often fail routinely as a consequence of medical-device-centered infections. The incidence rate for catheter-associated urinary tract infections is between 3% and 7% for each day of use, which means that infection is inevitable when resident for sufficient time. The O'Neill Review on antimicrobial resistance estimates that, left unchecked, ten million people will die annually from drug-resistant infections by 2050. Development of biomaterials resistant to bacterial colonization can play an important role in reducing device-associated infections. However, rational design of new biomaterials is hindered by the lack of quantitative structure-activity relationships (QSARs). Here, the development of a predictive QSAR is reported for bacterial biofilm formation on a range of polymers, using calculated molecular descriptors of monomer units to discover and exemplify novel, biofilm-resistant (meth-)acrylate-based polymers. These predictions are validated successfully by the synthesis of new monomers which are polymerized to create coatings found to be resistant to biofilm formation by six different bacterial pathogens: Pseudomonas aeruginosa, Proteus mirabilis, Enterococcus faecalis, Klebsiella pneumoniae, Escherichia coli, and Staphylococcus aureus.

High-Throughput Assessment and Modeling of a Polymer Library Regulating Human Dental Pulp-Derived Stem Cell Behavior

The identification of biomaterials that modulate cell responses is a crucial task for tissue engineering and cell therapy. The identification of novel materials is complicated by the immense number of synthesizable polymers and the time required for testing each material experimentally. In the current study, polymeric biomaterial-cell interactions were assessed rapidly using a microarray format. The attachment, proliferation, and differentiation of human dental pulp stem cells (hDPSCs) were investigated on 141 homopolymers and 400 diverse copolymers. The copolymer of isooctyl acrylate and 2-(methacryloyloxy)ethyl acetoacetate achieved the highest attachment and proliferation of hDPSC, whereas high cell attachment and differentiation of hDPSC were observed on the copolymer of isooctyl acrylate and trimethylolpropane ethoxylate triacrylate. Computational models were generated, relating polymer properties to cellular responses. These models could accurately predict cell behavior for up to 95% of materials within a test set. The models identified several functional groups as being important for supporting specific cell responses. In particular, oxygen-containing chemical moieties, including fragments from the acrylate/acrylamide backbone of the polymers, promoted cell attachment. Small hydrocarbon fragments originating from polymer pendant groups promoted cell proliferation and differentiation. These computational models constitute a key tool to direct the discovery of novel materials within the enormous chemical space available to researchers.

Error analysis of the spectral shift for partial least squares models in Raman spectroscopy

Raman spectroscopy paired with the partial least squares (PLS) method is commonly used for quantitative or qualitative analysis of complex samples. However, spectral shift induced by different Raman spectroscopy, different environment or different measured time will decrease the accuracy of the PLS model. In this work, the processing algorithms that improve the accuracy by removing the noise, background and varying sources of other spectral interference were first reviewed. The error induced by the spectral shift was analyzed and the formulas of the error were derived. The formulas were then used to calculate the theoretical error in the example of discriminating human and nonhuman blood. A comparison of the actual errors obtained from the mathematical method and experiment with the theoretical value demonstrated the effectiveness of the equation. The compensation for nonhuman blood according to the average error demonstrated the improvement of the accuracy. Finally, the non-uniform sampling of the Raman shift by charge-coupled device (CCD) was considered in the error equation. An accurate error equation was obtained. This work could help improve the stability of PLS models in the case of the spectral shift of the spectrometer in Raman spectroscopy.

An improved partial least-squares regression method for Raman spectroscopy

It is known that the performance of partial least-squares (PLS) regression analysis can be improved using the backward variable selection method (BVSPLS). In this paper, we further improve the BVSPLS based on a novel selection mechanism. The proposed method is based on sorting the weighted regression coefficients, and then the importance of each variable of the sorted list is evaluated using root mean square errors of prediction (RMSEP) criterion in each iteration step. Our Improved BVSPLS (IBVSPLS) method has been applied to leukemia and heparin data sets and led to an improvement in limit of detection of Raman biosensing ranged from 10% to 43% compared to PLS. Our IBVSPLS was also compared to the jack-knifing (simpler) and Genetic Algorithm (more complex) methods. Our method was consistently better than the jack-knifing method and showed either a similar or a better performance compared to the genetic algorithm.

Rapid Nanogram Scale Screening Method of Microarrays to Evaluate Drug-Polymer Blends Using High-Throughput Printing Technology

A miniaturized, high-throughput assay was optimized to screen polymer-drug solid dispersions using a 2-D Inkjet printer. By simply printing nanoliter amounts of polymer and drug solutions onto an inert surface, drug/polymer microdots of tunable composition were produced in an easily addressable microarray format. The amount of material printed for each dried spot ranged from 25 ng to 650 ng. These arrays were used to assess the stability of drug/polymer dispersions with respect to recrystallization, using polarized light microscopy. One array with a panel of 6 drugs formulated at different ratios with a poly(vinylpyrrolidone-vinyl acetate) (PVPVA) copolymer was developed to estimate a possible bulk (gram-scale) approximation threshold from the final printed nanoamount of formulation. Another array was printed at a fixed final amount of material to establish a literature comparison of one drug formulated with different commercial polymers for validation. This new approach may offer significant efficiency in pharmaceutical formulation screening, with each experiment in the nanomicro-array format requiring from 3 up to 6 orders of magnitude lower amounts of sample than conventional screening methods.

ToF-SIMS analysis of a polymer microarray composed of poly(meth)acrylates with C6 derivative pendant groups

Surface analysis plays a key role in understanding the function of materials, particularly in biological environments. Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) provides highly surface sensitive chemical information that can readily be acquired over large areas and has, thus, become an important surface analysis tool. However, the information‐rich nature of ToF‐SIMS complicates the interpretation and comparison of spectra, particularly in cases where multicomponent samples are being assessed. In this study, a method is presented to assess the chemical variance across 16 poly(meth)acrylates. Materials are selected to contain C₆ pendant groups, and ten replicates of each are printed as a polymer microarray. SIMS spectra are acquired for each material with the most intense and unique ions assessed for each material to identify the predominant and distinctive fragmentation pathways within the materials studied. Differentiating acrylate/methacrylate pairs is readily achieved using secondary ions derived from both the polymer backbone and pendant groups. Principal component analysis (PCA) is performed on the SIMS spectra of the 16 polymers, whereby the resulting principal components are able to distinguish phenyl from benzyl groups, mono‐functional from multi‐functional monomers and acrylates from methacrylates. The principal components are applied to copolymer series to assess the predictive capabilities of the PCA. Beyond being able to predict the copolymer ratio, in some cases, the SIMS analysis is able to provide insight into the molecular sequence of a copolymer. The insight gained in this study will be beneficial for developing structure–function relationships based upon ToF‐SIMS data of polymer libraries. © 2016 The Authors Surface and Interface Analysis Published by John Wiley & Sons Ltd.

Bacterial attachment to polymeric materials correlates with molecular flexibility and hydrophilicity

A new class of material resistant to bacterial attachment has been discovered that is formed from polyacrylates with hydrocarbon pendant groups. In this study, the relationship between the nature of the hydrocarbon moiety and resistance to bacteria is explored, comparing cyclic, aromatic, and linear chemical groups. A correlation is shown between bacterial attachment and a parameter derived from the partition coefficient and the number of rotatable bonds of the materials' pendant groups. This correlation is applicable to 86% of the hydrocarbon pendant moieties surveyed, quantitatively supporting the previous qualitative observation that bacteria are repelled from poly(meth)acrylates containing a hydrophilic ester group when the pendant group is both rigid and hydrophobic. This insight will help inform and predict the further development of polymers resistant to bacterial attachment.