Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763562
Title: Improved in silico methods for target deconvolution in phenotypic screens
Author: Mervin, Lewis
ISNI:       0000 0004 7651 7428
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Abstract:
Target-based screening projects for bioactive (orphan) compounds have been shown in many cases to be insufficiently predictive for in vivo efficacy, leading to attrition in clinical trials. Phenotypic screening has hence undergone a renaissance in both academia and in the pharmaceutical industry, partly due to this reason. One key shortcoming of this paradigm shift is that the protein targets modulated need to be elucidated subsequently, which is often a costly and time-consuming procedure. In this work, we have explored both improved methods and real-world case studies of how computational methods can help in target elucidation of phenotypic screens. One limitation of previous methods has been the ability to assess the applicability domain of the models, that is, when the assumptions made by a model are fulfilled and which input chemicals are reliably appropriate for the models. Hence, a major focus of this work was to explore methods for calibration of machine learning algorithms using Platt Scaling, Isotonic Regression Scaling and Venn-Abers Predictors, since the probabilities from well calibrated classifiers can be interpreted at a confidence level and predictions specified at an acceptable error rate. Additionally, many current protocols only offer probabilities for affinity, thus another key area for development was to expand the target prediction models with functional prediction (activation or inhibition). This extra level of annotation is important since the activation or inhibition of a target may positively or negatively impact the phenotypic response in a biological system. Furthermore, many existing methods do not utilize the wealth of bioactivity information held for orthologue species. We therefore also focused on an in-depth analysis of orthologue bioactivity data and its relevance and applicability towards expanding compound and target bioactivity space for predictive studies. The realized protocol was trained with 13,918,879 compound-target pairs and comprises 1,651 targets, which has been made available for public use at GitHub. Consequently, the methodology was applied to aid with the target deconvolution of AstraZeneca phenotypic readouts, in particular for the rationalization of cytotoxicity and cytostaticity in the High-Throughput Screening (HTS) collection. Results from this work highlighted which targets are frequently linked to the cytotoxicity and cytostaticity of chemical structures, and provided insight into which compounds to select or remove from the collection for future screening projects. Overall, this project has furthered the field of in silico target deconvolution, by improving the performance and applicability of current protocols and by rationalizing cytotoxicity, which has been shown to influence attrition in clinical trials.
Supervisor: Bender, Andreas ; Engkvist, Ola Sponsor: BBSRC ; AstraZeneca
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.763562  DOI:
Keywords: Cheminformatics ; Mode of action ; In silico ; Protein Target Prediction ; Orthologue ; Chemical space ; AstraZeneca ; Chemistry Connect ; Bioactivity data ; Target deconvolution ; Target prediction ; MoA ; ChEMBL ; PubChem ; Functional prediction ; Sphere exclusion ; Random Forest ; Naive Bayes ; SVM ; Support Vector Machine ; AD-AUC ; Activation ; Inhibition ; Functional Effects ; Mechanism-of-action ; Mode-of-action ; Mechanism of action ; Phenotypic screens ; High throughput screens ; High content screens ; PR-AUC ; Applicability domain ; Venn Abers ; Platt scaling ; Isotonic regression scaling ; Python ; Scikit-learn ; RDKit
Share: