High-throughput Screening Research Articles

Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG).

While the terms "gene-by-gene interaction" (GxG) and "gene-by-environment interaction" (GxE) are widely recognized in the fields of quantitative and evolutionary genetics, "environment-byenvironment interaction" (ExE) is a term used less often. In this study, we find that environmentby-environment interactions are a meaningful driver of phenotypes, and moreover, that they differ across different genotypes (suggestive of ExExG). To support this conclusion, we analyzed a large dataset of roughly 1,000 mutant yeast strains with varying degrees of resistance to different antifungal drugs. Our findings reveal that the effectiveness of a drug combination, relative to single drugs, often differs across drug resistant mutants. Remarkably, even mutants that differ by only a single nucleotide change can have dramatically different drug x drug (ExE) interactions. We also introduce a new framework that more accurately predicts the direction and magnitude of ExE interactions for some mutants. Understanding how ExE interactions change across genotypes (ExExG) is crucial not only for modeling the evolution of pathogenic microbes, but also for enhancing our knowledge of the underlying cell biology and the sources of phenotypic variance within populations. While the significance of ExExG interactions has been overlooked in evolutionary and population genetics, these fields and others stand to benefit from understanding how these interactions shape the complex behavior of living systems.

Simultaneous Detection of Eight Dairy-Derived Components Using Double-Tube Multiplex qPCR Based TaqMan Probe.

The authentication of milk and dairy products has great significance for food fraud. The present investigation entailed the development of a novel method that amalgamates the double-tube approach with multiplex real-time polymerase chain reaction (PCR) technology, incorporating TaqMan probes, to facilitate the high-throughput screening and detection of animal-derived constituents within milk and dairy products. Eight dairy-derived animal-specific primers and probes were designed for the mitochondrial cytochrome b (Cytb) gene of eight dairy products, including cow, buffalo, yak, goat, sheep, horse, donkey, and camel. Through the developed double-tube detection assays, the above eight targets could be simultaneously identified with a detection limit of 0.00128-0.0064 ng/μL. The multiplex qPCR assay was effectively validated using simulated adulterated samples with different mixing ratios and demonstrated a detection limit of 0.1%. Upon analysis of 54 commercially available dairy products, a mislabeling rate of 33% was revealed. This method affords an efficacious means of detecting dairy product ingredients, thereby offering robust technical backing for market oversight and regulatory enforcement of milk and dairy products.

Engineered Age-Mimetic Breast Cancer Models Reveal Differential Drug Responses in Young and Aged Microenvironments.

Aging is one of the most significant risk factors for breast cancer. With the growing interests in the alterations of the aging breast tissue microenvironment, it has been identified that aging is related to tumorigenesis, invasion, and drug resistance. However, current pre-clinical disease models often neglect the impact of aging and sometimes result in worse clinical outcomes. In this study, we utilized aged animal-generated materials to create and validate a novel age-mimetic breast cancer model that generates an aging microenvironment for cells and alters cells towards a phenotype found in the aged environment. Furthermore, we utilized the age-mimetic models for 3D breast cancer invasion assessment and high-throughput screening of over 700 drugs in the FDA-approved drug library. We identified 36 potential effective drug targets and 34 potential drug targets with different drug responses in different age groups, demonstrating the potential of this age-mimetic breast cancer model for further in-depth breast cancer studies and drug development.

Cu−based bimetallic sites' p-d orbital hybridization promotes CO asymmetric coupling conversion to C2 products

Hampered by sluggish C−C coupling kinetics, the low selectivity and efficiency have limited industrial applications of CO2 reduction into valuable multi-carbon products. A direct coupling of CO molecules or their coupling after hydrogenation, followed by the final synthesis of C2 products, can help to overcome these limitations potentially. In this study, a detailed high-throughput screening of bimetallic site catalysts comprising copper (Cu) and 28 other metal (M) atoms was conducted. The metal atoms Cu and M were anchored on a carbon nanotube (CNT) with six nitrogen (N6) defects (CuMN6@CNT), which possesses effective dual active sites for C−C coupling. The calculated results demonstrate that the CuGaN6@CNT catalyst exhibited favorable selectivity, with low theoretical overpotentials of −0.23 and −0.34 eV for ethanol and ethylene, respectively, surpassing most reported catalysts. The synergistic effect of Ga and Cu sites, along with their p-d states hybridization, results in an enhancement of Cu's d state dispersion and energy barriers reduction for C−C coupling. Additionally, the strain effect of the substrate CNT exhibits a direct correlation with the catalytic performance of CuGaN6@CNT by adjusting the d-band center of the Cu site and p-band center of the Ga site. These findings provide a novel insights into the electrocatalytic reduction of CO into valuable C2 products using bimetallic single atom catalyst, offering significant guidance for future research endeavors in this field.

Exploration of phytoconstituents of Medhya Rasayana herbs to identify potential inhibitors for cerebroside sulfotransferase through high-throughput screening.

Cerebroside sulfotransferase (CST) is a key enzyme in sulfatide biosynthesis and regulation of the myelin sheath in the nervous system. To counter sulfatide accumulation with the deficiency of aryl sulfatase A, CST is considered a target protein in substrate reduction therapy in metachromatic leukodystrophy. In this study, 461 phytoconstituents from four herbs of Medhya Rasayana were screened using multi-pronged virtual screening methods including molecular docking, molecular dynamics (MD) simulation, and reverse pharmacophore analysis. The initial screening of the top 15 hits was based on the binding affinity of the compounds toward the CST substrate-binding site using the lowest free energy of a binding score cutoff of ≤ -7.5 kcal/mol, with the number of conformations in the largest cluster more than 75. The absorption, distribution, metabolism, and excretion (ADME) and toxicity-based pharmacokinetic analysis delivered the top four hits: 18alpha-glycyrrhetinic acid, lupeol, alpha carotene, and beta-carotene, with high blood-brain barrier permeability and negligible toxicity. Furthermore, a 100-ns simulation of protein-ligand complexes with a trajectory analysis of structural deviation, compactness, intramolecular interactions, principal component analysis, free energy landscape, and dynamic cross-correlation analysis showed the binding potential and positioning of the four hits in the binding pocket. Thus, an in-depth analysis of protein-ligand interactions from pre- and post-molecular dynamics simulation, along with reverse pharmacophore mapping, suggests that 18alpha-glycyrrhetinic acid is the most potent and specific CST inhibitor, while beta-carotene could be considered the second most potent compound for CST inhibition as it also exhibited overall stability throughout the simulation. Therefore, the computational drug screening approach applied in this study may contribute to the development of oral drugs as a therapeutic option for metachromatic leukodystrophy.

Disease models of Leigh syndrome: From yeast to organoids.

Leigh syndrome (LS) is a severe mitochondrial disease that results from mutations in the nuclear or mitochondrial DNA that impairs cellular respiration and ATP production. Mutations in more than 100 genes have been demonstrated to cause LS. The disease most commonly affects brain development and function, resulting in cognitive and motor impairment. The underlying pathogenesis is challenging to ascertain due to the diverse range of symptoms exhibited by affected individuals and the variability in prognosis. To understand the disease mechanisms of different LS-causing mutations and to find a suitable treatment, several different model systems have been developed over the last 30 years. This review summarizes the established disease models of LS and their key findings. Smaller organisms such as yeast have been used to study the biochemical properties of causative mutations. Drosophila melanogaster, Danio rerio, and Caenorhabditis elegans have been used to dissect the pathophysiology of the neurological and motor symptoms of LS. Mammalian models, including the widely used Ndufs4 knockout mouse model of complex I deficiency, have been used to study the developmental, cognitive, and motor functions associated with the disease. Finally, cellular models of LS range from immortalized cell lines and trans-mitochondrial cybrids to more recent model systems such as patient-derived induced pluripotent stem cells (iPSCs). In particular, iPSCs now allow studying the effects of LS mutations in specialized human cells, including neurons, cardiomyocytes, and even three-dimensional organoids. These latter models open the possibility of developing high-throughput drug screens and personalized treatments based on defined disease characteristics captured in the context of a defined cell type. By analyzing all these different model systems, this review aims to provide an overview of past and present means to elucidate the complex pathology of LS. We conclude that each approach is valid for answering specific research questions regarding LS, and that their complementary use could be instrumental in finding treatment solutions for this severe and currently untreatable disease.

Microscopic Phage Adsorption Assay: High-throughput quantification of virus particle attachment to host bacterial cells.

Phages, viruses of bacteria, play a pivotal role in Earth's biosphere and hold great promise as therapeutic and diagnostic tools in combating infectious diseases. Attachment of phages to bacterial cells is a crucial initial step of the interaction. The classic assay to quantify the dynamics of phage attachment involves co-culturing and enumeration of bacteria and phages, which is laborious, lengthy, hence low-throughput, and only provides ensemble estimates of model-based adsorption rate constants. Here, we utilized fluorescence microscopy and particle tracking to obtain trajectories of individual virus particles interacting with cells. The trajectory durations quantified the heterogeneity in dwell time, the time that each phage spends interacting with a bacterium. The average dwell time strongly correlated with the classically-measured adsorption rate constant. We successfully applied this technique to quantify host-attachment dynamics of several phages including those targeting key bacterial pathogens. This approach should benefit the field of phage biology by providing highly quantitative, model-free readouts at single-virus resolution, helping to uncover single-virus phenomena missed by traditional measurements. Owing to significant reduction in manual effort, our method should enable rapid, high-throughput screening of a phage library against a target bacterial strain for applications such as therapy or diagnosis.

Unbiased high-throughput screening of drug-repurposing libraries identifies small-molecule inhibitors of clot retraction

AbstractPlatelet clot retraction, the ultimate phase of platelet thrombus formation, is critical for clot stabilization. It requires functional αIIbβ3 receptors, fibrin, and the integrated actions of the actin-myosin contractile and cytoskeletal systems. Disturbances in clot retraction have been associated with both bleeding and thrombosis. We recently demonstrated that platelets treated with the αIIbβ3 antagonist peptide RGDW, which eliminates fibrinogen-mediated platelet aggregation, are still able to retract clots. We have exploited this observation to develop an unbiased, functional high-throughput assay to identify small-molecule inhibitors of fibrin-mediated clot retraction adapted for a 384-well plate format. We tested 9710 compounds from drug-repurposing libraries (DRLs). These libraries contain compounds that are either US Food and Drug Administration approved or have undergone preclinical/clinical development. We identified 27 compounds from the Library of Pharmacologically Active Compounds library as inhibitors of clot retraction, of which 14 are known inhibitors of platelet function. From the DRLs, we identified 135 compounds (1.6% hit rate). After extensive curation, these compounds were categorized based on the activity of their reported target. Multiple kinase and phosphodiesterase inhibitors with known antiplatelet effects were identified, along with multiple deubiquitination and receptor inhibitors, as well as compounds that have not previously been reported to have antiplatelet activity. Studies of 1 of the deubiquitination inhibitors (degrasyn) suggest that its effects are downstream of thrombin-induced platelet-fibrinogen interactions and thus may permit the separation of platelet thrombin-induced aggregation-mediated events from clot retraction. Additional studies of the identified compounds may lead to novel mechanisms of inhibiting thrombosis.

A comprehensive study on optical properties of La2O3 and HfO2 for radiative cooling via multiscale simulations

Abstract Radiative cooling materials have gained prominence as a zero-energy solution to mitigate global warming. However, a comprehensive understanding of atomic-scale optical properties and macroscopic optical performance of radiative cooling materials remains elusive, limiting insights into the underlying physics of their optical response and cooling efficacy. La2O3 and HfO2, representing rare earth and third/fourth subgroup inorganic oxides respectively, which show promise for radiative cooling applications. In this work, we employed multiscale simulations to investigate the optical properties of La2O3 and HfO2 across a broad spectrum. First-principles calculations revealed their dielectric functions and intrinsic refractive indices and the results indicated that the slightly smaller bandgap of La2O3 compared to HfO2 induces a higher refractive index in the solar band. Additionally, three-phonon scattering was found to provide more accurate infrared optical properties than two-phonon scattering, which enhances the emissivity in the sky window. Monte Carlo simulations were also used to determine the macroscopic optical properties of La2O3 and HfO2 coatings. Based on simulated results, we identified that particle size and particle volume fraction play the dominant role in optical properties. Our findings underscore the potential of La2O3 and HfO2 nanocomposites for environmentally friendly cooling and offer a new approach for high-throughput screening of optical materials through multiscale simulations.

Shock value: advancing sustainable sensing materials

Shock value: advancing sustainable sensing materials Biomolecular crystals have emerged as exciting new sustainable piezoelectrics. Currently, little research is focused on developing these crystals as reliable, solid-state sensors to integrate into conventional electronic devices due to their water solubility, uncontrolled growth, variable response and electroding difficulties. Pb-FREE is taking on the challenge of accelerating the design, growth, and engineering of these novel piezoelectric materials through high-throughput computational screenings, novel crystal growth procedures, and standardised electromechanical characterisation and packaging.

Standardisation of high throughput microdilution antifungal susceptibility testing for Candida albicans and Cryptococcus neoformans.

The Clinical and Laboratory Standards Institute (CLSI) M27 guidelines are the recommended and most commonly used protocols for broth microdilution antifungal susceptibility testing of yeasts. However, these guidelines are limited to the use of 96-well assay plates, limiting assay capacity. With the increased risk of fungal resistance emerging in the community, it is important to have alternative protocols available, that offer higher throughput and can screen more than eight to ten potential antifungal compounds per plate. This study presents an optimised broth microdilution minimum inhibitory concentration (MIC) method for testing the susceptibility of yeasts in an efficient high throughput screening setup, with minimal growth variability and maximum reproducibility. We extend the M27 guidelines and optimise the conditions for 384-well plates. Validation of the assay was performed with ten clinically used antifungals (fluconazole, amphotericin B, 5-fluorocytosine, posaconazole, voriconazole, ketoconazole, itraconazole, caspofungin diacetate, anidulafungin and micafungin) against Candida albicans and Cryptococcus neoformans.

Identification of novel and potent inhibitors of SARS-CoV-2 main protease from DNA-encoded chemical libraries.

In vitro screening of large compound libraries with automated high-throughput screening is expensive and time-consuming and requires dedicated infrastructures. Conversely, the selection of DNA-encoded chemical libraries (DECLs) can be rapidly performed with routine equipment available in most laboratories. In this study, we identified novel inhibitors of SARS-CoV-2 main protease (Mpro) through the affinity-based selection of the DELopen library (open access for academics), containing 4.2 billion compounds. The identified inhibitors were peptide-like compounds containing an N-terminal electrophilic group able to form a covalent bond with the nucleophilic Cys145 of Mpro, as confirmed by x-ray crystallography. This DECL selection campaign enabled the discovery of the unoptimized compound SLL11 (IC50 = 30 nM), proving that the rapid exploration of large chemical spaces enabled by DECL technology allows for the direct identification of potent inhibitors avoiding several rounds of iterative medicinal chemistry. As demonstrated further by x-ray crystallography, SLL11 was found to adopt a highly unique U-shaped binding conformation, which allows the N-terminal electrophilic group to loop back to the S1' subsite while the C-terminal amino acid sits in the S1 subsite. MP1, a close analog of SLL11, showed antiviral activity against SARS-CoV-2 in the low micromolar range when tested in Caco-2 and Calu-3 (EC50 = 2.3 µM) cell lines. As peptide-like compounds can suffer from low cell permeability and metabolic stability, the cyclization of the compounds will be explored in the future to improve their antiviral activity.

An adaptable, fit-for-purpose screening approach with high-throughput capability to determine speed of action and stage specificity of anti-malarial compounds.

A revamped in vitro compound identification and activity profiling approach is required to meet the large unmet need for new anti-malarial drugs to combat parasite drug resistance. Although compound hit identification utilizing high-throughput screening of large compound libraries is well established, the ability to rapidly prioritize such large numbers for further development is limited. Determining the speed of action of anti-malarial drug candidates is a vital component of malaria drug discovery, which currently occurs predominantly in lead optimization and development. This is due in part to the capacity of current methods which have low throughput due to the complexity and labor intensity of the approaches. Here, we provide an adaptable screening paradigm utilizing automated high content imaging, including the development of an automated schizont maturation assay, which collectively can identify anti-malarial compounds, classify activity into fast and slow acting, and provide an indication of the parasite stage specificity, with high-throughput capability. By frontloading these critical biological parameters much earlier in the drug discovery pipeline, it has the potential to reduce lead compound attrition rates later in the development process. The capability of the approach in its alternative formats is demonstrated using three Medicines for Malaria Venture open access compound "boxes," namely Pathogen Box (malaria set-125 compounds), Global Health Priority Box [Malaria Box 2 (80 compounds) and zoonotic neglected diseases (80 compounds)], and the Pandemic Response Box (400 compounds). From a total of 685 compounds tested, 79 were identified as having fast ring-stage-specific activity comparable to that of artemisinin and therefore of high priority for further consideration and development.

Discovery of new acetamide derivatives of 5-indole-1,3,4-oxadiazol-2-thiol as inhibitors of HIV-1 Tat-mediated viral transcription.

Human immunodeficiency virus-1 (HIV-1) encodes a transcriptional factor called Tat, which is critical for viral transcription. Tat-mediated transcription is highly ordered apart from the cellular manner; therefore, it is considered a target for developing anti-HIV-1 drugs. However, drugs targeting Tat-mediated viral transcription are not yet available. Our high-throughput screen of a compound library employing a dual-reporter assay identified a 1,3,4-oxadiazole scaffold against Tat and HIV-1 infection. Furthermore, a serial structure-activity relation (SAR) study performed with biological assays found 1,3,4-oxadiazole derivatives (9 and 13) containing indole and acetamide that exhibited potent inhibitory effects on HIV-1 infectivity, with half-maximal effective concentrations (EC50) of 0.17 (9) and 0.24 µM (13), respectively. The prominent derivatives specifically interfered with the viral transcriptional step without targeting other infection step(s) and efficiently inhibited the HIV-1 replication cycle in the T cell lines and peripheral blood mononuclear cells infected with HIV-1. Additionally, compared to the wild type, the compounds exhibited similar potency against anti-retroviral drug-resistant HIV-1 strains. In a series of mode-of-action studies, the compounds inhibited the ejection of histone H3 for facilitating viral transcription on the long-terminal repeat (LTR) promoter. Furthermore, SAHA (a histone deacetylase inhibitor) treatment abolished the compound potency, revealing that the compounds can possibly target Tat-regulated epigenetic modulation of LTR to inhibit viral transcription. Overall, our screening identified novel 1,3,4-oxadiazole compounds that inhibited HIV-1 Tat, and subsequent SAR-based optimization led to the derivatives 9 and 13 development that could be a promising scaffold for developing a new class of therapeutic agents for HIV-1 infection.

Machine Learning Elucidates Design Features of Plasmid Deoxyribonucleic Acid Lipid Nanoparticles for Cell Type-Preferential Transfection.

To broaden the accessibility of cell and gene therapies, it is essential to develop and optimize nonviral, cell type-preferential gene carriers such as lipid nanoparticles (LNPs). While high-throughput screening (HTS) approaches have proven effective in accelerating LNP discovery, they are often costly, labor-intensive, and do not consistently yield actionable design rules that direct screening efforts toward the most relevant chemical and formulation parameters. In this study, we employed a machine learning (ML) workflow, utilizing well-curated plasmid DNA LNP transfection data sets across six cell types, to extract compositional and chemical insights from HTS studies. Our approach achieved prediction errors averaging between 5 and 10%, depending on the cell type. By applying SHapley Additive exPlanations to our ML models, we uncovered key composition-function relationships that govern cell type-preferential LNP transfection efficiency. Notably, we identified consistent LNP composition parameters that enhance in vitro transfection efficiency across diverse cell types, including a helper lipid molar percentage of charged lipids between 9 and 50% and the inclusion of cationic/zwitterionic helper lipids. Additionally, several parameters were found to modulate cell type-preferentiality, such as the total molar percentage of ionizable and helper lipids, N/P ratio, PEGylated lipid molar percentage of uncharged lipids, and hydrophobicity of the helper lipid. This study leverages HTS of compositionally diverse LNP libraries combined with ML analysis to elucidate the interactions between lipid components in LNP formulations, providing insights that contribute to the design of LNP compositions tailored for cell type-preferential transfection.

Data mining of PubChem bioassay records reveals diverse OXPHOS inhibitory chemotypes as potential therapeutic agents against ovarian cancer

Focused screening on target-prioritized compound sets can be an efficient alternative to high throughput screening (HTS). For most biomolecular targets, compound prioritization models depend on prior screening data or a target structure. For phenotypic or multi-protein pathway targets, it may not be clear which public assay records provide relevant data. The question also arises as to whether data collected from disparate assays might be usefully consolidated. Here, we report on the development and application of a data mining pipeline to examine these issues. To illustrate, we focus on identifying inhibitors of oxidative phosphorylation, a druggable metabolic process in epithelial ovarian tumors. The pipeline compiled 8415 available OXPHOS-related bioassays in the PubChem data repository involving 312,093 unique compound records. Application of PubChem assay activity annotations, PAINS (Pan Assay Interference Compounds), and Lipinski-like bioavailability filters yields 1852 putative OXPHOS-active compounds that fall into 464 clusters. These chemotypes are diverse but have relatively high hydrophobicity and molecular weight but lower complexity and drug-likeness. These chemotypes show a high abundance of bicyclic ring systems and oxygen containing functional groups including ketones, allylic oxides (alpha/beta unsaturated carbonyls), hydroxyl groups, and ethers. In contrast, amide and primary amine functional groups have a notably lower than random prevalence. UMAP representation of the chemical space shows strong divergence in the regions occupied by OXPHOS-inactive and -active compounds. Of the six compounds selected for biological testing, 4 showed statistically significant inhibition of electron transport in bioenergetics assays. Two of these four compounds, lacidipine and esbiothrin, increased in intracellular oxygen radicals (a major hallmark of most OXPHOS inhibitors) and decreased the viability of two ovarian cancer cell lines, ID8 and OVCAR5. Finally, data from the pipeline were used to train random forest and support vector classifiers that effectively prioritized OXPHOS inhibitory compounds within a held-out test set (ROCAUC 0.962 and 0.927, respectively) and on another set containing 44 documented OXPHOS inhibitors outside of the training set (ROCAUC 0.900 and 0.823). This prototype pipeline is extensible and could be adapted for focus screening on other phenotypic targets for which sufficient public data are available.Scientific contributionHere, we describe and apply an assay data mining pipeline to compile, process, filter, and mine public bioassay data. We believe the procedure may be more broadly applied to guide compound selection in early-stage hit finding on novel multi-protein mechanistic or phenotypic targets. To demonstrate the utility of our approach, we apply a data mining strategy on a large set of public assay data to find drug-like molecules that inhibit oxidative phosphorylation (OXPHOS) as candidates for ovarian cancer therapies.Graphical

Cell-based high-content approach for SARS-CoV-2 neutralization identifies unique monoclonal antibodies and PI3K pathway inhibitors.

The sudden rise of the SARS-CoV-2 virus and the delay in the development of effective therapeutics to mitigate it made evident a need for ways to screen for compounds that can block infection and prevent further pathogenesis and spread. Yet, identifying effective drugs efficacious against viral infection and replication with minimal toxicity for the patient can be difficult. Monoclonal antibodies were shown to be effective, yet as the SARS-CoV-2 mutated, these antibodies became ineffective. Small molecule antivirals were identified using pseudovirus constructs to recapitulate infection in non-human cells, such as Vero E6 cells. However, the impact was limited due to poor translation of these compounds in the clinical setting. This is partly due to the lack of similarity of screening platforms to the in vivo physiology of the patient and partly because drugs effective in vitro showed dose-limiting toxicities. In this study, we performed two high-throughput screens in human lung adenocarcinoma cells with authentic SARS-CoV-2 virus to identify both monoclonal antibodies that neutralize the virus and clinically useful kinase inhibitors to block the virus and prioritize minimal host toxicity. Using high-content imaging combined with single-cell and multidimensional analysis, we identified antibodies and kinase inhibitors that reduce virus infection without affecting the host. Our screening technique uncovered novel antibodies and overlooked kinase inhibitors (i.e. PIK3i, mTORi, multiple RTKi) that could be effective against SARS-CoV-2 virus. Further characterization of these molecules will streamline the repurposing of compounds for the treatment of future pandemics and uncover novel mechanisms viruses use to hijack and infect host cells.

Scalable, compressed phenotypic screening using pooled perturbations.

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.

Rational Molecular Design of a Fluorescent Probe for Selectively Sensing Human Cytochrome P450 2D6.

Cytochrome P450 2D6 (CYP2D6) is a key enzyme that mediates the metabolism of various drugs and endogenous substances in humans. However, its biological role in drug-drug interactions especially mechanism-based inactivation (MBI), and various diseases remains poorly understood, owing to the lack of molecular tools suitable for selectively monitoring CYP2D6 in complex biological systems. Herein, using a tailored molecular strategy, we developed a fluorescent probe BDPM for CYP2D6. BDPM exhibits excellent specificity and imaging capability for CYP2D6, making it suitable for the real-time monitoring of endogenous CYP2D6 activity in living bio-samples. Therefore, our tailored strategy proved useful for constructing the highly selective and enzyme-activated fluorescent probes. BDPM as a molecular tool to explore the critical roles of CYP2D6 in the pathogenesis of diseases, high-throughput screening of inhibitors and intensive investigation of CYP2D6-induced MBI in natural systems.

8738 Unveiling Potential Therapeutic Candidates for ACTH-Silent Pituitary Neuroendocrine Tumors Through High Throughput Drug Screening

Abstract Disclosure: J.M. Marques: None. N.D. D' Alessandre: None. G.D. Guardia: None. F.L. Craveiro: None. C. Grutzmacher: None. G.O. Silva: None. B.B. Mendonca: None. L.R. Carvalho: None. F. Fattahi: None. P.A. Galante: None. R.L. Batista: None. Introduction: ACTH-silent pituitary neuroendocrine tumours (PitNETs) are a subtype of PitNETs that arise from corticotroph cells of the anterior pituitary gland. They can show positive immunoreactivity for adrenocorticotropic hormone (ACTH) but with no clinical or laboratory evidence of hypercortisolism. ACTH-silent PitNETs display notably more aggressive behavior and treatment resistance. Currently, there is no established pharmacological therapy for this specific tumor subtype. Aim: To use high throughput drug screening to identify compounds that modulate ACTH secretion and tumor progression in ACTH-silent PitNETs. Methods: WTC11 cells (GM25256) were differentiated into pituitary using E6 with SB431542 10µM, SHH 200ng/mL, FGF8 100ng/mL and FGF10 50ng/mL until day 30. Pituitary cells were passaged in 384 well plates and treated with FDA-approved compound library with 3113 drugs at 1μM for 72h (#L1300, Selleckchem). Cells were stained with ACTH and imaged on ImageXpress® HCS. ACTH intensity was normalized to total cell number, measured by DAPI, using MetaXpress Software and compounds z-score was calculated, considering -2.5 for analysis. Gene and pathway enrichment analysis were performed and PitNET RNAseq data, publicly available (GSE207937), was used to determine target drugs based on similar target pathways and gene expression. To validate hits, we collected tumor samples from a patient with an aggressive silent ACTH PitNET. PitNET was histologically confirmed and hypercortisolism was excluded clinically and laboratory. Samples were cultured on DMEN/F12 with 10% FBS and 1% antibiotic and pituitary hormones were measured at days 3, 6 and 8. Cells were treated with 1uM of Letrozole, Linsitinib and Raloxifene for 48h and sample for RNA and hormone dosage were collected. Results: We identified 8 drugs that decreased ACTH in induced pituitary cells and selected 3 of them for validation. PitNET was successfully cultured and treated with selected drugs and ACTH secretion was detected at all periods (972.1 pg/mL to 10490 pg/mL), gonadotropins were below the detected level. Letrozole significantly reduced ACTH in the PitNET culture at day 8 (600.9 ± 25.8pg/mL, p<0,001). ACTH was also decreased in Linsitinib treated cells (932 ± 12.7pg/mL, p<0.05), while Raloxifene did not change ACTH secretion (980 ± 95.1pg/mL, p=0.061). Microscopically, PitNET cells aggregate in spheres and this seems to be affected by letrozole treatment, not influencing cell viability. Conclusion: We performed a high throughput drug screen using induced pituitary cells that led to the discovery of 3 compounds that might be used as an alternative for PitNET treatment. Pathway and gene expression analysis from target drugs and PitNET RNAseq may help in the understanding of drug effect in the PitNET context and improve the understanding of tumorigenesis and pharmacological treatment. Presentation: 6/1/2024