Molecular networks in drug discovery

Abstract

Despite the dramatic increase of global spending on drug discovery and development, the approval rate for new drugs is declining, due chiefly to toxicity and undesirable side effects. Simultaneously, the growth of available biomedical data in the postgenomic era has provided fresh insight into the nature of redundant and compensatory drug-target pathways. This stagnation in drug approval can be overcome by the novel concept of polypharmacology, which is built on the fundamental concept that drugs modulate multiple targets. Polypharmacology can be studied with molecular networks that integrate multidisciplinary concepts including cheminformatics, bioinformatics, and systems biology. In silico techniques such as structure- and ligand-based approaches can be employed to study molecular networks and reduce costs by predicting adverse drug reactions and toxicity in the early stage of drug development. By amalgamating strides in this informatics-driven era, designing polypharmacological drugs with molecular network technology exemplifies the next generation of therapeutics with less of-target properties and toxicity. In this review, we will first describe the challenges in drug discovery, and showcase successes using multitarget drugs toward diseases such as cancer and mood disorders. We will then focus on recent development of in silico polypharmacology predictions. Finally, our technologies in molecular network analysis will be presented.

Figure 1

PP121, a dual-inhibitor of PI3Ks and tyrosine kinases

Figure 2

A. Structure of Clozapine, an atypical antipsyphotic agent, B. Molecular network of Clozapine receptor targets for which the binding affinity (K_i) < 100nM. Octagon: serotonin receptors, Rhomb: muscarinic receptors, Circle: histamine receptors, Triangle: dopamine receptors, Trapezoid: adrenergic receptors.

Figure 3

Features of molecular network analysis tool interface: Using keyword searches for drug or target (here with dasatinib as an example), users can perform a series of functions including: navigate through drug-target maps, visualize targets in Jmol, view structures and external resource pages, download SDF files for similar compounds, etc.

Figure 4

The 3-level network of imatinib based on the target annotation of the drug. Using the same concept in Figure 3, we extended the networks of imatinib to multiple levels and it resulted in the network on the left. Those drugs at the same level (proximity to imatinib) are in the same colors. For example, the query compound in the center of the network in blue, and its inner most cluster (obscured) contains tyrosine kinase inhibitor such as dasatinib and sunitinib, whereas the blue, green, and red clusters contain the antiviral zanamivir, the anti-inflamitory difluisal, and the anti-cancer cisplatin, respectively.