Hasil untuk "Therapeutics. Pharmacology"

Eric Cramer, Laura M. Heiser, Young Hwan Chang

Multiplex tissue imaging (MTI) enables high- dimensional, spatially resolved measurements of the tumor microenvironment (TME), but most clinical datasets are tempo- rally undersampled and longitudinally limited, restricting direct inference of underlying spatiotemporal dynamics and effective intervention timing. Agent-based models (ABMs) provide mech- anistic, stochastic simulators of TME evolution; yet their high- dimensional state space and uncertain parameterization make direct control design challenging. This work presents a reduced- order, simulation-driven framework for therapeutic strategy design using ABM-derived trajectory ensembles. Starting from a nominal ABM, we systematically perturb biologically plausible parameters to generate a set of simulated trajectories and construct a low-dimensional trajectory landscape describing TME evolution. From time series of spatial summary statistics extracted from the simulations, we learn a probabilistic Markov State Model (MSM) that captures metastable states and the transitions between them. To connect simulation dynamics with clinical observations, we map patient MTI snapshots onto the landscape and assess concordance with observed spatial phenotypes and clinical outcomes. We further show that conditioning the MSM on dominant governing parameters yields group-specific transition models to formulate a finite-horizon Markov Decision Process (MDP) for treatment scheduling. The resulting framework enables simulation-grounded therapeutic policy design for partially observed biological systems without requiring longitudinal patient measurements.

Kyle Adams, Julia Bruner, Salma Ameziane et al.

Triple-negative breast cancer (TNBC) is an aggressive disease with high mortality and limited treatment options, due to its lack of receptors that have targeted therapies available. The tumor microenvironment (TME) plays a critical role in TNBC progression and therapeutic resistance. In this work, we developed a novel mathematical model to describe key cellular interactions within the TNBC TME, informed by current literature and expert input. Our model consists of a system of ordinary differential equations representing five interacting cell populations: M2 macrophages, cancer-associated fibroblasts, TNBC tumor cells, cytotoxic T lymphocytes, and regulatory T cells. We performed global sensitivity analysis to determine which model parameters most strongly influence tumor burden over a clinically-relevant treatment timeframe. The pathways associated with the most-influential parameters correspond to biological mechanisms that are consistent with known and emerging therapeutic strategies in TNBC, including stromal-mediated tumor support. These results highlight key regulatory interactions within the TNBC TME and provide a quantitative framework for hypothesis generation and future investigation of combination treatment strategies.

Michael Petrides, Aliki Peletidi, Evangelia Nena et al.

Background Approximately 50 million individuals across the globe are impacted by epilepsy, leading to fear, discrimination, psychiatric issues, high costs, and social stigma. Proper diagnosis and treatment could allow up to 70% of those affected to live seizure-free. Community pharmacists have significant potential to actively participate in epilepsy patient care, beyond merely dispensing medications. The objective of this study was to systematically review and assess the roles of pharmacists in epilepsy care, focusing on pharmacist-led interventions and services for patients with epilepsy.Methods Following PRISMA 2020 guidelines, the review included cross-sectional, retrospective cohort, and qualitative/quantitative studies on pharmacist-led epilepsy interventions in community and outpatient settings. Searches were conducted in Scopus, PubMed Central, and Science Direct for studies published through the end of 2023. Two evaluators independently reviewed and chose studies, and the data was analysed using Microsoft Excel®. Quality assessment was performed using the MMAT tool.Results Five eligible studies were included, covering 457 participants. Studies originated from the USA (n = 3), Netherlands (n = 1), and Palestine (n = 1). They evaluated pharmacist-led interventions in epilepsy, including medication adherence, quality of life, and pharmacist’s integration in epilepsy care.Conclusion This review underscores the possible contributions of pharmacists in epilepsy care, stressing the importance of pharmacist-led interventions to enhance medication adherence and the quality of life for individuals with epilepsy. Future research should evaluate the effectiveness and cost-effectiveness of these services, including disease management and patient education. Increasing awareness among pharmacists and patients about pharmacists’ contributions is crucial for improving epilepsy care.

Igbayilola Yusuff Dimeji, Adekola Saheed Ayodeji

Central obesity, atherogenic dyslipidemia, insulin resistance, and hypertension are among the metabolic dysregulations associated with metabolic syndrome (MetS). Insulin resistance and chronic low-grade inflammation are 2 of the many acquired and genetic components that make up the pathophysiology of MetS. MetS is strongly linked to a greater risk of diabetes and cardiovascular disease in the absence of effective treatment. To create effective intervention strategies and preventative techniques, the MetS process needs to be thoroughly examined. Recent research has emphasized the critical roles that metabolic endotoxemia and the gut microbiota play in the pathophysiology of MetS. The manipulation of gut microbiota‒host metabolism interactions has been linked to several factors, including bile acid metabolism, short-chain fatty acid metabolism, and inflammation caused by malfunction of the gut barrier. Pharmacological treatments for the gut microbiota are becoming increasingly popular as treatment alternatives. This brief message highlights some of the most recent developments in pharmaceutical strategies for preventing both gut dysbiosis and systemic low-grade inflammation caused by endotoxemia. Antibiotics, prebiotics, probiotics, synbiotics, postbiotics, and metabolite modulators produced from the microbiota are all used in these tactics. Particular focus is placed on next-generation treatments such as small chemical inhibitors of microbial‒host interactions, bacteriophage therapy, and tailored probiotics. Significance Statement: Pharmacologic alteration of the gut microbiota to target endotoxemia, a major cause of systemic inflammation, is a viable next-generation treatment for metabolic syndrome. These treatments help stop lipopolysaccharide translocation, restore metabolic balance, and improve insulin sensitivity by strengthening the gut barrier and changing the makeup of microbes. This method improves lipid metabolism, decreases chronic inflammation, and targets the underlying causes of disease. As a result, to improve results, treatment is moving from just managing symptoms to changing the disease itself.

Maria Venera Menzo

La frattura dell'omero prossimale rappresenta un trauma comune, con un impatto significativo sulla funzionalità dell'arto superiore. Le opzioni terapeutiche variano tra approccio conservativo e chirurgico, mentre la gestione riabilitativa gioca un ruolo cruciale nel recupero del movimento e nella riduzione della disabilità. La letteratura esistente evidenzia una carenza di studi in questo ambito. Questo lavoro si propone di confrontare i risultati funzionali di pazienti con frattura di omero prossimale trattati con strategie chirurgiche e non chirurgiche, che hanno svolto un programma riabilitativo rispetto a quelli che non l’hanno eseguito, con un'ulteriore distinzione basata sulla tempistica dell'intervento riabilitativo.

Yuxi Liu, Yanlin Cao, Yugang Li et al.

Focusing on vaccines available to adults and not in the immunization schedule, this study investigates the preferences and factors influencing adults in selecting vaccination clinic locations. It aims to provide strategic insights for boosting vaccination rates by analyzing adults’ decision-making factors. This contributes to developing more efficient, patient-focused vaccination strategies that tackle vaccine hesitancy and improve access to vaccination sites. We conducted a cross-sectional study through the “YueMiao” platform from November 1 to December 10, 2023, using convenience and purposive sampling to engage 2014 participants. We collected data via online surveys that included questions about sociodemographic characteristics, sources of vaccination clinic information, clinic satisfaction, and the impact of site selection on vaccination decisions. Our findings reveal that adults’ site preferences for vaccination are influenced by gender, age, income, and vaccination history. Participants showed a strong preference for locations that offer convenience, efficiency, transparent pricing, and a comfortable environment. Analysis of service satisfaction at these clinics indicates that vaccinated individuals report higher satisfaction with appointment systems, wait times, and service hours than those unvaccinated. Furthermore, the preference for vaccination sites consistently aligns with the vaccine type, with a majority opting for community health service centers. Our results suggest that public health strategies should concentrate on enhancing site convenience, service quality, and information transparency to elevate adult vaccination rates. Future initiatives should aim to increase public trust in vaccines, improve the selection and quality of vaccination sites, and effectively utilize digital technology for spreading vaccination information.

Yuhong Zhang, Chenghang Li, Boya Wang et al.

Tumor-immune interactions are central to cancer progression and treatment outcomes. In this study, we present a stochastic agent-based model that integrates cellular heterogeneity, spatial cell-cell interactions, and drug resistance evolution to simulate tumor growth and immune response in a two-dimensional microenvironment. The model captures dynamic behaviors of four major cell types--tumor cells, cytotoxic T lymphocytes, helper T cells, and regulatory T cells--and incorporates key biological processes such as proliferation, apoptosis, migration, and immune regulation. Using this framework, we simulate tumor progression under different therapeutic interventions, including radiotherapy, targeted therapy, and immune checkpoint blockade. Our simulations reproduce emergent phenomena such as immune privilege and spatial immune exclusion. Quantitative analyses show that all therapies suppress tumor growth to varying degrees and reshape the tumor microenvironment. Notably, combination therapies--especially targeted therapy with immunotherapy--achieve the most effective tumor control and delay the emergence of resistance. Additionally, sensitivity analyses reveal a nonlinear relationship between treatment intensity and therapeutic efficacy, highlighting the existence of optimal dosing thresholds. This work demonstrates the utility of agent-based modeling in capturing complex tumor-immune dynamics and provides a computational platform for optimizing cancer treatment strategies. The model is extensible, biologically interpretable, and well-suited for future integration with experimental or clinical data.

Adham M. Alkhadrawi, Kyungsu Kim, Arif M. Rahman et al.

We developed an explainable deep learning framework integrating protein-protein interaction (PPI) network centrality metrics with node embeddings for cancer therapeutic target prioritization. A high-confidence PPI network was constructed from STRING database interactions, computing six centrality metrics: degree, strength, betweenness, closeness, eigenvector centrality, and clustering coefficient. Node2Vec embeddings captured latent network topology. Combined features trained XGBoost and neural network classifiers using DepMap CRISPR essentiality scores as ground truth. Model interpretability was assessed through GradientSHAP analysis quantifying feature contributions. We developed a novel blended scoring approach combining model probability predictions with SHAP attribution magnitudes for enhanced gene prioritization. Our framework achieved state-of-the-art performance with AUROC of 0.930 and AUPRC of 0.656 for identifying the top 10\% most essential genes. GradientSHAP analysis revealed centrality measures contributed significantly to predictions, with degree centrality showing strongest correlation ($ρ$ = -0.357) with gene essentiality. The blended scoring approach created robust gene prioritization rankings, successfully identifying known essential genes including ribosomal proteins (RPS27A, RPS17, RPS6) and oncogenes (MYC). This study presents a human-based, combinatorial in silico framework successfully integrating network biology with explainable AI for therapeutic target discovery. The framework provides mechanistic transparency through feature attribution analysis while maintaining state-of-the-art predictive performance. Its reproducible design and reliance on human molecular datasets demonstrate a reduction-to-practice example of next-generation, animal-free modeling for cancer therapeutic target discovery and prioritization.

Zifang He, Longtao Yang, Peiyao Ma

m5C modification is a type of RNA methylation modification, and its major methyltransferase, NSUN2, catalyzes m5C modification. NSUN2 is overexpressed in a variety of cancers, and it affects the metabolism of RNA from target genes by affecting the level of m5C modification in cancer cells, which in turn promotes the development of cancers and is associated with poor prognosis. This review summarizes the mechanisms by which NSUN2 and m5C play a pro-cancer role in various cancers, and the relationship between NSUN2 and the prognosis of various cancers, with the aim of identifying NSUN2 as a prognostic indicator and a target for future cancer therapy, and to provide a clearer therapeutic idea and direction for the future treatment of cancer.

Mónica Moreira-Rodrigues, Estibaliz Ampuero, Ursula Wyneken et al.

Samira Pakravan, Nikolaos Evangelou, Maxime Usdin et al.

Digital health technologies (DHT), such as wearable devices, provide personalized, continuous, and real-time monitoring of patient. These technologies are contributing to the development of novel therapies and personalized medicine. Gaining insight from these technologies requires appropriate modeling techniques to capture clinically-relevant changes in disease state. The data generated from these devices is characterized by being stochastic in nature, may have missing elements, and exhibits considerable inter-individual variability - thereby making it difficult to analyze using traditional longitudinal modeling techniques. We present a novel pharmacology-informed neural stochastic differential equation (SDE) model capable of addressing these challenges. Using synthetic data, we demonstrate that our approach is effective in identifying treatment effects and learning causal relationships from stochastic data, thereby enabling counterfactual simulation.

Binglu Wang, Ching Jin, Chaoming Song et al.

Despite the vast literature on the diffusion of innovations that impacts a broad range of disciplines, our understanding of the abandonment of innovations remains limited yet is essential for a deeper understanding of the innovation lifecycle. Here, we analyze four large-scale datasets that capture the temporal and structural patterns of innovation abandonment across scientific, technological, commercial, and pharmacological domains. The paper makes three primary contributions. First, across these diverse domains, we uncover one simple pattern of preferential abandonment, whereby the probability for individuals or organizations to abandon an innovation increases with time and correlates with the number of network neighbors who have abandoned the innovation. Second, we find that the presence of preferential abandonment fundamentally alters the way in which the underlying ecosystem breaks down, inducing a novel structural collapse in networked systems commonly perceived as robust against abandonments. Third, we derive an analytical framework to systematically understand the impact of preferential abandonment on network dynamics, pinpointing specific conditions where it may accelerate, decelerate, or have an identical effect compared to random abandonment, depending on the network topology. Together, these results deepen our quantitative understanding of the abandonment of innovation within networked social systems, with implications for the robustness and functioning of innovation communities. Overall, they demonstrate that the dynamics of innovation abandonment follow simple yet reproducible patterns, suggesting that the uncovered preferential abandonment may be a generic property of the innovation lifecycle.

Heather Marriott, Renata Kabiljo, Guy P Hunt et al.

Abstract Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80–90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk .

Giorgos Lysandrou, Roma English Owen, Vanja Popovic et al.

It is essential that healthcare professionals and members of the healthcare community can access and easily understand patient experiences in the real world, so that care standards can be improved and driven towards personalised drug treatment. Social media platforms and message boards are deemed suitable sources of patient experience information, as patients have been observed to discuss and exchange knowledge, look for and provide support online. This paper tests the hypothesis that not all online patient experience information can be treated and collected in the same way, as a result of the inherent differences in the way individuals talk about their journeys, in different therapeutic domains and or data sources. We used linguistic analysis to understand and identify similarities between datasets, across patient language, between data sources (Reddit, SocialGist) and therapeutic domains (cardiovascular, oncology, immunology, neurology). We detected common vocabulary used by patients in the same therapeutic domain across data sources, except for immunology patients, who use unique vocabulary between the two data sources, and compared to all other datasets. We combined linguistically similar datasets to train classifiers (CNN, transformer) to accurately identify patient experience posts from social media, a task we refer to as patient voice classification. The cardiovascular and neurology transformer classifiers perform the best in their respective comparisons for the Reddit data source, achieving F1-scores of 0.865 and 1.0 respectively. The overall best performing classifier is the transformer classifier trained on all data collected for this experiment, achieving F1-scores ranging between 0.863 and 0.995 across all therapeutic domain and data source specific test datasets.

Tianlai Chen, Madeleine Dumas, Rio Watson et al.

Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation. The computational design of protein-based binders presents unique opportunities to access "undruggable" targets, but have often relied on stable 3D structures or structure-influenced latent spaces for effective binder generation. In this work, we introduce PepMLM, a target sequence-conditioned generator of de novo linear peptide binders. By employing a novel span masking strategy that uniquely positions cognate peptide sequences at the C-terminus of target protein sequences, PepMLM fine-tunes the state-of-the-art ESM-2 pLM to fully reconstruct the binder region, achieving low perplexities matching or improving upon validated peptide-protein sequence pairs. After successful in silico benchmarking with AlphaFold-Multimer, outperforming RFDiffusion on structured targets, we experimentally verify PepMLM's efficacy via fusion of model-derived peptides to E3 ubiquitin ligase domains, demonstrating endogenous degradation of emergent viral phosphoproteins and Huntington's disease-driving proteins. In total, PepMLM enables the generative design of candidate binders to any target protein, without the requirement of target structure, empowering downstream therapeutic applications.

Tanish Jain

The positive therapeutic effect of viewing pet images online has been well-studied. However, it is difficult to obtain large-scale production of such content since it relies on pet owners to capture photographs and upload them. I use a Generative Adversarial Network-based framework for the creation of fake pet images at scale. These images are uploaded on an Instagram account where they drive user engagement at levels comparable to those seen with images from accounts with traditional pet photographs, underlining the applicability of the framework to be used for pet-therapy social media content.

Huizhi Kang, Zhuo Zuo, Ru Lin et al.

Microneedle patch (MNP) is an alternative to the oral route and subcutaneous injection with unique advantages such as painless administration, good compliance, and fewer side effects. Herein, we report MNP as a prominent strategy for drug delivery to treat local or systemic disease. Hyaluronic acid (HA) has advantageous properties, such as human autologous source, strong water absorption, biocompatibility, and viscoelasticity. Therefore, the Hyaluronic acid microneedle patch (HA MNP) occupies a large part of the MNP market. HA MNP is beneficial for wound healing, targeted therapy of certain specific diseases, extraction of interstitial skin fluid (ISF), and preservation of drugs. In this review, we summarize the benefits of HA and cross-linked HA (x-HA) as an MNP matrix. Then, we introduce the types of HA MNP, delivered substances, and drug distribution. Finally, we focus on the biomedical application of HA MNP as an excellent drug carrier in some specific diseases and the extraction and analysis of biomarkers. We also discuss the future development prospect of HA MNP in transdermal drug delivery systems (TDDS).

Laura Bayón-Cordero, Laura Bayón-Cordero, Laura Bayón-Cordero et al.

Oligodendrocytes are the myelin forming cells of the central nervous system, and their vulnerability to excitotoxicity induced by glutamate contributes to the pathogenesis of neurological disorders including brain ischemia and neurodegenerative diseases, such as multiple sclerosis. In addition to glutamate receptors, oligodendrocytes express GABA receptors (GABAR) that are involved in their survival and differentiation. The interactions between glutamate and GABAergic systems are well documented in neurons, under both physiological and pathological conditions, but this potential crosstalk in oligodendrocytes has not been studied in depth. Here, we evaluated the protective effect of GABAR agonists, baclofen (GABAB) and muscimol (GABAA), against AMPA-induced excitotoxicity in cultured rat oligodendrocytes. First, we observed that both baclofen and muscimol reduced cell death and caspase-3 activation after AMPA insult, proving their oligoprotective potential. Interestingly, analysis of the cell-surface expression of calcium-impermeable GluR2 subunits in oligodendrocytes revealed that GABAergic agonists significantly reverted GluR2 internalization induced by AMPA. We determined that baclofen and muscimol also impaired AMPA-induced intracellular calcium increase and subsequent mitochondrial membrane potential alteration, ROS generation, and calpain activation. However, AMPA-triggered activation of Src, Akt, JNK and CREB was not affected by baclofen or muscimol. Overall, our results suggest that GABAR activation initiates alternative molecular mechanisms that attenuate AMPA-mediated apoptotic excitotoxicity in oligodendrocytes by interfering with expression of GluR subunits in membranes and with calcium-dependent intracellular signaling pathways. Together, these findings provide evidence of GABAR agonists as potential oligodendroglial protectants in central nervous system disorders.

article Editorial

4 декабря 2021 г. на 81-м году жизни скончался бывший директор Нижегородского НИИ детской гастроэнтерологии профессор Анатолий Иванович Волков.

Md Sorwer Alam Parvez, Manash Kumar Saha, Md. Ibrahim et al.

Prominently accountable for the upsurge of COVID-19 cases as the world attempts to recover from the previous two waves, Omicron has further threatened the conventional therapeutic approaches. Omicron is the fifth variant of concern (VOC), which comprises more than 10 mutations in the receptor-binding domain (RBD) of the spike protein. However, the lack of extensive research regarding Omicron has raised the need to establish correlations to understand this variant by structural comparisons. Here, we evaluate, correlate, and compare its genomic sequences through an immunoinformatic approach with wild and mutant RBD forms of the spike protein to understand its epidemiological characteristics and responses towards existing drugs for better patient management. Our computational analyses provided insights into infectious and pathogenic trails of the Omicron variant. In addition, while the analysis represented South Africa's Omicron variant being similar to the highly-infectious B.1.620 variant, mutations within the prominent proteins are hypothesized to alter its pathogenicity. Moreover, docking evaluations revealed significant differences in binding affinity with human receptors, ACE2 and NRP1. Owing to its characteristics of rendering existing treatments ineffective, we evaluated the drug efficacy against their target protein encoded in the Omicron through molecular docking approach. Most of the tested drugs were proven to be effective. Nirmatrelvir (Paxlovid), MPro 13b, and Lopinavir displayed increased effectiveness and efficacy, while Ivermectin showed the best result against Omicron.