Hasil untuk "Therapeutics. Pharmacology"

M. Mangan, M. Mangan, E. Olhava et al.

R. Califf

K. Sriram, P. Insel

Mandana T. Manzari, Yosi Shamay, Hiroto Kiguchi et al.

R. Su, Lei Dong, Yangchan Li et al.

Fat mass and obesity-associated protein (FTO), an RNA N6-methyladenosine (m6A) demethylase, plays oncogenic roles in various cancers, presenting an opportunity for the development of effective targeted therapeutics. Here, we report two potent small-molecule FTO inhibitors that exhibit strong anti-tumor effects in multiple types of cancers. We show that genetic depletion and pharmacological inhibition of FTO dramatically attenuate leukemia stem/initiating cell self-renewal and reprogram immune response by suppressing expression of immune checkpoint genes, especially LILRB4. FTO inhibition sensitizes leukemia cells to T cell cytotoxicity and overcomes hypomethylating agent-induced immune evasion. Our study demonstrates that FTO plays critical roles in cancer stem cell self-renewal and immune evasion and highlights the broad potential of targeting FTO for cancer therapy.

Weiya Zhang, M. Doherty, Nigel K. Arden et al.

Francesco Dettori, Matteo Forasassi, Lorenzo Veronese et al.

Conversational agents are increasingly used as support tools along mental therapeutic pathways with significant societal impacts. In particular, empathy is a key non-functional requirement in therapeutic contexts, yet current chatbot development practices provide no systematic means to specify or verify it. This paper envisions a framework integrating natural language processing and formal verification to deliver empathetic therapy chatbots. A Transformer-based model extracts dialogue features, which are then translated into a Stochastic Hybrid Automaton model of dyadic therapy sessions. Empathy-related properties can then be verified through Statistical Model Checking, while strategy synthesis provides guidance for shaping agent behavior. Preliminary results show that the formal model captures therapy dynamics with good fidelity and that ad-hoc strategies improve the probability of satisfying empathy requirements.

Ming-xing Gong, Shan-shan Li, He Huang et al.

BackgroundIsotretinoin is a vitamin A derivative widely used for moderate-to-severe acne, and is known to cause multiple systemic and ocular adverse events. Refractive errors, such as myopia and astigmatism, are commonly considered reversible after discontinuation of isotretinoin treatment. However, the visual changes and subsequent prognosis after re-exposure to isotretinoin are rarely reported.Case PresentationA 21-year-old male initiated oral isotretinoin at 10 mg twice daily for acne vulgaris. Twelve days later, the patient developed acute visual deterioration. The eye examination revealed mild myopic astigmatism in the right eye and mild myopia in the left eye. The patient’s visual acuity returned to normal at approximately 20 days after isotretinoin cessation. Then, the patient later self-restarted isotretinoin (10 mg, twice daily) for 10 days, which again resulted in decreased visual acuity. Although the patient discontinued isotretinoin, the visual acuity did not recover to baseline over a 14-month follow-up period. The Naranjo Adverse Drug Reaction Probability Scale was eight points, indicating a “probable” causal relationship between isotretinoin and refractive error.ConclusionMyopia and astigmatism might happen concurrently after isotretinoin administration. In addition, recurrent and persistent refractive errors might occur upon restarting isotretinoin in patients with prior history of isotretinoin-related ocular complications. Clinicians should consult patients to take extra precautions on isotretinoin medication when they have a history of ocular disease or ocular complications with isotretinoin administration.

Wang M, Wang T, Wu N et al.

Mei Wang,1,2 Tianqi Wang,1 Nan Wu,2 Yutao Zhang,3,4 Jindi Jia,1 Shengguo Zhao,1,2 Jie Yan,1,2 Airong Wang,1 Dajiang Yuan1,5 1College of Anesthesiology, Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 2Department of Anesthesiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 3College of Second Clinical Medical, Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 4Department of Orthopedics, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 5Department of the Party Committee, Shanxi Cardiovascular Hospital, Taiyuan, Shanxi, People’s Republic of ChinaCorrespondence: Dajiang Yuan, Department of the Party Committee, Shanxi Cardiovascular Hospital, Taiyuan, Shanxi, People’s Republic of China, Tel +86-186-3617-1219, Email yuandajiang@sina.comPurpose: To investigate whether patient-controlled sedation (PCS) with remimazolam provides more precise sedation control and superior overall outcomes compared to conventional sedation (CS) in elderly patients undergoing lower limb surgery under spinal anesthesia.Patients and Methods: 120 patients aged ≥ 65 years (ASA physical status II~III) were randomized to receive either PCS (n=60) or CS (n=60). PCS received a loading dose (1 mg), background infusion (0.75 mg·h− 1), and patient-demand boluses (0.5 mg; lockout interval 60s). CS received a loading dose (0.05~0.1 mg·kg− 1) followed by a continuous infusion (0.1~0.3 mg·kg− 1·h− 1). The primary outcome was the proportion of patients who maintained optimal sedation, defined as a Ramsay Sedation Scale score of 3~4 with supportive bispectral index (BIS) monitoring. Secondary outcomes included onset/recovery time, hemodynamics, drug consumption, adverse events, and satisfaction.Results: The proportion maintaining optimal sedation was higher in the PCS group (100% vs 88.3%, P < 0.05). PCS also showed lower total drug use, shorter recovery time (both P < 0.001), and higher scores for comfort, satisfaction, and cooperation (all P < 0.05). Hemodynamic stability was better in the PCS group, with higher mean arterial pressure at T2 through T6 timepoints (mean difference 4.5 mmHg, P = 0.019) and a lower hypotension incidence (10.0% vs 23.3%, P = 0.050).Conclusion: For elderly patients undergoing lower limb surgery under spinal anesthesia, patient-controlled sedation with remimazolam facilitates more precise sedation control. It ensures a higher proportion of optimal sedation depth while reducing drug consumption, accelerating recovery, and enhancing patient comfort and satisfaction. Under the conditions of this study, this approach constitutes a safe, effective, and individualized sedation strategy.Keywords: remimazolam, patient-controlled sedation, elderly, spinal anesthesia

Hao-Xiang Jiang, Chao-Ran Cai, Ji-Qiang Zhang et al.

We propose a coupled dynamical model of resource allocation and epidemic spread, inspired by the hierarchical structure of real-world therapeutic resource allocation. In this framework, network nodes are assigned distinct roles as either resource allocators or resource recipients. As the average number of links per recipient from allocators increases, the prevalence exhibits one of four distinct response patterns across conditions: monotonically increasing, monotonically decreasing, U-shaped trend, or a sudden decrease with large fluctuations. A mechanistic analysis uncovers three central insights: (i) a trade-off between efficient resource allocation and infection risk faced by allocators, (ii) the critical need to avoid resource redundancy when therapeutic efficiency is high, and (iii) the emergence of cascade-induced bistability in the coupled system.

Giuseppe Sacco, Giovanni Bussi, Guido Sanguinetti

Predicting the secondary structure of RNA is a core challenge in computational biology, essential for understanding molecular function and designing novel therapeutics. The field has evolved from foundational but accuracy-limited thermodynamic approaches to a new data-driven paradigm dominated by machine learning and deep learning. These models learn folding patterns directly from data, leading to significant performance gains. This review surveys the modern landscape of these methods, covering single-sequence, evolutionary-based, and hybrid models that blend machine learning with biophysics. A central theme is the field's "generalization crisis," where powerful models were found to fail on new RNA families, prompting a community-wide shift to stricter, homology-aware benchmarking. In response to the underlying challenge of data scarcity, RNA foundation models have emerged, learning from massive, unlabeled sequence corpora to improve generalization. Finally, we look ahead to the next set of major hurdles-including the accurate prediction of complex motifs like pseudoknots, scaling to kilobase-length transcripts, incorporating the chemical diversity of modified nucleotides, and shifting the prediction target from static structures to the dynamic ensembles that better capture biological function. We also highlight the need for a standardized, prospective benchmarking system to ensure unbiased validation and accelerate progress.

Junkai Ji, Zhangfan Yang, Dong Xu et al.

Drug discovery is a time-consuming and expensive process, with traditional high-throughput and docking-based virtual screening hampered by low success rates and limited scalability. Recent advances in generative modelling, including autoregressive, diffusion, and flow-based approaches, have enabled de novo ligand design beyond the limits of enumerative screening. Yet these models often suffer from inadequate generalization, limited interpretability, and an overemphasis on binding affinity at the expense of key pharmacological properties, thereby restricting their translational utility. Here we present Trio, a molecular generation framework integrating fragment-based molecular language modeling, reinforcement learning, and Monte Carlo tree search, for effective and interpretable closed-loop targeted molecular design. Through the three key components, Trio enables context-aware fragment assembly, enforces physicochemical and synthetic feasibility, and guides a balanced search between the exploration of novel chemotypes and the exploitation of promising intermediates within protein binding pockets. Experimental results show that Trio reliably achieves chemically valid and pharmacologically enhanced ligands, outperforming state-of-the-art approaches with improved binding affinity (+7.85%), drug-likeness (+11.10%) and synthetic accessibility (+12.05%), while expanding molecular diversity more than fourfold. By combining generalization, plausibility, and interpretability, Trio establishes a closed-loop generative paradigm that redefines how chemical space can be navigated, offering a transformative foundation for the next era of AI-driven drug discovery.

Mohammad Amin Abbasi, Hassan Naderi

This study presents PsychoLexTherapy, a framework for simulating psychotherapeutic reasoning in Persian using small language models (SLMs). The framework tackles the challenge of developing culturally grounded, therapeutically coherent dialogue systems with structured memory for multi-turn interactions in underrepresented languages. To ensure privacy and feasibility, PsychoLexTherapy is optimized for on-device deployment, enabling use without external servers. Development followed a three-stage process: (i) assessing SLMs psychological knowledge with PsychoLexEval; (ii) designing and implementing the reasoning-oriented PsychoLexTherapy framework; and (iii) constructing two evaluation datasets-PsychoLexQuery (real Persian user questions) and PsychoLexDialogue (hybrid simulated sessions)-to benchmark against multiple baselines. Experiments compared simple prompting, multi-agent debate, and structured therapeutic reasoning paths. Results showed that deliberate model selection balanced accuracy, efficiency, and privacy. On PsychoLexQuery, PsychoLexTherapy outperformed all baselines in automatic LLM-as-a-judge evaluation and was ranked highest by human evaluators in a single-turn preference study. In multi-turn tests with PsychoLexDialogue, the long-term memory module proved essential: while naive history concatenation caused incoherence and information loss, the full framework achieved the highest ratings in empathy, coherence, cultural fit, and personalization. Overall, PsychoLexTherapy establishes a practical, privacy-preserving, and culturally aligned foundation for Persian psychotherapy simulation, contributing novel datasets, a reproducible evaluation pipeline, and empirical insights into structured memory for therapeutic reasoning.

P. McDonald, J. Winum, C. Supuran et al.

Carbonic anhydrase IX (CAIX) is a hypoxia-inducible enzyme that is overexpressed by cancer cells from many tumor types, and is a component of the pH regulatory system invoked by these cells to combat the deleterious effects of a high rate of glycolytic metabolism. CAIX functions to help produce and maintain an intracellular pH (pHi) favorable for tumor cell growth and survival, while at the same time participating in the generation of an increasingly acidic extracellular space, facilitating tumor cell invasiveness. Pharmacologic interference of CAIX catalytic activity using monoclonal antibodies or CAIX-specific small molecule inhibitors, consequently disrupting pH regulation by cancer cells, has been shown recently to impair primary tumor growth and metastasis. Many of these agents are in preclinical or clinical development and constitute a novel, targeted strategy for cancer therapy.

Cong Fu, Xiner Li, Blake Olson et al.

Structure-based drug design (SBDD) is crucial for developing specific and effective therapeutics against protein targets but remains challenging due to complex protein-ligand interactions and vast chemical space. Although language models (LMs) have excelled in natural language processing, their application in SBDD is underexplored. To bridge this gap, we introduce a method, known as Frag2Seq, to apply LMs to SBDD by generating molecules in a fragment-based manner in which fragments correspond to functional modules. We transform 3D molecules into fragment-informed sequences using SE(3)-equivariant molecule and fragment local frames, extracting SE(3)-invariant sequences that preserve geometric information of 3D fragments. Furthermore, we incorporate protein pocket embeddings obtained from a pre-trained inverse folding model into the LMs via cross-attention to capture protein-ligand interaction, enabling effective target-aware molecule generation. Benefiting from employing LMs with fragment-based generation and effective protein context encoding, our model achieves the best performance on binding vina score and chemical properties such as QED and Lipinski, which shows our model's efficacy in generating drug-like ligands with higher binding affinity against target proteins. Moreover, our method also exhibits higher sampling efficiency compared to atom-based autoregressive and diffusion baselines with at most ~300x speedup.

Alan Nawzad Amin, Nate Gruver, Yilun Kuang et al.

To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or by learning from large antibody databases to predict only typical antibodies. Unfortunately, the space of typical antibodies is enormous to search, and experiments often fail to find suitable antibodies on a budget. We introduce Clone-informed Bayesian Optimization (CloneBO), a Bayesian optimization procedure that efficiently optimizes antibodies in the lab by teaching a generative model how our immune system optimizes antibodies. Our immune system makes antibodies by iteratively evolving specific portions of their sequences to bind their target strongly and stably, resulting in a set of related, evolving sequences known as a clonal family. We train a large language model, CloneLM, on hundreds of thousands of clonal families and use it to design sequences with mutations that are most likely to optimize an antibody within the human immune system. We propose to guide our designs to fit previous measurements with a twisted sequential Monte Carlo procedure. We show that CloneBO optimizes antibodies substantially more efficiently than previous methods in realistic in silico experiments and designs stronger and more stable binders in in vitro wet lab experiments.

Anastasios Apsemidis, Nikolaos Demiris

The mean survival is the key ingredient of the decision process in several applications, notably in health economic evaluations. It is defined as the area under the complete survival curve, thus necessitating extrapolation of the observed data. This may be achieved in a more stable manner by borrowing long term evidence from registry and demographic data. Such borrowing can be seen as an implicit bias-variance trade-off in unseen data. In this article we employ a Bayesian mortality model and transfer its projections in order to construct the baseline population that acts as an anchor of the survival model. We then propose extrapolation methods based on flexible parametric polyhazard models which can naturally accommodate diverse shapes, including non-proportional hazards and crossing survival curves, while typically maintaining a natural interpretation. We estimate the mean survival and related estimands in three cases, namely breast cancer, cardiac arrhythmia and advanced melanoma. Specifically, we evaluate the survival disadvantage of triple-negative breast cancer cases, the efficacy of combining immunotherapy with mRNA cancer therapeutics for melanoma treatment and the suitability of implantable cardioverter defibrilators for cardiac arrhythmia. The latter is conducted in a competing risks context illustrating how working on the cause-specific hazard alone minimizes potential instability. The results suggest that the proposed approach offers a flexible, interpretable and robust approach when survival extrapolation is required.

Yushuai Wu, Ting Zhang, Hao Zhou et al.

The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i.e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials. One of the reasons is the inadequacy of Cross-drug Response Evaluation (CRE) during the late stages of drug R&D. Although in-silico CRE models bring a promising solution, existing methodologies are restricted to early stages of drug R&D, such as target and cell-line levels, offering limited improvement to clinical success rates. Herein, we introduce DeepCRE, a pioneering AI model designed to predict CRE effectively in the late stages of drug R&D. DeepCRE outperforms the existing best models by achieving an average performance improvement of 17.7% in patient-level CRE, and a 5-fold increase in indication-level CRE, facilitating more accurate personalized treatment predictions and better pharmaceutical value assessment for indications, respectively. Furthermore, DeepCRE has identified a set of six drug candidates that show significantly greater effectiveness than a comparator set of two approved drugs in 5/8 colorectal cancer organoids. This demonstrates the capability of DeepCRE to systematically uncover a spectrum of drug candidates with enhanced therapeutic effects, highlighting its potential to transform drug R&D.

Rion Brattig Correia, Jordan C. Rozum, Leonard Cross et al.

Objective: We report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and researchers in making decisions about care and self-management. Materials and Methods: myAURA rests on the federation of an unprecedented collection of heterogeneous data resources relevant to epilepsy, such as biomedical databases, social media, and electronic health records. A generalizable, open-source methodology was developed to compute a multi-layer knowledge graph linking all this heterogeneous data via the terms of a human-centered biomedical dictionary. Results: The power of the approach is first exemplified in the study of the drug-drug interaction phenomenon. Furthermore, we employ a novel network sparsification methodology using the metric backbone of weighted graphs, which reveals the most important edges for inference, recommendation, and visualization, such as pharmacology factors patients discuss on social media. The network sparsification approach also allows us to extract focused digital cohorts from social media whose discourse is more relevant to epilepsy or other biomedical problems. Finally, we present our patient-centered design and pilot-testing of myAURA, including its user interface, based on focus groups and other stakeholder input. Discussion: The ability to search and explore myAURA's heterogeneous data sources via a sparsified multi-layer knowledge graph, as well as the combination of those layers in a single map, are useful features for integrating relevant information for epilepsy. Conclusion: Our stakeholder-driven, scalable approach to integrate traditional and non-traditional data sources, enables biomedical discovery and data-powered patient self-management in epilepsy, and is generalizable to other chronic conditions.

Guisheng Zhou, Junzhi Zhang, Hongli Guo et al.

Detection of biomarkers was extremely important for the early diagnosis, prognosis, and therapy optimization of diseases. The purpose of this study was to investigate the differences in serum metabolites between patients with heart failure (HF) and healthy control (HC) and to diagnose HF qualitatively. In this study, serum samples from 83 patients with HF and 35 HCs were used as the research subjects for untargeted metabolomic analysis using ultraperformance liquid chromatography combined with quadrupole-time of flight mass spectrometry (UPLC-QTOF/MS) technology. Potential biomarkers were screened and validated using the orthogonal partial least squares discriminant analysis (OPLS-DA), random forest (RF), binary logistic regression (BLR), and receiver operating characteristic (ROC) analysis. The results indicated that a total of 43 metabolites were considered as differentially expressed metabolites (DEMs). Among these DEMs, glycodeoxycholate was identified as a specific biomarker of HF. A ROC curve analysis for HC versus HF discrimination showed an area under the ROC curve (AUC) of 0.9853 (95% CI: 0.9859–1.0000), a sensitivity of 95%, and a specificity of 100%. Hence, glycodeoxycholate might serve as a potential biomarker for HF. Furthermore, the amino acid metabolism was screened as the most significantly altered pathway in patients with HF. By identifying serum biomarkers and analyzing metabolic pathways, our study provided opportunities to enhance the understanding of the pathogenesis and early diagnosis of HF.