Recent progress in three-dimensionally-printed dosage forms from a pharmacist perspective.

OBJECTIVE: Additive manufacturing (AM), commonly known as 3D printing (3DP), has opened new frontiers in pharmaceutical applications. This review is aimed to summarise the recent development of 3D-printed dosage forms, from a pharmacists' perspective. METHODS: Keywords including additive manufacturing, 3D printing and drug delivery were used for literature search in PubMed, Excerpta Medica Database (EMBASE) and Web of Science, to identify articles published in the year 2020. RESULTS: For each 3DP study, the active pharmaceutical ingredients, 3D printers and materials used for the printing were tabulated and discussed. 3DP has found its applications in various dosage forms for oral delivery, transdermal delivery, rectal delivery, vaginal delivery, implant and bone scaffolding. Several topics were discussed in detail, namely patient-specific dosing, customisable drug administration, multidrug approach, varying drug release, compounding pharmacy, regulatory progress and future perspectives. AM is expected to become a common tool in compounding pharmacies to make polypills and personalised medications. CONCLUSION: 3DP is an enabling tool to fabricate dosage forms with intricate structure designs, tailored dosing, drug combinations and controlled release, all of which lend it to be highly conducive to personalisation, thereby revolutionising the future of pharmacy practice.

Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering.

Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several extensions have been developed to improve the original k-means clustering method such as k-means ++ and kernel k-means. K-means is a linear clustering method; that is, it divides the objects into linearly separable groups, while kernel k-means is a non-linear technique. Kernel k-means projects the elements to a higher dimensional feature space using a kernel function, and then groups them. Different kernel functions may not perform similarly in clustering of a data set and, in turn, choosing the right kernel for an application could be challenging. In our previous work, we introduced a weighted majority voting method for clustering based on normalized mutual information (NMI). NMI is a supervised method where the true labels for a training set are required to calculate NMI. In this study, we extend our previous work of aggregating the clustering results to develop an unsupervised weighting function where a training set is not available. The proposed weighting function here is based on Silhouette index, as an unsupervised criterion. As a result, a training set is not required to calculate Silhouette index. This makes our new method more sensible in terms of clustering concept.

Weighted Mutual Information for Aggregated Kernel Clustering.

Background: A common task in machine learning is clustering data into different groups based on similarities. Clustering methods can be divided in two groups: linear and nonlinear. A commonly used linear clustering method is K-means. Its extension, kernel K-means, is a non-linear technique that utilizes a kernel function to project the data to a higher dimensional space. The projected data will then be clustered in different groups. Different kernels do not perform similarly when they are applied to different datasets. Methods: A kernel function might be relevant for one application but perform poorly to project data for another application. In turn choosing the right kernel for an arbitrary dataset is a challenging task. To address this challenge, a potential approach is aggregating the clustering results to obtain an impartial clustering result regardless of the selected kernel function. To this end, the main challenge is how to aggregate the clustering results. A potential solution is to combine the clustering results using a weight function. In this work, we introduce Weighted Mutual Information (WMI) for calculating the weights for different clustering methods based on their performance to combine the results. The performance of each method is evaluated using a training set with known labels. Results: We applied the proposed Weighted Mutual Information to four data sets that cannot be linearly separated. We also tested the method in different noise conditions. Conclusions: Our results show that the proposed Weighted Mutual Information method is impartial, does not rely on a single kernel, and performs better than each individual kernel specially in high noise.

Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions.

BACKGROUND: In data analysis and machine learning, we often need to identify and quantify the correlation between variables. Although Pearson's correlation coefficient has been widely used, its value is reliable only for linear relationships and Distance correlation was introduced to address this shortcoming. METHODS: Distance correlation can identify linear and nonlinear correlations. However, its performance drops in noisy conditions. In this paper, we introduce the Association Factor (AF) as a robust method for identification and quantification of linear and nonlinear associations in noisy conditions. RESULTS: To test the performance of the proposed Association Factor, we modeled several simulations of linear and nonlinear relationships in different noise conditions and computed Pearson's correlation, Distance correlation, and the proposed Association Factor. CONCLUSION: Our results show that the proposed method is robust in two ways. First, it can identify both linear and nonlinear associations. Second, the proposed Association Factor is reliable in both noiseless and noisy conditions.

Discriminant Analysis of Lung Cancer Using Nonlinear Clustering of Copy Numbers.

Background: Patient survival is not optimal for non-small cell lung cancer (NSCLC) patients, recurrence rate is high, and hence, early detection is crucial to increase the patient's survival. Gene-cancer mapping intends to discover associated genes with cancers and due to advances in high-throughput genotyping, screening for disease loci on a genome-wide scale is now possible. DNA copy numbers can potentially be used to identify cancer from normal cells in early detection of cancer.Methods: We use a nonlinear clustering method, so-called kernel K-means to separate cancer from normal samples. Kernel K-means is applied to the copy numbers obtained for each chromosome to cluster 63 paired cancer-blood samples (total of 126 samples) into two groups. Clustering performance is evaluated using true and false-positive rates, true and false-negative rates, and a nonlinear criterion, normalized mutual information (NMI).Results: Copy numbers of paired cancer-blood samples for 63 NSCLC patients are used in this study. Kernel K-means was applied to cluster 126 samples in two groups using copy numbers on each chromosome separately. The clustering results for 22 chromosomes are evaluated and discriminant power of them in identifying cancer is computed. We identified the top five and bottom five chromosomes based on their discriminant power.Conclusions: The results reveal high discriminant power of chromosomes 8, 5, 1, 3, and 19 for identifying cancer with the highest sensitivity of 75% yielded by chromosome 5. Bottom 5 chromosomes 9, 6, 4, 13, and 21 show low discriminant power with the accuracy of below 54% where true cancer and normal samples are grouped into substantially overlapping groups using copy numbers. This indicates the similarities of copy numbers obtained for cancer and normal samples on these chromosomes.

Geometrical optimisation of a personalised microneedle eye patch for transdermal delivery of anti-wrinkle small peptide.

Acetyl-hexapeptide-3 (AHP-3) is a small peptide with good anti-wrinkle efficacy and safety profile. However, due to its hydrophilicity and high molecular weight, its skin permeation is generally poor. An innovative microneedle (MN) patch such as the curved, flexible or personalised MN patch is a viable avenue to deliver AHP-3. However, the well-researched geometrical relationship of MN on a flat MN patch cannot be assumed for these novel MN patches due to a complex mix of axial and shear forces. In this study, 3D printing was used for the fabrication of various MN patches with different MN geometries and curvatures. Both mechanical strength and skin penetration efficiency were used to determine the optimal MN geometry. The optimal MN geometry was then applied to the fabrication of a personalized MN patch (PMNP) for anti-wrinkle therapy, via 3D printing. In all, the general principles of MN geometrical effects on mechanical strength and skin penetration efficiency for a curved and a flat MN patch were similar. A MN height of 800 µm, tip diameter of 100 µm, interspacing of 800 µm and base diameter of 400 µm was observed to be the optimal MN geometry across all curvatures. In vitro skin permeation study demonstrated enhanced transdermal delivery of AHP-3 using the fabricated PMNP. Therefore, PMNP with optimized MN geometry can potentially be a novel approach to augment transdermal delivery of AHP-3 for effective wrinkle management.

Identifying Similarities and Disparities Between DNA Copy Number Changes in Cancer and Matched Blood Samples.

Background: Non-small cell lung cancer (NSCLC) is the first cause of cancer-related mortality for men and women in the United States. In spite of curative resection in early-stage, patient survival is not optimal and recurrence rate is high. Consequently, early detection and staging is essential to increase the patient's survival.Methods: Copy number (CN) changes in cancer populations have been broadly investigated to identify CN gains and deletions associated with cancer. In contrast, in this research, we quantify the similarities and disparities between cancer and paired peripheral blood samples using maximal information coefficient (MIC). We then detect the spatial locations with substantially high and the spatial locations with very low MICs in each chromosome. These locations can potentially help with early diagnosis, treatment, and prevention of cancer by identifying the similarities and disparities between cancer and healthy tissues.Results: Lung cancer data used in this project contains CN pairs for cancer and blood (non-involved) samples for 63 subjects. MIC was obtained to quantify the relation (linear or nonlinear) between cancer-blood pair samples for 63 subjects at each location for each chromosome. MIC values above a high threshold and MIC values below a low threshold were located. Among them top five (with lowest MIC's and with highest MIC's) were identified for each chromosome. For these identified locations, a high MIC score indicates high similarity between blood (non-involved) and cancer samples, while a low MIC score shows lack of similarity between the two samples.Conclusions: The results showed that a few chromosomes have a large number of MICs exceeding a high threshold. These locations can potentially be used to identify early indicators of NSCLC. In contrast, second group of chromosomes have several locations with small MICs which are potential candidates to develop biomarkers for discriminating cancer from the matched blood sample. Moreover, there is a third group of chromosomes with a large number of MICs exceeding a high threshold and a large set of MICs below a low threshold. These locations can help with both finding early indicators of cancer and developing biomarkers for discriminating cancer from non-involved tissue.

Method for detecting voxelwise changes in fluorodeoxyglucose-positron emission tomography brain images via background adjustment in cancer clinical trials.

An important challenge to using fluorodeoxyglucose-positron emission tomography (FDG-PET) in clinical trials of brain tumor patients is to identify malignant regions whose metabolic activity shows significant changes between pretreatment and a posttreatment scans in the presence of high normal brain background metabolism. This paper describes a semiautomated processing and analysis pipeline that is able to detect such changes objectively with a given false detection rate. Image registration and voxelwise comparison of the pre- and posttreatment images were performed. A key step is adjustment of the observed difference by the estimated background change at each voxel, thereby overcoming the confounding effect of spatially heterogeneous metabolic activity in the brain. Components of the proposed method were validated via phantom experiments and computer simulations. It achieves a false response volume accuracy of 0.4% at a significance threshold of 3 standard deviations. It is shown that the proposed methodology can detect lesion response with 100% accuracy with a tumor-to-background-ratio as low as 1.5, and it is not affected by the background brain glucose metabolism change. We also applied the method to FDG-PET patient images from a clinical trial to assess treatment effects of lapatinib, which demonstrated significant changes in metabolism corresponding to tumor regions.

FDR control of detected regions by multiscale matched filtering.

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed technique is a multi-scale kernel regression in conjunction with statistical multiple testing for region detection while controlling the false discovery rate (FDR) and maximizing the signal to noise ratio (SNR) via matched filtering. This is achieved by considering a one-dimensional (1D) region detection problem as its equivalent 0D (zero dimensional) peak detection problem. The detection method does not require a priori knowledge of the shape of the non-zero regions. However, if the shape of the non-zero regions is known a priori, e.g. rectangular pulse, the signal regions can also be reconstructed from the detected peaks, seen as their topological point representatives. Simulations show that the method can effectively perform signal detection and reconstruction in the simulated data under high noise conditions, while controlling the FDR of detected regions and their reconstructed length.

Detection of Local DNA Copy Number Changes in Lung Cancer Population Analyses Using A Multi-Scale Approach.

Emerging advances in genomic sequencing have prompted the development of new computational methods for studying the genomic sources of human diseases. This paper presents a recent statistical approach for detection of local regions with significant copy number alterations (CNAs) in lung cancer population. Mapping such regions is of interest as they are potentially associated with lung cancer. Conventional application of multiple testing methods corresponds to testing for CNAs at each probe separately and thresholding the t-statistics as test statistics. Due to the large number of probes, this approach often fails to detect CNA regions. In contrast, the proposed method uses the heights of located peaks and improves the detection power. This is achieved by taking advantage of the spatial structure in the data as well as reducing the number of tests in the multiple comparisons problem. In copy number analysis, it is common to apply segmentation or change detection tools to each individual genomic sample. However, since segmentation results vary among subjects, it becomes difficult to find the common genomic regions in population analyses. Our approach solves this problem by performing the analysis using summary statistics to study at population level directly. Hence, the region detection is performed on the summary t-statistic map. The proposed method is applied to lung cancer data and shows promise for detection of local regions with significant CNAs.

Tissue engineering based on electrochemical desorption of an RGD-containing oligopeptide.

This paper describes a non-invasive approach for efficient detachment of cells adhered to a gold substrate via a specific oligopeptide. Detachment is effected by an electrical stimulus. The oligopeptide contains cysteine, which spontaneously forms a gold-thiolate bond on a gold surface. This chemical adsorption reaches > 95% equilibrium within 10 min after immersion of a gold-coated substrate in a solution containing the peptide. The peptide is reversibly desorbed from the surface within 5 min of application of a negative electrical potential. By taking advantage of this simple adsorption and desorption mechanism, cells can be grown on an oligopeptide-functionalized gold surface and can be efficiently detached as single cells or cell sheets by application of a negative electrical potential. This approach was also applied to the surface of gold-coated microrods. Capillary-like microchannels were formed in collagen gel by transferring endothelial cells to the internal surfaces of the microchannels. During subsequent perfusion culture, the enveloped endothelial cells migrated into the collagen gel and formed luminal structures, which sprouted from the microchannels. This technique has the potential to provide a fundamental tool for the engineering of thick cell sheets as well as vascularized tissues and organs.

An automated two-phase system for hydrogel microbead production.

Polymeric beads have been used for protection and delivery of bioactive materials, such as drugs and cells, for different biomedical applications. Here, we present a generic two-phase system for the production of polymeric microbeads of gellan gum or alginate, based on a combination of in situ polymerization and phase separation. Polymer droplets, dispensed using a syringe pump, formed polymeric microbeads while passing through a hydrophobic phase. These were then crosslinked, and thus stabilized, in a hydrophilic phase as they crossed through the hydrophobic-hydrophilic interface. The system can be adapted to different applications by replacing the bioactive material and the hydrophobic and/or the hydrophilic phases. The size of the microbeads was dependent on the system parameters, such as needle size and solution flow rate. The size and morphology of the microbeads produced by the proposed system were uniform, when parameters were kept constant. This system was successfully used for generating polymeric microbeads with encapsulated fluorescent beads, cell suspensions and cell aggregates proving its ability for generating bioactive carriers that can potentially be used for drug delivery and cell therapy.

Designer hydrophilic regions regulate droplet shape for controlled surface patterning and 3D microgel synthesis.

A simple technique is presented for controlling the shapes of micro- and nanodrops by patterning surfaces with special hydrophilic regions surrounded by hydrophobic boundaries. Finite element method simulations link the shape of the hydrophilic regions to that of the droplets. Shaped droplets are used to controllably pattern planar surfaces and microwell arrays with microparticles and cells at the micro- and macroscales. Droplets containing suspended sedimenting particles, initially at uniform concentration, deposit more particles under deeper regions than under shallow regions. The resulting surface concentration is thus proportional to the local fluid depth and agrees well with the measured and simulated droplet profiles. A second application is also highlighted in which shaped droplets of prepolymer solution are crosslinked to synthesize microgels with tailored 3D geometry.

Drug-eluting microarrays for cell-based screening of chemical-induced apoptosis.

Traditional high-throughput screening (HTS) is carried out in centralized facilities that require extensive robotic liquid and plate handling equipment. This model of HTS is restrictive as such facilities are not accessible to many researchers. We have designed a simple microarray platform for cell-based screening that can be carried out at the benchtop. The device creates a microarray of 2100 individual cell-based assays in a standard microscope slide format. A microarray of chemical-laden hydrogels addresses a matching array of cell-laden microwells thus creating a microarray of sealed microscale cell cultures each with unique conditions. We demonstrate the utility of the device by screening the extent of apoptosis and necrosis in MCF-7 breast cancer cells in response to exposure to a small library of chemical compounds. From a set of screens we produced a rank order of chemicals that preferentially induce apoptosis over necrosis in MCF-7 cells. Treatment with doxorubicin induced high levels of apoptosis in comparison with staurosporine, ethanol, and hydrogen peroxide, whereas treatment with 100 µM ethanol induced minimal apoptosis with high levels of necrosis. We anticipate broad application of the device for various research and discovery applications as it is easy to use, scalable, and can be fabricated and operated with minimal peripheral equipment.

A probabilistic cell model in background corrected image sequences for single cell analysis.

BACKGROUND: Methods of manual cell localization and outlining are so onerous that automated tracking methods would seem mandatory for handling huge image sequences, nevertheless manual tracking is, astonishingly, still widely practiced in areas such as cell biology which are outside the influence of most image processing research. The goal of our research is to address this gap by developing automated methods of cell tracking, localization, and segmentation. Since even an optimal frame-to-frame association method cannot compensate and recover from poor detection, it is clear that the quality of cell tracking depends on the quality of cell detection within each frame. METHODS: Cell detection performs poorly where the background is not uniform and includes temporal illumination variations, spatial non-uniformities, and stationary objects such as well boundaries (which confine the cells under study). To improve cell detection, the signal to noise ratio of the input image can be increased via accurate background estimation. In this paper we investigate background estimation, for the purpose of cell detection. We propose a cell model and a method for background estimation, driven by the proposed cell model, such that well structure can be identified, and explicitly rejected, when estimating the background. RESULTS: The resulting background-removed images have fewer artifacts and allow cells to be localized and detected more reliably. The experimental results generated by applying the proposed method to different Hematopoietic Stem Cell (HSC) image sequences are quite promising. CONCLUSION: The understanding of cell behavior relies on precise information about the temporal dynamics and spatial distribution of cells. Such information may play a key role in disease research and regenerative medicine, so automated methods for observation and measurement of cells from microscopic images are in high demand. The proposed method in this paper is capable of localizing single cells in microwells and can be adapted for the other cell types that may not have circular shape. This method can be potentially used for single cell analysis to study the temporal dynamics of cells.

Fabrication of three-dimensional porous cell-laden hydrogel for tissue engineering.

For tissue engineering applications, scaffolds should be porous to enable rapid nutrient and oxygen transfer while providing a three-dimensional (3D) microenvironment for the encapsulated cells. This dual characteristic can be achieved by fabrication of porous hydrogels that contain encapsulated cells. In this work, we developed a simple method that allows cell encapsulation and pore generation inside alginate hydrogels simultaneously. Gelatin beads of 150-300 microm diameter were used as a sacrificial porogen for generating pores within cell-laden hydrogels. Gelation of gelatin at low temperature (4 degrees C) was used to form beads without chemical crosslinking and their subsequent dissolution after cell encapsulation led to generation of pores within cell-laden hydrogels. The pore size and porosity of the scaffolds were controlled by the gelatin bead size and their volume ratio, respectively. Fabricated hydrogels were characterized for their internal microarchitecture, mechanical properties and permeability. Hydrogels exhibited a high degree of porosity with increasing gelatin bead content in contrast to nonporous alginate hydrogel. Furthermore, permeability increased by two to three orders while compressive modulus decreased with increasing porosity of the scaffolds. Application of these scaffolds for tissue engineering was tested by encapsulation of hepatocarcinoma cell line (HepG2). All the scaffolds showed similar cell viability; however, cell proliferation was enhanced under porous conditions. Furthermore, porous alginate hydrogels resulted in formation of larger spheroids and higher albumin secretion compared to nonporous conditions. These data suggest that porous alginate hydrogels may have provided a better environment for cell proliferation and albumin production. This may be due to the enhanced mass transfer of nutrients, oxygen and waste removal, which is potentially beneficial for tissue engineering and regenerative medicine applications.

Constrained watershed method to infer morphology of mammalian cells in microscopic images.

Precise information about the size, shape, temporal dynamics, and spatial distribution of cells is beneficial for the understanding of cell behavior and may play a key role in drug development, regenerative medicine, and disease research. The traditional method of manual observation and measurement of cells from microscopic images is tedious, expensive, and time consuming. Thus, automated methods are in high demand, especially given the increasing quantity of cell data being collected. In this article, an automated method to measure cell morphology from microscopic images is proposed to outline the boundaries of individual hematopoietic stem cells (HSCs). The proposed method outlines the cell regions using a constrained watershed method which is derived as an inverse problem. The experimental results generated by applying the proposed method to different HSC image sequences showed robust performance to detect and segment individual and dividing cells. The performance of the proposed method for individual cell segmentation for single frame high-resolution images was more than 97%, and decreased slightly to 90% for low-resolution multiframe stitched images.

Directed assembly of cell-laden hydrogels for engineering functional tissues.

Tissue engineering aims to develop functionalized tissues for organ replacement or restoration. Biodegradable scaffolds have been used in tissue engineering to support cell growth and maintain mechanical and biological properties of tissue constructs. Ideally cells on these scaffolds adhere, proliferate, and deposit matrix at a rate that is consistent with scaffold degradation. However, the cellular rearrangement within these scaffolds often does not recapitulate the architecture of the native tissues. Directed assembly of tissue-like structures is an attractive alternative to scaffold-based approach for tissue engineering which potentially can build tissue constructs with biomimetic architecture and function. In directed assembly, shape-controlled microstructures are fabricated in which organized structures of different cell types can be used as tissue building blocks. To fabricate tissue building blocks, hydrogels are commonly used as biomaterials for cell encapsulation to mimic the matrix in vivo. The hydrogel-based tissue building blocks can be arranged in pre-defined architectures by various directed tissue assembly techniques. In this paper, recent advances in directed assembly-based tissue engineering are summarized as an emerging alternative to meet challenges associated with scaffold-based tissue engineering and future directions are addressed.

Future approaches to organ regeneration: microscale environments, stem cell engineering, and self-assembly of living tissues.

A promising means to address the limited supply of donor tissue is through the generation of artificial organs consisting of cells and materials. Progress towards this goal is limited by three main obstacles namely the generation of a sufficient number of cells specific to the organ, the arrangement of these cells in a functional tissue architecture and the delivery of nutrients and removal of waste from the tissue mass. This chapter describes the emerging approaches that may be achieved by the control of stem cell differentiation, control of the local tissue environment on the microscale, and the generation of complex structures containing multiple cell types.

Watershed deconvolution for cell segmentation.

Cell segmentation and/or localization is the first stage of a (semi)automatic tracking system. We addressed the cell localization problem in our previous work where we characterized a typical blood stem cell in a microscopic image as an approximately circular object with dark interior and bright boundary. We also addressed the modelling of adjacent and dividing cells in our previous work as a deconvolution method to model individual blood stem cell as well as adjacent and dividing blood stem cells where an optimization algorithm was combined with a template matching method to segment cell regions and locate the cell centers. Our previous cell deconvolution method is capable of modelling different cell types with changes in the model parameters. However in cases where either a complex parameterized shape is needed to model a specific cell type, or in place of cell center localization, an exact cell segmentation is needed, this method will not be effective. In this paper we propose a method to achieve cell boundary segmentation. Considering cell segmentation as an inverse problem, we assume that cell centers are located in advance. Then, the cell segmentation will be solved by finding cell regions for optimal representation of cell centers while a template matching method is effectively employed to localize cell centres.