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Predicting Blood-Brain Permeation from Three-Dimensional Molecular Structure

Predicting Blood-Brain Permeation from Three-Dimensional Molecular Structure. Patrizia Crivori, Gabriele Cruciani, Pierre-Alain Carrupt, and Bernard Testa. J. Med. Chem 2000 43: 2204-2216 Presented by Ankit Garg. To cross or not to cross?.

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Predicting Blood-Brain Permeation from Three-Dimensional Molecular Structure

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  1. Predicting Blood-Brain Permeation from Three-Dimensional Molecular Structure Patrizia Crivori, Gabriele Cruciani, Pierre-Alain Carrupt, and Bernard Testa. J. Med. Chem 2000 43: 2204-2216 Presented by Ankit Garg

  2. To cross or not to cross? • Some drugs must cross the blood-brain barrier (BBB), others absolutely should not. • Experimentally screening for BBB permeability is expensive.

  3. Many factors influence BBB permeability • H-bonding capacity • Hydrophobicity • Ionization profile • Molecular size • Lipophilicity • Flexibility • Plasma protein binding • Active efflux from CNS • Metabolism

  4. The VolSurf difference • Past approaches have emphasized one or a few factors, without regard to the rest: • Lipophilicity • Solvatochromatic parameters • Topological indices • VolSurf uses 72 descriptors derived from 3D molecular interaction fields.

  5. Multivariate Analysis: PCA • Represent multivariate data along a few, principal component axes. Output Plots Multivariate Data

  6. Two Sets of Data: • “Training” set was based on 44 compounds used in prior work. • “External Prediction” set was based on 108 wide-ranging drugs from literature. • Compounds in both sets had well-characterized BBB behavior.

  7. Choosing Descriptors • VolSurf descriptors are obtained directly from 3D molecular interaction fields. Not sure what this means. • Main difference appears to be that VolSurf descriptors have a clear chemical meaning.

  8. Encouraging 1st Results! PCA on the training set

  9. PCA on BBB+ compounds of the second data set 90% Accuracy! (40 out of 44)

  10. PCA on BBB- compounds of the second data set 65% Accuracy! (46 out of 71)

  11. Reasons for differences in accuracy • Many BBB- compounds passively defuse into the brain but: • are then metabolized before acting • are actively effluxed • At least one compound, Mequitazine, is likely misclassified in the literature based on the results of this study and another one as well.

  12. PLS discriminant analysis: Finding two latent variables Unlike PCA, relies on training, so descriptors can be differentially biased >90% accurate, though with a confidence interval

  13. Most important descriptors Though some descriptors are clearly more important, in general a balance of many descriptors control BBB permeability

  14. Questions: • Paper notes that it is impossible to model mixtures of stereoisomers in 3D? Why is this the case? • To account for conformational variety in the tested molecules, the authors used two different programs to generate low-energy conformations for the same molecule. The data they then generated for these pair complements was similar, suggesting that the conformational differences mattered little. Is this analysis convincing? • What are the relative strengths and weaknesses of using PCA vs. PLS discriminant analysis?

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