Shifting from the single to the multitarget paradigm in drug discovery

Exploration of small-molecule inhibitors targeting Hsp110 as novel therapeutics

The heat shock protein (HSP) 110 family has a key role as a unique class of molecular chaperones maintaining cellular proteostasis in eukaryotes. Abnormal activation of Hsp110 has been implicated in several diseases. Given its important role in pathogenesis, Hsp110 has become a novel drug target for disease diagnosis and targeted therapy. Thus, targeting Hsp110 or its interactions with client proteins offers new therapeutic approaches. Recent studies of small-molecule inhibitors that target Hsp110 in vitro and in vivo have yielded encouraging results. In this review, we provide an overview of novel therapeutics targeting Hsp110, mainly inhibitors of protein-protein interactions (PPIs), together with a brief discussion of the relevant challenges, opportunities, and future directions.

Development of novel dual-target drugs against visceral leishmaniasis and combinational study with miltefosine

The dual-target inhibitors (ZINC000008876351 and ZINC000253403245) were identified by utilizing an advanced computational drug discovery method by targeting two critical enzymes such as FeSODA (Iron superoxide dismutase) and TryR (Trypanothione reductase) within the antioxidant defense system of Leishmania donovani (Ld). In vitro enzyme inhibition kinetics reveals that both the compound's ability to inhibit the function of enzyme LdFeSODA and LdTryR with inhibition constant (Ki) value in the low μM range. Flow cytometry analysis, specifically at IC50 and 2X IC50 doses of both the compounds, the intracellular ROS was significantly increased as compared to the untreated control. The compounds ZINC000253403245 and ZINC000008876351 exhibited strong anti-leishmanial activity in a dose-dependent manner against both the promastigote and amastigote stages of the parasite. The data indicate that these molecules hold promise as potential anti-leishmanial agents for developing new treatments against visceral leishmaniasis, specifically targeting the LdFeSODA and LdTryR enzymes. Additionally, the in vitro MTT assay shows that combining these compounds with miltefosine produces a synergistic effect compared to miltefosine alone. This suggests that the compounds can boost miltefosine's effectiveness by synergistically inhibiting the growth of L. donovani promastigotes. Given the emergence of miltefosine resistance in some Leishmania strains, these findings are particularly significant.

Sertraline as a Multi-Target Modulator of AChE, COX-2, BACE-1, and GSK-3β: Computational and In Vivo Studies

Alzheimer's disease (AD) is a neurodegenerative disorder associated with the dysregulation of several key enzymes, including acetylcholinesterase (AChE), cyclooxygenase-2 (COX-2), glycogen synthase kinase 3β (GSK-3β), β-site amyloid precursor protein cleaving enzyme 1 (BACE-1), and caspase-3. In this study, machine learning algorithms such as Random Forest (RF), Gradient Boost (GB), and Extreme Gradient Boost (XGB) were employed to screen US-FDA approved drugs from the ZINC15 database to identify potential dual inhibitors of COX-2 and AChE. The models were trained using molecules obtained from the ChEMBL database, with 5039 molecules for AChE and 3689 molecules for COX-2. Specifically, 1248 and 3791 molecules were classified as active and inactive for AChE, respectively, while 858 and 2831 molecules were classified as active and inactive for COX-2. The three machine learning models achieved prediction accuracies ranging from 92% to 95% for both AChE and COX-2. Virtual screening of US-FDA drugs from the ZINC15 database identified sertraline (SETL) as a potential dual inhibitor of AChE and COX-2. Further docking studies of SETL in the active sites of AChE and COX-2, as well as BACE-1, GSK-3β, and caspase-3, revealed strong binding affinities for all five proteins. In vivo validation was conducted using a lipopolysaccharide (LPS)-induced rat model pretreated with SETL for 30 days. The results demonstrated a significant decrease in the levels of AChE (p < 0.001), BACE-1 (p < 0.01), GSK-3β (p < 0.05), and COX-2 (p < 0.05). Additionally, the downstream effects were evaluated, showing significant decreases in the apoptosis marker caspase-3 (p < 0.05) and the oxidative stress marker malondialdehyde (MDA) (p < 0.001), indicating that SETL is clinically localized in its effectiveness, mitigating both enzymatic activity and the associated pathological changes of cognitive impairment and AD.

Computer-Aided Identification and Design of Ligands for Multi-Targeting Inhibition of a Molecular Acute Myeloid Leukemia Network

BACKGROUND/OBJECTIVES

Acute myeloid leukemia (AML) is characterized by therapeutic failure and long-term risk for disease relapses. As several therapeutic targets participate in networks, they can rewire to eventually evade single-target drugs. Hence, multi-targeting approaches are considered on the expectation that interference with many different components could synergistically hinder activation of alternative pathways and demolish the network one-off, leading to complete disease remission.

METHODS

Herein, we established a network-based, computer-aided approach for the rational design of drug combinations and de novo agents that interact with many AML network components simultaneously.

RESULTS

A reconstructed AML network guided the selection of suitable protein hubs and corresponding multi-targeting strategies. For proteins responsive to existing drugs, a greedy algorithm identified the minimum amount of compounds targeting the maximum number of hubs. We predicted permissible combinations of amiodarone, artenimol, fostamatinib, ponatinib, procaine, and vismodegib that interfere with 3-8 hubs, and we elucidated the pharmacological mode of action of procaine on DNMT3A. For proteins that do not respond to any approved drugs, namely cyclins A1, D2, and E1, we used structure-based de novo drug design to generate a novel triple-targeting compound of the chemical formula C15H15NO5, with favorable pharmacological and drug-like properties.

CONCLUSIONS

Overall, by integrating network and structural pharmacology with molecular modeling, we determined two complementary strategies with the potential to annihilate the AML network, one in the form of repurposable drug combinations and the other as a de novo synthesized triple-targeting agent. These target-drug interactions could be prioritized for preclinical and clinical testing toward precision medicine for AML.

Combining Data-Driven and Structure-Based Approaches in Designing Dual PARP1-BRD4 Inhibitors for Breast Cancer Treatment

Poly(ADP-ribose) polymerase 1 (PARP1) inhibitors have revolutionized the treatment of many cancers with DNA-repairing deficiencies via synthetic lethality. Advocated by the polypharmacology concept, recent evidence discovered that a significantly synergistic effect in increasing the death of cancer cells was observed by simultaneously perturbating the enzymatic activities of bromodomain-containing protein 4 (BRD4) and PARP1. Here, we developed a novel cheminformatics approach combined with a structure-based method aiming to facilitate the design of dual PARP1-BRD4 inhibitors. Instead of linking pharmacophores, the developed approach first identified merged pharmacophores (a pool of amide-containing ring systems), from which phenanthridin-6(5H)-one was further prioritized. Based on this starting point, several small molecules were rationally designed, among which HF4 exhibited low micromolar inhibitory activity against BRD4 and PARP1, particularly exhibiting strong inhibition of BRD4 BD1 with an IC50 value of 204 nM. Furthermore, it demonstrated potent antiproliferative effects against breast cancer gene-deficient and proficient breast cancer cell lines by arresting cell cycle progression and impeding DNA damage repair. Collectively, our systematic efforts to design lead-like molecules have the potential to open doors for the exploration of dual PARP1-BRD4 inhibitors as a promising avenue for breast cancer treatment. Furthermore, the developed approach can be extended to systematically design inhibitors targeting PARP1 and other related targets.

Design and synthesis of new 1,2,4-oxadiazole/quinazoline-4-one hybrids with antiproliferative activity as multitargeted inhibitors

Introduction

The combination of BRAF and tyrosine kinase (TK) inhibitors has been demonstrated to be highly effective in inhibiting tumor development and is an approach for overcoming resistance in clinical trials. Accordingly, a novel series of 1,2,4-oxadiazole/quinazoline-4-one hybrids was developed as antiproliferative multitargeted inhibitors.

Methods

The structures of the newly synthesized compounds 9a-o were validated using IR, NMR, MS, and elemental techniques. 9a-o were tested as antiproliferative agents.

Results and Discussion

The results showed that the majority of the tested compounds showed significant antiproliferative action with 9b, 9c, 9h, 9k, and 9l being the most potent. Compounds 9b, 9c, 9h, 9k, and 9l were tested as EGFR and BRAFV600E inhibitors. These in vitro tests revealed that compounds 9b, 9c, and 9h are strong antiproliferative agents that may act as dual EGFR/BRAFV600E inhibitors. 9b, 9c, and 9h were further investigated for their inhibitory effect on mutant EGFR (EGFRT790M), and the results showed that the tested compounds had considerable inhibitory action. Cell cycle study and apoptosis detection demonstrated that compound 9b exhibits cell cycle arrest at the G2/M transition. Molecular docking simulations reveal the binding mechanism of the most active antiproliferative agents.

Exploring potential therapeutic combinations for castration-sensitive prostate cancer using supercomputers: a proof of concept study

To address the challenge of finding new combination therapies against castration-sensitive prostate cancer, we introduce Vini, a computational tool that predicts the efficacy of drug combinations at the intracellular level by integrating data from the KEGG, DrugBank, Pubchem, Protein Data Bank, Uniprot, NCI-60 and COSMIC databases. Vini is a computational tool that predicts the efficacy of drugs and their combinations at the intracellular level. It addresses the problem comprehensively by considering all known target genes, proteins and small molecules and their mutual interactions involved in the onset and development of cancer. The results obtained point to new, previously unexplored combination therapies that could theoretically be promising candidates for the treatment of castration-sensitive prostate cancer and could prevent the inevitable progression of the cancer to the incurable castration-resistant stage. Furthermore, after analyzing the obtained triple combinations of drugs and their targets, the most common targets became clear: ALK, BCL-2, mTOR, DNA and androgen axis. These results may help to define future therapies against castration-sensitive prostate cancer. The use of the Vini computer model to explore therapeutic combinations represents an innovative approach in the search for effective treatments for castration-sensitive prostate cancer, which, if clinically validated, could potentially lead to new breakthrough therapies.

Medicinal polypharmacology-a scientific glossary of terminology and concepts

Medicinal polypharmacology is one answer to the complex reality of multifactorial human diseases that are often unresponsive to single-targeted treatment. It is an admittance that intrinsic feedback mechanisms, crosstalk, and disease networks necessitate drugs with broad modes-of-action and multitarget affinities. Medicinal polypharmacology grew to be an independent research field within the last two decades and stretches from basic drug development to clinical research. It has developed its own terminology embedded in general terms of pharmaceutical drug discovery and development at the intersection of medicinal chemistry, chemical biology, and clinical pharmacology. A clear and precise language of critical terms and a thorough understanding of underlying concepts is imperative; however, no comprehensive work exists to this date that could support researchers in this and adjacent research fields. In order to explore novel options, establish interdisciplinary collaborations, and generate high-quality research outputs, the present work provides a first-in-field glossary to clarify the numerous terms that have originated from various individual disciplines.

Innovative Therapeutic Strategies in Alzheimer's Disease: A Synergistic Approach to Neurodegenerative Disorders

Alzheimer's disease (AD) remains a significant challenge in the field of neurodegenerative disorders, even nearly a century after its discovery, due to the elusive nature of its causes. The development of drugs that target multiple aspects of the disease has emerged as a promising strategy to address the complexities of AD and related conditions. The immune system's role, particularly in AD, has gained considerable interest, with nanobodies representing a new frontier in biomedical research. Advances in targeting antibodies against amyloid-β (Aβ) and using messenger RNA for genetic translation have revolutionized the production of antibodies and drug development, opening new possibilities for treatment. Despite these advancements, conventional therapies for AD, such as Cognex, Exelon, Razadyne, and Aricept, often have limited long-term effectiveness, underscoring the need for innovative solutions. This necessity has led to the incorporation advanced technologies like artificial intelligence and machine learning into the drug discovery process for neurodegenerative diseases. These technologies help identify therapeutic targets and optimize lead compounds, offering a more effective approach to addressing the challenges of AD and similar conditions.

Flexible scaffold-based cheminformatics approach for polypharmacological drug design

Effective treatments for complex central nervous system (CNS) disorders require drugs with polypharmacology and multifunctionality, yet designing such drugs remains a challenge. Here, we present a flexible scaffold-based cheminformatics approach (FSCA) for the rational design of polypharmacological drugs. FSCA involves fitting a flexible scaffold to different receptors using different binding poses, as exemplified by IHCH-7179, which adopted a "bending-down" binding pose at 5-HT2AR to act as an antagonist and a "stretching-up" binding pose at 5-HT1AR to function as an agonist. IHCH-7179 demonstrated promising results in alleviating cognitive deficits and psychoactive symptoms in mice by blocking 5-HT2AR for psychoactive symptoms and activating 5-HT1AR to alleviate cognitive deficits. By analyzing aminergic receptor structures, we identified two featured motifs, the "agonist filter" and "conformation shaper," which determine ligand binding pose and predict activity at aminergic receptors. With these motifs, FSCA can be applied to the design of polypharmacological ligands at other receptors.

Exploring acetaminophen prodrugs and hybrids: a review

This critical review highlights the advances in developing new molecules for treating pain syndrome, an important issue for human health. Acetaminophen (APAP, known as paracetamol) and nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly used in clinical practice despite their adverse effects. Research is being conducted to develop innovative drugs with improved pharmaceutical properties to mitigate these effects. A more practical way to achieve that is to study well-known and time-tested drugs in their molecular combinations. Accordingly, the present work explores APAP and their combined chemical entities, i.e., prodrugs (soft drugs), codrugs (mutual prodrugs), and hybrids. Due to their molecular structure, APAP prodrugs or codrugs could be considered merged or conjugated hybrids; all these names are very fluid terms. This article proposed a structural classification of these entities to better analyze their advances. So, the following: carrier-linked O-modified APAP, -linked N-modified APAP derivatives (prodrugs), and direct- and spacer-N,O-linked APAP hybrids (codrugs) are the central parts of this review and are examined, especially ester and amide NSAID-APAP molecules. The C-linked APAP and nitric oxide (NO)-releasing APAP hybrids were also briefly discussed. Prime examples of APAP-based drugs such as propacetamol, benorylate, acetaminosalol, nitroparacetamol, and agent JNJ-10450232 weave well into this classification. The proposed classification is the first and original, giving a better understanding of the SAR studies for new pain relievers research and the design development for the analgesic APAP-(or NSAID)-based compounds.

Design and Synthesis of Dual-Target Inhibitors Targeting Androgen Receptors and Glucocorticoid Receptors to Overcome Antiandrogen Resistance in Castration-Resistant Prostate Cancer

Androgen receptor (AR) antagonists play important roles in the treatment of castration-resistant prostate cancer (CRPC). The glucocorticoid receptor (GR) upregulation leads to drug resistance for clinically used antiandrogens. Therefore, blocking AR/GR signaling simultaneously has become an efficient strategy to overcome the drug resistance of CRPC. Our previous work indicated that Z19 could inhibit the activity of both AR and GR. Herein, we optimized the structure of Z19 and identified GA32 as a potent AR/GR dual inhibitor. GA32 efficiently reduced the mRNA and protein levels of AR/GR downstream genes. GA32 efficiently inhibited the proliferation of enzalutamide resistance CRPC both in vitro and in vivo. GA32 could directly bind to AR and GR, and the predicted binding modes for GA32 with AR/GR suggested that GA32 binds to the AR or GR hormone binding pocket. This work provides a potential lead compound with dual AR/GR inhibitory activity to conquer the drug resistance of CRPC.

Advances in structure-based drug design: The potential for precision therapeutics in psychiatric disorders

Over the years, the field of GPCR drug design has undergone a remarkable evolution, fueled by advancements in science and technology. This evolution has given rise to a diverse range of ideas and approaches in structure-based drug design, bolstering the versatility and strength of the GPCR drug design toolbox. This review encapsulates the iterative development process, navigating challenges and opportunities in structure-based drug design within GPCRs. With a focused emphasis on its impact on psychiatric disorders, the review accentuates recent advancements and delves into the potentials unlocked by emerging technologies. The review explores the intricate interplay between scientific progress and iterative refinement, offering profound insights into the potential pathways that lie ahead for GPCR drug design.

Dual-targeting compounds possessing enhanced anticancer activity via microtubule disruption and histone deacetylase inhibition

Dual-targeting anticancer agents 4-29 are designed by combining the structural features of purine-type microtubule-disrupting compounds and HDAC inhibitors. A library of the conjugate compounds connected by appropriate linkers was synthesized and found to possess HDACs inhibitory activity and render microtubule fragmentation by activating katanin, a microtubule-severing protein. Among various zinc-binding groups, hydroxamic acid shows the highest inhibitory activity of Class I HDACs, which was also reconfirmed by three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore prediction. The purine-hydroxamate conjugates exhibit enhanced cytotoxicity against MDA-MB231 breast cancer cells, H1975 lung cancer cells, and various clinical isolated non-small-cell lung cancer cells with different epidermal growth factor receptor (EGFR) status. Pyridyl substituents could be used to replace the C2 and N9 phenyl moieties in the purine-type scaffold, which can help to improve the solubility under physiological conditions, thus increasing cytotoxicity. In mice treated with the purine-hydroxamate conjugates, the tumor growth rate was significantly reduced without causing toxic effects. Our study demonstrates the potential of the dual-targeting purine-hydroxamate compounds for cancer monotherapy.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Discovery of natural multi-targets neuraminidase inhibitor glycosides compounds against influenza A virus through network pharmacology, virtual screening, molecular dynamics simulation, and in vitro experiment

Influenza virus continually challenges both human and animal health. Moreover, influenza viruses are easy to mutate. In a certain degree, vaccines may not catch up with rapid mutant paces of viruses. Anti-influenza drugs NIs (neuraminidase inhibitors) are one of the best choices. Therefore, based on ADMET properties, eight optimal natural multi-targets NIs glycosides compounds (IC50  = 0.094-97.275 μM) are found from radix glycyrrhizae, flos sophorae, caulis spatholobi, radix astragali, radix glycyrrhizae, semen astragali complanati, and common fenugreek seed through network pharmacology, molecular docking, dynamics simulation, quantum chemistry, and in vitro experiment. Moreover, mechanism research illustrates these natural compounds treat influenza A virus through key targets TLR4, TNF, and IL6 (high fever, acute respiratory distress syndrome), MAPK1, and MAPK3 (MAPK signaling pathway, viral RNP export, and viral protein expression), IL1B (NOD-like receptor signaling pathway, suppressed maturation of pro-IL-1β and pro-IL-18), CASP3 (apoptosis), AKT1 (inhibited premature apoptosis), and EP300 (viral myocarditis, chemoattraction of monocytes and macrophages, T-cell activation antibody response).

PaSTe. Blockade of the Lipid Phenotype of Prostate Cancer as Metabolic Therapy: A Theoretical Proposal

BACKGROUND

Prostate cancer is the most frequently diagnosed malignancy in 112 countries and is the leading cause of death in eighteen. In addition to continuing research on prevention and early diagnosis, improving treatments and making them more affordable is imperative. In this sense, the therapeutic repurposing of low-cost and widely available drugs could reduce global mortality from this disease. The malignant metabolic phenotype is becoming increasingly important due to its therapeutic implications. Cancer generally is characterized by hyperactivation of glycolysis, glutaminolysis, and fatty acid synthesis. However, prostate cancer is particularly lipidic; it exhibits increased activity in the pathways for synthesizing fatty acids, cholesterol, and fatty acid oxidation (FAO).

OBJECTIVE

Based on a literature review, we propose the PaSTe regimen (Pantoprazole, Simvastatin, Trimetazidine) as a metabolic therapy for prostate cancer. Pantoprazole and simvastatin inhibit the enzymes fatty acid synthase (FASN) and 3-hydroxy-3-methylglutaryl- coenzyme A reductase (HMGCR), therefore, blocking the synthesis of fatty acids and cholesterol, respectively. In contrast, trimetazidine inhibits the enzyme 3-β-Ketoacyl- CoA thiolase (3-KAT), an enzyme that catalyzes the oxidation of fatty acids (FAO). It is known that the pharmacological or genetic depletion of any of these enzymes has antitumor effects in prostatic cancer.

RESULTS

Based on this information, we hypothesize that the PaSTe regimen will have increased antitumor effects and may impede the metabolic reprogramming shift. Existing knowledge shows that enzyme inhibition occurs at molar concentrations achieved in plasma at standard doses of these drugs.

CONCLUSION

We conclude that this regimen deserves to be preclinically evaluated because of its clinical potential for the treatment of prostate cancer.

Multi-target Compounds against Trypanosomatid Parasites and Mycobacterium tuberculosis

Multi-target drug treatment has become popular as a substitute for traditional monotherapy. Monotherapy can lead to resistance and side effects. Multi-target drug discovery is gaining importance as data on bioactivity becomes more abundant. The design of multi-target drugs is expected to be an important development in the pharmaceutical industry in the near future. This review presents multi-target compounds against trypanosomatid parasites (Trypanosoma cruzi, T. brucei, and Leishmania sp.) and tuberculosis (Mycobacterium tuberculosis), which mainly affect populations in socioeconomically unfavorable conditions. The article analyzes the studies, including their chemical structures, viral strains, and molecular docking studies, when available. The objective of this review is to establish a foundation for designing new multi-target inhibitors for these diseases.

Systemic Convergent Multitarget Interactions of Plant Polyphenols Revealed by Affinity-Based Protein Profiling of Bone Cells Using C-Glucosidic Vescal(ag)in-Bearing Chemoproteomic Probes

The ellagitannins vescalagin and vescalin, known as actin-dependent inhibitors of osteoclastic bone resorption, were mounted onto chemical probes to explore their interactions with bone cell proteins by means of affinity-based chemoproteomics and bioinformatics. The chemical reactivity of the pyrogallol units of these polyphenols toward oxidation into electrophilic ortho-quinones was exploited using NaIO4 to promote the covalent capture of target proteins, notably those expressed at lower abundance and those interacting with polyphenols at low-to-moderate levels of affinity. Different assays revealed the multitarget nature of both ellagitannins, with 100-370 statistically significant proteins captured by their corresponding probes. A much higher number of proteins were captured from osteoclasts than from osteoblasts. Bioinformatic analyses unveiled a preference for the capture of proteins having phosphorylated ligands and GTPase regulators and enabled the identification of 33 potential target proteins with systemic relevance to osteoclast differentiation and activity, as well as to the regulation of actin dynamics.

Structure, function and drug discovery of GPCR signaling

G protein-coupled receptors (GPCRs) are versatile and vital proteins involved in a wide array of physiological processes and responses, such as sensory perception (e.g., vision, taste, and smell), immune response, hormone regulation, and neurotransmission. Their diverse and essential roles in the body make them a significant focus for pharmaceutical research and drug development. Currently, approximately 35% of marketed drugs directly target GPCRs, underscoring their prominence as therapeutic targets. Recent advances in structural biology have substantially deepened our understanding of GPCR activation mechanisms and interactions with G-protein and arrestin signaling pathways. This review offers an in-depth exploration of both traditional and recent methods in GPCR structure analysis. It presents structure-based insights into ligand recognition and receptor activation mechanisms and delves deeper into the mechanisms of canonical and noncanonical signaling pathways downstream of GPCRs. Furthermore, it highlights recent advancements in GPCR-related drug discovery and development. Particular emphasis is placed on GPCR selective drugs, allosteric and biased signaling, polyphamarcology, and antibody drugs. Our goal is to provide researchers with a thorough and updated understanding of GPCR structure determination, signaling pathway investigation, and drug development. This foundation aims to propel forward-thinking therapeutic approaches that target GPCRs, drawing upon the latest insights into GPCR ligand selectivity, activation, and biased signaling mechanisms.

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.

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.

Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks

Many therapies in clinical trials are based on single drug-single target relationships. To further extend this concept to multi-target approaches using multi-targeted drugs, we developed a machine learning pipeline to unravel the target landscape of kinase inhibitors. This pipeline, which we call 3D-KINEssence, uses a new type of protein fingerprints (3D FP) based on the structure of kinases generated through a 3D convolutional neural network (3D-CNN). These 3D-CNN kinase fingerprints were matched to molecular Morgan fingerprints to predict the targets of each respective kinase inhibitor based on available bioactivity data. The performance of the pipeline was evaluated on two test sets: a sparse drug-target set where each drug is matched in most cases to a single target and also on a densely-covered drug-target set where each drug is matched to most if not all targets. This latter set is more challenging to train, given its non-exclusive character. Our model's root-mean-square error (RMSE) based on the two datasets was 0.68 and 0.8, respectively. These results indicate that 3D FP can predict the target landscape of kinase inhibitors at around 0.8 log units of bioactivity. Our strategy can be utilized in proteochemometric or chemogenomic workflows by consolidating the target landscape of kinase inhibitors.

In silico design and computational evaluation of novel 2-arylaminopyrimidine-based compounds as potential multi-targeted protein kinase inhibitors: application for the native and mutant (T315I) Bcr-Abl tyrosine kinase

An integrated computational approach to drug discovery was used to identify novel potential inhibitors of the native and mutant (T315I) Bcr-Abl tyrosine kinase, the enzyme playing a key role in the pathogenesis of chronic myeloid leukemia (CML). This approach included i) design of chimeric molecules based on the 2-arylaminopyrimidine fragment, the main pharmacophore of the Abl kinase inhibitors imatinib and nilotinib used in the clinic for the CML treatment, ii) molecular docking of these compounds with the ATP-binding site of the native and mutant Abl kinase, iii) refinement of the ligand-binding poses by the quantum chemical method PM7, iv) molecular dynamics simulations of the ligand/Abl complexes, and v) prediction of the ligand/Abl binding affinity in terms of scoring functions of molecular docking, machine learning, quantum chemistry, and molecular dynamics. As a result, five top-ranking compounds able to effectively block the enzyme catalytic site were identified. According to the data obtained, these compounds exhibit close modes of binding to the Abl kinase active site that are mainly provided by hydrogen bonds and multiple van der Waals contacts. The identified compounds show high binding affinity to the native and mutant Abl kinase comparable with the one calculated for the FDA-approved kinase-targeted inhibitors imatinib, nilotinib, and ponatinib used in the calculations as a positive control. The results obtained testify to the predicted drug candidates against CML may serve as good scaffolds for the design of novel anticancer agents able to target the ATP-binding pocket of the native and mutant Abl kinase.Communicated by Ramaswamy H. Sarma.

Neuroprotective Potential of Seed Extracts: Review of In Vitro and In Vivo Studies

INTRODUCTION

Neurodegenerative diseases are characterized by neuronal dysfunction and death. Studies suggest that some seed extracts have a neuroprotective effect. Considering the increased incidence of these diseases and the need for new effective therapies with fewer side effects, this review aimed to assess the evidence of the efficacy and safety of seed extracts in experimental models of neurodegeneration.

MATERIAL AND METHOD

The search was carried out through studies published between 2000 and 2021 in Science Direct, PubMed, Scientific Electronic Library Online (SciELO), and Latin American Literature in Health Sciences (LILACS) databases, in which the effects of seed extracts in in vitro and in vivo experimental models of neurodegeneration were investigated. Based on the eligibility criteria, 47 studies were selected for this review.

RESULTS

In the in vitro models, the neuroprotection of the seed extracts was a result of their antioxidant, anti-inflammatory, and anti-apoptotic properties. In the in vivo models, neuroprotection resulted from the antioxidant and anti-inflammatory properties, a decrease in motor deficits, an improvement in learning and memory, as well as the increased release of neurotransmitters. The results show promise for the future of clinical research on new therapies for neurodegenerative diseases. However, the studies are still limited, which does not allow us to extrapolate the results to human beings with ND.

CONCLUSIONS

Therefore, clinical trials are needed in order to prove the results of the in vitro and in vivo studies, as well as to assess the ideal, safe, and effective dose of these seed extracts in patients with neurodegenerative diseases.

4-(5-Amino-pyrazol-1-yl)benzenesulfonamide derivatives as novel multi-target anti-inflammatory agents endowed with inhibitory activity against COX-2, 5-LOX and carbonic anhydrase: Design, synthesis, and biological assessments

In the current medical era, the single target inhibition paradigm of drug discovery has given way to the multi-target design concept. As the most intricate pathological process, inflammation gives rise to a variety of diseases. There are several drawbacks to the single target anti-inflammatory drugs currently available. Herein, we present the design and synthesis of a novel series of 4-(5-amino-pyrazol-1-yl)benzenesulfonamide derivatives (7a-j) with COX-2, 5-LOX and carbonic anhydrase (CA) inhibitory activities as potential multi-target anti-inflammatory agents. The pharmacophoric 4-(pyrazol-1-yl)benzenesulfonamide moiety in Celecoxib was used as the core scaffold and different substituted phenyl and 2-thienyl tails were grafted via a hydrazone linker to enhance inhibitory activity against hCA IX and XII isoforms, yielding target pyrazoles 7a-j. All reported pyrazoles were evaluated for their inhibitory activity against COX-1, COX-2, and 5-LOX. Pyrazoles 7a, 7b, and 7j showed the best inhibitory activities against the COX-2 isozyme (IC₅₀ = 49, 60 and 60 nM, respectively) and against 5-LOX (IC₅₀ = 2.4, 1.9, and 2.5 μM, respectively) with excellent SI indices (COX-1/COX-2) of 212.24, 208.33, and 158.33, respectively. In addition, the inhibitory activities of pyrazoles 7a-j were evaluated against four different hCA isoforms I, II, IX, and XII. Both transmembrane hCA IX and XII isoforms were potently inhibited by pyrazoles 7a-j with K_I values in the nanomolar range; 13.0-82.1 nM and 5.8-62.0 nM, respectively. Furthermore, pyrazoles 7a and 7b with the highest COX-2 activity and selectivity indices were evaluated in vivo for their analgesic, anti-inflammatory, and ulcerogenic activities. The serum level of the inflammatory mediators was then measured in order to confirm the anti-inflammatory activities of pyrazoles 7a and 7b.

1,2-Dibenzoylhydrazine as a Multi-Inhibitor Compound: A Morphological and Docking Study

In the framework of the multitarget inhibitor study, we report an in silico analysis of 1,2-dibenzoylhydrazine (DBH) with respect to three essential receptors such as the ecdysone receptor (EcR), urease, and HIV-integrase. Starting from a crystallographic structural study of accidentally harvested crystals of this compound, we performed docking studies to evaluate the inhibitory capacity of DBH toward three selected targets. A crystal morphology prediction was then performed. The results of our molecular modeling calculations indicate that DBH is an excellent candidate as a ligand to inhibit the activity of EcR receptors and urease. Docking studies also revealed the activity of DBH on the HIV integrase receptor, providing an excellent starting point for developing novel inhibitors using this molecule as a starting lead compound.

Recent advances in dopamine D2 receptor ligands in the treatment of neuropsychiatric disorders

Dopamine is a biologically active amine synthesized in the central and peripheral nervous system. This biogenic monoamine acts by activating five types of dopamine receptors (D_1-5 Rs), which belong to the G protein-coupled receptor family. Antagonists and partial agonists of D₂ Rs are used to treat schizophrenia, Parkinson's disease, depression, and anxiety. The typical pharmacophore with high D₂ R affinity comprises four main areas, namely aromatic moiety, cyclic amine, central linker and aromatic/heteroaromatic lipophilic fragment. From the literature reviewed herein, we can conclude that 4-(2,3-dichlorophenyl), 4-(2-methoxyphenyl)-, 4-(benzo[b]thiophen-4-yl)-1-substituted piperazine, and 4-(6-fluorobenzo[d]isoxazol-3-yl)piperidine moieties are critical for high D₂ R affinity. Four to six atoms chains are optimal for D₂ R affinity with 4-butoxyl as the most pronounced one. The bicyclic aromatic/heteroaromatic systems are most frequently occurring as lipophilic appendages to retain high D₂ R affinity. In this review, we provide a thorough overview of the therapeutic potential of D₂ R modulators in the treatment of the aforementioned disorders. In addition, this review summarizes current knowledge about these diseases, with a focus on the dopaminergic pathway underlying these pathologies. Major attention is paid to the structure, function, and pharmacology of novel D₂ R ligands, which have been developed in the last decade (2010-2021), and belong to the 1,4-disubstituted aromatic cyclic amine group. Due to the abundance of data, allosteric D₂ R ligands and D₂ R modulators from patents are not discussed in this review.

Oral herbal medicine for treatment of postherpetic neuralgia: A protocol for systematic review and meta-analysis

BACKGROUND

Postherpetic neuralgia (PHN) is the most common complication of shingling. Herbal medicine for PHN is clinically used to improve chronic pain and other symptoms that reduce the quality of life (QoL), such as sleep disturbances and emotional problems. This study aims to describe methods for synthesizing evidence for the effectiveness and safety of herbal medicine for treating PHN.

METHODS

Randomized controlled trials using herbal medicine in patients with PHN will be included in this review. Electronic databases such as MEDLINE via PubMed, EMBASE, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, China National Knowledge Infrastructure, WanFang, KoreaMed, Research Information Service System, Korean Studies Information Service System, Database Periodical Information Academic, Oriental Medicine Advanced Searching Integrated System, and Korea Citation Index will be searched without language limitations from their inception to September 2022. Two authors will perform quality assessments independently using the Cochrane risk-of-bias tool. The primary outcome will be pain intensity, and the secondary outcomes will be global impression, anxiety, depression, sleep disturbance, QoL, and safety. All data from eligible studies will be synthesized for meta-analysis.

RESULTS

This study will be a systematic review and meta-analysis to evaluate the effectiveness and safety of oral herbal medicine for treatment of PHN.

CONCLUSION

This study will provide evidence for herbal medicine as a potential treatment for PHN which is advantageous not only for patients but also for researchers and policymakers.

Design, Synthesis and Evaluation of New Multifunctional Benzothiazoles as Photoprotective, Antioxidant and Antiproliferative Agents

A current trend of research in the health field is toward the discovery of multifunctional compounds, capable of interacting with multiple biological targets, thus simplifying multidrug therapies and improving patient compliance. The aim of this work was to synthesize new multifunctional chemical entities bearing a benzothiazole nucleus, a structure that has attracted increasing interest for the great variety of biological actions that it can perform, and already used as a scaffold in several multifunctional drugs. Compounds are reported, divided into two distinct series, synthetized and tested in vitro for the antioxidant, and include UV-filtering and antitumor activities. DPPH and FRAP tests were chosen to outline an antioxidant activity profile against different radical species. The UV-filtering activity was investigated, pre- and post-irradiation, through evaluation of a O/W sunscreen standard formulation containing 3% of the synthetic compounds. The antitumor activity was investigated both on human melanoma cells (Colo-38) and on immortalized human keratinocytes as a control (HaCat). A good antiproliferative profile in terms of IC₅₀ was chosen as a mandatory condition to further investigate apoptosis induction as a possible cytotoxicity mechanism through the Annexin V test. Compound BZTcin4 was endowed with excellent activity and a selectivity profile towards Colo-38, supported by a good antioxidant capacity and an excellent broad-spectrum photoprotective profile.

Progress and Development of Carbazole Scaffold Based as Potential Anti- Alzheimer Agents Using MTDL Approach

Abstract:

Alzheimer’s is a neurodegenerative disease (NDs) found in old age people with associated most common symptom dementia. MTDLs (Multi-Target Direct Ligand strategy) is based on a combination of two or more bioactive pharmacophores into a single molecule and this phenomenon has received a great attention in the new era of modern drug discovery and emerging as a choice to treat this complex Alzheimer’s disease (AD). In last fifteen years, many research groups designed, and synthesized new carbazole integrated molecules linked with other bioactive pharmacophores like thiazoles, carvedilol, α- naphthylaminopropan-2-ol, tacrine, ferulic acid, piperazine, coumarin, chalcones, stilbene, benzyl piperidine, adamantane, quinoline, phthalocyanines, α-amino phosphonate, thiosemicarbazones, hydrazones, etc. derivatives using MTDLs approach to confront AD. The present review entails the scientific data on carbazole hybrids as potential Anti-Alzheimer activities from 2007 to 2021 that have shown potential anti-Alzheimer activities through multiple target pathways thereby promising hope for new drug development to confront AD.

Establishment and Evaluation of Dual HDAC/BET Inhibitors as Therapeutic Options for Germ Cell Tumors and Other Urological Malignancies

Urological malignancies represent major challenges for clinicians, with annually rising incidences. In addition, cisplatin treatment induced long-term toxicities and the development of therapy resistance emphasize the need for novel therapeutics. In this study, we analyzed the effects of novel histone deacetylase (HDAC) and bromodomain and extraterminal domain-containing (BET) inhibitors to combine them into a potent HDAC-BET-fusion molecule and to understand their molecular mode-of-action. Treatment of (cisplatin-resistant) germ cell tumors (GCT), urothelial, renal, and prostate carcinoma cells with the HDAC, BET, and dual inhibitors decreased cell viability, induced apoptosis, and affected the cell cycle. Furthermore, a dual inhibitor considerably decreased tumor burden in GCT xenograft models. On a molecular level, correlating RNA- to ATAC-sequencing data indicated a considerable induction of gene expression, accompanied by site-specific changes of chromatin accessibility after HDAC inhibitor application. Upregulated genes could be linked to intra- and extra-cellular trafficking, cellular organization, and neuronal processes, including neuroendocrine differentiation. Regarding chromatin accessibility on a global level, an equal distribution of active or repressed DNA accessibility has been detected after HDAC inhibitor treatment, questioning the current understanding of HDAC inhibitor function. In summary, our HDAC, BET, and dual inhibitors represent a new treatment alternative for urological malignancies. Furthermore, we shed light on new molecular and epigenetic mechanisms of the tested epi-drugs, allowing for a better understanding of the underlying modes-of-action and risk assessment for the patient.

Drug repositioning: A bibliometric analysis

Drug repurposing has become an effective approach to drug discovery, as it offers a new way to explore drugs. Based on the Science Citation Index Expanded (SCI-E) and Social Sciences Citation Index (SSCI) databases of the Web of Science core collection, this study presents a bibliometric analysis of drug repurposing publications from 2010 to 2020. Data were cleaned, mined, and visualized using Derwent Data Analyzer (DDA) software. An overview of the history and development trend of the number of publications, major journals, major countries, major institutions, author keywords, major contributors, and major research fields is provided. There were 2,978 publications included in the study. The findings show that the United States leads in this area of research, followed by China, the United Kingdom, and India. The Chinese Academy of Science published the most research studies, and NIH ranked first on the h-index. The Icahn School of Medicine at Mt Sinai leads in the average number of citations per study. Sci Rep, Drug Discov. Today, and Brief. Bioinform. are the three most productive journals evaluated from three separate perspectives, and pharmacology and pharmacy are unquestionably the most commonly used subject categories. Cheng, FX; Mucke, HAM; and Butte, AJ are the top 20 most prolific and influential authors. Keyword analysis shows that in recent years, most research has focused on drug discovery/drug development, COVID-19/SARS-CoV-2/coronavirus, molecular docking, virtual screening, cancer, and other research areas. The hotspots have changed in recent years, with COVID-19/SARS-CoV-2/coronavirus being the most popular topic for current drug repurposing research.

Synthesis and Biological Evaluation of Indole-2-Carboxamides with Potent Apoptotic Antiproliferative Activity as EGFR/CDK2 Dual Inhibitors

The apoptotic antiproliferative actions of our previously reported CB1 allosteric modulators 5-chlorobenzofuran-2-carboxamide derivatives VIIa–j prompted us to develop and synthesise a novel series of indole-2-carboxamide derivatives 5a–k, 6a–c, and 7. Different spectroscopic methods of analysis were used to validate the novel compounds. Using the MTT assay method, the novel compounds were examined for antiproliferative activity against four distinct cancer cell lines. Compounds 5a–k, 6a–c, and 7 demonstrated greater antiproliferative activity against the breast cancer cell line (MCF-7) than other tested cancer cell lines, and 5a–k (which contain the phenethyl moiety in their backbone structure) demonstrated greater potency than 6a–c and 7, indicating the importance of the phenethyl moiety for antiproliferative action. Compared to reference doxorubicin (GI₅₀ = 1.10 µM), compounds 5d, 5e, 5h, 5i, 5j, and 5k were the most effective of the synthesised derivatives, with GI₅₀ ranging from 0.95 µM to 1.50 µM. Compounds 5d, 5e, 5h, 5i, 5j, and 5k were tested for their inhibitory impact on EGFR and CDK2, and the results indicated that the compounds tested had strong antiproliferative activity and are effective at suppressing both CDK2 and EGFR. Moreover, the studied compounds induced apoptosis with high potency, as evidenced by their effects on apoptotic markers such as Caspases 3, 8, 9, Cytochrome C, Bax, Bcl2, and p53.

Discovery of spirooxindole-derived small-molecule compounds as novel HDAC/MDM2 dual inhibitors and investigation of their anticancer activity

Simultaneous inhibition of more than one target is considered to be a novel strategy in cancer therapy. Owing to the importance of histone deacetylases (HDACs) and p53-murine double minute 2 (MDM2) interaction in tumor development and their synergistic effects, a series of MDM2/HDAC bifunctional small-molecule inhibitors were rationally designed and synthesized by incorporating an HDAC pharmacophore into spirooxindole skeletons. These compounds exhibited good inhibitory activities against both targets. In particular, compound 11b was demonstrated to be most potent for MDM2 and HDAC, reaching the enzyme inhibition of 68% and 79%, respectively. Compound 11b also showed efficient antiproliferative activity towards MCF-7 cells with better potency than the reference drug SAHA and Nutlin-3. Furthermore, western blot analysis revealed that compound 11b increased the expression of p53 and Ac-H4 in MCF-7 cells in a dose-dependent manner. Our results indicate that dual inhibition of HDAC and MDM2 may provide a novel and efficient strategy for the discovery of antitumor drug in the future.

Inhibitory Effects of 7-Methylguanine and Its Metabolite 8-Hydroxy-7-Methylguanine on Human Poly(ADP-Ribose) Polymerase 1

Previously, we have found that a nucleic acid metabolite, 7-methylguanine (7mGua), produced in the body can have an inhibitory effect on the poly(ADP-ribose) polymerase 1 (PARP1) enzyme, an important pharmacological target in anticancer therapy. In this work, using an original method of analysis of PARP1 activity based on monitoring fluorescence anisotropy, we studied inhibitory properties of 7mGua and its metabolite, 8-hydroxy-7-methylguanine (8h7mGua). Both compounds inhibited PARP1 enzymatic activity in a dose-dependent manner, however, 8h7mGua was shown to be a stronger inhibitor. The IC₅₀ values for 8h7mGua at different concentrations of the NAD+ substrate were found to be 4 times lower, on average, than those for 7mGua. The more efficient binding of 8h7mGua in the PARP1 active site is explained by the presence of an additional hydrogen bond with the Glu988 catalytic residue. Experimental and computational studies did not reveal the effect of 7mGua and 8h7mGua on the activity of other DNA repair enzymes, indicating selectivity of their inhibitory action.

Toxicological Properties of 7-Methylguanine, and Preliminary Data on its Anticancer Activity

7-Methylguanine (7-MG) competitively inhibits the DNA repair enzyme poly(ADP-ribose) polymerase (PARP) and RNA-modifying enzyme tRNA-guanine transglycosylase (TGT) and represents a potential anticancer drug candidate. Furthermore, as a natural compound, it could escape the serious side effects characteristic for approved synthetic PARP inhibitors. Here we present a comprehensive study of toxicological and carcinogenic properties of 7-MG. It was demonstrated that 7-MG does not induce mutations or structural chromosomal abnormalities, and has no blastomogenic activity. A treatment regimen with 7-MG has been established in mice (50 mg/kg per os, 3 times per week), exerting no adverse effects or changes in morphology. Preliminary data on the 7-MG anticancer activity obtained on transplantable tumor models support our conclusions that 7-MG can become a promising new component of chemotherapy.

KNIME workflow for retrieving causal drug and protein interactions, building networks, and performing topological enrichment analysis demonstrated by a DILI case study

As an alternative to one drug-one target approaches, systems biology methods can provide a deeper insight into the holistic effects of drugs. Network-based approaches are tools of systems biology, that can represent valuable methods for visualizing and analysing drug-protein and protein-protein interactions. In this study, a KNIME workflow is presented which connects drugs to causal target proteins and target proteins to their causal protein interactors. With the collected data, networks can be constructed for visualizing and interpreting the connections. The last part of the workflow provides a topological enrichment test for identifying relevant pathways and processes connected to the submitted data. The workflow is based on openly available databases and their web services. As a case study, compounds of DILIRank were analysed. DILIRank is the benchmark dataset for Drug-Induced Liver Injury by the FDA, where compounds are categorized by their likeliness of causing DILI. The study includes the drugs that are most likely to cause DILI ("mostDILI") and the ones that are not likely to cause DILI ("noDILI"). After selecting the compounds of interest, down- and upregulated proteins connected to the mostDILI group were identified; furthermore, a liver-specific subset of those was created. The downregulated sub-list had considerably more entries, therefore, network and causal interactome were constructed and topological pathway enrichment analysis was performed with this list. The workflow identified proteins such as Prostaglandin G7H synthase 1 and UDP-glucuronosyltransferase 1A9 as key participants in the potential toxic events disclosing the possible mode of action. The topological network analysis resulted in pathways such as recycling of bile acids and salts and glucuronidation, indicating their involvement in DILI. The KNIME pipeline was built to support target and network-based approaches to analyse any sets of drug data and identify their target proteins, mode of actions and processes they are involved in. The fragments of the pipeline can be used separately or can be combined as required.

Effects of Linkers and Substitutions on Multitarget Directed Ligands for Alzheimer’s Diseases: Emerging Paradigms and Strategies

Alzheimer’s disease (AD) is multifactorial, progressive and the most predominant cause of cognitive impairment and dementia worldwide. The current “one-drug, one-target” approach provides only symptomatic relief to the condition but is unable to cure the disease completely. The conventional single-target therapeutic approach might not always induce the desired effect due to the multifactorial nature of AD. Hence, multitarget strategies have been proposed to simultaneously knock out multiple targets involved in the development of AD. Herein, we provide an overview of the various strategies, followed by the multitarget-directed ligand (MTDL) development, rationale designs and efficient examples. Furthermore, the effects of the linkers and substitutional functional groups on MTDLs against various targets of AD and their modes of action are also discussed.

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.

SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning

Identifying compound-protein (drug-target, DTI) interactions (CPI) accurately is a key step in drug discovery. Including virtual screening and drug reuse, it can significantly reduce the time it takes to identify drug candidates and provide patients with timely and effective treatment. Recently, more and more researchers have developed CPI's deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and R2 (degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and R2 = 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet.

Multitarget-Based Virtual Screening for Identification of Herbal Substances toward Potential Osteoclastic Targets

Osteoporosis is a complex bone disease indicating porous bone with low bone mass density and fragility. Cathepsin K, V-ATPase, and αVβ3 integrin are exhibited as novel targets for osteoporosis treatment. Our preliminary study uses a state-of-the-art method, including target-based virtual screening and clustering methods to determine promising candidates with multitarget properties. Phytochemicals with osteoprotective properties from the literature are used to elucidate the molecular interactions toward three targets. The binding scores of compounds are normalized and rescored. The K-means and hierarchical clustering methods are applied to filter and define the promising compounds, and the silhouette analysis is supposed to validate the clustering method. We explore 108 herbal compounds by virtual screening and the cluster approach, and find that rutin, sagittatoside A, icariin, and kaempferitrin showed strong binding affinities against Cathepsin K, V-ATPase, and αVβ3 integrin. Dockings of candidates toward three targets also provide the protein-ligand interactions and crucial amino acids for binding. Our study provides a straightforward and less time-consuming approach to exploring the new multitarget candidates for further investigations, using a combination of in silico methods.

Benzothiazole Derivatives as Multifunctional Antioxidant Agents for Skin Damage: Structure–Activity Relationship of a Scaffold Bearing a Five-Membered Ring System

Skin diseases often give multifactorial damages; therefore, the development of multifunctional compounds represents a suitable approach especially against disorders that are induced by oxidative stress. Thus, taking into account the successful results we achieved on benzimidazoles, we have devised a new series of isosteric benzothiazoles and investigated their antioxidant, photoprotective, antifungal and antiproliferative activity. Particular attention has been paid to synergistic antioxidant and photoprotective properties. For compounds 9a and 10a, a multifunctional profile was outlined, supported by an excellent filtering capacity, mainly UVB, which has higher capacities than those of the reference PBSA which is currently in the market as a UV sunscreen filter. The two compounds were also the best in terms of growth inhibition of dermatophytes and Candida albicans, and 10a also showed good antioxidant activity. Furthermore, 9a was also effective on melanoma tumor cells (SK-Mel 5), making these compounds good candidates in the development of new skin protective and preventive agents.

Polypharmacological Approaches for CNS Diseases: Focus on Endocannabinoid Degradation Inhibition

Polypharmacology breaks up the classical paradigm of “one-drug, one target, one disease” electing multitarget compounds as potential therapeutic tools suitable for the treatment of complex diseases, such as metabolic syndrome, psychiatric or degenerative central nervous system (CNS) disorders, and cancer. These diseases often require a combination therapy which may result in positive but also negative synergistic effects. The endocannabinoid system (ECS) is emerging as a particularly attractive therapeutic target in CNS disorders and neurodegenerative diseases including Parkinson’s disease (PD), Alzheimer’s disease (AD), Huntington’s disease (HD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), stroke, traumatic brain injury (TBI), pain, and epilepsy. ECS is an organized neuromodulatory network, composed by endogenous cannabinoids, cannabinoid receptors type 1 and type 2 (CB₁ and CB₂), and the main catabolic enzymes involved in the endocannabinoid inactivation such as fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL). The multiple connections of the ECS with other signaling pathways in the CNS allows the consideration of the ECS as an optimal source of inspiration in the development of innovative polypharmacological compounds. In this review, we focused our attention on the reported polypharmacological examples in which FAAH and MAGL inhibitors are involved.

wSDTNBI: a novel network-based inference method for virtual screening

In recent years, the rapid development of network-based methods for the prediction of drug-target interactions (DTIs) provides an opportunity for the emergence of a new type of virtual screening (VS), namely, network-based VS. Herein, we reported a novel network-based inference method named wSDTNBI. Compared with previous network-based methods that use unweighted DTI networks, wSDTNBI uses weighted DTI networks whose edge weights are correlated with binding affinities. A two-pronged approach based on weighted DTI and drug-substructure association networks was employed to calculate prediction scores. To show the practical value of wSDTNBI, we performed network-based VS on retinoid-related orphan receptor γt (RORγt), and purchased 72 compounds for experimental validation. Seven of the purchased compounds were confirmed to be novel RORγt inverse agonists by in vitro experiments, including ursonic acid and oleanonic acid with IC50 values of 10 nM and 0.28 μM, respectively. Moreover, the direct contact between ursonic acid and RORγt was confirmed using the X-ray crystal structure, and in vivo experiments demonstrated that ursonic acid and oleanonic acid have therapeutic effects on multiple sclerosis. These results indicate that wSDTNBI might be a powerful tool for network-based VS in drug discovery.

Sauropus brevipes ethanol extract negatively regulates inflammatory responses in vivo and in vitro by targeting Src, Syk and IRAK1

CONTEXT

Sauropus brevipes Müll. Arg. (Phyllanthaceae) has been used as an effective ingredient in a decoction for the treatment of diarrhoea. However, there was no report on its modulatory role in inflammation.

OBJECTIVE

This study investigates anti-inflammatory effect of S. brevipes in various inflammation models.

MATERIALS AND METHODS

The aerial part of S. brevipes was extracted with 95% ethanol to produce Sb-EE. RAW264.7 cells pre-treated with Sb-EE were stimulated by lipopolysaccharide (LPS), and Griess assay and PCR were performed. High-performance liquid chromatography (HPLC) analysis, luciferase assay, Western blotting and kinase assay were employed. C57BL/6 mice (10 mice/group) were orally administered with Sb-EE (200 mg/kg) once a day for five days, and peritonitis was induced by an intraperitoneal injection of LPS (10 mg/kg). ICR mice (four mice/group) were orally administered with Sb-EE (20 or 200 mg/kg) or ranitidine (positive control) twice a day for two days, and EtOH/HCl was orally injected to induce gastritis.

RESULTS

Sb-EE suppressed nitric oxide (NO) release (IC₅₀=34 µg/mL) without cytotoxicity and contained flavonoids (quercetin, luteolin and kaempferol). Sb-EE (200 µg/mL) reduced the mRNA expression of inducible NO synthase (iNOS). Sb-EE blocked the activities of Syk and Src, while inhibiting interleukin-1 receptor associated kinases (IRAK1) by 68%. Similarly, orally administered Sb-EE (200 mg/kg) suppressed NO production by 78% and phosphorylation of Src and Syk in peritonitis mice. Sb-EE also decreased inflammatory lesions in gastritis mice.

DISCUSSION AND CONCLUSIONS

This study demonstrates the inhibitory effect of Sb-EE on the inflammatory response, suggesting that Sb-EE can be developed as a potential anti-inflammatory agent.

Quasi-Supervised Strategies for Compound-Protein Interaction Prediction

In-silico compound-protein interaction prediction addresses prioritization of drug candidates for experimental biochemical validation because the wet-lab experiments are time-consuming, laborious and costly. Most machine learning methods proposed to that end approach this problem with supervised learning strategies in which known interactions are labeled as positive and the rest are labeled as negative. However, treating all unknown interactions as negative instances may lead to inaccuracies in real practice since some of the unknown interactions are bound to be positive interactions waiting to be identified as such. In this study, we propose to address this problem using the Quasi-Supervised Learning (QSL) algorithm. In this framework, potential interactions are predicted by estimating the overlap between a true positive dataset of compound-protein pairs with known interactions and an unknown dataset of all the remaining compound-protein pairs. The potential interactions are then identified as those in the unknown dataset that overlap with the interacting pairs in the true positive dataset in terms of the associated similarity structure. We also address the class-imbalance problem by modifying the conventional cost function of the QSL algorithm. Experimental results on GPCR and Nuclear Receptor datasets show that the proposed method can identify actual interactions from all possible combinations.