Screening Of Compound Libraries Research Articles

Proteome integral solubility alteration via label-free DIA approach (PISA-DIA), game changer in drug target deconvolution.

Drug protein-target identification in past decades required screening compound libraries against known proteins to determine drugs binding to specific protein. Protein targets used in drug-target screening were selected predominantly used laborious genetic manipulation assays. In 2013, a team led by Pär Nordlund from Karolinska Institutet(Stockholm, Sweden) developed Cellular Thermal Shift Assay (CETSA), a method which, for the first time, enabled the possibility of drug protein-target identification in the complex cellular proteome. High throughput, quantitative mass spectrometry (MS) proteomics appeared as a compatible analytical method of choice to complement CETSA, aka Thermal Protein Profiling assay (TPP). Since the seminal CETSA-MS/ TPP-MS publications, different protein-target deconvolution strategies emerged including Proteome Integral Solubility Alteration (PISA). The work of Emery-Corbin etal.(Proteomics2024,2300644), titled Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA), introduces Data-Independent Acquisition (DIA) as a quantification method, opening new avenues in drug target-deconvolution field. Application of DIA for target deconvolution offers attractive alternative to widely used data dependent methodology.

Pharmacophore-guided in-silico discovery of SIRT1 inhibitors for targeted cancer therapy

Epigenetic modifier, Sirtuin (SIRTs) is a family of seven isoforms (SIRT1-7) and nicotinamide adenine dinucleotide (NAD+) dependent class III histone deacetylase (HDACs) protein. SIRT1 in association with the p53 protein can regulate crucial cell processes such as glucose metabolism, lipid metabolism, mitochondrial biogenesis, DNA repair, oxidative stress, apoptosis, and inflammation through the process of deacetylation. When SIRT1 deacetylates p53, it loses its tumor suppression property. To promote apoptosis and decrease cell proliferation by inhibiting SIRT1 protein and ultimately raising the acetylation of p53 to regain its tumor suppressor function. Though we have many SIRT1 protein inhibitors, they exhibited off-target effects and inefficiency at the clinical trial stage. This study has been executed to identify more potentially effective and reliable SIRT1 inhibitors that can perform better than the existing options. To do so, pharmacophore-based screening of compound libraries followed by virtual screening, pharmacokinetic, drug-likeness, and toxicity studies were conducted which gave 42 compounds to evaluate further. Subsequently, exhaustive molecular docking and molecular dynamics simulation predicted four potential hits to inhibit the SIRT1 protein better than the reference compound. Further studies such as principal components analysis, free energy landscape, and estimation of binding free energy were done which concluded Hit4 (PubChem ID: 55753455) to be a novel and potent SIRT1 small molecule inhibitor among the others. The total binding free energy for Hit4 was found to be -44.68kcal/mol much better than the reference complex i.e., -29.38kcal/mol.

Fluorescence-Based HTS Assays for Ion Channel Modulation in Drug Discovery Pipelines.

Ion channels represent a druggable family of transmembrane pore-forming proteins with important (patho)physiological functions. While electrophysiological measurement (manual patch clamp) remains the only direct method for detection of ion currents, it is a labor-intensive technique. Although automated patch clamp instruments have become available to date, their high costs limit their use to large pharma companies or commercial screening facilities. Therefore, fluorescence-based assays are particularly important for initial screening of compound libraries. Despite their numerous disadvantages, they are highly amenable to high-throughput screening and in many cases, no sophisticated instrumentation or materials are required. These features predispose them for implementation in early phases of drug discovery pipelines (hit identification), even in an academic environment. This review summarizes the advantages and pitfalls of individual methodological approaches for identification of ion channel modulators employing fluorescent probes (i. e., membrane potential and ion flux assays) with emphasis on practical aspects of their adaptation to high-throughput format.

Development of a high-throughput screening system targeting the protein-protein interactions between PRL and CNNM

Phosphatase of regenerating liver (PRL) is an oncogenic protein that promotes tumor progression by directly binding to cyclin M (CNNM) membrane proteins and inhibiting their Mg2+ efflux activity. In this study, we have developed a high-throughput screening system to detect the interactions between PRL and CNNM proteins based on homogenous time-resolved fluorescence resonance energy transfer (HTR-FRET, HTRF). We optimized the tag sequences attached to the recombinant proteins of the CNNM4 CBS domains and PRL3 lacking the carboxyl terminal CAAX motif, and successfully detected the interaction by observing the FRET signal in the mixture of the tagged proteins and fluorophore-conjugated antibodies. Moreover, we performed compound library screening using this system and discovered several compounds that could efficiently inhibit the PRL-CNNM interaction. Characterization of one candidate compound revealed that it was relatively stable compared with thienopyridone, a known inhibitor of the PRL-CNNM interaction. The candidate compound can also inhibit PRL function in cells: suppression of CNNM-dependent Mg2+ efflux, and has sufficient in vitro drug metabolism and pharmacokinetic properties. Overall, these results demonstrate the effectiveness of this screening system for identifying novel inhibitors of the PRL-CNNM interaction, which could contribute to the development of novel anti-cancer drugs.

Machine Learning-Driven Data Valuation for Optimizing High-Throughput Screening Pipelines.

In the rapidly evolving field of drug discovery, high-throughput screening (HTS) is essential for identifying bioactive compounds. This study introduces a novel application of data valuation, a concept for evaluating the importance of data points based on their impact, to enhance drug discovery pipelines. Our approach improves active learning for compound library screening, robustly identifies true and false positives in HTS data, and identifies important inactive samples in an imbalanced HTS training, all while accounting for computational efficiency. We demonstrate that importance-based methods enable more effective batch screening, reducing the need for extensive HTS. Machine learning models accurately differentiate true biological activity from assay artifacts, streamlining the drug discovery process. Additionally, importance undersampling aids in HTS data set balancing, improving machine learning performance without omitting crucial inactive samples. These advancements could significantly enhance the efficiency and accuracy of drug development.

Angelic acid triggers ferroptosis in colorectal cancer cells via targeting and impairing NRF2 protein stability.

Ferroptosis is a unique programmed cell death driven by iron-dependent phospholipid peroxidation. Tumor cells that escape from the conventional therapies appear more sensitive to ferroptosis. Therefore, it is extremely urgent to find safe and efficient active ingredients that induce ferroptosis in tumor cells. Herein, we identified that angelic acid, as a potent anti-tumor active ingredient in Angelica sinensis, profoundly sensitizes CRC cells to ferroptosis via a natural compound library screen. We revealed that angelic acid treatment is sufficient to predispose CRC cells to ferroptosis phenotype, evidenced by malondialdehyde (MDA) accumulation, lipid peroxidation, and upregulation of ferroptosis-associated markers CHAC1 and PTGS2, which is abolished by ferroptosis inhibitor Fer-1. Moreover, the results of network pharmacology showed that NRF2 is critical for angelic acid-mediated CRC cell ferroptosis. Mechanistically, angelic acid exerted its ferroptosis induction via directly binding and facilitating the degradation of NRF2. In addition, the syngeneic mouse models revealed that angelic acid boosts CRC cell sensitivity to ferroptosis inducer sulfasalazine with essentially no toxicity in vivo. Collectively, our findings highlighted a previously unrecognized anti-tumor mechanism of angelic acid and represented an appealing therapeutic strategy for CRC treatment.

Lymphoma and Leukemia Cell Vulnerabilities and Resistance Identified by Compound Library Screens.

Response to anticancer agents is often restricted to subsets of patients. Recognition of factors underlying this heterogeneity and identification of biomarkers associated with response to drugs would greatly improve the efficacy of drug treatment. Platforms that can comprehensively map cellular response to compounds in high-throughput provide a unique tool to identify associated biomarkers and provide hypotheses for mechanisms underlying variable response. Such screens can be performed on cell lines and short-term cultures of primary cells to take advantage of the respective models' strength, which include, e.g., the ability to silence genes or introduce somatic mutations to cell lines. Cohorts of patient samples represent the natural diversity of cancers, including rarer mutations and combinatorial patterns of mutations that are often absent from existing cell lines. We here summarize a simple and scalable method for the measurement of viability after drug exposure based on ATP measurements as a surrogate for viability, which we use to measure and understand drug response in cell lines and primary cells.

Enhancing Cellular Homeostasis: Targeted Botanical Compounds Boost Cellular Health Functions in Normal and Premature Aging Fibroblasts.

The human skin, the body's largest organ, undergoes continuous renewal but is significantly impacted by aging, which impairs its function and leads to visible changes. This study aimed to identify botanical compounds that mimic the anti-aging effects of baricitinib, a known JAK1/2 inhibitor. Through in silico screening of a botanical compound library, 14 potential candidates were identified, and 7 were further analyzed for their effects on cellular aging. The compounds were tested on both normal aged fibroblasts and premature aging fibroblasts derived from patients with Hutchinson-Gilford Progeria Syndrome (HGPS). Results showed that these botanical compounds effectively inhibited the JAK/STAT pathway, reduced the levels of phosphorylated STAT1 and STAT3, and ameliorated phenotypic changes associated with cellular aging. Treatments improved cell proliferation, reduced senescence markers, and enhanced autophagy without inducing cytotoxicity. Compounds, such as Resveratrol, Bisdemethoxycurcumin, Pinosylvin, Methyl P-Hydroxycinnamate, cis-Pterostilbene, and (+)-Gallocatechin, demonstrated significant improvements in both control and HGPS fibroblasts. These findings suggest that these botanical compounds have the potential to mitigate age-related cellular alterations, offering promising strategies for anti-aging therapies, particularly for skin health. Further in vivo studies are warranted to validate these results and explore their therapeutic applications.

Genetically encoded fluorescent sensors for visualizing polyamine levels, uptake, and distribution.

Polyamines are abundant and physiologically essential biomolecules that play a role in numerous processes, but are disrupted in diseases such as cancer, and cardiovascular and neurological disorders. Despite their importance, measuring free polyamine concentrations and monitoring their metabolism and uptake in cells in real-time remains impossible due to the lack of appropriate biosensors. Here we engineered, characterized, and validated the first genetically encoded biosensors for polyamines, named iPASnFRs. We demonstrate the utility of iPASnFR for detecting polyamine import into mammalian cells, to the cytoplasm, mitochondria, and the nucleus. We demonstrate that these sensors are useful to probe the activity of polyamine transporters and to uncover biochemical pathways underlying the distribution of polyamines amongst organelles. The sensors powered a high-throughput small molecule compound library screen, revealing multiple compounds in different chemical classes that strongly modulate cellular polyamine levels. These sensors will be powerful tools to investigate the complex interplay between polyamine uptake and metabolic pathways, address open questions about their role in health and disease, and enable screening for therapeutic polyamine modulators.

Exploring Natural Compounds Targeting the Bacterial SHV Protein to Combat Antibiotic Resistance: A Biocomputational Study

Background: Antibiotic-resistant (AR) bacteria are rapidly spreading worldwide, posing a serious threat to antibiotic efficacy. Bacterial infections have emerged as a persistent threat following decades of antibiotic use. Sulfhydryl variable (SHV) is a well-known bacterial enzyme linked to AR. SHV has a high degree of genetic diversity, resulting in the existence of numerous distinct variants. Methods: The PyRx AutoDock VINA was used to conduct in-silico screening of a natural compound library to assess their interaction with the SHV-1 protein. SwissADME web tools were used to predict the physicochemical, drug-likeness, and ADMET properties of the selected compounds.Result: The compounds PSCdb00708, PSCdb00149, PSCdb00698, and PSCdb00175 bind strongly to the SHV-1 protein and interact strongly with the SHV-1 active site residues, as well as having several amino acid residue interactions in common with avibactam. These compounds exhibited higher binding affinity values than avibactam. Furthermore, these compounds demonstrated no violation of drug-likeness.Conclusion: The compounds PSCdb00708, PSCdb00149, PSCdb00698, and PSCdb00175 can be employed as SHV-1 inhibitors in the management of AR. However, experimental validation is required to optimize them as SHV-1 inhibitors.Keywords: Antibiotic-resistant; Antibiotic; Virtual screening; AutoDock; SHV-1 protein

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.

Allosteric modulation of the CXCR4:CXCL12 axis by targeting receptor nanoclustering via the TMV-TMVI domain

CXCR4 is a ubiquitously expressed chemokine receptor that regulates leukocyte trafficking and arrest in both homeostatic and pathological states. It also participates in organogenesis, HIV-1 infection, and tumor development. Despite the potential therapeutic benefit of CXCR4 antagonists, only one, plerixafor (AMD3100), which blocks the ligand-binding site, has reached the clinic. Recent advances in imaging and biophysical techniques have provided a richer understanding of the membrane organization and dynamics of this receptor. Activation of CXCR4 by CXCL12 reduces the number of CXCR4 monomers/dimers at the cell membrane and increases the formation of large nanoclusters, which are largely immobile and are required for correct cell orientation to chemoattractant gradients. Mechanistically, CXCR4 activation involves a structural motif defined by residues in TMV and TMVI. Using this structural motif as a template, we performed in silico molecular modeling followed by in vitro screening of a small compound library to identify negative allosteric modulators of CXCR4 that do not affect CXCL12 binding. We identified AGR1.137, a small molecule that abolishes CXCL12-mediated receptor nanoclustering and dynamics and blocks the ability of cells to sense CXCL12 gradients both in vitro and in vivo while preserving ligand binding and receptor internalization.

Pharmacophore-Based Study: An In Silico Perspective for the Identification of Potential New Delhi Metallo-β-lactamase-1 (NDM-1) Inhibitors.

In the ongoing battle against antibiotic-resistant bacteria, New Delhi metallo-β-lactamase-1 (NDM-1) has emerged as a significant therapeutic challenge due to its ability to confer resistance to a broad range of β-lactam antibiotics. This study presents a pharmacophore-based virtual screening, docking, and molecular dynamics simulation approach for the identification of potential inhibitors targeting NDM-1, a critical enzyme associated with antibiotic resistance. Through the generation of a pharmacophore model and subsequent virtual screening of compound libraries, candidate molecules (ZINC29142850 (Z1), ZINC78607001 (Z2), and ZINC94303138 (Z3)) were prioritized based on their similarity to known NDM-1 binder (hydrolyzed oxacillin (0WO)). Molecular docking studies further elucidated the binding modes and affinities of the selected compounds towards the active site of NDM-1. These compounds demonstrated superior binding affinities to the enzyme compared to a control compound (-7.30 kcal/mol), with binding scores of -7.13, -7.92, and -8.10 kcal/mol, respectively. Binding interactions within NDM-1's active site showed significant interactions with critical residues such as His250, Asn220, and Trp93 for these compounds. Subsequent molecular dynamics simulations were conducted to assess the stability of the ligand-enzyme complexes, showing low root mean square deviation (RMSD) values between 0.5 and 0.7 nm for Z1, Z2, which indicate high stability. Z2's compactness in principal component analysis (PCA) suggests that it can stabilize particular protein conformations more efficiently. Z2 displays a very cohesive landscape with a notable deep basin, suggesting a very persistent conformational state induced by the ligand, indicating robust binding and perhaps efficient inhibition. Z2 demonstrates the highest binding affinity among the examined compounds with a binding free energy of -25.68 kcal/mol, suggesting that it could offer effective inhibition of NDM-1. This study highlights the efficacy of computational tools in identifying novel antimicrobial agents against resistant bacteria, accelerating drug discovery processes.

Identification of STAM-binding protein as a target for the treatment of gemcitabine resistance pancreatic cancer in a nutrient-poor microenvironment

Pancreatic cancer (PC) is a highly malignant solid tumor whose resistance to gemcitabine (GEM) chemotherapy is a major cause of poor patient prognosis. Although PC is known to thrive on malnutrition, the mechanism underlying its chemotherapy resistance remains unclear. The current study analyzed clinical tissue sample databases using bioinformatics tools and observed significantly upregulated expression of the deubiquitinase STAMBP in PC tissues. Functional experiments revealed that STAMBP knockdown remarkably increases GEM sensitivity in PC cells. Multiple omics analyses suggested that STAMBP enhances aerobic glycolysis and suppresses mitochondrial respiration to increase GEM resistance in PC both in vitro and in vivo. STAMBP knockdown decreased PDK1 levels, an essential regulator of the aerobic glycolytic process, in several cancers. Mechanistically, STAMBP promoted the PDK1-mediated Warburg effect and chemotherapy resistance by modulating E2F1 via direct binding to E2F1 and suppressing its degradation and ubiquitination. High-throughput compound library screening using three-dimensional protein structure analysis and drug screening identified the FDA drug entrectinib as a potent GEM sensitizer and STAMBP inhibitor, augmenting the antitumor effect of GEM in a patient-derived xenograft (PDX) model. Overall, we established a novel mechanism, via the STAMBP–E2F1–PDK1 axis, by which PC cells become chemoresistant in a nutrient-poor tumor microenvironment.

An artificial intelligence accelerated virtual screening platform for drug discovery

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

New Highly Selective Antivirals for Chikungunya Virus identified from the Screening of a Drug-Like Compound Library.

Chikungunya fever is a mosquito-borne disease caused by Chikungunya virus (CHIKV). Treatment of CHIKV infections is currently supportive and does not limit viral replication or symptoms of persistent chronic arthritis. Although there are multiple compounds reported as antivirals active against CHIKV in vitro, there are still no effective and safe antivirals. Thus, active research aims at the identification of new chemical structures with antiviral activity. Here, we report the screen of the Pandemic Response Box library of small molecules against a fully infectious CHIKV reporter virus. Our screening approach successfully identified previously reported CHIKV antiviral compounds within this library and further expanded potentially active hits, supporting the use of reporter-virus-based assays in high-throughput screening format as a reliable tool for antiviral drug discovery. Four molecules were identified as potential drug candidates against CHIKV: MMV1634402 (Brilacidin) and MMV102270 (Diphyllin), which were previously shown to present broad-spectrum antiviral activities, in addition to MMV1578574 (Eravacycline), and the antifungal MMV689401 (Fluopicolide), for which their antiviral potential is uncovered here.

Identification of novel PHGDH inhibitors based on computational investigation: an all-in-one combination strategy to develop potential anti-cancer candidates.

Biological studies have elucidated that phosphoglycerate dehydrogenase (PHGDH) is the rate-limiting enzyme in the serine synthesis pathway in humans that is abnormally expressed in numerous cancers. Inhibition of the PHGDH activity is thought to be an attractive approach for novel anti-cancer therapy. The development of structurally diverse novel PHGDH inhibitors with high efficiency and low toxicity is a promising drug discovery strategy. A ligand-based 3D-QSAR pharmacophore model was developed using the HypoGen algorithm methodology of Discovery Studio. The selected pharmacophore model was further validated by test set validation, cost analysis, and Fischer randomization validation and was then used as a 3D query to screen compound libraries with various chemical scaffolds. The estimated activity, drug-likeness, molecular docking, growing scaffold, and molecular dynamics simulation processes were applied in combination to reduce the number of virtual hits. The potential candidates against PHGDH were screened based on estimated activity, docking scores, predictive absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties, and molecular dynamics simulation. Finally, an all-in-one combination was employed successfully to design and develop three potential anti-cancer candidates.

Identification of Environmental Compounds That May Trigger Early Female Puberty by Activating Human GnRHR and KISS1R.

There has been an alarming trend toward earlier puberty in girls, suggesting the influence of an environmental factor(s). As the reactivation of the reproductive axis during puberty is thought to be mediated by the hypothalamic neuropeptides kisspeptin and gonadotropin-releasing hormone (GnRH), we asked whether an environmental compound might activate the kisspeptin (KISS1R) or GnRH receptor (GnRHR). We used GnRHR or KISS1R-expressing HEK293 cells to screen the Tox21 10K compound library, a compendium of pharmaceuticals and environmental compounds, for GnRHR and KISS1R activation. Agonists were identified using Ca2+ flux and phosphorylated extracellularly regulated kinase (p-ERK) detection assays. Follow-up studies included measurement of genes known to be upregulated upon receptor activation using relevant murine or human cell lines and molecular docking simulation. Musk ambrette was identified as a KISS1R agonist, and treatment with musk ambrette led to increased expression of Gnrh1 in murine and human hypothalamic cells and expansion of GnRH neuronal area in developing zebrafish larvae. Molecular docking demonstrated that musk ambrette interacts with the His309, Gln122, and Gln123 residues of the KISS1R. A group of cholinergic agonists with structures similar to methacholine was identified as GnRHR agonists. When applied to murine gonadotrope cells, these agonists upregulated Fos, Jun, and/or Egr1. Molecular docking revealed a potential interaction between GnRHR and 5 agonists, with Asn305 constituting the most conservative GnRHR binding site. In summary, using a Tox21 10K compound library screen combined with cellular, molecular, and structural biology techniques, we have identified novel environmental agents that may activate the human KISS1R or GnRHR.

Uncovering the antidiabetic potential of heart-friendly and diuretic bioactive compounds through computer-based drug design

Avicenna, a pioneer of modern medicine, recommended diuretic therapy to treat diabetes. Like Avicenna's approach, current medicine frequently prescribes oral antidiabetic pills with diuretic and hypoglycemic effects by blocking the absorption of sodium and glucose. To this end, the paper sought natural compounds with potential antidiabetic, cardioprotective, and diuretic properties through computer-based drug design (CADD) techniques, targeting the inhibition of SGLT2 proteins. We identified several bioactive compounds from various sources exhibiting potential multifunctionality through high-throughput virtual screening (HTVS) of vast compound libraries. Subsequent molecular docking and dynamics simulations were employed to assess these compounds' binding efficacy and stability with their respective targets, alongside ADMET prediction, to evaluate their pharmacokinetic and safety profiles. The top hits, phenylalanyltryptophan, tyrosyl-tryptophan, tyrosyl-tyrosine, celecoxib, and DIBOA trihexose, had superior docking scores ranging from −11,4 to −9,8 kcal/mol. The molecular dynamics simulations displayed steady interactions between target proteins and biocompounds throughout 100 ns without significant conformational shifts. These findings lay the groundwork for lead optimization and preclinical testing. This meticulous process ensures the safety and efficacy of potential treatments, marking a meaningful step toward developing innovative treatments for managing diabetes and its associated health complications.