The purpose of this diploma thesis is to understand financing of drug development from an enterprise risk management perspective as well as to critically assess the efficiency of the ISO framework and risk management techniques used for determining whether to fund drug development or not. The diploma thesis is divided into theoretical and practical part. The first part starts with perception and assessment of uncertainty and risk in the past. It describes how risk-averse individuals attempted to deal with uncertainty and different risk. This is followed by the evolution of traditional risk management into the fast developing enterprise risk management. The text further analyses commonly used risk management standards COSO ERM and ISO 31000:2009. However, the main focus is on the critical assessment of analytical tools which are frequently used for evaluating and assessing risks, especially financial ones, during drug development. The theoretical part is finished by a drug development process, whose phases are briefly described. The practical part was written in co-operation with AstraZeneca, a top-notch pharmaceutical company. The overview of its business is preceded by an explanation of current issues in the pharmaceutical industry. Furthermore, the risk analysis is conducted with respect to the ISO framework. Subsequently, selected risk assessment techniques are applied on the simplified financial model of two different drugs, which was created based on AstraZeneca's real data. These risk assessment tools are used in different phases of drug development so it could be seen clearly how the results are changing during a project. The outcomes of this risk analysis are compared with original plans used by AstraZeneca which were used for deciding whether to fund drug development or not.

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.