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Predicting the Ionization Behavior of Drugs in Tissue in MALDI and MALDI-2 Mass Spectrometry Imaging Using Machine

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Machine learning predicts ionization efficiency in Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and MALDI-2 imaging (MSI). This approach aids pharmaceutical research by improving detection limit predictions for drugs and metabolites in tissues.

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Computational Chemistry

Background:

  • Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and its imaging variant (MSI) are crucial for analyzing biomolecules, particularly drug distribution in pharmaceutical research.
  • Predicting ion yields in MALDI(-2) analysis is challenging, complicating the determination of detection limits for drug metabolites.
  • Postionization techniques like MALDI-2 enhance signal intensity but do not fully resolve ion yield predictability issues.

Purpose of the Study:

  • To develop a machine learning (ML) model for predicting ionization efficiency in MALDI(-2)-MSI.
  • To address the challenge of unpredictable ion yields in MALDI-MSI, thereby improving the planning of pharmaceutical studies.
  • To explore the utility of ML in predicting ionizability for general MALDI(-2)-MSI applications.

Main Methods:

  • Utilized a dataset of ~1200 drug-like compounds with existing MALDI and MALDI-2 data (positive and negative ion modes).
  • Trained six different ML models using physicochemical properties and 2D structures of the compounds.
  • Employed SHAP analysis to understand the contribution of various parameters to the prediction accuracy.

Main Results:

  • Machine learning models were successfully developed to predict ionization efficiency in MALDI(-2)-MSI.
  • SHAP analysis indicated that a multifactorial approach, involving numerous parameters, is crucial for accurate prediction, rather than a few specific functional groups.
  • The study demonstrated the feasibility of using ML to predict ion yields, offering a significant improvement over current methods.

Conclusions:

  • Machine learning provides a powerful tool for predicting ion yields in MALDI(-2)-MSI, enhancing its utility in pharmaceutical research.
  • This ML-based approach can aid in optimizing study design by providing better estimates of detection limits for drugs and metabolites.
  • The developed methodology has broader implications for predicting ionizability in various MALDI(-2)-MSI applications beyond pharmaceuticals.