Network-based approaches in drug discovery and early development

Abstract

Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target-drug combination with high potential for yielding clinical success within the efficacy-toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification.

Figure 1

Horizontal and vertical integration. The complexities faced in target identification result from the interactions of spatiotemporal scales, and the vertical integration of these layers is the key to identifying drug targets that can overcome the current shortcomings associated with unpredicted toxicities and failures due to lack of efficacy.

Figure 2

Discrete and continuous dynamic modeling of directed graphs. (a) Illustration of predicted output for different modeling methodologies for a theoretical network with a given connectivity. (b) Changes in output D in response to upregulation of A for a continuous system. (c) Layers of information networks and their interactions constitute the challenges of drug-target identification. Squares represent genomic information characterized by gene regulatory networks, and triangles represent proteins, which constitute the bulk of the interactomic network. Ultimately, these systems give rise to changes in metabolic flux, represented by circles. As an example of signal transduction, (d) the quantal change in state A results in downstream signaling and upregulation of state D, (e) which can be recapitulated with a discrete dynamic model (DDM). The linkage between time steps of DDMs and continuous time to event in hypothetical continuous systems is shown by the shaded region linked by a dashed line. The Boolean network for the model in panel a is also listed in panel e.

Figure 3

The canonical network topology of the species represents all of the possible connections (e.g., genomic, proteomic, and metabolic) that are possible within that species. Individual patients or groups of patients can be represented as a sample from the species canonical network to produce the individual canonical network—all of the possible connections within this sample. Through differentiation, aging in the patient, and disease progression, components of the individual canonical network will be active in different tissues within the body.