Network pharmacology: drug re-purposing and discovery of multi-drug therapies by analytical approaches
What challenges are we solving?
The intrinsic robustness of living systems against perturbations is a key factor that explains why many single-target drugs have been found to provide poor efficacy or lead to significant side effects. Rather than trying to design selective ligands that target individual receptors only, network polypharmacology aims to modify multiple cellular targets to tackle the compensatory mechanisms and robustness of disease-associated cellular systems. However, the exponentially increasing number of potential drug target combinations makes the pure experimental approach quickly unfeasible, and translates into a need for design principles to determine the most promising target combinations to effectively control complex disease systems.
How will our project provide a solution ?
Although diseased cells may harbor hundreds of genomic alterations, only a subset of these alterations is driving the disease initiation and progression; these are known as (disease specific) essential genes.
Our approach focuses on controlling disease-specific essential genes, as acting upon them is guaranteed to kill the diseased (and only the diseased!) cells. Our new machine learning algorithms identify nodes targetable by FDA-approved drugs, which lead to controlling essential genes, through (sometimes many) cascading effects in the network. Additionally, since essential genes are crucial for disease proliferation, targeting even a subset of them induces significant gains.
For example, in a recent study we found that a combination of 5 already available FDA-approved drugs has the potential of controlling over 19 breast-cancer essential genes.
What kind of business potential is the solution associated with?
Our approach can pinpoint not only pairs or triples of available drugs for cumulative therapy testing, but can analytically predict combinations of 6-10 drugs (or drug-compounds) which are mathematically proved to have an influence over disease specific essential genes. The use of already existing, and approved, drugs in our predicted cumulative therapy is a significant advantage for a pharmaceutical company, as in the case of successful experimental testing it does not need to go through the extremely lengthy and costly process of approving a new drug.
Also, our personalized disease network control analysis can be added to the list of services offered by specialized bio-medical data analysis companies offering personalized medical data analysis services. This will ultimately allow the health providers to offer personalized therapies based on our high-end analysis.
What kind of business associates are we looking for?
The currently proposed analytical approach for disease network control through drug re-purposing has considerable benefits for the competitiveness of three business sectors: for pharmaceutical companies, for health service companies with strong R&D activities in new therapies development, and for companies offering specialized bio-medical data analysis and personalized medicine solutions. Thus, we are interested in associating/collaborating mainly with companies spanning these three sectors.
For the pharmaceutical sector (and for those companies involved in multi-drug therapy development and testing), our approach can pinpoint not only pairs (or triples) of available drugs for cumulative therapy testing, but can analytically predict combinations of 6-10 drugs (or drug-compounds) which are mathematically backed to have an influence over disease specific essential genes.
For those companies offering personalized medical data analysis services, by collaborating with us, such companies can provide personalized disease network control analysis to their clients, thus augmenting their list of available services. This will ultimately allow the corresponding health providers to offer personalized therapies based on our high-end analysis.
Key persons involved in the project:
Other senior researchers involved in the project: Dr. Vladimir Rogojin, Dr. Sepinoud Azimi