Current anticancer treatments are inefficient against glioblastoma multiforme (GBM), which remains one of the most aggressive and lethal cancers. Evidence has shown the presence of glioblastoma stem cells (GSCs), which are chemoradioresistant and associated with high invasive capabilities in normal brain tissues. Moreover, accumulating studies have indicated that radiotherapy contributes to abnormalities in cell cycle checkpoints, including the G₁/S and S phases, which may potentially lead to resistance to radiation. Through computational simulations using bioinformatics, we identified several GBM oncogenes that are involved in regulating the cell cycle. Cyclin B1 (CCNB1) is one of the cell cycle-related genes that was found to be upregulated in GBM. Overexpression of CCNB1 was demonstrated to be associated with higher grades, proliferation, and metastasis of GBM. Additionally, increased expression levels of CCNB1 were reported to regulate activation of mitogen-activated protein kinase 7 (MAPK7) in the G₂/M phase, which consequently modulates mitosis; additionally, in clinical settings, MAPK7 was demonstrated to promote resistance to temozolomide (TMZ) and poor patient survival. Therefore, MAPK7 is a potential novel drug target due to its dysregulation and association with TMZ resistance in GBM. Herein, we identified MAPK7/extracellular regulated kinase 5 (ERK5) genes as being overexpressed in GBM tumors compared to normal tissues. Moreover, our analysis revealed increased levels of the cell division control protein homolog (CDC42), a protein which is also involved in regulating the cell cycle through the G₁ phase in GBM tissues. This therefore suggests crosstalk among CCNB1/CDC42/MAPK7/cluster of differentiation 44 (CD44) oncogenic signatures in GBM through the cell cycle. We further evaluated a newly synthesized small molecule, SJ10, as a potential target agent of the CCNB1/CDC42/MAPK7/CD44 genes through target prediction tools and found that SJ10 was indeed a target compound for the above-mentioned genes; in addition, it displayed inhibitory activities against these oncogenes as observed from molecular docking analysis.

Ovarian cancer remains the third most prevalent and deadliest gynecologic malignancy worldwide, with most patients eventually developing resistance to platinum-based chemotherapy. This highlights a critical unmet need for innovative multitargeted therapies to address current treatment challenges. In this study, we identified 35 differentially expressed genes (DEGs) through integrated analysis of four GEO ovarian cancer datasets, with validation using TCGA data. Gene Ontology (GO) and KEGG enrichment analyses highlighted key tumor-associated pathways, and protein-protein interaction (PPI) network modeling prioritized CDK1 and TTK as high-value therapeutic targets. We evaluated the association between molecular genomic features and drug responses across the NCI-60 ovarian cancer cell line panel (IGROV1, OVCAR-3, OVCAR-4, OVCAR-5, OVCAR-8, NCI/ADR-RES, and SK-OV-3), using a series of salicylanilide-derived compounds and four FDA-approved drugs (cabozantinib, paclitaxel, rapamycin, and niclosamide) from the NCI Developmental Therapeutics Program (DTP). Among these, NSC765690 (MCC22) emerged as the most promising candidate. It demonstrated potent antiproliferative activity, high target selectivity, and strong binding affinity to both CDK1 and TTK. Multi-omics integration, combined with AI-driven network modeling, further elucidated NSC765690's mechanism of action and its relevance to ovarian cancer pathogenesis. Additionally, ADMET and pharmacokinetic profiling confirmed its favorable drug-like properties and low predicted toxicity. Collectively, these findings establish NSC765690 as a potent dual-target inhibitor and exemplify a rational, data-driven drug discovery pipeline for overcoming chemotherapy resistance in ovarian cancer.

The NLRP3 inflammasome is a key regulator of the innate immune response and a promising therapeutic target in inflammation-driven diseases. This study aimed to identify potent nature inspired small molecules using AI-guided in silico techniques integrated with NCI-60 high-throughput assays. We developed a machine learning-driven platform that combines pharmacophore modeling, molecular docking, MDS, and RNNs to prioritize candidate compounds. Among these, NSC828779 emerged as a lead scaffold, demonstrating high binding affinity to the ATP-binding site of NLRP3 and superior interaction energy and stability compared to known inhibitors. Docking scores were strongest for NLRP3 (-10.5 kcal/mol), caspase-1 (-8.6 kcal/mol), and ASC (-8.5 kcal/mol), outperforming MCC950, glyburide, and other reference compounds. MDS confirmed the stability of the NLRP3-ASC-caspase-1 complex, supported by RMSD and RMSF analyses showing enhanced conformational integrity. ADMET profiling predicted favorable drug-likeness, solubility, moderate lipophilicity, and low toxicity. Mechanistically, NSC828779 may act as a multi-mechanistic NLRP3 inhibitor by disrupting protein-protein interactions, inhibiting NF-κB signaling, and inducing autophagy. These results establish NSC828779 as a promising candidate for treating inflammation-related disorders and underscore the utility of AI-driven drug discovery platforms in identifying novel inflammasome-targeted therapeutics. Further in vitro and in vivo validation is warranted to support its clinical development.

With rapid pharmaceutical development, hepatic clearance has become crucial for assessing drug metabolism. Rapid and effective estimation of hepatic clearance can be achieved through in vitro-to-in vivo extrapolation (IVIVE) without complicated animal experiments. However, this well-established method frequently leads to significant underestimation, limiting its clinical applicability. We propose a series of optimizations to address these limitations. Metoprolol was selected as the target drug. In vitro microsomal assays, ex situ isolated perfused rat liver (IPRL) experiments, and in vivo pharmacokinetic studies in rats were performed to systematically investigate the factors that contribute to IVIVE deviation. To rectify the previously overlooked errors in IVIVE, we incorporated the apparent volume of distribution to refine the estimation of the intrinsic hepatic clearance derived from the Michaelis-Menten equation. Additionally, a more cytosolic-like environment was provided for microsome experiments to better simulate in vivo reactions. By integrating the findings from the IPRL experiments and pharmacokinetic studies, we identified the optimal hepatic clearance model from the perspective of liver drug metabolism-driving concentrations. Ultimately, our results indicate that the IVIVE-predicted hepatic clearance increased from the previously underestimated value of 28.1 to ∼70 mL/min/kg, which is closer to the in vivo value of 73.9 mL/min/kg. Moreover, the HEPES-KOH buffer system exhibits superior performance under these conditions. We anticipate that the improved IVIVE method will provide a more comprehensive and accurate framework for predicting hepatic clearance, paving the way for its application in clinical drug dosage adjustments and new drug development.

Background/Objectives: Hepatic clearance is important in determining clinical drug administration strategies. Achieving accurate hepatic clearance predictions through in vitro-to-in vivo extrapolation (IVIVE) relies on appropriate model selection, which is a critical step. Although numerous models have been developed to estimate drug dosage, some may fail to predict liver drug clearance owing to inappropriate hepatic clearance models during IVIVE. To address this limitation, an in silico-based model selection approach for optimizing hepatic clearance predictions was introduced in a previous study. The current study extends this strategy by verifying the accuracy of the selected models using ex situ experimental data, particularly for drugs whose model choices are influenced by protein binding. Methods: Commonly prescribed drugs were classified according to their hepatic extraction ratios and protein-binding properties. Building on previous studies that employed multinomial logistic regression analysis for model selection, a three-phase classification method was implemented to identify five representative drugs: diazepam, diclofenac, rosuvastatin, fluoxetine, and tolbutamide. Subsequently, an isolated perfused rat liver (IPRL) system was used to evaluate the accuracy of the in silico method. Results: As the unbound fraction increased for diazepam and diclofenac, the most suitable predictive model shifted from the initially preferred well-stirred model (WSM) to the modified well-stirred model (MWSM). For rosuvastatin, the MWSM provided a more accurate prediction. These three capacity-limited, binding-sensitive drugs conformed to the outcomes predicted by the multinomial logistic regression analysis. Fluoxetine was best described by the WSM, which is consistent with its flow-limited classification. For tolbutamide, a representative capacity-limited, binding-insensitive drug, no significant differences were observed among the various models. Conclusions: These findings demonstrate the accuracy of an in silico-based model selection approach for predicting liver metabolism and highlight its potential for guiding dosage adjustments. Furthermore, the IPRL system serves as a practical tool for validating the accuracy of the results derived from this approach.

In this study, we developed and characterized a gelatin‐based foam dressing for application in the medical device field. We utilized gelatin as the substrate and prepared the dressing through foaming and freeze‐drying processes. This dressing exhibited a favorable pore distribution, with an average pore size of approximately 60–70 µm, enabling efficient absorption of wound exudate and promoting wound healing. We conducted cell viability analyses on four cell lines (3T3, L929, Hs68, CG1519) exposed to the fluids released from the crosslinked gelatin foam dressing, with excellent biocompatibility demonstrated across all cell lines. In wound healing studies performed in BALB/cByJNarl mice, the crosslinked gelatin foam dressing exhibited a pronounced effect in promoting functional tissue regeneration and skin repair at the wound site. An analysis of the wound closure rate, based on wound area images acquired during dressing changes, revealed a recovery speed comparable to that observed for a commercially available dressing. Mice treated with the crosslinked gelatin foam dressing consistently demonstrated a stable wound closure rate. Pathological sections of wounds displayed regenerative tissues and epidermal layers, highlighting the wound healing efficacy of our developed dressing. Liver and kidney sections showed no presence of neutrophils or abnormal lymphocyte infiltration, indicating the absence of significant drug toxicity in mice. These findings collectively demonstrate the favorable safety profile of the crosslinked gelatin foam dressing within biological systems. Overall, the crosslinked gelatin foam dressing developed in this study shows promise for use in wound dressing applications.