Abstract The blood-brain barrier (BBB) constitutes a dynamic membrane primarily evolved to protect the brain from exposure to harmful xenobiotics. The distribution of synthesized drugs across the blood-brain barrier (BBB) is a vital parameter to consider in drug discovery projects involving a central nervous system (CNS) target, since the molecules should be capable of crossing the major hurdle, BBB. In contrast, the peripherally acting drugs have to be designed optimally to minimize brain exposure which could possibly result in undue side effects. It is thus important to establish the BBB permeability of molecules early in the drug discovery pipeline. Previously, most of the in-silico attempts for the prediction of brain exposure have relied on the total drug distribution between the blood plasma and the brain. However, it is now understood that the unbound brain-to-plasma concentration ratio ( Kp,uu,brain) is the parameter that precisely indicates the BBB availability of compounds. Kp,uu,brain describes the free drug concentration of the drug molecule in the brain, which, according to the free drug hypothesis, is the parameter that causes the relevant pharmacological response at the target site. Current work involves revisiting a model built in 2011 and uploaded in an in-house server and checking for its performance on the data collected since then. This gave a satisfying result showing the stability of the model. The old dataset was then further extended with the temporal dataset in order to update the model. This is important to maintain a substantial chemical space so as to ensure a good predictability with unknown data. Using other methods and descriptors not used in the previous study, a further improvement in the model performance was achieved. Attempts were also made in order to interpret the model by identifying the most influential descriptors in the model.Popular science summary: Predictive model for unbound brain-to-plasma concentration ratio Blood-brain barrier (BBB) is a dynamic interface evolved to protect the brain from exposure to toxic xenobiotics and to maintain homeostasis. Distribution of drugs across BBB is critical for any drug discovery project. A drug designed for a target in brain has to pass through the BBB in sufficient concentrations to elicit the desired therapeutic effect. On the other hand, a drug designed for a non-CNS target should be kept away from the brain to avoid fatal side effects. Unbound brain-to-plasma concentration ratio, Kp,uu,brain is a parameter that describes the distribution of a molecule across the BBB. It represents the free drug concentration in the brain, which is the fraction that elicits the pharmacological effect on the CNS. The experimental measurement of this parameter is time consuming and laborious. Computational prediction of such properties thus prove to be of a great utility in reducing the time and resources spent by aiding in the early elimination of compounds possessing undesirable qualities. This helps in reducing late stage compound attrition (failure rate) which has always been a major problem for pharmaceutical industries. Quantitative Structure Activity Relationship (QSAR) is an approach that attempts to establish a meaningful relationship between the chemical structure of a molecule and its chemical/biological activity. Once established, this relationship can be used to predict the activity of a new compound based on its chemical structure. In a typical QSAR experiment, the chemical structures are often represented in terms of numerical values called molecular descriptors. The thesis work utilized machine learning algorithm (Support Vector Machine and Random forest) to define the structure -activity relationship. A predictive model for estimating the unbound brain-to-plasma concentration ratio (Kp,uu,brain) was developed based on a training set of in-house compounds and was mounted in an in-house program (C-lab) in 2011 for routine use. The thesis project involved validating the existing model and updating the model by extending the dataset with the data collected since 2011. Different combinations of Machine Learning algorithms, modeling approaches and molecular descriptors (calculated numerical values representing of chemical structures) were used to build the models. Further, combining the prediction from these models, consensus models were built and validated. Two-class classification models were also evaluated based on categorizing compounds into BBB positive (crosses BBB) or negative (does not cross BBB). The validation of the old model using temporal test set (Kp,uu,brain data collected since 2011) gave a promising result showing stability and good predictive power. However, it is very important to keep the chemical space updated, which defines the purpose for updating the model. The new model (a consensus model with five components) shows a significant improvement in terms of the predictive power along with an improvement in the classification performance. This model will be uploaded to C-lab and will be accessible for use within AstraZeneca. Advisors: Hongming Chen, Ola Engkvist (Computational Chemistry, AstraZeneca R&D Mölndal) Master´s Degree Project 60 credits in Bioinformatics (2014) Department of Biology., Lund Universit
