Automated Data Processing and Annotation for Untargeted Plasma Metabolomics in TKI-Associated ADR Research

Tyrosine kinase inhibitors (TKIs) are increasingly utilized in the treatment of various malignancies. However, their use is frequently associated with adverse drug reactions (ADRs), which may compromise both therapeutic adherence and efficacy. The aim of this project is to establish an untargeted metabolomics approach for the identification of predictive serum biomarkers indicative of ADR frequency and severity during TKI therapy.

Sample preparation is based on protein precipitation, optimized for metabolite recovery and matrix reduction. Analytical measurements are performed using UHPLC coupled to HRMS (qTOF X500R, Sciex) operated in SWATH mode. Data acquisition is conducted in both positive and negative ionization to maximize metabolic coverage. For data analysis, an R-based processing pipeline is being developed, incorporating automated modules for feature detection and alignment, performed with the xcms package, as well as data preprocessing (missing value imputation, signal drift correction, etc.) and data annotation using the CAMERA package. Subsequent statistical evaluation includes both univariate and multivariate methods to identify potential biomarkers.

By combining the systematic analytical workflow with integrative statistical evaluation, this project introduces an innovative strategy for the discovery of novel metabolic ADR biomarkers, which will be applied to serum samples originating from a non-interventional study, in which samples were longitudinally collected at predefined time points from patients with renal cell carcinoma undergoing TKI treatment. These biomarkers could enable the early detection of ADR risks associated with TKI therapy and, in the long term, contribute to the personalized management of TKI-based treatment regimens, supported by machine learning-based biomarker selection.

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