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Computational Prediction of CNS Drug Exposure Based on a Novel In Vivo Dataset

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Abstract

Purpose

To develop a computational model for predicting CNS drug exposure using a novel in vivo dataset.

Methods

The brain-to-plasma (B:P) ratio of 43 diverse compounds was assessed following intravenous administration to Swiss Outbred mice. B:P ratios were subjected to PLS modeling using calculated molecular descriptors. The obtained results were transferred to a qualitative setting in which compounds predicted to have a B:P ratio > 0.3 were sorted as high CNS exposure compounds and those below this value were sorted as low CNS exposure compounds. The model was challenged with an external test set consisting of 251 compounds for which semi-quantitative values of CNS exposure were available in the literature.

Results

The dataset ranged more than 1700-fold in B:P ratio, with 16 and 27 compounds being sorted as low and high CNS exposure drugs, respectively. The model was a one principal component model based on five descriptors reflecting molecular shape, electronegativity, polarisability and charge transfer, and allowed 74% of the compounds in the training set and 76% of the test set to be predicted correctly.

Conclusion

A qualitative computational model has been developed which accurately classifies compounds as being high or low CNS exposure drugs based on rapidly calculated molecular descriptors.

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Acknowledgments and Disclosures

The authors would like to thank Dr. Thomas J. Raub for his assistance and guidance in development and validation of the mouse brain uptake assay. We thank SimulationsPlus (Lancaster, CA) for providing the Department of Pharmacy, Uppsala University, with a reference site license for the software ADMET Predictor. Financial support from the Swedish Agency for Innovation Systems (Grant 2010-00966) for Christel Bergström’s Marie Curie Fellowship at Monash University is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Pharmacy Drug Optimization and Pharmaceutical Profiling Platform, Uppsala University, Uppsala Biomedical Center, P.O. Box 580, SE-751 23, Uppsala, Sweden

    Christel A. S. Bergström

  2. Drug Delivery, Disposition and Dynamics Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, Victoria, 3052, Australia

    Christel A. S. Bergström & Joseph A. Nicolazzo

  3. Centre for Drug Candidate Optimisation Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, Victoria, 3052, Australia

    Susan A. Charman

Authors
  1. Christel A. S. Bergström
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  2. Susan A. Charman
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  3. Joseph A. Nicolazzo
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Corresponding author

Correspondence to Joseph A. Nicolazzo.

Electronic Supplementary Material

The supporting information contains information relating to the chemical space of training set and its applicability for prediction of the test set (Figure 1), the literature used to compile the test set (Table 1), information relating to the specific activity and site of radiolabel for the probe compounds (Table 2), and results of the CNS exposure predictions of the external test set (Table 3).

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Bergström, C.A.S., Charman, S.A. & Nicolazzo, J.A. Computational Prediction of CNS Drug Exposure Based on a Novel In Vivo Dataset. Pharm Res 29, 3131–3142 (2012). https://doi.org/10.1007/s11095-012-0806-5

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  • DOI: https://doi.org/10.1007/s11095-012-0806-5

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