Drug Development Research最新文献

Ischemic stroke is the third leading cause of death and disability worldwide. Cerebral ischemia-reperfusion injury leads to severe neuroinflammation, in which microglial polarization plays a key role. Dehydrocorydaline (DHC), the main alkaloid of Corydalis yanhusuo, has anti-inflammatory and neuroprotective effects. However, it is unknown if it controls microglial polarization to lessen cerebral ischemia-reperfusion injury. A mouse middle cerebral artery occlusion (MCAO) model and an in vitro microglial oxygen-glucose deprivation/reoxygenation (OGD/R) model were used. 2,3,5-triphenyltetrazolium chloride (TTC) staining, neurological function scores, flow cytometry, Enzyme-Linked Immunosorbent Assay, and Western blot were used to evaluate the efficacy and mechanism of DHC. A rescue experiment was also conducted using the Janus Kinase 1 (JAK1) agonist coumermycin A1 (CMA1). Compared with the MCAO group, DHC treatment significantly reduced cerebral infarction volume and significantly improved neurological function scores, motor coordination, and sensorimotor function. DHC effectively reduced hippocampal neuronal damage. At the same time, DHC treatment significantly decreased the level of pro-inflammatory factors in brain tissue, while increasing the level of anti-inflammatory factors. DHC significantly reduced the expression of M1 phenotype markers and upregulated the expression of M2 phenotype markers. Mechanistically, DHC significantly inhibited the phosphorylation levels of JAK1 and Signal Transducer and Activator of Transcription 3 (STAT3), while the JAK1 agonist CMA1 completely reversed the above-mentioned protective effects of DHC. DHC reduces neuroinflammation and has a neuroprotective impact against cerebral ischemia-reperfusion injury by blocking the JAK1/STAT3 signaling pathway and encouraging microglia to polarize to the M2 phenotype.

Sepsis, a life-threatening condition triggered by dysregulated host response to infection, poses significant global health challenges. Identifying lipopolysaccharide (LPS)-related biomarkers and underlying mechanisms remains critical, yet underexplored. We integrated bulk microarray datasets and single-cell RNA-seq data from the Gene Expression Omnibus to identify LPS-related genes associated with sepsis. scRNA-seq was used for cell clustering, annotation, AUCell scoring, and cell-cell communication analysis. Differentially expressed LRGs were screened from both bulk and single-cell datasets and intersected. Three machine learning algorithms—least absolute shrinkage and selection operator regression, support vector machine–recursive feature elimination, and extreme gradient boosting—were applied to select robust diagnostic biomarkers. Gene expression was validated via qRT-PCR. Diagnostic and prognostic models were constructed and validated in independent cohorts. Seven key LRGs were identified. The diagnostic model achieved high AUCs (> 0.89) across validation cohorts, while the prognostic model effectively stratified patients into distinct survival groups. High-risk groups showed increased myeloid-derived suppressor cell and macrophage infiltration, activation of inflammatory pathways, and unique intercellular communication networks. scRNA-seq revealed cell-type-specific LRGs expression, particularly in myeloid populations. We established and validated a robust LPS-related biomarker model that integrates bulk microarray and single-cell transcriptomics, offering novel diagnostic, prognostic, and therapeutic insights for sepsis.

This study aimed to explore the therapeutic effect and underlying mechanism of α7 nicotinic acetylcholine receptor (α7nAChR) mediated by adeno-associated virus serotype 9 (AAV9) on dextran sulfate sodium (DSS)-induced ulcerative colitis (UC) in mice. Male C57BL/6 mice were randomly divided into the blank control group, model group, AAV-α7nAChR low/medium/high-dose groups, and AAV-GFP group. A chronic UC model was established, with interventions administered via intraperitoneal injection. Meanwhile, an acute UC model was set up, together with control groups treated with the α7nAChR antagonist methyllycaconitine (MLA) and 5-aminosalicylic acid (5-ASA). A series of indicators were detected, including body weight, Disease Activity Index (DAI), colonic pathological changes, inflammatory factors, pathway-related proteins, and T-regulatory (T-reg)/T helper 17 (Th-17) cell balance. Results demonstrated that AAV9-α7nAChR ameliorated UC symptoms in mice in a dose-dependent manner: it relieved body weight loss and hematochezia, restored colon length and spleen weight, and alleviated colonic mucosal damage. Furthermore, it activated the cholinergic anti-inflammatory pathway (CAP), inhibited the NF-κB/NLRP3 inflammatory axis, repaired the intestinal barrier (by upregulating ZO-1 and occludin), and restored the T-reg/Th-17 immune balance. The therapeutic efficacy of the high-dose AAV9-α7nAChR group was superior to that of the 5-ASA group, while MLA could suppress its therapeutic effect. This study preliminarily clarified the multi-target mechanism of AAV9-α7nAChR in treating UC, providing an experimental foundation for the clinical gene therapy of UC.

In this study, a series of novel iridium(III) complexes incorporating 2-phenylimidazo[4,5-f][1,10]phenanthroline ligands with different substituents (methyl (1a), isopropyl (2a), methoxy (3a), phenyl (4a)) were evaluated for their in vitro inhibitory activities against anticholinesterase (acetylcholinesterase [AChE], butyrylcholinesterase [BChE]) and carbonic anhydrase [hCA I] and [hCA II]) enzymes. Among the tested compounds, 2a demonstrated exceptional potency with IC₅₀ values of 66.5 ± 9.06 nM for AChE and 26.45 ± 5.00 nM for BChE, significantly outperforming tacrine. Compound 4a also exhibited strong inhibition of hCA I (IC₅₀ = 33.0 ± 7.09 nM) and hCA II (IC₅₀ = 41.79 ± 8.09 nM), surpassing the reference drug acetazolamide. Molecular docking studies revealed that compound 4a exhibited the strongest binding affinity with BChE (PDB: 4BDS) at -12.06 kcal/mol, while compound 2a showed strong binding to hCA II (PDB: 3HS4) at -8.768 kcal/mol. Molecular dynamics simulations over 150 ns confirmed the structural stability of the protein-ligand complexes. Cell viability assays showed that compounds 1a–4a decreased PC3 prostate cancer cell viability in a concentration-dependent manner, with IC₅₀ values of 10.09, 3.95, 11.39, and 4.51 µM, respectively. This comprehensive study highlights iridium(III) complexes as multitarget therapeutic agents with potent anticholinesterase and carbonic anhydrase inhibitory properties, offering promise for treating neurodegenerative and metabolic disorders.

Integrating artificial intelligence (AI) into drug discovery revolutionizes pharmaceutical research by significantly accelerating the identification, optimization, and development of novel therapeutics. Conventional drug discovery methods, known for high costs, lengthy timelines, and low success rates, are increasingly being augmented by AI-based technologies, including machine learning (ML), deep learning (DL), and reinforcement learning (RL). These advanced computational approaches enhance key processes, such as target identification, virtual screening, de novo drug design, toxicity prediction, and the optimization of pharmacokinetic and pharmacodynamic profiles, dramatically increasing overall efficiency. AI-driven primary and secondary screening methods improve cell classification, compound prioritization, and drug-target interaction predictions, substantially shortening the progression from preclinical phases to clinical trials. Additionally, AI enables retrosynthesis prediction and reaction yield modeling, optimizing chemical synthesis pathways and reducing the need for resource-intensive experimental procedures. AI's integration into clinical trials has notably improved patient stratification, biomarker discovery, and adaptive trial designs, ultimately delivering more precise and economically feasible therapeutic interventions. Furthermore, AI supports polypharmacological approaches through multitarget drug discovery, drug repurposing (finding new uses for existing drugs), and adverse effect prediction, thereby advancing personalized medicine. Despite these transformative advantages, it's important to note that AI in drug discovery also has limitations, such as ensuring data quality, improving model interpretability, gaining regulatory acceptance, and addressing ethical concerns. This review comprehensively explores the impact of AI throughout the drug discovery pipeline, emphasizing its critical role in expediting the development of life-saving medications and outlining future directions for continued pharmaceutical innovation driven by AI.

Tuberculosis is a global emergency. Despite two decades of intense research to understand and cure the disease, biological uncertainties prevail and hamper the therapeutic progress. Increasing incidences of multidrug-resistant (MDR) and extensively drug resistant (XDR) Mycobacterium tuberculosis strains further complicate tuberculosis control. Modern research, therefore, focuses on development of new antitubercular drugs to treat drug-resistant tuberculosis and shorten the duration of the standard chemotherapy. Fortunately, the recent accelerated approval of delamanid and bedaquiline for use in MDR and XDR tuberculosis has reinvigorated antitubercular research. Although progresses in tuberculosis drug discovery are being made following the availability of M. tuberculosis genome and progressions in molecular biology, novel drug targets and leads are continuously required to strengthen the tuberculosis therapeutic pipeline. Discovery of new targets has eventually promoted tuberculosis therapy, and will keep paving the foundation for generating the future wave of tuberculosis drug leads. This review summarizes important M. tuberculosis drug targets such as F₁/F₀ ATP synthase, isocitrate lyase, β-ketoacyl-ACP synthases KasA and KasB, QcrB, and mycobactin biosynthesis enzymes MbtA and MbtI, and drugs under preclinical and clinical development stages that aim at making drug discovery breakthroughs. It also discusses the strategies that entail discovery of new mycobacterial therapeutic determinants and provide ideas for development of more efficient drugs. The article comprehensively provides a better understanding of M. tuberculosis drug targets, along with opening new ventures for tuberculosis control and treatment.

Triple-negative breast cancer (TNBC) is an aggressive subtype that is characterized by a high metastatic capacity, limited therapeutic options, and a poor prognosis, largely because of its stemness properties and aberrant signaling. Dynorphin A, an endogenous opioid peptide, has been identified as a potential modulator of cancer progression, although its role in TNBC remains unclear. This study demonstrates that the Dynorphin/κ-opioid receptor (KOR) signaling axis is markedly diminished in TNBC patients and TNBC cell lines, suggesting its tumor-suppressive function. Functional assays revealed that Dynorphin A suppresses TNBC cell proliferation, migration, invasion, and stemness while inducing apoptosis. Cell cycle analysis showed Dynorphin A induced G1/S phase arrest. Mechanistically, Dynorphin A blocked epithelial−mesenchymal transition (EMT) by restoring E-cadherin and reducing N-cadherin, Vimentin, and Snail. Morphological analysis confirmed a reversion from mesenchymal to epithelial phenotype. Furthermore, it attenuated stem cell-like properties as indicated by lower levels of CD44, CD133, and Octamer-binding transcription factor 4 (OCT4). Notably, Dynorphin A disrupted zinc finger E-box binding homeobox 1 (ZEB1)-mediated Wnt/β-catenin signaling, and forced expression of ZEB1 partially rescued β-catenin levels, confirming that ZEB1 is a downstream mediator. These findings identify Dynorphin A as a promising endogenous inhibitor of TNBC progression that targets both malignancy and stemness via the ZEB1/β-catenin axis.

Cholangiocarcinoma (CCA) is a type of cancer that has a rather high mortality rate and arises from cholangiocytes. Due to its aggressive nature, CCA is considered a rare and highly advanced form of malignancy. This research is detailed around which role does ferroptosis play in CCA and what impact does it have on CCA progression as well as on treatment resistance. A systematic assessment of previously published works was conducted to understand the cellular mechanisms of ferroptosis, the pertinent biological networks, and its possible therapeutic targets. During the research, we discovered that the sensitivity of CCA cells to ferroptosis associated cell death is increased because they have dysregulated iron metabolism and uncontrolled lipid peroxidation. Sensitivity to ferroptosis is regulated by important proteins such as acyl-CoA synthetase long-chain family member 4 (ACSL4) and solute carrier family 7 member 11 (SLC7A11). In addition, the ferroptosis inducers erastin and RSL3 are capable of enhancing the efficacy of traditional therapies and seeking solutions for the chemoresistance problem. The hurdles to be overcome are finding reliable biomarkers for the prediction of ferroptosis sensitivity and designing targeted delivery systems for minimal off-target effects. Clinically, these techniques offer novel concepts in the treatment of CCA, making further research key to these conclusions being adopted in practice.