Exploring In Silico Prediction of the Unbound Brain‐to‐Plasma Drug Concentration Ratio: Model Validation, Renewal, and Interpretation. Issue 3 (24th December 2014)

• Record Type: Journal Article
• Authors: Varadharajan, Srinidhi; Winiwarter, Susanne; Carlsson, Lars; Engkvist, Ola; Anantha, Ajay; Kogej, Thierry; Fridén, Markus; Stålring, Jonna; Chen, Hongming
• Is Part Of: Journal of pharmaceutical sciences. Volume 104:Issue 3(2015:Mar.)
• Page Start: 1197
• Page End: 1206
• Publication Date: 2014-12-24
• Subjects: Pharmacy -- Periodicals; 615.1
• DOI: 10.1002/jps.24301
• Languages: English
• ISSNs: 0022-3549

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Recently, we built an <italic>in silico</italic> model to predict the unbound brain‐to‐plasma concentration ratio (<italic>K</italic>_{p, uu, brain}), a measure of the distribution of a compound between the blood plasma and the brain. Here, we validate the previous model with new additional data points expanding the chemical space and use that data also to renew the model. The model building process was similar to our previous approach; however, a new set of descriptors, molecular signatures, was included to facilitate the model interpretation from a structure perspective. The best consensus model shows better predictive power than the previous model (<italic>R</italic>² = 0.6 vs. <italic>R</italic>² = 0.53, when the same 99 compounds were used as test set). The two‐class classification accuracy increased from 76% using the previous model to 81%. Furthermore, the atom‐summarized gradient based on molecular signature descriptors was proposed as an interesting new approach to interpret the <italic>K</italic>_{p, uu, brain} machine learning model and scrutinize structure <italic>K</italic>_{p, uu, brain} relationships for investigated compounds. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:1197–1206, 2015</p> </abstract>