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Although public–private collaboration abounds in addressing societal grand challenges, little is known about its performance relative to private-sector collaboration. Drawing from the innovation collaboration literature, and qualitative insights from interviews and contracts, we argue that collaboration risk impairs task performance of public–private versus private–private collaboration. We attribute this to two collaboration risk mechanisms: cooperation problems arising from misaligned social versus economic incentives, and coordination problems due to differences in organizational governance and processes. We test our hypotheses using the development history of 2,496 antimicrobial drugs from 1995–2019. After accounting for endogenous selection into collaboration, we find that public–private collaboration is less effective than private–private collaboration. However, this performance gap reduces for innovation tasks under high technological uncertainty (i.e., in the discovery stage and for developing new drug action mechanisms). To probe the distinctive cooperation and coordination mechanisms, we also examine innovation task efficiency. Findings reveal a wider gap in task effectiveness but not efficiency between public–private and private–private collaboration, suggesting more significant cooperation than coordination problems. Our theoretical and practical insights pertain to whether, when, and how public–private and private–private collaboration offer viable pathways for socially beneficial innovation tasks. [ABSTRACT FROM AUTHOR]

OBJECTIVE: This study aims to evaluate published clinical trials of ramucirumab to assess the risk/benefit profile and burden over time for patients. BACKGROUND: The burden of oncologic drug development on patients paired with increasing clinical trial failure rates emphasizes the need for reform of drug development. Identifying and addressing patterns of excess burden can guide policy, ensure evidence-based protections for trial participants, and improve medical decision-making. METHODS: On May 25, 2023 a literature search was performed on Pubmed/MEDLINE, Embase, Cochrane CENTRAL, and ClinicalTrials.gov for clinical trials using ramucirumab as monotherapy or in combination with other interventions for cancer treatment. Authors screened titles and abstracts for potential inclusion in a masked, duplicate fashion. Following data screening, data was extracted in a masked, duplicate fashion. Trials were classified as positive when meeting their primary endpoint and safety, negative or indeterminate. RESULTS: Ramucirumab was initially approved for gastric cancer but has since been tested in 20 cancers outside of its FDA approved indications. In our analysis of ramucirumab trials, there were a total of 10,936 participants and 10,303 adverse events reported. Gains in overall survival and progression-free survival for patients were 1.5 and 1.2 months, respectively. FDA-approved indications have reported more positive outcomes in comparison to off-label indications. CONCLUSION: We found that FDA-approved indications for ramucirumab had better efficacy outcomes than non-approved indications. However, a concerning number of adverse events were observed across all trials assessed. Participants in ramucirumab randomized controlled trials saw meager gains in overall survival when evaluated against a comparison group. Clinicians should carefully weigh the risks associated with ramucirumab therapy given its toxicity burden and poor survival gains.

Cardiovascular organ-on-a-chip (OoC) devices are composed of engineered or native functional tissues that are cultured under controlled microenvironments inside microchips. These systems employ microfabrication and tissue engineering techniques to recapitulate human physiology. This review focuses on human OoC systems to model cardiovascular diseases, to perform drug screening, and to advance personalized medicine. We also address the challenges in the generation of organ chips that can revolutionize the large-scale application of these systems for drug development and personalized therapy.

Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.

Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institutes L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

BackgroundAccording to the new AASLD Practice Guidance, all patients with primary sclerosing cholangitis (PSC) should be considered for participation in clinical trials. However, PSC's rarity has posed challenges to characterizing patient interest in trial participation and identifying predictors of patient willingness to participate in drug trials.MethodsPSC Partners Seeking a Cure developed the "Our Voices" survey to inform the development of the Externally-Led Patient-Focused Drug Development Forum, an FDA initiative to capture patient experiences and perspectives on drug development.ResultsOf 797 survey respondents from over 30 countries, 536 (67%) identified slowing disease progression as the most important outcome. Eighty-nine percent identified their hepatologist/gastroenterologist as someone they would approach for advice about trials. Although 61% reported being willing to participate in drug trials, only 26% had ever been asked to participate. Notable barriers to trial involvement included unknown long-term risks (71%), long travel times to the study center (32%), and a liver biopsy requirement (27%). On multivariable logistic regression, pruritus (OR 1.62, 95% CI: 1.09-2.40, p = 0.017) was positively associated with willingness to participate in disease-modifying therapy trials, while jaundice (OR 0.34, 95% CI: 0.19-0.61, p < 0.001) and inflammatory bowel disease (OR 0.64, 95% CI: 0.42-0.98, p = 0.038) were negatively associated. Pruritus (OR 2.25, 95% CI: 1.50-3.39, p < 0.001) was also independently associated with willingness to participate in symptom treatment trials.ConclusionsMost patients with PSC report interest in participating in clinical trials, but few have been asked to participate. Referral of patients with PSC by their hepatologist/gastroenterologist to clinical trials and patient education on trial participation are vital to closing the gap between trial interest and participation. Pruritus may serve as a key indicator of patient interest in trial participation.

Atherosclerotic coronary artery disease (CAD) is the causal pathological process driving most major adverse cardiovascular events (MACE) worldwide. The complex development of atherosclerosis manifests as intimal plaque which occurs in the presence or absence of traditional risk factors. There are numerous effective medications for modifying CAD but new pharmacologic therapies require increasingly large and expensive cardiovascular outcome trials to assess their potential impact on MACE and to obtain regulatory approval. For many disease areas, nearly a half of drugs are approved by the U.S. Food & Drug Administration based on beneficial effects on surrogate endpoints. For cardiovascular disease, only low-density lipoprotein cholesterol and blood pressure are approved as surrogates for cardiovascular disease. Valid surrogates of CAD are urgently needed to facilitate robust evaluation of novel, beneficial treatments and inspire investment. Fortunately, advances in non-invasive imaging offer new opportunity for accelerating CAD drug development. Coronary computed tomography angiography (CCTA) is the most advanced candidate, with the ability to measure accurately and reproducibly characterize the underlying causal disease itself. Indeed, favourable changes in plaque burden have been shown to be associated with improved outcomes, and CCTA may have a unique role as an effective surrogate endpoint for therapies that are designed to improve CAD outcomes. CCTA also has the potential to de-risk clinical endpoint-based trials both financially and by enrichment of participants at higher likelihood of MACE. Furthermore, total non-calcified, and high-risk plaque volume, and their change over time, provide a causally linked measure of coronary artery disease which is inextricably linked to MACE, and represents a robust surrogate imaging biomarker with potential to be endorsed by regulatory authorities. Global consensus on specific imaging endpoints and protocols for optimal clinical trial design is essential as we work towards a rigorous, sustainable and staged pathway for new CAD therapies.

Autophagy is a form of programmed cell degradation that enables the maintenance of homeostasis in response to extracellular stress stimuli. Autophagy is primarily activated by starvation and mediates the degradation, removal, or recycling of cell cytoplasm, organelles, and intracellular components in eukaryotic cells. Autophagy is also involved in the pathogenesis of human diseases, including several cancers. Human uveal melanoma (UM) is the primary intraocular malignancy in adults and has an extremely poor prognosis; at present there are no effective therapies. Several studies have suggested that autophagy is important in UM. By understanding the mechanisms of activation of autophagy in UM it may be possible to develop biomarkers to provide more definitive disease prognoses and to identify potential drug targets for the development of new therapeutic strategies. This article reviews the current information regarding autophagy in UM that could facilitate biomarker and drug development.

The Whey acidic protein four-disulfide core (WFDC) protein family consists of proteins with one or more WFDC domains which are ubiquitously expressed throughout the body of human and perform a wide range of functions, including antiprotease, antibacterial, and immunomodulatory functions. Aberrant expression of WFDC proteins is associated with human diseases. However, review on the WFDC protein family is limited and insufficient. Furthermore, a systematic summary of the underlying mechanisms of WFDC protein activity is lacking. In this review, we give a summary of the structural basis and molecular function of these proteins and review the immune regulatory mechanisms and signaling pathways of WFDC proteins in the development of certain diseases. Furthermore, we discuss the diagnostic and prognostic potential of multiple WFDC proteins in the aforementioned conditions, as well as their prospective use. At last, we also discuss the progress of WFDC protein in clinical trials and put forward some research difficulties and the directions of follow-up research. Our review highlights the functional diversity and clinical significance of WFDC proteins family, while providing potential targets for drug development and innovative therapeutic strategies, this review lays the foundation and direction for future research on WFDC proteins. [ABSTRACT FROM AUTHOR]

Biofilm formation by Staphylococcus aureus is a major challenge in clinical settings due to its role in persistent infections. The AgrA protein, a key regulator in biofilm development, is a promising target for therapeutic intervention. This study investigates the antibiofilm potential of halogenated phenazine compounds by targeting AgrA and explores their molecular interactions to provide insights for drug development. We employed molecular docking, molecular dynamics simulations, and computational mutagenesis to evaluate the binding of halogenated phenazine compounds (C1 to C7, HP, and HP‐14) to AgrA. Binding free energy analysis was performed to assess the affinity of these compounds for the AgrA‐DNA complex. Additionally, the impact of these compounds on AgrA's structural conformation and salt bridge interactions was examined. The binding‐free energy analysis revealed that all compounds enhance binding affinity compared to the Apo form of AgrA, which has a ΔGbind of −80.75 kcal/mol. The strongest binding affinities were observed with compounds C7 (−113.84 kcal/mol), HP‐14 (−115.23 kcal/mol), and HP (−112.28 kcal/mol), highlighting their effectiveness. Molecular dynamics simulations demonstrated that these compounds bind at the hydrophobic cleft of AgrA, disrupting essential salt bridge interactions between His174‐Glu163 and His174‐Glu226. This disruption led to structural conformational changes and reduced DNA binding affinity, aligning with experimental findings on biofilm inhibition. The halogenated phenazine compounds effectively inhibit biofilm formation by targeting AgrA, disrupting its DNA‐binding function. The study supports the potential of these compounds as antibiofilm agents and provides a foundation for rational drug design targeting the AgrA‐DNA interaction. Future research should focus on further optimizing these lead compounds and exploring additional active sites on AgrA to develop novel treatments for biofilm‐associated infections. [ABSTRACT FROM AUTHOR]

Background: Cell type prediction is crucial to cell type identification of genomics, cancer diagnosis and drug development, and it can solve the time-consuming and difficult problem of cell classification in biological experiments. Therefore, a computational method is urgently needed to classify and predict cell types using single-cell Hi-C data. In previous studies, there is a lack of convenient and accurate method to predict cell types based on single-cell Hi-C data. Deep neural networks can form complex representations of single-cell Hi-C data and make it possible to handle the multidimensional and sparse biological datasets. Results: We compare the performance of SCANN with existing methods and analyze the model by using five different evaluation metrics. When using only ML1 and ML3 datasets, the ARI and NMI values of SCANN increase by 14% and 11% over those of scHiCluster respectively. However, when using all six libraries of data, the ARI and NMI values of SCANN increase by 63% and 88% over those of scHiCluster respectively. These findings show that SCANN is highly accurate in predicting the type of independent cell samples using single-cell Hi-C data. Conclusions: SCANN enhances the training speed and requires fewer resources for predicting cell types. In addition, when the number of cells in different cell types was extremely unbalanced, SCANN has higher stability and flexibility in solving cell classification and cell type prediction using the single-cell Hi-C data. This predication method can assist biologists to study the differences in the chromosome structure of cells between different cell types. [ABSTRACT FROM AUTHOR]

Introduction: Anticoagulants have been discovered and developed over past 100 years. At the beginning unfractionated heparin found its applications, just later to fade into the background of newer and more effective drugs. Patients have been treated with more and more progressive medications -- Low-Molecule-Weight Heparin (LMWH), Vitamin K Antagonists (VKA) and Novel Oral Anticoagulants (NOAC). All of them have many indications but most importantly they are used in prophylaxis and treatment of venous thromboembolism (VTE). Scientists and physicians have been working for years to come up with the perfect drug that has fewer contraindications, side effects and doesn't need continuous anticoagulant monitoring. As for today, deemed a turning point in anticoagulant therapy are inhibitors of factor XI. It is a ground-breaking innovation as it ensures high prevention of thrombotic episodes and guarantees intact physiological hemostasis. Current State of Knowledge: The coagulation cascade and molecules part taking in that have been discovered and described extensively and in detail. Historically, the demand for anticoagulants was always a burning subject. A huge progress has been made in the last one hundred years. Treatment which clinicians are using currently is a combination of drugs used in the past and the novel forms. Unfractionated heparin is the oldest out of all and despite its simple action mechanism and adverse effects, is still a requested drug. Its lighter form, low-molecule-weight-heparin (LMWH) is an enhancement to the previous treatment due to its higher bioavailability and fewer side effects. Vitamin K antagonists (VKAs) are widely spread in medical environment thanks to their expanded mechanism of action, oral administration and reversibility of their overdose, as well as their well-developed anti-side effect therapy. Novel Oral Anticoagulants (NOACs) have been introduced to the market about 10 years ago. In spite of NOACs short period of clinical use, they were a huge change to the previous treating methods. No need of constant checking the coagulation times was a great convenience to both patients and doctors. Currently, the newest innovation in anticoagulant therapy are inhibitors of factor XI. Even though it still undergoes clinical trials, the outcome is promising for the future. Summary: The present article discusses history of anticoagulant drugs, their mechanism of action and usage but also focuses on the recent perspectives and developments as new anticoagulant drugs are being put to the test in therapeutic trials. The review underlines the importance and a big demand for improvements in old therapeutic methods and exploring the new, more suitable ones. [ABSTRACT FROM AUTHOR]

The identification of potential interactions and relationships between diseases and drugs is significant in public health care and drug discovery. As we all know, experimenting to determine the drug-disease interactions is very expensive in both time and money. However, there are still many drug-disease associations that are still undiscovered and potential. Therefore, the development of computational methods to explore the relationship between drugs and diseases is very important and essential. Many computational methods for predicting drug-disease associations have been developed based on known interactions to learn potential interactions of unknown drug-disease pairs. In this paper, we propose 3 new main groups of meta-paths based on the heterogeneous biological network of drug-protein-disease objects. For each meta-path, we design a machine learning model, then an integrated learning method is formed by these models. We evaluated our approach on 3 standard datasets which are DrugBank, OMIM, and Gottlieb's dataset. Experimental results demonstrate that the proposed method is better than some recent methods such as EMP-SVD, LRSSL, MBiRW, MPG-DDA, SCMFDD,... in some measures such as AUC, AUPR, and F1-score. [ABSTRACT FROM AUTHOR]

Inhibiting α‐glucosidase, a key enzyme in carbohydrate hydrolysis, can potentially control diabetes mellitus. Although commercially available drugs effectively lower blood glucose levels to mitigate hyperglycemia, their associated gastrointestinal side effects necessitate the exploration of explicit inhibitors with minimal side effects. We studied a library of 80 secondary metabolites ascribed to their antidiabetic properties and screened the most potent inhibitors using a comprehensive in silico approach, including molecular docking, DFT study, MD simulation, PCA, MMPBSA calculation, and pharmacokinetic analysis. Scolopianate A and ponasterone A emerged as potent α‐glucosidase inhibitors, forming hydrogen bonds with the catalytic diad and exhibiting prominent binding affinities of −8 kcal/mol. Further analyses encompassed significant reactivity at the catalytic site and stable protein‐ligand complexes, as asserted by 100 ns RMSD, RMSF, Rg, SASA, H‐bond, and PCA‐based free energy landscape analysis. The subsequent analysis of MMPBSA indicating a higher binding free energy of these compounds compared to reference ligand, acarbose further strengthened the validity of these findings. Furthermore, drug‐like behavior and favorable ADMET profiles affirmed scolopianate A and ponasterone A as robust α‐glucosidase inhibitors. These findings highlight their promise for future drug development targeting α‐glucosidase inhibition in diabetes management; however, more research needs to be conducted. [ABSTRACT FROM AUTHOR]

Enzyme reactions have numerous applications in diverse disciplines of science like chemistry, biology and biomechanics. In this study, we examine the role and act of enzymes in chemical reactions which is considered in the frame of fractional order model. The proposed model includes system of four equations which are studied via Caputo fractional operator. The systems of non-linear equations are evaluated by a semi-analytical approach called q -homotopy analysis transform method. The uniqueness and existence of the solutions has been investigated through fixed point theorem. The solutions of the proposed model are achieved through the considered method and the obtained outcomes are in the form of series which shows rapid convergence. The solutions are computed and graphs are plotted for the obtained results using mathematica software. The achieved results by the proposed method are unique and illustrate the significant dynamics of the considered model via 3D plots and graphs. The results of this study demonstrate the importance and effectiveness of projected derivative and technique in the analysis of time dependent fractional mathematical models. This study also gives an idea to extend the applications of enzymatic reactions in drug development, bio mechanics, and chemical reactions in various cellular metabolisms. Also, enzymatic reactions have a vital role in the fields of the food industry for processing food, in biotechnology for the manufacture of biofuels, and in metabolic engineering to design metabolic pathways. [ABSTRACT FROM AUTHOR]

Background: Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease. Methods: In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations. Results: Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated. Conclusions: Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases. [ABSTRACT FROM AUTHOR]

Many drugs can act on multiple targets or disease pathways, regardless of their original purpose. Drug repurposing involves reevaluating existing compounds for new medical uses. This can include repositioning approved drugs, redeveloping unapproved drugs, or repurposing any chemical, nutraceutical, or biotherapeutic product for new applications. Traditional drug development is slow, expensive, and has high failure rates. Drug repurposing can speed up the process, costing less and saving time. This approach can save 6–7 years of early-stage research time. Drug repurposing benefits from existing compounds with optimized structures and approved for clinical use with associated structure-activity relationship publications, supporting the development of new effective compounds. Drug repurposes can now utilize advanced in silico screening enabled by artificial intelligence (AI) and sophisticated tissue and organ-level in vitro models. These models more accurately replicate human physiology and improve the selection of existing drugs for further pre-clinical testing and, eventually, clinical trials for new indications. This mini-review discusses some examples of drug repurposing and novel strategies for further development of compounds for targets of the arachidonic acid cascade. In particular, we will delve into the prospect of repurposing antiplatelet agents for cancer prevention and addressing the emerging noncanonical functionalities of 5-lipoxygenase, potentially for leukemia therapy. [ABSTRACT FROM AUTHOR]

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification. Using a new computational pipeline for identification of drug-selective transcriptomic responses and FAERS data, the authors identified potential pathways and genomic variants indicative of cancer drug cardiotoxicity in iPSC-derived cardiomyocytes. [ABSTRACT FROM AUTHOR]

Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties early in drug development is essential for selecting compounds with optimal pharmacokinetics and minimal toxicity. Existing ADMET-related benchmark sets are limited in utility due to their small dataset sizes and the lack of representation of compounds used in drug discovery projects. These shortcomings hinder their application in model building for drug discovery. To address this issue, we propose a multi-agent data mining system based on Large Language Models that effectively identifies experimental conditions within 14,401 bioassays. This approach facilitates merging entries from different sources, culminating in the creation of PharmaBench. Additionally, we have developed a data processing workflow to integrate data from various sources, resulting in 156,618 raw entries. Through this workflow, we constructed PharmaBench, a comprehensive benchmark set for ADMET properties, which comprises eleven ADMET datasets and 52,482 entries. This benchmark set is designed to serve as an open-source dataset for the development of AI models relevant to drug discovery projects. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases are associated with chronic neuroinflammation in the brain, which can result in microglial phagocytosis of live synapses and neurons that may contribute to cognitive deficits and neuronal loss. The microglial P2Y6 receptor (P2Y6R) is a G-protein coupled receptor, which stimulates microglial phagocytosis when activated by extracellular uridine diphosphate, released by stressed neurons. Knockout or inhibition of P2Y6R can prevent neuronal loss in mouse models of Alzheimer's disease (AD), Parkinson's disease, epilepsy, neuroinflammation and aging, and prevent cognitive deficits in models of AD, epilepsy and aging. This review summarises the known roles of P2Y6R in the physiology and pathology of the brain, and its potential as a therapeutic target to prevent neurodegeneration and other brain pathologies. [ABSTRACT FROM AUTHOR]

This study describes a complex human in vitro model for evaluating anti‐inflammatory drug response in the alveoli that may contribute to the reduction of animal testing in the pre‐clinical stage of drug development. The model is based on the human alveolar epithelial cell line Arlo co‐cultured with macrophages differentiated from the THP‐1 cell line, creating a physiological biological microenvironment. To mimic the three‐dimensional architecture and dynamic expansion and relaxation of the air‐blood‐barrier, they are grown on a stretchable microphysiological lung‐on‐chip. For validating the in vitro model, three different protocols have been developed to demonstrate the clinically established anti‐inflammatory effect of glucocorticoids to reduce certain inflammatory markers after different pro‐inflammatory stimuli: (1) an inflammation caused by bacterial LPS (lipopolysaccharides) to simulate an LPS‐induced acute lung injury measured best with cytokine IL‐6 release; (2) an inflammation caused by LPS at ALI (air‐liquid interface) to investigate aerosolized anti‐inflammatory treatment, measured with chemokine IL‐8 release; and (3) an inflammation with a combination of human inflammatory cytokines TNFα and IFNγ to simulate a critical cytokine storm leading to epithelial barrier disruption, where the eventual weakening or protection of the epithelial barrier can be measured. In all cases, the presence of macrophages appeared to be crucial to mediating inflammatory changes in the alveolar epithelium. LPS induction led to inflammatory changes independently of stretch conditions. Dynamic stretch, emulating breathing‐like mechanics, was essential for in vitro modeling of the clinically relevant outcome of epithelial barrier disruption upon TNFα/IFNγ‐induced inflammation. [ABSTRACT FROM AUTHOR]

Motivation Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining representations that encompass these varied structures is crucial for a holistic understanding of molecules in drug-related contexts. Results In this study, we introduce an innovative multi-view contrastive learning framework for molecular representation, denoted as MolMVC. Initially, we use a Transformer encoder to capture 1D sequence information and a Graph Transformer to encode the intricate 2D and 3D structural details of molecules. Our approach incorporates a novel attention-guided augmentation scheme, leveraging prior knowledge to create positive samples tailored to different molecular data views. To align multi-view molecular positive samples effectively in latent space, we introduce an adaptive multi-view contrastive loss (AMCLoss). In particular, we calculate AMCLoss at various levels within the model to effectively capture the hierarchical nature of the molecular information. Eventually, we pre-train the encoders via minimizing AMCLoss to obtain the molecular representation, which can be used for various down-stream tasks. In our experiments, we evaluate the performance of our MolMVC on multiple tasks, including molecular property prediction (MPP), drug-target binding affinity (DTA) prediction and cancer drug response (CDR) prediction. The results demonstrate that the molecular representation learned by our MolMVC can enhance the predictive accuracy on these tasks and also reduce the computational costs. Furthermore, we showcase MolMVC's efficacy in drug repositioning across a spectrum of drug-related applications. Availability and implementation The code and pre-trained model are publicly available at https://github.com/Hhhzj-7/MolMVC. [ABSTRACT FROM AUTHOR]

Proteasomes are essential molecular machines responsible for the degradation of proteins in eukaryotic cells. Altered proteasome activity has been linked to neurodegeneration, auto-immune disorders and cancer. Despite the relevance for human disease and drug development, no method currently exists to monitor proteasome composition and interactions in vivo in animal models. To fill this gap, we developed a strategy based on tagging of proteasomes with promiscuous biotin ligases and generated a new mouse model enabling the quantification of proteasome interactions by mass spectrometry. We show that biotin ligases can be incorporated in fully assembled proteasomes without negative impact on their activity. We demonstrate the utility of our method by identifying novel proteasome-interacting proteins, charting interactomes across mouse organs, and showing that proximity-labeling enables the identification of both endogenous and small-molecule-induced proteasome substrates. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is a neurodegenerative condition characterized by the loss of dopaminergic neurons and the accumulation of Lewy-body protein aggregates containing misfolded α-synuclein (α-syn) in a phosphorylated form. The lack of effective models for drug screens has hindered drug development studies for PD. However, the recent development of in vitro brain-like organoids provides a new opportunity for evaluating therapeutic agents to slow the progression of this chronic disease. Methods: In this study, we used a 3D brain-like organoid model to investigate the potential of repurposing Tilorone, an anti-viral drug, for impeding the propagation of α-synucleinopathy. We assessed the effect of Tilorone on the uptake of fluorescently labeled α-syn preformed fibrils (sPFF) and sPFF-induced apoptosis using confocal microscopy. We also examined Tilorone's impact on the phosphorylation of endogenous α-syn induced by pathogenic sPFF by immunoblotting midbrain-like organoid extracts. Additionally, quantitative RT-PCR and proteomic profiling of sPFF-treated organoids were conducted to evaluate the global impact of Tilorone treatment on tissue homeostasis in the 3D organoid model. Results: Tilorone inhibits the uptake of sPFF in both mouse primary neurons and human midbrain-like organoids. Tilorone also reduces the phosphorylation of endogenous α-syn induced by pathogenic α-syn fibrils and mitigates α-syn fibril-induced apoptosis in midbrain-like organoids. Proteomic profiling of fibril-treated organoids reveals substantial alterations in lipid homeostasis by α-syn fibrils, which are reversed by Tilorone treatment. Given its safety profile in clinics, Tilorone may be further developed as a therapeutic intervention to alleviate the propagation of synucleinopathy in PD patients. [ABSTRACT FROM AUTHOR]

In the post-antibiotic era, antivirulence therapies are becoming refractory to the clinical application of existing antimicrobial regimens. Moreover, in an attempt to explore alternate intervention strategies, drug repurposing is gaining attention over development of novel drugs/antimicrobials. With the prevalence of multidrug resistance and high medical burden associated with Pseudomonas aeruginosa, there is an urgent need to devise novel therapeutics to combat this bacterial pathogen. In this context, the present study was undertaken to scrutinize the anti-quorum sensing (QS) and antivirulence potential of commonly consumed drugs such as fexofenadine (FeX), ivermectin (IvM), nitrofurantoin (NiT), levocetrizine (LvC), atorvastatin (AtS), and aceclofenac (AcF), against P. aeruginosa. The methodology involved assessment of antibacterial activity against P. aeruginosa PAO1 and quorum quenching (QQ) potential using Agrobacterium tumefaciens NTL4 biosensor strain. The antivirulence prospects were investigated by estimating the production of hallmark virulence factors in P. aeruginosa accompanied by molecular docking to predict drug associations with the QS receptors. Interestingly, all the drugs harbored antibacterial, anti-QS, and antivirulence potential in vitro, which consequently disrupted QS circuits and attenuated pseudomonal virulence phenotypically by significantly lowering the production of pyocyanin, hemolysin, pyochelin, and total bacterial protease in vitro. Moreover, the findings were validated by computational studies that predicted strong molecular interactions between the test drugs and QS receptors of P. aeruginosa. Hence, this study is the first to suggest the prospect of repurposing FeX, IvM, NiT, LvC, AtS, and AcF against P. aeruginosa. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common neurodegenerative disorder, characterized pathologically by extracellular deposition of β-amyloid (Aβ) into senile plaques and intracellular accumulation of hyperphosphorylated tau (pTau) as neurofibrillary tangles. Clinically, AD patients show memory deterioration with varying cognitive dysfunctions. The exact molecular mechanisms underlying AD are still not fully understood, and there are no efficient drugs to stop or reverse the disease progression. In this review, we first provide an update on how the risk factors, including APOE variants, infections and inflammation, contribute to AD; how Aβ and tau become abnormally accumulated and how this accumulation plays a role in AD neurodegeneration. Then we summarize the commonly used experimental models, diagnostic and prediction strategies, and advances in periphery biomarkers from high-risk populations for AD. Finally, we introduce current status of development of disease-modifying drugs, including the newly officially approved Aβ vaccines, as well as novel and promising strategies to target the abnormal pTau. Together, this paper was aimed to update AD research progress from fundamental mechanisms to the clinical diagnosis and therapies. [ABSTRACT FROM AUTHOR]

Introduction: Qualitative research methods can be used to obtain a deeper understanding of patient experience by collecting information in the patients’ own words about their encounters, perspectives, and feelings. In this study, patients with schizophrenia were interviewed to capture their voice and to complement the quantitative data typically obtained in clinical trials. Methods: Semi-structured exit interviews were conducted with 41 patients who completed or prematurely discontinued from a phase 3, open-label trial (NCT02873208). The interview guide included open-ended questions on current and prior disease burden, symptoms, quality of life, and treatment experiences. Steps taken to reduce interview stress and secure the validity of data included interviewer sensitivity training specific to mental health conditions and schizophrenia, use of in-person interviews whenever possible and use of videoconferencing for remote interviews to promote trust and comfort, and working closely with clinical site staff to identify patient eligibility and willingness to participate. Transcripts based on audio recordings were content coded and analyzed using thematic analysis; a post-hoc quantitative content analysis was conducted. Results: Patients reported that the symptoms of schizophrenia negatively impacted their work, relationships, self-esteem, emotional health, and daily activities. Most patients had positive experiences with medications that alleviated hallucinations, depression, and anxiety. However, side effects of medications were associated with negative impacts on physical, emotional, behavioral, and cognitive health. Lack of energy/drowsiness, weight gain, mood changes, and involuntary movements were the most common side effects reported with the use of antipsychotic medications. Patients reported unmet treatment needs related to better symptom control and to improved social and physical functioning. Conclusion: Collection of qualitative information within a schizophrenia clinical development process provides value and insights into patients’ views on burden of illness, experiences with previous medications, and experiences following participation in a clinical trial and can inform design for future studies. [ABSTRACT FROM AUTHOR]

Background: Chemicalsmay lead to acute liver injuries, posing a serious threat to human health. Achieving the precise safety profile of a compound is challenging due to the complex and expensive testing procedures. In silico approaches will aid in identifying the potential risk of drug candidates in the initial stage of drug development and thus mitigating the developmental cost. Methods: In current studies, QSAR models were developed for hepatotoxicity predictions using the ensemble strategy to integrate machine learning (ML) and deep learning (DL) algorithms using various molecular features. A large dataset of 2588 chemicals and drugswas randomly divided into training (80%) and test (20%) sets, followed by the training of individual base models using diverse machine learning or deep learning based on three different kinds of descriptors and fingerprints. Feature selection approaches were employed to proceed with model optimizations based on the model performance. Hybrid ensemble approaches were further utilized to determine the method with the best performance. Results: The voting ensemble classifier emerged as the optimal model, achieving an excellent prediction accuracy of 80.26%, AUC of 82.84%, and recall of over 93% followed by bagging and stacking ensemble classifiers method. The model was further verified by an external test set, internal 10-fold cross-validation, and rigorous benchmark training, exhibiting much better reliability than the published models. Conclusion: The proposed ensemble model offers a dependable assessment with a good performance for the prediction regarding the risk of chemicals and drugs to induce liver damage. [ABSTRACT FROM AUTHOR]

Over the last decade, a new paradigm for cancer therapies has emerged which leverages the immune system to act against the tumor. The novel mechanism of action of these immunotherapies has also introduced new challenges to drug development. Biomarkers play a key role in several areas of early clinical development of immunotherapies including the demonstration of mechanism of action, dose finding and dose optimization, mitigation and prevention of adverse reactions, and patient enrichment and indication prioritization. We discuss statistical principles and methods for establishing the prognostic, predictive aspect of a (set of) biomarker and for linking the change in biomarkers to clinical efficacy in the context of early development studies. The methods discussed are meant to avoid bias and produce robust and reproducible conclusions. This review is targeted to drug developers and data scientists interested in the strategic usage and analysis of biomarkers in the context of immunotherapies. [ABSTRACT FROM AUTHOR]

The selection and combination of dose regimens for antimalarials involve complex considerations including pharmacokinetic and pharmacodynamic interactions. In this study, we use immediate ex vivo P. falciparum field isolates to evaluate the effect of cabamiquine and pyronaridine as standalone treatments and in combination therapy. We feed the data into a pharmacometrics model to generate an interaction map and simulate meaningful clinical dose ratios. We demonstrate that the pharmacometrics model of parasite growth and killing provides a detailed description of parasite kinetics against cabamiquine-susceptible and resistant parasites. Pyronaridine monotherapy provides suboptimal killing rates at doses as high as 720 mg. In contrast, the combination of a single dose of 330 mg cabamiquine and 360 mg pyronaridine provides over 90% parasite killing in most of the simulated patients. The described methodology that combines a rapid, 3R-compliant in vitro method and modelling to set meaningful doses for new antimalarials could contribute to clinical drug development. Here the authors use drug susceptibility data from Plasmodium falciparum field isolates in a pharmacometric model to evaluate cabamiquine and pyronaridine efficacy, both individually and in combination. The combined treatment shows over 90% parasite reduction in most simulated cases, providing valuable guidance for clinical dose selection in real-world settings. [ABSTRACT FROM AUTHOR]

N-methyl-D-aspartate (NMDA) receptors are heterotetrametric ion channels composed of two obligatory GluN1 subunits and two alternative GluN2 or GluN3 subunits, forming GluN1-N2, GluN1-N3, and GluN1-N2-N3 type of NMDA receptors. Extensive research has focused on the functional and structural properties of conventional GluN1–GluN2 NMDA receptors due to their early discovery and high expression levels. However, the knowledge of unconventional GluN1-N3 NMDA receptors remains limited. In this study, we modeled the GluN1-N3A, GluN1-N3B, and GluN1-N3A-N3B NMDA receptors using deep-learned protein-language predication algorithms AlphaFold and RoseTTAFold All-Atom. We then compared these structures with GluN1-N2 and GluN1-N3A receptor cryo-EM structures and found that GluN1-N3 receptors have distinct properties in subunit arrangement, domain swap, and domain interaction. Furthermore, we predicted the agonist- or antagonist-bound structures, highlighting the key molecular–residue interactions. Our findings shed new light on the structural and functional diversity of NMDA receptors and provide a new direction for drug development. This study uses advanced AI algorithms to model GluN1-N3 NMDA receptors, revealing unique structural properties and interactions compared to conventional GluN1-N2 receptors. By highlighting key molecular–residue interactions and predicting ligand-bound structures, our research enhances the understanding of NMDA receptor diversity and offers new insights for targeted drug development. [ABSTRACT FROM AUTHOR]

The endoplasmic reticulum (ER) is a unique organelle responsible for protein synthesis and processing, lipid synthesis in eukaryotic cells, and the replication of many animal viruses is closely related to ER. A considerable number of viral proteins are synthesised during viral infection, resulting in the accumulation of unfolded and misfolded proteins in ER, which in turn induces endoplasmic reticulum stress (ERS). ERS further drives three signalling pathways (PERK, IRE1, and ATF6) of the cellular unfolded protein response (UPR) to respond to the ERS. In numerous studies, ERS has been shown to mediate autophagy, a highly conserved cellular degradation mechanism to maintain cellular homeostasis in eukaryotic cells, through the UPR to restore ER homeostasis. ERS-mediated autophagy is closely linked to the occurrence and development of numerous viral diseases in animals. Host cells can inhibit viral replication by regulating ERS-mediated autophagy, restoring the ER's normal physiological process. Conversely, many viruses have evolved strategies to exploit ERS-mediated autophagy to achieve immune escape. These strategies include the regulation of PERK-eIF2α-Beclin1, PERK-eIF2α-ATF4-ATG12, IRE1α-JNK-Beclin1, and other signalling pathways, which provide favourable conditions for the replication of animal viruses in host cells. The ERS-mediated autophagy pathway has become a hot topic in animal virological research. This article reviews the most recent research regarding the regulatory functions of ERS-mediated autophagy pathways in animal viral infections, emphasising the underlying mechanisms in the context of different viral infections. Furthermore, it considers the future direction and challenges in the development of ERS-mediated autophagy targeting strategies for combating animal viral diseases, which will contribute to unveiling their pathogenic mechanism from a new perspective and provide a scientific reference for the discovery and development of new antiviral drugs and preventive strategies. [ABSTRACT FROM AUTHOR]

Furin is an important proteolytic enzyme, converting several proteins from inactive precursors to their active forms. Recently, proteo-genomic analyses in European and East Asian populations suggested a causal association of furin with ischaemic heart disease, and there is growing interest in its role in cardiovascular disease (CVD) aetiology. In this narrative review, we present a critical appraisal of evidence from population studies to assess furin's role in CVD risk and potential as a drug target for CVD. Whilst most observational studies report positive associations between furin expression and CVD risk, some studies report opposing effects, which may reflect the complex biological roles of furin and its substrates. Genetic variation in FURIN is also associated with CVD and its risk factors. We found no evidence of current clinical development of furin as a drug target for CVD, although several phase 1 and 2 clinical trials of furin inhibitors as a type of cancer immunotherapy have been completed. The growing field of proteo-genomics in large-scale population studies may inform the future development of furin and other potential drug targets to improve the treatment and prevention of CVD. [ABSTRACT FROM AUTHOR]

The Target-Based Virtual Screening approach is widely employed in drug development, with docking or molecular dynamics techniques commonly utilized for this purpose. This systematic review (SR) aimed to identify in silico therapeutic targets for treating Diabetes mellitus (DM) and answer the question: What therapeutic targets have been used in in silico analyses for the treatment of DM? The SR was developed following the guidelines of the Preferred Reporting Items Checklist for Systematic Review and Meta-Analysis, in accordance with the protocol registered in PROSPERO (CRD42022353808). Studies that met the PECo strategy (Problem, Exposure, Context) were included using the following databases: Medline (PubMed), Web of Science, Scopus, Embase, ScienceDirect, and Virtual Health Library. A total of 20 articles were included, which not only identified therapeutic targets in silico but also conducted in vivo analyses to validate the obtained results. The therapeutic targets most frequently indicated in in silico studies were GLUT4, DPP-IV, and PPARγ. In conclusion, a diversity of targets for the treatment of DM was verified through both in silico and in vivo reassessment. This contributes to the discovery of potential new allies for the treatment of DM. [ABSTRACT FROM AUTHOR]

Marine algal toxins have garnered significant attention in the research community for their unique biochemical properties and potential medical applications. These bioactive compounds, produced by microalgae, pose significant risks due to their high toxicity, yet offer promising therapeutic benefits. Despite extensive research identifying over 300 marine algal toxins, including azaspiracids, brevetoxins, cyclic imines, and yessotoxins, gaps remain in the understanding of their pharmacological potential. In this paper, we critically review the classification, bioactive components, toxicology, pharmacological activities, and mechanisms of these toxins, with a particular focus on their clinical applications. Our motivation stems from the increasing interest in marine algal toxins as candidates for drug development, driven by their high specificity and affinity for various biological receptors. We aim to bridge the gap between toxicological research and therapeutic application, offering insights into the advantages and limitations of these compounds in comparison to other bioactive substances. This review not only enhances the understanding of marine algal toxins' complexity and diversity, but also highlights their extensive application potential in medicine and bioscience, providing a foundation for future research and development in this field. [ABSTRACT FROM AUTHOR]

Simple Summary: TGF-β is an important cytokine shown to drive oncogenesis in head and neck squamous cell carcinoma (HNSCC) through its diverse influences on the tumor microenvironment. While this cytokine is vital in maintaining tissue homeostasis in normal head and neck epithelia, in cancer, it paradoxically drives metastasis, angiogenesis, immune evasion, and therapy resistance. Despite promising preclinical data, the outcome of clinical trials of TGF-β inhibitors for HNSCC has been suboptimal. Patient stratification is warranted to improve this targeted therapy. Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cause of cancer worldwide according to GLOBOCAN estimates from 2022. Current therapy options for recurrent or metastatic disease are limited to conventional cytotoxic chemotherapy and immunotherapy, with few targeted therapy options readily available. Recent single-cell transcriptomic analyses identified TGF-β signaling as an important mediator of functional interplays between cancer-associated fibroblasts and a subset of mesenchymal cancer cells. This signaling was shown to drive invasiveness, treatment resistance, and immune evasion. These data provide renewed interest in the TGF-β pathway as an alternative therapeutic target, prompting a critical review of previous clinical data which suggest a lack of benefit from TGF-β inhibitors. While preclinical data have demonstrated the great anti-tumorigenic potential of TGF-β inhibitors, the underwhelming results of ongoing and completed clinical trials highlight the difficulty actualizing these benefits into clinical practice. This topical review will discuss the relevant preclinical and clinical findings for TGF-β inhibitors in HNSCC and will explore the potential role of patient stratification in the development of this therapeutic strategy. [ABSTRACT FROM AUTHOR]

Transdermal drug delivery (TDD) has gained clinical approval over several decades, with extensive research dedicated to novel drug and device development. Despite notable research progress, the market adoption of TDD devices has not met anticipated levels, with oral administration and injection remaining predominant delivery methods. To maximize the potential of TDD, we identify bottlenecks hindering its widespread clinical application and propose promising research avenues. We begin by analyzing stringent demands necessary to truly benefit patients, addressing significant challenges in biomechanics, nanomedicine, and flexible electronics. Subsequently, we delve into skin anatomy, enhancement strategies, nano-carriers, and their underlining mechanisms, highlighting the importance and framework of quantitative modeling. Based on these discussions, we highlight the core strength of TDD, such as automatic precise administration based on feedback and high delivery efficiencies, especially applicable to localized conditions (e.g., central nervous system diseases, tumors). Finally, we envision the future of intelligent TDD device and its operation scenario, aiming to steer research efforts toward faster translation of laboratory innovations into widely used products for sufferers. [ABSTRACT FROM AUTHOR]

Simple Summary: The developments of radioligand therapy, immunotherapy, and targeted chemotherapy have led to dozens of clinical trials targeting tumor-associated surface antigens in metastatic prostate cancer. Emerging clinical data on the development of these new therapies were summarized and discussed. There is an unmet need to develop new treatments for metastatic prostate cancer. With the development of targeted radioligand therapies, bispecific T cell engagers, antibody–drug conjugates and chimeric antigen receptor T cell (CAR T) therapies, tumor-associated cell surface antigens have emerged as new therapeutic targets in metastatic prostate cancer. Ongoing and completed clinical trials targeting prostate-specific membrane antigen (PSMA), six transmembrane epithelial antigens of the prostate 1 (STEAP1), kallikrein-related peptidase 2 (KLK2), prostate stem cell antigen (PSCA), and delta-like protein 3 (DLL3) in metastatic prostate cancer were reviewed. Strategies for sequential or combinational therapy were discussed. [ABSTRACT FROM AUTHOR]

Simple Summary: The first Onco Summit: The Latin American (LATAM) Chapter was held from 19–20 May 2023, in Rio de Janeiro, Brazil. This educational initiative aimed to discuss the current challenges in cancer care, both globally and in LATAM, and foster the exchange of best practices to address these challenges. The Summit brought together more than 30 international and regional experts and more than 300 oncology specialists to deliberate on the advancements and barriers in oncology, with a focus on strategies tailored to the specific needs of LATAM. This report provides an overview of the key discussions among cancer specialists at the Summit. The Onco Summit 2023: The Latin American (LATAM) Chapter took place over two days, from 19–20 May 2023, in Brazil. The event aimed to share the latest updates across various oncology disciplines, address critical clinical challenges, and exchange best practices to ensure optimal patient treatment. More than 30 international and regional speakers and more than 300 oncology specialists participated in the Summit. The Summit discussions centered on common challenges and therapeutic advances in cancer care, with a specific focus on the unique obstacles faced in LATAM and examples of adaptable strategies to address these challenges. The Summit also facilitated the establishment of a network of oncologists, hematologists, and scientists in LATAM, enabling collaboration to improve cancer care, both in this region and globally, through drug development and clinical research. This report summarizes the key discussions from the Summit for the global and LATAM oncology community. [ABSTRACT FROM AUTHOR]

Prevention and treatment of arterial hypertension is of great importance. Improvement of existing medicines through the use of alternative routes of administration can enhance the pharmacotherapeutic characteristics of active pharmaceutical ingredients (APIs). Transdermal therapeutic systems (TTS) allow for delivery of a drug through intact skin into the systemic circulation, while minimizing side effects. The development of transdermal formulations of antihypertensive drugs, Lisinopril dihydrate in particular, has practical and scientific significance. Optimization of the algorithm for the development of transdermal drugs involves in vitro preformulation studies of the membrane permeability of APIs and the identification of factors that affect this process. The tests were performed by dialysis through a semipermeable hydrophilic membrane using a Valia-Chien diffusion device. The effect of the initial concentration of Lisinopril dihydrate on the flux rate Is was investigated. Different donor concentrations of Lisinopril dihydrate were tested at three levels, 10, 20 and 30 mg/ml, accordingly. It was noted that the permeation process of Lisinopril dihydrate under model conditions corresponds to zero-order kinetics and is characterized by a uniform speed. The correlation coefficient (R2) for all the kinetic equations was 0.999. A linear dependence of the flux velocity of Lisinopril dihydrate on the initial concentration was indicated. The concentration of Lisinopril dihydrate in the donor solution of 30 mg/ml was found to be optimal for further stages of pharmaceutical development of the transdermal formulation. The studies of Lisinopril dihydrate permeability allow to give a positive assessment of the acceptability of this API for use in the transdermal formulation and the development of TTS. [ABSTRACT FROM AUTHOR]

For millennia, people have successfully treated a wide range of illnesses, including bacterial infections, with different plant parts or extracts. "MDR strain" typically refers to a "multi-drug resistant strain" of a bacteria. In the context of infectious diseases, a multi-drug resistant strain refers to a strain of the pathogen that has acquired resistance to multiple drugs that are commonly used to treat infections caused by that specific pathogen. MDR strains can present significant challenges in healthcare settings as they limit the effectiveness of standard treatments and may require more aggressive or specialized approaches to manage the infection. The discdiffusion method was used in this investigation to test the antimicrobial properties of Petroleum ether, acetone, ethanol, methanol, and aqueous extracts of Ficus auriculata leaves against four bacterial strains namely Salmonella enteric serovar typhi, Salmonella enteric ser Paratyphi (MDR strain) Pseudomonas aeruginosa and Escherichia coli. The findings showed that all of the investigated organisms (zone of inhibition of 0.5 ± 0.15 & 18 ± 1.7 mm) were significantly inhibited by the petroleum ether, ethanol, and methanol extracts, with the exception of Salmonella typhi (an 18 mm inhibitory zone). The restricted area (=5) indicated moderate activity in the aqueous extracts. It's crucial to remember that antimicrobial activity analysis of plant extracts is just one step in the process of identifying potential natural antimicrobial agents. Further studies, including the identification and isolation of specific bioactive compounds, toxicology assessments, and clinical trials, are required before any plant extract can be considered for use as a drug development. [ABSTRACT FROM AUTHOR]

A transition from nonhuman animal models to engineered microphysiological systems (MPS), such as organoids and organ-on-achip technologies, would signal a paradigm shift in biomedical research. Despite MPS' potential to more accurately model human physiology, reduce high failure rates of drugs in clinical trials, and limit unnecessary animal use, widespread adoption is hampered by public opinion and lack of scalability, standardization, and current regulatory uptake. This article suggests how 5 key concepts (awareness, access, education, application, and rewards) could help address these barriers. These concepts are part of a framework that underscores a need to integrate MPS into mainstream biomedical research and to better promote ethical responsibility for the means of biomedical innovation. [ABSTRACT FROM AUTHOR]

Historically, most discussions about nonhuman animal experimentation consider what has become known as the 3 R's: refinement, reduction, and replacement. Refinement and reduction receive the most attention, but recent modeling advances suggest that suitable replacement of nonhuman animal testing would bolster human research and increase translatability to human health outcomes. This article discusses these modeling advances and advocates their use, especially as replacements to nonpredictive nonhuman animal protocols, and discusses growing momentum in biomedical research communities and federal agencies that favors replacement of animal testing. [ABSTRACT FROM AUTHOR]

Antibody–drug conjugates (ADCs) represent a crucial component of targeted therapies in gastric cancer, potentially altering traditional treatment paradigms. Many ADCs have entered rigorous clinical trials based on biological theories and preclinical experiments. Modality trials have also been conducted in combination with monoclonal antibody therapies, chemotherapies, immunotherapies, and other treatments to enhance the efficacy of drug coordination effects. However, ADCs exhibit limitations in treating gastric cancer, including resistance triggered by their structure or other factors. Ongoing intensive researches and preclinical experiments are yielding improvements, while enhancements in drug development processes and concomitant diagnostics during the therapeutic period actively boost ADC efficacy. The optimal treatment strategy for gastric cancer patients is continually evolving. This review summarizes the clinical progress of ADCs in treating gastric cancer, analyzes the mechanisms of ADC combination therapies, discusses resistance patterns, and offers a promising outlook for future applications in ADC drug development and companion diagnostics. [ABSTRACT FROM AUTHOR]

Identifying drug–drug interactions (DDIs) is an important aspect of drug design research, and predicting DDIs serves as a crucial guarantee for avoiding potential adverse effects. Current substructure‐based prediction methods still have some limitations: (i) The process of substructure extraction does not fully exploit the graph structure information of drugs, as it only evaluates the importance of different radius substructures from a single perspective. (ii) The process of constructing drug representations has overlooked the significant impact of relation embedding on optimizing drug representations. In this work, we propose a substructure‐aware graph neural network incorporating relation features (RFSA‐DDI) for DDI prediction, which introduces a directed message passing neural network with substructure attention mechanism based on graph self‐adaptive pooling (GSP‐DMPNN) and a substructure‐aware interaction module incorporating relation features (RSAM). GSP‐DMPNN utilizes graph self‐adaptive pooling to comprehensively consider node features and local drug information for adaptive extraction of substructures. RSAM interacts drug features with relation representations to enhance their respective features individually, highlighting substructures that significantly impact predictions. RFSA‐DDI is evaluated on two real‐world datasets. Compared to existing methods, RFSA‐DDI demonstrates certain advantages in both transductive and inductive settings, effectively handling the task of predicting DDIs for unseen drugs and exhibiting good generalization capability. The experimental results show that RFSA‐DDI can effectively capture valuable structural information of drugs more accurately for DDI prediction, and provide more reliable assistance for potential DDIs detection in drug development and treatment stages. [ABSTRACT FROM AUTHOR]

[Display omitted] • This is the most systematic review of flavonoid drugs and clinical candidates to date. • 19 flavonoid drugs are approved for the market, while 36 candidates in clinical phases. • 52.6% of the marketed flavonoid drugs and 44.4% of clinical candidates are natural sources. • 30% of the marketed flavonoid drugs are used for the treatment of cardiovascular diseases. • 50% of the suspended clinical candidates are antineoplastic and immunomodulating agents. • Unexpectedly, flavonoid glycosides account for 36.8% of the marketed flavonoid drugs. Flavonoids are one of the most important metabolites with vast structural diversity and a plethora of potential pharmacological applications, which have drawn considerable attention in the laboratory. Nevertheless, it remains uncertain how many candidates were progressed to clinical application. We carried out a critical review of natural and semi-synthetic flavonoid drugs and candidates undergoing different clinical phases worldwide by applying an adequate search method and conducted a brief cheminformatic and bioinformatic analysis. It was expected that the obtained results might narrow the screening scope and reduce the cost of drug research and development. To our knowledge, this is the most systematic summarization of flavonoid-based drugs and clinical candidates to date. It was found that a total of 19 flavonoid-based drugs have been approved for the market, and of these, natural flavonoids accounted for 52.6%. Besides, a total of 36 flavonoid-based clinical candidates are undergoing or suspended in different phases, and of these, natural flavonoids account for 44.4%. Thus, natural flavonoids remain the best option for finding novel agents/active templates, and when investigated in conjunction with synthetic chemicals and biologicals, they offer the potential to discover novel structures that can lead to effective agents against a variety of human diseases. Additionally, flavonoid-based marketed drugs have been successful in cardiovascular treatment, and the related drugs account for more than 30% of marketed drugs. However, the use of flavonoids as antineoplastic and immunomodulating agents is not likely for approximately 50% of the candidates suspended in the clinical stage. Interestingly, the marketed drugs covered a broader range of chemical spaces based on size, polarity, and three-dimensional structure compared to the clinical candidates. In addition, flavonoid glycosides with poor oral bioavailability account for 36.8% of the marketed drugs, and thus, they could be thoroughly investigated. [ABSTRACT FROM AUTHOR]

An integrated platform was developed for NP-inspired PROTAC design, target identification and drug discovery. As a proof-of-concept study, this platform was applied to target characterization of 3-fluoro-10-hydroxylevodiamine, which highlighted the superiority of PROTAC technology in target identification of NPs and accelerated the process of NPs-based drug discovery. [Display omitted] • A new strategy combining PROTAC derivatization, quantitative proteomic analysis and binding affinity validation was developed for target identification and drug discovery of natural products. • The PROTAC-based target identitication strategy was successfully applied to characterize REXO4 as a direct target of 3-fluoro-10-hydroxylevodiamine. • Evodiamine-inspired PROTAC 13c showed potent antitumor activity and reduced toxic side effects through REXO4 degradation. Natural products (NPs) play a crucial role in the development of therapeutic drugs. However, it is still highly challenging to identify the targets of NPs. Besides, NPs usually exert their pharmacological activities via acting on multiple targets or pathways, which also poses great difficulties for the target identification of NPs. Inspired by our continuous efforts in designing drug-like protein degraders, this study introduced a successful example for the target identification and drug discovery of natural products evodiamine by employing PROTAC technology. Taking advantages of proteolysis targeting chimera (PROTAC), herein an integrated strategy combining PROTAC derivatization, quantitative proteomic analysis and binding affinity validation was developed for target identification and drug discovery of antitumor NP evodiamine. In this study, both highly potent PROTACs and negative controls were designed for quantitative proteomic analysis. Furthermore, REXO4 was confirmed as a direct target of 3-fluoro-10-hydroxylevodiamine, which induced cell death through ROS. In addition, the PROTAC 13c effectively degraded REXO4 both in vitro and in vivo , leading to potent antitumor activities and reduced toxic side effects. In summary, we developed an integrated strategy for the target identification and drug discovery of NPs, which was successfully applied to the PROTAC derivatization and target characterization of evodiamine. This proof-of-concept study highlighted the superiority of PROTAC technology in target identification of NPs and accelerated the process of NPs-based drug discovery, exhibiting broad application in NP-based drug development. [ABSTRACT FROM AUTHOR]

Small nucleic acid drugs, composed of nucleotides, represent a novel class of pharmaceuticals that differ significantly from conventional small molecule and antibody-based therapeutics. These agents function by selectively targeting specific genes or their corresponding messenger RNAs (mRNAs), further modulating gene expression and regulating translation-related processes. Prominent examples within this category include antisense oligonucleotides (ASO), small interfering RNAs (siRNAs), microRNAs (miRNAs), and aptamers. The emergence of small nucleic acid drugs as a focal point in contemporary biopharmaceutical research is attributed to their remarkable specificity, facile design, abbreviated development cycles, expansive target spectrum, and prolonged activity. Overcoming challenges such as poor stability, immunogenicity, and permeability issues have been addressed through the integration of chemical modifications and the development of drug delivery systems. This review provides an overview of the current status and prospective trends in small nucleic acid drug development. Commencing with a historical context, we introduce the primary classifications and mechanisms of small nucleic acid drugs. Subsequently, we delve into the advantages of the U.S. Food and Drug Administration (FDA) approved drugs and mainly discuss the challenges encountered during their development. Apart from researching chemical modification and delivery system that efficiently deliver and enrich small nucleic acid drugs to target tissues, promoting endosomal escape is a critical scientific question and important research direction in siRNA drug development. Future directions in this field will prioritize addressing these challenges to facilitate the clinical transformation of small nucleic acid drugs. This review provides an overview of the current status and prospective trends in small nucleic acid drug development, including the development history, drug classifications, and mechanisms, and mainly discusses the advantages of these drugs and the challenges encountered during their development. [Display omitted] [ABSTRACT FROM AUTHOR]

Background and Objectives Envafolimab is the first and only globally approved subcutaneously injectable PD-L1 antibody for the treatment of instability-high (MSI-H) or DNA mismatch repair deficient (dMMR) advanced solid tumors in adults, including those with advanced colorectal cancer that has progressed after treatment with a fluoropyrimidine, oxaliplatin, and irinotecan. The aim of this investigation was to examine the pharmacokinetic and exposure-response (E-R) profile of envafolimab in patients with solid tumors to support the approval of fixed and alternative dose regimens. Methods In this study, a population pharmacokinetic (PopPK) modeling approach will be employed to quantitatively evaluate intrinsic and extrinsic covariates. Additionally, PopPK-estimated exposure parameters were used to evaluate E-R relationship for safety and efficacy to provide a theoretical basis for recommending optimal treatment regimens. Simulations were performed on the dosing regimens of body weight-based regimen of 2.50 mg/kg QW, fixed dose 150 mg QW, and 300 mg Q2W for the selection of alternative dosing regimens. Data from 4 clinical studies (NCT02827968, NCT03101488, NCT03248843, and NCT03667170) were utilized. Results The PopPK dataset comprised 182 patients with 1810 evaluable envafolimab concentration records. Finally, a one-compartment model incorporating first-order absorption, first-order linear elimination, and time-dependent elimination according to an E max function was found to accurately describe the concentration-time data of envafolimab in patients with advanced solid tumors. Creatinine clearance and country were identified as statistically significant factors affecting clearance, but had limited clinical significance. A relative flat exposure-response relationship was observed between early measures of safety and efficacy to verify that no dose adjustment is required. Simulation results indicated that 2.50 mg/kg QW, 150 mg QW, and 300 mg Q2W regimen yield similar steady-state exposure. Conclusions No statistically significant difference was observed between weight-based and fixed dose regimens. Model-based simulation supports the adoption of a 150 mg weekly or 300 mg biweekly dosing regimen of envafolimab in the solid tumor population, as these schedules effectively balance survival benefits and safety risks. [ABSTRACT FROM AUTHOR]