Improving the Accuracy of Permeability Data to Gain Predictive Power: Assessing Sources of Variability in Assays...

Area of Science:

• Pharmacology and Drug Discovery
• Computational Chemistry
• Biotechnology

Background:

• Predicting compound permeation across biological membranes is crucial for drug efficacy, pharmacokinetics, and safety.
• Caco-2 cell monolayers are widely used in vitro models to assess intestinal permeability, generating numerous apparent permeability coefficient (Papp) values.
• Large datasets of Papp values enable the development of quantitative structure-permeability relationships (QSPRs) using artificial intelligence.

Purpose of the Study:

• To address the challenge of multiple Papp values for the same compound due to experimental protocol variations.
• To systematically and quantitatively assess the sources of variability in Papp data within and between laboratories.
• To interpret the impact of this variability on QSPR modeling and drug permeability prediction accuracy.

Main Methods:

• Review and analysis of existing literature and databases containing Papp values.
• Quantitative assessment of variability in Papp data across different experimental conditions and laboratories.
• Systematic evaluation of common sources contributing to data inconsistency.

Main Results:

• Significant variability exists within and between laboratories for Papp values, impacting QSPR model reliability.
• Differences in experimental protocols are identified as a primary cause of data inconsistency.
• The magnitude and impact of this variability on predictive modeling are quantitatively assessed.

Conclusions:

• Compiling consistent and reliable Papp data is essential for developing robust QSPRs.
• Strategies for obtaining standardized data are proposed to enhance the predictive power of QSPRs.
• Improved QSPRs will facilitate more accurate predictions of drug permeability, aiding drug discovery and development.