Abstract
Of late, lorlatinib has played an increasingly pivotal role in the treatment of brain metastasis from non-small cell lung cancer. However, its pharmacokinetics in the brain and the mechanism of entry are still controversial. The purpose of this study was to explore the mechanisms of brain penetration by lorlatinib and identify potential biomarkers for the prediction of lorlatinib concentration in the brain. Detection of lorlatinib in lorlatinib-administered mice and control mice was performed using liquid chromatography and mass spectrometry. Metabolomics and transcriptomics were combined to investigate the pathway and relationships between metabolites and genes. Multilayer perceptron was applied to construct an artificial neural network model for prediction of the distribution of lorlatinib in the brain. Nine biomarkers related to lorlatinib concentration in the brain were identified. A metabolite-reaction-enzyme-gene interaction network was built to reveal the mechanism of lorlatinib. A multilayer perceptron model based on the identified biomarkers provides a prediction accuracy rate of greater than 85%. The identified biomarkers and the neural network constructed with these metabolites will be valuable for predicting the concentration of drugs in the brain. The model provides a lorlatinib to treat tumor brain metastases in the clinic.
Highlights
Lung cancer is the leading cause of cancer death in china and worldwide, and was responsible for an estimated 1.76 million deaths in 2018 (World Health Organization, 2018; Cao and Chen, 2019)
The above characteristics have been further confirmed in clinical trials: lorlatinib had a mean cerebrospinal fluid to plasma concentration ratio of 0.75 confirming significant CNS penetration, had an IC response rate of 63% in brain metastasis patients previously administered with at least one anaplastic lymphoma kinase (ALK) inhibitor, confirming superior CNS activity compared to first-generation TKIs (Serritella and Bestvina, 2020; Xia et al, 2020)
9 noteworthy differential metabolites contributing to the altered metabolic profiles of experimental groups were identified, and they were enriched in 4 major metabolic pathways, namely, Sphingolipid metabolism, Glycerophospholipid metabolism, Thiamine metabolism and Synthesis and degradation of ketone bodies
Summary
Lung cancer is the leading cause of cancer death in china and worldwide, and was responsible for an estimated 1.76 million deaths in 2018 (World Health Organization, 2018; Cao and Chen, 2019). Non-small cell lung cancer (NSCLC) accounts for up to 85% of all lung cancers (Majem et al, 2019). As a known complication of NSCLC, arises in about 10% of patients at the initial diagnosis of NSCLC and in approximately 30% patients with advanced NSCLC adenocarcinoma (Barnholtz-Sloan et al, 2004; Singh et al, 2020). For patients with anaplastic lymphoma kinase (ALK)-positive NSCLC, this frequent complication occurs in roughly 30% of patients even at the time of initial diagnosis, and in about 60% of patients over the course of first-line therapy (Guérin et al, 2015; Johung et al, 2016). Progressive deterioration of neurological and cognitive functioning caused by brain metastasis will reduce the patient’s quality of life (Wanleenuwat and Iwanowski, 2020)
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