High-throughput Screening Technologies Research Articles

Insights into the drug screening approaches in leishmaniasis.

Leishmaniasis, a tropically neglected disease, is responsible for the high mortality and morbidity ratio in poverty-stricken areas. Currently, no vaccine is available for the complete cure of the disease. Current chemotherapeutic regimens face the limitations of drug resistance and toxicity concerns indicating a great need to develop better chemotherapeutic leads that are orally administrable, potent, non-toxic, and cost-effective. The anti-leishmanial drug discovery process accelerated the desire for large-scale drug screening assays and high-throughput screening (HTS) technology to identify new chemo-types that can be used as potential drug molecules to control infection. Using the HTS approach, about one million compounds can be screened daily within the shortest possible time for biological activity using automation tools, miniaturized assay formats, and large-scale data analysis. Classical and modern in vitro screening assays have led to the progression of active compounds further to ex vivo and in vivo studies. In the present review, we emphasized on the HTS approaches employed in the leishmanial drug discovery program. Recent in vitro screening assays are widely explored to discover new chemical scaffolds. Developing appropriate experimental animal models and their related techniques is necessary to understand the pathophysiological processes and disease host responses, paving the way for unraveling novel therapies against leishmaniasis.

High-Throughput Screening of Stapled Helical Peptides in Drug Discovery

Therapeutic peptides have revolutionized treatment for a number of human diseases. In particular, the past two decades have witnessed rapid progress of stapled helical peptides in drug discovery. Stapled helical peptides are chemically modified and constrained in their bioactive α-helical conformation. Compared to unstabilized linear peptides, stapled helical peptides exhibit superior binding affinity and selectivity, enhanced membrane permeability, and improved metabolic stability, presenting exciting promise for targeting otherwise challenging protein-protein interfaces. In this Perspective, we summarize recent applications of high-throughput screening technologies for identification of potent stapled helical peptides with optimized binding properties. We expect to provide a broad reference to accelerate the development of stapled helical peptides as the next generation of therapeutic peptides for various human diseases.

Autonomous high-throughput screening technology for accelerating drug molecule discovery and synthesis

<p indent="0mm">The discovery and synthesis of pharmaceutical small molecules is one of the important decisive steps in the process of new drug research and development. The traditional small molecule design and synthesis methods, however, is becoming increasingly difficult to meet the iteration and development speed of new drug molecules in modern human society. The recent emergence of chemical artificial intelligence and high-throughput screening (HTS) technology is a new direction to break through the bottleneck of traditional research and development efficiency. This review will focus on the development status of automatic HTS device driven by artificial intelligence decision algorithm, mainly including: (1) High-throughput screening device technology. Batch and flow HTS modes, reaction process intensification for HTS screening, and the analytical characterization technology for HTS are discussed. (2) Intelligent decision-making algorithm for driving HTS, discussing the autonomous decision algorithms for driving the automation device to realize intelligent autonomous exploration, target property optimization and high-quality experimental data generation. Finally, this review will discuss how to further develop intelligent HTS technology to accelerate the synthesis of pharmaceutical molecules in the future.

Drug synergy discovery of tavaborole and aminoglycosides against Escherichia coli using high throughput screening

High incidences of urinary tract infection (UTI) of aminoglycosides-resistant E.coli causes a severe burden for public health. A new therapeutic strategy to ease this crisis is to repurpose non-antibacterial compounds to increase aminoglycosides sensibility against multidrug resistant E.coli pathogens. Based on high throughput screening technology, we profile the antimicrobial activity of tavaborole, a first antifungal benzoxaborole drug for onychomycosis treatment, and investigate the synergistic interaction between tavaborole and aminoglycosides, especially tobramycin and amikacin. Most importantly, by resistance accumulation assay, we found that, tavaborole not only slowed resistance occurrence of aminoglycosides, but also reduced invasiveness of E.coli in combination with tobramycin. Mechanistic studies preliminary explored that tavaborole and aminoglycosides lead to mistranslation, but would be still necessary to investigate more details for further research. In addition, tavaborole exhibited low systematic toxicity in vitro and in vivo, and enhanced aminoglycoside bactericidal activity in mice peritonitis model. Collectively, these results suggest the potential of tavaborole as a novel aminoglycosides adjuvant to tackle the clinically relevant drug resistant E. coli and encourages us to discover more benzoxaborole analogues for circumvention of recalcitrant infections.

High-Throughput Preparation and Characterization of ZrMoTaW Refractory Multi-Principal Element Alloy Film

In this work, high-throughput screening technology is applied to four-member refractory multi-principal element alloys (RMPEAs) films with high W content. The exploration of refractory metals such as W is strictly limited by the high melting temperature in this work; a multi-gradient deposition method was introduced to overcome this obstacle. By adjusting the power and distance from the target to the sample, component Zr11Mo11Ta25W53 with the best hardening performance was successfully obtained. The uniformity of the material library was analyzed from the perspectives of phase structure and micromorphology. With the help of Hume-Rothery theory and XRD analysis, it is shown that the film has a stable bcc structure. It is believed that film uniformity, nanoscale size, preferential orientation, surface roughness, and solution mechanism are the pivotal factors to improve hardness performance, especially for high W components. The hardness and modulus of elasticity can reach 20 GPa and 300 GPa, respectively, and the H/Er and H3/Er2 values are 0.067 and 0.065, showing the best wear resistance in many samples.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment.

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

Overview of research and development of polypeptide drugs and traditional Chinese medicine-peptides

Peptide is a compound consisting of 2-50 amino acids, which is intermediate between small molecule and protein. It is characterized by a variety of biological activities, easy absorption, strong specific targeting, and few side effects and has become one of the hotspots in biomedical research in recent years. Chinese medicine contains a large number of peptides. The traditional processing methods such as decocting and boiling can effectively boost peptides to exert their due biological activities. At present, however, the research on Chinese medicinal components in laboratory generally employs high-concentration alcohol extraction method, which may cause the peptides to be ignored in many natural Chinese medicines. Substantial studies have revealed that the peptides in Chinese medicine are important material basis responsible for the traditional efficacy. Based on years of research and literature retrieval, this study put forward the concept of &quot;traditional Chinese medicine(TCM)-peptides&quot;, referring to the components consisting of two or more amino acids with molecular weight between small molecules and proteins that can express the efficacy of Chinese medicine. Furthermore, this study also summarized the extraction and separation of TCM-peptides, and structure determination methods and routes, predicted the research prospect of modern research methods of TCM-peptides based on &quot;holistic view&quot; and big data. The artificial intelligence prediction was combined with high-throughput screening technology to improve the discovery efficiency and accuracy of TCM-peptides, and holographic images between TCM-peptides and biological targets were established to provide references for the innovative drug design and related health product development of TCM-peptides based on TCM theories.

Omics-based high throughput toxicity screening and risk assessment of pollutants

<p indent="0mm">Screening priority pollutants with high toxicity and risk potential from a large number of environmental chemical pollutants is the prerequisite for prevention and control of environmental risks. After entering the living system, toxic pollutants can induce adverse outcomes through a series of disturbed molecular targets and biological pathways. Existing high-throughput toxicity screening methods rely on few and isolated cellular or molecular endpoints, making it difficult to capture the key biological processes that cause toxicity and to determine the direct relationship between chemical pollutants and disease. Meanwhile, the awareness is increasing that we are exposed to a true cocktail of chemicals, among which only a fraction has been identified through analytical methods. Integrated bioanalytical approaches, such as effect-directed analysis and toxicity identification evaluation (TIE), have been used to identify risk drivers in complex mixture pollutants. However, it is still difficult to elucidate the molecular toxicological drivers of chemical stress in the environment. Toxicogenomics take advantage of genome-wide coverage, high throughput, and big data capacity that are associated with omics technologies to achieve technology innovation in screening and risk assessment of chemical pollutants. We reviewed the technical advantages and the recent progress of toxicogenomic methodology in high-throughput toxicological screening and risk assessment of chemical pollutants from four aspects with specific research cases. To identify the molecular events initiated by toxic pollutants, toxicogenomics could uncover a large number of molecular events and pathways potentially disturbed at the early development stage of toxicity. Dose-dependent transcriptomes for high-throughput chemical testing have been developed to quantitatively assess responses induced by chemical pollutants at the molecular level, which improve the sensitivity of identifying bioactivity by 1–3 orders of magnitude. To uncover the biological pathways of adverse health outcome by chemical pollutants, systemic toxicological assessment by omics strategies provide mechanistic information for chemical pollution screening and health risk assessment at multiple biological levels. Through biological pathway enrichment analysis, such as the gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway, the molecular mechanistic information of toxicity could be captured. Moreover, multi-omics integration allows for multi-scale, comprehensive biological network analysis, which provide a systematic understanding of toxicity pathways or adverse outcome pathways (AOPs) to guide ecological and human health risk assessments. For human diseases diagnostics, functional genomics provide a new tool to explore mechanisms of individual disease risk and susceptibility under toxic pollutants exposure. Variations of individual susceptibility to toxicant-induced disease are largely due to genetic susceptibility across different populations. The direct relationship among pollutants, genes and adverse outcomes could be established by combining functional genomics and molecular epidemiological approaches. Finally, for risk management of complex mixture pollutants, it is important to assess the overall toxicity effects of the pool of pollutants and identify the key toxic pollutant. The combination of high-throughput bioassays and chemical analysis can be used to identify the highly bioactive fraction and to screen the key chemical pollutant that pose ecological and health risks. Recently, progresses have been made in integrating transcriptomic techniques with molecular toxicity identification and evaluation (mTIE) to assess the overall effects of mixtures and distinguish the key pollutants in inducing the corresponding biological responses. Particularly, dose-response transcriptome could be used to quantitatively evaluate the overall toxic effects and to guide the key toxicant identification by high mechanistic resolution. In summary, the development of omics-based high-throughput toxicity screening technologies will strongly support accurate environmental risk assessment of conventional and new pollutants in the future.

Machine Learning and High Throughput Synthesis Acceleration of the Discovery of Alkaline Electrolyte Oxygen Evolution Reaction Electrocatalysts

Accelerating the development and discovery of new catalysts is vital for advancing many electrochemical energy conversion technologies (EECT) required to achieve a sustainable future utilizing carbon-free fuel, a circular economy, and to meet the grand energy challenges of the 21st century. The oxygen evolution reaction (OER) is at the heart of many EECT such as water and carbon dioxide electrolyzers, fuel cells, and metal-oxygen batteries. The sluggish kinetics of oxygen electrocatalysis, resulting high overpotential necessary to attain practical current densities, and the high cost of the state-of-the-art OER platinum group metal (PGM) and precious metals catalysts (i.e., IrO2 and RuO2) limit the cost-effective implementation and development of several promising electrolysis technologies.1-3 The development of alternative PGM-free OER catalysts, with comparable or superior activity and durability to the PGM catalysts and derived from earth-abundant materials has thus been an active research area for decades.The application of perovskite oxides as PGM-free electrocatalysts for the OER in alkaline environments has seen significant research interest in the last decade, with tri-metallic and tetra-metallic compounds showing activities comparable to PGM-based catalysts.4,5 The chemical space of these compounds is exceptionally large, yet the development of new perovskite oxides with high OER performance (activity and durability) has been limited and often discovered through trial and error, a time and cost inefficient route that restricted the discovery of more advanced materials. Recent advances in high-performance computing, machine learning (ML), and high throughput material synthesis and screening technologies have enabled high-throughput catalyst design and discovery.4-10 This presentation will describe how the machine learning and high throughput synthesis technologies worked synergistically to accelerate the discovery of alkaline oxygen evolution reaction electrocatalysts. The role of ML in accelerating the materials synthesis and the role of high throughput synthesis in optimizing the ML model predictions will be discussed.AcknowledgmentsThis work was supported by the U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E) under the DIFFERENTIATE program. This work was authored in part by Argonne National Laboratory, a U.S. Department of Energy (DOE) Office of Science laboratory operated for DOE by UChicago Argonne, LLC under contract no. DE-AC02-06CH11357.References Katsounaros, Ioannis, Serhiy Cherevko, Aleksandar R. Zeradjanin, and Karl JJ Mayrhofer. "Oxygen electrochemistry as a cornerstone for sustainable energy conversion." Angewandte Chemie International Edition53, no. 1 (2014): 102-121.Lee, Youngmin, Jin Suntivich, Kevin J. May, Erin E. Perry, and Yang Shao-Horn. "Synthesis and activities of rutile IrO2 and RuO2 nanoparticles for oxygen evolution in acid and alkaline solutions." The journal of physical chemistry letters3, no. 3 (2012): 399-404.Cherevko, S. et al. Oxygen and hydrogen evolution reactions on Ru, RuO2, Ir, and IrO2 thin film electrodes in acidic and alkaline electrolytes: A comparative study on activity and stability. Today 262, 170–180 (2016).Nahar, Lamia, Ahmed A. Farghaly, Richard J. Alan Esteves, and Indika U. Arachchige. "Shape controlled synthesis of Au/Ag/Pd nanoalloys and their oxidation-induced self-assembly into electrocatalytically active aerogel monoliths." Chemistry of Materials29, no. 18 (2017): 7704-7715.Farghaly, Ahmed A., Rezaul K. Khan, and Maryanne M. Collinson. "Biofouling-resistant platinum bimetallic alloys." ACS applied materials & interfaces10, no. 25 (2018): 21103-21112.Khan, Rezaul K., Ahmed A. Farghaly, Tiago A. Silva, Dexian Ye, and Maryanne M. Collinson. "Gold-Nanoparticle-Decorated Titanium Nitride Electrodes Prepared by Glancing-Angle Deposition for Sensing Applications." ACS Applied Nano Materials2, no. 3 (2019): 1562-1569.Farghaly, Ahmed A., Mai Lam, Christopher J. Freeman, Badharinadh Uppalapati, and Maryanne M. Collinson. "Potentiometric measurements in biofouling solutions: comparison of nanoporous gold to planar gold." Journal of The Electrochemical Society163, no. 4 (2015): H3083.Suntivich, Jin, Kevin J. May, Hubert A. Gasteiger, John B. Goodenough, and Yang Shao-Horn. "A perovskite oxide optimized for oxygen evolution catalysis from molecular orbital principles." Science334, no. 6061 (2011): 1383-1385.Hwang, Jonathan, Zhenxing Feng, Nenian Charles, Xiao Renshaw Wang, Dongkyu Lee, Kelsey A. Stoerzinger, Sokseiha Muy et al. "Tuning perovskite oxides by strain: electronic structure, properties, and functions in (electro) catalysis and ferroelectricity." Materials Today31 (2019): 100-118.Gómez-Bombarelli, Rafael, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik. "Automatic chemical design using a data-driven continuous representation of molecules." ACS central science4, no. 2 (2018): 268-276.

SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning.

BackgroundIn cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)–powered informational view that integrates the relevant scientific fields and explores new territories.ResultsHere, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers.ConclusionsThe performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques.

Discovery of novel compounds as potent activators of Sirt3

Among the sirtuin enzymes, Sirt3 is one of the most important deacetylases as it regulates acetylation levels in mitochondria, which are linked to the metabolism of multiple organs and therefore involved in many types of age-related human diseases such as cancer, heart diseases and metabolic diseases. Given the dearth of direct activators of Sirt3, the identification of new modulators could be a key step in the development of new therapeutics. Here we report the discovery of Sirt3 modulators, including activators, through the use of DNA encoded library technology (DEL) and computational high-throughput screening methodologies. Top hits from both screenings against Sirt3 were evaluated according to their activity and affinity. Our best activator is more potent than the previously reported activator Honokiol. Docking studies suggest that our activators identified from virtual screening interact with Sirt3 at a site similar to Honokiol, whereas the activators identified from DEL selection interact with Sirt3 at an atypical site. Our results establish the attractiveness of these high-throughput screening technologies in identifying novel and potent Sirt3 activators and, therefore, in associated therapeutic applications.

Accelerating PROTAC drug discovery: Establishing a relationship between ubiquitination and target protein degradation

PROTACs have emerged as a new class of drugs that can target the “undruggable” proteome by hijacking the ubiquitin proteasome system. Despite PROTACs’ success, most current PROTACs interface with a limited number of E3 ligases, hindering their expansion to many challenging therapeutic uses. Currently, PROTAC drug discovery relies heavily on traditional Western blotting and reporter gene assays which are insensitive and prone to artifacts, respectively. New reliable methods to monitor true PROTAC function (i.e., ubiquitination and subsequent degradation of targets at physiological expression levels) without external tags are essential to accelerate the PROTAC discovery process and to address many unmet therapeutic areas. In this study, we developed a new high-throughput screening technology using “TUBEs” as ubiquitin-binding entities to monitor PROTAC-mediated poly-ubiquitination of native target proteins with exceptional sensitivity. As a proof of concept, targets including BRD3, Aurora A Kinase, and KRAS were used to demonstrate that ubiquitination kinetics can reliably establish the rank order potencies of PROTAC with variable ligands and linkers. PROTAC-treated cell lysates with the highest levels of endogenous target protein ubiquitination - termed “UbMax” - display excellent correlations with DC50 values obtained from traditional Western blots with the added benefits of being high throughput, providing improved sensitivity, and reducing technical errors.

Boosters for adeno-associated virus (AAV) vector (r)evolution

Adeno-associated virus (AAV) is one of the most exciting and most versatile templates for engineering of gene-delivery vectors for use in human gene therapy, owing to the existence of numerous naturally occurring capsid variants and their amenability to directed molecular evolution. As a result, the field has witnessed an explosion of novel “designer” AAV capsids and ensuing vectors over the last two decades, which have been isolated from comprehensive capsid libraries generated through technologies such as DNA shuffling or peptide display, and stratified under stringent positive and/or negative selection pressures. Here, we briefly highlight a panel of recent, innovative and transformative methodologies that we consider to have exceptional potential to advance directed AAV capsid evolution and to thereby accelerate AAV vector revolution. These avenues comprise original technologies for (i) barcoding and high-throughput screening of individual AAV variants or entire capsid libraries, (ii) selection of transduction-competent AAV vectors on the DNA level, (iii) enrichment of expression-competent AAV variants on the RNA level, as well as (iv) high-resolution stratification of focused AAV capsid libraries on the single-cell level. Together with other emerging AAV engineering stratagems, such as rational design or machine learning, these pioneering techniques promise to provide an urgently needed booster for AAV (r)evolution.

Combining genetically encoded biosensors with droplet microfluidic system for enhanced glutaminase production by Bacillus amyloliquefaciens

A high-throughput screening technology for single-cell by fluorescence-activated droplet sorting (FADS) has been applied to improve industrial enzymes and production strains, which usually require fluorophore-modified substrates to measure enzyme activity in microfluidic droplets. However, the difficulty of developing fluorophore-modified substrates limits their further application. Here, we employed the intensity-based glutamate-sensing fluorescent reporter (iGluSnFR) within microfluidic droplet sorting for screening Bacillus amyloliquefaciens variants with high glutaminase production. A rapid microplate assay based on the biosensor was developed to quantify glutaminase activity over 700-fold faster than high-performance liquid chromatography (HPLC). The novel FADS workflow was established subsequently for high-throughput screening of glutaminase-producing strains and achieved an enrichment ratio of 56 times. Furthermore, a 105 whole-genome mutant library was sorted, obtaining a clone that over 47 % increase in glutaminase production. Compared with the microtiter plate screening system, the biosensor-based microfluidic platform can give a 5 × 102 screening rate enhancement and decrease reagent consumption by 1.8 × 104 times. This strategy based on genetically encoded fluorescent biosensors will accelerate the application of FADS to the directed evolution of industrial enzymes and cell factories.

The Landscape of Virus-Host Protein-Protein Interaction Databases.

Knowledge of virus-host interactomes has advanced exponentially in the last decade by the use of high-throughput screening technologies to obtain a more comprehensive landscape of virus-host protein–protein interactions. In this article, we present a systematic review of the available virus-host protein–protein interaction database resources. The resources covered in this review are both generic virus-host protein–protein interaction databases and databases of protein–protein interactions for a specific virus or for those viruses that infect a particular host. The databases are reviewed on the basis of the specificity for a particular virus or host, the number of virus-host protein–protein interactions included, and the functionality in terms of browse, search, visualization, and download. Further, we also analyze the overlap of the databases, that is, the number of virus-host protein–protein interactions shared by the various databases, as well as the structure of the virus-host protein–protein interaction network, across viruses and hosts.

Emerging Bioanalytical Devices and Platforms for Rapid Detection of Pathogens in Environmental Samples.

The development of robust bioanalytical devices and biosensors for infectious pathogens is progressing well with the advent of new materials, concepts, and technology. The progress is also stepping towards developing high throughput screening technologies that can quickly identify, differentiate, and determine the concentration of harmful pathogens, facilitating the decision-making process for their elimination and therapeutic interventions in large-scale operations. Recently, much effort has been focused on upgrading these analytical devices to an intelligent technological platform by integrating them with modern communication systems, such as the internet of things (IoT) and machine learning (ML), to expand their application horizon. This review outlines the recent development and applications of bioanalytical devices and biosensors to detect pathogenic microbes in environmental samples. First, the nature of the recent outbreaks of pathogenic microbes such as foodborne, waterborne, and airborne pathogens and microbial toxins are discussed to understand the severity of the problems. Next, the discussion focuses on the detection systems chronologically, starting with the conventional methods, advanced techniques, and emerging technologies, such as biosensors and other portable devices and detection platforms for pathogens. Finally, the progress on multiplex assays, wearable devices, and integration of smartphone technologies to facilitate pathogen detection systems for wider applications are highlighted.

Plant Flavonoid Production in Bacteria and Yeasts

Flavonoids, a major group of secondary metabolites in plants, are promising for use as pharmaceuticals and food supplements due to their health-promoting biological activities. Industrial flavonoid production primarily depends on isolation from plants or organic synthesis, but neither is a cost-effective or sustainable process. In contrast, recombinant microorganisms have significant potential for the cost-effective, sustainable, environmentally friendly, and selective industrial production of flavonoids, making this an attractive alternative to plant-based production or chemical synthesis. Structurally and functionally diverse flavonoids are derived from flavanones such as naringenin, pinocembrin and eriodictyol, the major basic skeletons for flavonoids, by various modifications. The establishment of flavanone-producing microorganisms can therefore be used as a platform for producing various flavonoids. This review summarizes metabolic engineering and synthetic biology strategies for the microbial production of flavanones. In addition, we describe directed evolution strategies based on recently-developed high-throughput screening technologies for the further improvement of flavanone production. We also describe recent progress in the microbial production of structurally and functionally complicated flavonoids via the flavanone modifications. Strategies based on synthetic biology will aid more sophisticated and controlled microbial production of various flavonoids.

Application and prospects of high-throughput screening for in vitro neurogenesis.

Over the past few decades, high-throughput screening (HTS) has made great contributions to new drug discovery. HTS technology is equipped with higher throughput, minimized platforms, more automated and computerized operating systems, more efficient and sensitive detection devices, and rapid data processing systems. At the same time, in vitro neurogenesis is gradually becoming important in establishing models to investigate the mechanisms of neural disease or deve lopmental processes. However, challenges remain in generating more mature and functional neurons with specific subtypes and in establishing robust and standardized three-dimensional (3D) in vitro models with neural cells cultured in 3D matrices or organoids representing specific brain regions. Here, we review the applications of HTS technologies on in vitro neurogenesis, especially aiming at identifying the essential genes, chemical small molecules and adaptive microenvironments that hold great prospects for generating functional neurons or more reproductive and homogeneous 3D organoids. We also discuss the developmental tendency of HTS technology, e.g., so-called next-generation screening, which utilizes 3D organoid-based screening combined with microfluidic devices to narrow the gap between in vitro models and in vivo situations both physiologically and pathologically.

Abstract 3898: Discovering novel therapies in the treatment of osteosarcoma

Abstract Introduction: This research aims to discover novel therapies in the treatment of osteosarcoma (OS). OS is the most common primary bone cancer in pediatrics. 5-year overall survival is around 70% for patients presenting with localized OS, and less than 30% for patients with relapsed or metastatic disease. OS remains difficult to cure in large part due to the heterogenous property of the tumor and high rate of somatic mutations. Methods: Using high throughput screening (HTS), 320 drugs purchased from Selleck have been tested against 3 OS cell lines - HOS, LM7, and 143B - each of which harbors different mutations seen in OS. The Echo® 650 Series Next Generation Acoustic Liquid Handler is a robotic machine used for HTS. On day -1, cells from each cell line were added to 384 well plates based on calculations from cell line density testing. On day 0, 3 drug concentrations (10uM, 1uM and 0.1uM) for 320 drugs were added to each cell line using the Echo® machine and HTS technology. On day 8, the inhibitory concentration was tested among all cell lines. Our standard for successful cell inhibition included drugs with an inhibitory concentration of ≥98% (IC98) in ≥2 drug concentrations on all 3 cell lines. Additionally, orthotopic xenograft models of immunocompromised mice have been established. 10 OS models have been implanted intratibially in mice from 5 patient samples (3 samples received from consented patients treated at Lurie Children’s Hospital, 2 samples gifted from St. Jude Children’s Research Hospital) and 5 cell lines (gifted from Texas Children’s Hospital). Results: 32 drugs exhibited an IC98 on at least 1 cell line. 14 of 320 drugs met the inclusion criteria of an IC98 on all 3 cell lines, 10 of the 14 drugs have never been reported as being effective in osteosarcoma cell inhibition. These drugs are: BRL-15572 (dihydrochloride), PH-797804, Apalutamide (ARN-509), AMG-458, Ipatasertib (GDC-0068), Mifepristone, Prucalopride, VU 0361737, Olopatadine HCl, and AZ 3146. Multiple of these agents have proven to be effective in inhibiting other cancer types/solid tumors and will be of interest for in vivo testing. 5 orthotopic xenograft murine models have successfully grown, including 1 patient tumor sample from Lurie, 1 sample from St. Jude, and 3 cell lines. All 5 of the established orthotopic xenograft models have demonstrated metastatic disease to different organs including to the lungs. Tumor growth has been confirmed through gross observation, magnetic resonance imaging, computed tomography and microscopic evaluation. Conclusion: The in vitro test results are promising and provide the groundwork to proceed with in vivo testing on orthotopic xenograft models. These models will be used for drug testing of the narrowed drug agents, with tumor growth inhibition and survival time of the drug treatment group monitored against a control group without drug exposure. Results from in vivo testing will be proposed for future clinical trials for treating pediatric osteosarcoma. Citation Format: Casey Mehrhoff, Yuchen Du, Sophie Xiao, Robert Byrd, Chris Tsz-Kwong Man, Daniele Procissi, David Walterhouse, Xiao Nan Li. Discovering novel therapies in the treatment of osteosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3898.

Aptamer-Based High-Throughput Screening Model for Efficient Selection and Evaluation of Natural Ingredients against SGIV Infection

Singapore grouper iridovirus (SGIV) causes high economic losses in mariculture. Effective drugs for managing SGIV infection are urgently required. Medicinal plant resources are rich in China. Medicinal plants have a long history and significant curative effects in the treatment of many diseases. Reverse-transcription quantitative real-time PCR is the most commonly used method for detecting virus infection and assessing antiviral efficacy with high accuracy. However, their applications are limited due to high reagent costs and complex time-consuming operations. Aptamers have been applied in some biosensors to achieve the accurate detection of pathogens or diseases through signal amplification. This study aimed to establish an aptamer-based high-throughput screening (AHTS) model for the efficient selection and evaluation of medicinal plants components against SGIV infection. Q2-AHTS is an expeditious, rapid method for selecting medicinal plant drugs against SGIV, which was characterized as being dram, high-speed, sensitive, and accurate. AHTS strategy reduced work intensity and experimental costs and shortened the whole screening cycle for effective ingredients. AHTS should be suitable for the rapid selection of effective components against other viruses, thus further promoting the development of high-throughput screening technology.