BDDCS, the Rule of 5 and drugability

Overcoming challenges in dermal and transdermal delivery of herbal therapeutics with polymeric microneedles

Natural products are generally preferred medications owing to their low toxicity and irritancy potential. However, a good number of herbal therapeutics (HT) exhibit solubility, permeability and stability issues that eventually affect oral bioavailability. Transdermal administration has been successful in resolving some of these issues which has lead in commercialization of a few herbal transdermal products. Polymeric Microneedles (MNs) has emerged as a promising platform in transdermal delivery of HT that face problems in permeating the skin. Several biocompatible and biodegradable polymers used in the fabrication of MNs have been discussed. MNs have been exploited for cutaneous delivery of HT in management of skin ailments like skin cancer, acne, chronic wounds and hypertrophic scar. Considering the clinical need, MNs are explored for systemic delivery of potent HT for management of diverse disorders like asthma, disorders of central nervous system and nicotine replacement as it obviates first pass metabolism and elicits a quicker onset of therapeutic response. MNs of HT have found good number of aesthetic applications in topical delivery of HT to the skin. Interestingly, MNs have emerged as an attractive option as a minimally invasive diagnostic aid in sampling biomarkers from plants, skin and ocular interstitial fluid. The review updates the progress made by MN technology of HT for multiple therapeutic interventions along with the future challenges. An attempt is made to illustrate the challenging formulation strategies employed in the fabrication of polymeric MNs of HT. Efforts are on to extend the potential applications of polymeric MNs to HT for diverse therapeutic applications.

De Novo Drug Design Using Transformer-Based Machine Translation and Reinforcement Learning of an Adaptive Monte Carlo Tree Search

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder-Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder-Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder-Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases.

Insights from in silico exploration of major curcumin analogs targeting human dipeptidyl peptidase IV

The goal of this work is to use a variety of in-silico techniques to identify anti-diabetic agents against DPP-IV enzyme from five main curcumin analogues. To produce the successful molecules, five main curcumin analogues were docked into the active site of DPP-IV enzyme. In comparison to the control molecule (Saxagliptin, -6.9 kcal/mol), all the compounds have the highest binding affinity (-7.6 to -7.7 kcal/mol) for the DPP-IV enzyme. These compounds underwent further testing for studies on drug-likeness, pharmacokinetics, and acute toxicity to see the efficacy and safety of compounds. To assess the stability of the docking complex and the binding posture identified during the docking experiment, our study got THC as the lead compound, which was then exposed to 200 ns of molecular dynamic simulation and PCA analysis. Additionally, DFT calculations were conducted to determine the thermodynamic, molecular orbital, and electrostatic potential characteristics of lead compound. Overall, the lead chemical has shown strong drug-like properties, is non-toxic, and has a sizable affinity for the DPP-IV enzyme.Communicated by Ramaswamy H. Sarma.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

In vitro optimization of crushed drug-sensitive antituberculosis medication when administered via a nasogastric tube

IMPORTANCE

The incidence of tuberculosis (TB) in intensive care units (ICUs) can be as high as 3% in high-burden settings, translating to more than 7,500 patients admitted to the ICU annually. In resource-limited settings, the lack or absence of intravenous formulations of drug-sensitive antituberculosis medications necessitates healthcare practitioners to crush, dissolve, and administer the drugs to critically ill patients via a nasogastric tube (NGT). This off-label practice has been linked to plasma concentrations below the recommended target concentrations, particularly of rifampicin and isoniazid, leading to clinical failure and the development of drug resistance. Optimizing the delivery of crushed drug-sensitive antituberculosis medication via the NGT to critically ill patients is of utmost importance.

An in silico analysis of the interaction of marine sponge-derived bioactive compounds with type 2 diabetes mellitus targets DPP-4 and PTP1B

Type 2 diabetes is a medical condition involving elevated blood glucose levels resulting from impaired or improper insulin utilization. As the number of type 2 diabetes cases increases each year, there is an urgent need to develop novel drugs having new targets and/or complementing existing therapeutic protocols. In this regard, marine sponge-derived compounds hold great potential due to their potent biological activity and structural diversity. In this study, a small library of 50 marine sponge-derived compounds were examined for their activity towards type 2 diabetes targets, namely dipeptidyl peptidase-4 (DPP-4) and protein tyrosine phosphatase 1B (PTP1B). The compounds were first subjected to molecular docking on protein models based on their respective co-crystal structures to assess binding free energies (BFE) and conformations. Clustering analysis yielded BFE that ranged from 24.54 kcal/mol to -9.97 kcal/mol for DPP-4, and from -4.98 kcal/mol to -8.67 kcal/mol for PTP1B. Interaction analysis on the top ten compounds with the most negative BFE towards each protein target showed similar intermolecular interactions and key interacting residues as in the previously solved co-crystal structure. These compounds were subjected to absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling to characterize drug-likeness and combining the results from these analyses, (S)-6'-debromohamacanthin B was identified as a potential multi-target inhibitor of DPP-4 and PTP1B, having favorable protein interaction, no Lipinski violations, good gastrointestinal (GI) tract absorption, blood-brain barrier (BBB) penetration, and no predicted toxicity. Finally, the interaction of (S)-6'-debromohamacanthin B with the two proteins was validated using molecular dynamics simulations over 100 ns through RMSD, radius of gyration, PCA, and molecular mechanics Poisson-Boltzmann surface area (MMPBSA) confirming favorable interactions with the respective proteins.Communicated by Ramaswamy H. Sarma.

In-silico evaluation of natural alkaloids against the main protease and spike glycoprotein as potential therapeutic agents for SARS-CoV-2

Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2) is the causative agent of COVID-19 pandemic, which has resulted in global fatalities since late December 2019. Alkaloids play a significant role in drug design for various antiviral diseases, which makes them viable candidates for treating COVID-19. To identify potential antiviral agents, 102 known alkaloids were subjected to docking studies against the two key targets of SARS-CoV-2, namely the spike glycoprotein and main protease. The spike glycoprotein is vital for mediating viral entry into host cells, and main protease plays a crucial role in viral replication; therefore, they serve as compelling targets for therapeutic intervention in combating the disease. From the selection of alkaloids, the top 6 dual inhibitory compounds, namely liensinine, neferine, isoliensinine, fangchinoline, emetine, and acrimarine F, emerged as lead compounds with favorable docked scores. Interestingly, most of them shared the bisbenzylisoquinoline alkaloid framework and belong to Nelumbo nucifera, commonly known as the lotus plant. Docking analysis was conducted by considering the key active site residues of the selected proteins. The stability of the top three ligands with the receptor proteins was further validated through dynamic simulation analysis. The leads underwent ADMET profiling, bioactivity score analysis, and evaluation of drug-likeness and physicochemical properties. Neferine demonstrated a particularly strong affinity for binding, with a docking score of -7.5025 kcal/mol for main protease and -10.0245 kcal/mol for spike glycoprotein, and therefore a strong interaction with both target proteins. Of the lead alkaloids, emetine and fangchinoline demonstrated the lowest toxicity and high LD50 values. These top alkaloids, may support the body's defense and reduce the symptoms by their numerous biological potentials, even though some properties naturally point to their direct antiviral nature. These findings demonstrate the promising anti-COVID-19 properties of the six selected alkaloids, making them potential candidates for drug design. This study will be beneficial in effective drug discovery and design against COVID-19 with negligible side effects.

Sulfonamide Derivatives: Recent Compounds with Potent Anti-alzheimer's Disease Activity

Facile synthetic procedures and broad spectrum of biological activities are special attributes of sulfonamides. Sulfonamide derivatives have demonstrated potential as a class of compounds for the treatment of Alzheimer's disease (AD). Recent sulfonamide derivatives have been reported as prospective anti-AD agents, with a focus on analogues that significantly inhibit the function of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes and exhibit remarkable antioxidant and anti-inflammatory properties, all of which are critical for the treatment of AD. Sulfonamide- mediated activation of nuclear factor erythroid 2-related factor 2 (NRF2), a key regulator of the endogenous antioxidant response, has also been suggested as a potential therapeutic approach in AD. Additionally, it has been discovered that a number of sulfonamide derivatives show selectivity for the β- and γ-secretase enzymes and a significant reduction of amyloid B (Aβ) aggregation, which have been implicated in AD. The comparative molecular docking of benzenesulfonamide and donepezil, an AD reference drug showed comparable anti-AD activities. These suggest that sulfonamide derivatives may represent a new class of drugs for the treatment of AD. Thus, the current review will focus on recent studies on the chemical synthesis and evaluation of the anti-AD properties, molecular docking, pharmacological profile, and structure-activity relationship (SAR) of sulfonamide derivatives, as well as their potential anti-AD mechanisms of action. This paper offers a thorough assessment of the state of the art in this field of study and emphasizes the potential of sulfonamide derivatives synthesized during the 2012-2023 period as a new class of compounds for the treatment of AD.

Design of Inhibitors That Target the Menin-Mixed-Lineage Leukemia Interaction

The prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL fusion proteins. A protein called menin is an important oncogenic cofactor for these MLL fusion proteins, thus providing a new avenue for treatments against this subset of acute leukemias. In this study, we report results using the structure-based drug design (SBDD) approach to discover potential novel MLL-mediated leukemia inhibitors from natural products against menin. The three-dimensional (3D) protein model was derived from Protein Databank (Protein ID: 4GQ4), and EasyModeller 4.0 and I-TASSER were used to fix missing residues during rebuilding. Out of the ten protein models generated (five from EasyModeller and I-TASSER each), one model was selected. The selected model demonstrated the most reasonable quality and had 75.5% of residues in the most favored regions, 18.3% of residues in additionally allowed regions, 3.3% of residues in generously allowed regions, and 2.9% of residues in disallowed regions. A ligand library containing 25,131 ligands from a Chinese database was virtually screened using AutoDock Vina, in addition to three known menin inhibitors. The top 10 compounds including ZINC000103526876, ZINC000095913861, ZINC000095912705, ZINC000085530497, ZINC000095912718, ZINC000070451048, ZINC000085530488, ZINC000095912706, ZINC000103580868, and ZINC000103584057 had binding energies of -11.0, -10.7, -10.6, -10.2, -10.2, -9.9, -9.9, -9.9, -9.9, and -9.9 kcal/mol, respectively. To confirm the stability of the menin-ligand complexes and the binding mechanisms, molecular dynamics simulations including molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations were performed. The amino acid residues that were found to be potentially crucial in ligand binding included Phe243, Met283, Cys246, Tyr281, Ala247, Ser160, Asn287, Asp185, Ser183, Tyr328, Asn249, His186, Leu182, Ile248, and Pro250. MI-2-2 and PubChem CIDs 71777742 and 36294 were shown to possess anti-menin properties; thus, this justifies a need to experimentally determine the activity of the identified compounds. The compounds identified herein were found to have good pharmacological profiles and had negligible toxicity. Additionally, these compounds were predicted as antileukemic, antineoplastic, chemopreventive, and apoptotic agents. The 10 natural compounds can be further explored as potential novel agents for the effective treatment of MLL-mediated leukemia.

In silico analysis of luteolin derivatives as antibacterial agents targeting DNA gyrase and CTX-M-15 extended-spectrum β-lactamase of Escherichia coli

Luteolin exhibited antibacterial activity against Escherichia coli and its chemical structure similar to that of ciprofloxacin (CPF) which works by inhibiting DNA gyrase. Filtrate from passion fruit extract containing luteolin and its derivatives could inhibit extended-spectrum β-lactamase (ESBL)-producing E. coli. Antibacterial compounds that can also inhibit ESBL will be valuable compounds to overcome the problem of resistant bacteria. This study aimed to ensure the potency of luteolin and luteolin derivatives targeting DNA gyrase and ESBL by in silico approach. Docking simulation of ligands L1-L14 was performed using AutoDock Vina, and pharmacokinetics and toxicity (absorption, distribution, metabolism, excretion, and toxicity) profiles were predicted by pKCSM online. The docking result revealed higher binding affinity on DNA gyrase (PDB.1KZN) of 12 luteolin derivatives (energy <-7.6 kcal/mol) compared to CPF and higher affinity (energy <-6.27 kcal/mol) of all compounds than clavulanic acid against ESBL CTX-M-15 (PDB.4HBU). The compounds could be absorbed through the human intestine moderately, which showed low permeability to blood-brain barrier, nontoxic and nonhepatotoxic. The most active luteolin glycoside (L6) is capable to inhibit DNA gyrase and ESBL from E. coli which provided the potential against resistant bacteria and was promoted as lead compounds to be developed further.

Blood-brain barrier transporters: a translational consideration for CNS delivery of neurotherapeutics

INTRODUCTION

Successful neuropharmacology requires optimization of CNS drug delivery and, by extension, free drug concentrations at brain molecular targets. Detailed assessment of blood-brain barrier (BBB) physiological characteristics is necessary to achieve this goal. The 'next frontier' in CNS drug delivery is targeting BBB uptake transporters, an approach that requires evaluation of brain endothelial cell transport processes so that effective drug accumulation and improved therapeutic efficacy can occur.

AREAS COVERED

BBB permeability of drugs is governed by tight junction protein complexes (i.e., physical barrier) and transporters/enzymes (i.e., biochemical barrier). For most therapeutics, a component of blood-to-brain transport involves passive transcellular diffusion. Small molecule drugs that do not possess acceptable physicochemical characteristics for passive permeability may utilize putative membrane transporters for CNS uptake. While both uptake and efflux transport mechanisms are expressed at the brain microvascular endothelium, uptake transporters can be targeted for optimization of brain drug delivery and improved treatment of neurological disease states.

EXPERT OPINION

Uptake transporters represent a unique opportunity to optimize brain drug delivery by leveraging the endogenous biology of the BBB. A rigorous understanding of these transporters is required to improve translation from the bench to clinical trials and stimulate the development of new treatment paradigms for neurological diseases.

A Computational Study of Green Tea Extracts and their Derivatives as Potential Inhibitors for Squalene Monooxygenase

BACKGROUND

According to the World Health Organisation, cardiovascular complications have been recognized as the leading course of death between 2000 and 2019. Cardiovascular complications are caused by excess LDL cholesterol in the body or arteries that can build up to form a plaque. There are drugs currently in clinical use called statins that target HMGCoA reductase. However, these drugs result in several side effects. This work investigated using computational approaches to lower cholesterol by investigating green tea extracts as an inhibitors for squalene monooxygenase (the second-rate-controlling step in cholesterol synthesis).

METHODS

Pharmacophore modeling was done to identify possible pharmacophoric sites based on the pIC50 values. The best hypothesis generated by pharmacophore modeling was further validated by atom-based 3D QSAR, where 70% of the data set was treated as the training set. Prior molecular docking ADMET studies were done to investigate the physiochemical properties of these molecules. Glide docking was performed, followed by molecular dynamics to evaluate the protein conformational changes.

RESULTS

Pharmacophore results suggest that the best molecules to interact with the biological target should have at least one hydrogen acceptor (A5), two hydrogen donors (D9 and D10), and two benzene rings (R14 and R15) for green tea polyphenols and theasinensin A. ADMET result shows that all molecules in this class have low oral adsorption. Molecular docking results showed that some green tea polyphenols have good binding affinities, with most of these structures having a docking score of less than -10 kcal/mol. Molecular dynamics further illustrated that the best-docked ligands perfectly stay within the active site over a 100 ns simulation.

CONCLUSION

The results obtained from this study suggest that green tea polyphenols have the potential for inhibition of squalene monooxygenase, except for theasinensin A.

Design, In Silico Molecular Docking, and ADMET Prediction of Amide Derivatives of Chalcone Nucleus as EGFR Inhibitors for the Treatment of Cancer

BACKGROUND

Cancer is a devastating disease. Many studies have shown that the primary causes of the aggressive and resistant types of cancer are the overexpression of receptors and growth factors, activation of oncogenes, and the inactivation of tumour suppressor genes. One such receptor is the epidermal growth factor receptor (EGFR), which is used as a drug target for the treatment of cancer.

OBJECTIVE

This study aimed to develop the new chemical entities of amide derivatives of chalcone as EGFR inhibitors using structure-activity relationship (SAR) studies, molecular docking, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) studies.

METHOD

New chemical entities (NCE) were designed based on literature findings. The Schrodinger 13.4 software was used for the molecular docking study. While Quickprop and Pro Tox-II online tools were used for ADME and toxicity prediction, respectively. .

RESULT

In this work, all compounds were subjected to an in-silico ADMET analysis. After pharmacokinetic and toxicity profile predictions, the molecules were further analysed by molecular docking. As a result of molecular docking, molecules AC9 and AC19 showed comparable docking scores compared to standard Afatinib.

CONCLUSION

Molecules AC9 and AC19 showed good docking scores and a promising ADMET profile. In the future, these derivatives can be further evaluated for wet lab studies and determination of their biological activity.

Analysis of Marrubiin in Marrubium alysson L. Extract Using Advanced HPTLC: Chemical Profiling, Acetylcholinesterase Inhibitory Activity, and Molecular Docking

The main purpose of this work is to investigate the phytochemical composition of Marrubium alysson L. non-polar fraction. GC/MS analysis was used to evaluate the plant extract's saponifiable and unsaponifiable matter. Although M. alysson L. lipoidal matter saponification produced 30.3% of fatty acid methyl esters and 69.7% of unsaponifiable matter. Phytol was the most dominant substance in the unsaponifiable materials. Notably, marrubiin which is one of the most prominent metabolites of Marrubium alysson L. was not detected through our adopted GC/MS technique. Thus, further characterization was proceeded through simple and rapid HPTLC analysis which successfully managed to identify marrubiin. Based on the regression equation, the concentration of marrubiin in M. alysson L. extract was 14.09 mg/g of dry extract. Concerning acetylcholinesterase inhibitory activity, both the crude M. alysson L. total methanolic extract and the non-polar fraction displayed reasonable inhibitory activity against acetylcholinesterase (AChE), whereas the pure compound marrubiin was considered to be the most effective and potent AChE inhibitor, with an IC50 value of 52.66 (µM). According to the molecular docking studies, potential sites of interaction between the pure chemical marrubiin and AChE were examined. The results show that Tyr124 on AChE residue was critical to the activity of the aforementioned drug. Based on the depicted marrubin AChE inhibition activity and reported safety profile, this chemical metabolite is considered as a promising lead compound for further pre-clinical investigation as well as drug development and optimization.

Potentiality of bioactive compounds as inhibitor of M protein and F protein function of human respiratory syncytial virus

The human respiratory syncytial virus (RSV) creates a pandemic every year in several countries in the world. Lack of target therapeutics and absence of vaccines have prompted scientists to create novel vaccines or small chemical treatments against RSV's numerous targets. The matrix (M) protein and fusion (F) glycoprotein of RSV are well characterized and attractive drug targets. Five bioactive compounds from Alnus japonica (Thunb.) Steud. were taken into consideration as lead compounds. Drug-likeness characters of them showed the drugs are non-toxic and non-mutagenic and mostly lipophobic. Molecular docking reveals that all bioactive compounds have better binding and better inhibitory effect than ribavirin which is currently used against RSV. Praecoxin A appeared as the best lead compound between them. It creates 7 different types of bonds with amino acids of M protein and 5 different types of bonds with amino acids of F protein. Van der Waals interactions highly influenced the binding energies. Molecular dynamic simulations represent the non-deviated and less fluctuating nature of praecoxin A. Principal Component Analysis showed praecoxin A complex with RSV matrix protein is more stable than ribavirin complex. This study will help to develop a new drug to inhibit RSV. All ligands were minimized through semi-empirical PM3 process with MOPAC. Toxicity was tested by ProTox-II server. Molecular docking studies were carried out using AutoDock 4.2. Molecular dynamics simulations for 100 ns were carried out through GROMACS 5.12 MD and GROMOS96 43a1 force field. The graphs were produced by GROMACS's XMGrace program.

Graphical abstract

Screening of phytochemicals from Clerodendrum inerme (L.) Gaertn as potential anti-breast cancer compounds targeting EGFR: an in-silico approach

Breast cancer (BC) is the most prevalent malignancy among women around the world. The epidermal growth factor receptor (EGFR) is a tyrosine kinase receptor (RTK) of the ErbB/HER family. It is essential for triggering the cellular signaling cascades that control cell growth and survival. However, perturbations in EGFR signaling lead to cancer development and progression. Hence, EGFR is regarded as a prominent therapeutic target for breast cancer. Therefore, in the current investigation, EGFR was targeted with phytochemicals from Clerodendrum inerme (L.) Gaertn (C. inerme). A total of 121 phytochemicals identified by gas chromatography-mass spectrometry (GC-MS) analysis were screened against EGFR through molecular docking, ADMET analyses (Absorption, Distribution, Metabolism, Excretion, and Toxicity), PASS predictions, and molecular dynamics simulation, which revealed three potential hit compounds with CIDs 10586 [i.e. alpha-bisabolol (-6.4 kcal/mol)], 550281 [i.e. 2,(4,4-Trimethyl-3-hydroxymethyl-5a-(3-methyl-but-2-enyl)-cyclohexene) (-6.5 kcal/mol)], and 161271 [i.e. salvigenin (-7.4 kcal/mol)]. The FDA-approved drug gefitinib was used to compare the inhibitory effects of the phytochemicals. The top selected compounds exhibited good ADMET properties and obeyed Lipinski's rule of five (ROF). The molecular docking analysis showed that salvigenin was the best among the three compounds and formed bonds with the key residue Met 793. Furthermore, the molecular mechanics generalized born surface area (MMGBSA) calculations, molecular dynamics simulation, and normal mode analysis validated the binding affinity of the compounds and also revealed the strong stability and compactness of phytochemicals at the docked site. Additionally, DFT and DOS analyses were done to study the reactivity of the compounds and to further validate the selected phytochemicals. These results suggest that the identified phytochemicals possess high inhibitory potential against the target EGFR and can treat breast cancer. However, further in vitro and in vivo investigations are warranted towards the development of these constituents into novel anti-cancer drugs.Communicated by Ramaswamy H. Sarma.

In silico design of novel bioactive molecules to treat breast cancer with chlorogenic acid derivatives: a computational and SAR approach

Introduction: Cancer is a vast group of diseases comprising abnormal cells that multiply and grow uncontrollably, and it is one of the top causes of death globally. Several types of cancers are diagnosed, but the incidence of breast cancer, especially in postmenopausal women, is increasing daily. Chemotherapeutic agents used to treat cancer are generally associated with severe side effects on host cells, which has led to a search for safe and potential alternatives. Therefore, the present research has been conducted to find novel bioactive molecules to treat breast cancer with chlorogenic acid and its derivatives. Chlorogenic acid was selected because of its known activity in the field. Methods: Several chlorogenic acid derivatives were subjected to computational studies such as molecular docking, determination of absorption, distribution, metabolism, and excretion (ADME), druglikeness, toxicity, and prediction of activity spectra for substances (PASS) to develop a potential inhibitor of breast cancer. The Protein Data Bank (PDB) IDs used for docking purposes were 7KCD, 3ERT, 6CHZ, 3HB5, and 1U72. Result: Exhaustive analysis of results has been conducted by considering various parameters, like docking score, binding energy, types of interaction with important amino acid residues in the binding pocket, ADME, and toxicity data of compounds. Among all the selected derivatives, CgE18, CgE11, CgAm13, CgE16, and CgE9 have astonishing interactions, excellent binding energy, and better stability in the active site of targeted proteins. The docking scores of compound CgE18 were -11.63 kcal/mol, -14.15 kcal/mol, and -12.90 kcal/mol against breast cancer PDB IDs 7KCD, 3HB5, and 1U72, respectively. The docking scores of compound CgE11 were -10.77 kcal/mol and -9.11 kcal/mol against breast cancer PDB IDs 3ERT and 6CHZ, respectively, whereas the docking scores of epirubicin hydrochloride were -3.85 kcal/mol, -6.4 kcal/mol, -8.76 kcal/mol, and -10.5 kcal/mol against PDB IDs 7KCD, 3ERT, 6CHZ, and 3HB5. The docking scores of 5-fluorouracil were found to be -5.25 kcal/mol, -3.43 kcal/mol, -3.73 kcal/mol, and -5.29 kcal/mol against PDB IDs 7KCD, 3ERT, 6CHZ, and 3HB5, which indicates the designed compounds have a better docking score than some standard drugs. Conclusion: Taking into account the results of molecular docking, drug likeness analysis, absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation, and PASS, it can be concluded that chlorogenic acid derivatives hold promise as potent inhibitors for the treatment of breast cancer.

Lead-oriented synthesis of epigenetic relevant scaffolds

A simple and rational method to rank lead-likeness of molecules using continuous evaluation functions was hereby developed. This strategy proved to be competitive against known methods and finally helped in driving synthetic efforts towards candidates of interest for epigenetic applications against HDAC6, BRD4 and EZH2.

Diverse Drug Classes Partition into Human Sweat: Implications for Both Sweat Fundamentals and for Therapeutic Drug Monitoring

ABSTRACT

Therapeutic drug monitoring to optimize drug therapy typically relies on the inconvenience of repeated plasma sampling. Sweat is a potential alternative biofluid convenient for sampling. However, limited information exists regarding the range of drugs excreted in sweat and their correlation with plasma concentrations. This study evaluated drugs in sweat and plasma of an ambulatory clinical cohort. Pilocarpine-induced sweat was collected from ambulatory participants at a single instance using an absorbent nylon mesh, followed by concurrent blood sampling for ratio and correlation analyses. In a model drug study, the pharmacokinetics of acetaminophen in sweat and plasma were compared. Of the 14 drugs and 2 metabolites monitored in the clinical study, all compounds were present in sweat and plasma; however, the sweat-to-plasma ratio varied substantially across the drugs. Opioids and methocarbamol demonstrated the highest concentrations in sweat, sometimes exceeding plasma concentrations. Selected antidepressants and muscle relaxants were also detected in sweat at a 2-10-fold dilution to the plasma. Others, such as gabapentin and pregabalin, were highly diluted (>30-fold) in sweat compared with plasma. Together, these data suggest that molecular attributes, specifically hydrophobicity (logP) and charge state at physiologic pH (7.4), enable reasonable prediction of sweat-to-plasma drug correlation. These findings demonstrated that sweat could be used as an alternative biofluid for therapeutic drug monitoring. The findings also suggest that although it has been broadly accepted that small hydrophobic molecules most likely have a strong plasma correlation, there is a small window of hydrophobicity and charge state that permits sweat partitioning.

Novel pseudonucleosides and sulfamoyl-oxazolidinone β-D-glucosamine derivative as anti-COVID-19: design, synthesis, and in silico study

New pseudonucleosides containing cyclic sulfamide moiety and sulfamoyl β-D-glucosamine derivative are described. These pseudonucleosides are synthesized in good yields starting from chlorosulfonyl isocyanate and β-D-glucosamine hydrochloride in five steps; (protection, acetylation, removal of the Boc group, sulfamoylation, and cyclization). Further, novel glycosylated sulfamoyloxazolidin-2-one is prepared in three steps; carbamoylation, sulfamoylation, and intramolecular cyclization. The structures of the synthesized compounds were confirmed by usual spectroscopic and spectrometric methods NMR, IR, MS, and EA. Interesting molecular docking of the prepared pseudonucleosides and (Beclabuvir, Remdesivir) drugs with SARS-CoV-2/Mpro (PDB:5R80) was conducted using the same parameters for a fair comparison. A low binding affinity of the synthesized compounds compared to the Beclabuvir and other analysis showed that pseudonucleosides have the ability to inhibit SARS-CoV-2. After the motivating results of molecular docking study, the complex between the SARS-CoV-2 Mpro and compound 7 was subjected to 100 ns molecular dynamics (MD) simulation using Desmond module of Schrodinger suite, during which the receptor-ligand complex showed substantial stability after 10 ns of MD simulation. Also, we studied the prediction of absorption, distribution, properties of metabolism, excretion, and toxicity (ADMET) of the synthesized compounds.Communicated by Ramaswamy H. Sarma.

Homology Modeling, Screening, and Identification of Potential FOXO6 Inhibitors Curtail Gastric Cancer Progression: an In Silico Drug Repurposing Approach

Gastric cancer is the world's second leading cause of cancer-related fatalities, with the epidemiology changing over the previous several decades. FOXOs are the O subfamily of the forkhead box (FOX) transcription factor family, which consists of four members: FOXO1, FOXO3, FOXO4, and FOXO6. FOXO6 mRNA and protein levels are increased in gastric cancer tissues. FOXO6 forced overexpression enhances gastric cancer cell growth, while knockdown decreases proliferation. In our study, the GEPIA, Kaplan-Meier, KEGG, and STRING databases were used to determine FOXO6 mRNA expression, overall survival ratio, interactive pathways, and top 10 associated proteins in gastric cancer respectively. Due to the lack of a solved structure for FOXO6, homology modeling was performed to obtain a 3D structure model, and we used anti-cancer drugs and small molecules to target FOXO6 for identifying a potential selective FOXO6 inhibitor. The chemical composition of the proteins and ligands has a significant impact on docking procedure performance. With this in mind, a critical evaluation of the performance of three regularly used docking routines was carried out: MVD, AutoDock Vina in PyRx, and ArgusLab. The binding affinities, docking scores, and intermolecular interactions were used as assessment criteria. In the study, the porfimer sodium showed excellent binding affinity to the FOXO6 protein. The major three docking software packages were used to analyze the scoring/H-bonding energy and intermolecular interactions. Based on the results, we concluded that FOXO6 was upregulated in gastric cancer and the ligand porfimer sodium emerges as a promising potential FOXO6 inhibitor to curtail gastric cancer progression.

Synergistic effect of antagonists to KRas4B/PDE6 molecular complex in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among all human cancers as it is highly resistant to chemotherapy. K-Ras mutations usually trigger the development and progression of PDAC. We hypothesized that compounds stabilizing the KRas4B/PDE6δ complex could serve as PDAC treatments. Using in silico approaches, we identified the small molecules C14 and P8 that reduced K-Ras activation in primary PDAC cells. Importantly, C14 and P8 significantly prevented tumor growth in patient-derived xenotransplants. Combined treatment with C14 and P8 strongly increased cytotoxicity in PDAC cell lines and primary cultures and showed strong synergistic antineoplastic effects in preclinical murine PDAC models that were superior to conventional therapeutics without causing side effects. Mechanistically, C14 and P8 reduced tumor growth by inhibiting AKT and ERK signaling downstream of K-RAS leading to apoptosis, specifically in PDAC cells. Thus, combined treatment with C14 and P8 may be a superior pharmaceutical strategy to improve the outcome of PDAC.

A Network Pharmacology and Molecular-Docking-Based Approach to Identify the Probable Targets of Short-Chain Fatty-Acid-Producing Microbial Metabolites against Kidney Cancer and Inflammation

(1) Background: A large and diverse microbial population exists in the human intestinal tract, which supports gut homeostasis and the health of the host. Short-chain fatty acid (SCFA)-secreting microbes also generate several metabolites with favorable regulatory effects on various malignancies and immunological inflammations. The involvement of intestinal SCFAs in kidney diseases, such as various kidney malignancies and inflammations, has emerged as a fascinating area of study in recent years. However, the mechanisms of SCFAs and other metabolites produced by SCFA-producing bacteria against kidney cancer and inflammation have not yet been investigated. (2) Methods: We considered 177 different SCFA-producing microbial species and 114 metabolites from the gutMgene database. Further, we used different online-based database platforms to predict 1890 gene targets associated with metabolites. Moreover, DisGeNET, OMIM, and Genecard databases were used to consider 13,104 disease-related gene targets. We used a Venn diagram and various protein-protein interactions (PPIs), KEGG pathways, and GO analyses for the functional analysis of gene targets. Moreover, the subnetwork of protein-protein interactions (through string and cytoscape platforms) was used to select the top 20% of gene targets through degree centrality, betweenness centrality, and closeness centrality. To screen the possible candidate compounds, we performed an analysis of the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of metabolites and then found the best binding affinity using molecular docking simulation. (3) Results: Finally, we found the key gene targets that interact with suitable compounds and function against kidney cancer and inflammation, such as MTOR (with glycocholic acid), PIK3CA (with 11-methoxycurvularin, glycocholic acid, and isoquercitrin), IL6 (with isoquercitrin), PTGS2 (with isoquercitrin), and IGF1R (with 2-amino-1-methyl-6-phenylimidazo[4,5-b] pyridine, isoquercitrin), showed a lower binding affinity. (4) Conclusions: This study provides evidence to support the positive effects of SCFA-producing microbial metabolites that function against kidney cancer and inflammation and makes integrative research proposals that may be used to guide future studies.

Molecular modeling, dynamic simulation, and metabolic reactivity studies of quinazoline derivatives to investigate their anti-angiogenic potential by targeting wild EGFRwt and mutant EGFRT790M receptor tyrosine kinases

Non-small cell lung cancer, head and neck cancer, glioblastoma, and various other cancer types often demonstrate persistent elevation in EGFR tyrosine kinase activity due to acquired mutations in its kinase domain. Any alteration in the EGFR is responsible for triggering the upregulation of tumor angiogenic pathways, such as the PI3k-AKT-mTOR pathway, MAPK-ERK pathway and PLC-Ƴ pathway, which are critically involved in promoting tumor angiogenesis in cancer cells. The emergence of frequently occurring EGFR kinase domain mutations (L858R/T790M/C797S) that confer resistance to approved therapeutic agents has presented a significant challenge for researchers aiming to develop effective and well-tolerated treatments against tumor angiogenesis. In this study, we directed our efforts towards the rational design and development of novel quinazoline derivatives with the potential to act as antagonists against both wild-type and mutant EGFR. Our approach encompasing the application of advanced drug design strategies, including structure-based virtual screening, molecular docking, molecular dynamics, metabolic reactivity and cardiotoxicity prediction studies led to the identification of two prominent lead compounds: QU648, for EGFRwt inhibition and QU351, for EGFRmt antagonism. The computed binding energies of selected leads and their molecular dynamics simulations exhibited enhanced conformational stability of QU648 and QU351 when compared to standard drugs Erlotinib and Afatinib. Notably, the lead compounds also demonstrated promising pharmacokinetic properties, metabolic reactivity, and cardiotoxicity profiles. Collectively, the outcomes of our study provide compelling evidence supporting the potential of QU648 and QU351 as prominent anti-angiogenic agents, effectively inhibiting EGFR activity across various cancer types harboring diverse EGFR mutations.Communicated by Ramaswamy H. Sarma.

Proteomic approaches advancing targeted protein degradation

Targeted protein degradation (TPD) is an emerging modality for research and therapeutics. Most TPD approaches harness cellular ubiquitin-dependent proteolytic pathways. Proteolysis-targeting chimeras (PROTACs) and molecular glue (MG) degraders (MGDs) represent the most advanced TPD approaches, with some already used in clinical settings. Despite these advances, TPD still faces many challenges, pertaining to both the development of effective, selective, and tissue-penetrant degraders and understanding their mode of action. In this review, we focus on progress made in addressing these challenges. In particular, we discuss the utility and application of recent proteomic approaches as indispensable tools to enable insights into degrader development, including target engagement, degradation selectivity, efficacy, safety, and mode of action.

The search for new efficient inhibitors of SARS-COV-2 through the De novo drug design developed by artificial intelligence

The pandemic caused by Sars-CoV-2 is a viral infection that has generated one of the most significant health problems worldwide. Previous studies report the main protease (Mpro) as a potential target for this virus, as it is considered a crucial enzyme in mediating replication and viral transcription. This work presented the construction of new bioactive compounds for possible inhibition. The De novo molecular design of drugs method in the incremental construction of a ligant model within a receptor model was used, producing new structures with the help of artificial intelligence. The research algorithm and the scoring function responsible for predicting orientation and affinity in the molecular target at the time of coupling showed, as a result of the simulation, the compound with the highest bioaffinity value, Hit 998, with the energy of -17.62 kcal/mol, and synthetic viability close to 50%. While hit 1103 presented better synthetic viability (80%), its affinity energy of -10.28 kcal/mol. Both were compared with the reference linker N3, with a binding affinity of -7.5 kcal/mol. ADMET tests demonstrated that simulated compounds have a low risk of metabolic activation and do not exert effective distribution in the CNS, suggesting a pharmacokinetic mechanism based on local action, even with high topological polarity, which resulted in low oral bioavailability. In conclusion, MMGBSA, H-bonds, RMSD, SASA, and RMSF values were also obtained through molecular dynamics to verify the stability of the receptor-ligant complex within the active protein site to seek new therapeutic propositions in the fight against the pandemic.Communicated by Ramaswamy H. Sarma.

Reversible and irreversible inhibitors of coronavirus Nsp15 endoribonuclease

The emergence of severe acute respiratory syndrome coronavirus 2, the causative agent of coronavirus disease 2019, has resulted in the largest pandemic in recent history. Current therapeutic strategies to mitigate this disease have focused on the development of vaccines and on drugs that inhibit the viral 3CL protease or RNA-dependent RNA polymerase enzymes. A less-explored and potentially complementary drug target is Nsp15, a uracil-specific RNA endonuclease that shields coronaviruses and other nidoviruses from mammalian innate immune defenses. Here, we perform a high-throughput screen of over 100,000 small molecules to identify Nsp15 inhibitors. We characterize the potency, mechanism, selectivity, and predicted binding mode of five lead compounds. We show that one of these, IPA-3, is an irreversible inhibitor that might act via covalent modification of Cys residues within Nsp15. Moreover, we demonstrate that three of these inhibitors (hexachlorophene, IPA-3, and CID5675221) block severe acute respiratory syndrome coronavirus 2 replication in cells at subtoxic doses. This study provides a pipeline for the identification of Nsp15 inhibitors and pinpoints lead compounds for further development against coronavirus disease 2019 and related coronavirus infections.

Computational modelling of potential Zn-sensitive non-β-lactam inhibitors of imipenemase-1 (IMP-1)

Antibiotic resistance (AR) remains one of the leading global health challenges, mostly implicated in disease-related deaths. The Enterobacteriaceae-producing metallo-β-lactamases (MBLs) are critically involved in AR pathogenesis through Zn-dependent catalytic destruction of β-lactam antibiotics, yet with limited successful clinical inhibitors. The efficacy of relevant broad-spectrum β-lactams including imipenem and meropenem are seriously challenged by their susceptibility to the Zn-dependent carbapenemase hydrolysis, as such, searching for alternatives remains imperative. In this study, computational molecular modelling and virtual screening methods were extensively applied to identify new putative Zn-sensitive broad-spectrum inhibitors of MBLs, specifically imipenemase-1 (IMP-1) from the IBScreen database. Three ligands, STOCK3S-30154, STOCK3S-30418 and STOCK3S-30514 selectively displayed stronger binding interactions with the enzymes compared to reference inhibitors, imipenem and meropenem. For instance, the ligands showed molecular docking scores of -9.450, -8.005 and -10.159 kcal/mol, and MM-GBSA values of -40.404, -31.902 and -33.680 kcal/mol respectively against the IMP-1. Whereas, imipenem and meropenem showed docking scores of -9.038 and -10.875 kcal/mol, and MM-GBSA of -31.184 and -32.330 kcal/mol respectively against the enzyme. The ligands demonstrated good thermodynamic stability and compactness in complexes with IMP-1 throughout the 100 ns molecular dynamics (MD) trajectories. Interestingly, their binding affinities and stabilities were significantly affected in contacts with the remodelled Zn-deficient IMP-1, indicating sensitivity to the carbapenemase active Zn site, however, with non-β-lactam scaffolds, tenable to resist catalytic hydrolysis. They displayed ideal drug-like ADMET properties, thus, representing putative Zn-sensitive non-β-lactam inhibitors of IMP-1 amenable for further experimental studies.

Molecular modelling of SdiA protein by selected flavonoid and terpenes compounds to attenuate virulence in Klebsiella pneumoniae

Klebsiella pneumoniae is one of the perturbing multidrug resistant (MDR) and ESKAPE pathogens contributing to the mounting morbidity, mortality and extended rate of hospitalization. Its virulence, often regulated by quorum sensing (QS) reinforces the need to explore alternative and prospective antivirulence agents, relatively from plants secondary metabolites. Computer aided drug discovery using molecular modelling techniques offers advantage to investigate prospective drugs to combat MDR pathogens. Thus, this study employed virtual screening of selected terpenes and flavonoids from medicinal plants to interrupt the QS associated SdiA protein in K. pneumoniae to attenuate its virulence. 4LFU was used as a template to model the structure of SdiA. ProCheck, Verify3D, Ramachandran plot scores, and ProSA-Web all attested to the model's good quality. Since SdiA protein in K. pneumoniae leads to the expression of virulence, 31 prospective bioactive compounds were docked for antagonistic potential. The stability of the protein-ligand complex, atomic motions and inter-atomic interactions were further investigated through molecular dynamics simulations (MDS) at 100 ns production runs. The binding free energy was estimated using the molecular mechanics/poisson-boltzmann surface area (MM/PB-SA). Furthermore, the drug-likeness properties of the studied compounds were validated. Docking studies showed phytol possesses the highest binding affinity (-9.205 kcal/mol) while glycitein had -9.752 kcal/mol highest docking score. The MDS of the protein in complex with the best-docked compounds revealed phytol with the highest binding energy of -44.2625 kcal/mol, a low root-mean-square deviation (RMSD) value of 1.54 Å and root-mean-square fluctuation (RMSF) score of 1.78 Å. Analysis of the drug-likeness properties prediction and bioavailability of these compounds revealed their conformed activity to lipinski's rules with bioavailability scores of 0.55 F. The studied terpenes and flavonoids compounds effectively thwart SdiA protein, therefore regulate inter- or intra cellular communication and associated in virulence Enterobacteriaceae, serving as prospective antivirulence drugs.Communicated by Ramaswamy H. Sarma.

Transcript-Level In Silico Analysis of Alzheimer's Disease-Related Gene Biomarkers and Their Evaluation with Bioactive Flavonoids to Explore Therapeutic Interactions

Alzheimer's disease (AD) is a progressive brain disorder that can significantly affect the quality of life. We used a variety of in silico tools to investigate the transcript-level mutational impact of exonic missense rare variations (single nucleotide polymorphisms, SNPs) on protein function and to identify potential druggable protein cavities that correspond to potential therapeutic targets for the management of AD. According to the NIA-AA (National Institute on Aging-Alzheimer's Association) framework, we selected three AD biomarker genes (APP, NEFL, and MAPT). We systematically screened transcript-level exonic rare SNPs from these genes with a minor allele frequency of 1% in 1KGD (1000 Genomes Project Database) and gnomAD (Genome Aggregation Database). With downstream functional effect predictions, a single variation (rs182024939: K > N) of the MAPT gene with nine transcript SNPs was identified as the most pathogenic variation from the large dataset of mutations. The machine learning consensus classifier predictor categorized these transcript-level SNPs as the most deleterious variations, resulting in a large decrease in protein structural stability (ΔΔG kcal/mol). The bioactive flavonoid library was screened for drug-likeness and toxicity risk. Virtual screening of eligible flavonoids was performed using the MAPT protein. Identification of druggable protein-binding cavities showed VAL305, GLU655, and LYS657 as consensus-interacting residues present in the MAPT-docked top-ranked flavonoid compounds. The MM/PB(GB)SA analysis indicated hesperetin (-5.64 kcal/mol), eriodictyol (-5.63 kcal/mol), and sakuranetin (-5.60 kcal/mol) as the best docked flavonoids with the near-native binding pose. The findings of this study provide important insights into the potential of hesperetin as a promising flavonoid that can be utilized for further rational drug design and lead optimization to open new gateways in the field of AD therapeutics.

A computational investigation of thymidylate synthase inhibitors through a combined approach of 3D-QSAR and pharmacophore modelling

Thymidylate synthase (TS) is a crucial target of cancer drug discovery and is mainly involved in the De novo synthesis of the DNA precursor thymine. In the present study, to generate reliable models and identify a few promising molecules, we combined QSAR modelling with the pharmacophore hypothesis-generating technique. Input molecules were clustered on their similarity, and a cluster of 74 molecules with a pyrimidine moiety was chosen as the set for 3D-QSAR and pharmacophore modelling. Atom-based and field-based 3D-QSAR models were generated and statistically validated with R2 > 0.90 and Q2 > 0.75. The common pharmacophore hypothesis(CPH) generation identified the best six-point model ADHRRR. Using these best models, a library of FDA-approved drugs was screened for activity and filtered via molecular docking, ADME profiling, and molecular dynamics simulations. The top ten promising TS-inhibiting candidates were identified, and their chemical features profitable for TS inhibitors were explored.Communicated by Ramaswamy H. Sarma.

Phytocompound screening, antioxidant activity and molecular docking studies of pomegranate seed: a preventive approach for SARS-CoV-2 pathogenesis

A global hazard to public health has been generated by the coronavirus infection 2019 (COVID-19), which is spreading quickly. Pomegranate is a strong source of antioxidants and has demonstrated a number of pharmacological characteristics. This work was aimed to analyze the phytochemicals present in ethanolic pomegranate seed extract (PSE) and their in vitro antioxidant potential and further in-silico evaluation for antiviral potential against crystal structure of two nucleocapsid proteins i.e., N-terminal RNA binding domain (NRBD) and C-terminal Domain (CTD) of SARS-CoV-2. The bioactive components from ethanolic extract of PSE were assessed by gas chromatography-mass spectroscopy (GC-MS). Free radical scavenging activity of PSE was determined using DPPH dye. Molecular docking was executed through the Glide module of Maestro software. Lipinski's 5 rule was applied for drug-likeness characteristics using cheminformatics Molinspiration software while OSIRIS Data Warrior V5.5.0 was used to predict possible toxicological characteristics of components. Thirty-two phytocomponents was detected in PSE by GC-MS technique. Free radical scavenging assay revealed the high antioxidant capacity of PSE. Docking analysis showed that twenty phytocomponents from PSE exhibited good binding affinity (Docking score ≥ - 1.0 kcal/mol) towards NRBD and CTD nucleocapsid protein. This result increases the possibility that the top 20 hits could prevent the spread of SARS-CoV-2 by concentrating on both nucleocapsid proteins. Moreover, molecular dynamics (MD) simulation using GROMACS was used to check their binding efficacy and internal dynamics of top complexes with the lowest docking scores. The metrics root mean square deviation (RMSD), root mean square fluctuation (RMSF), intermolecular hydrogen bonding (H-bonds) and radius of gyration (Rg) revealed that the lead phytochemicals form an energetically stable complex with the target protein. Majority of the phytoconstituents exhibited drug-likeness with non-tumorigenic properties. Thus, the PSE phytoconstituents could be useful source of drug or nutraceutical development in SARS-CoV-2 pathogenesis.

Short-term treatment with risperidone ameliorated 1,2-diacetylbenzene-induced liver dysfunction

1,2-Diacetylbenze (C10H10O2, DAB) is a potential inducer or activator of toxic mechanisms. DAB exerts high absorption by the gastrointestinal tract and high blood-brain barrier penetration. However, only the effects of DAB on the central nervous system were reported, with a dearth of evidence of DAB's effects on the liver, which is more susceptible to toxic substances. Risperidone, an atypical antipsychotic drug, has been shown to protect against DAB-induced cognitive impairment in an animal model. Risperidone was found to have little or no effect on the liver after short-term administration. The question of whether risperidone can protect against DAB-induced liver dysfunction, particularly after short-term administration, is unknown. Thus, this study aimed to assess the hepatoprotective effects of risperidone on DAB-induced liver dysfunction in male C57BL/6 mice treated with DAB 5 mg/kg for 1 week and risperidone 0.125-0.25 mg/kg for 2 weeks. After exposure to DAB 5 mg/kg for 1 week, we found that DAB induced liver damage by increasing liver function biomarkers (GGT, ALT, and AST), reactive oxygen species, nitric oxide, and proinflammatory cytokines (IL-1α, IL-1β, IL-6, IL-12, and TNF- α), activating apoptosis (elevated Caspase-3 and Bax levels and reduced Bcl2 level), TLR4/JNK/NF-κB, Jak2/Stat5 pathways, and suppressing Jak2/Stat3 and IRS1/PI3K/AKT/MDM2 pathways. After a 2-week course of treatment, risperidone was able to lessen these effects; the higher dose (0.25 mg/kg) appeared to be more effective than the lower dose (0.125 mg/kg). To strengthen findings from in vivo analysis, in silico analysis also found three targets (Stat3, Caspase-3, AKT, IL-1β), two miRNAs (miR-26b-5p and miR-34a-5p), two transcription factors (NFKB1 and NFKB2), and numerous pathways ("AGE-RAGE signaling pathway in diabetic complications", "hepatitis B", "alcoholic liver disease", "apoptosis", and "liver cirrhosis") as the key molecular processes involved in the pathogenesis of DAB-induced liver damage and targeted by risperidone. The physicochemical characteristics and pharmacokinetics of DAB and risperidone also support the toxic effects of DAB and the beneficial properties of risperidone in the liver. In conclusion, these findings reflect the therapeutic effects of risperidone on DAB-induced liver dysfunction after 1 week and 2 weeks exposure to DAB and risperidone, respectively.

Identification of PPA1 inhibitor candidates for potential repurposing in cancer medicine

Inorganic pyrophosphatase 1 (PPA1) is pivotal to cellular metabolism as it facilitates the hydrolysis of PPi-a by-product of various metabolic processes that influence cell growth and differentiation. Overexpression of PPA1 enzyme has been linked to diminished patient survival and was shown to influence tumor cell dynamics, thereby positioning it as a potential therapy target for a variety of cancers including colorectal cancer, diffuse large B-cell lymphoma, and lung adenocarcinoma. Despite this therapeutic promise, there are no known inhibitors of PPA1 as of today. In this study, we searched for potential PPA1 inhibitors using a molecular docking screen of 30 470 compounds with a history of clinical trials and/or US Food and Drug Administration approval. We specifically targeted the active pocket that coincides with the established catalytic domain. Our screen identified promising hits, which we further subjected to ADMET (absorption, distribution, metabolism, excretion, and toxicity) filtering. Subsequent molecular dynamics (MD) analyses were conducted on devazepide, quinotolast, and tarazepide-the three substances that successfully navigated all filters. MD analyses reinforced the stability of the protein-ligand complexes and confirmed ligand binding, as substantiated by our root mean square deviation, radius of gyration and secondary structures of proteins analyses. Furthermore, Molecular Mechanics Poisson-Boltzmann Surface Area calculations post-MD identified devazepide and quinotolast as showing higher binding affinities; being supported by principal component analysis, free energy landscape, and dynamic cross-correlation matrix results. Overall, our study reveals devazepide and quinotolast as potential candidates for PPA1 inhibition which could be considered for repurposing studies that need further experimental validation. These results not only reveal a potential for clinical repurposing for PPA1 inhibition but they also offer valuable insights into the development of future compounds for targeting the crucial PPA1 enzyme.

Unveiling the potential of marine compounds as quorum sensing inhibitors targeting Pseudomonas aeruginosa's LasI: A computational study using molecular docking and molecular dynamics

Antimicrobial resistance (AMR) poses a significant threat to global public health, with multidrug-resistant Pseudomonas aeruginosa being a leading cause of mortality, accounting for 18%-61% of deaths annually. The quorum sensing (QS) systems of P. aeruginosa, particularly the LasI-LasR system, play a crucial role in promoting biofilm formation and expression of virulent genes, which contribute to the development of AMR. This study focuses on LasI, the mediator of biofilm formation for identifying its inhibitors from a marine compound database comprising of 32 000 compounds using molecular docking and molecular simulation techniques. The virtual screening and docking experiments demonstrated that the top 10 compounds exhibited favorable docking scores of <-7.19 kcal/mol compared to the reported inhibitor 3,5,7-Trihydroxyflavone with a docking score of -3.098 kcal/mol. Additionally, molecular mechanics/Poisson-Boltzmann generalized born surface area (MM-GBSA) analyses were conducted to assess these compounds' suitability for further investigation. Out of 10 compounds, five compounds demonstrated high MM-GBSA binding energy (<-35.33 kcal/mol) and were taken up for molecular dynamics simulations to evaluate the stability of the protein-ligand complex over a 100 ns period. Based on root mean square deviation, root mean square fluctuation, radius of gyration, and hydrogen bond interactions analysis, three marine compounds, namely MC-2 (CMNPD13419) and MC-3 (CMNPD1068), exhibited consistent stability throughout the simulation. Therefore, these compounds show potential as promising LasI inhibitors and warrant further validation through in vitro and in vivo experiments. By exploring the inhibitory effects of these marine compounds on P. aeruginosa's QS system, this research aims to contribute to the development of novel strategies to combat AMR.

An efficient new method of ytterbium(III) triflate catalysis approach to the synthesis of substituted pyrroles: DFT, ADMET, and molecular docking investigations

In this study, the one-pot synthetic methodology for the preparation of substituted pyrroles with diethyl acetylene-dicarboxylate is reported for the various pyrrole derivatives via the Trifimow synthesis process from oximes. This method also offers the literature as a cyclization pathway using a ytterbium triflate catalyst. Another importance of this study is the use of pyrrole derivatives in pharmaceuticals, biological processes, and agrochemicals. From this point of view, the development of a new catalyst in synthetic organic chemistry and the difference in the method is also important. The syntheses of the target substituted pyrroles are accomplished in high yields. Also, all synthesized structures were confirmed by 1H NMR, 13C NMR, and IR spectra. The DFT computations were leveraged for structural and spectroscopic validation of the compounds. Then, FMO and NBO analyses were subsequently employed to elucidate the reactivity characteristics and intramolecular interactions within these compounds. Also, ADMET indices were ascertained to assess potential pharmacokinetic properties, drug-like qualities, and possible adverse effects of these compounds. Last, optimized molecules were analyzed by molecular docking methods against crystal structures of Bovine Serum Albumin and Leukemia Inhibitory Factor, and their binding affinities, interaction details, and inhibition constants were determined.

Pharmacokinetic correlation of structurally modified chalcone derivatives as promising leads to treat tuberculosis

In this study, we evaluated the potential of curated structurally modified chalcone derivatives as anti-tuberculosis (TB) agents through computer-aided drug design. Compounds from the flavonoid family known as chalcones were identified by the chemical group 1,3-diaryl-2-propen-1-one. After a search of the literature, 14 outstanding structurally modified chalcones were selected and evaluated for inhibitory activity against Mycobacterium tuberculosis H37Rv targets. The therapeutic potential of the chalcones was directly based on the drug-likeness and pharmacokinetic properties of the synthesized compounds. Prompt drug selection and personalized therapy are required to prevent TB from progressing and spreading to others. Pharmacokinetic parameters helps in the identification of lead molecule, at the earlier stages of drug development.

Gibbs Energy and Gene Expression Combined as a New Technique for Selecting Drug Targets for Inhibiting Specific Protein-Protein Interactions

One of the most important aspects of successful cancer therapy is the identification of a target protein for inhibition interaction. Conventionally, this consists of screening a panel of genes to assess which is mutated and then developing a small molecule to inhibit the interaction of two proteins or to simply inhibit a specific protein from all interactions. In previous work, we have proposed computational methods that analyze protein-protein networks using both topological approaches and thermodynamic quantification provided by Gibbs free energy. In order to make these approaches both easier to implement and free of arbitrary topological filtration criteria, in the present paper, we propose a modification of the topological-thermodynamic analysis, which focuses on the selection of the most thermodynamically stable proteins and their subnetwork interaction partners with the highest expression levels. We illustrate the implementation of the new approach with two specific cases, glioblastoma (glioma brain tumors) and chronic lymphatic leukoma (CLL), based on the publicly available patient-derived datasets. We also discuss how this can be used in clinical practice in connection with the availability of approved and investigational drugs.

Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications

The term "druggability" describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and seven β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ("variant vulnerability"), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ("drug applicability"). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G × G × E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

In silico identification of potential drug-like molecules against G glycoprotein of Nipah virus by molecular docking, DFT studies, and molecular dynamic simulation

Nipah virus (NiV) is a novel zoonotic pathogen that belongs to the Paramyxovirus family. The pathogen has infected a number of people in countries like Bangladesh, India, Singapore, and Malaysia with high mortality rates. Although the NiV has been classified as a biosafety level four pathogen (BSL-4), there is no drug approved for treatment against it. In this study, the G glycoprotein of the NiV was chosen as an antiviral target. Based on ADMET criteria, BBB- and BBB + group compounds were screened out of the Gold & platinum Asinex library containing 211620 compounds. After careful evaluation, the selected ligands were then virtually screened to identify the potential inhibitors against the G glycoprotein of the NiV through molecular docking, density functional theory (DFT), and molecular dynamic (MD) simulation studies. In our study we identified 5-(1,3-Benzodioxol-5-yl)-2-[(3-fluorobenzyl)sulfanyl]-5,8-dihydropyrido[2,3-d]pyrimidine-4,7(1H,6H)-dione (from BBB- group) and 7,7-Dimethyl-1-(4-methylphenyl)-3-(4-morpholinylcarbonyl)-7,8-dihydro-2,5(1H,6H)-quinolinedione) (from BBB + group) as potential compounds for the prevention and treatment of NiV related diseases.Communicated by Ramaswamy H. Sarma.

Solubility-Permeability Interplay in Facilitating the Prediction of Drug Disposition Routes, Extent of Absorption, Food Effects, Brain Penetration and Drug Induced Liver Injury Potential

Here I detail the use of measures of permeability rate and solubility in predicting drug disposition characteristics through the utilization of the Biopharmaceutics Drug Disposition Classification System (BDDCS) and the Extended Clearance Classification System (ECCS) as well as the accuracy of the systems in predicting the major route of elimination and the extent of oral absorption of a new small molecule therapeutics. I compare the BDDCS and ECCS with the FDA Biopharmaceutics Classification System (BCS). I also detail the use of the BCS in predicting food effects and the BDDCS in predicting brain disposition of small molecule therapeutics and in validating DILI predictive metrics. This review provides an update of the current status of these classification systems and their uses in the drug development process.

De novo design of bioactive phenol and chromone derivatives for inhibitors of Spike glycoprotein of SARS-CoV-2 in silico

This work presents the synthesis of 12 phenol and chromone derivatives, prepared by the analogs, and the possibility of conducting an in silico study of its derivatives as a therapeutic alternative to combat the SARS-CoV-2, pathogen responsible for COVID-19 pandemic, using its S-glycoprotein as a macromolecular target. After the initial screening for the ranking of the products, it was chosen which structure presented the best energy bond with the target. As a result, derivative 4 was submitted to a molecular growth study using artificial intelligence, where 8436 initial structures were obtained that passed through the interaction filters and similarity to the active glycoprotein pocket through the MolAICal computational package. Thus, 557 Hits with active configuration were generated, which is very promising compared to the BLA reference link for inhibiting the biological target. Molecular dynamics also simulated these compounds to verify their stability within the active protein site to seek new therapeutic propositions to fight against the pandemic. The Hit 48 and 250 are the most active compounds against SARS-CoV-2. In summary, the results show that the Hit 250 would be more active than the natural compound, which could be further developed for further testing against SARS-CoV-2. The study employs the de novo approach to design new drugs, combining artificial intelligence and molecular dynamics simulations to create efficient molecular structures. This research aims to contribute to the development of effective therapeutic strategies against the pandemic.

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.

In Silico Analysis of Pyeongwi-San Involved in Inflammatory Bowel Disease Treatment Using Network Pharmacology, Molecular Docking, and Molecular Dynamics

BACKGOUND

Pyeongwi-san (PWS) is a widely used formula for treating digestive disorders in Korea and China. Inflammatory bowel disease (IBD) is characterized by progressive inflammation of the gastrointestinal tract. Emerging evidence supports the protective effect of PWS against IBD, but specific mechanisms are still elusive.

METHODS

Active compounds of PWS were screened from the medicinal materials and chemical compounds in Northeast Asian traditional medicine (TM-MC) in the consideration of drug-likeness and oral bioavailability. Target candidates of active compounds were predicted using the ChEMBL database. IBD-related targets were obtained from the GeneCards and DisGeNET databases. The network of composition-targets-disease was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed. Molecular docking was used to simulate the binding affinity of active compounds on target proteins and molecular dynamics was used to validate the molecular docking result.

RESULTS

A total of 26 core target proteins of PWS were related to IBD. Enrichment analysis suggested that PWS is highly associated with tumor necrosis factor signaling pathway, apoptosis, and the collapse of tight junctions. Moreover, molecular docking and molecular dynamics simulation proposed β-eudesmol and (3R,6R,7S)-1,10-bisaboladien-3-ol to ameliorate IBD through the binding to TNF and MMP9, respectively.

CONCLUSION

Present in silico analysis revealed potential pathways and insight of PWS to regulate IBD. These results imply that the therapeutic effect of PWS might be achieved via an inhibitory effect.

Evaluating chemotherapeutic potential of soya-isoflavonoids against high penetrance genes in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive molecular subtype of breast cancer (BC) associated with a poor prognosis. Owing to the structural similarity with 17-β-estradiol, consumption of soya-isoflavonoids are associated with a reduced rate of hormone-receptive BC incidence, but their role in TNBC is not deciphered in detail. This present study thus aims to investigate the therapeutic binding dynamics of dietary soya-flavonoids with the six high penetrance (HP) receptors in TNBC, viz. BRCA1, BRCA2, PALB2, PTEN, STK11 and TP53. Out of the 14 soya-flavonoids screened based on ADMET descriptors and several other physicochemical, bioavailability, drug and lead-likeness properties, four hits were shortlisted (Daidzein, Genistein, Glycitein and Biochanin A). Docking and molecular dynamics (MD) simulation revealed Genistein as the most potential multi-target inhibitor of the six TNBC HP genes. Additionally, Genistein exhibited excellent binding specificity with PTEN, a potent mediator of the PI3K signaling pathway in TNBC. The binding interaction of PTEN and Genistein was further compared against a standardized FDA-approved chemotherapeutic inhibitor, Olaparib, computed through various MD trajectory analysis, principal component analysis and computation of free energy landscape. This study reveals a comparatively better binding dynamics of PTEN-Genistein than PTEN-Olaparib. With a significant global surge in biomarker-based precision therapeutics in oncology, the results of this exhaustive in-silico study thus encourage the prospect of validating PTEN as a druggable target of Genistein, a unique drug-receptor combination in the future.Communicated by Ramaswamy H. Sarma.

Methods of Analysis and Identification of Betulin and Its Derivatives

This scientific work presents practical and theoretical material on the methods of analysis and identification of betulin and its key derivatives. The properties of betulin and its derivatives, which are determined by the structural features of this class of compounds and their tendency to form dimers, polymorphism and isomerization, are considered. This article outlines ways to improve not only the bioavailability but also the solubility of triterpenoids, as well as any hydrophobic drug substances, through chemical transformations by introducing various functional groups, such as carboxyl, hydroxyl, amino, phosphate/phosphonate and carbonyl. The authors of this article summarized the physicochemical characteristics of betulin and its compounds, systematized the literature data on IR and NMR spectroscopy and gave the melting temperatures of key acids and aldehydes based on betulin.

Triose Phosphate Isomerase Structure-Based Virtual Screening and In Vitro Biological Activity of Natural Products as Leishmania mexicana Inhibitors

Cutaneous leishmaniasis (CL) is a public health problem affecting more than 98 countries worldwide. No vaccine is available to prevent the disease, and available medical treatments cause serious side effects. Additionally, treatment failure and parasite resistance have made the development of new drugs against CL necessary. In this work, a virtual screening of natural products from the BIOFACQUIM and Selleckchem databases was performed using the method of molecular docking at the triosephosphate isomerase (TIM) enzyme interface of Leishmania mexicana (L. mexicana). Finally, the in vitro leishmanicidal activity of selected compounds against two strains of L. mexicana, their cytotoxicity, and selectivity index were determined. The top ten compounds were obtained based on the docking results. Four were selected for further in silico analysis. The ADME-Tox analysis of the selected compounds predicted favorable physicochemical and toxicological properties. Among these four compounds, S-8 (IC50 = 55 µM) demonstrated a two-fold higher activity against the promastigote of both L. mexicana strains than the reference drug glucantime (IC50 = 133 µM). This finding encourages the screening of natural products as new anti-leishmania agents.

Potency of Xanthone Derivatives from Garcinia mangostana L. for COVID-19 Treatment through Angiotensin-Converting Enzyme 2 and Main Protease Blockade: A Computational Study

ACE2 and Mpro in the pathology of SARS-CoV-2 show great potential in developing COVID-19 drugs as therapeutic targets, due to their roles as the "gate" of viral entry and viral reproduction. Of the many potential compounds for ACE2 and Mpro inhibition, α-mangostin is a promising candidate. Unfortunately, the potential of α-mangostin as a secondary metabolite with the anti-SARS-CoV-2 activity is hindered due to its low solubility in water. Other xanthone isolates, which also possess the xanthone core structure like α-mangostin, are predicted to be potential alternatives to α-mangostin in COVID-19 treatment, addressing the low drug-likeness of α-mangostin. This study aims to assess the potential of xanthone derivative compounds in the pericarp of mangosteen (Garcinia mangostana L.) through computational study. The study was conducted through screening activity using molecular docking study, drug-likeness prediction using Lipinski's rule of five filtration, pharmacokinetic and toxicity prediction to evaluate the safety profile, and molecular dynamic study to evaluate the stability of formed interactions. The research results showed that there were 11 compounds with high potential to inhibit ACE2 and 12 compounds to inhibit Mpro. However, only garcinone B, in addition to being indicated as active, also possesses a drug-likeness, pharmacokinetic, and toxicity profile that was suitable. The molecular dynamic study exhibited proper stability interaction between garcinone B with ACE2 and Mpro. Therefore, garcinone B, as a xanthone derivative isolate compound, has promising potential for further study as a COVID-19 treatment as an ACE2 and Mpro inhibitor.

Promising disruptors of p53-MDM2 dimerization from some medicinal plant phytochemicals: a molecular modeling study

Cancer is a major global health issue that has a high mortality rate. p53, which functions as a tumor suppressor, is critical in preventing tumor development by regulating the cell cycle and inducing apoptosis in damaged cells. However, the tumor suppressor function of p53 is effectively inhibited by its direct interaction with the hydrophobic cleft of MDM2 protein via multiple mechanisms As a result, restoring p53 activity by blocking the p53-MDM2 protein-protein interaction has been proposed as a compelling therapeutic strategy for cancer treatment. The use of molecular docking and phytochemical screening procedures are appraised to inhibit MDM2's hydrophobic cleft and disrupt the p53-MDM2 interaction. For this purpose, a library of 51 bioactive compounds from 10 medicinal plants was compiled and subjected to structure-based virtual screening. Out of these, only 3 compounds (Atalantoflavone, Cudraxanthone 1, and Ursolic acid) emerged as promising inhibitors of MDM2-p53 based on their binding affinities (-9.1 kcal/mol, -8.8 kcal/mol, and -8.8 kcal/mol respectively) when compared to the standard (-8.8 kcal/mol). Moreover, these compounds showed better pharmacokinetic and drug-like profiling than the standard inhibitor (Chromonotriazolopyrimidine 1). Finally, the 100 ns MD simulation analysis confirmed no significant perturbation in the conformational dynamics of the simulated binary complexes when compared to the standard. In particular, Ursolic acid was found to satisfy the molecular enumeration the most compared to the other inhibitors. Our overall molecular modeling finding shows why these compounds may emerge as potent arsenals for cancer therapeutics. Nonetheless, extensive experimental and clinical research is needed to augment their use in clinics.Communicated by Ramaswamy H. Sarma.

Today's drug discovery and the shadow of the rule of 5

INTRODUCTION

The rule of 5 developed by Lipinski et al., a landmark and prescient piece of scholarship, focused the minds of drug hunters by systematically characterizing the physical make-up of drug molecules for the first time, noting many sub-optimal compounds identified by high-throughput screening practices. Its profound influence on thinking and practices, whilst providing benefit, perhaps etched the guidelines too strongly in the minds of some drug hunters who applied the bounds too literally without understanding the implications of the underlying statistics.

AREAS COVERED

This opinion is based on recent key developments that take thinking, measurements, and standards beyond those first set out, particularly the influences of molecular weight and the understanding, measurement, and calculation of lipophilicity.

EXPERT OPINION

Techniques and technologies for physicochemical estimations set new standards. It is timely to celebrate the significance and influence of the rule of 5, whilst taking thinking to new levels with better characterizations. The shadow of the rule of 5 may be long, but it is not dark, as new measurements, predictions and principles emerge as guiding lights in the design and prioritization of higher-quality molecules redefining the meaning of beyond the rule of 5.