Abstract

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.Computer designed of a Safe and immunogenic pharmacophoric activator mimicking physicochemical properties of the MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) inadjuvantwith PF-3512676 and GM-CSF with promising clinical outcome in metastatic melanoma using a new cluster of algorithms and a Ligand-Based Virtual Screening approach through a Support Vector and Information Fusion Bayesian Machine.

Keywords

Towards Structural Systems, Pharmacology Study, Complex Diseases, Personalized Medicine, Computer designed, immunogenic, pharmacophoric, activator, mimicking, physicochemical, properties, MART-1 (26-35,27L), gp100 (209-217, 210M), tyrosinase (368-376, 370D), adjuvant, PF-3512676, GM-CSF, promising, clinical outcome, metastatic melanoma, new cluster of algorithms. Ligand-Based, Virtual Screening approach through a Support Vector and Information Fusion Bayesian Machine.