Browsing by Subject "OATP1B1"

Personalized medicine tailors therapies for the patient based on predicted risk factors. Some tools used for making predictions on the safety and efficacy of drugs are genetics and metabolomics. This thesis focuses on identifying biomarkers for the activity level of the drug transporter organic anion transporting polypep-tide 1B1 (OATP1B1) from data acquired from untargeted metabolite profiling. OATP1B1 transports various drugs, such as statins, from portal blood into the hepatocytes. OATP1B1 is a genetically polymorphic influx transporter, which is expressed in human hepatocytes. Statins are low-density lipoprotein cholesterol-lowering drugs, and decreased or poor OATP1B1 function has been shown to be associated with statin-induced myopathy. Based on genetic variability, individuals can be classified to those with normal, decreased or poor OATP1B1 function. These activity classes were employed to identify metabolomic biomarkers for OATP1B1. To find the most efficient way to predict the activity level and find the biomarkers that associate with the activity level, 5 different machine learning models were tested with a dataset that consisted of 356 fasting blood samples with 9152 metabolite features. The models included both a Random Forest regressor and a classifier, Gradient Boosted Decision Tree regressor and classifier, and a Deep Neural Network regressor. Hindrances specific for this type of data was the collinearity between the features and the large amount of features compared to the number of samples, which lead to issues in determining the important features of the neural network model. To adjust to this, the data was clustered according to their Spearman’s rank-order correlation ranks. Feature importances were calculated using two methods. In the case of neural network, the feature importances were calculated with permutation feature importance using mean squared error, and random forest and gradient boosted decision trees used gini impurity. The performance of each model was measured, and all classifiers had a poor ability to predict decreasead and poor function classes. All regressors performed very similarly to each other. Gradient boosted decision tree regressor performed the best by a slight margin, but random forest regressor and neural network regressor performed nearly as well. The best features from all three models were cross-referenced with the features found from y-aware PCA analysis. The y-aware PCA analysis indicated that 14 best features cover 95% of the explained variance, so 14 features were picked from each model and cross-referenced with each other. Cross-referencing highest scoring features reported by the best models found multiple features that showed up as important in many models.Taken together, machine learning methods provide powerful tools to identify potential biomarkers from untargeted metabolomics data.