Identification of potential drug targets in kinetic networks described by ordinary differential equations
Over the last decade the productivity of the pharma industry has been constantly declining. Less and less drugs against new diseases are admitted to the market each year. This is mainly due to the fact that an increasing number of drug candidates fail for a lack of in vivo activity or for their toxicity in clinical trials. In order to reduce this failure rate, the targets against which new drugs are developed have to be chosen more carefully. This can be done with the help of methods from Systems Biology with which the dynamical effects of hypothetical drugs can be modelled in silico. The combination of mathematical models with experimental data will improve the target selection and will make the resulting drugs less likely to fail in clinical trials. Within this work I have developed a framework for the application of kinetic models in the drug development process. Furthermore, I have developed methods and tools that support researchers in pursuing the framework. This includes methods for the automated retrieval of mathematical models that describe processes relevant to an investigated disease, methods for the integration of knowledge stored in these models, and the investigation of the combined information for potential drug targets. For the priorisation of drug targets I propose different objectives and methods. Depending on the diseases, one can either choose to only consider the efficacy of drugs against potential targets or one can decide to incorporate information on potential side-effects in the considered or in alternative models. These objective can then be used in exhaustive searches for optimal combinations of hypothetical drugs. Apart from this identification of optimal treatments, I introduce methods that allow for the discovery of treatment alternatives, which can be useful when drugs against a selected target are hard or even impossible to create. Furthermore, I discuss methods for the investigation of synergisms and antagonisms amongst hypothetical drugs. Knowledge about these drug combination effects can be exploited to create treatments with fewer side-effects or treatments against which resistances are less likely to develop. In order to prove the relevance of the investigated methods, these are applied to two example systems, the glycolysis in Trypanosoma brucei, the pathogen causing the African sleeping sickness, and the arachidonic acid pathway in different human cells. The obtained results generally agree with the knowledge available in the literature but extend the understanding of drug effects on these networks.