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Updated: Jun 6, 2026

Quantifying Antibody-Dependent Cellular Cytotoxicity in a Tumor Spheroid Model: Application for Drug Discovery
13:19

Quantifying Antibody-Dependent Cellular Cytotoxicity in a Tumor Spheroid Model: Application for Drug Discovery

Published on: April 26, 2024

Predicting cytotoxicity from heterogeneous data sources with Bayesian learning.

Sarah R Langdon1, Joanna Mulgrew, Gaia V Paolini

  • 1Department of Chemistry and Biology, Pfizer Global Research and Development, Sandwich Laboratories, Sandwich, Kent, CT13 9NJ, UK. wvanhoorn@accelrys.com.

Journal of Cheminformatics
|December 15, 2010
PubMed
Summary

A new computational model predicts general cytotoxicity early in drug discovery. This model integrates data from over 80 assays, enabling faster identification of potentially toxic compounds and optimizing assay selection for drug development.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Toxicology

Background:

  • Utilized data from over 80 cytotoxicity assays from internal and public sources.
  • Investigated feasibility of a general cytotoxicity model using diverse assay formats.
  • Aimed to identify potentially cytotoxic compounds early in drug discovery.

Purpose of the Study:

  • Develop a computational model for general cytotoxicity prediction.
  • Highlight potentially cytotoxic compound series early in the drug discovery pipeline.
  • Integrate diverse cytotoxicity assay data into a unified predictive model.

Main Methods:

  • Developed Bayesian models using Scitegic FCFP_6 fingerprints and physical property descriptors.
  • Identified mutually predictive assay pairs using ROC scores (>0.60).

Related Experiment Videos

Last Updated: Jun 6, 2026

Quantifying Antibody-Dependent Cellular Cytotoxicity in a Tumor Spheroid Model: Application for Drug Discovery
13:19

Quantifying Antibody-Dependent Cellular Cytotoxicity in a Tumor Spheroid Model: Application for Drug Discovery

Published on: April 26, 2024

  • Merged data from 48 connected assays into a training set of 145,590 compounds.

    • Main Results:

    • Visualized assay inter-predictivity in a network graph.
    • Observed that mutual predictivity between assay pairs (A, B) and (B, C) did not guarantee predictivity for (A, C).
    • Derived and validated a general cytotoxicity model using cross-validation and FDA-approved drugs.

    Conclusions:

    • Generated a predictive model to accelerate drug discovery by enabling early identification of cytotoxic compounds.
    • Demonstrated that different assay formats can be mutually predictive, reducing redundant testing.
    • Facilitated selection of optimal assay panels or corporate standards based on predictivity.
    • Provided a comprehensive dataset of known cytotoxic compounds from the Pfizer collection.
    • Aided in designing new compounds with desired cytotoxicity profiles by comparing model output with in vitro safety data.