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Updated: May 11, 2026

In Vitro Modeling of Fat Deposition in Metabolic Dysfunction-Associated Steatotic Liver Disease
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Modeling phospholipidosis induction: reliability and warnings.

Laura Goracci1, Martina Ceccarelli, Daniela Bonelli

  • 1Laboratory for Chemometrics and Cheminformatics, Chemistry Department, University of Perugia, Via Elce di Sotto 8, I-06123 Perugia, Italy. laura@chemiome.chm.unipg.it

Journal of Chemical Information and Modeling
|May 23, 2013
PubMed
Summary
This summary is machine-generated.

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Drug-induced phospholipidosis (PLD) prediction models require high-quality data. This study identifies database inconsistencies and proposes a curated dataset to improve in silico PLD prediction accuracy.

Area of Science:

  • Pharmacology
  • Toxicology
  • Computational Chemistry

Background:

  • Drug-induced phospholipidosis (PLD) involves phospholipid accumulation in lysosomes.
  • Early prediction of PLD is crucial during drug discovery.
  • Existing in silico models for PLD prediction rely on experimental data quality.

Purpose of the Study:

  • To analyze difficulties and errors in generating databases for PLD prediction models.
  • To construct and evaluate a new database of compounds for PLD prediction.
  • To propose a curated database for improved in silico PLD assessment.

Main Methods:

  • Compilation of a new database of 466 publicly available compounds from seven literature sources.
  • Comparative analysis of PLD classifications across selected databases.

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  • Application of Partial Least Squares Discriminant Analysis (PLS-DA) to assess data quality and metabolic factors.

    • Main Results:

    • Inconsistencies were found in PLD classifications within existing databases.
    • The PLS-DA approach highlighted anomalies related to metabolism and data quality.
    • A new curated database of 331 compounds was developed.

    Conclusions:

    • Data quality is paramount for accurate in silico PLD prediction models.
    • Metabolism and data curation are essential considerations for developing reliable PLD prediction methods.
    • The proposed curated database can enhance the accuracy of future PLD prediction tools.