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Computer Regression Models for P-Glycoprotein Transport of Drugs

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Pharmaceutical Chemistry Journal Aims and scope

Regression models of the cellular substrate specificity of 177 drugs for P-glycoprotein were built using linear regression, random forest, and support vector methods. QSAR modeling used a full-trial search of all possible combinations of the seven most significant molecular descriptors with clear physicochemical interpretations. The statistics of the obtained models were satisfactory according to an internal cross-validation and external validation tests using 44 new compounds. H-bond descriptors were components of almost all most significant QSAR models. This confirmed that H-bonds played an important role in penetration of the compounds through the blood–brain barrier. The developed statistical models could be used to assess P-glycoprotein transport of investigational new drugs.

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Correspondence to V. Yu. Grigorev.

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Translated from Khimiko-Farmatsevticheskii Zhurnal, Vol. 52, No. 12, pp. 12 – 16, December, 2018.

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Grigorev, V.Y., Solodova, S.L., Polianczyk, D.E. et al. Computer Regression Models for P-Glycoprotein Transport of Drugs. Pharm Chem J 52, 975–979 (2019). https://doi.org/10.1007/s11094-019-01936-x

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  • DOI: https://doi.org/10.1007/s11094-019-01936-x

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