Screening Of Drug Candidates Research Articles

Intrinsic fluorescence hydrogels for ON/OFF screening of antidiabetic drugs: assessing α-glucosidase inhibition by acarbose.

Diabetes remains one of the most prevalent chronic diseases globally, significantly impacting mortality ratetables. The development of effective treatments for controlling glucose level in blood is critical to improve the quality of life of patients with diabetes. In this sense, smart optical sensors using hydrogels, responsive to external stimuli, have emerged as a revolutionary approach to diabetes care. In this study, changes in the optical properties of a hydrogel are employed for monitoring α-glucosidase activity, a critical enzyme involved in diabetes mellitus type II due to its role in breaking terminal α-glycosidic bonds, releasing α-glucose. The enzyme is encapsulated within a triazine-based hydrogel that exhibits intrinsic blue fluorescence. Upon hydrolysis of the substrate p-nitrophenyl-α-D-glucopyranoside (p-NPG) by α-glucosidase, the fluorescence is quenched due to the release of p-nitrophenol (PNP). However, when exposed to potential antidiabetic drugs, the enzyme's activity is inhibited, and the hydrogel's fluorescence remains intact. This ON/OFF fluorescence-based assay enables rapid screening of drug candidates by evaluating their ability to inhibit α-glucosidase enzymatic activity. Sensor optimization involves conducting swelling studies, fluorescent assays, reusability tests and a trial with a real antidiabetic drug. This innovative approach holds potential for enhancing antidiabetic drug screening and management, offering a more accessible and efficient solution compared to traditional biosensors.

Unraveling the Synergistic Neuroprotective Mechanism of Natural Drug Candidates Targeting TRPV1 and TRPM8 on an Ischemic Stroke.

The development of multitargeted drugs is urgent for ischemic stroke. TRPV1 and TRPM8 are important targets of ischemic stroke. Previous drug candidate screening has identified that muscone, l-borneol, and ferulic acid may target TRPV1 and TRPM8 for ischemic stroke. However, the mechanisms of these drug candidates on targets were ill-informed. Therefore, firstly, a tongue-tissue biosensor was constructed. It explored the activation or inhibition mechanisms of drug candidates targeting TRPV1 and TRPM8 in a near-physiological environment. It was found that muscone could specifically inhibit TRPM8 and selectively activate TRPV1, while l-borneol exhibited the opposite effect. It suggested a synergistic network between these two drug candidates. Furthermore, more selective protein biosensors were developed to delve deeper into the synergistic mechanisms. A strong synergistic effect of muscone and l-borneol was proved. Molecular docking revealed that the synergistic effect was caused by different action sites, respectively. Subsequently, the synergistic effect of muscone and l-borneol was further confirmed by hypoxic nerve injury models of Caenorhabditis elegans (C. elegans) and antithrombus and anti-ischemic models of zebrafish. Ultimately, through nontargeted metabolomics, it was found that muscone and l-borneol mainly regulated Ca2+ concentration and energy metabolism by pathways such as purine and amino acid metabolisms. In conclusion, this research identified critical targets and synergistic drug candidates for multitarget neuroprotection of ischemic stroke. In addition, it has systemically demonstrated the feasibility of the integration of tissue/protein biosensors and metabolomics for the research and development of multitarget drugs. Compared to other screening and validation methods for drugs and targets, the biosensors we developed not only achieved higher sensitivity and specificity in complex physiological environments, ensuring a wider detection range, but also greatly saved biological samples. Simultaneously, they could be extended to other complex systems, such as biomarker screening in clinical samples and exosomes isolated from stem cells.

Chemistry and biology of natural stilbenes: an update.

Covering: 2009 up to the end of 2023Stilbenes, an emblematic group of polyphenols, have attracted the attention of numerous researchers owing to their intriguing polycyclic architectures and diverse bioactivities. In this updated review, natural stilbenes were analysed, especially oligomeric stilbenes, which are an emblematic group of polyphenols that harbor intriguing polycyclic architectures and diverse bioactivities compared with those previously anticipated. Oligomeric stilbenes with unique skeletons comprise a large majority of natural stilbenes owing to their structural changes and different substitutions on the phenyl rings. These compounds can be promising sources of lead compounds for studying new drugs and medicines. In addition, the exploration of unusual structures of oligomeric stilbenes such as polyflavanostilbenes A and B, analysing their absolute stereochemistry, and improving their yield using synthetic biology methods have recently gained interest. This review provides a systematic overview of 409 new stilbenes, which were isolated and identified over time from January 2009 to December 2023, focusing on the classification and biomimetic syntheses of oligomeric stilbenes, in addition to presenting meaningful insights into their structural diversity and biological activities, which will inspire further investigations of biological activities, structure-activity relationships, and screening of drug candidates.

Evaluation of Vaccines and Therapeutics Against Marburg Virus in Nonhuman Primate Models.

Marburg virus (MARV) has caused sporadic outbreaks of severe hemorrhagic fever in Africa in humans and nonhuman primates (NHPs) and has the potential to be used as a biological weapon. Currently, there are no licensed vaccines or therapeutics to respond to outbreaks or deliberate misuse. Vaccine and therapeutic efficacy testing against MARV requires animal models that accurately mimic human disease. In vitro testing in cell culture cannot appropriately model the complex immunological host responses required to accurately predict efficacy in humans, which will ultimately be required for licensure of a medical countermeasure (MCM). While small animal models for MARV have been valuable for dissecting disease processes and the screening of vaccine and drug candidates, there are several caveats to their use including required adaptation of the virus, lack of host-specific reagents, or the need of an immunocompromised host. Conversely, the NHP MARV disease model addresses all shortcomings of small animal models and closely recapitulates all hallmark features of human disease. As such, NHPs have served as the "gold standard" for testing filovirus MCMs and will most likely be required for regulatory approval. Here, we describe the use of NHPs for vaccine and therapeutic evaluation against MARV.

Development and Use of DJ-1 Affinity Microcolumns to Screen and Study Small Drug Candidates for Parkinson’s Disease

BackgroundDJ-1 is a protein whose mutation causes rare heritable forms of Parkinson’s disease (PD) and is of interest as a target for treating PD and other disorders. This work used high performance affinity microcolumns to screen and examine the binding of small molecules to DJ-1, as could be used to develop new therapeutics or to study the role of DJ-1 in PD. Non-covalent entrapment was used to place microgram quantities of DJ-1 in an unmodified form within microcolumns, which were then used in multiple studies to analyze binding by model compounds and possible drug candidates to DJ-1. ResultsSeveral factors were examined in optimizing the entrapment method, including the addition of a reducing agent to maintain a reduced active site cysteine residue in DJ-1, the concentration of DJ-1 employed, and the entrapment times. Isatin was used as a known binding agent (dissociation constant, ∼2.0 μM) and probe for DJ-1 activity. This compound gave good retention on 2.0 cm × 2.1 mm inner diameter DJ-1 microcolumns made under the final entrapment conditions, with a typical retention factor of 14 and elution in ∼8 min at 0.50 mL/min. These DJ-1 microcolumns were used to evaluate the binding of small molecules that were selected in silico to bind or not to bind DJ-1. A compound predicted to have good binding with DJ-1 gave a retention factor of 122, an elution time of ∼15 min at 0.50 mL/min, and an estimated dissociation constant for this protein of 0.5 μM. SignificanceThese chromatographic tools can be used in future work to screen additional possible binding agents for DJ-1 or adapted for examining drug candidates for other proteins. This work represents the first time protein entrapment has been deployed with DJ-1, and it is the first experimental confirmation of binding to DJ-1 by a small lead compound selected in silico.

IPSC-derived human sensory neurons reveal a subset of TRPV1 antagonists as anti-pruritic compounds

Signaling interplay between the histamine 1 receptor (H1R) and transient receptor potential cation channel subfamily V member 1 (TRPV1) in mediating histaminergic itch has been well-established in mammalian models, but whether this is conserved in humans remains to be confirmed due to the difficulties in obtaining human sensory neurons (SNs) for experimentation. Additionally, previously reported species-specific differences in TRPV1 function indicate that use of human SNs is vital for drug candidate screening to have a higher chance of identifying clinically effective TRPV1 antagonists. In this study, we built a histamine-dependent itch model using peripheral SNs derived from human induced pluripotent stem cells (hiPSC-SNs), which provides an accessible source of human SNs for pre-clinical drug screening. We validated channel functionality using immunostaining, calcium imaging, and multielectrode array (MEA) recordings, and confirmed the interdependence of H1R and TRPV1 signalling in human SNs. We further tested the amenability of our model for pre-clinical studies by screening multiple TRPV1 antagonists in parallel, identifying SB366791 as a potent inhibitor of H1R activation and potential candidate for alleviating histaminergic itch. Notably, some of the results using our model corroborated with efficacy and side effect findings from human clinical trials, underscoring the importance of this species-specific platform. Taken together, our results present a robust in vitro human model for histaminergic itch, which can be used to further interrogate the molecular basis of human SN function as well as screen for TRPV1 activity-modifying compounds for a number of clinical indications.

Identify potential drug candidates within a high-quality compound search space.

The identification of potential effective drug candidates is a fundamental step in new drug discovery, with profound implications for pharmaceutical research and the healthcare sector. While many computational methods have been developed for such predictions and have yielded promising results, two challenges persist: (i) The cold start problem of new drugs, which increases the difficulty of prediction due to lack of historical data or prior knowledge. (ii) The vastness of the compound search space for potential drug candidates. In this study, we present a promising method that not only enhances the accuracy of identifying potential novel drug candidates but also refines the search space. Drawing inspiration from solutions to the cold start problem in recommender systems, we apply 'learning to rank' techniques to the field of new drug discovery. Furthermore, we propose using three similarity metrics to condense the compound search space into compact yet high-quality spaces, allowing for more efficient screening of potential drug candidates. Experimental results from two widely used datasets demonstrate that our method outperforms other state-of-the-art approaches in the new drug cold-start scenario. Additionally, we have verified that it is feasible to identify potential drug candidates within these high-quality compound search spaces. To our knowledge, this study is the first to address drug cold-start problem in such a confined space, potentially providing valuable insights and guidance for drug screening.

Druggability of Pharmaceutical Compounds Using Lipinski Rules with Machine Learning

In the field of pharmaceutical research, identifying promising pharmaceutical compounds is a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental criterion, but evaluating many compounds manually requires significant resources and time. However, the integration of computational techniques in drug discovery in its early stages has significantly transformed the pharmaceutical industry, enabling further efficient screening and selection of possible drug candidates. Therefore, this study explores RO5 using algorithms of Machine Learning (ML), offering a comprehensive method to predict the druggability of pharmaceutical compounds. The study developed, evaluated, and validated the performance metrics of multiple supervised machine learning models. The best model was used to build an application that can predict and classify potential drug candidates. The findings revealed promising capabilities across all models for drug classification. Among all the explored models, Random Forest (RF), Extreme Gradient Boost (XGBoost), and Decision Tree (DT) classifiers demonstrated exceptional performance, achieving near-perfect accuracy of 99.94%, 99.81% and 99.87% respectively. This highlights the robustness of ensemble learning methods in classifying compounds based on RO5 adherence. The comparative analysis of these models underscores the importance of considering balanced accuracy, precision, F1-score, recall, and Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) score, interpretability, and computational efficiency when choosing between ML algorithms in drug discovery. The DrugCheckMaster application was subsequently developed using the most predictive model and is now available on Render (https://capstone-project-dc7w.onrender.com/).

Graph Neural Networks and Molecular Docking as Two Complementary Approaches for Virtual Screening: A Case Study on Cruzain

AbstractMolecular docking is one of the most widely used techniques for virtual screening (VS) of potential drug candidates. Despite its popularity, docking accuracy is often limited due to the trade‐off between speed and precision required for screening large compound libraries. In the present work, we leverage graph convolutional networks (GCNs), a state‐of‐the‐art deep neural network architecture, to enhance docking capacity for prioritizing active compounds from a library of ∼200,000 compounds screened against Cruzain. We propose strategies to integrate both techniques into a single VS pipeline. By applying the GCN as a pre‐docking filter, the compound library was enriched with active molecules, resulting in higher hit rates in subsequent docking screenings. Additionally, to further enhance the docking performance, the GCN‐learned atomic embeddings were directly incorporated into the docking process through pharmacophoric restraints. Unlike common approaches that use deep learning (DL) scoring functions to rank pre‐generated docking poses, the approaches we propose here have the advantage that only compounds that passed the DL filters need to be screened by the more computationally demanding docking method. This work might serve as a proof of concept for combining deep learning and classical docking in drug discovery.

Enhancing drug-target interaction predictions in context of neurodegenerative diseases using bidirectional long short-term memory in male Swiss albino mice pharmaco-EEG analysis

Background and ObjectiveEmerging diseases like Parkinson or Alzheimer's, which are not curable, endanger human mental health and are challenging to research. Drug target interactions (DTI) are pivotal in the screening of candidate drugs and focus on a small pool of drug targets. Electroencephalogram shows the responses to psychotropic medicines in the brain bioelectric activity. Synaptic activity can be analyzed by using Local Field Potential recordings obtained from micro-electrodes implanted in the brain. The aim is to evaluate the effects of drug on brain bioelectric activity and increase the drug classification accuracy. The ultimate goal is to advance our understanding of how drugs affect synaptic activity and open the door to more focused treatment for neurodegenerative diseases. MethodsIn this study, Pharmaco-EEG recordings are processed using Advanced neural network models, particularly Convolutional Neural Networks, to assess the effects of medications. The five different medicines used in this study are Ephedrine, Fluoxetine, Kratom, Morphine, and Saline. The signals observed are local field potential signals. To overcome some limits of DTI prediction, we propose Bidirectional Long Short-Term Memory (LSTM) for the categorization of intracranial EEG (i-EEG) data, departing from standard approaches. Similar EEG patterns are presumably caused by drugs that work by homologous pharmacological pathways, producing similar psychotropic effects. To improve accuracy and reduce training loss, our study introduces a bidirectional LSTM model for classification along with Bayesian optimization ResultsHigh recall, precision, and F1-Scores, particularly a 95% F1-Score for morphine, ephedrine, fluoxetine, and saline, suggest good performance in predicting these drug classes. Kratom produces a somewhat lower recall of 94%, but a high F1-Score of 97% and perfect precision of 1.00. The weighted average F1-Score, macro average, and overall accuracy are all consistently high (around 97%), indicating that the model works well throughout the spectrum of drugs. ConclusionsImproved model performance was demonstrated by using a diversified dataset with five drug categories and bidirectional LSTM boosted with Bayesian optimization for hyperparameter tuning. From earlier limited-category models, it represents a substantial advancement.

Physiological liver microtissue 384-well microplate system for preclinical hepatotoxicity assessment of therapeutic small molecule drugs.

Hepatotoxicity can lead to the discontinuation of approved or investigational drugs. The evaluation of the potential hepatoxicity of drugs in development is challenging because current models assessing this adverse effect are not always predictive of the outcome in human beings. Cell lines are routinely used for early hepatotoxicity screening, but to improve the detection of potential hepatotoxicity, in vitro models that better reflect liver morphology and function are needed. One such promising model is human liver microtissues. These are spheroids made of primary human parenchymal and nonparenchymal liver cells, which are amenable to high throughput screening. To test the predictivity of this model, the cytotoxicity of 152 FDA (US Food & Drug Administration)-approved small molecule drugs was measured as per changes in ATP content in human liver microtissues incubated in 384-well microplates. The results were analyzed with respect to drug label information, drug-induced liver injury (DILI) concern class, and drug class. The threshold IC50ATP-to-Cmax ratio of 176 was used to discriminate between safe and hepatotoxic drugs. "vMost-DILI-concern" drugs were detected with a sensitivity of 72% and a specificity of 89%, and "vMost-DILI-concern" drugs affecting the nervous system were detected with a sensitivity of 92% and a specificity of 91%. The robustness and relevance of this evaluation were assessed using a 5-fold cross-validation. The good predictivity, together with the in vivo-like morphology of the liver microtissues and scalability to a 384-well microplate, makes this method a promising and practical in vitro alternative to 2D cell line cultures for the early hepatotoxicity screening of drug candidates.

Optimization of a Lethal, Combat-Relevant Model of Sterile Inflammation in Mice for Drug Candidate Screening.

Extensive trauma, commonly seen in wounded military Service Members, often leads to a severe sterile inflammation termed systemic inflammatory response syndrome (SIRS), which can progress to multiple organ dysfunction syndrome (MODS) and death. MODS is a serious threat to wounded Service Members, historically causing 10% of all deaths in trauma admissions at a forward deployed combat hospital. The importance of this problem will be exacerbated in large-scale combat operations, in which evacuation will be delayed and care of complex injuries at lower echelons of care may be prolonged. The main goal of this study was to optimize an existing mouse model of lethal SIRS/MODS as a therapeutic screening platform for the evaluation of immunomodulatory drugs. Male C57BL/6 mice were euthanized, and the bones and muscles were collected and blended into a paste termed tissue-bone matrix (TBX). The TBX at 12.5%-20% relative to body weight of each recipient mouse was implanted into subcutaneous pouches created on the dorsum of anesthetized animals. Mice were observed for clinical scores for up to 48 hours postimplantation and euthanized at the preset point of moribundity. To test effects of anesthetics on TBX-induced mortality, animals received isoflurane or ketamine/xylazine (K/X). In a separate set of studies, mice received TBX followed by intraperitoneal injection with 20 mg/kg or 40 mg/kg Eritoran or a placebo carrier. All Eritoran studies were performed in a blinded fashion. We observed that K/X anesthesia significantly increased the lethality of the implanted TBX in comparison to inhaled anesthetics. Although all the mice anesthetized with isoflurane and implanted with 12.5% TBX survived for 24 hours, 60% of mice anesthetized with K/X were moribund by 24 hours postimplantation. To mimic more closely the timing of lethal SIRS/MODS following polytrauma in human patients, we extended observation to 48 hours. We performed TBX dose-response studies and found that as low as 15%, 17.5%, and 20% TBX caused moribundity/mortality in 50%, 80%, and 100% mice, respectively, over a 48-hour time period. With 17.5% TBX, we tested if moribundity/mortality could be rescued by anti-inflammatory drug Eritoran, a toll-like receptor 4 antagonist. Neither 20 mg/kg nor 40 mg/kg doses of Eritoran were found to be effective in this model. We optimized a TBX mouse model of SIRS/MODS for the purpose of evaluating novel therapeutic interventions to prevent trauma-related pathophysiologies in wounded Service Members. Negative effects of K/X on lethality of TBX should be further evaluated, particularly in the light of widespread use of ketamine in treatment of pain. By mimicking muscle crush, bone fracture, and necrosis, the TBX model has pleiotropic effects on physiology and immunology that make it uniquely valuable as a screening tool for the evaluation of novel therapeutics against trauma-induced SIRS/MODS.

High-Throughput Assessment of Bile Salt Export Pump Inhibition Using RapidFire-MS and DESI-MS.

The bile salt export pump (BSEP) assay is widely used to evaluate the potential for drug-induced liver injury (DILI) early in the drug discovery process. While traditional liquid chromatography-mass spectrometry (LC-MS)-based approaches have been utilized for BSEP activity testing, they have intrinsic limitations in either throughput or the requirement for sample preparation and are difficult to scale up in order to screen drug candidates. Here we demonstrate the use of two different high-throughput MS methods based on solid-phase extraction (SPE) and desorption electrospray ionization (DESI) for high-throughput BSEP activity assessment in a label-free manner, with minimal needs for sample workup, at sampling rates of ∼11 and ∼5.5 s/sample, respectively. Both approaches were validated, compared, and successfully applied to the evaluation of 96 drug candidates for the inhibition of taurocholic acid (TCA) transport using BSEP vesicles.

The future of medicine: an outline attempt using state-of-the-art business and scientific trends.

Currently, there is a lot of discussion about the future of medicine. From research and development to regulatory approval and access to patients until the withdrawal of a medicinal product from the market, there have been many challenges and a lot of barriers to overcome. In parallel, the business environment changes rapidly. So, the big question is how the pharma ecosystem will evolve in the future. The current literature about the latest business and scientific evolutions and trends was reviewed. In the business environment, vast changes have taken place via the development of the internet as well as the Internet of Things. A new approach to production has emerged in a frame called Creative Commons; producer and consumer may be gradually identified in the context of the same process. As technology rapidly evolves, it is dominated by Artificial Intelligence (AI), its subset, Machine Learning, and the use of Big Data and Real-World Data (RWD) to produce Real-World Evidence (RWE). Nanotechnology is an inter-science field that gives new opportunities for the manufacturing of devices and products that have dimensions of a billionth of a meter. Artificial Neural Networks and Deep Learning (DL) are mimicking the use of the human brain, combining computer science with new theoretical foundations for complex systems. The implementation of these evolutions has already been initiated in the medicinal products' lifecycle, including screening of drug candidates, clinical trials, pharmacovigilance (PV), marketing authorization, manufacturing, and the supply chain. This has emerged as a new ecosystem which features characteristics such as free online tools and free data available online. Personalized medicine is a breakthrough field where tailor-made therapeutic solutions can be provided customized to the genome of each patient. Various interactions take place as the pharma ecosystem and technology rapidly evolve. This can lead to better, safer, and more effective treatments that are developed faster and with a more solid, data-driven and evidence-concrete approach, which will drive the benefit for the patient.

The French Medicinal Chemistry Society-SCT, an Historical Member of EFMC.

The French Society of Medicinal Chemistry or " Société de Chimie Thérapeutique " (SCT) was founded in 1966. Since its inception, its mission has been to promote knowledge in the main fields of pharmaceutical research and development, in particular the research and validation of biological targets of therapeutic interest, the screening, design and optimization of drug candidates, chemical biology, medicinal chemistry, pharmacokinetics, metabolism and toxicity. Since 1964, the Society has organized an annual international congress (RICT), and later thematic days for young researchers and workshops on specific topics. The SCT is also a member of the European Federation for Medicinal Chemistry (EFMC) and organized the International Symposium on Medicinal Chemistry (ISMC) in Nice in 2022. Several new trends can be identified in the activities of the SCT, such as the organization of regular webinars, but also the recent creation of the Young MedChem Forum, as well as the distribution of a newsletter reporting scientific achievements in the French community and abroad, and an improved presence on social networks. These trends are in line with the current changes in the field in terms of scientific progress, means of communication in the community and with the public and inclusiveness.

Application of Generative Artificial Intelligence in Predicting Membrane Partitioning of Drugs: Combining Denoising Diffusion Probabilistic Models and MD Simulations Reduces the Computational Cost to One-Third.

The optimal interaction of drugs with plasma membranes and membranes of subcellular organelles is a prerequisite for desirable pharmacology. Importantly, for drugs targeting the transmembrane lipid-facing sites of integral membrane proteins, the relative affinity of a drug to the bilayer lipids compared to the surrounding aqueous phase affects the partitioning, access, and binding of the drug to the target site. Molecular dynamics (MD) simulations, including enhanced sampling techniques such as steered MD, umbrella sampling (US), and metadynamics, offer valuable insights into the interactions of drugs with the membrane lipids and water in atomistic detail. However, these methods are computationally prohibitive for the high-throughput screening of drug candidates. This study shows that applying denoising diffusion probabilistic models (DDPMs), a generative AI method, to US simulation data reduces the computational cost significantly. Specifically, the models used only partial (one-third) data from the US simulations and reproduced the complete potential of mean force (PMF) profiles for three FDA-approved drugs (β2-adrenergic agonists) and ∼20 biologically relevant chemicals with known experimentally characterized bilayer locations. Intriguingly, the model can predict the solvation-free energies for partitioning and crossing the bilayer, preferred bilayer locations (low-energy well), and orientations of the ligands with high accuracy. The results indicate that DDPMs can be used to characterize the complete membrane partitioning profile of drug molecules using fewer umbrella sampling simulations at select positions along the bilayer normal (z-axis), irrespective of their amphiphilic-lipophilic-cephalophilic characteristics.

CXCL9, IL2RB, and SPP1, potential diagnostic biomarkers in the co-morbidity pattern of atherosclerosis and non-alcoholic steatohepatitis

Non-alcoholic steatohepatitis (NASH) is a hepatocyte inflammation based on hepatocellular steatosis, yet there is no effective drug treatment. Atherosclerosis (AS) is caused by lipid deposition in the endothelium, which can lead to various cardiovascular diseases. NASH and AS share common risk factors, and NASH can also elevate the risk of AS, causing a higher morbidity and mortality rate for atherosclerotic heart disease. Therefore, timely detection and diagnosis of NASH and AS are particularly important. In this study, differential gene expression analysis and weighted gene co-expression network analysis were performed on the AS (GSE100927) and NASH (GSE89632) datasets to obtain common crosstalk genes, respectively. Then, candidate Hub genes were screened using four topological algorithms and externally validated in the GSE43292 and GSE63067 datasets to obtain Hub genes. Furthermore, immune infiltration analysis and gene set variation analysis were performed on the Hub genes to explore the underlying mechanisms. The DGIbd database was used to screen candidate drugs for AS and NASH. Finally, a NASH model was constructed using free fatty acid-induced human L02 cells, an AS model was constructed using lipopolysaccharide-induced HUVECs, and a co-morbidity model was constructed using L02 cells and HUVECs to verify Hub gene expression. The result showed that a total of 113 genes common to both AS and NASH were identified as crosstalk genes, and enrichment analysis indicated that these genes were mainly involved in the regulation of immune and metabolism-related pathways. 28 candidate Hub genes were screened according to four topological algorithms, and CXCL9, IL2RB, and SPP1 were identified as Hub genes after in vitro experiments and external dataset validation. The ROC curves and SVM modeling demonstrated the good diagnostic efficacy of these three Hub genes. In addition, the Hub genes are strongly associated with immune cell infiltration, especially macrophages and γ–δ T cell infiltration. Finally, five potential therapeutic drugs were identified. has-miR-185 and hsa-miR-335 were closely related to AS and NASH. This study demonstrates that CXCL9, IL2RB, and SPP1 may serve as potential biomarkers for the diagnosis of the co-morbidity patterns of AS and NASH and as potential targets for drug therapy.

Recent advances in protein precipitation-based methods for drug-target screening

Drug targets are biological macromolecules that bind drug molecules in vivo. Therefore, the system-wide identification of drug targets plays a vital role in fully understanding the mechanism of drug action, efficacy, and side effects. The unbiased screening of drug targets may accelerate the process of drug discovery and candidate screening. Mass spectrometry is a key tool for large-scale protein identification and accurate quantification owing to its high acquisition speed, resolution, and sensitivity. Mass spectrometry-based proteomics has been widely used for drug-target screening. It can systematically identify the protein-target landscape of a drug and elucidate drug-protein interactions. Commonly used drug-target characterization methods, such as labeling-based affinity enrichment, require the chemical derivatization of drug molecules, which is not only time-consuming but may also affect the affinity of the drug towards its targets. Furthermore, the spatial effects of the derivatization groups may block interactions between the drug and its targets. Considering the disadvantages of affinity-enrichment methods, strategies that do not require chemical derivatization have received widespread attention. Proteins may undergo denaturation, unfolding, and precipitation under different conditions such as high temperatures, extreme pH, denaturants, and mechanical stress. Binding to small-molecule drugs may alter the folding balance of target proteins. The conformational stability of target proteins can be stabilized by binding with drugs, and protein-drug complexes are more resistant than free proteins to the precipitation induced by different conditions. Based on this mechanism, various large-scale drug-target identification methods using protein precipitation have been developed by combining proteomics and mass spectrometry analysis, including thermal proteome profiling and solvent-, mechanical stress-, and pH-induced protein precipitation. These methods have been successfully applied to the characterization of small-molecule drug targets. In this review, we describe the protein precipitation-based methods used for the high-throughput discovery of drug targets and elucidation of the interactions between drugs and proteins in the past decade. We also summarize the characteristics of each method and discuss their application potential in drug-efficacy evaluation and drug discovery.

Anti-aging and immunomodulatory role of caffeine in Drosophila larvae.

Drug repurposing is a promising approach to identify new pharmacological indications for drugs that have already been established. However, there is still a limitation in the availability of a high-throughput in vivo preclinical system that is suitable for screening and investigating new pharmacological indications. The aim of this study was to introduce the application of Drosophila larvae as an in vivo platform to screen drug candidates with anti-aging and immunomodulatory activities. To determine whether Drosophila larvae can be utilized for assessing anti-aging and immunomodulatory activities, phenotypical and molecular assays were conducted using wildtype and mutant lines of Drosophila. The utilization of mutant lines (PGRP-LBΔ and Psh[1];;ModSP[KO]) mimics the autoinflammatory and immunodeficient conditions in humans, thereby enabling a thorough investigation of the effects of various compounds. The phenotypical assay was carried out using survival and locomotor observation in Drosophila larvae and adult flies. Meanwhile, the molecular assay was conducted using the RT-qPCR method. In vivo survival analysis revealed that caffeine was relatively safe for Drosophila larvae and exhibited the ability to extend Drosophila lifespan compared to the untreated controls, suggesting its anti-aging properties. Further analysis using the RT-qPCR method demonstrated that caffeine treatment induced transcriptional changes in the Drosophila larvae, particularly in the downstream of NF-κB and JAK-STAT pathways, two distinct immune-related pathways homologue to humans. In addition, caffeine enhanced the survival of Drosophila autoinflammatory model, further implying its immunosuppressive activity. Nevertheless, this compound had minimal to no effect on the survival of Staphylococcus aureus-infected wildtype and immunodeficient Drosophila, refuting its antibacterial and immunostimulant activities. Overall, our results suggest that the anti-aging and immunosuppressive activities of caffeine observed in Drosophila larvae align with those reported in mammalian model systems, emphasizing the suitability of Drosophila larvae as a model organism in drug repurposing endeavors, particularly for the screening of newly discovered chemical entities to assess their immunomodulatory activities before proceedings to investigations in mammalian animal models.

Fluorescence-Guided Spatial Drug Screening in 3D Colorectal Cancer Spheroids.

The limited recapitulation of critical cancer features in 2D cultures causes poor translatability of preclinical results from in vitro assays to in vivo tumor models. This contributes to slow drug development with a low success rate. 3D cultures better recapitulate the tumor microenvironment, enabling more accurate predictions when screening drug candidates and improving the development of chemotherapeutics. Platinum (Pt) (IV) compounds are promising prodrugs designed to reduce the severe systemic toxicity of widely used Food and Drug Administration (FDA)-approved Pt(II) drugs such as cisplatin. Here, this work presents spatiotemporal evaluations in 3D colorectal cancer (CRC) spheroids of mitochondria-targeting Pt(IV) complexes. CRC spheroids provide a greater pathophysiological recapitulation of in vivo tumors than 2D cultures by a marked upregulation of the ABCG2 chemoresistance marker expression. Furthermore, new 3D-staining protocols are introduced to evaluate the real-time decrease in mitochondria membrane potential (ΔΨ) in CRC spheroids, and a Pt-sensing dye to quantify the Pt mitochondrial accumulation. Finally, this work demonstrates a correlation between in vitro results and the efficacy of the compounds in vivo. Overall, the CRC spheroids represent a fast and cost-effective model to assess the behavior of Pt compounds in vitro and predict their translational potential in CRC treatment.