Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jul 4, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Data mining the NCI60 to predict generalized cytotoxicity.

Adam C Lee¹, Kerby Shedden, Gustavo R Rosania

¹Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, USA.

Journal of Chemical Information and Modeling

|July 1, 2008

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Blended mobile health and wellness coaching enhances student engagement in mental health care.

Academia mental health & well-being·2026

Same author

Brief interactive lifestyle preventive medicine video education in the primary care clinic: Protocol for a randomized clinical trial.

PloS one·2026

Same author

Urinary clusterin as a biomarker of human kidney disease progression and response to the endothelin receptor antagonist atrasentan: An exploratory analysis from the SONAR trial.

Nature communications·2026

Same author

Association between placental malaria, postnatal linear growth, and body mass index in the Dogon Longitudinal Study, Mali.

Malaria journal·2026

Same author

ASET: an end-to-end pipeline for quantification and visualization of allele specific expression.

BMC bioinformatics·2025

Same author

Associations of latitude and photoperiod with sleep duration in a yearlong study of US physicians.

Sleep medicine·2025

Same journal

Mapping Evolution of Molecules across Biochemistry with Assembly Theory.

Journal of chemical information and modeling·2026

Same journal

Structural Proteomics-Based Deciphering of Hydrophobic Packing Fingerprints Informing Protein Thermostability in TIM Barrels.

Journal of chemical information and modeling·2026

Same journal

Bridging between Structure-Based and Data-Driven Affinity Prediction.

Journal of chemical information and modeling·2026

Same journal

Reinforcement Learning-Driven Multiproperty Optimization in Molecular Design Using Multicontext Transcriptome Data.

Journal of chemical information and modeling·2026

Same journal

EnsembleCycPerm: Interpretable Modeling of Cyclic Peptide Permeability through Solvent-Dependent Conformational Ensembles.

Journal of chemical information and modeling·2026

Same journal

Resolving Conformational Preferences of Monosaccharides from <sup>1</sup>H and <sup>13</sup>C NMR Chemical Shifts Using an Integrated MD and QM Approach.

Journal of chemical information and modeling·2026

See all related articles

This study uses principal components analysis (PCA) to identify nonspecific cytotoxicity in drug discovery, reducing R&D costs. Predictive models accurately flag cytotoxic molecules, saving significant time and resources.

Area of Science:

Computational chemistry
Drug discovery
Toxicology

Background:

Eliminating cytotoxic compounds early in drug discovery reduces research and development costs.
Nonspecific cytotoxicity is a major factor contributing to compound failure.
Predictive modeling can aid in identifying and mitigating cytotoxic liabilities.

Purpose of the Study:

To apply principal components analysis (PCA) to understand and quantify nonspecific cytotoxicity.
To develop predictive models for identifying cytotoxic and non-cytotoxic substances in silico.
To establish a model for predicting mean log GI50 using a subset of NCI60 cancer cell lines.

Main Methods:

Principal Components Analysis (PCA) was used for data mining and variance analysis.

More Related Videos

High-Content Screening Assay for the Identification of Antibody-Dependent Cellular Cytotoxicity Modifying Compounds

High-Content Screening Assay for the Identification of Antibody-Dependent Cellular Cytotoxicity Modifying Compounds

Published on: August 18, 2023

Related Experiment Videos

Last Updated: Jul 4, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

High-Content Screening Assay for the Identification of Antibody-Dependent Cellular Cytotoxicity Modifying Compounds

High-Content Screening Assay for the Identification of Antibody-Dependent Cellular Cytotoxicity Modifying Compounds

Published on: August 18, 2023

A decision tree model utilizing MACCS keys was developed to classify substances based on mean log GI50.

A linear model using least-squares regression was established to predict mean log GI50 from a subset of NCI60 cell lines.

Main Results:

PCA revealed that approximately 89% of the log GI50 variance is attributable to nonspecific cytotoxicity.
The decision tree model achieved over 83% accuracy in classifying substances as cytotoxic or non-cytotoxic.
The linear model demonstrated high predictive power (R^2 = 0.99, RMSE = 0.09) for mean log GI50 using nine cell lines.

Conclusions:

Nonspecific cytotoxicity significantly impacts drug discovery, and PCA effectively quantifies this effect.
In silico predictive models, including decision trees and linear regression, can accurately identify cytotoxic compounds.
These predictive models offer a valuable tool for flagging cytotoxic molecules in chemical libraries, optimizing drug discovery pipelines.