Literature DB >> 30359532

Drug-Homogeneity Index in Mass-Spectrometry Imaging.

Mridula Prasad1,2,3, Geert Postma3, Lavinia Morosi4, Silvia Giordano5, Raffaella Giavazzi4, Maurizio D'Incalci4, Francesca Falcetta4, Enrico Davoli5, Jeroen Jansen3, Pietro Franceschi1.   

Abstract

Enhancing drug penetration in solid tumors is an interesting clinical issue of considerable importance. In preclinical research, mass-spectrometry imaging is a promising technique for visualizing drug distribution in tumors under different treatment conditions and its application in this field is rapidly increasing. However, in view of the huge variability among MSI data sets, drug homogeneity is usually manually assessed by an expert, and this approach is biased by interobserver variability and lacks reproducibility. We propose a new texture-based feature, the drug-homogeneity index (DHI), which provides an objective, automated measure of drug homogeneity in MSI data. A simulation study on synthetic data sets showed that previously known texture features do not give an accurate picture of intratumor drug-distribution patterns and are easily influenced by the tumor-tissue morphology. The DHI has been used to study the distribution profile of the anticancer drug paclitaxel in various xenograft models, which were either pretreated or not pretreated with antiangiogenesis compounds. The conclusion is that drug homogeneity is better in the pretreated condition, which is in agreement with previous experimental findings published by our group. This study shows that DHI could be useful in preclinical studies as a new parameter for the evaluation of protocols for better drug penetration.

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Year:  2018        PMID: 30359532     DOI: 10.1021/acs.analchem.8b01870

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  5 in total

1.  Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence.

Authors:  Theodore Alexandrov
Journal:  Annu Rev Biomed Data Sci       Date:  2020-04-13

2.  Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues.

Authors:  Dan Guo; Kylie Bemis; Catherine Rawlins; Jeffrey Agar; Olga Vitek
Journal:  Bioinformatics       Date:  2019-07-15       Impact factor: 6.937

3.  Evaluation and comparison of unsupervised methods for the extraction of spatial patterns from mass spectrometry imaging data (MSI).

Authors:  Mridula Prasad; Geert Postma; Pietro Franceschi; Lutgarde M C Buydens; Jeroen J Jansen
Journal:  Sci Rep       Date:  2022-09-20       Impact factor: 4.996

4.  A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging data.

Authors:  Mridula Prasad; Geert Postma; Pietro Franceschi; Lavinia Morosi; Silvia Giordano; Francesca Falcetta; Raffaella Giavazzi; Enrico Davoli; Lutgarde M C Buydens; Jeroen Jansen
Journal:  Gigascience       Date:  2020-11-25       Impact factor: 6.524

5.  PEGylated recombinant human hyaluronidase (PEGPH20) pre-treatment improves intra-tumour distribution and efficacy of paclitaxel in preclinical models.

Authors:  Lavinia Morosi; Marina Meroni; Maurizio D'Incalci; Roberta Frapolli; Paolo Ubezio; Ilaria Fuso Nerini; Lucia Minoli; Luca Porcu; Nicolò Panini; Marika Colombo; Barbara Blouw; David W Kang; Enrico Davoli; Massimo Zucchetti
Journal:  J Exp Clin Cancer Res       Date:  2021-09-10
  5 in total

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