Elucidating the molecular landscape of the stratum corneum.

SignificanceSkin is recognized as an intricate assembly of molecular components, which facilitate cell signaling, metabolism, and protein synthesis mechanisms in order to offer protection, regulation, and sensation to the body. Our study takes significant steps to characterize in more detail the complex chemistry of the skin, in particular by generating a better understanding of the uppermost layer, the stratum corneum. Using a state-of-the-art 3D OrbiSIMS technique, we were able to observe the depth distribution, in situ, for a wide range of molecular species. This unprecedented molecular characterization of skin provides information that has the potential to benefit research into fundamental processes, such as those associated with skin aging and disease, and the development and delivery of effective topical formulations.

Metabolomic and Proteomic Analysis of ApoE4-Carrying H4 Neuroglioma Cells in Alzheimer's Disease Using OrbiSIMS and LC-MS/MS.

Growing clinical evidence reveals that systematic molecular alterations in the brain occur 20 years before the onset of AD pathological features. Apolipoprotein E4 (ApoE4) is one of the most significant genetic risk factors for Alzheimer's disease (AD), which is not only associated with the AD pathological features such as amyloid-ß deposition, phosphorylation of tau proteins, and neuroinflammation but is also involved in metabolism, neuron growth, and synaptic plasticity. Multiomics, such as metabolomics and proteomics, are applied widely in identifying key disease-related molecular alterations and disease-progression-related changes. Despite recent advances in the development of analytical technologies, screening the entire profile of metabolites remains challenging due to the numerous classes of compounds with diverse chemical properties that require different extraction processes for mass spectrometry. In this study, we utilized Orbitrap Secondary Ion Mass Spectrometry (OrbiSIMS) as a chemical filtering screening tool to examine molecular alterations in ApoE4-carried neuroglioma cells compared to wild-type H4 cells. The findings were compared using liquid chromatography (LC)-MS/MS targeted metabolomics analysis for the confirmation of specific metabolite classes. Detected alterations in peptide fragments by OrbiSIMS provided preliminary indications of protein changes. These were extensively analyzed through proteomics to explore ApoE4's impact on proteins. Our metabolomics approach, combining OrbiSIMS and LC-MS/MS, revealed disruptions in lipid metabolism, including glycerophospholipids and sphingolipids, as well as amino acid metabolism, encompassing alanine, aspartate, and glutamate metabolism; aminoacyl-tRNA biosynthesis; glutamine metabolism; and taurine and hypotaurine metabolism. Further LC-MS/MS proteomics studies confirmed the dysfunction in amino acid and tRNA aminoacylation metabolic processes, and highlighted RNA splicing alterations influenced by ApoE4.

Merging Modular Molecular Design with High Throughput Screening of Cell Adhesion on Antimicrobial Supramolecular Biomaterials.

A polymer microarray based on the supramolecular ureido-pyrimidinone (UPy) moiety is fabricated to screen antimicrobial materials for their ability to support cell adhesion. UPy-functionalized additives, either cell-adhesive, antimicrobial or control peptides, are used, and investigated in different combinations at different concentrations, resulting in a library of 194 spots. These are characterized on composition and morphology to evaluate the microarray fabrication. Normal human dermal fibroblasts are cultured on the microarrays and cell adhesion to the spots is systematically analyzed. Results demonstrate enhanced cell adhesion on spots with combinations including the antimicrobial peptides. This study clearly proves the power of the high throughput approach in combination with supramolecular molecules, to screen additive libraries for desired biological response.

Toward Comprehensive Analysis of the 3D Chemistry of Pseudomonas aeruginosa Biofilms.

Bacterial biofilms are structured communities consisting of cells enmeshed in a self-generated extracellular matrix usually attached to a surface. They contain diverse classes of molecules including polysaccharides, lipids, proteins, nucleic acids, and diverse small organic molecules (primary and secondary metabolites) which are organized to optimize survival and facilitate dispersal to new colonization sites. In situ characterization of the chemical composition and structure of bacterial biofilms is necessary to fully understand their development on surfaces relevant to biofouling in health, industry, and the environment. Biofilm development has been extensively studied using confocal microscopy using targeted fluorescent labels providing important insights into the architecture of biofilms. Recently, cryopreparation has been used to undertake targeted in situ chemical characterization using Orbitrap secondary ion mass spectrometry (OrbiSIMS), providing a label-free method for imaging biofilms in their native state. Although the high mass resolution of OrbiSIMS enables more confident peak assignments, it is still very challenging to assign most of the peaks in the spectra due to complexity of SIMS spectra and lack of automatic peak assignment methods. Here, we analyze the same OrbiSIMS depth profile data generated from the frozen-hydrated biofilm, but employ a new untargeted chemical filtering process utilizing mass spectral databases to assign secondary ions to decipher the large number of fragments present in the SIMS spectra. To move towards comprehensive analysis of different chemistries in the sample, we apply a molecular formula prediction approach which putatively assigns 81% of peaks in the 3D OrbiSIMS depth profile analysis. This enables us to catalog over 1000 lipids and their fragments, 3500 protein fragments, 71 quorum sensing-related molecules (2-alkyl-4-quinolones and N-acylhomoserine lactones), 150 polysaccharide fragments, and glycolipids simultaneously from one data set and map these separated molecular classes spatially through a Pseudomonas aeruginosa biofilm. Assignment of different chemistries in this sample facilitates identification of differences between biofilms grown on biofilm-promoting and biofilm-resistant polymers.

Predictive Molecular Design and Structure-Property Validation of Novel Terpene-Based, Sustainably Sourced Bacterial Biofilm-Resistant Materials.

Presented in this work is the use of a molecular descriptor, termed the α parameter, to aid in the design of a series of novel, terpene-based, and sustainable polymers that were resistant to biofilm formation by the model bacterial pathogen Pseudomonas aeruginosa. To achieve this, the potential of a range of recently reported, terpene-derived monomers to deliver biofilm resistance when polymerized was both predicted and ranked by the application of the α parameter to key features in their molecular structures. These monomers were derived from commercially available terpenes (i.e., α-pinene, ß-pinene, and carvone), and the prediction of the biofilm resistance properties of the resultant novel (meth)acrylate polymers was confirmed using a combination of high-throughput polymerization screening (in a microarray format) and in vitro testing. Furthermore, monomers, which both exhibited the highest predicted biofilm anti-biofilm behavior and required less than two synthetic stages to be generated, were scaled-up and successfully printed using an inkjet "valve-based" 3D printer. Also, these materials were used to produce polymeric surfactants that were successfully used in microfluidic processing to create microparticles that possessed bio-instructive surfaces. As part of the up-scaling process, a novel rearrangement was observed in a proposed single-step synthesis of α-terpinyl methacrylate via methacryloxylation, which resulted in isolation of an isobornyl-bornyl methacrylate monomer mixture, and the resultant copolymer was also shown to be bacterial attachment-resistant. As there has been great interest in the current literature upon the adoption of these novel terpene-based polymers as green replacements for petrochemical-derived plastics, these observations have significant potential to produce new bio-resistant coatings, packaging materials, fibers, medical devices, etc.

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.

Mechanisms of lipid preservation in archaeological clay ceramics revealed by mass spectrometry imaging.

Traces of lipids, absorbed and preserved for millennia within the inorganic matrix of ceramic vessels, act as molecular fossils and provide manifold information about past people's subsistence, diet, and rituals. It is widely assumed that lipids become preserved after adsorption into nano- to micrometer-sized pores, but to this day the distribution of these lipids in the ceramics was virtually unknown, which severely limits our understanding about the process of lipid preservation. Here we use secondary ion mass spectrometry (SIMS) imaging for direct in situ analysis of lipids absorbed in 700- to 2,000-y-old archaeological pottery. After sectioning from larger sherds, wall cross-sections of smaller fragments were used for SIMS analysis. Lipids were found in relatively large zones of 5- to 400-µm diameter, which does not support the notion of absorption only into individual nanometer-scale pores but indicates that more macroscopic structures in the ceramics are involved in lipid preservation as well. Furthermore, lipids were found concentrated on calcium carbonate inclusions in the ceramics, which suggests that precipitation of fatty acids as calcium salts is an important aspect of lipid preservation in archaeological samples. This has important implications for analytical methods based on extraction of lipids from archaeological ceramics and needs to be considered to maximize the yield and available information from each unique sample.

Single-Cell Metabolic Profiling of Macrophages Using 3D OrbiSIMS: Correlations with Phenotype.

Macrophages are important immune cells that respond to environmental cues acquiring a range of activation statuses represented by pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes at each end of their spectrum. Characterizing the metabolic signature (metabolic profiling) of different macrophage subsets is a powerful tool to understand the response of the human immune system to different stimuli. Here, the recently developed 3D OrbiSIMS instrument is applied to yield useful insight into the metabolome from individual cells after in vitro differentiation of macrophages into naïve, M1, and M2 phenotypes using different cytokines. This analysis strategy not only requires more than 6 orders of magnitude less sample than traditional mass spectrometry approaches but also allows the study of cell-to-cell variance. Characteristic metabolites in macrophage subsets are identified using a targeted lipid and data-driven multivariate approach highlighting amino acids and other small molecules. The diamino acids alanylasparagine and lipid sphingomyelin SM(d18/16:0) are uniquely found in M1 macrophages, while pyridine and pyrimidine are observed at increased intensity in M2 macrophages, findings which link to known biological pathways. The first demonstration of this capability illustrates the great potential of direct cell analysis for in situ metabolite profiling with the 3D OrbiSIMS to probe functional phenotype at the single-cell level using molecular signatures and to understand the response of the human body to implanted devices and immune diseases.

Molecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets.

Modern mass spectrometry techniques produce a wealth of spectral data, and although this is an advantage in terms of the richness of the information available, the volume and complexity of data can prevent a thorough interpretation to reach useful conclusions. Application of molecular formula prediction (MFP) to produce annotated lists of ions that have been filtered by their elemental composition and considering structural double bond equivalence are widely used on high resolving power mass spectrometry datasets. However, this has not been applied to secondary ion mass spectrometry data. Here, we apply this data interpretation approach to 3D OrbiSIMS datasets, testing it for a series of increasingly complex samples. In an organic on inorganic sample, we successfully annotated the organic contaminant overlayer separately from the substrate. In a more challenging purely organic human serum sample we filtered out both proteins and lipids based on elemental compositions, 226 different lipids were identified and validated using existing databases, and we assigned amino acid sequences of abundant serum proteins including albumin, fibronectin, and transferrin. Finally, we tested the approach on depth profile data from layered carbonaceous engine deposits and annotated previously unidentified lubricating oil species. Application of an unsupervised machine learning method on filtered ions after performing MFP from this sample uniquely separated depth profiles of species, which were not observed when performing the method on the entire dataset. Overall, the chemical filtering approach using MFP has great potential in enabling full interpretation of complex 3D OrbiSIMS datasets from a plethora of material types.

The Galapagos Chip Platform for High-Throughput Screening of Cell Adhesive Chemical Micropatterns.

In vivo cells reside in a complex extracellular matrix (ECM) that presents spatially distributed biochemical and -physical cues at the nano- to micrometer scales. Chemical micropatterning is successfully used to generate adhesive islands to control where and how cells attach and restore cues of the ECM in vitro. Although chemical micropatterning has become a powerful tool to study cell-material interactions, only a fraction of the possible micropattern designs was covered so far, leaving many other possible designs still unexplored. Here, a high-throughput screening platform called "Galapagos chip" is developed. It contains a library of 2176 distinct subcellular chemical patterns created using mathematical algorithms and a straightforward UV-induced two-step surface modification. This approach enables the immobilization of ligands in geometrically defined regions onto cell culture substrates. To validate the system, binary RGD/polyethylene glycol patterns are prepared on which human mesenchymal stem cells are cultured, and the authors observe how different patterns affect cell and organelle morphology. As proof of concept, the cells are stained for the mechanosensitive YAP protein, and, using a machine-learning algorithm, it is demonstrated that cell shape and YAP nuclear translocation correlate. It is concluded that the Galapagos chip is a versatile platform to screen geometrical aspects of cell-ECM interaction.

Time resolved growth of (N)-polycyclic aromatic hydrocarbons in engine deposits uncovered with OrbiSIMS depth profiling.

Carbonaceous deposits are ubiquitous, being formed on surfaces in engines, fuel systems and on catalysts operating at high temperatures for hydrocarbon transformations. In internal combustion engines, their formation negatively affects worldwide vehicle emissions and fuel economy, leading to premature deaths and environmental damage. Deposit composition and formation pathways are poorly understood due to their insolubility and the intrinsic complexity of their layered carbonaceous matrix. Here, we apply the in situ high mass resolving power capabilities of 3D Orbitrap secondary ion mass spectrometry (3D OrbiSIMS) argon cluster depth profiling on 16 lab grown deposits and evidence common molecular distributions in deposit depth and in positions relative to the combustion chamber. We observe the products of the growth of both planar and curved polycyclic aromatic hydrocarbons to form small fullerenes over time in the engine and propose possible formation pathways which explain the molecular distributions observed. These include alkyl scission, cyclisation of aliphatic side chains and hydrogen abstraction C2H2 addition to form larger aromatic structures. We apply this pathway to previously unidentified nitrogen containing structures in deposits including quinolines and carbazoles. For the first time, 3D OrbiSIMS results were compared and validated with data from atmospheric pressure matrix assisted laser desorption ionization MS. The comprehensive characterization provided will help the development of a new generation of chemical additives to reduce deposits, and thus improve vehicle emissions and global air quality.

AbaM Regulates Quorum Sensing, Biofilm Formation, and Virulence in Acinetobacter baumannii.

Acinetobacter baumannii possesses a single divergent luxR/luxRI-type quorum-sensing (QS) locus named abaR/abaI This locus also contains a third gene located between abaR and abaI, which we term abaM, that codes for an uncharacterized member of the RsaM protein family known to regulate N-acylhomoserine lactone (AHL)-dependent QS in other beta- and gammaproteobacteria. Here, we show that disruption of abaM via a T26 insertion in A. baumannii strain AB5075 resulted in increased production of N-(3-hydroxydodecanoyl)-l-homoserine lactone and enhanced surface motility and biofilm formation. In contrast to the wild type and the abaI::T26 mutant, the virulence of the abaM::T26 mutant was completely attenuated in a Galleria mellonella infection model. Transcriptomic analysis of the abaM::T26 mutant revealed that AbaM differentially regulates at least 76 genes, including the csu pilus operon and the acinetin 505 lipopeptide biosynthetic operon, that are involved in surface adherence, biofilm formation and virulence. A comparison of the wild type, abaM::T26 and abaI::T26 transcriptomes, indicates that AbaM regulates ∼21% of the QS regulon including the csu operon. Moreover, the QS genes (abaI and abaR) were among the most upregulated in the abaM::T26 mutant. A. baumanniilux-based abaM reporter gene fusions revealed that abaM expression is positively regulated by QS but negatively autoregulated. Overall, the data presented in this work demonstrates that AbaM plays a central role in regulating A. baumannii QS, virulence, surface motility, and biofilm formation.IMPORTANCEAcinetobacter baumannii is a multiantibiotic-resistant pathogen of global health care importance. Understanding Acinetobacter virulence gene regulation could aid the development of novel anti-infective strategies. In A. baumannii, the abaR and abaI genes that code for the receptor and synthase components of an N-acylhomoserine (AHL) lactone-dependent quorum sensing system (QS) are separated by abaM Here, we show that although mutation of abaM increased AHL production, surface motility, and biofilm development, it resulted in the attenuation of virulence. AbaM was found to control both QS-dependent and QS-independent genes. The significance of this work lies in the identification of AbaM, an RsaM ortholog known to control virulence in plant pathogens, as a modulator of virulence in a human pathogen.

Sequential Orbitrap Secondary Ion Mass Spectrometry and Liquid Extraction Surface Analysis-Tandem Mass Spectrometry-Based Metabolomics for Prediction of Brain Tumor Relapse from Sample-Limited Primary Tissue Archives.

We present here a novel surface mass spectrometry strategy to perform untargeted metabolite profiling of formalin-fixed paraffin-embedded pediatric ependymoma archives. Sequential Orbitrap secondary ion mass spectrometry (3D OrbiSIMS) and liquid extraction surface analysis-tandem mass spectrometry (LESA-MS/MS) permitted the detection of 887 metabolites (163 chemical classes) from pediatric ependymoma tumor tissue microarrays (diameter: <1 mm; thickness: 4 µm). From these 163 classes, 60 classes were detected with both techniques, whilst LESA-MS/MS and 3D OrbiSIMS individually allowed the detection of another 83 and 20 unique metabolite classes, respectively. Through data fusion and multivariate analysis, we were able to identify key metabolites and corresponding pathways predictive of tumor relapse, which were retrospectively confirmed by gene expression analysis with publicly available data. Altogether, this sequential mass spectrometry strategy has shown to be a versatile tool to perform high-throughput metabolite profiling on sample-limited tissue archives.

Droplet Microfluidic Optimisation Using Micropipette Characterisation of Bio-Instructive Polymeric Surfactants.

Droplet microfluidics can produce highly tailored microparticles whilst retaining monodispersity. However, these systems often require lengthy optimisation, commonly based on a trial-and-error approach, particularly when using bio-instructive, polymeric surfactants. Here, micropipette manipulation methods were used to optimise the concentration of bespoke polymeric surfactants to produce biodegradable (poly(d,l-lactic acid) (PDLLA)) microparticles with unique, bio-instructive surface chemistries. The effect of these three-dimensional surfactants on the interfacial tension of the system was analysed. It was determined that to provide adequate stabilisation, a low level (0.1% (w/v)) of poly(vinyl acetate-co-alcohol) (PVA) was required. Optimisation of the PVA concentration was informed by micropipette manipulation. As a result, successful, monodisperse particles were produced that maintained the desired bio-instructive surface chemistry.

ToF-SIMS and Machine Learning for Single-Pixel Molecular Discrimination of an Acrylate Polymer Microarray.

Combinatorial approaches to materials discovery offer promising potential for the rapid development of novel polymer systems. Polymer microarrays enable the high-throughput comparison of material physical and chemical properties-such as surface chemistry and properties like cell attachment or protein adsorption-in order to identify correlations that can progress materials development. A challenge for this approach is to accurately discriminate between highly similar polymer chemistries or identify heterogeneities within individual polymer spots. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) offers unique potential in this regard, capable of describing the chemistry associated with the outermost layer of a sample with high spatial resolution and chemical sensitivity. However, this comes at the cost of generating large scale, complex hyperspectral imaging data sets. We have demonstrated previously that machine learning is a powerful tool for interpreting ToF-SIMS images, describing a method for color-tagging the output of a self-organizing map (SOM). This reduces the entire hyperspectral data set to a single reconstructed color similarity map, in which the spectral similarity between pixels is represented by color similarity in the map. Here, we apply the same methodology to a ToF-SIMS image of a printed polymer microarray for the first time. We report complete, single-pixel molecular discrimination of the 70 unique homopolymer spots on the array while also identifying intraspot heterogeneities thought to be related to intermixing of the polymer and the pHEMA coating. In this way, we show that the SOM can identify layers of similarity and clusters in the data, both with respect to polymer backbone structures and their individual side groups. Finally, we relate the output of the SOM analysis with fluorescence data from polymer-protein adsorption studies, highlighting how polymer performance can be visualized within the context of the global topology of the data set.

Cryo-OrbiSIMS for 3D Molecular Imaging of a Bacterial Biofilm in Its Native State.

Secondary ion mass spectrometry (SIMS) is gaining popularity for molecular imaging in the life sciences because it is label-free and allows imaging in two and three dimensions. The recent introduction of the OrbiSIMS has significantly improved the utility for biological imaging through combining subcellular spatial resolution with high-performance Orbitrap mass spectrometry. SIMS instruments operate in high-vacuum, and samples are typically analyzed in a freeze-dried state. Consequently, the molecular and structural information may not be well-preserved. We report a method for molecular imaging of biological materials, preserved in a native state, by using an OrbiSIMS instrument equipped with cryogenic sample handling and a high-pressure freezing protocol compatible with mass spectrometry. The performance is demonstrated by imaging a challenging sample (>90% water) of a mature Pseudomonas aeruginosa biofilm in its native state. The 3D distribution of quorum sensing signaling molecules, nucleobases, and bacterial membrane molecules is revealed with high spatial-resolution and high mass-resolution. We discover that analysis in the frozen-hydrated state yields a 10 000-fold increase in signal intensity for polar molecules such as amino acids, which has important implications for SIMS imaging of metabolites and pharmaceuticals.

The 3D OrbiSIMS-label-free metabolic imaging with subcellular lateral resolution and high mass-resolving power.

We report the development of a 3D OrbiSIMS instrument for label-free biomedical imaging. It combines the high spatial resolution of secondary ion mass spectrometry (SIMS; under 200 nm for inorganic species and under 2 µm for biomolecules) with the high mass-resolving power of an Orbitrap (>240,000 at m/z 200). This allows exogenous and endogenous metabolites to be visualized in 3D with subcellular resolution. We imaged the distribution of neurotransmitters-gamma-aminobutyric acid, dopamine and serotonin-with high spectroscopic confidence in the mouse hippocampus. We also putatively annotated and mapped the subcellular localization of 29 sulfoglycosphingolipids and 45 glycerophospholipids, and we confirmed lipid identities with tandem mass spectrometry. We demonstrated single-cell metabolomic profiling using rat alveolar macrophage cells incubated with different concentrations of the drug amiodarone, and we observed that the upregulation of phospholipid species and cholesterol is correlated with the accumulation of amiodarone.

Improved Extraction Repeatability and Spectral Reproducibility for Liquid Extraction Surface Analysis-Mass Spectrometry Using Superhydrophobic-Superhydrophilic Patterning.

A major problem limiting reproducible use of liquid extraction surface analysis (LESA) array sampling of dried surface-deposited liquid samples is the unwanted spread of extraction solvent beyond the dried sample limits, resulting in unreliable data. Here, we explore the use of the Droplet Microarray (DMA), which consists of an array of superhydrophilic spots bordered by a superhydrophobic material giving the potential to confine both the sample spot and the LESA extraction solvent in a defined area. We investigated the DMA method in comparison with a standard glass substrate using LESA analysis of a mixture of biologically relevant compounds with a wide mass range and different physicochemical properties. The optimized DMA method was subsequently applied to urine samples from a human intervention study. Relative standard deviations for the signal intensities were all reduced at least 3-fold when performing LESA-MS on the DMA surface compared with a standard glass surface. Principal component analysis revealed more tight clusters indicating improved spectral reproducibility for a human urine sample extracted from the DMA compared to glass. Lastly, in urine samples from an intervention study, more significant ions (145) were identified when using LESA-MS spectra of control and test urine extracted from the DMA. We demonstrate that DMA provides a surface-assisted LESA-MS method delivering significant improvement of the surface extraction repeatability leading to the acquisition of more robust and higher quality data. The DMA shows potential to be used for LESA-MS for controlled and reproducible surface extraction and for acquisition of high quality, qualitative data in a high-throughput manner.

Extrusion 3D Printing of Paracetamol Tablets from a Single Formulation with Tunable Release Profiles Through Control of Tablet Geometry.

An extrusion-based 3D printer was used to fabricate paracetamol tablets with different geometries (mesh, ring and solid) from a single paste-based formulation formed from standard pharmaceutical ingredients. The tablets demonstrate that tunable drug release profiles can be achieved from this single formulation even with high drug loading (> 80% w/w). The tablets were evaluated for drug release using a USP dissolution testing type I apparatus. The tablets showed well-defined release profiles (from immediate to sustained release) controlled by their different geometries. The dissolution results showed dependency of drug release on the surface area/volume (SA/V) ratio and the SA of the different tablets. The tablets with larger SA/V ratios and SA had faster drug release. The 3D printed tablets were also evaluated for physical and mechanical properties including tablet dimension, drug content, weight variation and breaking force and were within acceptable range as defined by the international standards stated in the US Pharmacopoeia. X-ray powder diffraction, differential scanning calorimetry and attenuated total reflectance Fourier transform infrared spectroscopy were used to identify the physical form of the active and to assess possible drug-excipient interactions. These data again showed that the tablets meet USP requirement. These results clearly demonstrate the potential of 3D printing to create unique pharmaceutical manufacturing, and potentially clinical, opportunities. The ability to use a single unmodified formulation to achieve defined release profiles could allow, for example, relatively straightforward personalization of medicines for individuals with different metabolism rates for certain drugs and hence could offer significant development and clinical opportunities.

Intracellular Drug Uptake-A Comparison of Single Cell Measurements Using ToF-SIMS Imaging and Quantification from Cell Populations with LC/MS/MS.

ToF-SIMS is a label-free imaging method that has been shown to enable imaging of amiodarone in single rat macrophage (NR8383) cells. In this study, we show that the method extends to three other cell lines relevant to drug discovery: human embryonic kidney (HEK293), cervical cancer (HeLa), and liver cancer (HepG2). There is significant interest in the variation of drug uptake at the single cell level, and we use ToF-SIMS to show that there is great diversity between individual cells and when comparing each of the cell types. These single cell measurements are compared to quantitative measurements of cell-associated amiodarone for the population using LC/MS/MS and cell counting with flow cytometry. NR8383 and HepG2 cells uptake the greatest amount of amiodarone with an average of 2.38 and 2.60 pg per cell, respectively, and HeLa and Hek 293 have a significantly lower amount of amiodarone at 0.43 and 0.36 pg per cell, respectively. The amount of cell-associated drug for the ensemble population measurement (LC/MS/MS) is compared with the ToF-SIMS single cell data: a similar amount of drug was detected per cell for the NR8383, and HepG2 cells at a greater level than that for the HEK293 cells. However, the two techniques did not agree for the HeLa cells, and we postulate potential reasons for this.