Discovery Applications Research Articles

The Russian priority in development of general intravenous anesthesia (Report II)

The article considers evaluation of works of colleagues and contemporaries of N. P. Kravkov, the creator of "Russian narcosis" with hedonal. The hidden and explicit, objective and subjective factors that prevented spreading of application of discovery of Russian scientists, both in Russia and abroad, are established. The dependence of self-esteem of Russian surgeons of XIX - early XX centuries on recognition of their scientific efforts in Germany is emphasized.

Regiodivergent and Enantioselective Synthesis of Cyclic Sulfones via Ligand‐Controlled Nickel‐Catalyzed Hydroalkylation

Cyclic sulfones have demonstrated important applications in drug discovery. However, the catalytic and enantioselective synthesis of chiral cyclic sulfones remains challenging. Herein, we develop nickel‐catalyzed regiodivergent and enantioselective hydroalkylation of sulfolenes to streamline the synthesis of chiral alkyl cyclic sulfones. The method has broad scope and high functional group tolerance. The regioselectivity can be controlled by ligands only. A neutral PYROX ligand favors C3‐alkylation whereas an anionic BOX ligand favors C2‐alkylation. This control is kinetic in origin as the C2‐bound Ni intermediates are always thermodynamically more stable. Reactivity study of a wide range of relevant Ni intermediates reveal a NiI/NiIII catalytic cycle with a NiII‐H species as the resting state. The regio‐ and enantio‐determining step is the insertion of this NiII‐H species into 2‐sulfolene. This work provides an efficient catalytic method for the synthesis of an important class of organic compounds and enhances the mechanistic understanding of Ni‐catalyzed stereoselective hydroalkylation.

Optimization of protocols for blood-derived extracellular vesicles for studies in liver diseases

Aim: Extracellular vesicles (EVs) have gained significant attention for their diagnostic and therapeutic potential in various diseases, including liver disorders. This study focuses on optimizing the isolation and characterization of small EVs from plasma and serum samples in patients with liver diseases, aiming to advance our understanding and potential clinical applications of EVs. Methods: Blood samples were collected from patients with end-stage liver disease (ESLD) enlisted in the TransplantLines Cohort and Biobank Study, and healthy donors. We employed differential ultracentrifugation (DUC) to evaluate three distinct protocols: a 3-step DUC, a washing step omitted [samples without washing (WW)], and a contaminant-depleted plasma (CDP) protocol. RNA isolation methodologies were compared, involving the use of TRI-reagent or the commercial AllPrep DNA/RNA kit. Further insights into EV composition were obtained through proteomic analyses, comparing samples subjected to traditional cell lysis (L) with those processed without lysis (NL). Results: We successfully isolated EVs from both plasma and serum samples as confirmed by the presence of specific EV markers, including CD9, CD63, CD81, and tumor susceptibility gene 101 (TSG-101). While some contaminants remained, such as albumin and lipoproteins, the protocol selected to continue EVs analysis was the 3-step protocol. Transmission electron microscopy (TEM) and nanotracking analysis (NTA) further confirmed EVs presence. RNA extraction was achieved using TRI-reagent, but not with the commercial kit highlighting the importance of selecting an appropriate method for RNA isolation. Finally, proteomics analysis showed that lysed samples were significantly more enriched in proteins compared to non-lysed samples, although protein variability was still present in both groups. Conclusions: Optimizing EV isolation techniques is essential for harnessing their potential in liver disease diagnosis and therapy. Further refinement of purification methods, a deep characterization of our cohort and understanding the variability and cargo within EVs will be crucial for future biomarker discovery and therapeutic applications in liver-related diseases.

Quantifying Antibody-Dependent Cellular Cytotoxicity in a Tumor Spheroid Model: Application for Drug Discovery.

Monoclonal antibody-based immunotherapy targeting tumor antigens is now a mainstay of cancer treatment. One of the clinically relevant mechanisms of action of the antibodies is antibody-dependent cellular cytotoxicity (ADCC), where the antibody binds to the cancer cells and engages the cellular component of the immune system, e.g., natural killer (NK) cells, to kill the tumor cells. The effectiveness of these therapies could be improved by identifying adjuvant compounds that increase the sensitivity of the cancer cells or the potency of the immune cells. In addition, undiscovered drug interactions in cancer patients co-medicated for previous conditions or cancer-associated symptoms may determine the success of the antibody therapy; therefore, such unwanted drug interactions need to be eliminated. With these goals in mind, we created a cancer ADCC model and describe here a simple protocol to find ADCC-modulating drugs. Since 3D models such as cancer cell spheroids are superior to 2D cultures in predicting in vivo responses of tumors to anticancer therapies, spheroid co-cultures of EGFP-expressing HER2+ JIMT-1 breast cancer cells and the NK92.CD16 cell lines were set up and induced with Trastuzumab, a monoclonal antibody clinically approved against HER2-positive breast cancer. JIMT-1 spheroids were allowed to form in cell-repellent U-bottom 96-well plates. On day 3, NK cells and Trastuzumab were added. The spheroids were then stained with Annexin V-Alexa 647 to measure apoptotic cell death, which was quantitated in the peripheral zone of the spheroids with an automated microscope. The applicability of our assay to identify ADCC-modulating molecules is demonstrated by showing that Sunitinib, a receptor tyrosine kinase inhibitor approved by the FDA against metastatic cancer, almost completely abolishes ADCC. The generation of the spheroids and image acquisition and analysis pipelines are compatible with high-throughput screening for ADCC-modulating compounds in cancer cell spheroids.

Production of Polyclonal Antibodies and Development of Competitive ELISA for Quantification of the Lantibiotic Paenibacillin

The discovery and biotechnological application of new antimicrobial peptides are impeded by a lack of sensitive methods for peptide quantification. Paenibacillin is an emerging antimicrobial lantibiotic that was discovered in Paenibacillus polymyxa OSY-DF ATCC PTA-7852, isolated from the fermented vegetable Kimchee. This lantibiotic has potency against many foodborne pathogenic and spoilage bacteria. To advance the research and application of paenibacillin, a rapid, specific, and sensitive detection and quantification immunoassay was developed. After anti-paenibacillin polyclonal antibodies (pAbs) were generated and purified, a competitive enzyme-linked immunosorbent assay (cELISA) was developed and optimized for paenibacillin quantification. The dynamic range of the cELISA was determined by using a three-parameter nonlinear regression model, achieving a correlation (R2) value of 0.95. The cELISA displayed high sensitivity, with the ability to detect paenibacillin at levels as low as 15.6 ng/mL, which is significantly lower than the limit of detection of the conventional antimicrobial assay (20 µg/mL paenibacillin). The cELISA successfully differentiated paenibacillin concentrations in cell-free crude supernatants of P. polymyxa wild type and its mutant strain when grown at 30 °C and 37 °C; higher paenibacillin levels were found in the mutant (0.248–0.276 µg/mL) than in the wild type (0.122–0.212 µg/mL) culture. These findings were validated by the transcriptional analysis of 11 paenibacillin biosynthetic genes, which were significantly upregulated (≥2-fold increase) in the mutant compared with the wild strain. Additionally, the cELISA exhibited high sensitivity by recovery of paenibacillin titers spiked at 2.5 and 10 µg/mL in de Man, Rogosa, and Sharpe (MRS) broth and diluted skim milk. These results suggest that the anti-paenibacillin pAbs and the developed cELISA could be valuable in quantifying paenibacillin in complex matrices and in aiding the discovery of paenibacillin-producing natural microbiota.

Deoxy-Arylation of Amides via a Tandem Hydrosilylation/Radical- Radical Coupling Sequence.

Vaska's complex is a prominent catalyst for the hydrosilylation of amides. The O-silyl hemiaminal intermediate formed in these processes has been demonstrated as an electrophile for nucleophilic additions. More recently, these intermediates have been shown to be suitable for single electron reduction to generate α-amino radicals. Leveraging the ability to generate α-amino radicals from these hemiaminals, we describe a two-step, one-pot, deoxy-arylation of amides utilizing iridium-catalyzed hydrosilylation and photoredox catalysis. This transformation can be tailored toward the late-stage functionalization of biologically relevant molecules, with drug discovery applications as shown in the streamlined synthesis of an NPY Y2 inhibitor.

Simple dynamic cell culture system reduces recording noise in microelectrode array recordings

Microelectrode arrays (MEAs) have applications in drug discovery, toxicology, and basic research. They measure the electrophysiological response of tissue cultures to quantify changes upon exposure to biochemical stimuli. Unfortunately, manual addition of chemicals introduces significant noise in the recordings. Here, we report a simple-to-fabricate fluidic system that addresses this issue. We show that cell cultures can be successfully established in the fluidic compartment under continuous flow conditions and that the addition of chemicals introduces minimal noise in the recordings. This dynamic cell culture system represents an improvement over traditional tissue culture wells used in MEAs, facilitating electrophysiology measurements.Graphical abstract

From stem cells to pancreatic β-cells: strategies, applications, and potential treatments for diabetes.

Loss and functional failure of pancreatic β-cells results in disruption of glucose homeostasis and progression of diabetes. Although whole pancreas or pancreatic islet transplantation serves as a promising approach for β-cell replenishment and diabetes therapy, the severe scarcity of donor islets makes it unattainable for most diabetic patients. Stem cells, particularly induced pluripotent stem cells (iPSCs), are promising for the treatment of diabetes owing to their self-renewal capacity and ability to differentiate into functional β-cells. In this review, we first introduce the development of functional β-cells and their heterogeneity and then turn to highlight recent advances in the generation of β-cells from stem cells and their potential applications in disease modeling, drug discovery and clinical therapy. Finally, we have discussed the current challenges in developing stem cell-based therapeutic strategies for improving the treatment of diabetes. Although some significant technical hurdles remain, stem cells offer great hope for patients with diabetes and will certainly transform future clinical practice.

Scalable protein production by Komagataella phaffii enabled by ARS plasmids and carbon source-based selection

BackgroundMost recombinant Komagataella phaffii (Pichia pastoris) strains for protein production are generated by genomic integration of expression cassettes. The clonal variability in gene copy numbers, integration loci and consequently product titers limit the aptitude for high throughput applications in drug discovery, enzyme engineering or most comparative analyses of genetic elements such as promoters or secretion signals. Circular episomal plasmids with an autonomously replicating sequence (ARS), an alternative which would alleviate some of these limitations, are inherently unstable in K. phaffii. Permanent selection pressure, mostly enabled by antibiotic resistance or auxotrophy markers, is crucial for plasmid maintenance and hardly scalable for production. The establishment and use of extrachromosomal ARS plasmids with key genes of the glycerol metabolism (glycerol kinase 1, GUT1, and triosephosphate isomerase 1, TPI1) as selection markers was investigated to obtain a system with high transformation rates that can be directly used for scalable production processes in lab scale bioreactors.ResultsIn micro-scale deep-well plate experiments, ARS plasmids employing the Ashbya gossypii TEF1 (transcription elongation factor 1) promoter to regulate transcription of the marker gene were found to deliver high transformation efficiencies and the best performances with the reporter protein (CalB, lipase B of Candida antarctica) for both, the GUT1- and TPI1-based, marker systems. The GUT1 marker-bearing strain surpassed the reference strain with integrated expression cassette by 46% upon re-evaluation in shake flask cultures regarding CalB production, while the TPI1 system was slightly less productive compared to the control. In 5 L bioreactor methanol-free fed-batch cultivations, the episomal production system employing the GUT1 marker led to 100% increased CalB activity in the culture supernatant compared to integration construct.ConclusionsFor the first time, a scalable and methanol-independent expression system for recombinant protein production for K. phaffii using episomal expression vectors was demonstrated. Expression of the GUT1 selection marker gene of the new ARS plasmids was refined by employing the TEF1 promoter of A. gossypii. Additionally, the antibiotic-free marker toolbox for K. phaffii was expanded by the TPI1 marker system, which proved to be similarly suited for the use in episomal plasmids as well as integrative expression constructs for the purpose of recombinant protein production.

Recent Advances in the Total Synthesis of Spirotryprostatin Alkaloids.

Spirotryprostatin alkaloids, a class of alkaloids with a unique spirocyclic indoledionepiperazine structure, were first extracted from the fermentation broth of Aspergillus fumigatus and have garnered significant attention in the fields of biology and pharmacology. The investigation into the pharmacological potential of this class of alkaloids has unveiled promising applications in drug discovery and development. Notably, certain spirotryprostatin alkaloids have demonstrated remarkable anti-cancer activity, positioning them as potential candidates for anti-tumor drug development. In recent years, organic synthetic chemists have dedicated efforts to devise efficient and viable strategies for the total synthesis of spirotryprostatin alkaloids, aiming to meet the demands within the pharmaceutical domain. The construction of the spiro-C atom within the spirotryprostatin scaffold and the chirality control at the spiro atomic center emerge as pivotal aspects in the synthesis of these compounds. This review categorically delineates the synthesis of spirotryprostatin alkaloids based on the formation mechanism of the spiro-C atom.

Recent Advances in Grayanane Diterpenes: Isolation, Structural Diversity, and Bioactivities from Ericaceae Family (2018-2024).

Diterpenes represent one of the most diverse and structurally complex families of natural products. Among the myriad of diterpenoids, grayanane diterpenes are particularly notable. These terpenes are characterized by their unique 5/7/6/5 tetracyclic system and are exclusive to the Ericaceae family of plants. Renowned for their complex structures and broad spectrum of bioactivities, grayanane diterpenes have become a primary focus in extensive phytochemical and pharmacological research. Recent studies, spanning from 2018 to January 2024, have reported a series of new grayanane diterpenes with unprecedented carbon skeletons. These compounds exhibit various biological properties, including analgesic, antifeedant, anti-inflammatory, and inhibition of protein tyrosine phosphatase 1B (PTP1B). This paper delves into the discovery of 193 newly identified grayanoids, representing 15 distinct carbon skeletons within the Ericaceae family. The study of grayanane diterpenes is not only a deep dive into the complexities of natural product chemistry but also an investigation into potential therapeutic applications. Their unique structures and diverse biological actions make them promising candidates for drug discovery and medicinal applications. The review encompasses their occurrence, distribution, structural features, and biological activities, providing invaluable insights for future pharmacological explorations and research.

Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX

Graph Neural Networks (GNNs) excel in compound property and activity prediction, but the choice of molecular graph representations significantly influences model learning and interpretation. While atom-level molecular graphs resemble natural topology, they overlook key substructures or functional groups and their interpretation partially aligns with chemical intuition. Recent research suggests alternative representations using reduced molecular graphs to integrate higher-level chemical information and leverages both representations for model. However, there is a lack of studies about applicability and impact of different molecular graphs on model learning and interpretation. Here, we introduce MMGX (Multiple Molecular Graph eXplainable discovery), investigating the effects of multiple molecular graphs, including Atom, Pharmacophore, JunctionTree, and FunctionalGroup, on model learning and interpretation with various perspectives. Our findings indicate that multiple graphs relatively improve model performance, but in varying degrees depending on datasets. Interpretation from multiple graphs in different views provides more comprehensive features and potential substructures consistent with background knowledge. These results help to understand model decisions and offer valuable insights for subsequent tasks. The concept of multiple molecular graph representations and diverse interpretation perspectives has broad applicability across tasks, architectures, and explanation techniques, enhancing model learning and interpretation for relevant applications in drug discovery.

How to differentiate induced pluripotent stem cells into sensory neurons for disease modelling: a functional assessment.

Human induced pluripotent stem cell (iPSC)-derived peripheral sensory neurons present a valuable tool to model human diseases and are a source for applications in drug discovery and regenerative medicine. Clinically, peripheral sensory neuropathies can result in maladies ranging from a complete loss of pain to severe painful neuropathic disorders. Sensory neurons are located in the dorsal root ganglion and are comprised of functionally diverse neuronal types. Low efficiency, reproducibility concerns, variations arising due to genetic factors and time needed to generate functionally mature neuronal populations from iPSCs remain key challenges to study human nociception in vitro. Here, we report a detailed functional characterization of iPSC-derived sensory neurons with an accelerated differentiation protocol ("Anatomic" protocol) compared to the most commonly used small molecule approach ("Chambers" protocol). Anatomic's commercially available RealDRG™ were further characterized for both functional and expression phenotyping of key nociceptor markers. Multiple iPSC clones derived from different reprogramming methods, genetics, age, and somatic cell sources were used to generate sensory neurons. Manual patch clamp was used to functionally characterize both control and patient-derived neurons. High throughput techniques were further used to demonstrate that RealDRGs™ derived from the Anatomic protocol are amenable to high throughput technologies for disease modelling. The Anatomic protocol rendered a purer culture without the use of mitomycin C to suppress non-neuronal outgrowth, while Chambers differentiations yielded a mix of cell types. Chambers protocol results in predominantly tonic firing when compared to Anatomic protocol. Patient-derived nociceptors displayed higher frequency firing compared to control subject with both, Chambers and Anatomic differentiation approaches, underlining their potential use for clinical phenotyping as a disease-in-a-dish model. RealDRG™ sensory neurons show heterogeneity of nociceptive markers indicating that the cells may be useful as a humanized model system for translational studies. We validated the efficiency of two differentiation protocols and their potential application for functional assessment and thus understanding the disease mechanisms from patients suffering from pain disorders. We propose that both differentiation methods can be further exploited for understanding mechanisms and development of novel treatments in pain disorders.

Highlights on Fluorine-containing Drugs Approved by U.S. FDA in 2023.

Fluorine continues to show its potential applications in drug discovery and development, as reflected by twelve drugs being fluorinated out of the fifty-five approved by the FDA in 2023. This concise review highlights the discovery of each of these fluorine-containing drugs in the past year, including its brand name, date of approval, composition, sponsors, indication, and mechanism of action. The relevant future trend is also briefly discussed.

HydraProt: A New Deep Learning Tool for Fast and Accurate Prediction of Water Molecule Positions for Protein Structures.

Water molecules are integral to the structural stability of proteins and vital for facilitating molecular interactions. However, accurately predicting their precise position around protein structures remains a significant challenge, making it a vibrant research area. In this paper, we introduce HydraProt (deep Hydration of Proteins), a novel methodology for predicting precise positions of water molecule oxygen atoms around protein structures, leveraging two interconnected deep learning architectures: a 3D U-net and a Multi-Layer Perceptron (MLP). Our approach starts by introducing a coarse voxel-based representation of the protein, which allows for rapid sampling of candidate water positions via the 3D U-net. These water positions are then assessed by embedding the water-protein relationship in the Euclidean space by means of an MLP. Finally, a postprocessing step is applied to further refine the MLP predictions. HydraProt surpasses existing state-of-the-art approaches in terms of precision and recall and has been validated on large data sets of protein structures. Notably, our method offers rapid inference runtime and should constitute the method of choice for protein structure studies and drug discovery applications. Our pretrained models, data, and the source code required to reproduce these results are accessible at https://github.com/azamanos/HydraProt.

Benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications.

Antibodies are proteins produced by our immune system that have been harnessed as biotherapeutics. The discovery of antibody-based therapeutics relies on analyzing large volumes of diverse sequences coming from phage display or animal immunizations. Identification of suitable therapeutic candidates is achieved by grouping the sequences by their similarity and subsequent selection of a diverse set of antibodies for further tests. Such groupings are typically created using sequence-similarity measures alone. Maximizing diversity in selected candidates is crucial to reducing the number of tests of molecules with near-identical properties. With the advances in structural modeling and machine learning, antibodies can now be grouped across other diversity dimensions, such as predicted paratopes or three-dimensional structures. Here we benchmarked antibody grouping methods using clonotype, sequence, paratope prediction, structure prediction, and embedding information. The results were benchmarked on two tasks: binder detection and epitope mapping. We demonstrate that on binder detection no method appears to outperform the others, while on epitope mapping, clonotype, paratope, and embedding clusterings are top performers. Most importantly, all the methods propose orthogonal groupings, offering more diverse pools of candidates when using multiple methods than any single method alone. To facilitate exploring the diversity of antibodies using different methods, we have created an online tool-CLAP-available at (clap.naturalantibody.com) that allows users to group, contrast, and visualize antibodies using the different grouping methods.

Interest of rare autoantibodies in autoimmune encephalitis and paraneoplastic neurological syndromes: the utility (or futility) of rare antibody discovery.

The increasing recognition and diagnosis of autoimmune encephalitis (AE) and paraneoplastic neurological syndromes (PNS) is partly due to neural autoantibody testing and discovery. The past two decades witnessed an exponential growth in the number of identified neural antibodies. This review aims to summarize recent rare antibody discoveries in the context of central nervous system (CNS) autoimmunity and evaluate the ongoing debate about their utility. In the last 5 years alone 15 novel neural autoantibody specificities were identified. These include rare neural antibody biomarkers of autoimmune encephalitis, cerebellar ataxia or other movement disorders, including multifocal presentations. Although the clinical applications of these rare antibody discoveries may be limited by the low number of positive cases, they still provide important diagnostic, prognostic, and therapeutic insights.

Synthesis, biological evaluation and in-silico ADMET studies of novel chalcones and dihydro pyrazoles of 2H-Chromene as anticancer agents

2H-chromenes and chalcones are biologically important scaffolds with applications in drug discovery. Their ease of synthesis and biological importance have attracted the attention of medicinal chemists worldwide. Inspired by this, we synthesized various substituted chalcones and dihydropyrazole derivatives bearing various aryl and hetero-aryl 2H-chromene moieties. The resulting compounds were evaluated for cytotoxicity in vitro against doxorubicin in triple-negative breast cancer cell lines (MDA-MB-231), estrogen receptor-positive breast cancer cell lines (MCF-7), and human brain glioblastoma (U-87 MG) cell lines. Compounds 6b and 6j exhibited anticancer activity against triple-negative breast cancer cell line (MDA-MB-231), with IC50 values of 4.02 µM and 3.35 µM, respectively. Further investigation involving in silico pharmacokinetic (ADME) and toxicity studies also revealed pivotal features for the favourable bioavailability of these derivatives.

Abstract 1848: The Cellular Thermal Shift assay and its applications in Target ID, MoA determination and biomarker discovery

Abstract With the Cellular Thermal Shift Assay (CETSA) celebrating its first decade since the PoC publication, the method has gained much interest in the basic science field as well as become an industry standard in applied drug discovery setting. In the last few years it has become apparent that in addition to providing information about drug-protein interaction, CETSA based thermal profiling of cells can help describe additional aspects of cell biology as it gives insight into the activating and rewiring of protein-protein interaction networks inside the cell upon stimuli. In the work presented here, we have used CETSA coupled to high resolution mass spectrometric readout to profile the response patterns of about 8000 proteins to several hundred molecular probes and marketed drugs. The goal has been to provide a map of wanted and unwanted effects following treatment and to correlate it to alternative readouts, by for example cellular imaging and cell death assays. We have applied the CETSA protocol for close to five hundred compounds, many in several cell-lines (HepG2, U87MG and K562). The experimental setup includes the step of treating the cells with a certain compound at a fixed concentration. After incubation for an hour, we heated the cells to a range of temperatures, followed by a lysis protocol and pooling of the samples. After this, the samples have been centrifuged to separate the soluble from insoluble fraction. The soluble fraction has then been prepared for MS acquisition using TMT based multiplexing. Typically, this allowed us to monitor changes in thermal stability among more than 8,000 proteins, as a consequence of compound treatment. This data reveals specific compound “fingerprints” that can be used to decipher mode of action and identify biomarkers. Despite the short incubation time, proteins with compound-induced thermal stability shifts are not only targets/off-targets (direct binders), but also downstream, and sometimes upstream, pathway members and general cellular responses. All data combined makes the basis of the Target Engagement Atlas. Here we can see clusters of compounds, sharing similar protein binding profiles. Among the most prominent ones are mTOR inhibitors, NSAIDs, as well as tubulin binders. Alternatively, one can use this resource to reveal clusters of “pharmacologically” associated protein networks. We show that pharmacological perturbation allows identification of clusters representing tight protein complexes (for example ribosomes), known metabolic pathways (folate biosynthesis), molecular functions (kinases), as well as networks of proteins linked only by ligand binding specificity. CETSA MS allows for unbiased, cellular, and molecular profiling of compound effects. Together with data from clinical settings, as well as in vivo/in vitro end point assays it is possible to correlate the CETSA patterns to wanted but also unwanted responses. Citation Format: Tomas Friman, Alexey Chernobrovkin, Tuomas Tolvanen, Erin Gilson, Stina Lundgren, Victoria Brehmer, Daniel Martinez Molina. The Cellular Thermal Shift assay and its applications in Target ID, MoA determination and biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1848.

Abstract 4712: GPCR panel establishment and application in drug discovery

Abstract G protein-coupled receptors (GPCRS) are the largest family of membrane proteins of cell surface receptors. It can be activated by various stimuli and play an important role in various physiological and pathological processes such as cell growth, proliferation, differentiation, metabolism, and homeostasis. Abnormal regulation of GPCR is associated with various human diseases, such as metabolic diseases, cardiovascular diseases, and eye diseases. G protein contains three subunits: α, β, and γ, which form a trimer. When extracellular ligands are bound to GPCRs which activate G protein and GTP phosphorylates Gα subunits, leading to the dissociation of Gα subunits from the trimer and dissociation from the β and γ subunits. In addition, Gα different subtypes of Gαs, Gαi and Gαq have different biological effects after activation, Gas-coupled receptors activate adenylyl cyclase (AC) to increase cAMP. In contrast, Gαi-coupled receptors activated adenylyl cyclase (AC) to inhibit cAMP. Gαq coupled receptors promote phospholipase C (PLC) to produce IP3, and IP3 can increase intracellular Ca2+ concentration. GPCR Panel With high general availability, which provides over 150 GPCR targets, covering wide families like 5-Hydroxytryptamine, adrenoceptors, Acetylcholine, dopamine, glucagon, and opioid receptors and having the selectivity of multiple species. It can conduct similar GPCR poly-link interaction and screening, used to study new GPCR drug development technology, detect the expression properties of GPCR receptors, reveal new GPCR structure and function, and screen GPCR drugs. We also constructed different assay platforms, including cAMP assay, Ca2+ flux assay, IP3 assay, β-arrestin recruitment assay and reporter assay to detect the different second messengers produced by G protein submits and GPCR-mediated multiple signaling pathways. This research constructed stable cell lines expressing GPCRs and provided related assays, which could be used to detect compounds' effects on GPCR targets successfully. In addition, the GPCR panel was applicable to both activating and inhibitory models, and the establishment of this platform provides a powerful tool for drug development of GPCR-related targets. Citation Format: Xiaoyan Wang, Ning Sui, Dandan Hu, Ziwei Zhang, Tiejun Bing. GPCR panel establishment and application in drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4712.