Hasil untuk "Therapeutics. Pharmacology"

G. Semenza

B. Leader, Quentin J. Baca, D. Golan

C. Szabó, H. Ischiropoulos, R. Radi

W. Stigelman, P. D.

Nikos Karamanos, Z. Piperigkou, A. Theocharis et al.

The extracellular matrix (ECM) constitutes a highly dynamic three-dimensional structural network comprised of macromolecules, such as proteoglycans/glycosaminoglycans (PGs/GAGs), collagens, laminins, fibronectin, elastin, other glycoproteins and proteinases. In recent years, the field of PGs has expanded rapidly. Due to their high structural complexity and heterogeneity, PGs mediate several homeostatic and pathological processes. PGs consist of a protein core and one or more covalently attached GAG chains, which provide the protein cores with the ability to interact with several proteins. The GAG building blocks of PGs significantly influence the chemical and functional properties of PGs. The primary goal of this comprehensive review is to summarize major achievements and paradigm-shifting discoveries made on the PG/GAG chemistry-biology axis, focusing on structural variability, structure-function relationships, metabolic, molecular, and epigenetic mechanisms underlying their synthesis. Recent insights related to exosome biogenesis, degradation, and cell signaling, their status as diagnostic tools and potential pharmacological targets in diseases as well as current applications in nanotechnology and biotechnology are addressed. Moreover, issues related to docking studies, molecular modeling, GAG/PG interaction networks, and their integration are discussed.

Subhas Nandy, Monica Manohar, Ashis K Sen

Intracellular delivery of biomolecules remains a critical challenge in both basic cell biology and translational therapeutics. We introduce Programmable Acoustic Standing-wave Transfection (PAST), a microfluidic tool that leverages dynamically programmable ultrasonic fields to transiently permeabilize cell membranes and enhance biomolecular transport within cell clusters. By generating programmable acoustic potential landscapes, PAST drives cells through cycles of hydrodynamic and acoustic stresses that induce reversible pore formation, enabling diffusion-based delivery without chemical carriers or contrast agents. Experimental studies demonstrate controlled influx and efflux dynamics across multiple biomolecular species, with transport rates tunable via acoustic power, frequency modulation, and duty cycles. Theoretical scaling and numerical simulations reveal that membrane tension, pore energetics, and acoustic field distributions collectively govern transmembrane transport of biomolecules. Post-treatment assays confirm high cellular viability and sustained proliferation, underscoring the biocompatibility of the method. Remarkably, effective diffusivity estimates derived from model predictions closely match experimental transport timescales. Together, these findings establish PAST as a programmable, high-throughput, and non-invasive intracellular delivery platform, offering new opportunities for precision drug screening, gene editing, and mechanistic exploration of cellular membrane biophysics.

Jose-Maria Blanc, Isabel Jimeno-Sanz, Valentín Hernández-Barrera et al.

Herpes zoster (HZ) is a vaccine-preventable disease with increasing incidence and hospitalization burden, particularly among older adults and immunocompromised individuals, who have an increased risk. In 2021, Spain introduced systematic vaccination with the recombinant zoster vaccine (RZV). We conducted a retrospective, descriptive study using hospital discharge data from the Spanish Minimum Basic DataSet (MBDS) for the years 2022–2023. Hospitalization rates (HR), mortality rates (MR), case fatality rates (CFR), length of stay, comorbidities, and costs were analyzed nationally and for the region of Madrid. A total of 16,277 HZ-related hospitalizations were recorded in Spain, with 80% occurring in individuals aged ≥65 y. The HR was 16.85 per 100,000 inhabitants, and the CFR was 7.44%. In Madrid, 3263 hospitalizations were recorded, with a higher HR (23.73 per 100,000) and CFR (6.41%) compared to the national average. Complicated HZ cases accounted for over 64% of hospitalizations nationally and 69% in Madrid. Total hospitalization costs were €98.1 million in Spain and €21.4 million in Madrid. This is the first study to assess HZ hospitalization burden in Spain and Madrid following the introduction of RZV. The findings highlight the substantial toll of HZ on older and immunocompromised populations. Future studies with longer follow-up are needed to assess vaccine impact.

Matthew L. Pearn, I. Niesman, Junji Egawa et al.

John Mellnik, Jack Scannell

Consider two similar drug companies with access to similar chemical libraries and synthesis methods, who each run an R&D program. The programs have the same number of stages, which each take the same amount of time, with the same costs, with the same historic stepwise progression rates, and which aim to address the same therapeutic indication. Now let us suppose one of these companies invests in new scientific tools that make it unusually good at critical progression decisions, while the other company does not. How do we assess the difference in value between the two programs? Surprisingly, standard discounted cash flow valuation methods, such as risk-adjusted net present value (rNPV), ubiquitous in drug industry portfolio management and venture capital, are largely useless in this case. They fail to value the decisions that make drug candidates more or less valuable because rNPV conflates wrong decisions to progress bad candidates with right decisions to progress good ones. The purpose of this paper is to set out a new class of valuation model that logically links the value of therapeutic assets with the value of "decisions tools" that are used to design, optimize, and test those assets. Our model makes clear the interaction between asset value and decision tool value. It also makes clear the downstream consequences of better, or worse, upstream decisions. This new approach may support more effective allocation of R&D capital; helping fund therapeutic assets that are developed using good decision tools, and funding better decision tools to distinguish between good and bad therapeutic assets.

Subin Kim, Hoonrae Kim, Jihyun Lee et al.

Recent studies have explored the use of large language models (LLMs) in psychotherapy; however, text-based cognitive behavioral therapy (CBT) models often struggle with client resistance, which can weaken therapeutic alliance. To address this, we propose a multimodal approach that incorporates nonverbal cues, which allows the AI therapist to better align its responses with the client's negative emotional state. Specifically, we introduce a new synthetic dataset, Mirror (Multimodal Interactive Rolling with Resistance), which is a novel synthetic dataset that pairs each client's statements with corresponding facial images. Using this dataset, we train baseline vision language models (VLMs) so that they can analyze facial cues, infer emotions, and generate empathetic responses to effectively manage client resistance. These models are then evaluated in terms of both their counseling skills as a therapist, and the strength of therapeutic alliance in the presence of client resistance. Our results demonstrate that Mirror significantly enhances the AI therapist's ability to handle resistance, which outperforms existing text-based CBT approaches. Human expert evaluations further confirm the effectiveness of our approach in managing client resistance and fostering therapeutic alliance.

Aseem Srivastava, Zuhair Hasan Shaik, Tanmoy Chakraborty et al.

In mental health counseling, a variety of earlier studies have focused on dialogue modeling. However, most of these studies give limited to no emphasis on the quality of interaction between a patient and a therapist. The therapeutic bond between a patient and a therapist directly correlates with effective mental health counseling. It involves developing the patient's trust on the therapist over the course of counseling. To assess the therapeutic bond in counseling, we introduce trust as a therapist-assistive metric. Our definition of trust involves patients' willingness and openness to express themselves and, consequently, receive better care. We conceptualize it as a dynamic trajectory observable through textual interactions during the counseling. To facilitate trust modeling, we present MENTAL-TRUST, a novel counseling dataset comprising manual annotation of 212 counseling sessions with first-of-its-kind seven expert-verified ordinal trust levels. We project our problem statement as an ordinal classification task for trust quantification and propose a new benchmark, TrustBench, comprising a suite of classical and state-of-the-art language models on MENTAL-TRUST. We evaluate the performance across a suite of metrics and lay out an exhaustive set of findings. Our study aims to unfold how trust evolves in therapeutic interactions.

Mohammad Amin Abbasi, Farnaz Sadat Mirnezami, Ali Neshati et al.

We present HamRaz, a culturally adapted Persian-language dataset for AI-assisted mental health support, grounded in Person-Centered Therapy (PCT). To reflect real-world therapeutic challenges, we combine script-based dialogue with adaptive large language models (LLM) role-playing, capturing the ambiguity and emotional nuance of Persian-speaking clients. We introduce HamRazEval, a dual-framework for assessing conversational and therapeutic quality using General Metrics and specialized psychological relationship measures. Human evaluations show HamRaz outperforms existing baselines in empathy, coherence, and realism. This resource contributes to the Digital Humanities by bridging language, culture, and mental health in underrepresented communities.

Na Xing, Jasmin Er, Ricardo M. Vidal et al.

At the onset of viral outbreaks, broad-spectrum antiviral materials are crucial before specific therapeutics become available. We report scalable, biodegradable black phosphorus (BP) hybrids that provide mutation-resilient virucidal protection. BP sheets, produced via an optimized mechanochemical process, are covalently functionalized with 2-azido-4,6-dichloro- 1,3,5-triazine to form P=N bonds. Fucoidan, a sulfated polysaccharide with intrinsic antiviral activity, and hydrophobic chains are then incorporated to achieve irreversible viral deactivation. The material exhibits strong antiviral inhibition and complete virucidal activity against multiple viruses, including recent severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants. It maintains high biocompatibility, remains effective against viral mutations, and is shelf stable for at least five month. The combination of biodegradability, scalable synthesis, and synergistic antiviral and virucidal mechanisms establishes BP-conjugates as a new class of highly efficient antivirals. They offer a broad spectrum antiviral solutions that could bridge the gap between antiviral medicines and general antiseptics.

Mariano Barone, Antonio Laudante, Giuseppe Riccio et al.

The extraction of pharmacological knowledge from regulatory documents has become a key focus in biomedical natural language processing, with applications ranging from adverse event monitoring to AI-assisted clinical decision support. However, research in this field has predominantly relied on English-language corpora such as DrugBank, leaving a significant gap in resources tailored to other healthcare systems. To address this limitation, we introduce DART (Drug Annotation from Regulatory Texts), the first structured corpus of Italian Summaries of Product Characteristics derived from the official repository of the Italian Medicines Agency (AIFA). The dataset was built through a reproducible pipeline encompassing web-scale document retrieval, semantic segmentation of regulatory sections, and clinical summarization using a few-shot-tuned large language model with low-temperature decoding. DART provides structured information on key pharmacological domains such as indications, adverse drug reactions, and drug-drug interactions. To validate its utility, we implemented an LLM-based drug interaction checker that leverages the dataset to infer clinically meaningful interactions. Experimental results show that instruction-tuned LLMs can accurately infer potential interactions and their clinical implications when grounded in the structured textual fields of DART. We publicly release our code on GitHub: https://github.com/PRAISELab-PicusLab/DART.

Qian Shao, Bang Du, Zepeng Li et al.

High failure rates in cardiac drug development necessitate virtual clinical trials via electrocardiogram (ECG) generation to reduce risks and costs. However, existing ECG generation models struggle to balance morphological realism with pathological flexibility, fail to disentangle demographics from genuine drug effects, and are severely bottlenecked by early-phase data scarcity. To overcome these hurdles, we propose the Multimodal Drug-Aware Diffusion Model (MM-DADM), the first generative framework for generating individualized drug-induced ECGs. Specifically, our proposed MM-DADM integrates a Dynamic Cross-Attention (DCA) module that adaptively fuses External Physical Knowledge (EPK) to preserve morphological realism while avoiding the suppression of complex pathological nuances. To resolve feature entanglement, a Causal Feature Encoder (CFE) actively filters out demographic noise to extract pure pharmacological representations. These representations subsequently guide a Causal-Disentangled ControlNet (CDC-Net), which leverages counterfactual data augmentation to explicitly learn intrinsic pharmacological mechanisms despite limited clinical data. Extensive experiments on $9,443$ ECGs across $8$ drug regimens demonstrate that MM-DADM outperforms $10$ state-of-the-art ECG generation models, improving simulation accuracy by at least $6.13\%$ and recall by $5.89\%$, while providing highly effective data augmentation for downstream classification tasks.

Srijit Seal, Maria-Anna Trapotsi, Ola Spjuth et al.

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other -omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded the ability of Cell Painting to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

Mina J. Kian, Mingyu Zong, Katrin Fischer et al.

Cognitive behavioral therapy (CBT) is a widely used therapeutic method for guiding individuals toward restructuring their thinking patterns as a means of addressing anxiety, depression, and other challenges. We developed a large language model (LLM)-powered prompt-engineered socially assistive robot (SAR) that guides participants through interactive CBT at-home exercises. We evaluated the performance of the SAR through a 15-day study with 38 university students randomly assigned to interact daily with the robot or a chatbot (using the same LLM), or complete traditional CBT worksheets throughout the duration of the study. We measured weekly therapeutic outcomes, changes in pre-/post-session anxiety measures, and adherence to completing CBT exercises. We found that self-reported measures of general psychological distress significantly decreased over the study period in the robot and worksheet conditions but not the chatbot condition. Furthermore, the SAR enabled significant single-session improvements for more sessions than the other two conditions combined. Our findings suggest that SAR-guided LLM-powered CBT may be as effective as traditional worksheet methods in supporting therapeutic progress from the beginning to the end of the study and superior in decreasing user anxiety immediately after completing the CBT exercise.

Stephen Zhewen Lu, Ziqing Lu, Ehsan Hajiramezanali et al.

High-content phenotypic screening, including high-content imaging (HCI), has gained popularity in the last few years for its ability to characterize novel therapeutics without prior knowledge of the protein target. When combined with deep learning techniques to predict and represent molecular-phenotype interactions, these advancements hold the potential to significantly accelerate and enhance drug discovery applications. This work focuses on the novel task of HCI-guided molecular design. Generative models for molecule design could be guided by HCI data, for example with a supervised model that links molecules to phenotypes of interest as a reward function. However, limited labeled data, combined with the high-dimensional readouts, can make training these methods challenging and impractical. We consider an alternative approach in which we leverage an unsupervised multimodal joint embedding to define a latent similarity as a reward for GFlowNets. The proposed model learns to generate new molecules that could produce phenotypic effects similar to those of the given image target, without relying on pre-annotated phenotypic labels. We demonstrate that the proposed method generates molecules with high morphological and structural similarity to the target, increasing the likelihood of similar biological activity, as confirmed by an independent oracle model.

Jiseong Kim, Jin‐Su Kim, Dohyun Kim et al.

Abstract Human induced pluripotent stem cells (iPSCs) hold great promise for personalized medicine, as they can be differentiated into specific cell types, especially mesenchymal stem cells (MSCs). Therefore, our study sought to assess the feasibility of deriving MSCs from teratomas generated from human iPSCs. Teratomas serve as a model to mimic multilineage human development, thus enriching specific somatic progenitors and stem cells. Here, we discovered a small, condensed mass of MSCs within iPSC‐generated teratomas. Afterward, we successfully isolated MSCs from this condensed mass, which was a byproduct of teratoma development. To evaluate the characteristics and cell behaviors of iPSC‐derived MSCs (iPSC‐MSCs), we conducted comprehensive assessments using qPCR, immunophenotype analysis, and cell proliferation‐related assays. Remarkably, iPSC‐MSCs exhibited an immunophenotype resembling that of conventional MSCs, and they displayed robust proliferative capabilities, similar to those of higher pluripotent stem cell‐derived MSCs. Furthermore, iPSC‐MSCs demonstrated the ability to differentiate into multiple lineages in vitro. Finally, we evaluated the therapeutic potential of iPSC‐MSCs using an osteochondral defect model. Our findings demonstrated that teratomas are a promising source for the isolation of condensed MSCs. More importantly, our results suggest that iPSC‐MSCs derived from teratomas possess the capacity for tissue regeneration, highlighting their promise for future therapeutic applications.

Yao Deng, Shichao Xie, Wenhao Zhan et al.

This study aimed to assess the influence of varying dietary levels of astaxanthin (AST) on the growth, antioxidant capacity and lipid metabolism of juvenile swimming crabs. Six diets were formulated to contain different AST levels, and the analyzed concentration of AST in experimental diets were 0, 24.2, 45.8, 72.4, 94.2 and 195.0 mg kg⁻¹, respectively. Juvenile swimming crabs (initial weight 8.20 ± 0.01 g) were fed these experimental diets for 56 days. The findings indicated that the color of the live crab shells and the cooked crab shells gradually became red with the increase of dietary AST levels. Dietary 24.2 mg kg⁻¹ astaxanthin significantly improved the growth performance of swimming crab. the lowest activities of glutathione (GSH), total antioxidant capacity (T-AOC), superoxide dismutase (SOD) and peroxidase (POD) were found in crabs fed without AST supplementation diet. Crabs fed diet without AST supplementation showed lower lipid content and the activity of fatty acid synthetase (FAS) in hepatopancreas than those fed diets with AST supplementation, however, lipid content in muscle and the activity of carnitine palmitoyl transferase (CPT) in hepatopancreas were not significantly affected by dietary AST levels. And it can be found in oil red O staining that dietary 24.2 and 45.8 mg kg⁻¹ astaxanthin significantly promoted the lipid accumulation of hepatopancreas. Crabs fed diet with 195.0 mg kg⁻¹ AST exhibited lower expression of <i>ampk</i>, <i>foxo</i>, <i>pi3k</i>, <i>akt</i> and <i>nadph</i> in hepatopancreas than those fed the other diets, however, the expression of genes related to antioxidant such as <i>cMn-sod</i>, <i>gsh-px</i>, <i>cat</i>, <i>trx</i> and <i>gst</i> in hepatopancreas significantly down-regulated with the increase of dietary AST levels. In conclusion, dietary 24.2 and 45.8 mg kg⁻¹ astaxanthin significantly promoted the lipid accumulation of hepatopancreas and im-proved the antioxidant and immune capacity of hemolymph.