Clinical development of nintedanib for advanced non-small-cell lung cancer

Efficacy and safety of nintedanib in patients with non-small cell lung cancer, and novel insights in radiation-induced lung toxicity

Nintedanib is a tyrosine kinase inhibitor of fibroblast growth factor-, vascular endothelial growth factor-, and platelet-derived growth factor receptors. These three receptors promote new blood vessel formation and maintenance, which is essential for tumor growth and spread. Several trials have shown that nintedanib plays a substantial role in treating patients with non-small cell lung cancer (NSCLC) and idiopathic pulmonary fibrosis. Recently, several clinical trials of nintedanib to treat NSCLC have been reported. In this review, we focus on our current understanding of nintedanib treatment for advanced NSCLC patients and summarize the literature on using nintedanib in radiation-induced lung toxicity and the efficacy and tolerability of nintedanib.

Safety profile of tyrosine kinase inhibitors used in non-small-cell lung cancer: An analysis from the Italian pharmacovigilance database

Introduction

Non-small cell lung cancer (NSCLC) is often caused by molecular alterations that can be detected by predictive biomarkers including mutations or amplifications of several genes. Several tyrosine kinase inhibitors (TKIs) have been approved in Europe by the European Medicines Agency (EMA) for NSCLC. The aim of this study was to analyze the onset of adverse drug reactions (ADRs) related to TKIs in NSCLC through a spontaneous reporting system (SRS) database.

Methods

All ADR reports having as suspected drug afatinib (AFT), alectinib (ALEC), brigatinib (BRG), ceritinib (CER), crizotinib (CRIZ), erlotinib (ERL), gefitinib (GEF), lorlatinib (LORL), nintedanib (NTB), and osimertinib (OSI) recorded into the Report Reazioni Avverse dei Medicinali (RAM) system database for national data and into the Italian SRS database for Sicilian data and collected from 2006 to 2021 have been evaluated. A descriptive analysis of basal demographic and drug-related characteristics was performed. A case-by-case methodology was conducted paying particular attention to all serious ADR reports collected in Sicily, focusing on type of seriousness, age, sex, concomitant drugs, and comorbidities.

Results

Of the 3,048 Italian reports, most of ADRs were related to ERL (n = 1,448), followed by AFT (n = 435) and GEF (n = 366). ADR reports were slightly more frequent in females (52.2%) and in the age group >65 years (53.0%). A higher number of cases were related to skin disorders (n = 1,766; 57.9%), followed by gastrointestinal disorders (n = 1,024; 33.6%), general disorders and administration site conditions (n = 536; 17.6%), and infections (n = 483; 15.8%). The case-by-case assessment of Sicilian ADRs showed that 33 cases were serious (12.5%) and mainly involved ERL (n = 17; 51.5%), occurring in males with a higher onset of respiratory diseases (30.3%) such as respiratory failure, interstitial lung disease and dyspnea.

Discussion

The analysis of spontaneous ADR reports of TKIs confirmed, in general, well-known risks, which often include skin, gastrointestinal, general, liver, and respiratory diseases as well as infections. However, more attention should be paid to the occurrence of serious life-threatening ADRs including respiratory failure, interstitial lung disease, and cardiogenic shock, especially in young patients.

Learning important features from multi-view data to predict drug side effects

The problem of drug side effects is one of the most crucial issues in pharmacological development. As there are many limitations in current experimental and clinical methods for detecting side effects, a lot of computational algorithms have been developed to predict side effects with different types of drug information. However, there is still a lack of methods which could integrate heterogeneous data to predict side effects and select important features at the same time. Here, we propose a novel computational framework based on multi-view and multi-label learning for side effect prediction. Four different types of drug features are collected and graph model is constructed from each feature profile. After that, all the single view graphs are combined to regularize the linear regression functions which describe the relationships between drug features and side effect labels. L1 penalties are imposed on the regression coefficient matrices in order to select features relevant to side effects. Additionally, the correlations between side effect labels are also incorporated into the model by graph Laplacian regularization. The experimental results show that the proposed method could not only provide more accurate prediction for side effects but also select drug features related to side effects from heterogeneous data. Some case studies are also supplied to illustrate the utility of our method for prediction of drug side effects.

Nanoformulations of anticancer FGFR inhibitors with improved therapeutic index

Fibroblast growth factor receptor (FGFR) inhibitors like ponatinib and nintedanib are clinically approved for defined cancer patient cohorts but often exert dose-limiting adverse effects. Hence, we encapsulated the FGFR inhibitors ponatinib, PD173074, and nintedanib into polylactic acid nanoparticles and liposomes to enable increased tumor accumulation/specificity and reduce side effects. Different methods of drug loading were tested and the resulting formulations compared regarding average size distribution as well as encapsulation efficiency. Appropriate encapsulation levels were achieved for liposomal preparations only. Nanoencapsulation resulted in significantly decelerated uptake kinetics in vitro with clearly decreased short-term (up to 72 h) cytotoxicity at higher concentrations. However, in long-term clonogenic assays liposomal formations were equally or even more active as compared to the free drugs. Accordingly, in an FGFR inhibitor-sensitive murine osteosarcoma transplantation model (K7M2), only liposomal but not free ponatinib resulted in significant tumor growth inhibition (by 60.4%) at markedly reduced side effects.

Low-expression of TMEM100 is associated with poor prognosis in non-small-cell lung cancer

Transmembrane protein 100 (TMEM100) was first identified as a transcript from the mouse genome. Recent studies have demonstrated that TMEM100 is involved in hepatocellular carcinoma (HCC) malignancy. However, the distribution and clinical significance of TMEM100 in non-small-cell lung carcinoma (NSCLC) remains poorly understood. This study aims to explore the significance of TMEM100 expression in NSCLC. We found that TMEM100 expression was significantly reduced in NSCLC tissues when compared with that in adjacent normal lung tissues (P<0.001). Kaplan-Meier survival analysis showed that overall survival of patients with lower expressions of TMEM100 was significantly shorter (n=152, P<0.05). In addition, TMEM100 overexpression in NSCLC cell lines inhibited cell proliferation in vitro and in vivo. Transwell migration and invasion assay showed that TMEM100 significantly suppressed the migration and invasion of NSCLC cell lines. In contrast, knocking down TMEM100 promoted NSCLC proliferation and migration. Finally, we found that TMEM100 worked as a cancer suppressor gene mainly by inhibiting the TNF signaling pathway. In conclusion, TMEM100 acted as a tumor suppressor in NSCLC and may prove to be a potential prognostic biomarker and therapeutic target for NSCLC.

miRNA-296-3p modulates chemosensitivity of lung cancer cells by targeting CX3CR1

Lung cancer is the most common type of cancer-related death in developed countries. MicroRNAs (miRNAs) are small non-coding RNAs, which regulates gene expression in cancer. Recent studies demonstrate that the microRNA-293-3p (miR-293-3p) may play as an oncogene or a tumor suppressor. However, its expression and roles in non-small cell lung cancer (NSCLC) is not known. In this study, our purpose is to investigate the expression and roles of miR-296-3p in NSCLC. The findings indicated that miR296-3p inhibited NSCLC cell proliferation, enhance the drug resistance, and apoptosis. Data of luciferase reporter assays demonstrated that the CX3CR1 gene was a direct regulator of tumorsuppressive miR296-3p. Moreover, overexpressed CX3CR1 was confirmed in NSCLC clinical specimens. Inhibition of CX3CR1 could inhibit cancer cellular survival and increase chemotherapy sensitivity. There was a negative relationship between miR296-3p and CX3CR1 expression in NSCLC tissues. Our study elucidates that miR296-3p plays a suppressive role in NSCLC by inhibiting CX3CR1 expression.