Dataset related to article "Quantitative determination of niraparib and olaparib tumor distribution by mass spectrometry imaging"

The .zip file contains raw data related to the article "Quantitative determination of niraparib and olaparib tumor distribution by mass spectrometry imaging", available from https://www.ijbs.com/v16p1363.htm.

• The folder "fig 1 2 3 NIRA" contains raw data related to the experiments with niraparib presented in figures 1, 2 and 3 
• The folder "fig 1 2 3 OLA" contains raw data related to the experiments with olaparib presented in figures 1, 2 and 3 
• The folders "fig 4" and "fig 6" contain raw data related to figures 4 and 6, respectively.

For any additional information on how to read and reuse the dataset please contact Dr. Ubezio at paolo.ubezio@marionegri.it.

ABSTRACT OF THE MANUSCRIPT:

Rationale: Optimal intratumor distribution of an anticancer drug is fundamental to reach an active concentration in neoplastic cells, ensuring the therapeutic effect. Determination of drug concentration in tumor homogenates by LC-MS/MS gives important information about this issue but the spatial information gets lost. Targeted mass spectrometry imaging (MSI) has great potential to visualize drug distribution in the different areas of tumor sections, with good spatial resolution and superior specificity. MSI is rapidly evolving as a quantitative technique to measure the absolute drug concentration in each single pixel.

Methods: Different inorganic nanoparticles were tested as matrices to visualize the PARP inhibitors (PARPi) niraparib and olaparib. Normalization by deuterated internal standard and a custom preprocessing pipeline were applied to achieve a reliable single pixel quantification of the two drugs in human ovarian tumors from treated mice.

Results: A quantitative method to visualize niraparib and olaparib in tumor tissue of treated mice was set up and validated regarding precision, accuracy, linearity, repeatability and limit of detection. The different tumor penetration of the two drugs was visualized by MSI and confirmed by LC-MS/MS, indicating the homogeneous distribution and higher tumor exposure reached by niraparib compared to olaparib. On the other hand, niraparib distribution was heterogeneous in an ovarian tumor model overexpressing the multidrug resistance protein P-gp, a possible cause of resistance to PARPi.

Conclusions: The current work highlights for the first time quantitative distribution of PAPRi in tumor tissue. The different tumor distribution of niraparib and olaparib could have important clinical implications. These data confirm the validity of MSI for spatial quantitative measurement of drug distribution providing fundamental information for pharmacokinetic studies, drug discovery and the study of resistance mechanisms.