Backgrounds: Brain metastases occur in approximately 30% of patients with non-small-cell lung cancer (NSCLC). Therefore, the free drug concentration in cerebrospinal fluid (CSF) is strongly associated with the clinical efficacy.Purpose: The present study aimed to develop physiologically based pharmacokinetic (PBPK) models that can predict the steady-state trough concentration (C_trough) in plasma and CSF, as well as anaplastic lymphoma kinase (ALK) occupancy (AO), for three inhibitors: crizotinib (CRI), alectinib (ALE), and lorlatinib (LOR).Methods: To achieve this, population PBPK models were successfully developed and validated using multiple clinical pharmacokinetics (PK) and drug–drug interaction (DDI) studies, both in healthy subjects and patients.Results: The prediction-to-observation ratios for plasma AUC, C_max, and C_trough in heathy subjects and patients ranged between 0.5 and 2.0. In addition, PK profiles of CRI, ALE, and LOR in CSF aligned well with observed data. Moreover, the AUC and C_max ratios of the three inhibitors when co-administered with CYP3A4 inhibitors/inducers also matched with clinically observed values. Utilizing PK thresholds for effective plasma C_trough and AO values on wild-type and four ALK mutations in plasma and CSF, PBPK models were then combined with the mean and 95% confidence interval to predict optimal dosing regimens.Conclusions: Overall, these PBPK models provide valuable insights into determining appropriate dosing regimens for the three ALK inhibitors, understanding their effectiveness in brain metastasis therapy, and analyzing the underlying mechanisms of on-target resistance.

Background: Piper longum demonstrates significant therapeutic potential against gastric cancer (GC), but its underlying mechanisms remain incompletely understood. This study aimed to establish a comprehensive multi-omics framework to elucidate Piper longum's anti-cancer mechanisms. Methods: We integrated in vivo experiments, metabolomics, gut microbiota analysis, mass spectrometry, and network pharmacology to investigate Piper longum’s effects. In vivo studies assessed its dose-dependent inhibition of GC growth compared to standard chemotherapy (L-OHP + 5-FU). Metabolomics identified altered lipid metabolism pathways, while gut microbiota analysis evaluated its impact on microbial composition. Piperine was identified as the key active compound, and ACOT1 was pinpointed as a critical molecular target through integrated analysis. Results: Piper longum significantly suppressed gastric cancer (GC) growth in a dose-dependent manner, with high-dose treatment demonstrating superior efficacy compared to conventional chemotherapy (L-OHP + 5-FU). Metabolomic analysis revealed that its anti-cancer mechanism primarily involves the reprogramming of lipid metabolism pathways in GC cells, while gut microbiota assessment confirmed that it modulates intestinal flora composition without compromising microbial diversity, supporting its favorable safety profile. Mass spectrometry identified piperine as the key bioactive compound, and integrated metabolomics and network pharmacology further pinpointed ACOT1 as a critical molecular target, which interacts with piperine that confirmed by CETSA. Notably, high ACOT1 expression was associated with poor prognosis in GC patients, underscoring its therapeutic relevance. Conclusions: This study elucidates Piper longum's "component-target-pathway" mechanism in GC treatment, highlighting piperine-ACOT1-de novo lipogenesis regulatory pathway as a critical axis. Additionally, it establishes a robust multi-omics framework for evaluating traditional medicine efficacy, providing a theoretical foundation for Piper longum’s clinical application in GC therapy.

Introduction: Lung adenocarcinoma, the most common subtype of non‐small cell lung cancer, faces challenges such as drug resistance and tumor heterogeneity. N4‐acetylcytidine (ac4C) is an important RNA modification involved in cancer progression, but its role in lung adenocarcinoma remains unclear.Methods: This study analyzed transcriptomic and single‐cell RNA sequencing data from public databases to investigate the expression and clinical significance of ac4C‐related genes in lung adenocarcinoma. Ten machine learning algorithms were applied to develop and validate an ac4C‐related gene signature (ARGSig) for prognosis prediction across multiple independent cohorts.Results: Cells with high ac4C activity showed increased intercellular communication and activation of tumor‐associated pathways. The ARGSig model effectively stratified patients by survival outcomes and predicted sensitivity to immune checkpoint inhibitors and chemotherapy agents.Conclusion: ac4C modification and its related genes play a critical role in lung adenocarcinoma development. The ARGSig model provides a promising molecular tool for prognosis evaluation and personalized treatment guidance in lung adenocarcinoma patients.

The head and neck squamous cell carcinomas (HNSCC), which is the most common malignant tumor of head and neck region. Programmed cell death pathways, inclu