Novel Next-Generation Sequencing and Networks-Based Therapeutic Targets: Realistic and More Effective Drug Design and Discovery

Reductionist approaches and linear experimentation have expanded our knowledge in biology over the past century and represent till today the basis for the prevention, diagnosis and treatment of all diseases in clinical medicine. However, major diseases still remain incurable. All currently available drugs target a single gene or protein ignoring dynamics of highly complex biomolecular networks driving collectively gene expression and cell’s function. No surprise that most of these agents don’t cure common multifactorial disorders while available diagnostics and biomarkers are unable to predict tissue-specific cellular reactions to genetic and epigenetic alterations as well as drug effects in individual patients and populations. In this review we discuss latest advances in genome localization of genomewide association studies variants, whole genome/whole exome data analysis, protein-protein interactions networks databases, and more recent Encyclopedia of DNA Elements (ENCODE) data on regulatory networks including transcription factors-binding sites and genegene interactions. In addition challenges for a comprehensive analysis of intracellular signaling pathways network is described. Such analysis, despite genome-scale scarce data and lack of sophisticated methods to predict dynamics of a global hierarchy or ‘cloud”of biological networks, appears essential for the discovery of new therapeutic network targets, which could dramatically increase treatment efficacy, while minimizing at the same time major adverse effects. In this review we describe potential and challenges of modern approaches for applying next-generation sequencing and patient’s personal whole genome analysis for personalized treatment using available drugs. Additionally, we report why the discovery of next-generation drugs should be shifted from our linear world to motifsand network-associated disease integrating genome science and dynamics of network biology advances.