Abstract/Summary

The growing research and commercial pressures for novel therapeutics development accentuate why better strategies are needed for drug discovery. The costly nature of developing a pharmaceutical compound as well as the shrinking pool of ‘easy’ targets are some of the key reasons why there is a research paradigm shift towards integrative and systems biology driven approaches. Moreover, multifactorial aspects of many diseases require more innovative clinical strategies rather than just focusing on a single target. Cardiovascular diseases as well as associated immune components exemplify this complexity well. This thesis aimed to introduce a gradual and highly integrative analytical framework by incorporating a full range of studies from disease target selection to high-throughput virtual screening so that a cost-effective and efficient stratification of targets and associated compounds could be achieved. Heart failure served as a case study for complex diseases where the first in-depth omics study on cardiomyopathies helped to elucidate new therapeutic avenues. This research tied in with a development of a novel scoring function and integrated machine learning approach for multiple therapeutic target classification and exploration. Finally, all pieces of the introduced research were used to create a highly integrative in silico screening workflow. Some of the key results included the first reported molecular dynamics analyses for a complex immunotherapeutic target, c-Rel, as well as 15 new therapeutic compounds that could potentially modulate this transcription factor subunit. Thus, this dissertation provided several important improvements for target identification, validation, and drug discovery that could significantly advance current development strategies and accelerate new therapeutics production.