Hasil untuk "Therapeutics. Pharmacology"

Jingjie Ning, Xiangzhen Shen, Li Hou et al.

G protein-coupled receptors (GPCRs) govern diverse physiological processes and are central to modern pharmacology. Yet discovering GPCR modulators remains challenging because receptor activation often arises from complex allosteric effects rather than direct binding affinity, and conventional assays are slow, costly, and not optimized for capturing these dynamics. Here we present GPCR-Filter, a deep learning framework specifically developed for GPCR modulator discovery. We assembled a high-quality dataset of over 90,000 experimentally validated GPCR-ligand pairs, providing a robust foundation for training and evaluation. GPCR-Filter integrates the ESM-3 protein language model for high-fidelity GPCR sequence representations with graph neural networks that encode ligand structures, coupled through an attention-based fusion mechanism that learns receptor-ligand functional relationships. Across multiple evaluation settings, GPCR-Filter consistently outperforms state-of-the-art compound-protein interaction models and exhibits strong generalization to unseen receptors and ligands. Notably, the model successfully identified micromolar-level agonists of the 5-HT\textsubscript{1A} receptor with distinct chemical frameworks. These results establish GPCR-Filter as a scalable and effective computational approach for GPCR modulator discovery, advancing AI-assisted drug development for complex signaling systems.

Sulaiman Muhammad Musa, Usman Abubakar Haruna, Lukman Jibril Aliyu et al.

Examining the current situation of the vaccine supply chain in Africa, the article highlights the importance of AI technologies while outlining the prospects and problems in vaccine supply chain management in Africa. Despite the significance of vaccinations, many African children are unable to receive them due to logistical challenges and a lack of infrastructure. AI has the potential to increase productivity by streamlining logistics and inventory management, but it is hampered by issues with data privacy and technology infrastructure. This perspectiveoffers ways for utilizing AI to enhance vaccine supply chains in Africa, citing successful experiences in Nigeria, Malawi, Rwanda, and Ghana as examples of AI’s advantages. In order to improve healthcare outcomes and immunization coverage in Africa, cooperation among stakeholders is stressed.

Wang J, Lu S, Zhang C et al.

Jinglong Wang, Shijia Lu, Chenxiao Zhang, Junjie Wang, Huiru Wu, Guofei Li Department of Pharmacy, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People’s Republic of ChinaCorrespondence: Guofei Li, Department of Pharmacy, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Shenyang, 110004, People’s Republic of China, Tel/Fax +86-24-23925108, Email sylgf2009@163.comPropose: The co-treatment of ulcerative colitis with berberine hydrochloride (BBR), the Janus kinase(JAK) inhibitor Tofacitinib (TOFA), and Fligotinib (FIGA) is feasible and sophisticated in terms of mechanism. However, no studies have yet explored their interactions. This study aimed to establish a highly sensitive, specific, and reproducible HPLC-MS/MS method for investigating the pharmacokinetic interactions between BBR-TOFA and BBR-FIGA in rats.Methods: The analytes and internal standards were extracted from rat plasma using a mixed solvent of dichloromethane and ether (3:2 ratio). The mobile phase comprised a mixture of methanol (containing 0.1% formic acid) and water (containing 0.1% formic acid and 2 mm ammonium acetate), with a flow rate of 0.6 mL/min. Elution was performed in a gradient mode on a Phenomenex Kinetex column (50× 3.0 mm, 2.6 μm). A systematic methodological validation was conducted according to the standards of the Chinese Pharmacopoeia, covering aspects such as specificity, calibration curve and linearity, residual effects, precision and accuracy, recovery, matrix effect, dilution integrity, and stability.Results: All methodological validation parameters met the standards of the Chinese Pharmacopoeia, confirming the method’s suitability for simultaneously determining the concentrations of BBR, TOFA, and FIGA in rat plasma. Pharmacokinetic experimental results indicate that TOFA and FIGA have no significant effect on the plasma concentration of BBR across various pharmacokinetic parameters. However, due to BBR’s inhibition or induction of various drug-metabolizing enzymes, it significantly affects some of the pharmacokinetic parameters of TOFA and FIGA. Keywords: HPLC-MS/MS, Validation, Drug-drug interaction, Berberine hydrochloride, Tofacitinib, Filgotinib

Ruiwen Ding, Lin Li, Rajath Soans et al.

Inflammatory bowel disease (IBD) involves chronic inflammation of the digestive tract, with treatment options often burdened by adverse effects. Identifying biomarkers for personalized treatment is crucial. While immune cells play a key role in IBD, accurately identifying ulcer regions in whole slide images (WSIs) is essential for characterizing these cells and exploring potential therapeutics. Multiple instance learning (MIL) approaches have advanced WSI analysis but they lack spatial context awareness. In this work, we propose a weakly-supervised model called DomainGCN that employs a graph convolution neural network (GCN) and incorporates domain-specific knowledge of ulcer features, specifically, the presence of epithelium, lymphocytes, and debris for WSI-level ulcer prediction in IBD. We demonstrate that DomainGCN outperforms various state-of-the-art (SOTA) MIL methods and show the added value of domain knowledge.

Jie Gao, Jing Hu, Shanzhuo Zhang et al.

Antibody engineering is essential for developing therapeutics and advancing biomedical research. Traditional discovery methods often rely on time-consuming and resource-intensive experimental screening. To enhance and streamline this process, we introduce a production-grade, high-throughput platform built on HelixFold3, HelixDesign-Antibody, which utilizes the high-accuracy structure prediction model, HelixFold3. The platform facilitates the large-scale generation of antibody candidate sequences and evaluates their interaction with antigens. Integrated high-performance computing (HPC) support enables high-throughput screening, addressing challenges such as fragmented toolchains and high computational demands. Validation on multiple antigens showcases the platform's ability to generate diverse and high-quality antibodies, confirming a scaling law where exploring larger sequence spaces increases the likelihood of identifying optimal binders. This platform provides a seamless, accessible solution for large-scale antibody design and is available via the antibody design page of PaddleHelix platform.

Guangyi Liu, Yongqi Zhang, Xunyuan Liu et al.

Drug-drug interaction (DDI) prediction is critical for treatment safety. While large language models (LLMs) show promise in pharmaceutical tasks, their effectiveness in DDI prediction remains challenging. Inspired by the well-established clinical practice where physicians routinely reference similar historical cases to guide their decisions through case-based reasoning (CBR), we propose CBR-DDI, a novel framework that distills pharmacological principles from historical cases to improve LLM reasoning for DDI tasks. CBR-DDI constructs a knowledge repository by leveraging LLMs to extract pharmacological insights and graph neural networks (GNNs) to model drug associations. A hybrid retrieval mechanism and dual-layer knowledge-enhanced prompting allow LLMs to effectively retrieve and reuse relevant cases. We further introduce a representative sampling strategy for dynamic case refinement. Extensive experiments demonstrate that CBR-DDI achieves state-of-the-art performance, with a significant 28.7% accuracy improvement over both popular LLMs and CBR baseline, while maintaining high interpretability and flexibility.

Praveen Muralikrishnan, Jonathan W. P. Zajac, Caryn L. Heldt et al.

The stabilization of macromolecules is fundamental to developing biological formulations, such as vaccines and protein therapeutics. In this study, we employ coarse grained polymer models to investigate the impact of four sugars: $α$-glucose, $β$-fructose, trehalose, and sucrose on macromolecule stability. Free energy decomposition and preferential interaction analysis indicate that polymer-sugar interactions favor folding at low concentrations while driving unfolding at higher concentrations. In contrast, the polymer-solvent soft interaction entropy consistently favors unfolding across all sugar concentrations under study. At low sugar concentrations, polymer-solvent interactions predominantly govern stabilization, whereas at higher concentrations, entropic penalties dictate polymer stability. Local mixing entropy demonstrates that binary sugar mixtures introduce entropic contributions that preferentially stabilize the folded state. These findings contribute to a more nuanced understanding of sugar-based excipient stabilization mechanisms, offering guidance for the rational design of stable biological formulations.

Sean D. Huver

The characterization of ligand--receptor interactions is a cornerstone of modern pharmacology; however, current methods are hampered by limitations such as ensemble averaging and invasive labeling. We propose a theoretical quantum sensing solution, the Quantum Ligand-Binding Interrogator (QLI), designed to overcome these challenges. The QLI is a differential sensor, or gradiometer, that uses a pair of co-trapped atomic ions to perform label-free detection of the electric field gradient produced by a single ligand binding to its receptor in vitrified samples. This gradiometric approach provides robust common-mode rejection of background electric field noise. To bridge the gap between the cryogenic, ultra-high-vacuum environment required for the sensor and the biological sample, we propose an architecture based on a vitrified sample mounted on a scanning probe. This enables the detection of the electrostatic signature of a single molecule in a specific conformational state (e.g., bound vs.\ unbound). This paper details the conceptual framework of the QLI, the experimental architecture, the measurement protocol using entangled two-ion spin states, and an analysis of key engineering risks. Anchoring to state-of-the-art single-ion low-frequency sensitivities (sub-mV\,m$^{-1}$/\,$\sqrt{\mathrm{Hz}}$), we project SNR\,=\,1 in tens of seconds at a 10\,\textmu m ion--sample separation for $Δp \sim 20$\,D, with feasibility dominated by the (as yet unmeasured) electrostatic stability of vitrified samples. If realized, QLI would provide direct single-molecule measurements of binding-induced electric field changes, offering a new path for experimental validation of computational models of drug--receptor interactions.

Don Roosan, Saif Nirzhor, Rubayat Khan

Herbal compounds present complex toxicity profiles that are often influenced by both intrinsic chemical properties and pharmacokinetics (PK) governing absorption and clearance. In this study, we develop a quantum regression model to predict acute toxicity severity for herbal-derived compounds by integrating toxicity data from NICEATM with pharmacological features from TCMSP.

Leo Klarner, Tim G. J. Rudner, Garrett M. Morris et al.

Generative models have the potential to accelerate key steps in the discovery of novel molecular therapeutics and materials. Diffusion models have recently emerged as a powerful approach, excelling at unconditional sample generation and, with data-driven guidance, conditional generation within their training domain. Reliably sampling from high-value regions beyond the training data, however, remains an open challenge -- with current methods predominantly focusing on modifying the diffusion process itself. In this paper, we develop context-guided diffusion (CGD), a simple plug-and-play method that leverages unlabeled data and smoothness constraints to improve the out-of-distribution generalization of guided diffusion models. We demonstrate that this approach leads to substantial performance gains across various settings, including continuous, discrete, and graph-structured diffusion processes with applications across drug discovery, materials science, and protein design.

Eric Bousquet, Mauro Fava, Zachary Romestan et al.

Chirality refers to the asymmetry of objects that cannot be superimposed on their mirror image. It is a concept that exists in various scientific fields and has profound consequences. Although these are perhaps most widely recognized within biology, chemistry, and pharmacology, recent advances in chiral phonons, topological systems, crystal enantiomorphic materials, and magneto-chiral materials have brought this topic to the forefront of condensed matter physics research. Our review discusses the symmetry requirements and the features associated with structural chirality in inorganic materials. This allows us to explore the nature of phase transitions in these systems, the coupling between order parameters, and their impact on the material's physical properties. We highlight essential contributions to the field, particularly recent progress in the study of chiral phonons, altermagnetism, magnetochirality between others. Despite the rarity of naturally occurring inorganic chiral crystals, this review also highlights a significant knowledge gap, presenting challenges and opportunities for structural chirality mostly at the fundamental level, e.g., chiral displacive phase transitions and ferrochirality, possibilities of tuning and switching structural chirality by external means (electric, magnetic, or strain fields), whether chirality could be an independent order parameter, and whether structural chirality could be quantified, etc. Beyond simply summarising this field of research, this review aims to inspire further research in materials science by addressing future challenges, encouraging the exploration of chirality beyond traditional boundaries, and seeking the development of innovative materials with superior or new properties.

Zheyuan Hu, Nazanin Ahmadi Daryakenari, Qianli Shen et al.

Physics-informed machine learning (PIML) has emerged as a promising alternative to classical methods for predicting dynamical systems, offering faster and more generalizable solutions. However, existing models, including recurrent neural networks (RNNs), transformers, and neural operators, face challenges such as long-time integration, long-range dependencies, chaotic dynamics, and extrapolation, to name a few. To this end, this paper introduces state-space models implemented in Mamba for accurate and efficient dynamical system operator learning. Mamba addresses the limitations of existing architectures by dynamically capturing long-range dependencies and enhancing computational efficiency through reparameterization techniques. To extensively test Mamba and compare against another 11 baselines, we introduce several strict extrapolation testbeds that go beyond the standard interpolation benchmarks. We demonstrate Mamba's superior performance in both interpolation and challenging extrapolation tasks. Mamba consistently ranks among the top models while maintaining the lowest computational cost and exceptional extrapolation capabilities. Moreover, we demonstrate the good performance of Mamba for a real-world application in quantitative systems pharmacology for assessing the efficacy of drugs in tumor growth under limited data scenarios. Taken together, our findings highlight Mamba's potential as a powerful tool for advancing scientific machine learning in dynamical systems modeling. (The code will be available at https://github.com/zheyuanhu01/State_Space_Model_Neural_Operator upon acceptance.)

Parishmita Sarma, Carlo Marion C. Carino, Deeksha Seetharama et al.

Abstract Chemokine receptors constitute an important subfamily of G protein-coupled receptors (GPCRs), and they are critically involved in a broad range of immune response mechanisms. Ligand promiscuity among these receptors makes them an interesting target to explore multiple aspects of biased agonism. Here, we comprehensively characterize two chemokine receptors namely, CXCR4 and CXCR7, in terms of their transducer-coupling and downstream signaling upon their stimulation by a common chemokine agonist, CXCL12, and a small molecule agonist, VUF11207. We observe that CXCR7 lacks G-protein-coupling while maintaining robust βarr recruitment with a major contribution of GRK5/6. On the other hand, CXCR4 displays robust G-protein activation as expected but exhibits significantly reduced βarr-coupling compared to CXCR7. These two receptors induce distinct βarr conformations even when activated by the same agonist, and CXCR7, unlike CXCR4, fails to activate ERK1/2 MAP kinase. We also identify a key contribution of a single phosphorylation site in CXCR7 for βarr recruitment and endosomal localization. Our study provides molecular insights into intrinsic-bias encoded in the CXCR4-CXCR7 system with broad implications for drug discovery.

Anna Och, Marta Olech, Kamil Bąk et al.

<i>Berberis vulgaris</i> L. is currently widely studied for its antioxidant and chemopreventive properties, especially with regard to the beneficial properties of its fruits. Although the bark and roots have been well known and used in traditional medicine since ancient times, little is known about the other parts of this plant. The aim of the research was to determine the antioxidant and LOX inhibitory activity effects of extracts obtained from the leaves, fruits, and stems. Another aim of the work was to carry out the quantitative and qualitative analysis of phenolic acids, flavonoid aglycones, and flavonoid glycosides. The extracts were obtained with the use of ASE (accelerated solvent extraction). The total content of polyphenols was determined and was found to vary depending on the organ, with the highest amount of polyphenols found in the leaf extracts. The free radical scavenging activity of the extracts was determined spectrophotometrically in relation to the DPPH (2,2-diphenyl-1-picrylhydrazyl) radical, with results ranging from 63.9 mgTE/g for the leaves to 65.2 mgTE/g for the stem. Antioxidant activity was also assessed using the ABTS test. The lowest value was recorded for the barberry fruit (117.9 mg TE/g), and the highest level was found for the barberry leaves (140.5 mgTE/g). The oxygen radical absorbance capacity test (ORAC) showed the lowest value for the stem (167.7 mgTE/g) and the highest level for the leaves (267.8 mgTE/g). The range of the percentage inhibition of LOX was determined as well. The percentage inhibition of the enzyme was positively correlated with the sum of the flavonoids, TPC, TFC, and the content of selected flavonoids. Phenolic acids, flavonoid aglycones, and flavonoid glycosides were determined qualitatively and quantitatively in individual parts of <i>Berberis vulgaris</i> L. The content of phenolic acids, flavonoid aglycones, and flavonoid glycosides was determined with the LC-MS/MS method. The following phenolic acids were quantitatively and qualitatively identified in individual parts of <i>Berberis vulgaris</i> L.: gallic acid, 3-caffeoylquinic acid, protocatechuic acid, 5-caffeoylquinic acid, 4-caffeoylquinic acid, and caffeic acid. The flavonoid glycosides determined were: eleutheroside E, Eriodictyol-7-glucopyranoside, rutin, hyperoside, isoquercitin, luteoloside, narcissoside, naringenin-7-glucoside, isorhamnetin-3-glucoside, afzeline, and quercitrin. Flavonoid aglycones such as catechin, luteolin, quercetin, and eriodictyol were also determined qualitatively and quantitatively.

Shivsharan B. Dhadde, Shivsharan B. Dhadde, Mallinath S. Kalshetti et al.

The present study was undertaken to explore the effect of Chromium-D-phenylalanine (Cr (D-phe)3) on the reproduction and development of Drosophila melanogaster. Cr (D-phe)3 was synthesized and characterized by infrared spectral analysis, melting point (DSC), and UV spectral analysis. D. melanogaster was raised in corn flour agar medium containing 0, 5, 10, 15, and 20 μg/mL of Cr (D-phe)3. The effect of Cr (D-phe)3 was evaluated by observing the larval period, pupal period, percentage of egg hatching, morphometric analysis of eggs, larvae, pupae and adults, fertility, fecundity, lifespan of the emerged flies, and levels of antioxidant enzymes such as catalase, glutathione-S-transferase (GST), and superoxide dismutase (SOD) in the supernatant of flies homogenate suspension. The study results indicate that Cr (D-phe)3 showed beneficial effects on reproduction and development in D. melanogaster. Cr (D-phe)3 significantly improved the larval period, pupal period, percentage of egg hatching, morphometric characters of the larva, pupa, and adult, fertility, fecundity, and lifespan of D. melanogaster. Moreover, Cr (D-phe)3 also significantly elevated the levels of catalase (p &lt; 0.01), GST (p &lt; 0.05), and SOD (p &lt; 0.01) in D. melanogaster, and results were statistically significant at the dose of 15 μg/mL. The study results indicate that Cr (D-phe)3 has a positive effect on the reproduction and development of D. melanogaster. The literature review revealed that there is a strong relationship between the physiology of metabolism, oxidative stress and reproduction and development. Several studies propose that Cr(III) influences insulin sensitivity and thereby the metabolism of carbohydrates, proteins, and fats. Cr (D-phe)3 also has antioxidant and anti-inflammatory properties. Hence, the observed beneficial effects of Cr (D-phe)3 on reproduction and development of D. melanogaster may be attributed to its physiological effect on carbohydrate, protein, and lipid metabolism and its antioxidant and anti-inflammatory properties.

Harsh Parikh, Quinn Lanners, Zade Akras et al.

Recent statistical and reinforcement learning methods have significantly advanced patient care strategies. However, these approaches face substantial challenges in high-stakes contexts, including missing data, inherent stochasticity, and the critical requirements for interpretability and patient safety. Our work operationalizes a safe and interpretable framework to identify optimal treatment regimes. This approach involves matching patients with similar medical and pharmacological characteristics, allowing us to construct an optimal policy via interpolation. We perform a comprehensive simulation study to demonstrate the framework's ability to identify optimal policies even in complex settings. Ultimately, we operationalize our approach to study regimes for treating seizures in critically ill patients. Our findings strongly support personalized treatment strategies based on a patient's medical history and pharmacological features. Notably, we identify that reducing medication doses for patients with mild and brief seizure episodes while adopting aggressive treatment for patients in intensive care unit experiencing intense seizures leads to more favorable outcomes.

Pafadnam Ibrahim, Nièssan Kohio, Wêpari Charles Yaguibou et al.

Active ingredients containing fluorinated organic compounds such as Fluoroalkylamines or pyrimidine-based molecules are promising in the field of agriculture (pesticides and herbicides) and pharmacology (antibiotics). The massive use of these molecules will result in a massive increase in waste containing this type of molecule. Developed countries have restrictive waste management policies, which is not the case in developing countries. In the latter, we are witnessing a proliferation of storage areas and open-air disposal of waste, sometimes from developed countries. These practices have enormous consequences on the environment such as air, soil and water pollution and therefore on human health. One of the solutions already proven on solid waste would be the use of plasma torches. These torches can reach high temperatures (5,000 K to 20,000 K). However, the use of these means of treatment is not without danger since toxic or lethal molecules could be produced. In order to understand these difficulties, we propose to study the influence of air on the chemical composition of a plasma based on fluoroalkylamines (trifluoroethylamine: C2H4F3N, nonafluoropentylamine: C5H4F9N, etc.), at atmospheric pressure and at atmospheric pressure. local thermodynamic equilibrium (E.T.L), in a temperature range from 500 K to 20,000 K. In order to obtain the chemical composition of the plasma, we use the Gibbs free energy minimization method. The results obtained show that dangerous and toxic gaseous chemical species such as CF2, CO, HCN and HF appear at low temperatures with high concentration.

María Alías-Ferri, María Alías-Ferri, Manuela Pellegrini et al.

Cannabis is the most widely consumed illegal drug in the world and synthetic cannabinoids are increasingly gaining popularity and replacing traditional cannabis. These substances are a type of new psychoactive substance that mimics the cannabis effects but often are more severe. Since, people with opioids use disorder use widely cannabis, they are a population vulnerable to use synthetic cannabinoids. In addition, these substances are not detected by the standard test used in the clinical practice and drug-checking is more common in recreational settings. A cross-sectional study with samples of 301 opioid use disorder individuals was carried out at the addiction care services from Barcelona and Badalona. Urinalysis was performed by high-sensitivity gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography-high –resolution mass spectrometry (UHPLC-HRMS). Any synthetic cannabinoid was detected in 4.3% of the individuals and in 23% of these samples two or more synthetic cannabinoids were detected. Among the 8 different synthetic cannabinoids detected, most common were JWH-032 and JWH-122. Natural cannabis was detected in the 18.6% of the samples and only in the 0.7% of them THC was identified. Several different synthetic cannabinoids were detected and a non-negligible percentage of natural cannabis was detected among our sample. Our results suggest that the use of synthetic cannabinoids may be related to the avoidance of detection. In the absence of methods for the detection of these substances in clinical practice, there are insufficient data and knowledge making difficult to understand about this phenomenon among opioid use disorder population.

Nirmalya Manna, Ria Mukherjee, Parthasarathi Bhattacharya et al.

Background: COVID vaccines have been rolled out all over the world after emergency use authorization in the prevailing pandemic situation. However, hesitancy about its safety and efficacy exists among beneficiaries. Vaccine hesitancy can be a barrier to adequate immunization coverage. Aims and Objectives: This qualitative study was undertaken among health care workers in the rural field practice area of a tertiary care hospital, to find out their perceptions about COVID vaccines, and reasons behind hesitancy toward the same. Materials and Methods: Six focused group discussions (FGD) were held with the help of moderator. Each FGD had five members, so 30 members were included in the study. Health care workers included doctors, nurses, ANM/ASHAs, and other health care workers. Results: There were 17 males and 13 females. Doctors had a positive attitude toward vaccination, but other health care workers had mixed perception regarding vaccination. Most grass root level workers were sceptical about the efficacy of the vaccine. Conclusion: Mostly positive attitude toward COVID vaccines was observed in the present study. Hesitancy toward vaccines was observed in some health care workers, and it likely rooted from their inadequate knowledge about the vaccine. [Natl J Physiol Pharm Pharmacol 2022; 12(9.000): 1458-1462]

Jie-Jie Xu, Lin Zhu, Hai-Di Li et al.

Alcohol-induced liver injury (ALI) is associated with inflammatory responses regulated by macrophages. Activation of macrophages plays a crucial role in ALI while DNA methylation-regulated gene silencing is associated with inflammation processes in macrophages. Proline-Serine-Threonine Phosphatase Interacting Protein 2 (PSTPIP2), which belongs to the Fes/CIP4 homology-Bin/Amphiphysin/Rvs domain family of proteins and plays a role in macrophages. Previous studies have shown that Pstpip2 can be methylated. Herein, its expression was found to be significantly downregulated in primary liver macrophages isolated from EtOH-fed mice and EtOH-induced RAW264.7 cells. Overexpression of PSTPIP2 using liver-specific recombinant AAV serotype 9 (rAAV9)-PSTPIP2 in EtOH-fed mice dramatically alleviated liver injury and inflammatory responses. In addition, silencing of PSTPIP2 aggravated the alcohol-induced inflammatory response in vitro. Mechanistically, PSTPIP2 might affect macrophage-induced inflammatory responses by regulating the STAT1 and NF-κB signaling pathways. The downregulation of PSTPIP2 in ALI may be associated with DNA methylation. Methylation-specific PCR and western blotting analyses showed that EtOH induced abnormal DNA methylation patterns and increased the protein expression levels of DNMT1, DNMT3a, and DNMT3b. The chromatin immunoprecipitation assay showed that DNMT3a could directly bind to the Pstpip2 promoter and act as a principal regulator of PSTPIP2 expression. Moreover, silencing of DNMT3a significantly restored the EtOH-induced low expression of PSTPIP2 and inhibited EtOH-induced inflammation. Overall, these findings provide a detailed understanding of the possible functions and mechanisms of PSTPIP2 in ALI, thus providing new substantive research to elucidate the pathogenesis of ALI and investigate potential targeted treatment strategies.