Hasil untuk "Therapeutics. Pharmacology"

Keqi Wang, Keilung Choy, Eli Reiser et al.

In vitro transcription (IVT) plays a critical role in the manufacture of mRNA vaccines and therapeutics. Optimizing mRNA yield and ensuring product quality, such as capping efficiency and integrity, are essential but mechanistically complex. This study presents a modular mechanistic model of the IVT process to advance scientific understanding and improve predictive capability. The IVT reaction network is decomposed into interconnected modules describing (1) initiation and capping, (2) elongation and truncation, (3) termination and read-through, (4) mRNA degradation, (5) magnesium pyrophosphate precipitation, and (6) enzymatic degradation of pyrophosphate. Guided by biochemical principles and experimental data, kinetic models were developed for each module, accounting for mass balances, molecular complexation, and enzyme activity, and were subsequently assembled to capture coupled IVT dynamics. Multivariate residual analysis and Shapley value-based sensitivity analysis, guided by domain knowledge, were applied to iteratively improve model fidelity. These machine learning-driven analytics enabled identification of key mechanisms, supported in silico experimentation, and facilitated root-cause analysis. Combined with Gaussian-process-based batch Bayesian optimization for efficient parameter estimation, this framework establishes a scalable hybrid (mechanistic + machine learning) modeling platform that integrates heterogeneous data, accelerates model calibration, and supports rational design and optimization of mRNA manufacturing processes.

Karl Swärd, Karl-Erik Andersson, Bengt Uvelius

Membrane channels are central to bladder function, yet current understanding is shaped disproportionately by a few well-studied families such as TRPA1 and TRPV1. To provide a more balanced view, this review analyzed emerging human transcriptomic datasets to identify the channels most highly expressed in the urinary bladder and examined how they remodel in bladder outlet obstruction and denervation. Sixty-seven channels were prominently expressed at the mRNA level in GTEx bladder tissue, with correlation analyses and protein expression data assigning many to smooth muscle, urothelial, endothelial, or neuronal compartments. Several abundant channels remain largely unstudied in urological contexts, including CLIC4, CLCN3, TPCN1 and ANO10. Disease-associated remodeling revealed shared and model-specific patterns. Outlet obstruction produced marked upregulation of L-type Ca2+ channel auxiliary subunits and robust changes in CLIC-family channels, whereas denervation induced broader channel downregulation not explained by nerve loss alone. Three channels, Gja1, Piezo1 and Ano1, were concordantly altered in both conditions, suggesting coordinated changes within interstitial cell networks and mechanotransductive pathways. These findings highlight a diverse and incompletely explored bladder “channel-ome.” Expanding research beyond traditional targets may uncover new mechanisms underlying storage and voiding dysfunction and provide opportunities for therapeutic innovation in lower urinary tract disease.

Qin Wu, W. Ba-alawi, Geneviève Deblois et al.

Triple negative breast cancer (TNBC) is a deadly form of breast cancer due to the development of resistance to chemotherapy affecting over 30% of patients. New therapeutics and companion biomarkers are urgently needed. Recognizing the elevated expression of glucose transporter 1 (GLUT1, encoded by SLC2A1) and associated metabolic dependencies in TNBC, we investigated the vulnerability of TNBC cell lines and patient-derived samples to GLUT1 inhibition. We report that genetic or pharmacological inhibition of GLUT1 with BAY-876 impairs the growth of a subset of TNBC cells displaying high glycolytic and lower oxidative phosphorylation (OXPHOS) rates. Pathway enrichment analysis of gene expression data suggests that the functionality of the E2F pathway may reflect to some extent OXPHOS activity. Furthermore, the protein levels of retinoblastoma tumor suppressor (RB1) strongly correlate with the degree of sensitivity to GLUT1 inhibition in TNBC, where RB1-negative cells are insensitive to GLUT1 inhibition. Collectively, our results highlight a strong and targetable RB1-GLUT1 metabolic axis in TNBC and warrant clinical evaluation of GLUT1 inhibition in TNBC patients stratified according to RB1 protein expression levels. Triple negative breast cancer is a deadly form of breast cancer with limited therapeutic options. Here the authors show the efficacy of GLUT1 pharmacological inhibition against a subset of tumors expressing RB1, thereby identifying RB1 protein level as a biomarker of sensitivity to anti-GLUT1 therapy.

A. Vassiliou, A. Kotanidou, I. Dimopoulou et al.

The pulmonary endothelium is a metabolically active continuous monolayer of squamous endothelial cells that internally lines blood vessels and mediates key processes involved in lung homoeostasis. Many of these processes are disrupted in acute respiratory distress syndrome (ARDS), which is marked among others by diffuse endothelial injury, intense activation of the coagulation system and increased capillary permeability. Most commonly occurring in the setting of sepsis, ARDS is a devastating illness, associated with increased morbidity and mortality and no effective pharmacological treatment. Endothelial cell damage has an important role in the pathogenesis of ARDS and several biomarkers of endothelial damage have been tested in determining prognosis. By further understanding the endothelial pathobiology, development of endothelial-specific therapeutics might arise. In this review, we will discuss the underlying pathology of endothelial dysfunction leading to ARDS and emerging therapies. Furthermore, we will present a brief overview demonstrating that endotheliopathy is an important feature of hospitalised patients with coronavirus disease-19 (COVID-19).

N. Iwamoto, David C D Butler, N. Svrzikapa et al.

Chaithanya Purushottam Bhat, Pranav Suryawanshi, Aditya Guneja et al.

Efficient identification of promising drug candidates for nanomaterial-based delivery systems is essential for advancing next-generation therapeutics. In this work, we present a synergistic framework combining density functional theory (DFT) and machine learning (ML) to explore the adsorption behavior and electronic interactions of drugs on a novel 2D graphene allotrope, termed Graphsene (GrS). Graphsene, characterized by its porous ring topology and large surface area, offers an excellent platform for efficient adsorption and strong electronic coupling with drug molecules. A dataset comprising 67 drugs adsorbed on various 2D substrates was employed to train the ML model, which was subsequently applied to predict suitable drug candidates for GrS based on molecular size and adsorption energy criteria (database link provided in a later section). The ML model exhibited robust predictive accuracy, achieving a mean absolute error of 0.075 eV upon DFT validation, though its sensitivity to initialization highlighted the need for larger and more diverse datasets. DFT-based analyses, including adsorption energetics, projected density of states (PDOS), and Bader charge calculations, revealed pronounced charge transfer and electronic coupling between the drug molecules and the GrS surface, elucidating the fundamental nature of drug-substrate interactions. The study reveals that the integrated DFT-ML strategy offers a rapid, cost-efficient approach for screening and understanding drug-nanomaterial interactions, paving the way for data-driven design of advanced nanomaterial-enabled drug delivery systems.

Suthep Udomsawaengsup, Sathienrapong Chantawibul, Naranon Boonyuen et al.

ABSTRACT Data on the effects of Roux‐en‐Y gastric bypass (RYGB) surgery on lansoprazole pharmacokinetics in morbidly obese patients are limited. This study aimed to evaluate the impact of RYGB surgery on the pharmacokinetic profile of lansoprazole in Thai morbidly obese patients. Participants received 30 mg of lansoprazole twice daily for 7 days before surgery and continued the regimen for 6 weeks post‐surgery. Plasma lansoprazole concentrations were measured at predose (0), 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, and 8 h after dosing, both pre‐ and post‐surgery, using a validated high‐performance liquid chromatography technique. CYP2C19 genotyping classified participants as normal metabolizers (*1/*1) or intermediate metabolizers (*1/*2 and *1/*3). Pharmacokinetic parameters, including the area under the plasma concentration‐time curve from 0 to 8 h (AUC0–8 h), maximum plasma concentration (Cmax), and time to maximum concentration (Tmax), were compared before and after surgery. A total of 13 patients (mean age 37.0 ± 3.9 years; body mass index 54.0 ± 4.8 kg/m2) were enrolled. Post‐surgery, AUC0–8 h and Cmax decreased by 16% (p = 0.009) and 31% (p = 0.003), respectively, while Tmax remained unchanged. A 30% reduction in Cmax (p = 0.007) was observed in CYP2C19 normal metabolizers, whereas no significant changes were noted in intermediate metabolizers. In conclusion, RYGB surgery significantly reduced lansoprazole systemic exposure, particularly in CYP2C19 normal metabolizers. Further studies are needed to explore the clinical implications of these pharmacokinetic changes and develop optimized treatment strategies for post‐RYGB patients. Trial Registration: ClinicalTrials.gov identifier: TCTR20220118001

Xiaorui Kang, Xiaoyan Liu, Quan Zou et al.

IntroductionDrug combination therapy represents a promising strategy for addressing complex diseases, offering the potential for improved efficacy while mitigating safety concerns. However, conventional wet-lab experimentation for identifying optimal drug combinations is resource-intensive due to the vast combinatorial search space. To address this challenge, computational methods leveraging machine learning and deep learning have emerged to effectively navigate this space.MethodsIn this study, we introduce a Calibrated Deep Feature Aggregation (CDFA) framework for screening synergistic drug combinations. Concretely, CDFA utilizes a novel cell line representation based on the protein information and gene expression capturing complementary biological determinants of drug response. Besides, a novel feature aggregation network is proposed based on the Transformer to model the intricate interactions between drug pairs and cell lines through multi-head attention mechanisms, enabling discovery of non-linear synergy patterns. Furthermore, a method is introduced to quantify and calibrate the uncertainties associated with CDFA’s predictions, enhancing the reliability of the identified synergistic drug combinations.ResultsExperiments results have demonstrated that CDFA outperforms existing state-of-the-art deep learning models.DiscussionThe superior performance of CDFA stems from its biologically informed cell line representation, its ability to capture complex non-linear drug-cell interactions via attention mechanisms, and its enhanced reliability through uncertainty calibration. This framework provides a robust computational tool for efficient and reliable drug combination screening.

Ahnika Kline, Ana G. Cobián Güemes, Jennifer Yore et al.

The resurgence of phage therapy in Western societies has been in direct response to recent increases in antimicrobial resistance (AMR) that have ravaged many societies. While phage therapy as a concept has been around for over 100 years, it has largely been replaced by antibiotics due to their relative ease of use and their predictability in spectrum of activity. Now that antibiotics have become less reliable due to greater antibiotic resistance and microbiome disruption, phage therapy has once again become a viable and promising alternative, but it is not without its challenges. Much like the development of antibiotics, with deployment of phage therapeutics there will be a simultaneous need for diagnostics in the clinical laboratory. This review provides an overview of current challenges to widespread adoption of phage therapy with a focus on adoption in the clinical diagnostic laboratory. Current barriers include a lack of standard methodology and quality controls for phage susceptibility testing and selection, the absence of phage-antibiotic synergy testing, and the absence of standard methods to assay phage activity on biofilms. Additionally, there are a number of lab-specific administrative and regulatory barriers to widespread phage therapy adoption including the need for pharmacokinetic (PK) and pharmacodynamic (PD) assays, methods to account for changes in phages after passaging, an absence of regulatory guidance on what will be required for agency approvals of phages and how broad that approval will apply, and the increased need for lab personnel or automation to account for the work of testing large phage libraries against bacteria isolates.

Iram Pervaiz, Syed Muhammad Ahmed Rahim, Muhammad Zubair Ahmad Khan et al.

Background: Trismus or lockjaw characterized by limited mouth opening presents as complication associated with odontogenic infections commonly involving mandibular teeth. Due to limited literature providing insight to probable odontogenic causes of trismus, this study is oriented to determine the incidence of Trismus severity in association with fascial space infection resulting from odontogenic causes, i.e., pericoronitis, pulp infection, or periodontal infection. Methods: This study was conducted at the Department of Oral and Maxillofacial Surgery, Azra Naheed Dental College/ Chaudhry Muhammad Akram Dental Hospital from July 2024 to December 2024. In this descriptive cross-sectional study, following non-probability purposive sampling, 87 patients who had trismus secondary to fascial space infections were enrolled. All required demographic and clinical data were recorded in a purpose-designed form. The collected data were analyzed by the Chi-square test, using SPSS version 25. A p-value of ≤ 0.005 was considered significant. Results: The average mean value of Trismus was 23.5±5.5mm. Submandibular space infection most frequently involved the fascial space 42 (48.2%), and the mandibular third molar was frequently involved, offending tooth 43 (49.4%) in this study. The most common cause of odontogenic infection was Pulp infection/caries 58 (66.6%), followed by pericoronitis 27 (31.3%) and periodontal infection 2 (2.3%). Conclusion: Mandibular third molars are the most involved teeth, leading to fascial space infection and associated with increased severity of Trismus. The submandibular space is the most affected fascial space, and pulp infection is the major cause of odontogenic infection, leading to fascial space involvement.

Muhammad Akmal Raheem, Muhammad Ajwad Rahim, Ijaz Gul et al.

SARS-CoV-2, the highly contagious pathogen responsible for the COVID-19 pandemic, has persistent effects that begin four weeks after initial infection and last for an undetermined duration. These chronic effects are more harmful than acute ones. This review explores the long-term impact of the virus on various human organs, including the pulmonary, cardiovascular, neurological, reproductive, gastrointestinal, musculoskeletal, endocrine, and lymphoid systems, particularly in older adults. Regarding diagnosis, RT-PCR is the gold standard for detecting COVID-19, though it requires specialized equipment, skilled personnel, and considerable time to produce results. To address these limitations, artificial intelligence in imaging and microfluidics technologies offers promising alternatives for diagnosing COVID-19 efficiently. Pharmacological and non-pharmacological strategies are effective in mitigating the persistent impacts of COVID-19. These strategies enhance immunity in post-COVID-19 patients by reducing cytokine release syndrome, improving T cell response, and increasing the circulation of activated natural killer and CD8 T cells in blood and tissues. This, in turn, alleviates symptoms such as fever, nausea, fatigue, muscle weakness, and pain. Vaccines, including inactivated viral, live attenuated viral, protein subunit, viral vectored, mRNA, DNA, and nanoparticle vaccines, significantly reduce the adverse long-term effects of the virus. However, no vaccine has been reported to provide lifetime protection against COVID-19. Consequently, protective measures such as physical distancing, mask usage, and hand hygiene remain essential strategies. This review offers a comprehensive understanding of the persistent effects of COVID-19 on individuals of varying ages, along with insights into diagnosis, treatment, vaccination, and future preventative measures against the spread of SARS-CoV-2.

David Benrimoh, Caitrin Armstrong, Joseph Mehltretter et al.

INTRODUCTION: The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial intelligence (AI) model aiming to personalize treatment and improve outcomes, which was deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study. OBJECTIVES: 1) Develop a model capable of predicting probabilities of remission across multiple pharmacological treatments for adults with at least moderate major depression. 2) Validate model predictions and examine them for amplification of harmful biases. METHODS: Data from previous clinical trials of antidepressant medications were standardized into a common framework and included 9,042 adults with moderate to severe major depression. Feature selection retained 25 clinical and demographic variables. Using Bayesian optimization, a deep learning model was trained on the training set, refined using the validation set, and tested once on the held-out test set. RESULTS: In the evaluation on the held-out test set, the model demonstrated achieved an AUC of 0.65. The model outperformed a null model on the test set (p = 0.01). The model demonstrated clinical utility, achieving an absolute improvement in population remission rate in hypothetical and actual improvement testing. While the model did identify one drug (escitalopram) as generally outperforming the other drugs (consistent with the input data), there was otherwise significant variation in drug rankings. On bias testing, the model did not amplify potentially harmful biases. CONCLUSIONS: We demonstrate the first model capable of predicting outcomes for 10 different treatment options for patients with MDD, intended to be used at or near the start of treatment to personalize treatment. The model was put into clinical practice during the AIDME randomized controlled trial whose results are reported separately.

Dimitris Alevras, Mihir Metkar, Takahiro Yamamoto et al.

Recent advancements in quantum computing have opened new avenues for tackling long-standing complex combinatorial optimization problems that are intractable for classical computers. Predicting secondary structure of mRNA is one such notoriously difficult problem that can benefit from the ever-increasing maturity of quantum computing technology. Accurate prediction of mRNA secondary structure is critical in designing RNA-based therapeutics as it dictates various steps of an mRNA life cycle, including transcription, translation, and decay. The current generation of quantum computers have reached utility-scale, allowing us to explore relatively large problem sizes. In this paper, we examine the feasibility of solving mRNA secondary structures on a quantum computer with sequence length up to 60 nucleotides representing problems in the qubit range of 10 to 80. We use Conditional Value at Risk (CVaR)-based VQE algorithm to solve the optimization problems, originating from the mRNA structure prediction problem, on the IBM Eagle and Heron quantum processors. To our encouragement, even with ``minimal'' error mitigation and fixed-depth circuits, our hardware runs yield accurate predictions of minimum free energy (MFE) structures that match the results of the classical solver CPLEX. Our results provide sufficient evidence for the viability of solving mRNA structure prediction problems on a quantum computer and motivate continued research in this direction.

Christian S Sharma, Pravin S Lohar, Ajit M Zende

Background: A package insert is a document within a medication package that provides information about the product. Labels similarly covey information regarding the product but are printed on the packaging rather than separately within. Aims and Objectives: This study aimed to determine whether pharmaceuticals in India adhere to the national guidelines laid down by CDSCO with regards to the packaging and labeling of drugs. Materials and Methods: 100 drugs (labels + package inserts) were collected within duration of 1 month, beginning June 6th, 2023,–July 5th, 2023, from the central drug store of a tertiary care government hospital and nearby pharmacies. After entering pertinent data into a Microsoft Excel sheet, analysis was carried out using descriptive statistics. Results: Most companies (in and around 70%) followed requirements with respect to drug labels barring "warning about the drug" and use of the "red line," while package inserts showed divisive results, with patient-centric information such as posology, special warnings, and contraindications being followed by more than 80% of the manufacturer's while taking a backseat with regards to technical details such as shelf life and incompatibilities. Conclusion: Labels and package inserts do largely convey the information that they intend to, with scope for improvement from both companies and regulatory authorities in order to perfect the balance of information conveyed. [Natl J Physiol Pharm Pharmacol 2024; 14(4.000): 742-745]

IGVF Consortium

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations.

I. Schweigert, M. Biryukov, A. Polyakova et al.

Efficient and biologically safe mode of cold atmospheric plasma jet (CAPJ) is crucial for the development of CAPJ-based anticancer therapy. In the experiment and numerical simulations, by changing the pulse duration of a positive-pulsed voltage, we found the optimal CAPJ mode with regular streamer propagation. CAPJ regimes with a maximum discharge current at a temperature T<42 C substantially suppressed the viability of cancer cells. To enhance cell killing, gold nanoparticles (NPs) were added to the cells before and after the CAPJ exposure. Combination of CAPJ, generated with positive pulsed voltage, and gold nanoparticles decreased viability of NCI-H23 epithelial-like lung adenocarcinoma, A549 lung adenocarcinoma, BrCCh4e-134 breast adenocarcinoma and uMel1 uveal melanoma cells. Polyethylene glycol-modified nanoparticles with attached fluorescent label were used to visualize the uptake of NPs. We demonstrated that NPs efficiently entered the cells when were added to the cells just before CAPJ exposure or up to two hours afterwards. The efficiency ofNPs penetration into cells positively correlated with the induced cytotoxic effect: it was maximal when NPs was added to cells right before or immediately after CAPJ exposure. Summarizing, the treatment with optimal CAPJ modes in combination with modified NPs, bearing the cancer-addressed molecules and therapeutics may be next strategy of strengthening the CAPJ-based antitumor approaches.

Ioana Creanga-Murariu, Ioana Creanga-Murariu, Leontina Elena Filipiuc et al.

Cannabis enjoyed a “golden age” as a medicinal product in the late 19th, early 20th century, but the increased risk of overdose and abuse led to its criminalization. However, the 21st century have witnessed a resurgence of interest and a large body of literature regarding the benefits of cannabinoids have emerged. As legalization and decriminalization have spread around the world, cancer patients are increasingly interested in the potential utility of cannabinoids. Although eager to discuss cannabis use with their oncologist, patients often find them to be reluctant, mainly because clinicians are still not convinced by the existing evidence-based data to guide their treatment plans. Physicians should prescribe cannabis only if a careful explanation can be provided and follow up response evaluation ensured, making it mandatory for them to be up to date with the positive and also negative aspects of the cannabis in the case of cancer patients. Consequently, this article aims to bring some clarifications to clinicians regarding the sometimes-confusing various nomenclature under which this plant is mentioned, current legislation and the existing evidence (both preclinical and clinical) for the utility of cannabinoids in cancer patients, for either palliation of the associated symptoms or even the potential antitumor effects that cannabinoids may have.

Chao Pang, Yu Wang, Yi Jiang et al.

In this work, we propose MEDICO, a Multi-viEw Deep generative model for molecule generation, structural optimization, and the SARS-CoV-2 Inhibitor disCOvery. To the best of our knowledge, MEDICO is the first-of-this-kind graph generative model that can generate molecular graphs similar to the structure of targeted molecules, with a multi-view representation learning framework to sufficiently and adaptively learn comprehensive structural semantics from targeted molecular topology and geometry. We show that our MEDICO significantly outperforms the state-of-the-art methods in generating valid, unique, and novel molecules under benchmarking comparisons. In particular, we showcase the multi-view deep learning model enables us to generate not only the molecules structurally similar to the targeted molecules but also the molecules with desired chemical properties, demonstrating the strong capability of our model in exploring the chemical space deeply. Moreover, case study results on targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that by integrating molecule docking into our model as chemical priori, we successfully generate new small molecules with desired drug-like properties for the Mpro, potentially accelerating the de novo design of Covid-19 drugs. Further, we apply MEDICO to the structural optimization of three well-known Mpro inhibitors (N3, 11a, and GC376) and achieve ~88% improvement in their binding affinity to Mpro, demonstrating the application value of our model for the development of therapeutics for SARS-CoV-2 infection.

Elsa Fernandez, Jordi Sole-Casals, Pilar M. Calvo et al.

Dementia, and specially Alzheimer s disease (AD) and Mild Cognitive Impairment (MCI) are one of the most important diseases suffered by elderly population. Music therapy is one of the most widely used non-pharmacological treatment in the field of cognitive impairments, given that music influences their mood, behavior, the decrease of anxiety, as well as facilitating reminiscence, emotional expressions and movement. In this work we present HAIDA, a multi-platform support system for Musical Therapy oriented to cognitive impairment, which includes not only therapy tools but also non-invasive biometric analysis, speech, activity and hand activity. At this moment the system is on use and recording the first sets of data.

William Parke, Dalong Pang

Therapeutic protons acting on O18-substituted thymidine increase cytotoxicity in radio-resistant human cancer cells. We consider here the physics behind the irradiation during proton beam therapy and diagnosis using O18-enriched thymine in DNA, with attention to the effect of the presence of thymine-18 on cancer cell death.