A perspective on multi-target drug discovery and design for complex diseases

Novel dual-targeting inhibitors of NSD2 and HDAC2 for the treatment of liver cancer: structure-based virtual screening, molecular dynamics simulation, and in vitro and in vivo biological activity evaluations

Liver cancer exhibits a high degree of heterogeneity and involves intricate mechanisms. Recent research has revealed the significant role of histone lysine methylation and acetylation in the epigenetic regulation of liver cancer development. In this study, five inhibitors capable of targeting both histone lysine methyltransferase nuclear receptor-binding SET domain 2 (NSD2) and histone deacetylase 2 (HDAC2) were identified using a structure-based virtual screening approach. Notably, DT-NH-1 displayed a potent inhibition of NSD2 (IC50 = 0.08 ± 0.03 μM) and HDAC2 (IC50 = 5.24 ± 0.87 nM). DT-NH-1 also demonstrated a strong anti-proliferative activity against various liver cancer cell lines, particularly HepG2 cells, and exhibited a high level of biological safety. In an experimental xenograft model involving HepG2 cells, DT-NH-1 showed a significant reduction in tumour growth. Consequently, these findings indicate that DT-NH-1 will be a promising lead compound for the treatment of liver cancer with epigenetic dual-target inhibitors.

Isothiocyanate-Corticosteroid Conjugates against asthma: Unity makes strength

Asthma is a major noncommunicable disease, affecting both children and adults, and represents one of the major causes leading to high health care costs due to the need for chronic pharmacological treatments. The standard gold therapy of inflammation in asthmatic patients involves the use of glucocorticoids even if their chronic use is often related to serious adverse effects. Growing evidence suggests the biological relevance of hydrogen sulfide (H2S) in the pathogenesis of airway diseases. Hence, aiming to associate the beneficial effects of steroidal anti-inflammatory drugs (SAIDs) to H2S biological activity, we designed and synthesized novel multi-target molecules by chemically combining a group of glucocorticoids, usually employed in asthma treatment, with an isothiocyanate moiety, well-known for its H2S releasing properties. Firstly, the synthesized compounds have been screened for their H2S-releasing profile using an amperometric approach and for their in vitro effects on the degranulation process, using RBL-2H3 cell line. The physicochemical profile, in terms of solubility, chemical and enzymatic stability of the newly hybrid molecules, has been assessed at different physiological pH values and in esterase-rich medium (bovine serum albumin, BSA). The selected compound 5c, through both its corticosteroid and H2S releasing component, has been evaluated in vivo in experimental model of asthma. The compound 5c inhibited in vivo all asthma features with a significative effect on the restoration of pulmonary structure and reduction of lung inflammation.

Design, Synthesis, and Biological Evaluation of Ferulic Acid-Piperazine Derivatives Targeting Pathological Hallmarks of Alzheimer's Disease

Alzheimer's disease (AD) is the most prevalent cause of dementia and is characterized by low levels of acetyl and butyrylcholine, increased oxidative stress, inflammation, accumulation of metals, and aggregations of Aβ and tau proteins. Current treatments for AD provide only symptomatic relief without impacting the pathological hallmarks of the disease. In our ongoing efforts to develop naturally inspired novel multitarget molecules for AD, through extensive medicinal chemistry efforts, we have developed 13a, harboring the key functional groups to provide not only symptomatic relief but also targeting oxidative stress, able to chelate iron, inhibiting NLRP3, and Aβ1-42 aggregation in various AD models. 13a exhibited promising anticholinesterase activity against AChE (IC50 = 0.59 ± 0.19 μM) and BChE (IC50 = 5.02 ± 0.14 μM) with excellent antioxidant properties in DPPH assay (IC50 = 5.88 ± 0.21 μM) over ferulic acid (56.49 ± 0.62 μM). The molecular docking and dynamic simulations further corroborated the enzyme inhibition studies and confirmed the stability of these complexes. Importantly, in the PAMPA-BBB assay, 13a turned out to be a promising molecule that can efficiently cross the blood-brain barrier. Notably, 13a also exhibited iron-chelating properties. Furthermore, 13a effectively inhibited self- and metal-induced Aβ1-42 aggregation. It is worth mentioning that 13a demonstrated no symptom of cytotoxicity up to 30 μM concentration in PC-12 cells. Additionally, 13a inhibited the NLRP3 inflammasome and mitigated mitochondrial-induced reactive oxygen species and mitochondrial membrane potential damage triggered by LPS and ATP in HMC-3 cells. 13a could effectively reduce mitochondrial and cellular reactive oxygen species (ROS) in the Drosophila model of AD. Finally, 13a was found to be efficacious in reversing memory impairment in a scopolamine-induced AD mouse model in the in vivo studies. In ex vivo assessments, 13a notably modulates the levels of superoxide, catalase, and malondialdehyde along with AChE and BChE. These findings revealed that 13a holds promise as a potential candidate for further development in AD management.

Evolving Significance of Kinase Inhibitors in the Management of Alzheimer's Disease

Alzheimer's disease is a neurodegenerative problem with progressive loss of memory and other cognitive function disorders resulting in the imbalance of neurotransmitter activity and signaling progression, which poses the need of the potential therapeutic target to improve the intracellular signaling cascade brought by kinases. Protein kinase plays a significant and multifaceted role in the treatment of Alzheimer's disease, by targeting pathological mechanisms like tau hyperphosphorylation, neuroinflammation, amyloid-beta production and synaptic dysfunction. In this review, we thoroughly explore the essential protein kinases involved in Alzheimer's disease, detailing their physiological roles, regulatory impacts, and the newest inhibitors and compounds that are progressing into clinical trials. All the findings of studies exhibited the promising role of kinase inhibitors in the management of Alzheimer's disease. However, it still poses the need of addressing current challenges and opportunities involved with this disorder for the future perspective of kinase inhibitors in the management of Alzheimer's disease. Further study includes the development of biomarkers, combination therapy, and next-generation kinase inhibitors with increased potency and selectivity for its future prospects.

Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation

Dual-target drug design has gained significant attention in the treatment of complex diseases, such as cancers and autoimmune disorders. A widely employed design strategy is combining pharmacophores to leverage the knowledge of structure-activity relationships of both targets. Unfortunately, pharmacophore combination often struggles with long and expensive trial and error, because the protein pockets of the two targets impose complex structural constraints. In this study, we propose AIxFuse, a structure-aware dual-target drug design method that learns pharmacophore fusion patterns to satisfy the dual-target structural constraints simulated by molecular docking. AIxFuse employs two self-play reinforcement learning (RL) agents to learn pharmacophore selection and fusion by comprehensive feedback including dual-target molecular docking scores. Collaboratively, the molecular docking scores are learned by active learning (AL). Through collaborative RL and AL, AIxFuse learns to generate molecules with multiple desired properties. AIxFuse is shown to outperform state-of-the-art methods in generating dual-target drugs against glycogen synthase kinase-3 beta (GSK3β) and c-Jun N-terminal kinase 3 (JNK3). When applied to another task against retinoic acid receptor-related orphan receptor γ-t (RORγt) and dihydroorotate dehydrogenase (DHODH), AIxFuse exhibits consistent performance while compared methods suffer from performance drops, leading to a 5 times higher performance in success rate. Docking studies demonstrate that AIxFuse can generate molecules concurrently satisfying the binding mode required by both targets. Further free energy perturbation calculation indicates that the generated candidates have promising binding free energies against both targets.

Enhancing amyloid beta inhibition and disintegration by natural compounds: A study utilizing spectroscopy, microscopy and cell biology

Amyloid proteins and peptides play a pivotal role in the etiology of various neurodegenerative diseases, including Alzheimer's disease (AD). Synthetically designed small molecules/ peptides/ peptidomimetics show promise towards inhibition of various kinds of amyloidosis. However, exploration of compounds isolated from natural extracts having such potential is lacking. Herein, we have investigated the repurposing of a traditional Indian medicine Lasunadya Ghrita (LG) in AD. LG is traditionally used to treat gut dysregulation and mental illnesses. Various extracts of LG were obtained, characterized, and analyzed for inhibition of Aβ aggregation. Biophysical studies show that the water extract of LG (LGWE) is more potent in inhibiting Aβ peptide aggregation and defibrillation of Aβ40/Aβ42 aggregates. NMR studies showed that LGWE binds to the central hydrophobic area and C-terminal residues of Aβ40/Aβ42, thereby modulating the aggregation, and reducing cell membrane damage. Additionally, LGWE rescues Aβ toxicity in neuronal SH-SY5Y cells evident from decreases in ROS generation, membrane leakage, cellular apoptosis, and calcium dyshomeostasis. Notably, LGWE is non-toxic to neuronal cells and mouse models. Our study thus delves into the mechanistic insights of a repurposed drug LGWE with the potential to ameliorate Aβ induced neuroinflammation.

Pills of Multi-Target H2S Donating Molecules for Complex Diseases

Among the various drug discovery methods, a very promising modern approach consists in designing multi-target-directed ligands (MTDLs) able to modulate multiple targets of interest, including the pathways where hydrogen sulfide (H2S) is involved. By incorporating an H2S donor moiety into a native drug, researchers have been able to simultaneously target multiple therapeutic pathways, resulting in improved treatment outcomes. This review gives the reader some pills of successful multi-target H2S-donating molecules as worthwhile tools to combat the multifactorial nature of complex disorders, such as inflammatory-based diseases and cancer, as well as cardiovascular, metabolic, and neurodegenerative disorders.

Several major herb pairs containing Coptidis rhizoma: a review of key traditional uses, constituents and compatibility effects

Herb compatibility is the soul of traditional Chinese Medicine prescriptions. Coptidis rhizoma (CR) (Coptis chinensis Franch., Coptis deltoidea C.Y.Cheng et Hsiao, or Coptis teeta Wall.; family Ranunculaceae), is a well-known herb. The bitter and cold nature of CR can irritate the spleen and stomach, and certain ingredients in CR may trigger allergic reactions. Herb combinations can help alleviate the side effects caused by CR. Through data analysis and literature research, there are many herbs combined with CR have a high frequency, but only a few are currently used as formulae in clinical practice. The results showed that these six herb pairs are usually widely studied or used as prescriptions in the clinic. This paper describes the six herb pairs from the key traditional uses, changes in bioactive constituents, and compatibility effects, especially with Euodiae fructus (family Rutaceae), Scutellariae radix (family Lamiaceae), Magnoliae Officinalis cortex (family Magnoliaceae), Glycyrrhizae radix et rhizoma (family Fabaceae), Ginseng radix et rhizoma (family Araliaceae), and Aucklandiae radix (family Asteraceae), and found that herbs are more effective when used in combination. Therefore, it is feasible to establish some methods to study herb pairs comprehensively from different perspectives. This paper aims to provide the latest and most comprehensive information on the six herb pairs and summarize the pattern of CR compatibility effects. It aims to attract more attention, and further experimental studies will be conducted to investigate and evaluate the effects of herb pairs containing CR. These data can also provide valuable references for researchers and also provide more possibilities for future applications in clinical practice and new drug development.

Effect of theophylline on serum and milk pharmacokinetics of tylosin following intramuscular administration in lactating goats

AIM OF THE WORK

The study was conducted to evaluate the influence of theophylline pre-treatment on serum pharmacokinetics and milk elimination of tylosin following single intramuscular (IM) administrations in lactating goats.

METHODS AND RESULTS

In a cross-over study, tylosin was injected via intramuscular (IM) at a single dose of 15 mg/kg b.wt. After a one-month washout period goats received theophylline at a daily IM dose of 2 mg/kg b.wt. for seven consecutive days then tylosin was injected IM dose of 15 mg/kg b.wt. two hours after the last theophylline dosing. Blood samples were collected before and at 0.25, 0.5, 0.75, 1, 2, 4, 6, 8, 10, 12, and 24 h post-injection. Samples were left to clot and then centrifuged to yield serum. Milk samples were collected before and at 0.5, 1, 2, 4, 6, 8, 10, 12, 24, 48, and 72 h post-injection from each goat by hand milking. Tylosin serum concentrations were determined by high-performance liquid chromatography (HPLC). Tylosin concentrations versus time were analyzed by a noncompartmental method. Tylosin Cmax significantly declined from 1.73 ± 0.10 to 1.01 ± 0.11 µg/ml, and attained Tmax values of 2 and 1 h, respectively in theophylline-pretreated goats. Moreover, theophylline pretreatment significantly shortened the elimination half-life (t1/2el) from 6.94 to 1.98 h, t1/2ka from 0.62 to 0.36 h and the mean residence time (MRT) from 8.02 to 4.31 h, also Vz/F and AUCs decreased from 11.91 to 7.70 L/kg and from 12.64 to 4.57 µg*h/ml, respectively, consequently, theophylline enhanced the clearance (Cl/F) of tylosin from the body. Similarly, tylosin milk concentrations were significantly lower in theophylline-pretreated goats than in goats that received tylosin alone and were detected up to 24 and 72 h in both groups, respectively. Moreover, the t1/2el and AUCs were significantly decreased from 14.68 ± 1.97 to 4.72 ± 0.48 h, and from 181 to 67.20 µg*h/ml, respectively.

CONCLUSIONS

The withdrawal period for tylosin in goat milk is at least 72 h. Theophylline pretreatment significantly decreases serum and milk tylosin concentrations to subtherapeutic levels, which could have serious clinical consequences such as failure of therapy. This means that after administering tylosin to goats, milk from these animals should not be consumed for at least 96 h to ensure that the milk is free from residues of the antibiotic.

Novel binuclear copper(II) complexes with sulfanylpyrazole ligands: synthesis, crystal structure, fungicidal, cytostatic, and cytotoxic activity

New binuclear copper(II) [Cu(II)] tetraligand complexes (six examples) with sulfanylpyrazole ligands were synthesized. Electron spin resonance (ESR) studies have shown that in solution the complexes are transformed to the mononuclear one. Fungicidal properties against Candida albicans were found for the Cu complexes with benzyl and phenyl substituents. An in vitro evaluation of the cytotoxic properties of Cu chelates against HEK293, Jurkat, MCF-7, and THP-1 cells identified the Cu complex with the cyclohexylsulfanyl substituent in the pyrazole core as the lead compound, whereas the Cu complex without a sulfur atom in the pyrazole ligand had virtually no cytotoxic or fungicidal activity. The lead Cu(II) complex was more active than cisplatin. Effect of the S-containing Cu complex on apoptosis and cell cycle distribution has been investigated as well.

Advances in drug design and therapeutic potential of selective or multitarget 5-HT1A receptor ligands

5-HT1A receptor (5-HT1A-R) is a serotoninergic G-protein coupled receptor subtype which contributes to several physiological processes in both central nervous system and periphery. Despite being the first 5-HT-R identified, cloned and studied, it still represents a very attractive target in drug discovery and continues to be the focus of a myriad of drug discovery campaigns due to its involvement in numerous neuropsychiatric disorders. The structure-activity relationship studies (SAR) performed over the last years have been devoted to three main goals: (i) design and synthesis of 5-HT1A-R selective/preferential ligands; (ii) identification of 5-HT1A-R biased agonists, differentiating pre- versus post-synaptic agonism and signaling cellular mechanisms; (iii) development of multitarget compounds endowed with well-defined poly-pharmacological profiles targeting 5-HT1A-R along with other serotonin receptors, serotonin transporter (SERT), D2-like receptors and/or enzymes, such as acetylcholinesterase and phosphodiesterase, as a promising strategy for the management of complex psychiatric and neurodegenerative disorders. In this review, medicinal chemistry aspects of ligands acting as selective/preferential or multitarget 5-HT1A-R agonists and antagonists belonging to different chemotypes and developed in the last 7 years (2017-2023) have been discussed. The development of chemical and pharmacological 5-HT1A-R tools for molecular imaging have also been described. Finally, the pharmacological interest of 5-HT1A-R and the therapeutic potential of ligands targeting this receptor have been considered.

Drug Development for Alzheimer's and Parkinson's Disease: Where Do We Go Now?

Neurodegenerative diseases (NDs) are a set of progressive, chronic, and incurable diseases characterized by the gradual loss of neurons, culminating in the decline of cognitive and/or motor functions. Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common NDs and represent an enormous burden both in terms of human suffering and economic cost. The available therapies for AD and PD only provide symptomatic and palliative relief for a limited period and are unable to modify the diseases' progression. Over the last decades, research efforts have been focused on developing new pharmacological treatments for these NDs. However, to date, no breakthrough treatment has been discovered. Hence, the development of disease-modifying drugs able to halt or reverse the progression of NDs remains an unmet clinical need. This review summarizes the major hallmarks of AD and PD and the drugs available for pharmacological treatment. It also sheds light on potential directions that can be pursued to develop new, disease-modifying drugs to treat AD and PD, describing as representative examples some advances in the development of drug candidates targeting oxidative stress and adenosine A2A receptors.

Exploring the Potential Biological Activities of Pyrazole-Based Schiff Bases as Anti-Diabetic, Anti-Alzheimer's, Anti-Inflammatory, and Cytotoxic Agents: In Vitro Studies with Computational Predictions

In this innovative research, we aim to reveal pyrazole-based Schiff bases as new multi-target agents. In this context, we re-synthesized three sets of pyrazole-based Schiff bases, 5a-f, 6a-f, and 7a-f, to evaluate their biological applications. The data from in vitro biological assays (including antioxidant and scavenging activities, anti-diabetes, anti-Alzheimer's, and anti-inflammatory properties) of the pyrazole-based Schiff bases 5a-f, 6a-f, and 7a-f showed that the six pyrazole-based Schiff bases 5a, 5d, 5e, 5f, 7a, and 7f possess the highest biological properties among the compounds evaluated. The cytotoxicity against lung (A549) and colon (Caco-2) human cancer types, as well as normal lung (WI-38) cell lines, was evaluated. The data from the cytotoxicity investigation demonstrated that the three Schiff bases 5d, 5e, and 7a are active against lung (A549) cells, while the two Schiff bases 5e and 7a exhibited the highest cytotoxicity towards colon (Caco-2) cells. Additionally, the enzymatic activities against caspase-3 and Bcl-2 of the six pyrazole-based Schiff bases 5a, 5d, 5e, 5f, 7a, and 7f were evaluated. Furthermore, we assessed the in silico absorption, distribution, metabolism, and toxicity (ADMT) properties of the more potent pyrazole-based Schiff bases. After modifying the structures of the six pyrazole-based Schiff bases, we plan to further extend the studies in the future.

Exploring optimal drug targets through subtractive proteomics analysis and pangenomic insights for tailored drug design in tuberculosis

Tuberculosis (TB), caused by Mycobacterium tuberculosis, ranks among the top causes of global human mortality, as reported by the World Health Organization's 2022 TB report. The prevalence of M. tuberculosis strains that are multiple and extensive-drug resistant represents a significant barrier to TB eradication. Fortunately, having many completely sequenced M. tuberculosis genomes available has made it possible to investigate the species pangenome, conduct a pan-phylogenetic investigation, and find potential new drug targets. The 442 complete genome dataset was used to estimate the pangenome of M. tuberculosis. This study involved phylogenomic classification and in-depth analyses. Sequential filters were applied to the conserved core genome containing 2754 proteins. These filters assessed non-human homology, virulence, essentiality, physiochemical properties, and pathway analysis. Through these intensive filtering approaches, promising broad-spectrum therapeutic targets were identified. These targets were docked with FDA-approved compounds readily available on the ZINC database. Selected highly ranked ligands with inhibitory potential include dihydroergotamine and abiraterone acetate. The effectiveness of the ligands has been supported by molecular dynamics simulation of the ligand-protein complexes, instilling optimism that the identified lead compounds may serve as a robust basis for the development of safe and efficient drugs for TB treatment, subject to further lead optimization and subsequent experimental validation.

MDM2 Inhibitors for Cancer Therapy: The Past, Present, and Future

Since its discovery over 35 years ago, MDM2 has emerged as an attractive target for the development of cancer therapy. MDM2's activities extend from carcinogenesis to immunity to the response to various cancer therapies. Since the report of the first MDM2 inhibitor more than 30 years ago, various approaches to inhibit MDM2 have been attempted, with hundreds of small-molecule inhibitors evaluated in preclinical studies and numerous molecules tested in clinical trials. Although many MDM2 inhibitors and degraders have been evaluated in clinical trials, there is currently no Food and Drug Administration (FDA)-approved MDM2 inhibitor on the market. Nevertheless, there are several current clinical trials of promising agents that may overcome the past failures, including agents granted FDA orphan drug or fast-track status. We herein summarize the research efforts to discover and develop MDM2 inhibitors, focusing on those that induce MDM2 degradation and exert anticancer activity, regardless of the p53 status of the cancer. We also describe how preclinical and clinical investigations have moved toward combining MDM2 inhibitors with other agents, including immune checkpoint inhibitors. Finally, we discuss the current challenges and future directions to accelerate the clinical application of MDM2 inhibitors. In conclusion, targeting MDM2 remains a promising treatment approach, and targeting MDM2 for protein degradation represents a novel strategy to downregulate MDM2 without the side effects of the existing agents blocking p53-MDM2 binding. Additional preclinical and clinical investigations are needed to finally realize the full potential of MDM2 inhibition in treating cancer and other chronic diseases where MDM2 has been implicated. SIGNIFICANCE STATEMENT: Overexpression/amplification of the MDM2 oncogene has been detected in various human cancers and is associated with disease progression, treatment resistance, and poor patient outcomes. This article reviews the previous, current, and emerging MDM2-targeted therapies and summarizes the preclinical and clinical studies combining MDM2 inhibitors with chemotherapy and immunotherapy regimens. The findings of these contemporary studies may lead to safer and more effective treatments for patients with cancers overexpressing MDM2.

Neuroblastoma and its Target Therapies: A Medicinal Chemistry Review

Neuroblastoma (NB) is a childhood malignant tumour belonging to a group of embryonic tumours originating from progenitor cells of the sympathoadrenal lineage. The heterogeneity of NB is reflected in the survival rates of those with low and intermediate risk diseases who have survival rates ranging from 85 to 90 %. However, for those identified with high-risk Stage 4 NB, the treatment options are much more limited. For this group, current treatment consists of immunotherapy (monoclonal antibodies) in combination with anti-cancer drugs and has a 40 to 50 % survival rate. The purpose of this review is to summarise NB research from a medicinal chemistry perspective and to highlight advances in targeted drug therapy in the field. The review examines the medicinal chemistry of a number of drugs tested in research, some of which are currently under clinical trial. It concludes by proposing that future medicinal chemistry research into NB should consider other possible target therapies and adopt a multi-target drug approach rather than a one-drug-one-target approach for improved efficacy and less drug-drug interaction for the treatment of NB Stage 4 (NBS4) patients.

A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery

Traditional Chinese Medicine (TCM) has been used for thousands of years to treat human diseases. Recently, many databases have been devoted to studying TCM pharmacology. Most of these databases include information about the active ingredients of TCM herbs and their disease indications. These databases enable researchers to interrogate the mechanisms of action of TCM systematically. However, there is a need for comparative studies of these databases, as they are derived from various resources with different data processing methods. In this review, we provide a comprehensive analysis of the existing TCM databases. We found that the information complements each other by comparing herbs, ingredients, and herb-ingredient pairs in these databases. Therefore, data harmonization is vital to use all the available information fully. Moreover, different TCM databases may contain various annotation types for herbs or ingredients, notably for the chemical structure of ingredients, making it challenging to integrate data from them. We also highlight the latest TCM databases on symptoms or gene expressions, suggesting that using multi-omics data and advanced bioinformatics approaches may provide new insights for drug discovery in TCM. In summary, such a comparative study would help improve the understanding of data complexity that may ultimately motivate more efficient and more standardized strategies towards the digitalization of TCM.

System Pharmacological Approach to Investigate and Validate Multitargeted and Therapeutic Effect of Furocoumarins of Apium graveolens L. for Treatment of Kidney Disease

Background

System pharmacological approaches play important roles in drug discovery and development and in biomolecular exploration to investigate the multitarget therapeutic effects of phytochemicals for the treatment of acute and chronic ailments.

Objectives

The aim of the study was to apply a system pharmacological approach to investigate the multitarget therapeutic effects of furocoumarins of Apium graveolens L. for the treatment of kidney disease.

Methods

Several furocoumarins of Apium graveolens were screened from online databases. Network biology and poly-pharmacology analyses were performed to investigate the multitarget therapeutic effect of furocoumarins. The potential metabolites that showed significant interactions with various genes were selected for in silico docking analysis with CASP-3 and SOD proteins. In silico ADME analysis was also performed to investigate the pharmacokinetic behavior of targeted furocoumarins.

Results

Out of thirteen furocoumarins selected for analysis, six showed partial or significant interaction with SOD and CASP-3 proteins. These metabolites may alleviate kidney dysfunction by reducing oxidative and inflammatory stress, regulating apoptosis, slowing down the progression of diabetic nephropathy, and reducing hypertension and glomerular vascular rigidity. In silico docking analysis revealed bergapten as a potential therapeutic agent for kidney disease treatment. In silico docking analysis showed anglicine, imperatorin, and sphondin exhibited strong interaction with CASP-3 and SOD with binding energy -6.5, -7.2, -6.5 and -6.8, -6.2 -5.7 kcal/mol, respectively. These components exhibited greater conventional hydrogen bonding with CASP-3 and SOD than other furocoumarins. Furthermore, in silico ADME analysis of metabolites showed that all furocoumarins have a highly lipophilic nature, good skin permeability, and GI absorption, as well as good blood-brain permeability (BBB).

Conclusion

Furocoumarins reduce kidney dysfunction and associated pathophysiological complications via the reduction of glomerular vascular rigidity, diabetic nephropathy, and oxidative and inflammatory stress. However, further biomolecular and clinical examinations are necessary to validate and enhance the credibility of present findings.

Evaluation of the interaction between potent small molecules against the Nipah virus Glycoprotein in Malaysia and Bangladesh strains, accompanied by the human Ephrin-B2 and Ephrin-B3 receptors; a simulation approach

Malaysia reported the first human case of Nipah virus (NiV) in late September 1998 with encephalitis and respiratory symptoms. As a result of viral genomic mutations, two main strains (NiV-Malaysia and NiV-Bangladesh) have spread around the world. There are no licensed molecular therapeutics available for this biosafety level 4 pathogen. NiV attachment glycoprotein plays a critical role in viral transmission through its human receptors (Ephrin-B2 and Ephrin-B3), so identifying small molecules that can be repurposed to inhibit them is crucial to developing anti-NiV drugs. Consequently, in this study annealing simulations, pharmacophore modeling, molecular docking, and molecular dynamics were used to evaluate seven potential drugs (Pemirolast, Nitrofurantoin, Isoniazid Pyruvate, Eriodictyol, Cepharanthine, Ergoloid, and Hypericin) against NiV-G, Ephrin-B2, and Ephrin-B3 receptors. Based on the annealing analysis, Pemirolast for efnb2 protein and Isoniazid Pyruvate for efnb3 receptor were repurposed as the most promising small molecule candidates. Furthermore, Hypericin and Cepharanthine, with notable interaction values, are the top Glycoprotein inhibitors in Malaysia and Bangladesh strains, respectively. In addition, docking calculations revealed that their binding affinity scores are related to efnb2-pem (- 7.1 kcal/mol), efnb3-iso (- 5.8 kcal/mol), gm-hyp (- 9.6 kcal/mol), gb-ceph (- 9.2 kcal/mol). Finally, our computational research minimizes the time-consuming aspects and provides options for dealing with any new variants of Nipah virus that might emerge in the future.

De novo generation of dual-target ligands for the treatment of SARS-CoV-2 using deep learning, virtual screening, and molecular dynamic simulations

De novo generation of molecules with the necessary features offers a promising opportunity for artificial intelligence, such as deep generative approaches. However, creating novel compounds having biological activities toward two distinct targets continues to be a very challenging task. In this study, we develop a unique computational framework for the de novo synthesis of bioactive compounds directed at two predetermined therapeutic targets. This framework is referred to as the dual-target ligand generative network. Our approach uses a stochastic policy to explore chemical spaces called a sequence-based simple molecular input line entry system (SMILES) generator. The steps in the high-level workflow would be to gather and prepare the training data for both targets' molecules, build a neural network model and train it to make molecules, create new molecules using generative AI, and then virtually screen the newly validated molecules against the SARS-CoV-2 PLpro and 3CLpro drug targets. Results shows that novel molecules generated have higher binding affinity with both targets than the conventional drug i.e. Remdesivir being used for the treatment of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Target-based drug design strategies to overcome resistance to antiviral agents: opportunities and challenges

Viral infections have a major impact in human health. Ongoing viral transmission and escalating selective pressure have the potential to favor the emergence of vaccine- and antiviral drug-resistant viruses. Target-based approaches for the design of antiviral drugs can play a pivotal role in combating drug-resistant challenges. Drug design computational tools facilitate the discovery of novel drugs. This review provides a comprehensive overview of current drug design strategies employed in the field of antiviral drug resistance, illustrated through the description of a series of successful applications. These strategies include technologies that enhance compound-target affinity while minimizing interactions with mutated binding pockets. Furthermore, emerging approaches such as virtual screening, targeted protein/RNA degradation, and resistance analysis during drug design have been harnessed to curtail the emergence of drug resistance. Additionally, host targeting antiviral drugs offer a promising avenue for circumventing viral mutation. The widespread adoption of these refined drug design strategies will effectively address the prevailing challenge posed by antiviral drug resistance.

Recent advances in multitarget-directed ligands via in silico drug discovery

To combat multifactorial refractory diseases, such as cancer, cardiovascular, and neurodegenerative diseases, multitarget drugs have become an emerging area of research aimed at 'synthetic lethality' (SL) relationships associated with drug-resistance mechanisms. In this review, we discuss the in silico design of dual and triple-targeted ligands, strategies by which specific 'warhead' groups are incorporated into a parent compound or scaffold with primary inhibitory activity against one target to develop one small molecule that inhibits two or three molecular targets in an effort to increase potency against multifactorial diseases. We also discuss the analytical exploration of structure-activity relationships (SARs), physicochemical properties, polypharmacology, scaffold feature extraction of US Food and Drug Administration (FDA)-approved multikinase inhibitors (MKIs), and updates regarding the clinical status of dual-targeted chemotypes.

In silico evaluation of pharmacokinetics properties of withanolides and simulation of their biological activities against Alzheimer's disease

The withanolides are naturally occurring steroidal lactones found mainly in plants of the Solanaceae family. The subtribe Withaninae includes species like Withania sominifera, which are a source of many bioactive withanolides. In this work, we selected and evaluate the ADMET-related properties of 91 withanolides found in species of the subtribe Withaninae computationally, to predict the relationship between their structures and their pharmacokinetic profiles. We also evaluated the interaction of these withanolides with known targets of Alzheimer's disease (AD) through molecular docking and molecular dynamics. Withanolides presented favorable pharmacokinetic properties, like high gastrointestinal absorption, lipophilicity (logP ≤ 5), good distribution and excretion parameters, and a favorable toxicity profile. The specie Withania aristata stood out as an interesting source of the promising withanolides classified as 5-ene with 16-ene or 17-ene. These withanolides presented a favourable pharmacokinetic profile and were also highlighted as the best candidates for inhibition of AD-related targets. Our results also suggest that withanolides are likely to act as cholinesterase inhibitors by interacting with the catalytic pocket in an energy favorable and stable way.Communicated by Ramaswamy H. Sarma.

Quantitative live cell imaging of a tauopathy model enables the identification of a polypharmacological drug candidate that restores physiological microtubule interaction

Tauopathies such as Alzheimer's disease are characterized by aggregation and increased phosphorylation of the microtubule-associated protein tau. Tau's pathological changes are closely linked to neurodegeneration, making tau a prime candidate for intervention. We developed an approach to monitor pathological changes of aggregation-prone human tau in living neurons. We identified 2-phenyloxazole (PHOX) derivatives as putative polypharmacological small molecules that interact with tau and modulate tau kinases. We found that PHOX15 inhibits tau aggregation, restores tau's physiological microtubule interaction, and reduces tau phosphorylation at disease-relevant sites. Molecular dynamics simulations highlight cryptic channel-like pockets crossing tau protofilaments and suggest that PHOX15 binding reduces the protofilament's ability to adopt a PHF-like conformation by modifying a key glycine triad. Our data demonstrate that live-cell imaging of a tauopathy model enables screening of compounds that modulate tau-microtubule interaction and allows identification of a promising polypharmacological drug candidate that simultaneously inhibits tau aggregation and reduces tau phosphorylation.

Piperazine-2-carboxylic acid derivatives as MTDLs anti-Alzheimer agents: Anticholinesterase activity, mechanistic aspect, and molecular modeling studies

Development of Multitarget-Directed Ligands (MTDLs) is a promising approach to combat the complex etiologies of Alzheimer's disease (AD). Herein we report the design, synthesis, and characterization of a new series of 1,4-bisbenzylpiperazine-2-carboxylic acid derivatives 3-5(a-g), 7a-f, 8a-s, and their piperazine-2-yl-1,3,4-oxadiazole analogs 6a-g. In vitro inhibitory effect against Electrophorus electricus acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) from Equine serum was evaluated using modified Ellman's method, considering donepezil and tacrine as reference drugs. Lineweaver-Burk plot analysis of the results proved competitive inhibition of AChE and BChE with Ki values, in low micromolar range. The free carboxylic acid series 4a-g showed enhanced selectivity for AChE. Hence, 4c, 1,4-bis (4-chlorobenzyl)-piperazinyl-2-carboxylic acid), was the most active member of this series (Ki (AChE) = 10.18 ± 1.00 µM) with clear selectivity for AChE (SI ∼ 17.90). However, the hydroxamic acids 7a-f and carboxamides 8a-s congeners were more potent and selective inhibitors of BChE (SI ∼ 5.38 - 21862.5). Extraordinarily, 1,4-bis (2-chlorobenzyl)-piperazinyl-2-hydroxamic acid 7b showed promising inhibitory activity against BChE enzyme (Ki = 1.6 ± 0.08 nM, SI = 21862.5), that was significantly superior to that elicited by donepezil (Ki = 12.5 ± 2.6 µM) and tacrine (Ki = 17.3 ± 2.3 nM). Cytotoxicity assessment of 4c and 7b, on human neuroblastoma (SH-SY5Y) cell lines, revealed lower toxicity than staurosporine and was nearly comparable to that of donepezil. Molecular docking and molecular dynamics simulation afforded unblemished insights into the structure-activity relationships for AChE and BChE inhibition. The results showed stable binding with fair H-bonding, hydrophobic and/or ionic interactions to the catalytic and peripheral anionic sites of the enzymes. In silico predicted ADME and physicochemical properties of conjugates showed good CNS bioavailability and safety parameters. In this regard, compound (7b) might be considered as a promising inhibitor of BChE with an innovative donepezil-based anti-Alzheimer activity. Further assessments of the most potent AChE and BChE inhibitors as potential MTDLs anti-Alzheimer's agents are under investigation with our research group and will be published later.

A Cocktail of Natural Compounds Holds Promise for New Immunotherapeutic Potential in Head and Neck Cancer

OBJECTIVE

To obtain detailed understanding on the gene regulation of natural compounds in altering prognosis of head and neck squamous cell carcinomas (HNSC).

METHODS

Gene expression data of HNSC samples and peripheral blood mononuclear cells (PBMCs) of HNSC patients were collected from Gene Expression Omnibus (GEO). Differential gene expression analysis of GEO datasets were achieved by the GEO2R tool. Common differentially expressed gerres (DEGs) were screened by comparing DEGs of HNSC with those of PBMCs. The combination was further analyzed for regulating pathways and biological processes that were affected.

RESULTS

Totally 110 DEGs were retrieved and identified to be involved in biological processes related to tumor regulation. Then 102 natural compounds were screened for a combination such that the expression of all 110 commonly DEGs was altered. A combination of salidroside, ginsenoside Rd, oridonin, britanin, and scutellarein was chosen. A multifaceted, multi-dimensional tumor regression was showed by altering autophagy, apoptosis, inhibiting cell proliferation, angiogenesis, metastasis and inflammatory cytokines production.

CONCLUSIONS

This study has helped develop a unique combination of natural compounds that will markedly reduce the propensity of development of drug resistance in tumors and immune evasion by tumors. The result is crucial to developing a combinatorial natural therapeutic cocktail with accentuated immunotherapeutic potential.

Novel Multi-Target Agents Based on the Privileged Structure of 4-Hydroxy-2-quinolinone

In this work, the privileged scaffold of 4-hydroxy-2quinolinone is investigated through the synthesis of carboxamides and hybrid derivatives, as well as through their bioactivity evaluation, focusing on the ability of the molecules to inhibit the soybean LOX, as an indication of their anti-inflammatory activity. Twenty-one quinolinone carboxamides, seven novel hybrid compounds consisting of the quinolinone moiety and selected cinnamic or benzoic acid derivatives, as well as three reverse amides are synthesized and classified as multi-target agents according to their LOX inhibitory and antioxidant activity. Among all the synthesized analogues, quinolinone-carboxamide compounds 3h and 3s, which are introduced for the first time in the literature, exhibited the best LOX inhibitory activity (IC50 = 10 μM). Furthermore, carboxamide 3g and quinolinone hybrid with acetylated ferulic acid 11e emerged as multi-target agents, revealing combined antioxidant and LOX inhibitory activity (3g: IC50 = 27.5 μM for LOX inhibition, 100% inhibition of lipid peroxidation, 67.7% ability to scavenge hydroxyl radicals and 72.4% in the ABTS radical cation decolorization assay; 11e: IC50 = 52 μM for LOX inhibition and 97% inhibition of lipid peroxidation). The in silico docking results revealed that the synthetic carboxamide analogues 3h and 3s and NDGA (the reference compound) bind at the same alternative binding site in a similar binding mode.

Novel computational and drug design strategies for the inhibition of human T-cell leukemia virus 1-associated lymphoma by Astilbin derivatives

Human T-cell leukemia virus 1 (HTLV-1) associated lymphoma is a devastating malignancy triggered by HTLV-1 infections. We employeda comprehensive drug design and computational strategy in this work to explore the inhibitory activitiesof Astilbin derivatives against HTLV-1-associated lymphoma. We evaluated the stability, binding affinities, and various computational analysis of Astilbin derivatives against target proteins, such as HTLV-1 main protease and HTLV-1 capsid protein. The root mean square deviation (RMSD), root mean square fluctuation, radius of gyration, hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM) were applied to characterize these protein-ligand interactions further. Ligand-03 and ligand-04 exhibited notable binding affinity to HTLV-1 capsid protein, while ligand-05 displayed high binding affinity to HTLV-1 protease. MD simulation analysis revealed that ligand-03, bound to HTLV-1 capsid protein, demonstrated enhanced stability with lower RMSD values and fewer conformational changes, suggesting a promising binding orientation. Ligand-04, despite stable binding, exhibited increased structural deviations, making it less suitable. Ligand-05 demonstrated stable binding to HTLV-1 protease throughout the simulation period at 100 nanoseconds. Hydrogen bond analysis indicated that ligand-05 formed persistent hydrogen bonds with significantresidues, contributing to its stability. PCA highlighted ligand-03's more remarkable conformational changes, while DCCM showed ligand-05's distinct dynamics, indicating its different behavior in the complex. Furthermore, binding free energy calculations supported the favorable interactions of ligand-03 and ligand-04 with HTLV-1 capsid protein, while ligand-05 showed weaker interactions with HTLV-1 protease. Molecular electrostatic potential and frontier molecular orbital analyses provided insights into these compounds' charge distribution and stability. In conclusion, this research found Astilbin derivatives as potential inhibitors of HTLV-1-associated lymphoma. Future attempts at drug development will benefit from the steady interaction landscape provided by Ligand-03, Ligand-04 and Ligand-05, which showed the most attractive binding profile with the target protein. These results open up new opportunities for innovative drug development, and more experimental testing should be done between Astilbin derivatives and HTLV-1-associated lymphoma.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening against multiple Plasmodium falciparum kinases reveals antimalarial compounds

The continuous emergence of resistance against most frontline antimalarial drugs has led to countless deaths in malaria-endemic countries, counting 619,000 deaths in 2021, with mutation in drug targets being the sole cause. As mutation is correlated frequently with fitness cost, the likelihood of mutation emergence in multiple targets at a time is extremely low. Hence, multitargeting compounds may seem promising to address drug resistance issues with additional benefits like increased efficacy, improved safety profile, and the requirement of fewer pills compared to traditional single and combinational drugs. In this study, we attempted to use the High Throughput Virtual Screening approach to predict multitarget inhibitors against six chemically validated Plasmodium falciparum (Pf) kinases (PfPKG, PfMAP2, PfCDPK4, PfTMK, PfPK5, PfPI4K), resulting in 21 multitargeting hits. The molecular dynamic simulation of the top six complexes (Myricetin-MAP2, Quercetin-CDPK4, Myricetin-TMK, Quercetin-PKG, Salidroside-PK5, and Salidroside-PI4K) showed stable interactions. Moreover, hierarchical clustering reveals the structural divergence of the compounds from the existing antimalarials, indicating less chance of cross-resistance. Additionally, the top three hits were validated through parasite growth inhibition assays, with quercetin and myricetin exhibiting an IC50 value of 1.84 and 3.93 µM, respectively.

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI's cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI's predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.

Genomic and transcriptomic advances in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder and the most common motor neuron disease. ALS shows substantial clinical and molecular heterogeneity. In vitro and in vivo models coupled with multiomic techniques have provided important contributions to unraveling the pathomechanisms underlying ALS. To date, despite promising results and accumulating knowledge, an effective treatment is still lacking. Here, we provide an overview of the literature on the use of genomics, epigenomics, transcriptomics and microRNAs to deeply investigate the molecular mechanisms developing and sustaining ALS. We report the most relevant genes implicated in ALS pathogenesis, discussing the use of different high-throughput sequencing techniques and the role of epigenomic modifications. Furthermore, we present transcriptomic studies discussing the most recent advances, from microarrays to bulk and single-cell RNA sequencing. Finally, we discuss the use of microRNAs as potential biomarkers and promising tools for molecular intervention. The integration of data from multiple omic approaches may provide new insights into pathogenic pathways in ALS by shedding light on diagnostic and prognostic biomarkers, helping to stratify patients into clinically relevant subgroups, revealing novel therapeutic targets and supporting the development of new effective therapies.

Development of Potential Multi-Target Inhibitors for Human Cholinesterases and Beta-Secretase 1: A Computational Approach

Alzheimer's disease causes chronic neurodegeneration and is the leading cause of dementia in the world. The causes of this disease are not fully understood but seem to involve two essential cerebral pathways: cholinergic and amyloid. The simultaneous inhibition of AChE, BuChE, and BACE-1, essential enzymes involved in those pathways, is a promising therapeutic approach to treat the symptoms and, hopefully, also halt the disease progression. This study sought to identify triple enzymatic inhibitors based on stereo-electronic requirements deduced from molecular modeling of AChE, BuChE, and BACE-1 active sites. A pharmacophore model was built, displaying four hydrophobic centers, three hydrogen bond acceptors, and one positively charged nitrogen, and used to prioritize molecules found in virtual libraries. Compounds showing adequate overlapping rates with the pharmacophore were subjected to molecular docking against the three enzymes and those with an adequate docking score (n = 12) were evaluated for physicochemical and toxicological parameters and commercial availability. The structure exhibiting the greatest inhibitory potential against all three enzymes was subjected to molecular dynamics simulations (100 ns) to assess the stability of the inhibitor-enzyme systems. The results of this in silico approach indicate ZINC1733 can be a potential multi-target inhibitor of AChE, BuChE, and BACE-1, and future enzymatic assays are planned to validate those results.

A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs

Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy. Currently, numerous computational methods have been developed to predict the synergistic effects of anticancer drugs. However, there has been insufficient exploration of how to mine drug and cell line data at different granularity levels for predicting synergistic anticancer drug combinations. Therefore, this study proposes a granularity-level information fusion strategy based on the hypergraph transformer, named HypertranSynergy, to predict synergistic effects of anticancer drugs. HypertranSynergy introduces synergistic connections between cancer cell lines and drug combinations using hypergraph. Then, the Coarse-grained Information Extraction (CIE) module merges the hypergraph with a transformer for node embeddings. In the CIE module, Contranorm is a normalization layer that mitigates over-smoothing, while Gaussian noise addresses local information gaps. Additionally, the Fine-grained Information Extraction (FIE) module assesses fine-grained information's impact on predictions by employing similarity-aware matrices from drug/cell line features. Both CIE and FIE modules are integrated into HypertranSynergy. In addition, HypertranSynergy achieved the AUC of 0.93${\pm }$0.01 and the AUPR of 0.69${\pm }$0.02 in 5-fold cross-validation of classification task, and the RMSE of 13.77${\pm }$0.07 and the PCC of 0.81${\pm }$0.02 in 5-fold cross-validation of regression task. These results are better than most of the state-of-the-art models.

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

Machine learning algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Machine learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained machine learning model to screen a vast chemical library for new JAK2 inhibitors, the biological activities of which were reported. Reference JAK2 inhibitors, comprising 1911 compounds, have experimentally determined IC50 values. To generate the input to the machine learning model, reference compounds were subjected to RDKit, a cheminformatic toolkit, to extract molecular descriptors. A Random Forest Regression model from the Scikit-learn machine learning library was applied to obtain a predictive regression model and to analyze each molecular descriptor's role in determining IC50 values in the reference data set. Then, IC50 values of the library compounds, comprised of 1,576,903 compounds, were predicted using the generated regression model. Interestingly, some compounds that exhibit high IC50 values from the prediction were reported to possess JAK inhibition activity, which indicates the limitations of the prediction model. To confirm the JAK2 inhibition activity of predicted compounds, molecular docking and molecular dynamics simulation were carried out with the JAK inhibitor reference compound, tofacitinib. The binding affinity of docked compounds in the active region of JAK2 was also analyzed by the gmxMMPBSA approach. Furthermore, experimental validation confirmed the results from the computational analysis. Results showed highly comparable outcomes concerning tofacitinib. Conclusively, the machine learning model can efficiently improve the virtual screening of drugs and drug development.

A novel in silico scaffold-hopping method for drug repositioning in rare and intractable diseases

In the field of rare and intractable diseases, new drug development is difficult and drug repositioning (DR) is a key method to improve this situation. In this study, we present a new method for finding DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM). At first, we used a spleen associated tyrosine kinase inhibitor as a reference to evaluate the technique, and succeeded in scaffold-hopping maintaining the pharmacological activity. Then we applied this method to five drugs and obtained 144 compounds with diverse structures. Among these, 31 compounds were known to target the same proteins as their reference compounds and 113 compounds were known to target different proteins. We found that AI-AAM dominantly selected functionally similar compounds; thus, these selected compounds may represent improved alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of their references as well. This new "compound-target" information provided DR candidates that could be utilized for future drug development.

Molecular docking, molecular dynamics and binding free energy based identification of novel potential multitarget inhibitors of Nipah virus

Nipah virus (NiV) is one of the most common viral diseases affecting the brain and nervous system of the body. To date, there is no significant antiviral drug specifically designed to inhibit NiV. In the last ten years, there has been a significant increase in interest in multitarget drug development. Therefore, the reported work focuses on designing a multitarget inhibitor for NiV. Among the twelve designed compounds, five exhibited better drug-likeness and ADMET properties, hence being selected for further analysis. In a molecular docking study, these compounds possessed better binding affinity as compared to Favipiravir. The RMSD of these compounds was ≤2Å and the number of H-bonds signified the better stability of the complexes formed. The ΔGbind of C4, C6 and C7 was found to be comparatively higher than the other screened compounds, revealing their greater ability to bind efficiently with NiV-G, NiV-F and NiV-N receptors, respectively. Therefore, based on molecular docking, molecular dynamics, and MM/PBSA analysis, these compounds can act as potential inhibitors of multitargets of NiV.Communicated by Ramaswamy H. Sarma.

Targeting the epigenetic reader "BET" as a therapeutic strategy for cancer

Bromodomain and extraterminal (BET) proteins have the ability to bind to acetylated lysine residues present in both histones and non-histone proteins. This binding is facilitated by the presence of tandem bromodomains. The regulatory role of BET proteins extends to chromatin dynamics, cellular processes, and disease progression. The BET family comprises of BRD 2, 3, 4 and BRDT. The BET proteins are a class of epigenetic readers that regulate the transcriptional activity of a multitude of genes that are involved in the pathogenesis of cancer. Thus, targeting BET proteins has been identified as a potentially efficacious approach for the treatment of cancer. BET inhibitors (BETis) are known to interfere with the binding of BET proteins to acetylated lysine residues of chromatin, thereby leading to the suppression of transcription of several genes, including oncogenic transcription factors. Here in this review, we focus on role of Bromodomain and extra C-terminal (BET) proteins in cancer progression. Furthermore, numerous small-molecule inhibitors with pan-BET activity have been documented, with certain compounds currently undergoing clinical assessment. However, it is apparent that the clinical effectiveness of the present BET inhibitors is restricted, prompting the exploration of novel technologies to enhance their clinical outcomes and mitigate undesired adverse effects. Thus, strategies like development of selective BET-BD1, & BD2 inhibitors, dual and acting BET are also presented in this review and attempts to cover the chemistry needed for proper establishment of designed molecules into BRD have been made. Moreover, the review attempts to summarize the details of research till date and proposes a space for future development of BET inhibitor with diminished side effects. It can be concluded that discovery of isoform selective BET inhibitors can be a way forward in order to develop BET inhibitors with negligible side effects.

Echinacoside: A promising active natural products and pharmacological agents

Echinacoside, a natural phenylethanoid glycoside, was discovered and isolated from the garden plant Echinacea angustifolia DC., belonging to the Compositae family, approximately sixty years ago. Extensive investigations have revealed that it possesses a wide array of pharmacologically beneficial activities for human health, particularly notable for its neuroprotective and anticancer activity. Several crucial concerns surfaced, encompassing the recognition of active metabolites that exhibited inadequate bioavailability in their prototype form, the establishment of precise molecular signal pathways or targets associated with the aforementioned effects of echinacoside, and the scarcity of dependable clinical trials. Hence, the question remains unanswered as to whether scientific research can effectively utilize this natural compound. To support future studies on this natural product, it is imperative to provide a systematic overview and insights into potential future prospects. The current review provides a comprehensive analysis of the existing knowledge on echinacoside, encompassing its wide distribution, structural diversity and metabolism, diverse therapeutic applications, and improvement of echinacoside bioavailability for its potential utilization.

Molecular docking and dynamics guided approach to identify potential anti-inflammatory molecules as NRF2 activator to protect against drug-induced liver injury (DILI): a computational study

Inflammation and oxidative stress can contribute to the etiology of metabolic and chronic illnesses. The ability to prevent oxidative stress induced diseases such as cancer, cardiovascular disease, Alzheimer's disease, and others has been the subject of global research. Drug-induced liver injury (DILI) pathogenesis can be either due to oxidative stress or inflammatory response elicited by the drug, its metabolite, or herbal supplements. Our present research uses computational studies to identify a molecule with anti-inflammatory properties that can operate as an NRF2 activator. Acquiring and preparing the KEAP1-NRF2 Protein (PDB: 4L7D) with Schrodinger Suite was followed by developing a ligand library (Anti-inflammatory library downloaded from ChemDiv database). Molecular docking studies were performed in HTVS, SP, and XP modes, respectively. Based on the docking score, interaction, ADMET and binding free energy, the top ten compounds were selected and subjected to induced-fit docking (IFD) analysis for further study. The top three molecules were chosen for a molecular dynamics (MD) simulation study. Using the Desmond module of the Schrodinger Suite, the stability of the protein-ligand complex and protein-ligand contact throughout 100ns were evaluated during the MD simulation study. In our study, it was observed that three compounds exhibit exceptional stability and retain the essential interaction throughout the studies, and it is anticipated that these compounds may act as effective NRF2 activators. Further in vitro and in vivo assessments can be conducted to determine its potential to prevent DILI via acting as an NRF2 activator for future drug development.

Pharmacogenomic Analysis of Combined Therapies against Glioblastoma Based on Cell Markers from Single-Cell Sequencing

Glioblastoma is the most common and aggressive form of primary brain cancer and the lack of viable treatment options has created an urgency to develop novel treatments. Personalized or predictive medicine is still in its infancy stage at present. This research aimed to discover biomarkers to inform disease progression and to develop personalized prophylactic and therapeutic strategies by combining state-of-the-art technologies such as single-cell RNA sequencing, systems pharmacology, and a polypharmacological approach. As predicted in the pyroptosis-related gene (PRG) transcription factor (TF) microRNA (miRNA) regulatory network, TP53 was the hub gene in the pyroptosis process in glioblastoma (GBM). A LASSO Cox regression model of pyroptosis-related genes was built to accurately and conveniently predict the one-, two-, and three-year overall survival rates of GBM patients. The top-scoring five natural compounds were parthenolide, rutin, baeomycesic acid, luteolin, and kaempferol, which have NFKB inhibition, antioxidant, lipoxygenase inhibition, glucosidase inhibition, and estrogen receptor agonism properties, respectively. In contrast, the analysis of the cell-type-specific differential expression-related targets of natural compounds showed that the top five subtype cells targeted by natural compounds were endothelial cells, microglia/macrophages, oligodendrocytes, dendritic cells, and neutrophil cells. The current approach-using the pharmacogenomic analysis of combined therapies-serves as a model for novel personalized therapeutic strategies for GBM treatment.

Fundamental Determinants of the Accuracy of Equilibrium Constants for Affinity Complexes

The equilibrium constant of a chemical reaction is arguably the key thermodynamic parameter in chemistry; we naturally expect that equilibrium constants are determined accurately. The majority of equilibrium constants determined today are those of binding reactions that form affinity complexes, such as protein-protein, protein-DNA, and protein-small molecule. There is growing awareness that the determination of equilibrium constants for highly stable affinity complexes may be very inaccurate. However, fundamental (i.e., method-independent) determinants of accuracy are poorly understood. Here, we present a study that explicitly shows what the accuracy of equilibrium constants of affinity complexes depends on. This study reveals the critical importance of the choice of concentration of interacting components and creates a theoretical foundation for improving the accuracy of the equilibrium constants. The predicted influence of concentrations on accuracy was confirmed experimentally. The results of this fundamental study provide instructive guidance for experimentalists independently on the method they use.

In silico design and cell-based evaluation of two dual anti breast cancer compounds targeting Bcl-2 and GPER

According to WHO statistics, breast cancer (BC) disease represents about 2.3 million diagnosed and 685,000 deaths globally. Regarding histological classification of BC, the Estrogen (ER) and Progesterone (PR) receptors negative-expression cancer, named Triple-Negative BC (TNBC), represents the most aggressive type of this disease, making it a challenge for drug discovery. In this context, our research group, applying a well-established Virtual Screening (VS) protocol, in addition to docking and molecular dynamics simulations studies, yielded two ligands identified as 6 and 37 which were chemically synthesized and evaluated on MCF-7 and MDA-MB-231 cancer cell lines. Strikingly, 37 assayed on MDA-MB-231 (a TNBC cell model) depicted an outstanding value of 18.66 μM much lower than 65.67 μM yielded by Gossypol Bcl-2 inhibitor whose main disadvantage is to produce multiple toxic effects. Highlighted above, enforce the premise of the computational tools to find new therapeutic options against the most aggressive forms of breast cancer, as the results herein showed.

Sea Conch Peptides Hydrolysate Alleviates DSS-Induced Colitis in Mice through Immune Modulation and Gut Microbiota Restoration

Inflammatory bowel disease (IBD) is a persistent, lifelong inflammation of the digestive system. Dextran sulfate sodium is commonly used to induce colitis in experimental animal models, which causes epithelial damage, intestinal inflammation, mucin depletion, and dysbiosis of the gut microbiota. Various prebiotics, polysaccharides, and polypeptides are used for IBD treatment. In this study, we used a murine model utilizing BALB/c mice, with 10 mice per group, to investigate the treatment effect of sea conch peptide hydrolysate (CPH) on DSS-induced colitis mice. Colitis was induced through the administration of 2.5% DSS in drinking water over a seven-days period. Furthermore, on the eighth day of the experiment, sea conch peptide hydrolysate (CPH) at low (100 mg/kg), medium (200 mg/kg), and high (400 mg/kg) doses, which were continued for 14 days, were assessed for medicinal purposes in DSS-induced colitis mice. Our results showed that CPH treatment significantly alleviated the severity and symptoms of colitis. The epithelial integrity and histological damage were improved. Intestinal inflammation and inflammatory cell infiltration were improved. Furthermore, the expression of pro-inflammatory cytokines was reduced, and intestinal barrier integrity was restored by elevating the tight junction proteins. Moreover, 16s RNA sequencing revealed dysbiosis of the gut microbiota was observed upon DSS treatment, which was reinstated after CPH treatment. An increased level of Firmicutes and Lactobacillus was observed in the treatment groups. Finally, our results suggest that CPH would be recommended as a functional food source and also have the potential to be used as a medicinal product for different gastrointestinal disorders.

Vismodegib Identified as a Novel COX-2 Inhibitor via Deep-Learning-Based Drug Repositioning and Molecular Docking Analysis

Artificial intelligence algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Deep-learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained deep-learning model to screen an FDA-approved drug library for novel COX-2 inhibitors. Reference COX-2 data sets, composed of active and decoy compounds, were obtained from the DUD-E database. To extract molecular features, compounds were subjected to RDKit, a cheminformatic toolkit. GraphConvMol, a graph convolutional network model from DeepChem, was applied to obtain a predictive model from the DUD-E data sets. Then, the COX-2 inhibitory potential of the FDA-approved drugs was predicted using the trained deep-learning model. Vismodegib, an anticancer agent that inhibits the hedgehog signaling pathway by binding to smoothened, was predicted to inhibit COX-2. Noticeably, some compounds that exhibit high potential from the prediction were known to be COX-2 inhibitors, indicating the prediction model's liability. To confirm the COX-2 inhibition activity of vismodegib, molecular docking was carried out with the reference compounds of the COX-2 inhibitor, celecoxib, and ibuprofen. Furthermore, the experimental examination of COX-2 inhibition was also carried out using a cell culture study. Results showed that vismodegib exhibited a highly comparable COX-2 inhibitory activity compared to celecoxib and ibuprofen. In conclusion, the deep-learning model can efficiently improve the virtual screening of drugs, and vismodegib can be used as a novel COX-2 inhibitor.

Jian Yun Qing Hua Decoction inhibits malignant behaviors of gastric carcinoma cells via COL12A1 mediated ferroptosis signal pathway

BACKGROUND

Jian Yun Qing Hua Decoction (JYQHD), a traditional Chinese medicine decoction, which has been applied in the treatment of gastric cancer (GC). We attempt to confirm the anti-gastric cancer effect of JYQHD and explore the mechanism of JYQHD.

METHODS

Acute toxicity test was used to understand the toxicity of JYQHD. We studied the expression and prognostic outcome of COL12A1 within GC tissues through the network databases. Using several web-based databases, we analyzed the major components and targets of JYQHD, as well as known therapeutic targets in gastric cancer. The Venn diagram was utilized to obtain the overlapped genes. Lentiviral vector, shRNAs and plasmids, were used to transfect GC cells. Cell counting kit-8 (CCK8), sphere formation, malondialdehyde (MDA), glutathione (GSH), reactive oxygen species (ROS), Fe2+, transmission electron microscopy (TEM), quantitative Real-Time Polymerase Chain Reaction (qRT-PCR), Western-Blot (WB), and immunohistochemical (IHC) assays were employed to investigate the role and mechanism of COL12A1 and JYQHD in GC.

RESULTS

The results showed that JYQHD was non-toxic and safe. JYQHD inhibited growth and sphere formation ability through inducing the ferroptosis of GC cells, and suppressed the GC cells induced subcutaneous xenograft tumor growth. COL12A1 was highly expressed in gastric cancer tissues, indicating poor prognosis. COL12A1 specifically enhanced GC cell progression and stemness via suppressing ferroptosis. JYQHD down-regulated COL12A1 in order to suppress the stemness of GC cells via inducing ferroptosis.

CONCLUSION

COL12A1 inhibited ferroptosis and enhanced stemness in GC cells. JYQHD inhibited the development of GC cells by inhibiting cancer cell stemness via the ferroptosis pathway mediated by COL12A1.

Multitargeting in cardioprotection: An example of biaromatic compounds

A multitarget drug design approach is actively developing in modern medicinal chemistry and pharmacology, especially with regard to multifactorial diseases such as cardiovascular diseases, cancer, and neurodegenerative diseases. A detailed study of many well-known drugs developed within the single-target approach also often reveals additional mechanisms of their real pharmacological action. One of the multitarget drug design approaches can be the identification of the basic pharmacophore models corresponding to a wide range of the required target ligands. Among such models in the group of cardioprotectors is the linked biaromatic system. This review develops the concept of a "basic pharmacophore" using the biaromatic pharmacophore of cardioprotectors as an example. It presents an analysis of possible biological targets for compounds corresponding to the biaromatic pharmacophore and an analysis of the spectrum of biological targets for the five most known and most studied cardioprotective drugs corresponding to this model, and their involvement in the biological effects of these drugs.

Modulation of neurotrophic factors in the treatment of dementia, stroke and TBI: Effects of Cerebrolysin

Neurotrophic factors (NTFs) are involved in the pathophysiology of neurological disorders such as dementia, stroke and traumatic brain injury (TBI), and constitute molecular targets of high interest for the therapy of these pathologies. In this review we provide an overview of current knowledge of the definition, discovery and mode of action of five NTFs, nerve growth factor, insulin-like growth factor 1, brain derived NTF, vascular endothelial growth factor and tumor necrosis factor alpha; as well as on their contribution to brain pathology and potential therapeutic use in dementia, stroke and TBI. Within the concept of NTFs in the treatment of these pathologies, we also review the neuropeptide preparation Cerebrolysin, which has been shown to resemble the activities of NTFs and to modulate the expression level of endogenous NTFs. Cerebrolysin has demonstrated beneficial treatment capabilities in vitro and in clinical studies, which are discussed within the context of the biochemistry of NTFs. The review focuses on the interactions of different NTFs, rather than addressing a single NTF, by outlining their signaling network and by reviewing their effect on clinical outcome in prevalent brain pathologies. The effects of the interactions of these NTFs and Cerebrolysin on neuroplasticity, neurogenesis, angiogenesis and inflammation, and their relevance for the treatment of dementia, stroke and TBI are summarized.