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Computational Drug Discovery: Clinical Translation

Computational drug discovery has reduced the time and money required for research and development while increasing the potential number of drug leads in the drug discovery pipeline. Because of the vastly increasing improvements to software advancement and computing power, computational methods in the drug discovery pipeline have exploded in use. Researchers now have accessible software and improved technologies available for various stages of the drug discovery pipeline, from high-throughput screening to clinical trial simulations. With all of this available technology, has a computational approach actually developed into a viable drug? Yes!

Computational drug discovery used to identify potential inhibitors for the enzyme HIV-1 integrase. Credit US National Science Foundation2

One notable example is the use of AutoDock in the discovery of raltagravir, an FDA approved drug that inhibits the insertion of HIV DNA into human DNA, thus limiting the ability of the virus to infect new cells.1Here, researchers used docking software to characterize a potential location on the HIV-1 integrase enzyme that allows for the inhibition of enzyme activity and to screen novel ligands with a higher selective affinity for this site. Merck further developed the drug after the computational results were published and the drug was then approved for use in 2007 by HIV-infected patients.

These researchers have also used computational methods to identify five potential drug targets that can potentially treat trypanosomal disease, more commonly known as African sleeping sickness. Having five potential targets is a big deal, especially since there has only been one new drug for the treatment of African sleeping sickness that has appeared in the past 50 years!3Computational drug discovery opened up new possibilities and provided new leads in disease research where there has been little to no progress.

In fact, researchers have now found that computational methods have been able to screen currently available drugs and finding new uses for these drugs for other diseases.4 This “drug repositioning” has support from the NIH and this allows for decreased expenses in the search for developing completely novel drugs and offers new affordable ways to treat current diseases. Due to sheer computational power, the ability for scientists to quickly sift through large amounts of data and research allows for the development of new hypotheses and potential treatments that may have previously taken years to accomplish.

Computational drug discovery has advanced rapidly in recent years, and as technology improves and the amount of research and clinical data increases, we have the potential to efficiently develop new useful drugs. Perhaps orphan diseases will no longer require the dedication of the families of those affected by the disease as drug development becomes more efficient. The potential growth of finding new drugs can only go up, and computational methods will be certainly be an important asset in the drug discovery pipeline.