ArticleTranslational oncology2021
Establishment and application of a predictive model for gefitinib-induced severe rash based on pharmacometabolomic profiling and polymorphisms of transporters in non-small cell lung cancer.
Article in Translational oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Genetic Variants in theGenes · 2024Pooled it
- Influence of genetic polymorphisms on gefitinib pharmacokinetics and adverse drug reactions in non-small cell lung cancer patients.Cancer metastasis reviews · 2025Review
- Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication.Pharmaceuticals (Basel, Switzerland) · 2023Review
- SNPs in 3'UTR miRNA Target Sequences Associated with Individual Drug Susceptibility.International journal of molecular sciences · 2022Review
Corrections and comments
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Authors and funding
16 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRash 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.
methodsThe 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.
resultsThe 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).
conclusionsOur 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.
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