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AlphaScreen is one of the most widely used assay technologies in drug discovery due to its versatility, dynamic range and sensitivity. However, a presence of false positives and frequent hitters contributes to difficulties with an interpretation of measured HTS data. Although filters do exist to identify frequent hitters for AlphaScreen, they are frequently based on privileged scaffolds. The development of such filters is time consuming and requires deep domain knowledge. Recently, machine learning and artificial intelligence methods are emerging as important tools to advance drug discovery and chemoinformatics, including their application to identification of frequent hitters in screening assays. However, the relative performance and complementarity of the Machine Learning and scaffold‐based techniques has not yet been comprehensively compared. In this study, we analysed filters based on the privileged scaffolds with filters built using machine learning. Our results demonstrate that machine‐learning methods provide more accurate filters for identification of frequent hitters in AlphaScreen assays than scaffold‐based methods and can be easily redeveloped once new data are measured. We present highly accurate models to identify frequent hitters in AlphaScreen assays. [ABSTRACT FROM AUTHOR]

Blood analysis is crucial for early cancer screening and improving patient survival rates. However, developing an effective strategy for early cancer detection using high‐throughput blood analysis is still challenging. Herein, a novel automatic super‐hydrophobic platform is developed together with a deep learning (DL)‐based label‐free serum and surface‐enhanced Raman scattering (SERS), along with an automatic high‐throughput Raman spectrometer to build an effective point‐of‐care diagnosis system. A total of 695 high‐quality serum SERS spectra are obtained from 203 healthy volunteers, 77 leukemia M5, 94 hepatitis B virus, and 321 breast cancer patients. Serum SERS signals from the normal (n = 183) and patient (n = 443) groups are used to assess the DL model, which classify them with a maximum accuracy of 100%. Furthermore, when SERS is combined with DL, it exhibits excellent diagnostic accuracy (98.6%) for the external held‐out test set, indicating that this method can be used to develop a high throughput, rapid, and label‐free tool for screening diseases. [ABSTRACT FROM AUTHOR]

An imbalance of extracellular matrix (ECM) deposition and turnover is a hallmark of fibrotic pathologies as opposed to normal repair response to injury across several organs. Antifibrotic approaches to date have targeted multiple mechanisms and pathways involved in inflammation, angiogenesis, injury, wound repair, ECM biosynthesis, assembly, crosslinking and degradation. Many of these approaches have been unsuccessful which may in part be due to suboptimal models and the lack of validated functional ECM end points relevant to fibrosis. In addition, drug discovery and development for fibrotic diseases has been challenging due to the lack of translatability from in vivo models to the clinic. Targeting growth factor signaling pathways such as transforming growth factor beta (TGFβ), platelet-derived growth factor (PDGF), and fibroblast growth factor (FGF) are possible in simple recombinant cell models and the approval of the tyrosine kinase inhibitor, nintedanib (Ofev) is testament to the approach. However, drug targets directly impacting ECM synthesis, assembly or degradation have proven clinically intractable to date. The reasons for a lack of progress are many and include; non-traditional drug targets, lack of suitable high throughput screening assays and translational models, incomplete understanding of the role of the target. Here, we review the role of ECM in fibrosis, the challenges of ECM-targeted antifibrotic approaches, progress in the development of functional and biomarker-related ECM assays and where new translational models of fibrotic ECM remodeling could support drug discovery for fibrotic diseases. [ABSTRACT FROM AUTHOR]

Superhydrophobic surfaces are actively studied across a wide range of applications and industries, and are now finding increased use in the biomedical arena as substrates to control protein adsorption, cellular interaction, and bacterial growth, as well as platforms for drug delivery devices and for diagnostic tools. The commonality in the design of these materials is to create a stable or metastable air layer at the material surface, which lends itself to a number of unique properties. These activities are catalyzing the development of new materials, applications, and fabrication techniques, as well as collaborations across material science, chemistry, engineering, and medicine given the interdisciplinary nature of this work. The review begins with a discussion of superhydrophobicity, and then explores biomedical applications that are utilizing superhydrophobicity in depth including material selection characteristics, in vitro performance, and in vivo performance. General trends are offered for each application in addition to discussion of conflicting data in the literature, and the review concludes with the authors' future perspectives on the utility of superhydrophobic biomaterials for medical applications. [ABSTRACT FROM AUTHOR]

Polyethylene glycol (PEG) hydrogel is permeable to biomolecules, but its permeability depends on the molecular weight of monomers and the concentration of monomer solutions. In this study, we show that PEG hydrogel made from 20% to 30% of PEG700 monomer is permeable to amino acids yet impermeable to oligopeptides. Because of its unique permeability, the gel can be used to detect protease by separating amino acids from oligopeptides when proteases cleave the oligopeptides and release amino acids. Based on this principle, an UV crosslinked gel array is fabricated on a chip for simultaneous detection of protease in up to 40 samples with only 1 µl of volume required for each sample. As a proof of concept, the on-chip protease assays are used to detect trypsin in buffer and serum. The detection limits are 1.2 nM in buffer and 17.7 nM in serum, which are comparable to conventional protease assays. Moreover, because only 1 µl of liquid is required, as little as 1.2 fmol of trypsin can be detected by using the on-chip assay. The protease assay also shows good specificity for trypsin and chymotrypsin. The gel array chip could be a useful miniaturized platform for high-throughput detection of different proteases and screening of their inhibitors. [ABSTRACT FROM AUTHOR]

Abstract: Understanding neurotoxic mechanisms is a challenge of deciphering which genes and gene products in the developing or mature nervous system are targeted for disruption by chemicals we encounter in our environment. Our understanding of nervous system development and physiology is highly advanced due in large part to studies conducted in simple non-mammalian models. The paucity of toxicological data for the more than 80,000 chemicals in commercial use today, and the approximately 2000 new chemicals introduced each year, makes development of sensitive and rapid assays to screen for neurotoxicity paramount. In this article I advocate the use of Drosophila in the modern regimen of toxicological testing, emphasizing its unique attributes for assaying neurodevelopment and behavior. Features of the Drosophila model are reviewed and a generalized overall scheme for its use in toxicology is presented. Examples of where the fly has proven fruitful in evaluating common toxicants in our environment are also highlighted. Attention is drawn to three areas where development and application of the fly model might benefit toxicology the most: 1) optimizing sensitive endpoints for pathway-specific screening, 2) accommodating high throughput demands for analysis of chemical toxicant libraries, 3) optimizing genetic and molecular protocols for more rapid identification toxicant-by-gene interactions. While there are shortcomings in the Drosophila model, which exclude it from effective toxicological testing in certain arenas, conservation of fundamental cellular and developmental mechanisms between flies and man is extensive enough to warrant a central role for the Drosophila model in toxicological testing of today. [Copyright &y& Elsevier]

An unbiased, polygenic approach is needed to unravel the complex molecular bases of craniofacial development and disease. DNA microarrays, the current paradigm of genome-wide analysis, permit the simultaneous study of many thousands of genes, the ready identification of candidate molecules and pathways, and the compilation of gene expression profiles for whole systems – pathologic and embryonic alike. We survey the existing literature applying microarrays to craniofacial biology and highlight the value of animal models, particularly mice and chickens, to understanding molecular regulation in the craniofacial complex. We also emphasize the importance of functional studies and high-throughput assays to extracting useful data from microarray output. It is our goal to help put researchers and clinicians on the same page as microarray technology moves into the forefront of craniofacial biology. [ABSTRACT FROM AUTHOR]

A subset of cells within solid tumors become highly enlarged and enter a state of dormancy (sustained proliferation arrest) in response to anticancer treatment. Although dormant cancer cells might be scored as "dead" in conventional preclinical assays, they remain viable, secrete growth-promoting factors, and can give rise to progeny with stem cell-like properties. Furthermore, cancer cells exhibiting features of apoptosis (e.g., caspase-3 activation) following genotoxic stress can undergo a reversal process called anastasis and survive. Consistent with these observations, single-cell analysis of adherent cultures (solid tumor-derived cell lines with differing p53 status) has demonstrated that virtually all cells—irrespective of their size and morphology—that remain adherent to the culture dish for a long time (weeks) after treatment with anticancer agents exhibit the ability to metabolize 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl- tetrazolium bromide (MTT). The purpose of this commentary is to briefly review these findings and discuss the significance of single-cell (versus population averaged) observation methods for assessment of cancer cell viability and metabolic activity. [ABSTRACT FROM AUTHOR]