Partial least squares regression as a powerful tool for investigating large combinatorial polymer libraries

Exploring the Performance of Linear and Nonlinear Models of Time-of-Flight Secondary Ion Mass Spectrometry Spectra

Multivariate statistical tools and machine learning (ML) techniques can deconvolute hyperspectral data and control the disparity between the number of samples and features in materials science. Nevertheless, the importance of generating sufficient high-quality sample replicates in training data cannot be overlooked, as it fundamentally affects the performance of ML models. Here, we present a quantitative analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) spectra of a simple microarray system of two food dyes using partial least-squares (PLS, linear) and random forest (RF, nonlinear) algorithms. This microarray was generated by a high-throughput sample preparation and analysis workflow for fast and efficient acquisition of quality and reproducible spectra via ToF-SIMS. We drew insights from the bias-variance trade-off, investigated the performances of PLS and RF regression models as a function of training data size, and inferred the amount of data needed to construct accurate and reliable regression models. In addition, we found that the spectral concatenation of positive and negative ToF-SIMS spectra improved the model performances. This study provides an empirical basis for future design of high-throughput microarrays and multicomponent systems, for the purpose of analysis with ToF-SIMS and ML.

Evaluation of the relative potential for contact and doffing transmission of SARS-CoV-2 by a range of personal protective equipment materials

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-the causative agent of coronavirus disease 2019 (COVID-19)-has caused a global public health emergency. Personal protective equipment (PPE) is the primary defence against viral exposure in healthcare and community settings. However, the surfaces of PPE materials may trap virus for contact transmission or through laden aerosols generated during removal of PPE, through cleaning or during movement. In this study, the relative efficacy of current PPE materials in terms of virion adsorption to materials and their antiviral potency, has been evaluated on a wide range of PPE for the first time, including four polymer glove types, two types of scrubs, apron material, a mask, visor and a selection of other commercial polymers and products. Although differences in virion adsorption to the test materials were observed, none of the existing polymer-based PPE resulted in more than tenfold reduction in the SARS-CoV-2 titre within either 10 min or 30 min contact period. The wettability and surface chemistry of the test materials were analysed to investigate any correlations with their surface physicochemical properties. While no correlation was found between wettability and viral retention under air flow challenge, one secondary ion of m/z 101.03 (+) and three secondary ions of m/z 31.98 (-), 196.93 (-) and 394.33 (+) in ToF-SIMS data of the test materials showed positive and negative correlations with the viral retention, respectively, which was identified by PLS regression model, suggesting that the surface chemistry plays a role in determining the extent of virion adsorption. Our findings outline the material aspects that influence the efficacy of current PPE against SARS-CoV-2 transmission and give suggestions on the development of novel simple polymer-based PPE for better infection protection.

High-Throughput and Combinatorial Approaches for the Development of Multifunctional Polymers

High-throughput (HT) development of new multifunctional polymers is accomplished by the combination of different HT tools established in polymer sciences in the last decade. Important advances are robotic/HT synthesis of polymer libraries, the HT characterization of polymers, and the application of spatially resolved polymer library formats, explicitly microarray and gradient libraries. HT polymer synthesis enables the generation of material libraries with combinatorial design motifs. Polymer composition, molecular weight, macromolecular architecture, etc. may be varied in a systematic, fine-graded manner to obtain libraries with high chemical diversity and sufficient compositional resolution as model systems for the screening of these materials for the functions aimed. HT characterization allows a fast assessment of complementary properties, which are employed to decipher quantitative structure-properties relationships. Moreover, these methods facilitate the HT determination of important surface parameters by spatially resolved characterization methods, including time-of-flight secondary ion mass spectrometry and X-ray photoelectron spectroscopy. Here current methods for the high-throughput robotic synthesis of multifunctional polymers as well as their characterization are presented and advantages as well as present limitations are discussed.

High sensitivity analysis of nanogram quantities of glycosaminoglycans using ToF-SIMS

Glycosaminoglycans (GAGs) are important biopolymers that differ in the sequence of saccharide units and in post polymerisation alterations at various positions, making these complex molecules challenging to analyse. Here we describe an approach that enables small quantities (<200 ng) of over 400 different GAGs to be analysed within a short time frame (3-4 h). Time of flight secondary ion mass spectrometry (ToF-SIMS) together with multivariate analysis is used to analyse the entire set of GAG samples. Resultant spectra are derived from the whole molecules and do not require pre-digestion. All 6 possible GAG types are successfully discriminated, both alone and in the presence of fibronectin. We also distinguish between pharmaceutical grade heparin, derived from different animal species and from different suppliers, to a sensitivity as low as 0.001 wt%. This approach is likely to be highly beneficial in the quality control of GAGs produced for therapeutic applications and for characterising GAGs within biomaterials or from in vitro cell culture.

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.

Development of stable amorphous solid dispersion and quantification of crystalline fraction of lopinavir by spectroscopic-chemometric methods

This study aimed to improve the dissolution of the poorly soluble drug lopinavir (LPV) by preparing amorphous solid dispersions (ASDs) using solvent evaporation method. The ASD formulations were prepared with ternary mixtures of LPV, Eudragit® E100, and microcrystalline cellulose (MCC) at various weight ratios. The ASDs were subjected to solid-state characterization and in vitro drug dissolution testing. Chemometric models based on near infrared spectroscopy (NIR) and NIR-hyperspectroscopy (NIR-H) data were developed using the partial least squares (PLS) regression and externally validated to estimate the percent of the crystalline LPV in the ASD. Initially, the solid-state characterization data of ASDs showed transformation of the drug from crystalline to amorphous. Negligible fraction of crystalline LPV was present in the ASD (3%). Compared to pure LPV, ASDs showed faster and higher drug dissolution (<2% vs. 60.3-73.5%) in the first 15 min of testing. The ASD was stable against crystallization during stability testing at 40 °C/75% for a month. In conclusion, the prepared ASD was stable against devitrification and enhance the dissolution of LPV.

Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering

The research paradigm in biomaterials science and engineering is evolving from using low-throughput and iterative experimental designs towards high-throughput experimental designs for materials optimization and the evaluation of materials properties. Computational science plays an important role in this transition. With the emergence of the omics approach in the biomaterials field, referred to as materiomics, high-throughput approaches hold the promise of tackling the complexity of materials and understanding correlations between material properties and their effects on complex biological systems. The intrinsic complexity of biological systems is an important factor that is often oversimplified when characterizing biological responses to materials and establishing property-activity relationships. Indeed, in vitro tests designed to predict in vivo performance of a given biomaterial are largely lacking as we are not able to capture the biological complexity of whole tissues in an in vitro model. In this opinion paper, we explain how we reached our opinion that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field.

STATEMENT OF SIGNIFICANCE

In this opinion paper, we postulate that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field.

Engineering serendipity: High-throughput discovery of materials that resist bacterial attachment

Controlling the colonisation of materials by microorganisms is important in a wide range of industries and clinical settings. To date, the underlying mechanisms that govern the interactions of bacteria with material surfaces remain poorly understood, limiting the ab initio design and engineering of biomaterials to control bacterial attachment. Combinatorial approaches involving high-throughput screening have emerged as key tools for identifying materials to control bacterial attachment. The hundreds of different materials assessed using these methods can be carried out with the aid of computational modelling. This approach can develop an understanding of the rules used to predict bacterial attachment to surfaces of non-toxic synthetic materials. Here we outline our view on the state of this field and the challenges and opportunities in this area for the coming years.

STATEMENT OF SIGNIFICANCE

This opinion article on high throughput screening methods reflects one aspect of how the field of biomaterials research has developed and progressed. The piece takes the reader through key developments in biomaterials discovery, particularly focusing on need to reduce bacterial colonisation of surfaces. Such bacterial resistant surfaces are increasingly required in this age of antibiotic resistance. The influence and origin of high-throughput methods are discussed with insights into the future of biomaterials development where computational methods may drive materials development into new fertile areas of discovery. New biomaterials will exhibit responsiveness to adapt to the biological environment and promote better integration and reduced rejection or infection.

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.

Chemically diverse polymer microarrays and high throughput surface characterisation: a method for discovery of materials for stem cell culture†Electronic supplementary information (ESI) available. See DOI: 10.1039/c4bm00054dClick here for additional data file

Chemically diverse polymer microarrays as a powerful screening tool for the discovery of new materials for a variety of applications.

Materials discovery provides the opportunity to identify novel materials that are tailored to complex biological environments by using combinatorial mixing of monomers to form large libraries of polymers as micro arrays. The materials discovery approach is predicated on the use of the largest chemical diversity possible, yet previous studies into human pluripotent stem cell (hPSC) response to polymer microarrays have been limited to 20 or so different monomer identities in each study. Here we show that it is possible to print and assess cell adhesion of 141 different monomers in a microarray format. This provides access to the largest chemical space to date, allowing us to meet the regenerative medicine challenge to provide scalable synthetic culture ware. This study identifies new materials suitable for hPSC expansion that could not have been predicted from previous knowledge of cell-material interactions.

Combinatorial discovery of polymers resistant to bacterial attachment

Bacterial attachment and subsequent biofilm formation pose key challenges to the optimal performance of medical devices. In this study, we determined the attachment of selected bacterial species to hundreds of polymeric materials in a high-throughput microarray format. Using this method, we identified a group of structurally related materials comprising ester and cyclic hydrocarbon moieties that substantially reduced the attachment of pathogenic bacteria (Pseudomonas aeruginosa, Staphylococcus aureus and Escherichia coli). Coating silicone with these 'hit' materials achieved up to a 30-fold (96.7%) reduction in the surface area covered by bacteria compared with a commercial silver hydrogel coating in vitro, and the same material coatings were effective at reducing bacterial attachment in vivo in a mouse implant infection model. These polymers represent a class of materials that reduce the attachment of bacteria that could not have been predicted to have this property from the current understanding of bacteria-surface interactions.

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

Polymer microarrays are a key enabling technology for the discovery of novel materials. This technology can be further enhanced by expanding the combinatorial space represented on an array. However, not all materials are compatible with the microarray format and materials must be screened to assess their suitability with the microarray manufacturing methodology prior to their inclusion in a materials discovery investigation. In this study a library of materials expressed on the microarray format are assessed by light microscopy, atomic force microscopy and time-of-flight secondary ion mass spectrometry to identify compositions with defects that cause a polymer spot to exhibit surface properties significantly different from a smooth, round, chemically homogeneous 'normal' spot. It was demonstrated that the presence of these defects could be predicted in 85% of cases using a partial least square regression model based upon molecular descriptors of the monomer components of the polymeric materials. This may allow for potentially defective materials to be identified prior to their formation. Analysis of the PLS regression model highlighted some chemical properties that influenced the formation of defects, and in particular suggested that mixing a methacrylate and an acrylate monomer and/or mixing monomers with long and linear or short and bulky pendant groups will prevent the formation of defects. These results are of interest for the formation of polymer microarrays and may also inform the formulation of printed polymer materials generally.

Modelling human embryoid body cell adhesion to a combinatorial library of polymer surfaces

Designing materials to control biology is an intense focus of biomaterials and regenerative medicine research. Discovering and designing materials with appropriate biological compatibility or active control of cells and tissues is being increasingly undertaken using high throughput synthesis and assessment methods. We report a relatively simple but powerful machine-learning method of generating models that link microscopic or molecular properties of polymers or other materials to their biological effects. We illustrate the potential of these methods by developing the first robust, predictive, quantitative, and purely computational models of adhesion of human embryonic stem cell embryoid bodies (hEB) to the surfaces of a 496-member polymer micro array library.

Polymers with hydro-responsive topography identified using high throughput AFM of an acrylate microarray

Atomic force microscopy has been applied to an acrylate polymer microarray to achieve a full topographic characterisation. This process discovered a small number of hydro-responsive materials created from monomers with disparate hydrophilicities that show reversibility between pitted and protruding nanoscale topographies.

ToF-SIMS analysis of ocular tissues reveals biochemical differentiation and drug distribution

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to obtain mass spectra from three ocular tissues, the lens, the vitreous and the retina. All three tissues were extracted from control ovine eyes and ovine eyes treated with model drug. To identify variations in surface biochemistry of each ocular tissue, principal component analysis (PCA) was applied to ToF-SIMS data. Interesting physiological differences in Na(+) and K(+) distribution were shown across the three tissue types, with other elements including Ca(2+) and Fe(2+) distribution also detected. In addition to the identification of small molecules and smaller molecular fragments, larger molecules such as phosphocholine were also detected. The ToF-SIMS data were also used to identify the presence of a model drug compound (amitriptyline--chosen as a generic drug structure) within all three ocular tissues, with model drug detected predominantly across the vitreous tissue samples. This study demonstrates that PCA can be successfully applied to ToF-SIMS data from different ocular tissues and highlights the potential of coupling multivariate statistics with surface analytical techniques to gain a greater understanding of the biochemical composition of tissues and the distribution of pharmaceutically active small molecules within these tissues.

Polymer surface functionalities that control human embryoid body cell adhesion revealed by high throughput surface characterization of combinatorial material microarrays

High throughput materials discovery using combinatorial polymer microarrays to screen for new biomaterials with new and improved function is established as a powerful strategy. Here we combine this screening approach with high throughput surface characterization (HT-SC) to identify surface structure-function relationships. We explore how this combination can help to identify surface chemical moieties that control protein adsorption and subsequent cellular response. The adhesion of human embryoid body (hEB) cells to a large number (496) of different acrylate polymers synthesized in a microarray format is screened using a high throughput procedure. To determine the role of the polymer surface properties on hEB cell adhesion, detailed HT-SC of these acrylate polymers is carried out using time of flight secondary ion mass spectrometry (ToF SIMS), X-ray photoelectron spectroscopy (XPS), pico litre drop sessile water contact angle (WCA) measurement and atomic force microscopy (AFM). A structure-function relationship is identified between the ToF SIMS analysis of the surface chemistry after a fibronectin (Fn) pre-conditioning step and the cell adhesion to each spot using the multivariate analysis technique partial least squares (PLS) regression. Secondary ions indicative of the adsorbed Fn correlate with increased cell adhesion whereas glycol and other functionalities from the polymers are identified that reduce cell adhesion. Furthermore, a strong relationship between the ToF SIMS spectra of bare polymers and the cell adhesion to each spot is identified using PLS regression. This identifies a role for both the surface chemistry of the bare polymer and the pre-adsorbed Fn, as-represented in the ToF SIMS spectra, in controlling cellular adhesion. In contrast, no relationship is found between cell adhesion and wettability, surface roughness, elemental or functional surface composition. The correlation between ToF SIMS data of the surfaces and the cell adhesion demonstrates the ability to identify surface moieties that control protein adsorption and subsequent cell adhesion using ToF SIMS and multivariate analysis.

Tailored electroactive and quantitative ligand density microarrays applied to stem cell differentiation

The ability to precisely control the interactions between materials and mammalian cells at the molecular level is crucial to understanding the fundamental chemical nature of how the local environment influences cellular behavior as well as for developing new biomaterials for a range of biotechnological and tissue engineering applications. In this report, we develop and apply for the first time a quantitative electroactive microarray strategy that can present a variety of ligands with precise control over ligand density to study human mesenchymal stem cell (hMSC) differentiation on transparent surfaces with a new method to quantitate adipogenic differentiation. We found that both the ligand composition and ligand density influence the rate of adipogenic differentiation from hMSC's. Furthermore, this new analytical biotechnology method is compatible with other biointerfacial characterization technologies (surface plasmon resonance, mass spectrometry) and can also be applied to investigate a range of protein-ligand or cell-material interactions for a variety of systems biology studies or cell behavior based assays.

Predicting the physical properties of tablets from ATR-FTIR spectra using partial least squares regression

CONTEXT

The formulation of a new tablet is a time-consuming activity involving the preparation and testing of many different formulations with the aim of identifying one with the desired properties. In complex formulations it may not be clear which excipient is responsible for eliciting a particular property.

OBJECTIVE

To investigate partial least squares (PLS) regression analysis of ATR-FTIR spectra of tablets as a predictive and investigative tool in the formulation of novel tablet formulations.

MATERIALS

Magnesium stearate, lactose, acetylsalicylic acid and Ac-Di-Sol.

RESULTS AND DISCUSSION

ATR-FTIR spectra of a simple aspirin tablet formulation with varying amounts of the lubricant magnesium stearate were obtained. PLS models were built using the spectral data as the multivariate variable and various physical properties of the tablets as the univariate variables. PLS models that allowed good predications to be made for samples not included in the training set were obtained for tablet hardness and disintegration time. It was clear from PLS model regression coefficients that magnesium stearate was responsible for the variation in the tablets' physical properties.

CONCLUSION

PLS regression in combination with ATR-FTIR spectroscopy has been shown to be a useful approach for the prediction of the physical properties of tablets.

High throughput methods applied in biomaterial development and discovery

The high throughput discovery of new bio materials can be achieved by rapidly screening many different materials synthesised by a combinatorial approach to identify the optimal composition that fulfils a particular biomedical application. Here we review the literature in this area and conclude that for polymers this process is best achieved in a microarray format, which enable thousands of cell-material interactions to be monitored on a single chip. Polymer microarrays can be formed by printing pre-synthesised polymers or by printing monomers onto the chip where on-slide polymerisation is initiated. The surface properties of the material can be analysed and correlated to the biological performance using high throughput surface analysis, including time-of-flight secondary ion mass spectrometry (ToF-SIMS), X-ray photoelectron spectroscopy (XPS) and water contact angle (WCA) measurements. This approach enables the surface properties responsible for the success of a material to be understood, which in turn provides the foundations of future material design. The high throughput discovery of materials using polymer microarrays has been explored for many cell-based applications including the isolation of specific cells from heterogeneous populations, the attachment and differentiation of stem cells and the controlled transfection of cells. Further development of polymerisation techniques and high throughput biological assays amenable to the polymer microarray format will broaden the combinatorial space and biological phenomenon that polymer microarrays can explore, and increase their efficacy. This will, in turn, facilitate the discovery of optimised polymeric materials for many biomaterial applications.