Computational polypharmacology: a new paradigm for drug discovery

Abstract

Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to predict drug side effects and toxicities. To tackle this issue, an alternative approach, known as polypharmacology, is being adopted to study small molecule interactions with multiple targets. Apart from developing more potent and effective drugs, this approach allows for studies of off-target activities and the facilitation of drug repositioning. Although exhaustive polypharmacology studies in-vitro or in-vivo are not practical, computational methods of predicting unknown targets or side effects are being developed. Areas covered: This article describes various computational approaches that have been developed to study polypharmacology profiles of small molecules. It also provides a brief description of the algorithms used in these state-of-the-art methods. Expert opinion: Recent success in computational prediction of multi-targeting drugs has established polypharmacology as a promising alternative approach to tackle some of the daunting complications in drug discovery. This will not only help discover more effective agents, but also present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.

Keywords: Drug polypharmacology; computer-aided drug design; drug repurposing; in silico prediction; multi-targeting ligands.

Figure 1

Multiple targets of propoxyphene, along with its medical use and side effects.

Figure 2

The polypharmacology profile of imatinib, generated using the QIAGEN’s Ingenuity® Pathway Analysis (IPA®, QIAGEN Redwood City, www.qiagen.com/ingenuity).

Figure 3

Top: in the first phase, input data x is passed through hidden layers to the final output layer. Bottom: in the reconstruction phase, the output results o are passed from output layer to the previous visible layer.

Figure 4

Binding of the small molecule BI2536 in A) the binding pocket of PLK1 receptor (PDB ID: 2RKU); and B) the binding pocket of the first bromodomain of human BRD4 (PDB ID: 4OGI).