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Escitalopram, one of the Selective Serotonin Reuptake Inhibitors (SSRIs), has been widely used in the patients with major depression. In this study, a simple, sensitive and rapid method was established and validated for simultaneous quantification of Escitalopram (S-CT), desmethyl escitalopram (S-DCT), didemethyl escitalopram (S-DDCT) and escitalopram N-Oxide (S-NOCT) in human plasma by ultra-high performance liquid chromatography-tandem with mass spectrometry (UHPLC-MS/MS). Analytes were extracted from plasma by utilizing protein precipitation and then separated on a Hypersil GOLD C18 column (50 mm × 2.1 mm, 1.9 µm). The mobile phase was water: acetonitrile (70:30, v/v) with 0.25% formic acid at a flow-rate of 0.3 mL/min, within a 5 min run time. The mass analysis used positive electro-spray ionization (ESI) in selection reaction monitoring (SRM). The calibration ranges of the analytes were: S-CT: 2.0-200.0 ng/mL, S-DCT: 1.0-100.0 ng/mL, S-DDCT: 0.5-50.0 ng/mL, S-NOCT: 0.2-20.0 ng/mL. The method has been fully validated for selectivity, linearity, accuracy, precision, matrix effect, recovery, stability and carry over and all the results met the admissible limits according to the the US Food and Drug Administration guidelines. Mean plasma concentration (ng/mL) of S-CT, S-DCT, S-DDCT and S-NOCT in 93 depressed patients were 51.10 ± 45.73, 10.32 ± 15.25, 1.53 ± 1.79 and 0.87 ± 0.94, respectively. it is the first time that a UHPLC-MS/MS method for simultaneous quantification of S-CT and its 3 metabolites in human plasma was established and validated.

Gefitinib, the first approved oral epidermal growth factor receptor (EGFR) inhibitor, has been demonstrated effective in cancers with EGFR active mutations. In this study, we established and validated a method for determining gefitinib and its main metabolites, M605211, M387783, M537194 and M523595 in patients with non-small cell lung cancer (NSCLC) by liquid chromatography–tandem mass spectrometry (LC–MS/MS) method. The mobile phase was water: acetonitrile (35:65, v/v) with 0.1% formic acid at a flow‐rate of 0.35 mL/min, within a 3 min run time. Gefitinib and its main metabolites were separated on a X-Terra RP18 column (50 × 2.1 mm, 3.5 μm) at 40 ℃ and subjected to mass analysis using positive electro-spray ionization (ESI). The calibration ranges of gefitinib and M523595 were 0.5–1000 ng/mL, and other compounds were 0.05–100 ng/mL with the correlation coefficients (r2) ≥ 0.99. Accuracies ranged from 92.60%–107.58 and the inter- and intra-assay precision were less than 15% for all analytes in quality control samples. There was no significant matrix effect. The ranges of extraction recoveries were 86–105% for all analytes and IS. Thirty plasmas were obtained from Sun Yat-sen university cancer center. The mean plasma concentration of (± SD) of gefitinib M537194, M523595, M387783 and M605211 were 247.18 (± 140.39) ng/mL, 7.78 (± 6.74) ng/mL, 101.09 (± 93.44) ng/mL, 1.6 (± 0.9) ng/mL and 11.63 (± 4.98) ng/mL, respectively. The validated LC/MS/MS method was effectively used in the determination of gefitinib and its four metabolites in NSCLC patients.

Background: Gefitinib-induced rash is a sign of favorable outcomes for patients with non-small cell lung cancer (NSCLC). However, whether patients with more severe rash, the more survival benefits obtained from gefitinib is still unknown, and as for the severe rash a risk factor prediction model is needed. Method: A total of 346 patients were enrolled in this study. Gefitinib/metabolites and 9 gene polymorphisms, were determined by pharmacometabolomic and pharmacogenomics methods in an exploratory cohort. External cohort patients were enrolled to validate this model. Findings: The survival for patients with rash were significantly higher than patients without rash (p=0.0002, p= 0.0089)，but no difference was found between grade 1/2 or grade 3/4. Only the concentration of gefitinib, but not its metabolites, was associated with severe rash, and the cutoff value of gefitinib was 204.6 ng/mL conducted by ROC curve (AUC=0.685). A predictive model for severe rash was established: gefitinib concentration (OR = 11.523, 95%CI=2.898-64.016, p = 0.0016), SLC22A8 rs4149179(CT vs CC, OR = 3.156, 95%CI = 0.958-11.164, p = 0.0629), SLC22A1 rs4709400(CG vs CC, OR = 10.267, 95%CI = 2.067-72.465, p = 0.0087; GG vs CC, OR=5.103, 95%CI=1.032-33.938, p=0.061). This model was confirmed in validation cohort with an excellent predictive ability (AUC = 0.749, 95%CI = 0.710-0.951). Interpretation: Our finding demonstrated that patients developed any grade rash predicted improved survival, the gefitinib concentration and polymorphisms of SLC22A8 and SLC22A1 were recommended to manage severe rash. However, our model would require further validation in a biomarker-led trial. Trial Registration: This study was registered at ClinicalTrials.gov (NCT01994057). Funding Statement: This study was funded by the National Natural Science Foundation of China (Grant Nos. 81730103, 81320108027, 81573507, 81473283, 81173131 and 81973398), The National Key Research and Development Program (Grant Nos. 2017YFC0909303 and 2016YFC0905001), Guangdong Provincial Key Laboratory of Construction Foundation (Grant No. 2017B030314030), Science and Technology Program of Guangzhou（201607020031）, National Engineering and Technology Research Center for New drug Druggability Evaluation (Seed Program of Guangdong Province (No. 2017B090903004) and the 111 project (Grant: B16047). Declaration of Interests: The authors declare no conflict of interest. Ethics Approval Statement: The study was approved by Human Ethics of Sun Yat-sen University Cancer Center (B2013-038-1) and conducted in accordance with the principles of the Declaration of Helsinki and the Good Clinical Practice Guidelines of the International Conference on Harmonization.

Highlights • A total of 346 patients were enrolled in this study. • Severe rash (grade 3&4) did not gain more bonification compare to grade 1&2 rash. • Gefitinib and its four metabolites were detected in patients’ plasma. • A specific and sensitive predictive model were established based on pharmacometabolomic profiling and pharmacogenomics approach., Background Rash is a well-known predictor of survival for patients with gefitinib therapy with non-small cell lung cancer (NSCLC). However, whether patients with more severe rash obtain the more survival benefits from gefitinib is still unknown, and predicted model for severe rash is needed. Methods The relationship between gefitinib-induced rash and progression free survival (PFS) was primarily explored in the retrospective cohort. The association between rash and gefitinib/metabolites concentration and genetic polymorphisms were determined by pharmacometabolomic and pharmacogenomics methods in the exploratory cohort and validated in an external cohort. Results The survival for patients with rash was significantly higher than that of patients without rash (p = 0.0002, p = 0.0089), but no difference was found between grade 1/2 or grade 3/4. Only the concentration of gefitinib, but not its metabolites, was found to be associated with severe rash, and the cutoff value of gefitinib was 204.6 ng/mL conducted by ROC curve analysis (AUC=0.685). A predictive model for severe rash was established: gefitinib concentration (OR = 11.523, 95% CI = 2.898-64.016, p = 0.0016), SLC22A8 rs4149179(CT vs CC, OR = 3.156, 95% CI = 0.958–11.164, p = 0.0629), SLC22A1 rs4709400(CG vs CC, OR = 10.267, 95% CI = 2.067–72.465, p = 0.0087; GG vs CC, OR = 5.103, 95% CI = 1.032–33.938, p = 0.061). This model was confirmed in the validation cohort with an excellent predictive ability (AUC = 0.749, 95% CI = 0.710–0.951). Conclusions Our finding demonstrated that the incidence, not the severity, of gefitinib-induced rash predicted improved survival, the gefitinib concentration and polymorphisms of SLC22A8 and SLC22A1 were recommended to manage severe rash.