Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph

Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, mono-modal learning is inherently limited as it relies solely on a single modality of molecular representation, which restricts a comprehensive understanding of drug molecules. To overcome the limitations, we propose a multimodal fused deep learning (MMFDL) model to leverage information from different molecular representations. Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, Bidirectional Gated Recurrent Unit (BiGRU), and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, and molecular graphs, respectively. We evaluate the proposed triple-modal model using five fusion approaches on six molecule datasets, including Delaney, Llinas2020, Lipophilicity, SAMPL, BACE, and pKa from DataWarrior. The results show that the MMFDL model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in the random splitting test, outperforming mono-modal models in accuracy and reliability. Furthermore, we validate the generalization ability of our model in the prediction of binding constants for protein-ligand complex molecules, and assess the resilience capability against noise. Through analysis of feature distributions in chemical space and the assigned contribution of each modal model, we demonstrate that the MMFDL model shows the ability to acquire complementary information by using proper models and suitable fusion approaches. By leveraging diverse sources of bioinformatics information, multimodal deep learning models hold the potential for successful drug discovery.

MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/.

Experimental and computational biophysics to identify vasodilator drugs targeted at TRPV2 using agonists based on the probenecid scaffold

TRP channels are important pharmacological targets in physiopathology. TRPV2 plays distinct roles in cardiac and neuromuscular function, immunity, and metabolism, and is associated with pathologies like muscular dystrophy and cancer. However, TRPV2 pharmacology is unspecific and scarce at best. Using in silico similarity-based chemoinformatics we obtained a set of 270 potential hits for TRPV2 categorized into families based on chemical nature and similarity. Docking the compounds on available rat TRPV2 structures allowed the clustering of drug families in specific ligand binding sites. Starting from a probenecid docking pose in the piperlongumine binding site and using a Gaussian accelerated molecular dynamics approach we have assigned a putative probenecid binding site. In parallel, we measured the EC50 of 7 probenecid derivatives on TRPV2 expressed in Pichia pastoris using a novel medium-throughput Ca2+ influx assay in yeast membranes together with an unbiased and unsupervised data analysis method. We found that 4-(piperidine-1-sulfonyl)-benzoic acid had a better EC50 than probenecid, which is one of the most specific TRPV2 agonists to date. Exploring the TRPV2-dependent anti-hypertensive potential in vivo, we found that 4-(piperidine-1-sulfonyl)-benzoic acid shows a sex-biased vasodilator effect producing larger vascular relaxations in female mice. Overall, this study expands the pharmacological toolbox for TRPV2, a widely expressed membrane protein and orphan drug target.

SAEDC: Development of a technological solution for exploratory data analysis and statistics in cytotoxicity

INTRODUCTION

The intergovernmental organizations Organisation for Economic Co-operation and Development (OECD) and Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) have developed guidelines for the use of in vitro models for toxicological evaluation of chemicals. However, the presence of manual steps and the requirement of multiple tools for data analysis, apart from being costly and time-consuming, can inadvertently introduce errors by researchers.

OBJECTIVES

We have developed the SAEDC platform (Technological Solution for Exploratory Data Analysis and Statistics for Cytotoxicity, in Portuguese), which enables analysis of cytotoxicity data from assays following OECD Guideline No. 129.

METHODOLOGY

In vitro experimental data were used to compare with the analysis methodology suggested in the Guideline. We analyzed 117 data sets covering chemicals from Category I to Unclassified according to GHS classification.

RESULTS

The four-parameters of non-linear regression (4PL) calculated by the SAEDC platform showed no significant differences compared to standard methodology in any of the data sets (p > 0.05). The coefficient of determination (R-squared) also demonstrated not only a good fit of the 4PL model to the data but also significant similarity to values obtained by the conventional methodology. Finally, the SAEDC platform predicted LD50 values for the chemicals from IC50, using the Registry of Cytotoxicity (RC) regression models.

CONCLUSION

The comparison with the standard data analysis methodology revealed that SAEDC platform fulfills the requirements for cytotoxicity data analysis, generating reliable and accurate results with fewer steps performed by researchers. The use of SAEDC platform for obtaining toxicity values can reduce analysis time compared to the standard methodology proposed by regulatory agencies. Thus, automation of the analysis using the SAEDC platform has the potential to save time and resources for cytotoxicity researchers and laboratories while generating reliable results.

Fragment-based drug discovery and biological evaluation of novel cannabinol-based inhibitors of oxytosis/ferroptosis for neurological disorders

The oxytosis/ferroptosis regulated cell death pathway is an emerging field of research owing to its pathophysiological relevance to a wide range of neurological disorders, including Alzheimer's and Parkinson's diseases and traumatic brain injury. Developing novel neurotherapeutics to inhibit oxytosis/ferroptosis offers exciting opportunities for the treatment of these and other neurological diseases. Previously, we discovered cannabinol (CBN) as a unique, potent inhibitor of oxytosis/ferroptosis by targeting mitochondria and modulating their function in neuronal cells. To further elucidate which key pharmacophores and chemical space are essential to the beneficial effects of CBN, we herein introduce a fragment-based drug discovery strategy in conjunction with cell-based phenotypic screens using oxytosis/ferroptosis to determine the structure-activity relationship of CBN. The resulting information led to the development of four new CBN analogs, CP1-CP4, that not only preserve the sub-micromolar potency of neuroprotection and mitochondria-modulating activities seen with CBN in neuronal cell models but also have better druglike properties. Moreover, compared to CBN, the analog CP1 shows improved in vivo efficacy in the Drosophila model of mild traumatic brain injury. Together these studies identify the key molecular scaffolds of cannabinoids that contribute to neuroprotection against oxytosis/ferroptosis. They also highlight the advantageous approach of combining in vitro cell-based assays and rapid in vivo studies using Drosophila models for evaluating new therapeutic compounds.

Therapeutic potential of natural flavonoids in pulmonary arterial hypertension: A review

BACKGROUND

Pulmonary arterial hypertension (PAH) is a fatal disease caused by pulmonary vascular remodeling, with a high incidence and mortality. At present, many clinical drugs for treating PAH mainly exert effects by relaxing the pulmonary artery, with limited therapeutic effects, so the search for viable therapeutic agents continues uninterrupted. In recent years, natural flavonoids have shown promising potential in the treatment of cardiovascular diseases. It is necessary to comprehensively elucidate the potential of natural flavonoids to combat PAH.

PURPOSE

To evaluate the potential of natural flavonoids to hinder or slow down the occurrence and development of PAH, and to identify promising drug discovery candidates.

METHODS

Literature was collected from PubMed, Science Direct, Web of science, CNKI databases and Google scholar. The search terms used included "pulmonary arterial hypertension", "pulmonary hypertension", "natural products", "natural flavonoids", "traditional chinese medicine", etc., and several combinations of these keywords.

RESULTS

The resources, structural characteristics, mechanisms, potential and prospect strategies of natural flavonoids for treating PAH were summarized. Natural flavonoids offer different solutions as possible treatments for PAH. These mechanisms may involve various pathways and molecular targets related to the pathogenesis of PAH, such as inflammation, oxidative stress, vascular remodeling, genetic, ion channels, cell proliferation and autophagy. In addition, prospect strategies of natural flavonoids for anti-PAH including structural modification and nanomaterial delivery systems have been explored. This review suggests that the potential of natural flavonoids as alternative therapeutic agents in the prevention and treatment of PAH holds promise for future research and clinical applications.

CONCLUSION

Despite displaying the enormous potential of flavonoids in PAH, some limitations need to be further explored. Firstly, using advanced drug discovery tools, including computer-aided design and high-throughput screening, to further investigate the safety, biological activity, and precise mechanism of action of flavonoids. Secondly, exploring the structural modifications of these compounds is expected to optimize their efficacy. Lastly, it is necessary to conduct well controlled clinical trials and a comprehensive evaluation of potential side effects to determine their effectiveness and safety.

Discovery of a potent inhibitor, D-132, targeting AsfvPolX, via protein-DNA complex-guided pharmacophore screening and in vitro molecular characterizations

The heightened transmissibility and capacity of African swine fever virus (ASFV) induce fatal diseases in domestic pigs and wild boars, posing significant economic repercussions and global threats. Despite extensive research efforts, the development of potent vaccines or treatments for ASFV remains a persistent challenge. Recently, inhibiting the AsfvPolX, a key DNA repair enzyme, emerges as a feasible strategy to disrupt viral replication and control ASFV infections. In this study, a comprehensive approach involving pharmacophore-based inhibitor screening, coupled with biochemical and biophysical analyses, were implemented to identify, characterize, and validate potential inhibitors targeting AsfvPolX. The constructed pharmacophore model, Phar-PolX-S, demonstrated efficacy in identifying a potent inhibitor, D-132 (IC50 = 2.8 ± 0.2 µM), disrupting the formation of the AsfvPolX-DNA complex. Notably, D-132 exhibited strong binding to AsfvPolX (KD = 6.9 ± 2.2 µM) through a slow-on-fast-off binding mechanism. Employing molecular modeling, it was elucidated that D-132 predominantly binds in-between the palm and finger domains of AsfvPolX, with crucial residues (R42, N48, Q98, E100, F102, and F116) identified as hotspots for structure-based inhibitor optimization. Distinctively characterized by a 1,2,5,6-tetrathiocane with modifications at the 3 and 8 positions involving ethanesulfonates, D-132 holds considerable promise as a lead compound for the development of innovative agents to combat ASFV infections.

Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki-Miyaura, Hiyama and Liebeskind-Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

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

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

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.

Different receptor models show differences in ligand binding strength and location: a computational drug screening for the tick-borne encephalitis virus

The tick-borne encephalitis virus (TBE) is a neurotrophic disease that has spread more rapidly throughout Europe and Asia in the past few years. At the same time, no cure or specific therapy is known to battle the illness apart from vaccination. To find a pharmacologically relevant drug, a computer-aided drug screening was initiated. Such a procedure probes a possible binding of a drug to the RNA Polymerase of TBE. The crystal structure of the receptor, however, includes missing and partially modeled regions, which rendered the structure incomplete and of questionable use for a thorough drug screening procedure. The quality of the receptor model was addressed by studying three putative structures created. We show that the choice of receptor models greatly influences the binding affinity of potential drug molecules and that the binding location could also be significantly impacted. We demonstrate that some drug candidates are unsuitable for one model but show decent results for another. Without any prejudice on the three employed receptor models, the study reveals the imperative need to investigate the receptor structure before drug binding is probed whether experimentally or computationally.

Emerging opportunities of using large language models for translation between drug molecules and indications

A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.

Discovery of 9H-pyrimido[4,5-b]indole derivatives as dual RET/TRKA inhibitors

Aberrant RET kinase signaling is activated in numerous cancers including lung, thyroid, breast, pancreatic, and prostate. Recent approvals of selective RET inhibitors, pralsetinib and selpercatinib, has shifted the focus of RET kinase drug discovery programs towards the development of selective inhibitors. However, selective inhibitors invariably lose efficacy as the selective nature of the inhibitor places Darwinian-like pressure on the tumor to bypass treatment through the selection of novel oncogenic drivers. Further, selective inhibitors are restricted for use in tumors with specific genetic backgrounds that do not encompass diverse patient classes. Here we report the identification of a pyrimido indole RET inhibitor found to also have activity against TRK. This selective dual RET/TRK inhibitor can be utilized in tumors with both RET and TRK genetic backgrounds and can also provide blockade of NTRK-fusions that are selected for from RET inhibitor treatments. Efforts towards developing dual RET/TRK inhibitors can be beneficial in terms of encompassing more diverse patient classes while also achieving blockade against emerging resistance mechanisms.

Robenidine derivatives as potential antischistosomal drug candidates

Schistosomiasis caused by Schistosoma spp. is a disease that causes a considerable health burden to millions of people worldwide. The limited availability of effective drugs on the market and the increased risk of resistance development due to extensive usage, highlight the urgent need for new antischistosomal drugs. Recent studies have shown that robenidine derivatives, containing an aminoguanidine core, exhibit promising activities against Plasmodium falciparum, motivating further investigation into their efficacy against Schistosoma mansoni, due to their similar habitat and the resulting related cellular mechanisms like the heme detoxification pathway. The conducted phenotypic screening of robenidine and 80 derivatives against newly transformed schistosomula and adult Schistosoma mansoni yielded 11 candidates with low EC50 values for newly transformed schistosomula (1.12-4.63 μM) and adults (2.78-9.47 μM). The structure-activity relationship revealed that electron-withdrawing groups at the phenyl moiety, as well as the presence of methyl groups adjacent to the guanidine moiety, enhanced the activity of derivatives against both stages of Schistosoma mansoni. The two compounds 2,2'-Bis[(3-cyano-4-fluorophenyl)methylene] carbonimidic Dihydrazide Hydrochloride (1) and 2,2'-Bis[(4-difluoromethoxyphenyl) ethylidene] carbonimidic Dihydrazide Hydrochloride (19), were selected for an in vivo study in Schistosoma mansoni-infected mice based on their potency, cytotoxicity, pharmacokinetic-, and physicochemical properties, but failed to reduce the worm burden significantly (worm burden reduction <20%). Thus, robenidine derivatives require further refinements to obtain higher antischistosomal specificity and in vivo activity.

Data-oriented protein kinase drug discovery

The continued growth of data from biological screening and medicinal chemistry provides opportunities for data-driven experimental design and decision making in early-phase drug discovery. Approaches adopted from data science help to integrate internal and public domain data and extract knowledge from historical in-house data. Protein kinase (PK) drug discovery is an exemplary area where large amounts of data are accumulating, providing a valuable knowledge base for discovery projects. Herein, the evolution of PK drug discovery and development of small molecular PK inhibitors (PKIs) is reviewed, highlighting milestone developments in the field and discussing exemplary studies providing a basis for increasing data orientation of PK discovery efforts.

Quinoxaline derivatives: Recent discoveries and development strategies towards anticancer agents

Cancer is a leading cause of death and a major health problem worldwide. While many effective anticancer agents are available, most drugs currently on the market are not specific, raising issues like the common side effects of chemotherapy. However, recent research hold promises for the development of more efficient and safer anticancer drugs. Quinoxaline and its derivatives are becoming recognized as a novel class of chemotherapeutic agents with activity against different tumors. The present review compiles and discusses studies concerning the therapeutic potential of the anticancer activity of quinoxaline derivatives, covering articles published between January 2018 and January 2023.

Can artemisinin and its derivatives treat malaria in a host-directed manner?

Malaria is caused by an apicomplexan protozoan parasite, Plasmodium, and is transmitted through vectors. It remains a substantial health burden, especially in developing countries, leading to significant socioeconomic losses. Although the World Health Organization (WHO) has approved various antimalarial medications in the past two decades, the increasing resistance to these medications has worsened the situation. The development of drug resistance stems from genetic diversity among Plasmodium strains, impeding eradication efforts. Consequently, exploring innovative technologies and strategies for developing effective medications based on the host is crucial. Artemisinin and its derivatives (artemisinins) have been recommended by the WHO for treating malaria owing to their known effectiveness in killing the parasite. However, their potential to target the host for malaria treatment has not been investigated. This article concisely reviews the application of host-directed therapeutics, potential drug candidates targeting the host for treating malaria, and usage of artemisinins in numerous diseases. It underscores the importance of host-directed interventions for individuals susceptible to malaria, suggests the potential utility of artemisinins in host-directed malaria treatments, and posits that the modulation of host proteins with artemisinins may offer a means of intervening in host-parasite interactions. Further studies focusing on the host-targeting perspective of artemisinins can provide new insights into the mechanisms of artemisinin resistance and offer a unique opportunity for new antimalarial drug discovery.

Recent discovery and development of AXL inhibitors as antitumor agents

AXL, a receptor tyrosine kinase (RTK), plays a pivotal role in various cellular functions. It is primarily involved in processes such as epithelial-mesenchymal transition (EMT) in tumor cells, angiogenesis, apoptosis, immune regulation, and chemotherapy resistance mechanisms. Therefore, targeting AXL is a promising therapeutic approach for the treatment of cancer. AXL inhibitors that have entered clinical trials, such as BGB324(1), have shown promising efficacy in the treatment of melanoma and non-small cell lung cancer. Additionally, novel AXL-targeted drugs, such as AXL degraders, offer a potential solution to overcome the limitations of traditional small-molecule AXL inhibitors targeting single pathways. We provide an overview of the structure and biological functions of AXL, discusses its correlation with various cancers, and critically analyzes the structure-activity relationship of AXL small-molecule inhibitors in cellular contexts. Additionally, we summarize multiple research and development strategies, offering insights for the future development of innovative AXL inhibitors.

Two-point immobilization of M3 muscarinic receptor: a method for recognizing receptor antagonists in natural products

In the investigation of active ingredients from natural products, current technologies relying on drug-target affinity recognition analysis face significant challenges. This is primarily due to their limited specificity and inability to provide downstream pharmacodynamic information, such as agonistic or antagonistic activity. In this study, a two-point method was developed by immobilizing M3 acetylcholine receptor (M3R) through the combination of the conformation-specific peptide BJ-PRO-13a and the HaloTag trap system. We systematically assessed the specificity of the immobilized M3R using known M3R antagonists (pirenzepine and atropine) and agonists (cevimeline and pilocarpine). By frontal analysis and nonlinear chromatography, the performance of immobilized M3R was evaluated in terms of binding kinetics and thermodynamics of four drugs to the immobilized M3R. Additionally, we successfully identified two M3R antagonists within an extract from Daturae Flos (DF), specifically hyoscyamine and scopolamine. Our findings demonstrate that this immobilization method effectively captures receptor-ligand binding interactions and can discern receptor agonists from antagonists. This innovation enhances the efficiency of receptor chromatography to determine binding-affinity in the development of new drugs, offering promise for the screening and characterization of active compounds, particularly within complex natural products.

Computer-Aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review

Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.

Miniaturized screening and performance prediction of tailored subcutaneous extended-release formulations for preclinical in vivo studies

Microencapsulation of active pharmaceutical ingredients (APIs) for preparation of long acting injectable (LAI) formulations is an auspicious technique to enable preclinical characterization of a broad variety of APIs, ideally independent of their physicochemical and pharmacokinetic (PK) characteristics. During early API discovery, tunable LAI formulations may enable pharmacological proof-of-concept for the given variety of candidates by tailoring the level of plasma exposure over the duration of various timespans. Although numerous reports on small scale preparation methods for LAIs utilizing copolymers of lactic and glycolic acid (PLGA) and polymers of lactic acid (PLA) highlight their potential, application in formulation screening and use in preclinical in vivo studies is yet very limited. Transfer from downscale formulation preparation to in vivo experiments is hampered in early preclinical API screening by the large number of API candidates with simultaneously very limited available amount in the lower sub-gram scale, lack of formulation stability and deficient tunability of sustained release. We hereby present a novel comprehensive platform tool for tailored extended-release formulations, aiming to support a variety of preclinical in vivo experiments with ranging required plasma exposure levels and timespans. A novel small-scale spray drying process was successfully implemented by using an air brush based instrument for preparation of PLGA and PLA based formulations. Using Design of Experiments (DoE), required API amount of 250 mg was demonstrated to suffice for identification of dominant polymer characteristics with largest impact on sustained release capability for an individual API. BI-3231, a hydrophilic and weakly acidic small compound with good water solubility and permeability, but low metabolic stability, was used as an exemplary model for one of the many candidates during API discovery. Furthermore, an in vitro to in vivo correlation (IVIVC) of API release rate was established in mice, which enabled the prediction of in vivo plasma concentration plateaus after single subcutaneous injection, using only in vitro dissolution profiles of screened formulations. By tailoring LAI formulations and their doses for acute and sub-chronic preclinical experiments, we exemplary demonstrate the practical use for BI-3231. Pharmacological proof-of-concept could be enabled whilst circumventing the need of multiple administration as result of extensive hepatic metabolism and simultaneously superseding numerous in vivo experiments for formulation tailoring.

Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus

Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC90 2.95 μM; M. abscessus IC90 59.1 μM) and tribromsalan (M. tuberculosis IC90 76.92 μM; M. abscessus IC90 147.4 μM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism.

The application of brain organoid for drug discovery in mitochondrial diseases

Mitochondrial diseases are difficult to treat due to the complexity and multifaceted nature of mitochondrial dysfunction. Brain organoids are three-dimensional (3D) structures derived from human pluripotent stem cells designed to mimic brain-like development and function. Brain organoids have received a lot of attention in recent years as powerful tools for modeling human diseases, brain development, and drug screening. Screening compounds for mitochondrial diseases using brain organoids could provide a more physiologically relevant platform for drug discovery. Brain organoids offer the possibility of personalized medicine because they can be derived from patient-specific cells, allowing testing of drugs tailored to specific genetic mutations. In this article, we highlight how brain organoids offer a promising avenue for screening compounds for mitochondrial diseases and address the challenges and limitations associated with their use. We hope this review will provide new insights into the further progress of brain organoids for mitochondrial screening studies.

Elucidating the semantics-topology trade-off for knowledge inference-based pharmacological discovery

Leveraging AI for synthesizing the deluge of biomedical knowledge has great potential for pharmacological discovery with applications including developing new therapeutics for untreated diseases and repurposing drugs as emergent pandemic treatments. Creating knowledge graph representations of interacting drugs, diseases, genes, and proteins enables discovery via embedding-based ML approaches and link prediction. Previously, it has been shown that these predictive methods are susceptible to biases from network structure, namely that they are driven not by discovering nuanced biological understanding of mechanisms, but based on high-degree hub nodes. In this work, we study the confounding effect of network topology on biological relation semantics by creating an experimental pipeline of knowledge graph semantic and topological perturbations. We show that the drop in drug repurposing performance from ablating meaningful semantics increases by 21% and 38% when mitigating topological bias in two networks. We demonstrate that new methods for representing knowledge and inferring new knowledge must be developed for making use of biomedical semantics for pharmacological innovation, and we suggest fruitful avenues for their development.

PT-Finder: A multi-modal neural network approach to target identification

Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.

Development of dietary small molecules as multi-targeting treatment strategies for Alzheimer's disease

Cognitive dysfunction can occur both in normal aging and age-related neurological disorders, such as mild cognitive impairment and Alzheimer's disease (AD). These disorders have few treatment options due to side effects and limited efficacy. New approaches to slow cognitive decline are urgently needed. Dietary interventions (nutraceuticals) have received considerable attention because they exhibit strong neuroprotective properties and may help prevent or minimize AD symptoms. Biological aging is driven by a series of interrelated mechanisms, including oxidative stress, neuroinflammation, neuronal apoptosis, and autophagy, which function through various signaling pathways. Recent clinical and preclinical studies have shown that dietary small molecules derived from natural sources, including flavonoids, carotenoids, and polyphenolic acids, can modulate oxidative damage, cognitive impairments, mitochondrial dysfunction, neuroinflammation, neuronal apoptosis, autophagy dysregulation, and gut microbiota dysbiosis. This paper reviews research on different dietary small molecules and their bioactive constituents in the treatment of AD. Additionally, the chemical structure, effective dose, and specific molecular mechanisms of action are comprehensively explored. This paper also discusses the advantages of using nanotechnology-based drug delivery, which significantly enhances oral bioavailability, safety, and therapeutic effect, and lowers the risk of adverse effects. These agents have considerable potential as novel and safe therapeutic agents that can prevent and combat age-related AD.

Prediction of drug-target binding affinity based on deep learning models

The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, these techniques have not succeeded in reversing the low success rate of new drug development. In recent years, the continuous development of deep learning (DL) technology has brought new opportunities for drug discovery through the DTA prediction. This shift has moved the prediction of DTA from traditional machine learning methods to DL. The DL frameworks used for DTA prediction include convolutional neural networks (CNN), graph convolutional neural networks (GCN), and recurrent neural networks (RNN), and reinforcement learning (RL), among others. This review article summarizes the available literature on DTA prediction using DL models, including DTA quantification metrics and datasets, and DL algorithms used for DTA prediction (including input representation of models, neural network frameworks, valuation indicators, and model interpretability). In addition, the opportunities, challenges, and prospects of the application of DL frameworks for DTA prediction in the field of drug discovery are discussed.

Antiplatelet and Anticoagulant Effects of Two New Phenylpropanoid Sucrose Esters and Other Secondary Metabolites from the Aerial Part of Canna edulis

This study isolated pure compounds from Canna edulis aerial parts and assessed their antiplatelet and anticoagulant potential. Structural elucidation resulted in the identification of two new compounds: caneduloside A (1) and caneduloside B (2), and eleven known compounds: 6'-acetyl-3,6,2'-tri-p-coumaroyl sucrose (3), 6'-acetyl-3,6,2'-triferuloyl sucrose (4), tiliroside (5), afzelin (6), quercitrin (7), 2-hydroxycinnamaldehyde (8), cinnamic acid (9), 3,4-dimethoxycinnamic acid (10), dehydrovomifoliol (11), 4-hydroxy-3,5-dimethoxybenzaldehyde (12), and (S)-(-)-rosmarinic acid (13). Compounds 3, 4, 6-9, 13 were previously reported for antithrombotic properties. Hence, antithrombotic tests were conducted for 1, 2, 5, 10-12. All tested compounds demonstrated a dose-dependent antiaggregatory effect, and 10 and 12 were the most potent for both ADP and collagen activators. Additionally, 10 and 12 showed anticoagulant effects, with prolonged prothrombin time and activated partial thromboplastin time. The new compound 1 displayed antiplatelet and anticoagulant activity, while 2 mildly inhibited platelet aggregation. C. edulis is a potential source for developing antithrombotic agents.

Cheminformatics approach to identify andrographolide derivatives as dual inhibitors of methyltransferases (nsp14 and nsp16) of SARS-CoV-2

The Covid-19 pandemic outbreak has accelerated tremendous efforts to discover a therapeutic strategy that targets severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to control viral infection. Various viral proteins have been identified as potential drug targets, however, to date, no specific therapeutic cure is available against the SARS-CoV-2. To address this issue, the present work reports a systematic cheminformatic approach to identify the potent andrographolide derivatives that can target methyltransferases of SARS-CoV-2, i.e. nsp14 and nsp16 which are crucial for the replication of the virus and host immune evasion. A consensus of cheminformatics methodologies including virtual screening, molecular docking, ADMET profiling, molecular dynamics simulations, free-energy landscape analysis, molecular mechanics generalized born surface area (MM-GBSA), and density functional theory (DFT) was utilized. Our study reveals two new andrographolide derivatives (PubChem CID: 2734589 and 138968421) as natural bioactive molecules that can form stable complexes with both proteins via hydrophobic interactions, hydrogen bonds and electrostatic interactions. The toxicity analysis predicts class four toxicity for both compounds with LD50 value in the range of 500-700 mg/kg. MD simulation reveals the stable formation of the complex for both the compounds and their average trajectory values were found to be lower than the control inhibitor and protein alone. MMGBSA analysis corroborates the MD simulation result and showed the lowest energy for the compounds 2734589 and 138968421. The DFT and MEP analysis also predicts the better reactivity and stability of both the hit compounds. Overall, both andrographolide derivatives exhibit good potential as potent inhibitors for both nsp14 and nsp16 proteins, however, in-vitro and in vivo assessment would be required to prove their efficacy and safety in clinical settings. Moreover, the drug discovery strategy aiming at the dual target approach might serve as a useful model for inventing novel drug molecules for various other diseases.

Developing theragnostics for Alzheimer's disease: Insights from cancer treatment

The prevalence of Alzheimer's disease (AD) and its associated economic and societal burdens are on the rise, but there are no curative treatments for AD. Interestingly, this neurodegenerative disease shares several biological and pathophysiological features with cancer, including cell-cycle dysregulation, angiogenesis, mitochondrial dysfunction, protein misfolding, and DNA damage. However, the genetic factors contributing to the overlap in biological processes between cancer and AD have not been actively studied. In this review, we discuss the shared biological features of cancer and AD, the molecular targets of anticancer drugs, and therapeutic approaches. First, we outline the common biological features of cancer and AD. Second, we describe several anticancer drugs, their molecular targets, and their effects on AD pathology. Finally, we discuss how protein-protein interactions (PPIs), receptor inhibition, immunotherapy, and gene therapy can be exploited for the cure and management of both cancer and AD. Collectively, this review provides insights for the development of AD theragnostics based on cancer drugs and molecular targets.

Discovery of a SUCNR1 antagonist for potential treatment of diabetic nephropathy: In silico and in vitro studies

Diabetic nephropathy (DN) is one of the most severe complications of diabetes mellitus. Succinate Receptor 1 (SUCNR1), a member of the G-protein-coupled receptor (GPCR) family, represents a potential target for treatment of DN. Here, utilizing multi-strategy in silico virtual screening methods containing AlphaFold2 modelling, molecular dynamics (MD) simulation, ligand-based pharmacophore screening, molecular docking and machine learning-based similarity clustering, we successfully identified a novel antagonist of SUCNR1, AK-968/12117473 (Cpd3). Through extensive in vitro experiments, including dual-luciferase reporter assay, cellular thermal shift assay, immunofluorescence, and western blotting, we substantiated that Cpd3 could specifically target SUCNR1, inhibit the activation of NF-κB pathway, and ameliorate epithelial-mesenchymal transition (EMT) and extracellular matrix (ECM) deposition in renal tubular epithelial cells (NRK-52E) under high glucose conditions. Further in silico simulations revealed the molecular basis of the SUCNR1-Cpd3 interaction, and the in vitro metabolic stability assay indicated favorable drug-like pharmacokinetic properties of Cpd3. This work not only successfully pinpointed Cpd3 as a specific antagonist of SUCNR1 to serve as a promising candidate in the realm of therapeutic interventions for DN, but also provides a paradigm of dry-wet combined discovery strategies for GPCR-based therapeutics.

Repositioning of anti-infective compounds against monkeypox virus core cysteine proteinase: a molecular dynamics study

Monkeypox virus (MPXV) core cysteine proteinase (CCP) is one of the major drug targets used to examine the inhibitory action of chemical moieties. In this study, an in silico technique was applied to screen 1395 anti-infective compounds to find out the potential molecules against the MPXV-CCP. The top five hits were selected after screening and processed for exhaustive docking based on the docked score of ≤ -9.5 kcal/mol. Later, the top three hits based on the exhaustive-docking score and interaction profile were selected to perform MD simulations. The overall RMSD suggested that two compounds, SC75741 and ammonium glycyrrhizinate, showed a highly stable complex with a standard deviation of 0.18 and 0.23 nm, respectively. Later, the MM/GBSA binding free energies of complexes showed significant binding strength with ΔGTOTAL from -21.59 to -15 kcal/mol. This report reported the potential inhibitory activity of SC75741 and ammonium glycyrrhizinate against MPXV-CCP by competitively inhibiting the binding of the native substrate.

From stem cells to pancreatic β-cells: strategies, applications, and potential treatments for diabetes

Loss and functional failure of pancreatic β-cells results in disruption of glucose homeostasis and progression of diabetes. Although whole pancreas or pancreatic islet transplantation serves as a promising approach for β-cell replenishment and diabetes therapy, the severe scarcity of donor islets makes it unattainable for most diabetic patients. Stem cells, particularly induced pluripotent stem cells (iPSCs), are promising for the treatment of diabetes owing to their self-renewal capacity and ability to differentiate into functional β-cells. In this review, we first introduce the development of functional β-cells and their heterogeneity and then turn to highlight recent advances in the generation of β-cells from stem cells and their potential applications in disease modeling, drug discovery and clinical therapy. Finally, we have discussed the current challenges in developing stem cell-based therapeutic strategies for improving the treatment of diabetes. Although some significant technical hurdles remain, stem cells offer great hope for patients with diabetes and will certainly transform future clinical practice.

Identification of a new class of activators of the Hippo pathway with antitumor activity in vitro and in vivo

The Hippo pathway is a key regulator of tissue growth, organ size, and tumorigenesis. Activating the Hippo pathway by gene editing or pharmaceutical intervention has been proven to be a new therapeutic strategy for treatment of the Hippo pathway-dependent cancers. To now, a number of compounds that directly target the downstream effector proteins of Hippo pathway, including YAP and TEADs, have been disclosed, but very few Hippo pathway activators are reported. Here, we discovered a new class of Hippo pathway activator, YL-602, which inhibited CTGF expression in cells irrespective of cell density and the presence of serum. Mechanistically, YL-602 activates the Hippo pathway via MST1/2, which is different from known activators of Hippo pathway. In vitro, YL-602 significantly induced tumor cell apoptosis and inhibited colony formation of tumor cells. In vivo, oral administration of YL-602 substantially suppressed the growth of cancer cells by activation of Hippo pathway. Overall, YL-602 could be a promising lead compound, and deserves further investigation for its mechanism of action and therapeutic applications.

High-throughput prioritization of target proteins for development of new antileishmanial compounds

Leishmaniasis, a vector-borne disease, is caused by the infection of Leishmania spp., obligate intracellular protozoan parasites. Presently, human vaccines are unavailable, and the primary treatment relies heavily on systemic drugs, often presenting with suboptimal formulations and substantial toxicity, making new drugs a high priority for LMIC countries burdened by the disease, but a low priority in the agenda of most pharmaceutical companies due to unattractive profit margins. New ways to accelerate the discovery of new, or the repositioning of existing drugs, are needed. To address this challenge, our study aimed to identify potential protein targets shared among clinically-relevant Leishmania species. We employed a subtractive proteomics and comparative genomics approach, integrating high-throughput multi-omics data to classify these targets based on different druggability metrics. This effort resulted in the ranking of 6502 ortholog groups of protein targets across 14 pathogenic Leishmania species. Among the top 20 highly ranked groups, metabolic processes known to be attractive drug targets, including the ubiquitination pathway, aminoacyl-tRNA synthetases, and purine synthesis, were rediscovered. Additionally, we unveiled novel promising targets such as the nicotinate phosphoribosyltransferase enzyme and dihydrolipoamide succinyltransferases. These groups exhibited appealing druggability features, including less than 40% sequence identity to the human host proteome, predicted essentiality, structural classification as highly druggable or druggable, and expression levels above the 50th percentile in the amastigote form. The resources presented in this work also represent a comprehensive collection of integrated data regarding trypanosomatid biology.

Discovery and in vitro Evaluation of Novel Serotonin 5-HT2A Receptor Ligands Identified Through Virtual Screening

The 5-HT2A receptor is a molecular target of high pharmacological importance. Ligands of this protein, particularly atypical antipsychotics, are useful in the treatment of numerous mental disorders, including schizophrenia and major depressive disorder. Structure-based virtual screening using a 5-HT2A receptor complex was performed to identify novel ligands for the 5-HT2A receptor, serving as potential antidepressants. From the Enamine screening library, containing over 4 million compounds, 48 molecules were selected for subsequent experimental validation. These compounds were tested against the 5-HT2A receptor in radioligand binding assays. From the tested batch, six molecules were identified as ligands of the main molecular target and were forwarded to a more detailed in vitro profiling. This included radioligand binding assays at 5-HT1A, 5-HT7, and D2 receptors and functional studies at 5-HT2A receptors. These compounds were confirmed to show a binding affinity for at least one of the targets tested in vitro. Thus, the success rate for the inactive template-based screening reached 17%, while it was 9% for the active template-based screening. Similarity and fragment analysis indicated the structural novelty of the identified compounds. Pharmacokinetics for these molecules was determined using in silico approaches.

Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties

Cytochrome P450 enzymes are a superfamily of enzymes responsible for the metabolism of a variety of medicines and xenobiotics. Among the Cytochrome P450 family, five isozymes that include 1A2, 2C9, 2C19, 2D6, and 3A4 are most important for the metabolism of xenobiotics. Inhibition of any of these five CYP isozymes causes drug-drug interactions with high pharmacological and toxicological effects. So, the inhibition or non-inhibition prediction of these isozymes is of great importance. Many techniques based on machine learning and deep learning algorithms are currently being used to predict whether these isozymes will be inhibited or not. In this study, three different molecular or substructural properties that include Morgan, MACCS and Morgan (combined) and RDKit of the various molecules are used to train a distinct SVM model against each isozyme (1A2, 2C9, 2C19, 2D6, and 3A4). On the independent dataset, Morgan fingerprints provided the best results, while MACCS and Morgan (combined) achieved comparable results in terms of balanced accuracy (BA), sensitivity (Sn), and Mathews correlation coefficient (MCC). For the Morgan fingerprints, balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4 on an independent dataset ranged between 0.81 and 0.85, 0.61 and 0.70, 0.72 and 0.83, respectively. Similarly, on the independent dataset, MACCS and Morgan (combined) fingerprints achieved competitive results in terms of balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4, which ranged between 0.79 and 0.85, 0.59 and 0.69, 0.69 and 0.82, respectively.

Selective impact of ALK and MELK inhibition on ERα stability and cell proliferation in cell lines representing distinct molecular phenotypes of breast cancer

Breast cancer (BC) is a leading cause of global cancer-related mortality in women, necessitating accurate tumor classification for timely intervention. Molecular and histological factors, including PAM50 classification, estrogen receptor α (ERα), breast cancer type 1 susceptibility protein (BRCA1), progesterone receptor (PR), and HER2 expression, contribute to intricate BC subtyping. In this work, through a combination of bioinformatic and wet lab screenings, followed by classical signal transduction and cell proliferation methods, and employing multiple BC cell lines, we identified enhanced sensitivity of ERα-positive BC cell lines to ALK and MELK inhibitors, inducing ERα degradation and diminishing proliferation in specific BC subtypes. MELK inhibition attenuated ERα transcriptional activity, impeding E2-induced gene expression, and hampering proliferation in MCF-7 cells. Synergies between MELK inhibition with 4OH-tamoxifen (Tam) and ALK inhibition with HER2 inhibitors revealed potential therapeutic avenues for ERα-positive/PR-positive/HER2-negative and ERα-positive/PR-negative/HER2-positive tumors, respectively. Our findings propose MELK as a promising target for ERα-positive/PR-positive/HER2-negative BC and highlight ALK as a potential focus for ERα-positive/PR-negative/HER2-positive BC. The synergistic anti-proliferative effects of MELK with Tam and ALK with HER2 inhibitors underscore kinase inhibitors' potential for selective treatment in diverse BC subtypes, paving the way for personalized and effective therapeutic strategies in BC management.

Antimicrobial and anti-virulence activities of 4-shogaol from grains of paradise against gram-negative bacteria: Integration of experimental and computational methods

ETHNOPHARMACOLOGICAL RELEVANCE

Bacterial resistance to antibiotics is a growing global concern, highlighting the urgent need for new antimicrobial candidates. Aframomum melegueta was traditionally used for combating urinary tract and soft tissue infections, which implies its potential as an antimicrobial agent.

AIM OF STUDY

This study was designed to explore the antibacterial and anti-virulence capabilities of 4-shogaol isolated from A. melegueta seeds versus gram-negative bacteria: Serratia marcescens, Klebsiella pneumoniae, Acinetobacter baumannii, and the clinically important pathogen Pseudomonas aeruginosa.

MATERIALS AND METHODS

4-Shogeol was isolated from A. melegueta seeds and its MICs were determined for Acinetobacter baumannii (ATCC-17978), Pseudomonas aeruginosa (ATCC-27853), Klebsiella pneumoniae (ATCC-700603), and Serratia marcescens clinical isolate. The anti-efflux activity and effect on the bacterial cell membrane for the compound were evaluated. Furthermore, the anti-virulence activities of the compound were evaluated. The effects of 4-shogeol at sub-MIC on bacterial motility, biofilm formation, and production of virulent enzymes and pigments were assessed. The anti-quorum sensing activities of 4-shogeol were evaluated virtually and by quantification its effect on the expression of quorum sensing encoding genes. The in vivo protection assay was conducted to evaluate the effect of 4-shogaol on the P. aeruginosa capacity to induce pathogenesis in mice. Finally, the effect of shogaol-antibiotics combination was assessed.

RESULTS

The research revealed that 4-shogaol's antibacterial action primarily involves disrupting the bacterial cell membrane and efflux pumps. It also exhibited significant anti-virulence effects by reducing biofilm development and repressing virulence factors production, effectively protecting mice against P. aeruginosa infection. Furthermore, when combined with antibiotics, 4-shogaol demonstrated synergistic effects, leading to reduced minimum inhibitory concentrations (MICs) against P. aeruginosa. Its anti-virulence properties were linked to its ability to disrupt bacterial quorum sensing (QS) mechanisms, as evidenced by its interaction with QS receptors and downregulation of QS-related genes. Notably, in silico analysis indicated that 4-shogaol exhibited strong binding affinity to different P. aeruginosa QS targets.

CONCLUSION

These findings suggest that 4-shogaol holds promise as an effective anti-virulence agent that can be utilized in combination with antibiotics for treating severe infections caused by gram-positive bacteria.

Bio synthesis, comprehensive characterization, and multifaceted therapeutic applications of BSA-Resveratrol coated platinum nanoparticles

This study examines the manufacturing, characterization, and biological evaluation of platinum nanoparticles, which were synthesized by Enterobacter cloacae and coated with Bovine Serum Albumin (BSA) and Resveratrol (RSV). The formation of PtNPs was confirmed with the change of color from dark yellow to black, which was due to the bioreduction of platinum chloride by E. cloacae. BSA and RSV functionalization enhanced these nanoparticles' biocompatibility and therapeutic potential. TGA, SEM, XRD, and FTIR were employed for characterization, where PtNPs and drug conjugation-related functional groups were studied by FTIR. XRD confirmed the crystalline nature of PtNPs and Pt-BSA-RSV NPs, while TGA and SEM showed thermal stability and post-drug coating morphological changes. Designed composite was also found to be biocompatible in nature in hemolytic testing, indicating their potential in Biomedical applications. After confirmation of PtNPs based nanocaompsite synthesis, they were examined for anti-bacterial, anti-oxidant, anti-inflammatory, and anti-cancer properties. Pt-BSA-RSV NPs showed higher concentration-dependent DPPH scavenging activity, which measured antioxidant capability. Enzyme inhibition tests demonstrated considerable anti-inflammatory activity against COX-2 and 15-LOX enzymes. In in vitro anticancer studies, Pt-BSA-RSV NPs effectively killed human ovarian cancer cells. This phenomenon was demonstrated to be facilitated by the acidic environment of cancer, as the drug release assay confirmed the release of RSV from the NP formulation in the acidic environment. Finally, Molecular docking also demonstrated that RSV has strong potential as an anti-oxidant, antibacterial, anti-inflammatory, and anticancer agent. Overall, in silico and in vitro investigations in the current study showed good medicinal applications for designed nanocomposites, however, further in-vivo experiments must be conducted to validate our findings.

Vesicular monoamine transporter-2 inhibitor JPC-141 prevents methamphetamine-induced dopamine toxicity and blocks methamphetamine self-administration in rats

Previous research has demonstrated therapeutic potential for VMAT2 inhibitors in rat models of methamphetamine use disorder. Here, we report on the neurochemical and behavioral effects of 1-(2-methoxyphenethyl)-4-phenethypiperazine (JPC-141), a novel analog of lobelane. JPC-141 potently inhibited (Ki = 52 nM) [3H]dopamine uptake by VMAT2 in striatal vesicles with 50 to 250-fold greater selectivity for VMAT2 over dopamine, norepinephrine and serotonin plasmalemma transporters. Also, JPC-141 was 57-fold more selective for inhibiting VMAT2 over [3H]dofetilide binding to hERG channels expressed by HEK293, suggesting relatively low potential for cardiotoxicity. When administered in vivo to rats, JPC-141 prevented the METH-induced reduction in striatal dopamine content when given either prior to or after a high dose of METH, suggesting a reduction in METH-induced dopaminergic neurotoxicity. In behavioral assays, JPC-141 decreased METH-stimulated locomotor activity in METH-sensitized rats at doses of JPC-141 which did not alter locomotor activity in the saline control group. Moreover, JPC-141 specifically decreased iv METH self-administration at doses that had no effect on food-maintained responding. These findings support the further development of VMAT2 inhibitors as pharmacotherapies for individuals with methamphetamine use disorder.

Is there a biochemical basis for purinergic P2X3 and P2X4 receptor antagonists to be considered as anti-seizure medications?

Patients with epilepsy require improved medications. Purinergic receptors were identified as late as 1976 and are slowly emerging as potential drug targets for the discovery of antiseizure medications. While compounds interacting with these receptors have been approved for use as medicines (e.g., gefapixant for cough) and continue to be explored for a number of diseases (e.g., pain, cancer), there have been no purinergic receptor antagonists that have been advanced for epilepsy. There are very few studies on the channel conducting receptors, P2X3 and P2X4, that suggest their possible role in seizure generation or control. However, the limited data available provides some compelling reasons to believe that they could be valuable antiseizure medication drug targets. The data implicating P2X3 and P2X4 receptors in epilepsy includes the role played by ATP in neuronal excitability and seizures, receptor localization, increased receptor expression in epileptic brain, the involvement of these receptors in seizure-associated inflammation, crosstalk between these purinergic receptors and neuronal processes involved in seizures (GABAergic and glutamatergic neurotransmission), and the significant attenuation of seizures and seizure-like activity with P2X receptor blockade. The discovery of new and selective antagonists for P2X3 and P2X4 receptors is ongoing, armed with new structural data to guide rational design. The availability of safe, brain-penetrant compounds will likely encourage the clinical exploration of epilepsy as a disease entity.

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

N- and s-substituted Pyrazolopyrimidines: A promising new class of potent c-Src kinase inhibitors with prominent antitumor activity

In this work, readily achievable synthetic pathways were utilized for construction of a library of N/S analogues based on the pyrazolopyrimidine scaffold with terminal alkyl or aryl fragments. Subsequently, we evaluated the anticancer effects of these novel analogs against the proliferation of various cancer cell lines, including breast, colon, and liver lines. The results were striking, most of the tested molecules exhibited strong and selective cytotoxic activity against the MDA-MB-231 cancer cell line; IC50 1.13 µM. Structure-activity relationship (SAR) analysis revealed that N-substituted derivatives generally enhanced the cytotoxic effect, particularly with aliphatic side chains that facilitated favorable target interactions. We also investigated apoptosis, DNA fragmentation, invasion assay, and anti-migration effects, and discussed their underlying molecular mechanisms for the most active compound 7c. We demonstrated that 7c N-propyl analogue could inhibit MDA-MB-231 TNBC cell proliferation by inducing apoptosis through the regulation of vital proteins, namely c-Src, p53, and Bax. In addition, our results also revealed the potential of these compounds against tumor metastasis by downregulating the invasion and migration modes. Moreover, the in vitro inhibitory effect of active analogs against c-Src kinase was studied and proved that might be the main cause of their antiproliferative effect. Overall, these compelling results point towards the therapeutic potential of these derivatives, particularly those with N-substitution as promising candidates for the treatment of TNBC type of breast cancer.