Structure-based Pharmacophore Model Research Articles

In Silico Design, Chemical Synthesis, Biophysical and in Vitro Evaluation for the Identification of 1-Ethyl-1H-Pyrazolo[3,4-b]Pyridine-Based BRD9 Binders.

In this work, we report the identification of novel bromodomain-containing protein 9 (BRD9) binders through a virtual screening based on our developed 3D structure-based pharmacophore model. The in silico workflow here described led to the identification of a promising initial hit (1) featuring the 1-ethyl-1H-pyrazolo[3,4-b]pyridine motif which represented an unexplored chemotype for the development of a new class of BRD9 ligands. The encouraging biophysical results achieved for compound 1 prompted us to explore further tailored structural modification around the C-4 and C-6 positions of the central core. Hence, the design and synthesis of a set of 19 derivatives (2-20) were performed to extensively investigate the chemical space of BRD9 binding site. Among them, four compounds (5, 11, 12, and 19) stood out in biophysical assays as new valuable BRD9 ligands featuring IC50 values in the low-micromolar range. Noteworthy, a promising antiproliferative activity was detected in vitro for compound 5 on HeLa and A375 cancer cell line. The successful combination and application of in silico tools, chemical synthesis, and biological assays allowed to identify novel BRD9 binders and to expand the arsenal of promising chemical entities amenable to the recognition of this important epigenetic target.

Novel glucokinase activators: A structure-based pharmacophore modeling, QSAR analysis, and molecular dynamics approach

Novel glucokinase activators: A structure-based pharmacophore modeling, QSAR analysis, and molecular dynamics approach

Identification of Novel Bromodomain-Containing Protein 4 (BRD4) Binders through 3D Pharmacophore-Based Repositioning Screening Campaign.

A 3D structure-based pharmacophore model built for bromodomain-containing protein 4 (BRD4) is reported here, specifically developed for investigating and identifying the key structural features of the (+)-JQ1 known inhibitor within the BRD4 binding site. Using this pharmacophore model, 273 synthesized and purchased compounds previously considered for other targets but yielding poor results were screened in a drug repositioning campaign. Subsequently, only six compounds showed potential as BRD4 binders and were subjected to further biophysical and biochemical assays. Compounds 2, 5, and 6 showed high affinity for BRD4, with IC50 values of 0.60 ± 0.25 µM, 3.46 ± 1.22 µM, and 4.66 ± 0.52 µM, respectively. Additionally, these compounds were tested against two other bromodomains, BRD3 and BRD9, and two of them showed high selectivity for BRD4. The reported 3D structure-based pharmacophore model proves to be a straightforward and useful tool for selecting novel BRD4 ligands.

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach.

Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth. This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model. This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP. The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis. Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

Virtual Screening of Indonesian Herbal Compounds with Neuraminidase Inhibitor Activity against N2 Influenza Virus Protein: An in silico Study

Background: Neuraminidase inhibitor (NAI) is one of anti-influenza drugs recommended for use by the World Health Organization (WHO). However, after treatment with NAI drugs in human, resistance to influenza antiviral drugs is begun to rise. Therefore, identification of compounds from Indonesian herbal plants as natural inhibitors of the influenza virus neuraminidase protein needs to be conducted for the development of new anti-influenza drugs.Materials and methods: The crystal structure of the neuraminidase protein complex used in this study was obtained from the Protein Data Bank (PDB). Structure-based pharmacophore modeling was performed using LigandScout version 4.4.5 software. Indonesian herbal plant compounds were collected from the HerbalDB database. Protein and ligand processing was carried out using Autodock 4.2 software. The 3D interaction visualization was carried out with Autodock software, while 2D interaction visualization was carried out with LigPlot software. To determine the toxicity and drug-likeliness of the ligand, the test ligands that had the best docking results were predicted using SwissADME and AdmetSAR.Results: From the virtual screening results, 24 hits were found, and five compounds had the best binding energy among the 24 tested compounds, these were pollenitin (ΔG=-7.22 kcal/mol), OPC-4:0 (ΔG=-7.11 kcal/mol), 6-hydroxykaempferol (ΔG=-7.08 kcal/mol), 5,8-dihydroxy-7,4'-dimethoxyflavone (ΔG=-7.07 kcal/mol), and 3,5,6,7-tetrahydroxy-4'-methoxyflavone (ΔG=-6.95 kcal/mol). The best five compounds were then chosen for further analysis.Conclusion: OPC-4:0 is found to be the best compound for the NAI based on its binding energy, pharmacokinetics, toxicity, and drug-likeliness. Thus, OPC-4:0 might be a potential candidate as a NAI of HxN2 virus. Keywords: influenza, molecular docking, neuraminidase, resistance, virtual screening

Repositioning of Small Molecules through the Inverse Virtual Screening in silico Tool: Case of Benzothiazole-Based Inhibitors of Soluble Epoxide Hydrolase (sEH).

Computational techniques accelerate drug discovery by identifying bioactive compounds for specific targets, optimizing molecules with moderate activity, or facilitating the repositioning of inactive items onto new targets. Among them, the Inverse Virtual Screening (IVS) approach is aimed at the evaluation of one or a small set of molecules against a panel of targets for addressing target identification. In this work, a focused library of benzothiazole-based compounds was re-investigated by IVS. Four items, originally synthesized and tested on bromodomain-containing protein 9 (BRD9) but yielding poor binding, were critically re-analyzed, disclosing only a partial fit with 3D structure-based pharmacophore models, which, in the meanwhile, were developed for this target. Afterwards, these compounds were re-evaluated through IVS on a panel of proteins involved in inflammation and cancer, identifying soluble epoxide hydrolase (sEH) as a putative interacting target. Three items were subsequently confirmed as able to interfere with sEH activity, leading to inhibition percentages spanning from 70 % up to 30 % when tested at 10 μM. Finally, one benzothiazole-based compound emerged as the most promising inhibitor featuring an IC50 in the low micromolar range (IC50=6.62±0.13 μM). Our data confirm IVS as a predictive tool for accelerating the target identification and repositioning processes.

Developing Dynamic Structure-Based Pharmacophore and ML-Trained QSAR Models for the Discovery of Novel Resistance-Free RET Tyrosine Kinase Inhibitors Through Extensive MD Trajectories and NRI Analysis.

Activation of RET tyrosine kinase plays a critical role in the pathogenesis of various cancers, including non-small cell lung cancer, papillary thyroid cancers, multiple endocrine neoplasia type 2A and 2B (MEN2A, MEN2B), and familial medullary thyroid cancer. Gene fusions and point mutations in the RET proto-oncogene result in constitutive activation of RET signaling pathways. Consequently, developing effective inhibitors to target RET is of utmost importance. Small molecules have shown promise as inhibitors by binding to the kinase domain of RET and blocking its enzymatic activity. However, the emergence of resistance due to single amino acid changes poses a significant challenge. In this study, a structure-based dynamic pharmacophore-driven approach using E-pharmacophore modeling from molecular dynamics trajectories is proposed to select low-energy favorable hypotheses, and ML-trained QSAR models to predict pIC50 values of compounds. For this aim, extensive small molecule libraries were screened using developed ligand-based models, and potent compounds that are capable of inhibiting RET activation were proposed.

PharmaCore: The Automatic Generation of 3D Structure-Based Pharmacophore Models from Protein/Ligand Complexes.

In this work, we present PharmaCore: a new, completely automatic workflow aimed at generating three-dimensional (3D) structure-based pharmacophore models toward any target of interest. The proposed approach relies on using cocrystallized ligands to create the input files for generating the pharmacophore hypotheses, integrating not only the three-dimensional structural information on the ligand but also data concerning the binding mode of these molecules put in the protein cavity. We developed a Python library that, starting from the specific UniProt ID of the protein under investigation as the only element that requires user intervention, subsequently collects and aligns the corresponding structures bearing a known ligand in a fully automated fashion, bringing them all into the same coordinate system. The protocol includes a final phase in which the aligned small molecules are used to produce the pharmacophore hypotheses directly onto the protein structure using a specific software, e.g., Phase (Schrödinger LLC). To validate the entire procedure and highlight the possible applications in the field of drug discovery and repositioning, we first generated pharmacophores for soluble epoxide hydrolase (sEH) and compared with already-published ones. Then, we reproduced the binding profile of a reported selective binder of ATAD2 bromodomain (AM879), testing it against a panel of 1741 pharmacophores related to 16 epigenetic proteins and automatically generated with PharmaCore, finally disclosing putative unprecedented off-targets. The computational predictions were successfully validated with AlphaScreen assays, highlighting the applicability of the proposed workflow in drug discovery and repositioning. Finally, the process was also validated on tankyrase 2 and SARS-CoV-2 MPro, confirming the robustness of PharmaCore.

A structure-based pharmacophore modelling approach to identify and design new neprilysin (NEP) inhibitors: An in silico-based investigation

Neutral endopeptidase or neprilysin (NEP) cleaves the natriuretic peptides, bradykinin, endothelin, angiotensin II, amyloid β protein, substance P, etc., thus modulating their effects on heart, kidney, and other organs. NEP has a proven role in hypertension, heart disease, renal disease, Alzheimer's, diabetes, and some cancers. NEP inhibitor development has been in focus since the US FDA approved a combination therapy of angiotensin II type 1 receptor inhibitor (valsartan) and NEP inhibitor (sacubitril) for use in heart failure. Considering the importance of NEP inhibitors the present work focuses on the designing of a potential lead for NEP inhibition. A structure-based pharmacophore modelling approach was employed to identify NEP inhibitors from the pool of 1140 chemical entities obtained from the ZINC database. Based on the docking score and pivotal interactions, ten molecules were selected and subjected to binding free energy calculations and ADMET predictions. The top two compounds were studied further by molecular dynamics simulations to determine the stability of the ligand-receptor complex. ZINC0000004684268, a phenylalanine derivative, showed affinity and complex stability comparable to sacubitril. However, in silico studies indicated that it may have poor pharmacokinetic parameters. Therefore, the molecule was optimized using bioisosteric replacements, keeping the phenylalanine moiety intact, to obtain five potential lead molecules with an acceptable pharmacokinetic profile. The works thus open up the scope to further corroborate the present in silico findings with the biological analysis.

Hit discovery of potential CDK8 inhibitors and analysis of amino acid mutations for cancer therapy through computer-aided drug discovery

Cyclin-dependent kinase 8 (CDK8) has emerged as a promising target for inhibiting cancer cell function, intensifying efforts towards the development of CDK8 inhibitors as potential cancer therapeutics. Mutations in CDK8, a protein kinase, are also implicated as a primary factor associated with tumor formation. In this study, we identified potential inhibitors through virtual screening for CDK8 and single amino acid mutations in CDK8, namely D173A (Aspartate 173 mutate to Alanine), D189N (Aspartate 189 mutate to Asparagine), T196A (Threonine 196 mutate to Alanine) and T196D (Threonine 196 mutate to Aspartate). Four databases (CHEMBEL, ZINC, MCULE, and MolPort) containing 65,209,131 molecules have been searched to identify new inhibitors for CDK8 and its single mutations. In the first step, structure-based pharmacophore modeling in the Pharmit server was used to select the compounds to know the inhibitors. Then molecules with better predicted drug-like molecule properties were selected. The final filter used to select more effective inhibitors among the previously selected molecules was molecular docking. Finally, 13 hits for CDK8, 11 hits for D173A, 11 hits for D189N, 15 hits for T196A, and 12 hits for T196D were considered potential inhibitors. A majority of the virtual screening hits exhibited satisfactorily predict pharmacokinetic characteristics and toxicity properties.

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein’s crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

In silico analysis of small molecule compounds based on pharmacophore- and backbone-leaping peroxiredoxin 1 (PRDX1) inhibitors

Abnormal expression of PRDX has been found to play a significant role in the growth of colorectal cancer and other types of tumors. Despite the identification of several PRDX1 inhibitory compounds in recent years, none of them have been utilized in clinical treatments. Therefore, we conducted a virtual screening of 210,331 small molecules from the SPECS library using PRDX1 and multiple methods. From this screening, we identified 13 compounds with the highest scores from the molecular docking analysis. To further validate the accuracy of our pharmacophore model, we constructed a structure-based pharmacophore model and analyzed the receiver operating characteristic curve (ROC curve). Through this process, we selected nine compounds using skeleton jumping and virtual screening based on the highest pharmacophore model scores. Subsequently, we examined the ADMET properties of these nine compounds to assess their drug-forming potential, resulting in three compounds with the best drug properties. Finally, we assessed the binding stability of these three candidate molecules to proteins using molecular dynamics and MM-PBSA calculations. After a comprehensive evaluation, we found that compounds 6 and 9 formed stable complexes with PRDX1 proteins and could potentially serve as competitive inhibitors of PRDX1 substrates. Communicated by Ramaswamy H. Sarma

Mutant anaplastic lymphoma kinase inhibitor identification by integrated in silico approaches

ABSTRACT Non-small-cell lung cancer (NSCLC) is the primary form of lung cancer globally and remains a leading cause of mortality. Anaplastic lymphoma kinase (ALK) mutations, such as I1171N + L1198H, have been discovered to confer resistance to current ALK inhibitors, reducing their therapeutic effectiveness. Addressing drug resistance necessitates exploring selective inhibitors for innovative therapeutic approaches. In this study, a structure-based pharmacophore model, using ALK-approved inhibitors, was developed to screen an In-house database for potential mutant ALK inhibitors. Compounds with requisite pharmacophoric features were evaluated for binding potential against the I1171N + L1198H ALK mutant phenotype. Selected hits underwent assessment for chemical reactivity, and dynamics stability. The study identified five chemical scaffolds (NS1-5) with favorable binding modes and pharmacokinetic properties. The conformational ensembles featured the average RMSD values, ranging from 0.4 to 0.6 nm. RMSF analysis revealed consistent side chain fluctuations with reduced flexibility, while Rog analysis indicated convergence of most complexes. NS1 and NS5, in particular emerged as promising candidates, exhibiting remarkable performance than others, with binding free energies of −210.12 ± 9.94 and −163.68 ± 11.14 kcal/mol, respectively. These findings thus suggest further exploration and optimisation of NS1 and NS5 for mutant ALK inhibitors for the treatment of NSCLC.

Discovery of dual kinase inhibitors targeting VEGFR2 and FAK: structure-based pharmacophore modeling, virtual screening, and molecular docking studies

VEGFR2 and FAK signaling pathways are interconnected and have synergistic effects on tumor angiogenesis, growth, and metastasis. Thus, instead of the conventional targeting of each of these proteins individually with a specific inhibitor, the present work aimed to discover novel dual inhibitors targeting both VEGFR2 and FAK exploiting their association. To this end, receptor-based pharmacophore modeling technique was opted to generate 3D pharmacophore models for VEGFR2 and FAK type II kinase inhibitors. The generated pharmacophore models were validated by assessing their ability to discriminate between active and decoy compounds in a pre-compiled test set of VEGFR2 and FAK active compounds and decoys. ZINCPharmer web tool was then used to screen the ZINC database purchasable subset using the validated pharmacophore models retrieving 42,616 hits for VEGFR2 and 28,475 hits for FAK. Subsequently, they were filtered using various filters leaving 13,023 and 6,832 survived compounds for VEGFR2 and FAK, respectively, with 124 common compounds. Based on molecular docking simulations, thirteen compounds were found to satisfy all necessary interactions with VEGFR2 and FAK kinase domains. Thus, they are predicted to have a possible dual VEGFR2/FAK inhibitory activity. Finally, SwissADME web tool showed that compound ZINC09875266 is not only promising in terms of binding pattern to our target kinases, but also in terms of pharmacokinetic properties.

Novel 5-Substituted Oxindole Derivatives as Bruton's Tyrosine Kinase Inhibitors: Design, Synthesis, Docking, Molecular Dynamics Simulation, and Biological Evaluation.

Bruton's tyrosine kinase (BTK) is a non-RTK cytoplasmic kinase predominantly expressed by hemopoietic lineages, particularly B-cells. A new oxindole-based focused library was designed to identify potent compounds targeting the BTK protein as anticancer agents. This study used rational approaches like structure-based pharmacophore modeling, docking, and ADME properties to select compounds. Molecular dynamics simulations carried out at 20 ns supported the stability of compound 9g within the binding pocket. All the compounds were synthesized and subjected to biological screening on two BTK-expressing cancer cell lines, RAMOS and K562; six non-BTK cancer cell lines, A549, HCT116 (parental and p53-/-), U2OS, JURKAT, and CCRF-CEM; and two non-malignant fibroblast lines, BJ and MRC-5. This study resulted in the identification of four new compounds, 9b, 9f, 9g, and 9h, possessing free binding energies of -10.8, -11.1, -11.3, and -10.8 kcal/mol, respectively, and displaying selective cytotoxicity against BTK-high RAMOS cells. Further analysis demonstrated the antiproliferative activity of 9h in RAMOS cells through selective inhibition of pBTK (Tyr223) without affecting Lyn and Syk, upstream proteins in the BCR signaling pathway. In conclusion, we identified a promising oxindole derivative (9h) that shows specificity in modulating BTK signaling pathways.

Developing dynamic structure-based pharmacophore and QSAR models for the discovery of novel resistance-free RET tyrosine kinase inhibitors through extensive molecular dynamics trajectories for accelerated and successful virtual screening of ultra large ligand libraries

Developing dynamic structure-based pharmacophore and QSAR models for the discovery of novel resistance-free RET tyrosine kinase inhibitors through extensive molecular dynamics trajectories for accelerated and successful virtual screening of ultra large ligand libraries

Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modeling, molecular docking, MM-GBSA calculations and molecular dynamics studies.

Pin1 is a pivotal player in interactions with a diverse array of phosphorylated proteins closely linked to critical processes such as carcinogenesis and tumor suppression. Its axial role in cancer initiation and progression, coupled with its overexpression and activation in various cancers render it a potential candidate for the development of targeted therapeutics. While several known Pin1 inhibitors possess favorable enzymatic profiles, their cellular efficacy often falls short. Consequently, the pursuit of novel Pin1 inhibitors has gained considerable attention in the field of medicinal chemistry. In this study, we employed the Phase tool from Schrödinger to construct a structure-based pharmacophore model. Subsequently, 449,008 natural products (NPs) from the SN3 database underwent screening to identify compounds sharing pharmacophoric features with the native ligand. This resulted in 650 compounds, which then underwent molecular docking and binding free energy calculations. Among them, SN0021307, SN0449787 and SN0079231 showed better docking scores with values of -9.891, -7.579 and -7.097kcal/mol, respectively than the reference compound (-6.064kcal/mol). Also, SN0021307, SN0449787 and SN0079231 exhibited lower free binding energies (-57.12, -49.81 and -46.05kcal/mol, respectively) than the reference ligand (-37.75kcal/mol). Based on these studies, SN0021307, SN0449787, and SN0079231 showed better binding affinity that the reference compound. Further the validation of these findings, molecular dynamics simulations confirmed the stability of the ligand-receptor complex for 100ns with RMSD ranging from 0.6 to 1.8Å. Based on these promising results, these three phytochemicals emerge as promising lead compounds warranting comprehensive biological screening in future investigations. These compounds hold great potential for further exploration regarding their efficacy and safety as Pin1 inhibitors, which could usher in new avenues for combating cancer.

Exploration of SAM-I riboswitch inhibitors: In-Silico discovery of ligands to a new target employing multistage CADD approaches

Targeting riboswitches, regulatory elements responsible for the expression of essential genes, is taking central stage in the new era of antibacterial medications discovery due to the emergence of antibiotic resistance. The S-Adenosyl methionine-I (SAM-I) riboswitch works through transcription termination in a negative feedback manner modulated by the natural ligand SAM. SAM-I riboswitch is specific to bacteria and found mainly in gram-positive bacteria such as Bacillus anthracis. Analyzing the interactions of the co-crystallized structure of SAM-I riboswitch aptamer with its native ligand SAM clarified the needed chemical structural features to achieve binding. Acknowledging those features, structure-based and ligand-based pharmacophore models were built for filtration use in screening the OTAVA Chemical library and the Pubchem database. For further filtration enhancement, the physicochemical properties of SAM were used as a second filtration criterion. Compounds obtained as output from previous steps were energy minimized, and the lowest energy conformer structures were docked to SAM-I using MOE, v.2019.01. S-score and ligand interactions were used to assess the best hits. This yielded eight promising compounds to which molecular dynamics (MD) simulations with SAM-I aptamer were applied using GROMACS 2020.3 package affirming stable binding interactions and binding energetics similar to SAM. Moreover, pharmacokinetic and drug-like properties of those eight hits were assessed using SWISS-ADME. According to the combined computational methods and PK/Tox assessment, compound 20 was the most promising and thus can be considered a lead for future evaluation and optimization as a candidate new antibacterial agent targeting a new biomolecule eliciting a new mechanism of action.

Discovery of novel and potent dual-targeting AXL/HDAC2 inhibitors for colorectal cancer treatment via structure-based pharmacophore modelling, virtual screening, and molecular docking, molecular dynamics simulation studies, and biological evaluation

Colorectal cancer (CRC) is one of the most common cancers worldwide. Nowadays, owing to the complex mechanism of tumorigenesis, simultaneous inhibition of multiple targets is an important anticancer strategy. Recent studies have demonstrated receptor tyrosine kinase AXL (AXL) and histone deacetylase 2 (HDAC2) are closely associated with colorectal cancer. Herein, we identified five hit compounds concurrently targeting AXL and HDAC2 using virtual screening. Inhibitory experiments revealed these hit compounds potently inhibited AXL and HDAC2 in the nanomolar range. Among them, Hit-3 showed the strongest inhibitory effects which were better than that of the positive control groups. Additionally, MD assays showed that Hit-3 could bind stably to the AXL and HDAC2 active pockets. Further MTT assays demonstrated that Hit-3 showed potent anti-proliferative activity. Most importantly, Hit-3 exhibited significant in vivo antitumor efficacy in xenograft models. Collectively, this study is the first discovery of dual-targeting AXL/HDAC2 inhibitors for colorectal cancer treatment.

Marine-Derived Compounds as Potential Inhibitors of Hsp90 for Anticancer and Antimicrobial Drug Development: A Comprehensive In Silico Study.

Marine compounds constitute a diverse and invaluable resource for the discovery of bioactive substances with promising applications in the pharmaceutical development of anti-inflammatory and antibacterial agents. In this study, a comprehensive methodology was employed, encompassing pharmacophore modeling, virtual screening, in silico ADMET assessment (encompassing aspects of absorption, distribution, metabolism, excretion, and toxicity), and molecular dynamics simulations. These methods were applied to identify new inhibitors targeting the Hsp90 protein (heat shock protein 90), commencing with a diverse assembly of compounds sourced from marine origins. During the virtual screening phase, an extensive exploration was conducted on a dataset comprising 31,488 compounds sourced from the CMNPD database, characterized by a wide array of molecular structures. The principal objective was the development of structure-based pharmacophore models, a valuable approach when the pool of known ligands is limited. The pharmacophore model DDRRR was successfully constructed within the active sites of the Hsp90 crystal structure. Subsequent docking studies led to the identification of six compounds (CMNPD 22591, 9335, 10015, 360799, 15115, and 20988) demonstrating substantial binding affinities, each with values below -8.3 kcal/mol. In the realm of in silico ADMET predictions, five of these compounds exhibited favorable pharmacokinetic properties. Furthermore, molecular dynamics simulations and total binding energy calculations using MM-PBSA indicated that these marine-derived compounds formed exceptionally stable complexes with the Hsp90 receptor over a 100-nanosecond simulation period. These findings underscore the considerable potential of these novel marine compounds as promising candidates for anticancer and antimicrobial drug development.