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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]

Strategic collaborations are critical to the success of efforts aimed at discovering new drugs and therapies in the biopharmaceutical industry. These collaborations aim to leverage domain expertise, asymmetries in production costs and/or capabilities to improve efficiency. Despite increasing collaborations, the biopharmaceutical industry lacks a structured guideline for choosing contracts. We present contract models with effort-based formulations that capture the key characteristics of biopharmaceutical operations and analyse incentive mechanisms such as fixed payment, revenue-sharing, risk-sharing, and cost-sharing in biopharmaceutical collaborations. We show that traditional incentive schemes do not achieve supply chain coordination, although they are commonly used in the industry. We introduce a new contract model called fee-for-effort-and-output contract that encourages the parties to exert higher efforts by offering discounts on their operating costs, and show that this contract achieves coordination with an appropriate selection of contract parameters. We also investigate the efficiency of noncoordinating contracts with traditional incentive schemes and identify the capability and cost structures under which they achieve the highest efficiency possible, to both determine the next-best alternatives and explain their popularity in practice. [ABSTRACT FROM AUTHOR]

Specialty drugs treat complex, severe diseases and offer significant therapeutic advances. Despite their potential to transform patient care, little is known about the drivers of their diffusion. In this study, the authors develop a framework for the diffusion of specialty drugs that is motivated by the drugs' novelty, complexity, and importance, with a focus on the role of scientific evidence production. They propose that the effects of scientific evidence on specialty drug diffusion are multifaceted and generated through the three stages of the scientific evidence production process: unpublished clinical studies, publications in medical journals not cited in clinical guidelines, and clinical guidelines. The findings from the empirical analysis of two specialty drugs validate the framework, supporting the idea of multistage scientific evidence effects. In contrast, marketing activities do not have a significant effect. Although this could be attributed to specialty drug prescribers discounting information from commercial sources, it may also be due to limited marketing support. An additional analysis on a nonspecialty drug further validates the proposed framework. Calculations of scientific evidence contributions to trial prescriptions indicate that scientific evidence production can generate returns beyond the publication stage, which should provide specialty drug manufacturers with strong incentives to commit to quality and innovation. [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.

In theory, financial markets promote innovation by selectively allocating capital to high-quality projects. In this article, I show that equity markets can also inhibit innovation. In public firms, I find that short-term equity market declines cause pharmaceutical companies to abandon early-stage drug developments, irrespective of drug quality or changes in a firm's stock price. I show that financing constraints drive this behavior, highlighting that even short-term market fluctuations can have long-term effects on pharmaceutical innovation and prevent potentially life-saving drugs from progressing to the market. [ABSTRACT FROM AUTHOR]

Development of drug delivery systems is an extensively researched area in the biomedical field. In recent years, there has been an increasing interest in fabrication of biocompatible nanofibrous drug delivery systems. The present study describes the fabrication of calcium chloride‐crosslinked bionanocomposite hydrogels based on pectin for the use of gastro‐retentive delivery system. Sepiolite/famotidine/pectin nanocomposite hydrogel was fabricated by using Ionotropic gelation process. The thermal, structural and morphological properties were evaluated via different experimental techniques such as TGA‐DSC, FTIR, and SEM. The prepared hydrogels showed a pH sensitive swelling profile with maximum water absorption at pH 1.2. Analysis of in‐vitro drug release data revealed that the sepiolite/famotidine/pectin nanocomposite hydrogels exhibited a slight initial burst release, followed by a sustained release over a 24 h period. This was observed both in simulated gastric fluid and in phosphate‐buffered saline, suggesting that these hydrogels are well‐suited for prolonged drug delivery. To investigate the drug release kinetics, various models were applied. The release of famotidine was predominantly governed by diffusion, as described by the Korsmeyer‐Peppas model. [ABSTRACT FROM AUTHOR]

Genomic technologies have led to massive gains in our understanding of human gene function and disease relevance. Chemical biologists are a primary beneficiary of this information, which can guide the prioritization of proteins for chemical probe and drug development. The vast functional and structural diversity of disease-relevant proteins, however, presents challenges for conventional small molecule screening libraries and assay development that in turn raise questions about the broader "druggability" of the human proteome. Here, we posit that activity-based protein profiling (ABPP), by generating global maps of small molecule-protein interactions in native biological systems, is well positioned to address major obstacles in human biology-guided chemical probe and drug discovery. We will support this viewpoint with case studies highlighting a range of small molecule mechanisms illuminated by ABPP that include the disruption and stabilization of biomolecular (protein-protein/nucleic acid) interactions and underscore allostery as a rich source of chemical tools for historically "undruggable" protein classes. [Display omitted] In this review, Niphakis and Cravatt describe how activity-based protein profiling (ABPP) technologies generate global maps of small molecule-protein interactions in native systems, expanding the druggability of the human proteome. Highlighted are chemical tools discovered by ABPP, including those remodeling protein-protein interactions and acting through cryptic allosteric pockets. [ABSTRACT FROM AUTHOR]

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]

Aims Materials and Methods Results Conclusions The combination of cagrilintide and semaglutide (CagriSema) is being developed for the treatment of obesity and type 2 diabetes. The objective of this thorough QT study was to confirm that cagrilintide does not result in a clinically relevant prolongation in cardiac repolarization compared with placebo.This was a double‐blind study (NCT05804162) in which healthy participants were randomized to cagrilintide, administered as a once‐weekly subcutaneous injection dose escalated to 4.5 mg, or a placebo. The primary end point was the time‐matched change from baseline in Fridericia heart rate–corrected QT interval (QTcF) at 12‐, 24‐, 48‐ and 72 h after the last cagrilintide 4.5‐mg dose. To conclude that cagrilintide does not induce a clinically relevant prolongation, the upper limit of the two‐sided 90% confidence interval (CI) for the treatment difference at each of the four time points must fall below 10 ms. To establish QT assay sensitivity, participants in the placebo arms received a single 400‐mg oral moxifloxacin dose as a positive control and moxifloxacin placebo in a nested cross‐over fashion.A total of 105 participants received cagrilintide (n = 53) or placebo (n = 52). No clinically relevant QTcF prolongation occurred after the last cagrilintide 4.5‐mg dose; the upper limits of the two‐sided 90% CIs of the placebo‐adjusted QTcF changes from baseline were below 10 ms at all time points. QT assay sensitivity was demonstrated with moxifloxacin as a positive control.Cagrilintide did not result in clinically relevant QTcF prolongation, indicating no increased risk of ventricular tachyarrhythmias. [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]

Background Aims Methods Content Conclusions Catheter‐related thrombosis (CRT) is one of the major complications affecting patients with indwelling venous catheters, usually involving the upper extremity deep venous system. This condition can lead to potentially life‐threatening complications such as pulmonary embolism and sepsis. The risk of developing CRT varies depending on type of catheters and patient characteristics. Despite advances in materials and technologies, the actual incidence of CRT is still considerable. Available evidence on CRT management remains controversial, and clinical guidelines base their recommendations on data from non‐catheter related upper extremity or lower extremity deep venous thromboses.This narrative review aims to describe the epidemiology of CRT, to review the available evidence on its management and to highlight the current unmet needs.No formal search strategy was applied for the revision of the literature. The main sources of information used were Medline and guidelines from international societies.The management of CRT requires a careful balance between the risk of thrombus progression, recurrent events, and systemic embolization and the increased bleeding risk in often fragile patients. Open issues include the optimal management of the catheter and the type and duration of anticoagulant therapy. Direct oral anticoagulants are increasingly prescribed, representing an important alternative to the standard of care low molecular weight heparins in selected cases. The development of new anticoagulant drugs such as factors XI and XII inhibitors may offer further advantages in this context.The management of CRT is still challenging with constant need for updated evidence to support tailored approaches. [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]

Among clinical trialists, there has been a growing interest in using external data to improve decision‐making and accelerate drug development in randomized clinical trials (RCTs). Here we propose a novel approach that combines the propensity score weighting (PW) and the multi‐source exchangeability modelling (MEM) approaches to augment the control arm of a RCT in the rare disease setting. First, propensity score weighting is used to construct weighted external controls that have similar observed pre‐treatment characteristics as the current trial population. Next, the MEM approach evaluates the similarity in outcome distributions between the weighted external controls and the concurrent control arm. The amount of external data we borrow is determined by the similarities in pretreatment characteristics and outcome distributions. The proposed approach can be applied to binary, continuous and count data. We evaluate the performance of the proposed PW‐MEM method and several competing approaches based on simulation and re‐sampling studies. Our results show that the PW‐MEM approach improves the precision of treatment effect estimates while reducing the biases associated with borrowing data from external sources. [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]

AbstractThiazole is a sulfur and nitrogen-containing five-membered heteroaromatic ring in many FDA-approved drugs and numerous experimental leads. To facilitate access to this structure, several novel methodologies have been developed using easily accessible starting materials and more environmentally friendly protocols. This review focuses on all recently developed methods (2013–2024) that utilized homogeneous or heterogeneous catalyzed methods to synthesize 1,3-thiazole and provides thorough information on the diverse therapeutic applicability of thiazole derivatives in drug discovery and development. Furthermore, this review will ameliorate the understanding of synthetic and medicinal chemists in selecting green and ecological methodologies to synthesize thiazoles judicially. [ABSTRACT FROM AUTHOR]

In March 2022, the US Food and Drug Administration expanded indications of TRIUMEQ, a once‐daily fixed‐dose combination (FDC) containing abacavir (ABC), dolutegravir (DTG), and lamivudine (3TC) to include pediatric patients weighing at least 10 kg for the treatment of HIV‐1. Prior to this extension, the ABC 600 mg/DTG 50 mg/3TC 300 mg FDC tablet was approved for use only in the adult/adolescent population, weighing ≥40 kg while each component of the FDC was approved for its use in pediatric patients at least 3 months and older. A new child‐friendly formulation was developed as an FDC dispersible tablet (DT) of ABC 60 mg/DTG 5 mg/3TC 30 mg for pediatric patients with a body weight ≥ 6 kg. The present work demonstrates the utility of applying a model‐informed drug development (MIDD) approach to expedite ABC/DTG/3TC FDC approval for pediatric patients (≥10 to <40 kg) based on data from the existing individual components and formulation bridging. Population pharmacokinetic models developed for pediatric participants across all three components of ABC/DTG/3TC FDC were employed for exposure prediction and incorporated relative bioavailability data. The predicted plasma exposures of ABC, DTG, and 3TC for FDC doses were consistent with those observed for the single entities in pediatric and adult studies. Thus, safety and efficacy observed in the individual component studies could be adequately extrapolated to the FDC that results in similar exposure. The current work demonstrates the significance of MIDD approaches in facilitating expedited access to child‐friendly formulations in the HIV‐1 therapeutic area. [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]

The discovery of biomarkers, essential for successful drug development, is often hindered by the limited availability of tissue samples, typically obtained through core needle biopsies. Standard ‘omics platforms can consume significant amounts of tissue, forcing scientist to trade off spatial context for high-plex assays, such as genome-wide assays. While bulk gene expression approaches and standard single-cell transcriptomics have been valuable in defining various molecular and cellular mechanisms, they do not retain spatial context. As such, they have limited power in resolving tissue heterogeneity and cell–cell interactions. Current spatial transcriptomics platforms offer limited transcriptome coverage and have low throughput, restricting the number of samples that can be analyzed daily or even weekly. While the Digital Spatial Profiling (DSP) method does not provide single-cell resolution, it presents a significant advancement by enabling scalable whole transcriptome and ultrahigh-plex protein analysis from distinct tissue compartments and structures using a single tissue slide. These capabilities overcome significant constraints in biomarker analysis in solid tissue specimens. These advancements in tissue profiling play a crucial role in deepening our understanding of disease biology and in identifying potential therapeutic targets and biomarkers. To enhance the use of spatial biology tools in drug discovery and development, the DSP Scientific Consortium has created best practices guidelines. These guidelines, built on digital spatial profiling data and expertise, offer a practical framework for designing spatial studies and using current and future spatial biology platforms. The aim is to improve tissue analysis in all research areas supporting drug discovery and development. [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]

Direct installation of key functionalities in a molecule through C−H bond activation is one of the thrust areas as well as challenging task in organic synthesis. Particularly, introduction of chlorine in a molecule imparts additional benefits for further functionalizations as well as improves the electronic behaviour such as lipophilicity and polarity towards drug development process. The chlorinated molecules have also been established as efficient biologically relevant scaffolds. Current manuscript has been focused on the direct installation of the chlorine atom at various aromatic and aliphatic positions to produce functional molecules. The key highlight of the manuscript belongs to the site selectivity (regioselectivity) for the installation of chlorine functionality. Manuscript describes the advanced methods developed for the direct C−H chlorination reactions and further simplified for the chlorination reactions at various positions including aromatic (o‐, m‐, and p‐), benzylic, heteroaromatic, and aliphatic positions. Directing groups (DGs) and the coordination with the catalyst is the key for the enhancement of regioselectivities during direct C−H chlorination reactions. [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]

Heterocycles have gained recognition as vital components in approved drugs, drawing substantial attention from the scientific community. Ionic liquids (ILs) have been utilized for their transformative roles in heterocycle synthesis, showcasing distinctive properties that are pivotal in diverse chemical transformations, while also acting as effective catalysts and offering safer alternatives to volatile organic solvents. This account delves into the synthesis of nitrogen- and oxygen-containing heterocyclic structures, employing various ILs such as ammonium, cholinium, DABCO-based, DBU-based, guanidinium-based, imidazolium, phosphonium, pyridinium, and other miscellaneous examples. They have proven indispensable in facilitating reactions like the Fischer indole synthesis, the Biginelli reaction, Knoevenagel condensations and many more. Notably, the recyclability of ILs serves as a valuable asset, aiding in the completion of intricate synthetic pathways, multicomponent reactions, and one-pot syntheses, ultimately enhancing yields. This account, covering the literature published in 2022, seeks to guide researchers in selecting suitable ILs for specific chemical reactions that enable the synthesis of aza- and/or oxa-heterocycles. The described advancements represent promising prospects for drug development and other applications within the domain of heterocyclic chemistry. 1 Introduction 2 Construction of Heterocycles Catalyzed by Ionic Liquids 2.1 Ammonium Ionic Liquids 2.2 Cholinium-Based Ionic Liquids 2.3 DABCO-Based Ionic Liquids 2.4 DBU-Based Ionic Liquids 2.5 Guanidinium-Based Ionic Liquids 2.6 Imidazolium Ionic Liquids 2.7 Phosphonium Ionic Liquids 2.8 Pyridinium Ionic Liquids 2.9 Other Ionic Liquids 3 Summary and Outlook 4 Abbreviations [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]

The solid tumors provide a series of biological barriers in cellular microenvironment for designing drug delivery methods based on advanced stimuli-responsive materials. These intertumoral and intratumoral barriers consist of perforated endotheliums, tumor cell crowding, vascularity, lymphatic drainage blocking effect, extracellular matrix (ECM) proteins, hypoxia, and acidosis. Triggering opportunities have been drawn for solid tumor therapies based on single and dual stimuli-responsive drug delivery systems (DDSs) that not only improved drug targeting in deeper sites of the tumor microenvironments, but also facilitated the antitumor drug release efficiency. Single and dual stimuli-responsive materials which are known for their lowest side effects can be categorized in 17 main groups which involve to internal and external stimuli anticancer drug carriers in proportion to microenvironments of targeted solid tumors. Development of such drug carriers can circumvent barriers in clinical trial studies based on their superior capabilities in penetrating into more inaccessible sites of the tumor tissues. In recent designs, key characteristics of these DDSs such as fast response to intracellular and extracellular factors, effective cytotoxicity with minimum side effect, efficient permeability, and rate and location of drug release have been discussed as core concerns of designing paradigms of these materials. [ABSTRACT FROM AUTHOR]

Aim: To establish significant risk factors for the development of adverse drug effects (ADEs) in children and adolescents with an acute psychotic episode taking antipsychotics. Materials and Methods: The research team randomly selected 15 patient records each month for 3 years (2016–2018). Overall, 450 patient records were included (223 boys and 227 girls, mean age was 14.52 ± 2.21 years). Adverse effects were identified using the standard algorithm of the Global Trigger Tool method. A "trigger" is an indication that an adverse reaction is likely to occur, e.g., an antihistamine prescription on a prescribing list. When a trigger was detected, the case history was studied in further detail to confirm the occurrence of ADEs. We divided patients into two groups: the "children" group (under 12 years old) and the "adolescents" group (13 years and older). Data were analyzed using the statistical package IBM SPSS Statistics 23.0. Results: Of the 450 patient records, 402 (89.3%) had at least one trigger detected. In total, 126 case histories contained evidence of ADE (28%). The total number of ADEs per 1000 patient days was 5.39 and the number of ADEs per 100 admissions was 32.0. Among adolescents, two or more triggers per patient were significantly more frequently identified (61.3% vs. 44.6%; p = 0.001). ADEs were rare in "Children" compared with "Adolescents" (13.8% vs. 30.4%; p = 0.006). The logistic regression analysis confirmed high predictive role of "Adolescence" (odds ratio [OR] = 2.58; 95% confidence interval [CI] 1.22–5.4; p = 0.013), "Polypharmacy" (OR = 1.96; 95% CI 1.23–3.1; p = 0.004), and "First-life hospitalization" (OR = 2.17; 95% CI 1.34–3.48; p = 0.001) for ADE fact in patient records. Conclusion: We found that significant risk factors for ADEs to antipsychotics in patients with acute psychotic episode were adolescence (13 years and older), polypharmacy, and first-life hospitalization. The fact that children (i.e., younger than 13 years of age) are less likely to experience ADEs was not associated with high-risk drugs or higher doses in our study. [ABSTRACT FROM AUTHOR]