The emergence of genome-based drug repositioning

Abstract

In a pair of papers in this issue of Science Translational Medicine, Butte et al. provide a concrete example of how reinterpreting and comparing genome-wide metrics allows us to effectively hypothesize which drugs from one disease-indication can be repurposed for another disease. The shift toward integrative genome-wide computational approaches has precedence in insightful scalar theories of biological information. Here, we discuss how this recent work in drug repurposing adheres to and takes advantage of the concepts surrounding this information paradigm.

Figure 1. Scalar information flow in molecular biology and present day genomics

The central dogma of molecular biology was explicitly coined by Crick as an information model and is encompassed by a Blois' broader subsequent theory of biomedical information. These scalar theories remain salient to genome-based therapy as they appropriately frame the information intermediaries between molecular mechanisms and clinical-level phenotypes. Above, the flow of information from lower scales to higher ones is shown for the central dogma and for gene expression data. Conventional gene expression signature classifiers predictive of therapeutic utility correspond to an intermediate emergent property, measured by a genomic metric, consisting of a selected number of genes. In contrast, Butte proposes that a drug is a potential candidate for a disease treatment when their respective global genomic metrics are in opposition.

Figure 2. Biological Scales Contrasting Reductionist and Integrative Drug Repositioning Computations

A few exemplar bioinformatics and computational biology studies of drug targeting or repositioning are used to illustrate the major differences in approaches: (i) reduction to the molecular function on the left quadrants, and (ii) integrated properties of multiple molecules to the right. The vertical axis discriminates among different scales of biological substrates for genetic or genomic assays. As shown at the DNA base pair scale, the genetics of warfarin dosage elucidated from a genome-wide association study is illustrated on the lower left quadrant (18). Gene expression signature classifiers follow at the mRNA scale (). Integrated pathway-level scores of gene expression measures are shown in the lower right quadrant (19). High throughput screens (HTS) (11) of the upper left quadrant correspond to reductionism methods at the tissue level and contrast with increasingly integrative genome-wide tissular methods of the PREDICT algorithm (13) and that of Butte's research group conducted at the same biological scale (upper right quadrant, orange circles). Importantly, the genome-wide metric developed by Butte's group was applied to both a cellular-level disease (cancer) and a tissular one (IBD). As shown by the void in the uppermost part of the right quadrant, systemic diseases and multi-organ ones have not yet been explored by genome-wide metric and may require more comprehensive analyses than straightforward opposition of a genomic metric measured in surrogate tissues and drugs. Scales of biology (m): DNA base pairs, DNA, protein, organelle (mitochondria), cell, tissue, organ, system, organism.