Translational Bioinformatics Approaches to Drug Development

Abstract

Significance: A majority of therapeutic interventions occur late in the pathological process, when treatment outcome can be less predictable and effective, highlighting the need for new precise and preventive therapeutic development strategies that consider genomic and environmental context. Translational bioinformatics is well positioned to contribute to the many challenges inherent in bridging this gap between our current reactive methods of healthcare delivery and the intent of precision medicine, particularly in the areas of drug development, which forms the focus of this review.

Recent advances: A variety of powerful informatics methods for organizing and leveraging the vast wealth of available molecular measurements available for a broad range of disease contexts have recently emerged. These include methods for data driven disease classification, drug repositioning, identification of disease biomarkers, and the creation of disease network models, each with significant impacts on drug development approaches.

Critical issues: An important bottleneck in the application of bioinformatics methods in translational research is the lack of investigators who are versed in both biomedical domains and informatics. Efforts to nurture both sets of competencies within individuals and to increase interfield visibility will help to accelerate the adoption and increased application of bioinformatics in translational research.

Future directions: It is possible to construct predictive, multiscale network models of disease by integrating genotype, gene expression, clinical traits, and other multiscale measures using causal network inference methods. This can enable the identification of the "key drivers" of pathology, which may represent novel therapeutic targets or biomarker candidates that play a more direct role in the etiology of disease.

Joel Dudley, PhD

Figure 1.

Development of new pharmaceuticals. Despite a general trend towards increased R&D spending by U.S. pharmaceutical companies, the number of new molecular entity and new biologic entities by the U.S. Food and Drug Administration (FDA) has remained steady over the past decade. As demonstrated, the number of new applications has also remained steady, suggesting an increased difficulty developing new compounds beyond the early phases of drug development. R&D costs converted to 2012 U.S. dollars. R&D, research and development; NME, new molecular entity; NBE, new biological entity. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/wound

Figure 2.

Carving disease at the joints. With an increase in our collective ability to measure the different aspects of disease traits, comes an opportunity to rethink the ways in which we describe diseases, and classify them into communities, which reflect their underlying biology. (a) By incorporating available sources of sufficiently accurate, high-dimensional data which is entangled with a pathological process, (b) we will be able to produce disease signatures, which will allow comparison between instances of a disease, and between different diseases. (c) This will enable a classification system, which encodes the difference and similarities between diseases, according to the dynamic molecular networks distributed throughout key tissues/organ systems, and at useful multi-omic levels. (d) Such differences and similarities are useful to the extent that they catalyse new understandings of disease, including the transfer of knowledge between similar diseases, and the identification of key underlying principles of difference between dissimilar diseases. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/wound

Figure 3.

Beyond population-level evaluation of therapies. Traditionally, assessment of pharmaceutical safety and efficacy has been constrained by the application of a new therapy to a single class of patients, usually sharing a broad diagnosis. (a) This level of assessment necessitates combining heterogenous patient subtypes, which often possess different treatment responses or side-effect and toxicity risks. (b) By combining trial data with a range of rich biological data on each patient, investigators can now try to find predictive features, which will segregate with important aspects of treatment response. Such data can consist of single gene genotyping, through to protein biomarker concentrations, to identifying disease module gene activity signatures. Identifying these kinds of predictive markers empowers the tailoring of specific therapeutic strategies on the basis of a patient's biology. Overall, this approach can allow the retention of otherwise useful medications that might be predictably toxic, or perhaps only useful in a subset of patients. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/wound

Figure 4.

Drug repositioning strategies. Many computational approaches to drug repositioning capitalize on known relationships between diseases and drugs, with the accompanying possibility that shared features between drugs used to treat the same disease, or diseases that share treatments, implies a degree of meaningful similarity between some aspects of the linked entities, which can then be used to generalize existing treatments into new clinical contexts. A number of emerging methods integrate multiple sources of disease and drug based knowledge, which can enable even more sophisticated inferences about new therapeutic opportunities. (Figure reprinted with permission from Dudley et al.)

Figure 5.

Building causal models of disease. One of the exciting possibilities in systems medicine is the capacity to build models of disease which reflect the causal interactions underlying disease at the molecular level. These models can then be used to identify new diagnostic, prognostic and therapeutic approaches. One approach utilizes (a, b) gene expression microarray data from disease tissue to (c) identify modules of genes which are coexpressed, (d) and for each of these modules, derive an undirected network which best accounts for the pair-wise gene expression correlations. (e) By layering in matched DNA variation data for each patient, it becomes possible to transform the undirected network, into a causal network, which encodes the direction of functional regulatory relationships between its genes. By considering expression quantitative trait loci (genomic locations where sequence variation correlates with mRNA transcript abundance of one or more genes) alongside a pair of its associated genes, it is possible of look at conditional correlations to find the most likely causal model connecting the eQTL and the gene pair. (f) By iterating over all relevant gene pairs (with their associated eQTLs) in all the modules, it becomes possible to create a probabilistic causal network, and identify “local drivers” (blue)—genes which regulate many relevant downstream genes and even the most upstream, so-called global drivers (dark blue). This allows the identification of potentially high leverage molecular targets, facilitating the prioritisation of high-value validation experiments. eQTL, expression quantitative trait locus. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/wound

Figure 6.

Intertissue molecular networks. Dobrin et al. uncovered a TTC network between hypothalamus, liver, and adipose tissue in the context of obesity, deriving biologically relevant interactions which weren't present within any single tissue type. Center: Depiction of the network backbone, comprised of a limited number of highly correlated genes, where variation in one gene is likely to induce expression variation in a connected gene in a complementary tissue. Node coloring represents membership to different TTC subnetworks, with enrichment for GOBP terms of relevance to obesity traits. (Figure reprinted from Dobrin et al.) GOBP, Gene Ontology Biological Process; TTC, tissue–tissue coexpression. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/wound