Network Pharmacology Based Modelling: Application to the Discovery of Novel Drug Leads
Ben Allen, Colin Stubberfield, Sree Vadlamudi, Marie Weston, Jonny Wray
Network pharmacology models cells as networks of interacting proteins. Within this paradigm, a disease state is identified as a disorder of the entire network, and the target for pharmacological intervention becomes a set of proteins. Network modelling can be used to identify a set of key proteins and their interactions which can be targeted to correct the network disorder in a disease setting.
The chemoinformatic problem is then to identify compounds with desirable interactions with the set of proteins. The e-Therapeutics method starts by generating databases of protein-protein interactions (PPI) and compound-protein interactions (CPI), using a combination of public and proprietary collections of experimental results and computational modelling.
Biological and medical expertise is used to identify key proteins that participate in the networks that mediate disease targets of interest, and PPI data is used to construct networks based on the targets. This complex network of proteins is then analysed to identify multiple intervention points that, if impacted simultaneously, will disrupt the disease-related network. The next step is to identify potential small molecule modulators with the best overall impact in the disease-related network using proprietary computational methods. This search process takes account of the chemical biology profile of the molecules in proprietary CPI database. The lists of predicted active compounds are then triaged based on phys.chem profile, synthetic tractability and the shortlists of selected compounds are screened in cell based disease specific assays.
We believe that, by accounting more realistically for both the complexity of disease at the outset and for the many effects of the presence of a drug molecule in the body, our approach has the potential to discover more effective treatments. The e-Therapeutics network pharmacology approach has enabled us to successfully identify multiple new chemical entities for targeting Telomerase and the Hedgehog pathway in cancer and TNF alpha in anti-inflammatory disease projects. Lead optimisation is ongoing for the selected small molecule modulators. Details of the network analyses and cell-based screening results will be discussed.