Nat Biotechnol. 2025 May 12. doi: 10.1038/s41587-025-02697-7. Online ahead of print.

NO ABSTRACT

PMID:40355567 | DOI:10.1038/s41587-025-02697-7

Drug Ther Bull. 2025 May 12:dtb-2025-000016. doi: 10.1136/dtb.2025.000016. Online ahead of print.

NO ABSTRACT

PMID:40355247 | DOI:10.1136/dtb.2025.000016

Int Dent J. 2025 May 11;75(4):100827. doi: 10.1016/j.identj.2025.04.007. Online ahead of print.

ABSTRACT

Dental caries is one of the most prevalent chronic diseases among both children and adults, despite being largely preventable. This condition has significant negative impacts on human health and imposes a substantial economic burden. In recent years, scientists and dentists have increasingly started to utilize artificial intelligence (AI), particularly machine learning, to improve the efficiency of dental caries management. This study aims to provide an overview of the current knowledge about the AI-enabled approaches for dental caries management within the framework of personalized patient care. Generally, AI works as a promising tool that can be used by both dental professionals and patients. For dental professionals, it predicts the risk of dental caries by analyzing dental caries risk and protective factors, enabling to formulate personalized preventive measures. AI, especially those based on machine learning and deep learning, can also analyze images to detect signs of dental caries, assist in developing treatment plans, and help to make a risk assessment for pulp exposure during treatment. AI-powered tools can also be used to train dental students through simulations and virtual case studies, allowing them to practice and refine their clinical skills in a risk-free environment. Additionally, AI tracks brushing patterns and provides feedback to improve oral hygiene practices of the patients and the general population, thereby improving their understanding and compliance. This capability of AI can inform future research and the development of new strategies for dental caries management and control.

PMID:40354695 | DOI:10.1016/j.identj.2025.04.007

Bioinformatics. 2025 May 12:btaf298. doi: 10.1093/bioinformatics/btaf298. Online ahead of print.

ABSTRACT

MOTIVATION: Predicting protein-ligand binding affinity accurately and quickly is a major challenge in drug discovery. Recent advancements suggest that deep learning-based computational methods can effectively quantify binding affinity, making them a promising alternative. Environmental factors significantly influence the interactions between protein pockets and ligands, affecting the binding strength. However, many existing deep learning approaches tend to overlook these environmental effects, focusing instead on extracting features from proteins and ligands based solely on their sequences or structures.

RESULTS: We propose a deep learning method, EM-PLA, which is based on an environment-aware heterogeneous graph neural network and utilizes multimodal data. This method improves protein-ligand binding affinity prediction by incorporating environmental information derived from the biochemical properties of proteins and ligands. Specifically, EM-PLA employs a heterogeneous graph neural network(HGT) with environmental information to improve the calculation of non-covalent interactions, while also considering the interaction calculations between protein sequences and ligand sequences. We evaluate the performance of the proposed EM-PLA through comprehensive benchmark experiments for binding affinity prediction, demonstrating its superior performance and generalization capability compared to state-of-the-art baseline methods. Furthermore, by analyzing the results of the ablation experiments and integrating visual analyses and case studies, we validate the rationale of the proposed method. These results indicate that EM-PLA is an effective method for binding affinity prediction and may provide valuable insights for future applications.

AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/littlemou22/EM-PLA.

CONTACT: pzhang@tju.edu.com.

SUPPLEMENTARY INFORMATION: Supplementary data are available in the submitted files.

PMID:40354612 | DOI:10.1093/bioinformatics/btaf298

Anal Chem. 2025 May 12. doi: 10.1021/acs.analchem.5c01082. Online ahead of print.

ABSTRACT

Precise discrimination of the crucial substances, e.g., synthetic cannabinoids (SCs) that are composed of low-active chemical groups and structurally similar to each other with tiny differences, is a pressing need and of great significance for safeguarding public security and human health. The structure-relevant vibrational spectroscopic techniques, e.g., Raman spectroscopy, could reflect structural fingerprint information on the target; however, the algorithm-assisted phrasing is inevitable. This work achieved the accurate identification of CA series SCs by proposing an attention mechanism involving a CNN algorithm to phrase the Raman data. Specifically, these SCs have only one different chemical group compared to each other, the attention mechanism was introduced to intensify the computation on their structural difference from the massive data, realizing the accurate discrimination. Furthermore, how the spectral peaks corresponded to the specific structure was revealed, which plays a decisive role for the algorithm to distinguish these substances, and provides an instructive reference for differentiating other SCs based on Raman spectra. Hence, this work provides a research paradigm for applying the advanced CNN algorithm-aided Raman spectral analysis to sub-differentiate the substances, strengthening the understanding of spectral information from the sub-molecular level and propelling the integration of interdisciplinary areas.

PMID:40354573 | DOI:10.1021/acs.analchem.5c01082

PLoS One. 2025 May 12;20(5):e0319562. doi: 10.1371/journal.pone.0319562. eCollection 2025.

ABSTRACT

The increasing importance of deep learning in software development has greatly improved software quality by enabling the efficient identification of defects, a persistent challenge throughout the software development lifecycle. This study seeks to determine the most effective model for detecting defects in software projects. It introduces an intelligent approach that combines Temporal Convolutional Networks (TCN) with Antlion Optimization (ALO). TCN is employed for defect detection, while ALO optimizes the network's weights. Two models are proposed to address the research problem: (a) a basic TCN without parameter optimization and (b) a hybrid model integrating TCN with ALO. The findings demonstrate that the hybrid model significantly outperforms the basic TCN in multiple performance metrics, including area under the curve, sensitivity, specificity, accuracy, and error rate. Moreover, the hybrid model surpasses state-of-the-art methods, such as Convolutional Neural Networks, Gated Recurrent Units, and Bidirectional Long Short-Term Memory, with accuracy improvements of 21.8%, 19.6%, and 31.3%, respectively. Additionally, the proposed model achieves a 13.6% higher area under the curve across all datasets compared to the Deep Forest method. These results confirm the effectiveness of the proposed hybrid model in accurately detecting defects across diverse software projects.

PMID:40354496 | DOI:10.1371/journal.pone.0319562

PLoS One. 2025 May 12;20(5):e0317758. doi: 10.1371/journal.pone.0317758. eCollection 2025.

ABSTRACT

Eliminating flickering from digital images captured by cameras equipped with a rolling shutter is of paramount importance in computer vision applications. The ripple effect observed in an individual image is a consequence of the non-synchronized exposure of rolling shutters utilized in CMOS sensor-based cameras. To date, there have been only a limited number of studies focusing on the mitigation of flickering in single images. Furthermore, it is more feasible to eliminate these flickers with prior knowledge, such as camera specifications or matching images. To solve these problems, we present an unsupervised framework Super-Resolution Generative Adversarial Networks and Partition-Based Adaptive Filtering Technique (SRGAN-PBAFT) trained on unpaired images from end to end Deflickering of a single image. Flicker artifacts, which are commonly caused by dynamic lighting circumstances and sensor noise, can severely reduce an image's visual quality and authenticity. To enhance image resolution SRGAN is used, while Partition based Adaptive Filtering technique detects and mitigates flicker distortions successfully. Combining the strengths of deep learning and adaptive filtering results in a potent approach for restoring image integrity. Experimental results shows that the Proposed SRGAN-PBAFT method is effective, with major improvements in visual quality and flicker aberration reduction compared to existing methods.

PMID:40354494 | DOI:10.1371/journal.pone.0317758

PLOS Digit Health. 2025 May 12;4(5):e0000831. doi: 10.1371/journal.pdig.0000831. eCollection 2025 May.

ABSTRACT

Diabetic retinopathy (DR) is a frequent complication of diabetes, affecting millions worldwide. Screening for this disease based on fundus images has been one of the first successful use cases for modern artificial intelligence in medicine. However, current state-of-the-art systems typically use black-box models to make referral decisions, requiring post-hoc methods for AI-human interaction and clinical decision support. We developed and evaluated an inherently interpretable deep learning model, which explicitly models the local evidence of DR as part of its network architecture, for clinical decision support in early DR screening. We trained the network on 34,350 high-quality fundus images from a publicly available dataset and validated its performance on a large range of ten external datasets. The inherently interpretable model was compared to post-hoc explainability techniques applied to a standard DNN architecture. For comparison, we obtained detailed lesion annotations from ophthalmologists on 65 images to study if the class evidence maps highlight clinically relevant information. We tested the clinical usefulness of our model in a retrospective reader study, where we compared screening for DR without AI support to screening with AI support with and without AI explanations. The inherently interpretable deep learning model obtained an accuracy of .906 [.900-.913] (95%-confidence interval) and an AUC of .904 [.894-.913] on the internal test set and similar performance on external datasets, comparable to the standard DNN. High evidence regions directly extracted from the model contained clinically relevant lesions such as microaneurysms or hemorrhages with a high precision of .960 [.941-.976], surpassing post-hoc techniques applied to a standard DNN. Decision support by the model highlighting high-evidence regions in the image improved screening accuracy for difficult decisions and improved screening speed. This shows that inherently interpretable deep learning models can provide clinical decision support while obtaining state-of-the-art performance improving human-AI collaboration.

PMID:40354306 | DOI:10.1371/journal.pdig.0000831

J Exp Psychol Gen. 2025 May 12. doi: 10.1037/xge0001740. Online ahead of print.

ABSTRACT

Pertaining to goal orientation and achievement, agency is a fundamental aspect of human cognition and behavior. Accordingly, detecting and quantifying linguistic encoding of agency are critical for the analysis of human actions, interactions, and social dynamics. Available agency-quantifying computational tools rely on word-counting methods, which typically are insensitive to the semantic context in which the words are used and consequently prone to miscoding, for example, in case of polysemy. Additionally, some currently available tools do not take into account differences in the intensity and directionality of agency. In order to overcome these shortcomings, we present BERTAgent, a novel tool to quantify semantic agency in text. BERTAgent is a computational language model that utilizes the transformers architecture, a popular deep learning approach to natural language processing. BERTAgent was fine-tuned using textual data that were evaluated by human coders with respect to the level of conveyed agency. In four validation studies, BERTAgent exhibits improved convergent and discriminant validity compared to previous solutions. Additionally, the detailed description of BERTAgent's development procedure serves as a tutorial for the advancement of similar tools, providing a blueprint for leveraging the existing lexicographical data sets in conjunction with the deep learning techniques in order to detect and quantify other psychological constructs in textual data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

PMID:40354292 | DOI:10.1037/xge0001740

IEEE Trans Image Process. 2025 May 12;PP. doi: 10.1109/TIP.2025.3567205. Online ahead of print.

ABSTRACT

All-in-one image restoration, which seeks to handle multiple types of degradation within a unified model, has become a prominent research topic in computer vision. While existing deep learning models have achieved remarkable success in specific restoration tasks, extending these models to heterogenous degradations presents significant challenges. Current all-in-one methods predominantly concentrate on extracting degradation priors, often employing learned and fixed task prompts to guide the restoration process. However, these static prompts are inclined to generate an average distribution characteristics of degradations, unable to accurately depict the unique attribute of the given input, consequently providing suboptimal restoration results. To tackle these challenges, we propose a novel dynamic prompt approach called Degradation Prototype Assignment and Prompt Distribution Learning (DPPD). Our approach decouples the degradation prior extraction into two novel components: Degradation Prototype Assignment (DPA) and Prompt Distribution Learning (PDL). DPA anchors the degradation representations to predefined prototypes, providing discriminative and scalable representations. In addition, PDL models prompts as distributions rather than fixed parameters, facilitating dynamic and adaptive prompt sampling. Extensive experiments demonstrate that our DPPD framework can achieve significant performance improvement on different image restoration tasks. Codes are available at our project page https://github.com/Aitical/DPPD.

PMID:40354220 | DOI:10.1109/TIP.2025.3567205

IEEE J Biomed Health Inform. 2025 May 12;PP. doi: 10.1109/JBHI.2025.3569160. Online ahead of print.

ABSTRACT

Respiratory diseases present significant challenges to global health due to their high morbidity and mortality rates. Traditional diagnostic methods, such as chest radiographs and blood tests, often lead to unnecessary costs and resource strain, as well as potential risks of cross-contamination during these procedures. In recent years, contactless sensing and intelligent technologies, particularly multi-modal sound-based deep learning methods, have emerged as promising solutions for the early detection of respiratory diseases. While these methods have shown encouraging results, the integration of multi-modal features has not been sufficiently explored, which limits the enhancement of diagnostic accuracy. To address this issue, we introduce GAMMNet, a novel multi-modal neural network designed to enhance the detection of respiratory diseases by leveraging multi-modal sound data collected from contactless recording devices. GAMMNet utilizes a unique gating mechanism that adaptively regulates the influence of each modality on the classification results. Additionally, our model incorporates multi-head attention and linear transformation modules to further enhance classification performance. Our GAMMNet achieves state-of-the-art classification results, compared to existing deep learning based methods, on real-world multi-modal respiratory sound datasets. These findings demonstrate the robustness and effectiveness of GAMMNet in the contactless monitoring and early detection of respiratory diseases.

PMID:40354202 | DOI:10.1109/JBHI.2025.3569160

IEEE J Biomed Health Inform. 2025 May 12;PP. doi: 10.1109/JBHI.2025.3569726. Online ahead of print.

ABSTRACT

Breast cancer poses a serious threat to women's health, and its early detection is crucial for enhancing patient survival rates. While deep learning has significantly advanced mammographic image analysis, existing methods struggle to balance between view consistency with input adaptability. Furthermore, current models face challenges in accurately capturing multi-scale features, especially when subtle lesion variations across different scales are involved. To address this challenge, this paper proposes a Hybrid View Learning (HVL) paradigm that unifies traditional Single-View and Multi-View Learning approaches. The core component of this paradigm, our Attention-based Hybrid View Learning (AHVL) framework, incorporates two essential attention mechanisms: Contrastive Switch Attention (CSA) and Selective Pooling Attention (SPA). The CSA mechanism flexibly alternates between self-attention and cross-attention based on data integrity, integrating a pre-trained language model for contrastive learning to enhance model stability. Meanwhile, the SPA module employs multi-scale feature pooling and selection to capture critical features from mammographic images, overcoming the limitations of traditional models that struggle with fine-grained lesion detection. Experimental validation on the INbreast and CBIS-DDSM datasets shows that the AHVL framework outperforms both single-view and multi-view methods, especially under extreme view missing conditions. Even with an 80% missing rate on both datasets, AHVL maintains the highest accuracy and experiences the smallest performance decline in metrics like F1 score and AUC-PR, demonstrating its robustness and stability. This study redefines mammographic image analysis by leveraging attention-based hybrid view processing, setting a new standard for precise and efficient breast cancer diagnosis.

PMID:40354201 | DOI:10.1109/JBHI.2025.3569726

Proc Natl Acad Sci U S A. 2025 May 20;122(20):e2412218122. doi: 10.1073/pnas.2412218122. Epub 2025 May 12.

ABSTRACT

MEX-5 regulates the formation and dissolution of P granules in Caenorhabditis elegans embryos, yet the thermodynamic basis of its activity remains unclear. Here, using a time-resolved in vitro reconstitution system, we show that MEX-5 dissolves preassembled liquid-like PGL-3/RNA condensates by altering RNA availability and shifting the phase boundary. We develop a microfluidic assay to systematically analyze how MEX-5 influences phase separation. By measuring the contribution of PGL-3 to phase separation, we show that MEX-5 reduces the free energy of PGL-3, shifting the equilibrium toward dissolution. Our findings provide a quantitative framework for understanding how RNA-binding proteins modulate condensate stability and demonstrate the power of microfluidics in precisely mapping phase transitions.

PMID:40354522 | DOI:10.1073/pnas.2412218122

PLoS Comput Biol. 2025 May 12;21(5):e1013076. doi: 10.1371/journal.pcbi.1013076. Online ahead of print.

ABSTRACT

Epithelial-mesenchymal transition (EMT) plays an essential role in embryonic development, wound healing, and tumor progression. Partial EMT states have been linked to metastatic dissemination and drug resistance. Several interconnected feedback loops at the RNA and protein levels control the transition between different cellular states. Using a combination of mathematical modeling and experimental analyses in the TGFβ-responsive breast epithelial MCF10A cell model, we identify a central role for the tumor suppressor protein Deleted in Liver Cancer 1 (DLC1) during EMT. By extending a previous model of EMT comprising key transcription factors and microRNAs, our work shows that DLC1 acts as a positive regulator of TGFβ-driven EMT, mainly by promoting SNAIL1 expression. Our model predictions indicate that DLC1 loss impairs EMT progression. Experimental analyses confirm this prediction and reveal the acquisition of a partial EMT phenotype in DLC1-depleted cells. Furthermore, our model results indicate a possible EMT reversion to partial or epithelial states upon DLC1 loss in MCF10A cells induced toward mesenchymal phenotypes. The increased EMT plasticity of cells lacking DLC1 may explain its importance as a tumor suppressor.

PMID:40354489 | DOI:10.1371/journal.pcbi.1013076

PLoS Comput Biol. 2025 May 12;21(5):e1013098. doi: 10.1371/journal.pcbi.1013098. Online ahead of print.

ABSTRACT

Uncertainty quantification (UQ) is the process of systematically determining and characterizing the degree of confidence in computational model predictions. In systems biology, and particularly with dynamic models, UQ is critical due to the nonlinearities and parameter sensitivities that influence the behavior of complex biological systems. Addressing these issues through robust UQ enables a deeper understanding of system dynamics and more reliable extrapolation beyond observed conditions. Many state-of-the-art UQ approaches in this field are grounded in Bayesian statistical methods. While these frameworks naturally incorporate uncertainty quantification, they often require the specification of parameter distributions as priors and may impose parametric assumptions that do not always reflect biological reality. Additionally, Bayesian methods can be computationally expensive, posing significant challenges when dealing with large-scale models and seeking rapid, reliable uncertainty calibration. As an alternative, we propose using conformal predictions methods and introduce two novel algorithms designed for dynamic biological systems. These approaches can provide non-asymptotic guarantees, improving robustness and scalability across various applications, even when the predictive models are misspecified. Through several illustrative scenarios, we demonstrate that these conformal algorithms can serve as powerful complements-or even alternatives-to conventional Bayesian methods, delivering effective uncertainty quantification for predictive tasks in systems biology.

PMID:40354480 | DOI:10.1371/journal.pcbi.1013098

Cancer Res Commun. 2025 May 12. doi: 10.1158/2767-9764.CRC-24-0433. Online ahead of print.

ABSTRACT

Cisplatin-based neoadjuvant chemotherapy followed by radical cystectomy is the main treatment for muscle-invasive bladder cancer (MIBC). However, low survival rates highlight the necessity for new therapeutic strategies. Drug repurposing has emerged as a promising approach in cancer treatment, with various studies proposing the use of existing drugs for the treatment of bladder cancer (BC). In this context, we previously established an in silico repurposing strategy using patient -omics signatures, identifying drugs and compounds with the potential to reverse non-muscle invasive BC (NMIBC) to less aggressive subtypes. In the present study, we expanded our in silico approach to verify a list of compounds with potential anti-tumor activity against MIBC. We investigated the efficacy of the predicted candidates in a group of different BC cell lines, including NMIBC and MIBC. The most potent compound for decreasing cell viability was amiodarone, an anti-arrhythmic drug widely used in the field of cardiology. Amiodarone reduced cell proliferation and colony formation capacity, with a stronger effect on the most aggressive invasive models, validating our repurposing pipeline. The drug additionally induced cell death and inhibited the activity of mTOR and its target protein S6, suggesting that the anti-cancer effect of the drug is in part mediated by inhibition of the mTOR signaling pathway. Furthermore, the administration of amiodarone in a xenograft MIBC mouse model reduced tumor growth without inducing toxicity. Altogether, we demonstrated that amiodarone is a potential repurposed drug for BC, which might be especially effective in MIBC.

PMID:40353763 | DOI:10.1158/2767-9764.CRC-24-0433

Protein Pept Lett. 2025 May 8. doi: 10.2174/0109298665366911250416113831. Online ahead of print.

ABSTRACT

BACKGROUND: COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a highly pathogenic human coronavirus (CoV). For the treatment of COVID-19, various drugs, ayurvedic formulations, used for other diseases, were repurposed. Ayurveda and yoga exhibited a pivotal role in the treatment of COVID-19. Various medicinal plants, including garlic, tulsi, clove, cinnamon, ginger, black pepper, and turmeric, are recommended for the prevention of COVID-19 as immunity boosters along with their antiviral property.

OBJECTIVE: In view of the drug repurposing approach, the present work has been initiated with the broader objectives of screening and identification of phytoconstituents of Indian spices against targets, namely furin, 3C-like protease (3CL-PRO), NSP-9 RNA binding protein, papain-like protease, RNA dependent RNA polymerase (RDRP), spike protein concerned with life cycle of SARS-CoV-2 using in-silico tools.

METHOD: The phytoconstituents of Indian spices were screened for interaction with several targets using a molecular docking approach with the help of Discovery Studio 4.5 software. Furthermore, the pharmacokinetic analyses of selected ligands using ADMET and Lipinski's rule of five were also performed.

RESULT: In the present study, more than 35 active phytoconstituents of Indian spices were screened for interaction with several identified targets of Covid-19 using a molecular docking approach. The ligands, namely morin, gingerol, myristic acid, quercetin, gallic acid, octacosanal, and alliin were found to be the top interacting ligands with the targets analyzed.

CONCLUSION: Based on the present in-silico finding, the active components of spices could be considered for drug-lead compounds against COVID-19.

PMID:40353411 | DOI:10.2174/0109298665366911250416113831

Anim Cells Syst (Seoul). 2025 May 8;29(1):325-338. doi: 10.1080/19768354.2025.2489389. eCollection 2025.

ABSTRACT

Yes-associated protein (YAP), a key co-transcription factor of the Hippo pathway, is a promising drug target for cancer therapy due to its critical role in promoting cell proliferation, survival, and tumor progression when dysregulated. While most Hippo pathway-targeting drugs focus on disrupting TEAD-YAP interactions or modulating the MST or LATS kinase cascade, new approaches are needed to identify small molecules that regulate YAP activity. In this study, we conducted high-throughput screening of FDA-approved drugs to discover potential YAP modulators. Using a NanoBiT-based system, which enables real-time and quantitative measurement of protein interactions, combined with phenotype-based assays in EGFP-YAP-expressing cells, we identified compounds that activate or inhibit YAP function. Among the identified YAP regulators, the microtubule destabilizer vinorelbine promoted YAP nuclear localization and transcriptional activation, while the antipsychotic drug thioridazine enhanced YAP phosphorylation at Ser127, resulting in its cytoplasmic retention and reduced transcriptional activity, effectively suppressing cancer cell growth. These findings demonstrate the potential of FDA-approved drugs in modulating YAP activity and present a novel screening strategy for developing YAP-targeting therapeutics. Furthermore, this approach can be extended to identify modulators of other signaling pathways, advancing drug discovery for a wide range of diseases.

PMID:40353256 | PMC:PMC12064127 | DOI:10.1080/19768354.2025.2489389

Narra J. 2025 Apr;5(1):e1130. doi: 10.52225/narra.v5i1.1130. Epub 2025 Jan 17.

ABSTRACT

Tuberculosis (TB) remains a significant and deadly infection among pulmonary diseases caused by Mycobacterium tuberculosis, a highly adaptive bacterium. The ability of M. tuberculosis to evade certain drugs has been linked to its unique structure, particularly in the cell envelope, where the Ag85 complex proteins play an essential role in this part. The aim of this study was to utilize a drug repurposing strategy targeting the Ag85 complex proteins. This study utilized a computational approach with 120 selected drugs experimentally identified to inhibit Tuberculosis. A virtual screening molecular docking with Autodock Vina was used to filter the compounds and identify the strong binders to the Ag85 Complex. Molecular dynamics simulations employed the Gromacs Packages to evaluate the stability of each complex, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (RoG). Additionally, absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessments were conducted to gather more information about the drug-likeness of each hit compound. Three compounds, selamectin, imatinib, and eltrombopag were selected as potential drugs repurposed to inhibit the activity of the Ag85 complex enzyme, with binding affinities ranging between -10.560 kcal/mol and -11.422 kcal/mol. The MD simulation within 100 ns (3 replicas) showed that the average RMSD of each Ag85A complex was 0.15 nm-0.16 nm, RMSF was 0.09 nm-0.10 nm, and RoG was 1.80 nm-1.81 nm. For Ag85B, the average RMSD was 1.79 nm-1.80 nm, RMSF was 0.08 nm-0.09 nm, and RoG was 1.79 nm-1.80 nm. Then, for Ag85C, the mean RMSD was 0.16 nm-0.18 nm, RMSF was 0.09, and RoG was 1.77 nm. The study highlights that these promising results demonstrate the potential of some repurposed drugs in combating the Ag85 complex.

PMID:40352212 | PMC:PMC12059857 | DOI:10.52225/narra.v5i1.1130

J Psychopharmacol. 2025 May 12:2698811251337387. doi: 10.1177/02698811251337387. Online ahead of print.

ABSTRACT

INTRODUCTION: Psychopharmacotherapy with mirtazapine is commonplace. Lower serum concentrations of mirtazapine were reported in smokers due to CYP1A2 induction. However, no previous study that investigated CYP1A2 genetics and mirtazapine treatment considered CYP1A2-inducing parameters.

AIM: We aimed to investigate the association of CYP1A2 variants, mirtazapine serum concentration, and treatment outcome, considering the smoking status of the patients.

METHODS: Two depression cohorts were investigated for the association between serum concentration and treatment response of mirtazapine and CYP1A2-163C>A (rs762551) and -3860G>A (rs2069514) genotype groups, also considering smoking status, sex, and age of the patients. In total, 124 patients (82 non-smokers and 42 smokers) were eligible for the analyses.

RESULTS: Dose-corrected serum concentration (CD) of mirtazapine was associated with smoking status, sex, and age, with lower CD in smokers, females, and older patients. Considering non-smokers and genotype-grouped smokers, CD of mirtazapine in CYP1A2 normal metabolizer smokers (N = 6) did not differ from CD of non-smokers. By contrast, smokers carrying the CYP1A2*1A/*1F and *1F/*1F genotype groups showed 34.4% and 33.4% lower mirtazapine CD compared to non-smokers.

DISCUSSION: As yet, for clinical practice, it may be more relevant to focus on smoking status than on the CYP1A2 gene variants. Considering the relevant impact of smoking on the mirtazapine CD, physicians should monitor an increase in side effects due to the expected increase in mirtazapine serum concentrations. In these cases, measurement of mirtazapine CD and/or subsequent dosage reduction is recommended. The clinical relevance of CYP1A2 genotyping prior to treatment with drugs metabolized by CYP1A2 needs further investigation.

PMID:40353511 | DOI:10.1177/02698811251337387