High-throughput Screening Technologies Research Articles

Droplet microfluidic technologies for next-generation high-throughput screening

Droplet microfluidics has evolved into a promising platform for high-throughput screening (HTS), allowing for rapid and precise analysis of thousands of samples encapsulated within droplets. Droplet microfluidic platform offers versatility, high-throughput, and the ability to compartmentalize reactions for a wide range of applications including pharmaceutics, cell analysis, and combinatorial chemical analysis. While droplet-based microfluidics has made considerable advances in automating basic laboratory tasks, for instance, manipulation, storage, and analysis, there has been comparatively little advancement toward HTS applications. The complexity of the technology, the lack of standardization, and the challenges associated with screening large numbers of samples are all factors that have contributed to the limited adoption of droplet-based microfluidics in HTS. In this perspective, we provide a comprehensive overview of the progress of droplet microfluidics as a potential platform for next-generation HTS, specifically in the domain of droplet separation and library generation. We hope that this perspective will inspire further research in relevant academic fields and contribute to the development of innovative HTS strategies based on droplet microfluidic technologies.

Deubiquitinating enzyme MINDY1 is an independent risk factor for the maintenance of stemness and poor prognosis in liver cancer cells

Objective: To explore the key deubiquitinating enzymes that maintain the stemness of liver cancer stem cells and provide new ideas for targeted liver cancer therapy. Methods: The high-throughput CRISPR screening technology was used to screen the deubiquitinating enzymes that maintain the stemness of liver cancer stem cells. RT-qPCR and Western blot were used to analyze gene expression levels. Stemness of liver cancer cells was detected by spheroid-formation and soft agar colony formation assays. Tumor growth in nude mice was detected by subcutaneous tumor-bearing experiments. Bioinformatics and clinical samples were examined for the clinical significance of target genes. Results: MINDY1 was highly expressed in liver cancer stem cells. The expression of stem markers, the self-renewal ability of cells, and the growth of transplanted tumors were significantly reduced and inhibited after knocking out MINDY1, and its mechanism of action may be related to the regulation of the Wnt signaling pathway. The expression level of MINDY1 was higher in liver cancer tissues than that in adjacent tumors, which was closely related to tumor progression, and its high expression was an independent risk factor for a poor prognosis of liver cancer. Conclusion: The deubiquitinating enzyme MINDY1 promotes stemness in liver cancer cells and is one of the independent predictors of poor prognosis in liver cancer.

Reinventing Therapeutic Proteins: Mining a Treasure of New Therapies

Reinventing approved therapeutic proteins for a new dose, a new formulation, a new route of administration, an improved safety profile, a new indication, or a new conjugate with a drug or a radioactive source is a creative approach to benefit from the billions spent on developing new therapeutic proteins. These new opportunities were created only recently with the arrival of AI/ML tools and high throughput screening technologies. Furthermore, the complex nature of proteins offers mining opportunities that are not possible with chemical drugs; bringing in newer therapies without spending billions makes this path highly lucrative financially while serving the dire needs of humanity. This paper analyzes several practical reinventing approaches and suggests regulatory strategies to reduce development costs significantly. This should enable the entry of hundreds of new therapies at affordable costs.

Abstract 4275: Averon: informatics platform to discover Actionable Vulnerabilities Enabled by Rewired Oncogenic Networks

Abstract Missense mutations can change protein structure, cellular localization, and functions. Such changes lead to the rewiring of protein-protein interaction (PPI) networks, activation of oncogenic pathways, and acquisition of cancer hallmarks. However, the translation of the landscape of oncogenic mutations into clinically actionable biological models for cancer target discovery remains a major challenge. We address this challenge by leveraging the power of computational systems biology supported by high-throughput screening technologies. While some mutations can disrupt the PPIs, others may induce new PPIs that are not natural for the wild-type counterparts. Recently, we have established a quantitative High Throughput differential Screening (qHT-dS) platform [1] to discover such mutant-enabled or neomorph PPIs (neoPPIs). The screening of more than 13,000 mutant interactions revealed a landscape of gain-of-interactions encompassing both oncogenic and tumor suppressor mutations. This emerging neoPPI landscape may offer new mutant-directed therapeutic approaches for precision medicine. However, to infer clinically actionable mechanistic insights into how neoPPIs promote tumorigenesis special computational tools are needed. To inform the neoPPI-based target discovery, we develop a set of innovative informatics tools for discovering Actionable Vulnerabilities Enabled by Rewired Oncogenic Networks (Averon). Implemented in a widely-used Jupyter Notebook format, the Averon streamlines the identification of the oncogenic programs and clinically significant genes that are regulated by neoPPIs in cancer patients. The Averon can recapitulate well-established connectivity between known mutant-dependent PPIs and specific oncogenic pathways and reveal new, previously unknown mechanisms of neoPPI-mediated oncogenic signaling. To inform new therapeutic strategies in neoPPI-dependent cancers, Averon connects neoPPI-regulated genes with available approved drugs and clinical compounds. Together, the Averon provides a powerful informatics environment to determine therapeutically actionable vulnerabilities created by mutant-regulated protein-protein interactions to inform new personalized therapeutic strategies in cancer. Acknowledgments: This work was supported in part by NCI’s Informatics Technology for Cancer Research (ITCR) Program (R21CA274620, A.A.I.), Winship Cancer Institute #IRG-17-181-06 from the American Cancer Society (A.A.I.). Cancer Target Discovery and Development (CTD2) Network (U01CA217875, H.F.), NCI Emory Lung Cancer SPORE (P50CA217691, H.F.), Career Enhancement Program (A.A.I., P50CA217691), Winship Cancer Institute (NIH 5P30CA138292).

Abstract 441: Targeting MKK3-MYC complex to reduce racial survival disparity in breast cancer

Abstract Breast cancer is the most common cancer and the leading cause of cancer-related death in women. The triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer. African American women experience up to 2-fold higher incidence of TNBC as compared to White women. Moreover, African Americans die from breast cancer at up to 40% higher rate than White and Hispanic women. However, there is no therapeutic options are currently available to reduce survival disparity in breast cancer. To address this unmet medical need, we develop novel computational methods combined with experimental chemical biology and high-throughput screening (HTS) technologies. Using our specially designed bioinformatics workflow, we discovered that high expression of mitogen-activated protein kinase kinase 3 (MKK3) strongly correlates with worsened clinical outcomes in African American TNBC patients. The analysis of TNBC patient genomic profiles revealed that MKK3 promotes TNBC in part by activating the major tumor driver transcription factor MYC. MYC is a highly appealing therapeutic target in TNBC, however, no approved MYC inhibitors are available. We found that MKK3 binds and activates MYC through protein-protein interaction (PPI) in TNBC cells. Both genetic and pharmacological inhibition of the MKK3/MYC complex suppresses MYC activity and the viability of TNBC cells. Thus, MKK3/MYC interaction represents a novel vulnerability for therapeutic intervention in TNBC. By combining computational modeling with experimental HTS, we have discovered the first small molecule MKK3/MYC PPI inhibitors. For example, a quinoline derivative SGI-1027 disrupts the MKK3/MYC complex in cells and in vitro, inhibits MYC transcriptional activity, and suppresses the viability of TNBC cells. Together, our studies revealed a novel mechanism of TNBC tumorigenesis through MKK3-induced MYC activation, establish a new HTS to enable the discovery of potent and selective inhibitors to regulate MKK3/MYC complex, and may open new clinical strategies to reduce racial disparity in TNBC mortality. Acknowledgments: This work was supported in part by Mary Kay Ash Foundation Grant for Cancer Research (A.A.I.), Emory initiative on Biological Discovery through Chemical Innovation (A.A.I.), NCI’s Informatics Technology for Cancer Research (ITCR) Program (R21CA274620, A.A.I.), NCI Cancer Target Discovery and Development (CTD^2) Network (U01CA217875, H.F.), NCI Emory Lung Cancer SPORE (P50CA217691, H.F.), Career Enhancement Program (A.A.I., P50CA217691), Winship Cancer Institute (NIH 5P30CA138292).

Yupingfeng San exhibits anticancer effect in hepatocellular carcinoma cells via the MAPK pathway revealed by HTS2 technology.

Yupingfeng San (YPFS) is a classic rousing prescription in Chinese medicine, with widly clinical application and remarkably curative effect. It consists of three herbs named Astragalus mongholicus Bunge (Huangqi), Atractylodes rubra Dekker (Baizhu) and Saposhnikovia divaricata (Turcz.) Schischk. (Fangfeng), and has a variety of pharmacological activities including immune regulation, antioxidant, anti-tumor, regulation of cytokines, etc. AIM OF THE STUDY: It has been proved that YPFS exerts its anti-tumor effect through enhancing the systemic and local immune responses in tumor patients, moreover, it has the direct tumor-suppressing effect and can reduce the adverse reactions caused by radiotherapy and chemotherapy drugs. Therefore, in this study, we explored the potential anti-HCC mechanism of YPFS based on HTS2 technology and system pharmacology, aiming to provide a scientific basis for the clinical application of YPFS and a new strategy for Chinese medicine research. In this study, systems pharmacology plus high throughput sequencing-based high throughput screening(HTS2) technology, and experimental validation were used to investigate the therapeutic mechanisms and the chemical basis of YPFS in HCC treatment. Firstly, the potential therapeutic targets and signaling pathways of YPFS in the treatment of HCC were obtained through systems pharmacology. Subsequently, HTS2 technology combined with PPI network analysis were used to reveal potential therapeutic targets. Finally, the anti-HCC effects and underlying mechanisms of YPFS were further verified in vitro in human hepatocellular carcinoma cell lines. Moreover, the possible chemical basis was explored by drug target verification and molecular docking technology. In total, 183 active ingredients were predicted by YPFS screening and 49 anti-HCC targets were further identified. Most of these targets were enriched into the "MAPK pathway", and the expression of 37 genes was significantly changed after herb treatment. Among them, 5 key targets, including VEGFA, GRB2, JUN, PDGFRB and CDC42, were predicted by protein-protein interaction (PPI) network analysis. According to our results, YPFS inhibited the proliferation, induced the apoptosis and caused cell cycle arrest of HCC cells. In addition, YPFS significantly reduced P38 gene expression. Fangfeng, one of the three herbs in YPFS, significantly down-regulated the expression of more target genes than that of the other two herbs. Lastly, as revealed by molecular docking analysis, 4'-O-glucosyl-5-O-methylvisamminol, an active ingredient identified in Fangfeng, showed a high affinity for P38. Taken together, this study shows that YPFS possesses the activities of anti-proliferation and pro-apoptosis in treating HCC, which are achieved by inhibiting the MAPK signaling pathway. P38 is one of the critical targets of YPFS in treating HCC, which may be directly bound and inhibited by 4'-O-glucosyl-5-O-methylvisamminol, a compound derived from YPFS.

Targeted innovative design of Bt Cry toxin insecticidal mimics

Bt Cry toxin is the mostly studied and widely used biological insect resistance protein, which plays a leading role in the green control of agricultural pests worldwide. However, with the wide application of its preparations and transgenic insecticidal crops, the resistance to target pests and potential ecological risks induced by the drive are increasingly prominent and attracting much attention. The researchers seek to explore new insecticidal protein materials that can simulate the insecticidal function of Bt Cry toxin. This will help to escort the sustainable and healthy production of crops, and relieve the pressure of target pests' resistance to Bt Cry toxin to a certain extent. In recent years, the author's team has proposed that Ab2β anti-idiotype antibody has the property of mimicking antigen structure and function based on the "Immune network theory" of antibody. With the help of phage display antibody library and specific antibody high-throughput screening and identification technology, Bt Cry toxin antibody was designed as the coating target antigen, and a series of Ab2β anti-idiotype antibodies (namely Bt Cry toxin insecticidal mimics) were screened from the phage antibody library. Among them, the lethality of Bt Cry toxin insecticidal mimics with the strongest activity was close to 80% of the corresponding original Bt Cry toxin, showing great promise for the targeted design of Bt Cry toxin insecticidal mimics. This paper systematically summarized the theoretical basis, technical conditions, research status, and discussed the development trend of relevant technologies and how to promote the application of existing achievements, aiming to facilitate the research and development of green insect-resistant materials.

Recent Advances in Biologic Therapeutic N-Glycan Preparation Techniques and Analytical Methods for Facilitating Biomanufacturing Automation

N-glycosylation is a post-translational modification that occurs during the production of monoclonal antibody (mAb) therapeutics. During production of mAb based therapeutics the use of various hosts and cell culture additives attribute to glycan heterogeneity. The safety and efficacy of monoclonal antibodies with mechanism of actions that utilize Fc effector functions can be negatively impacted by glycan heterogeneity and thus is often considered a critical quality attribute (CQA). In this mini review, we discuss recent advances in mAb sample preparation specifically focused on denaturation, enzymatic processing, and released glycans derivatization methods. Additionally, we review the recent advances in characterization of released and intact N-glycans using chromatography, capillary electrophoresis, and mass spectrometry techniques with a focus on rapid, automated approaches that support analysis of glycosylation profiles of biopharmaceuticals. We delve into advances within sample preparation techniques that allow for rapid and robust sample preparation as well as how these techniques are being used for innovative at-line high-throughput screening and process analytical technology (PAT). The future of biomanufacturing is focused on decreasing process costs while increasing process understanding and quality for novel biologic candidates and biosimilars. Therefore, advances in PAT for biotherapeutics will positively influence current manufacturing practices and enable further bioprocess automation.

Yca1 metacaspase: diverse functions determine how yeast live and let die.

The Yca1 metacaspase was discovered due to its role in the regulation of apoptosis in Saccharomyces cerevisiae. However, the mechanisms that drive apoptosis in yeast remain poorly understood. Additionally, Yca1 and other metacaspase proteins have recently been recognized for their involvement in other cellular processes, including cellular proteostasis and cell cycle regulation. In this minireview, we outline recent findings on Yca1 that will enable the further study of metacaspase multifunctionality and novel apoptosis pathways in yeast and other nonmetazoans. In addition, we discuss advancements in high-throughput screening technologies that can be applied to answer complex questions surrounding the apoptotic and nonapoptotic functions of metacaspase proteins across a diverse range of species.

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. 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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.