A novel chromone-based as a potential inhibitor of ULK1 that modulates autophagy and induces apoptosis in colon cancer.

Aim: Chromones are promising for anticancer drug development. Methods & results: 12 chromone-based compounds were synthesized and tested against cancer cell lines. Compound 8 showed the highest cytotoxicity (LC50 3.2 µM) against colorectal cancer cells, surpassing 5-fluorouracil (LC50 4.2 µM). It suppressed colony formation, induced cell cycle arrest and triggered apoptotic cell death, confirmed by staining and apoptosis markers. Cell death was accompanied by enhanced reactive oxygen species formation and modulation of the autophagic machinery (autophagy marker light chain 3B (LC3B); adenosine monophosphate-activated protein kinase (AMPK); protein kinase B (PKB); UNC-51-like kinase (ULK)-1; and ULK2). Molecular docking and dynamic simulations revealed that compound 8 directly binds to ULK1. Conclusion: Compound 8 is a promising lead for autophagy-modulating anti-colon cancer drugs.

[Box: see text].

Targeted Therapy with Cisplatin-Loaded Calcium Citrate Nanoparticles Conjugated with Epidermal Growth Factor for Lung Cancer Treatment.

Lung cancer is the leading cause of cancer-related deaths worldwide with high incidence rates for new cases. Conventional cisplatin (CDDP) therapy has limitations due to severe side effects from nonspecific targeting. To address this challenge, nanomedicine offers targeted therapies. In this study, cisplatin-loaded calcium citrate nanoparticles conjugated with epidermal growth factor (CaCit@CDDP-EGF NPs) were synthesized. The resulting nanodrug had a size below 350 nm with a cation charge. Based on density functional theory (DFT), the CaCit@CDDP NP model containing two citrates substituted on two chlorides exhibited a favorable binding energy of -5.42 eV, and the calculated spectrum at 261 nm closely matched the experimental data. CaCit@CDDP-EGF NPs showed higher inhibition rates against EGFR-expressed and mutant carcinoma cells compared to those of cisplatin while displaying lower cytotoxicity to lung fibroblast cells. Integrating in vitro experiments with in silico studies, these nanoparticles hold promise as a novel nanomedicine for targeted therapy in clinical applications.

Evaluating solubility, stability, and inclusion complexation of oxyresveratrol with various ß-cyclodextrin derivatives using advanced computational techniques and experimental validation.

Oxyresveratrol (OXY), a natural stilbenoid in mulberry fruits, is known for its diverse pharmacological properties. However, its clinical use is hindered by low water solubility and limited bioavailability. In the present study, the inclusion complexes of OXY with ß-cyclodextrin (ßCD) and its three analogs, dimethyl-ß-cyclodextrin (DMßCD), hydroxypropyl-ß-cyclodextrin (HPßCD) and sulfobutylether-ß-cyclodextrin (SBEßCD), were investigated using in silico and in vitro studies. Molecular docking revealed two binding orientations of OXY, namely, 4',6'-dihydroxyphenyl (A-form) and 5,7-benzenediol ring (B-form). Molecular Dynamics simulations suggested the formation of inclusion complexes with ßCDs through two distinct orientations, with OXY/SBEßCD exhibiting maximum atom contacts and the lowest solvent-exposed area in the hydrophobic cavity. These results corresponded well with the highest binding affinity observed in OXY/SBEßCD when assessed using the MM/GBSA method. Beyond traditional simulation methods, Ligand-binding Parallel Cascade Selection Molecular Dynamics method was employed to investigate how the drug enters and accommodates within the hydrophobic cavity. The in silico results aligned with stability constants: SBEßCD (2060 M-1), HPßCD (1860 M-1), DMßCD (1700 M-1), and ßCD (1420 M-1). All complexes exhibited a 1:1 binding mode (AL type), with SBEßCD enhancing OXY solubility (25-fold). SEM micrographs, DSC thermograms, FT-IR and 1H NMR spectra confirm the inclusion complex formation, revealing novel surface morphologies, distinctive thermal behaviors, and new peaks. Notably, the inhibitory impact on the proliferation of breast cancer cell lines, MCF-7, exhibited by inclusion complexes particularly OXY/DMßCD, OXY/HPßCD, and OXY/SBEßCD were markedly superior compared to that of OXY alone.

Alpha and gamma mangostins inhibit wild-type B SARS-CoV-2 more effectively than the SARS-CoV-2 variants and the major target is unlikely the 3C-like protease.

Background: Anti-SARS-CoV-2 and immunomodulatory drugs are important for treating clinically severe patients with respiratory distress symptoms. Alpha- and gamma-mangostins (AM and GM) were previously reported as potential 3C-like protease (3CLpro) and Angiotensin-converting enzyme receptor 2 (ACE2)-binding inhibitors in silico. Objective: We aimed to evaluate two active compounds, AM and GM, from Garcinia mangostana for their antivirals against SARS-CoV-2 in live virus culture systems and their cytotoxicities using standard methods. Also, we aimed to prove whether 3CLpro and ACE2 neutralization were major targets and explored whether any additional targets existed. Methods: We tested the translation and replication efficiencies of SARS-CoV-2 in the presence of AM and GM. Initial and subgenomic translations were evaluated by immunofluorescence of SARS-CoV-2 3CLpro and N expressions at 16 h after infection. The viral genome was quantified and compared with the untreated group. We also evaluated the efficacies and cytotoxicities of AM and GM against four strains of SARS-CoV-2 (wild-type B, B.1.167.2, B.1.36.16, and B.1.1.529) in Vero E6 cells. The potential targets were evaluated using cell-based anti-attachment, time-of-drug addition, in vitro 3CLpro activities, and ACE2-binding using a surrogated viral neutralization test (sVNT). Moreover, additional targets were explored using combinatorial network-based interactions and Chemical Similarity Ensemble Approach (SEA). Results: AM and GM reduced SARS-CoV-2 3CLpro and N expressions, suggesting that initial and subgenomic translations were globally inhibited. AM and GM inhibited all strains of SARS-CoV-2 at EC50 of 0.70-3.05 µM, in which wild-type B was the most susceptible strain (EC50 0.70-0.79 µM). AM was slightly more efficient in the variants (EC50 0.88-2.41 µM), resulting in higher selectivity indices (SI 3.65-10.05), compared to the GM (EC50 0.94-3.05 µM, SI 1.66-5.40). GM appeared to be more toxic than AM in both Vero E6 and Calu-3 cells. Cell-based anti-attachment and time-of-addition suggested that the potential molecular target could be at the post-infection. 3CLpro activity and ACE2 binding were interfered with in a dose-dependent manner but were insufficient to be a major target. Combinatorial network-based interaction and chemical similarity ensemble approach (SEA) suggested that fatty acid synthase (FASN), which was critical for SARS-CoV-2 replication, could be a target of AM and GM. Conclusion: AM and GM inhibited SARS-CoV-2 with the highest potency at the wild-type B and the lowest at the B.1.1.529. Multiple targets were expected to integratively inhibit viral replication in cell-based system.

Machine learning-based QSAR and LB-PaCS-MD guided design of SARS-CoV-2 main protease inhibitors.

The global outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 virus had led to profound respiratory health implications. This study focused on designing organoselenium-based inhibitors targeting the SARS-CoV-2 main protease (Mpro). The ligand-binding pathway sampling method based on parallel cascade selection molecular dynamics (LB-PaCS-MD) simulations was employed to elucidate plausible paths and conformations of ebselen, a synthetic organoselenium drug, within the Mpro catalytic site. Ebselen effectively engaged the active site, adopting proximity to H41 and interacting through the benzoisoselenazole ring in a π-π T-shaped arrangement, with an additional π-sulfur interaction with C145. In addition, the ligand-based drug design using the QSAR with GFA-MLR, RF, and ANN models were employed for biological activity prediction. The QSAR-ANN model showed robust statistical performance, with an r2training exceeding 0.98 and an RMSEtest of 0.21, indicating its suitability for predicting biological activities. Integration the ANN model with the LB-PaCS-MD insights enabled the rational design of novel compounds anchored in the ebselen core structure, identifying promising candidates with favorable predicted IC50 values. The designed compounds exhibited suitable drug-like characteristics and adopted an active conformation similar to ebselen, inhibiting Mpro function. These findings represent a synergistic approach merging ligand and structure-based drug design; with the potential to guide experimental synthesis and enzyme assay testing.

Preferential Door for Ligand Binding and Unbinding Pathways in Inhibited Human Acetylcholinesterase.

Rising global population and increased food demands have resulted in the increased use of organophosphate pesticides (OPs), leading to toxin accumulation and transmission to humans. Pralidoxime (2-PAM), an FDA-approved drug, serves as an antidote for OP therapy. However, the atomic-level detoxification mechanisms regarding the design of novel antidotes remain unclear. This is the first study to examine the binding and unbinding pathways of 2-PAM to human acetylcholinesterase (HuAChE) through three identified doors using an enhanced sampling method called ligand-binding parallel cascade selection molecular dynamics (LB-PaCS-MD). Remarkably, LB-PaCS-MD could identify a predominant in-line binding mechanism through the acyl door at 63.79% ± 6.83%, also implicating it in a potential unbinding route (90.14% ± 4.22%). Interestingly, crucial conformational shifts in key residues, W86, Y341, and Y449, and the Ω loop significantly affect door dynamics and ligand binding modes. The LB-PaCS-MD technique can study ligand-binding pathways, thereby contributing to the design of antidotes and covalent drugs.

GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity.

The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.

Anilino-1,4-naphthoquinones as potent mushroom tyrosinase inhibitors: in vitro and in silico studies.

Tyrosinase, a pivotal enzyme in melanin synthesis, is a primary target for the development of depigmenting agents. In this work, in vitro and in silico techniques were employed to identify novel tyrosinase inhibitors from a set of 12 anilino-1,4-naphthoquinone derivatives. Results from the mushroom tyrosinase activity assay indicated that, among the 12 derivatives, three compounds (1, 5, and 10) demonstrated the most significant inhibitory activity against mushroom tyrosinase, surpassing the effectiveness of the kojic acid. Molecular docking revealed that all studied derivatives interacted with copper ions and amino acid residues at the enzyme active site. Molecular dynamics simulations provided insights into the stability of enzyme-inhibitor complexes, in which compounds 1, 5, and particularly 10 displayed greater stability, atomic contacts, and structural compactness than kojic acid. Drug likeness prediction further strengthens the potential of anilino-1,4-naphthoquinones as promising candidates for the development of novel tyrosinase inhibitors for the treatment of hyperpigmentation disorders.

Correction: Nukulkit et al. Eight Indole Alkaloids from the Roots of Maerua siamensis and Their Nitric Oxide Inhibitory Effects. Molecules 2022, 27, 7558.

After a proofreading check, some experimental data were inconsistent with the supplementary information in the original publication [...].

A new benzoquinone and a new stilbenoid from Paphiopedilum exul (Ridl.) Rolfe.

One new alkyl benzoquinone, paphionone (1), one new trans-stilbenoid, (E)-6,5'-dihydroxy-2,3'-dimethoxystilbene (2), and eight known stilbenoids and flavonoids (3-10) were isolated from the leaves and roots of Paphiopedilum exul (Orchidaceae). Their chemical structures were determined based on IR, ECD, MS and NMR analyses. Cytotoxicity of all isolated compounds towards human hepatocellular carcinoma (HepG2) cell line was examined in vitro by MTT assay. The para-hydroxybenzyl substituted stilbene 10 was potently cytotoxic to the cancer cells, with an IC50 value of 4.80 ± 1.10 µM (selectivity index = 20.83). All compounds were non-toxic to normal human embryo fibroblast (OUMS-36) cell line.

Enhancing bezlotoxumab binding to C. difficile toxin B2: insights from computational simulations and mutational analyses for antibody design.

Clostridioides difficile infection (CDI) is a significant concern caused by widespread antibiotic use, resulting in diarrhea and inflammation from the gram-positive anaerobic bacterium C. difficile. Although bezlotoxumab (Bez), a monoclonal antibody (mAb), was developed to address CDI recurrences, the recurrence rate remains high, partly due to reduced neutralization efficiency against toxin B2. In this study, we aimed to enhance the binding of Bez to C. difficile toxin B2 by combining computational simulations and mutational analyses. We identified specific mutations in Bez, including S28R, S31W/K, Y32R, S56W and G103D/S in the heavy chain (Hc), and S32F/H/R/W/Y in the light chain (Lc), which significantly improved binding to toxin B2 and formed critical protein-protein interactions. Through molecular dynamics simulations, several single mutations, such as HcS28R, LcS32H, LcS32R, LcS32W and LcS32Y, exhibited superior binding affinities to toxin B2 compared to Bez wild-type (WT), primarily attributed to Coulombic interactions. Combining the HcS28R mutation with four different mutations at residue LcS32 led to even greater binding affinities in double mutants (MTs), particularly HcS28R/LcS32H, HcS28R/LcS32R and HcS28R/LcS32Y, reinforcing protein-protein binding. Analysis of per-residue decomposition free energy highlighted key residues contributing significantly to enhanced binding interactions, emphasizing the role of electrostatic interactions. These findings offer insights into rational Bez MT design for improved toxin B2 binding, providing a foundation for developing more effective antibodies to neutralize toxin B2 and combat-related infections.Communicated by Ramaswamy H. Sarma.

Anti-proliferative Effects of Pinocembrin Isolated From Anomianthus dulcis on Hepatocellular Carcinoma Cells.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer. Anomianthus dulcis (Dunal) J.Sinclair (syn. Uvaria dulcis) has been used in Thai traditional medicine in various therapeutic indications. Phytochemical constituents of A. dulcis have been isolated and identified. However, their effects on liver cancer and the associated mechanisms have not been elucidated. METHODS: Dry flowers of A. dulcis were extracted using organic solvents, and chromatographic methods were used to purify the secondary metabolites. The chemical structures of the pure compounds were elucidated by analysis of spectroscopic data. Cytotoxicity against HCC cells was examined using SRB assay, and the effects on cell proliferation were determined using flow cytometry. The mechanisms underlying HCC inhibition were examined by molecular docking and verified by Western blot analysis. RESULTS: Among 3 purified flavonoids, pinocembrin, pinostrobin, and chrysin, and 1 indole alkaloid (3-farnesylindole), only pinocembrin showed inhibitory effects on the proliferation of 2 HCC cell lines, HepG2 and Li-7, whereas chrysin showed specific toxicity to HepG2. Pinocembrin was then selected for further study. Flow cytometric analyses revealed that pinocembrin arrested the HCC cell cycle at the G1 phase with a minimal effect on cell death induction. Pinocembrin exerted the suppression of STAT3, as shown by the molecular docking on STAT3 with a better binding affinity than stattic, a known STAT3 inhibitor. Pinocembrin also suppressed STAT3 phosphorylation at both Tyr705 and Ser727. Cell cycle regulatory proteins under the modulation of STAT3, namely cyclin D1, cyclin E, CDK4, and CDK6, are substantially suppressed in their expression levels. CONCLUSION: Pinocembrin extracted from A. dulcis exerted a significant growth inhibition on HCC cells via suppressing STAT3 signaling pathways and its downstream-regulated genes.

Comparative Study of Machine Learning-Based QSAR Modeling of Anti-inflammatory Compounds from Durian Extraction.

Quantitative structure-activity relationship (QSAR) analysis, an in silico methodology, offers enhanced efficiency and cost effectiveness in investigating anti-inflammatory activity. In this study, a comprehensive comparative analysis employing four machine learning algorithms (random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and artificial neural networks (ANNs)) was conducted to elucidate the activities of naturally derived compounds from durian extraction. The analysis was grounded in the exploration of structural attributes encompassing steric and electrostatic properties. Notably, the nonlinear SVR model, utilizing five key features, exhibited superior performance compared to the other models. It demonstrated exceptional predictive accuracy for both the training and external test datasets, yielding R2 values of 0.907 and 0.812, respectively; in addition, their RMSE resulted in 0.123 and 0.097, respectively. The study outcomes underscore the significance of specific structural factors (denoted as shadow ratio, dipole z, methyl, ellipsoidal volume, and methoxy) in determining anti-inflammatory efficacy. Thus, the findings highlight the potential of molecular simulations and machine learning as alternative avenues for the rational design of novel anti-inflammatory agents.

Efficiency of membrane fusion inhibitors on different hemagglutinin subtypes: insight from a molecular dynamics simulation perspective.

The challenge in vaccine development, along with drug resistance issues, has encouraged the search for new anti-influenza drugs targeting different viral proteins. Hemagglutinin (HA) glycoprotein, crucial in the viral replication cycle, has emerged as a promising therapeutic target. CBS1117 and JNJ4796 were reported to exhibit similar potencies against infectious group 1 influenza, which included H1 and H5 HAs; however, their potencies were significantly reduced against group 2 HA. This study aims to explore the molecular binding mechanisms and group specificity of these fusion inhibitors against both group 1 (H5) and group 2 (H3) HA influenza viruses using molecular dynamics simulations. CBS1117 and JNJ4796 exhibit stronger interactions with key residues within the H5 HA binding pocket compared to H3-ligand complexes. Hydrogen bonding and hydrophobic interactions involving residues, such as H381, Q401, T3251 (H5-CBS1117), T3181 (H5-JNJ4796), W212, I452, V482, and V522 predominantly contribute to stabilizing H5-ligand systems. In contrast, these interactions are notably weakened in H3-inhibitor complexes. Predicted protein-ligand binding free energies align with experimental data, indicating CBS1117 and JNJ4796's preference for heterosubtypic group 1 HA binding. Understanding the detailed atomistic mechanisms behind the varying potencies of these inhibitors against the two HA groups can significantly contribute to the development and optimization of effective HA fusion inhibitors. To accomplish this, the knowledge of the transition of HA from its pre- to post-fusion states, the molecular size of ligands, and their potential binding regions, could be carefully considered.Communicated by Ramaswamy H. Sarma.

Stimulating T cell responses against patient-derived breast cancer cells with neoantigen peptide-loaded peripheral blood mononuclear cells.

Breast cancer stands as a formidable global health challenge for women. While neoantigens exhibit efficacy in activating T cells specific to cancer and instigating anti-tumor immune responses, the accuracy of neoantigen prediction remains suboptimal. In this study, we identified neoantigens from the patient-derived breast cancer cells, PC-B-142CA and PC-B-148CA cells, utilizing whole-genome and RNA sequencing. The pVAC-Seq pipeline was employed, with minor modification incorporating criteria (1) binding affinity of mutant (MT) peptide with HLA (IC50 MT) ≤ 500 nm in 3 of 5 algorithms and (2) IC50 wild type (WT)/MT > 1. Sequencing results unveiled 2513 and 3490 somatic mutations, and 646 and 652 non-synonymous mutations in PC-B-142CA and PC-B-148CA, respectively. We selected the top 3 neoantigens to perform molecular dynamic simulation and synthesized 9-12 amino acid neoantigen peptides, which were then pulsed onto healthy donor peripheral blood mononuclear cells (PBMCs). Results demonstrated that T cells activated by ADGRL1E274K, PARP1E619K, and SEC14L2R43Q peptides identified from PC-B-142CA exhibited significantly increased production of interferon-gamma (IFN-γ), while PARP1E619K and SEC14L2R43Q peptides induced the expression of CD107a on T cells. The % tumor cell lysis was notably enhanced by T cells activated with MT peptides across all three healthy donors. Moreover, ALKBH6V83M and GAAI823T peptides from PC-B-148CA remarkably stimulated IFN-γ- and CD107a-positive T cells, displaying high cell-killing activity against target cancer cells. In summary, our findings underscore the successful identification of neoantigens with anti-tumor T cell functions and highlight the potential of personalized neoantigens as a promising avenue for breast cancer treatment.

FMO-guided design of darunavir analogs as HIV-1 protease inhibitors.

The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the need for antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR), responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is a potent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create DRV analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloud mode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Our findings indicate that the three designed analogs guided by FMO, 19-0-14-3, 19-8-10-0, and 19-8-14-3, are superior to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of our approach and its potential to be used in further studies for developing new antiretroviral drugs.

Design, Synthesis, and Characterization of Novel Styryl Dyes as Fluorescent Probes for Tau Aggregate Detection in Vitro and in Cells.

A series of novel styryl dye derivatives incorporating indolium and quinolinium core structures were successfully synthesized to explore their interacting and binding capabilities with tau aggregates in vitro and in cells. The synthesized dyes exhibited enhanced fluorescence emission in viscous environments due to the rotatable bond confinement in the core structure. Dye 4, containing a quinolinium moeity and featuring two cationic sites, demonstrated a 28-fold increase in fluorescence emission upon binding to tau aggregates. This dye could also stain tau aggregates in living cells, confirmed by cell imaging using confocal fluorescence microscopy. A molecular docking study was conducted to provide additional visualization and support for binding interactions. This work offers novel and non-cytotoxic fluorescent probes with desirable photophysical properties, which could potentially be used for studying tau aggregates in living cells, prompting further development of new fluorescent probes for early Alzheimer's disease detection.

Discovery of furopyridine-based compounds as novel inhibitors of Janus kinase 2: In silico and in vitro studies.

Janus kinase 2 (JAK2), one of the JAK isoforms participating in a JAK/STAT signaling cascade, has been considered a potential clinical target owing to its critical role in physiological processes involved in cell growth, survival, development, and differentiation of various cell types, especially immune and hematopoietic cells. Substantial studies have proven that the inhibition of this target could disrupt the JAK/STAT pathway and provide therapeutic outcomes for cancer, immune disorders, inflammation, and COVID-19. Herein, we performed docking-based virtual screening of 63 in-house furopyridine-based compounds and verified the first-round screened compounds by in vitro enzyme- and cell-based assays. By shedding light on the integration of both in silico and in vitro methods, we could elucidate two promising compounds. PD19 showed cytotoxic effects on human erythroblast cell lines (TF-1 and HEL) with IC50 values of 57.27 and 27.28 µM, respectively, while PD12 exhibited a cytotoxic effect on TF-1 with an IC50 value of 83.47 µM by suppressing JAK2/STAT5 autophosphorylation. In addition, all screened compounds were predicted to meet drug-like criteria based on Lipinski's rule of five, and none of the extreme toxicity features were found. Molecular dynamic simulations revealed that PD12 and PD19 could form stable complexes with JAK2 in an aqueous environment, and the van der Waals interactions were the main force driving the complex formation. Besides, all compounds sufficiently interacted with surrounding amino acids in all crucial regions, including glycine, catalytic, and activation loops. Altogether, PD12 and PD19 identified here could potentially be developed as novel therapeutic inhibitors disrupting the JAK/STAT pathway.

Enhancing solubility and stability of piperine using ß-cyclodextrin derivatives: computational and experimental investigations.

Piperine (PP), a natural alkaloid found in black pepper, possesses significant bioactivities. However, its use in pharmaceutical applications is hindered by low water solubility and susceptibility to UV light degradation. To overcome these challenges, we investigated the potential of ß-cyclodextrin (ßCD) and its derivatives with dimethyl (DMßCD), hydroxy-propyl (HPßCD) and sulfobutyl-ether (SBEßCD) substitutions to enhance the solubility and stability of PP. This study employed computational and experimental approaches to examine the complexation between PP and ßCDs. The results revealed the formation of two types of inclusion complexes: the P-form and M-form involving the insertion of piperidine moiety and the methylene-di-oxy-phenyl moiety, respectively. These complexes primarily rely on van der Waals interactions. Among the three derivatives, the PP/SBEßCD complex exhibited the highest stability followed by HPßCD, as attributed to maximum atom contacts and minimal solvent accessibility. Solubility studies confirmed the formation of inclusion complexes in a 1:1 ratio. Notably, the stability constant of the inclusion complex was approximately two-fold higher with SBEßCD and HPßCD compared to ßCD. The DSC thermograms provided confirmation of the formation of the inclusion complex between the host and guest. These findings highlight the potential of ßCD derivatives to effectively encapsulate PP, improving its solubility and presenting new opportunities for its pharmaceutical applications.Communicated by Ramaswamy H. Sarma.

Machine learning-guided design of potent darunavir analogs targeting HIV-1 proteases: A computational approach for antiretroviral drug discovery.

In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type-1 (HIV-1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one-dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild-type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy ( ΔΔ G bind ) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model-guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.