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Published on: April 16, 2019
In Silico Prediction and Validation of the Permeability of Small Molecules Across the Blood-Brain Barrier
Favour Ajao1, Dominique de Jong-Hoogland1, Jakob P Ulmschneider2
1Department of Chemistry, King's College London, London SE1 1DB, UK.
Abstract:
Understanding and predicting the ability of small-molecule drugs to cross the blood-brain barrier (BBB) is essential for developing treatments for neurodegenerative disorders such as Alzheimer's disease. In this study, we aim to computationally estimate BBB permeability for pharmacologically relevant molecules using an all-atom, unbiased molecular dynamics (MD) framework accelerated by elevated-temperature simulations. Our approach infers physiological permeabilities via elevated temperature passive diffusion trajectories, enabling quantitative ranking across a chemically diverse compound set. The computed permeabilities are compared with available in vitro and in silico data for control molecules. We further explore the molecular mechanisms underlying permeability differences through their free energy profiles and lipid contact analyses, revealing molecule-specific interactions with individual lipid species in the BBB membrane. This work introduces a novel combination of elevated-temperature MD and mechanistic decomposition to assess BBB permeability and applies it to candidate molecules with therapeutic potential in neurodegeneration.
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The Blood-brain Barrier
Reliability and Validity
Predicting Molecular Geometry
Permeability of Concrete
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Data Validation
Key parameters for method validation include:

