Quantification of oxaliplatin and lansoprazole in tumor tissue homogenates by LC–MS/MS: a fit-for-purpose bioanalytical approach

BACKGROUND: Quantification of drug concentrations within tumor tissue is essential for evaluating true intratumoral exposure, yet remains analytically challenging due to matrix complexity, protein binding, and drug instability. Existing LC-MS/MS methods for oxaliplatin and lansoprazole are primarily limited to plasma or pharmaceutical matrices. METHODS: This study describes the development and fit-for-purpose evaluation, guided by ICH M10 principles, of LC-MS/MS methods for quantifying oxaliplatin and lansoprazole in murine tumor tissue homogenates. Isocratic separation on a C18 column with an ammonium acetate-acetonitrile mobile phase and compound-specific MRM detection enabled selective analysis. RESULTS: The methods demonstrated acceptable linearity, agreement between nominal and back-calculated concentrations, controlled matrix effects, consistent extraction recovery, and satisfactory autosampler and freeze-thaw stability. The assays were successfully applied to determine intratumoral drug concentrations in control, monotherapy, and combination treatment groups. CONCLUSION: These matrix-matched LC-MS/MS methods provide a fit-for-purpose approach for exploratory intratumoral drug quantification and support more reliable assessment of pharmacologically relevant tumor exposure in preclinical studies.

Authors

Institutions

Publication Details

Journal
Bioanalysis
Published
2026-05-29
DOI
https://doi.org/10.1080/17576180.2026.2677734
Primary Topic
Cancer Treatment and Pharmacology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantification of oxaliplatin and lansoprazole in tumor tissue homogenates by LC–MS/MS: a fit-for-purpose bioanalytical approach

Erica Alves, Prabitha Prabhakaran, Gurupadayya Bannimath
Bioanalysis
Cancer Treatment and Pharmacology
article

Quantification of oxaliplatin and lansoprazole in tumor tissue homogenates by LC–MS/MS: a fit-for-purpose bioanalytical approach

Erica Alves, Prabitha Prabhakaran, Gurupadayya Bannimath
article en

Abstract

BACKGROUND: Quantification of drug concentrations within tumor tissue is essential for evaluating true intratumoral exposure, yet remains analytically challenging due to matrix complexity, protein binding, and drug instability. Existing LC-MS/MS methods for oxaliplatin and lansoprazole are primarily limited to plasma or pharmaceutical matrices. METHODS: This study describes the development and fit-for-purpose evaluation, guided by ICH M10 principles, of LC-MS/MS methods for quantifying oxaliplatin and lansoprazole in murine tumor tissue homogenates. Isocratic separation on a C18 column with an ammonium acetate-acetonitrile mobile phase and compound-specific MRM detection enabled selective analysis. RESULTS: The methods demonstrated acceptable linearity, agreement between nominal and back-calculated concentrations, controlled matrix effects, consistent extraction recovery, and satisfactory autosampler and freeze-thaw stability. The assays were successfully applied to determine intratumoral drug concentrations in control, monotherapy, and combination treatment groups. CONCLUSION: These matrix-matched LC-MS/MS methods provide a fit-for-purpose approach for exploratory intratumoral drug quantification and support more reliable assessment of pharmacologically relevant tumor exposure in preclinical studies.

Bioanalysis
JSS Academy of Higher Education and Research (IN)
Zero hunger
Openalex Percentile: Top 6%
Cancer Treatment and Pharmacology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.