Modeling and Screening Software Recycles Old Drugs For New Purposes

Biologics

The chemical structure of sirolimus, a common multipurpose immunosuppressant which researchers have identified as a potential anti-aging drug. Source: Wikipedia user Vaccinationist.

Growing interest from immunologists, cellular biologists and physicians is making cellular senescence a white-hot field of research that everyone expects to get even hotter.1 As grant money flows in, everyone scrambles to pin down aging pathways, develop therapies targeting them, and shuffle their drug candidates off to extensive in vivo testing. But what if there was a body of research which suggests that there are a number of drugs already on the market that have anti-aging activity? A new study published in the Biogerontology journal makes the case for the existence of a number of approved drugs that could be used in conjunction for effective treatment of cellular senescence.2 How did the researchers pick the anti-aging drugs out of the endless field of FDA approved therapies, though? With powerful molecular modeling software and database screening, of course.

Using a ligand homology modeling program, researchers were able to find drugs which would theoretically bind to the proteins of a few well-characterized aging pathways. The researchers then tested their screening findings in an invertebrate aging model. By taking this approach, the study’s authors joined a growing trend of repurposing old drugs for new applications like cancer and aging.3

Scraping Old Drugs

Surprisingly, the researchers found 29 different drugs on the market that would have very high binding affinity for aging pathway proteins. There’s an even bigger surprise, though: the database that the researchers queried only had 1347 drug entries. That number doesn’t include nutraceuticals, other known compounds, or even all of the drugs which were FDA approved a couple of years before the study.4  Increasing the size of the ligand screening pool would quickly deliver dozens more anti-aging drug candidates which could be put into action quickly.

The idea behind rapid mobilization of already characterized drugs is quite simple: if one drug has a partial anti-aging action, combining a bunch of drugs with partial actions may result in a stronger anti-aging effect downstream. It’s unlikely that by combining already approved treatments researchers will be able to drive senescence into total remission, but the new study’s combinatorial approach will be instrumental in producing stopgap aging therapies as well as adjunct therapies to future anti-aging drugs.

Critical to the effective implementation of multiple old treatments to the context of senescence is molecular modeling software. FINDSITEcomb is an experimentally validated ligand screening tool which the researchers used to do modeling of aging pathway targets and then to compare those models to drug binding data.5 Many researchers have recognized that computational tools for drug discovery have great potential, but if researchers are interested in taking the insights from computational screening into the clinic, they’ll need more than ligand binding database matches.6

After the initial drug candidates and targets are confirmed by a database query, researchers must perform additional modeling of their drug’s interactions with the other drugs and their targets too. The only way that old drugs will be useful in the context of senescence is in combination, after all.7 Without this additional modeling step before moving into the laboratory, there’s a good chance that researchers will unintentionally test combination treatments containing redundant drugs or drugs that target the same pathway in opposite ways, cancelling out any beneficial effects.

Avoiding Combination Drug Redundancy

It should be fairly simple to screen out redundant drugs that have the exact same target and mechanism of action, but that still leaves the issue of potential crosstalk or interference in the effects of the different drugs in a combination treatment.

Before taking their screening results into the laboratory, researchers need to model the following:

  • The interaction between each drug in the combination treatment and each intended original therapy target
  • The interaction between each drug in the combination treatment with the new intended senescence therapy target
  • The effect of each drug’s original ligand binding on the senescence therapy target; does binding to the drug’s original target occlude or disrupt the senescence pathway, or is it the same mechanism?
  • The anti-senescence target binding ability of each drug in the combination when delivered via the same route of administration as the original drug target; could metabolism or shear forces from injection disrupt the drug’s senescence target binding capability?

These basic consistency models are just the start of the computational work that researchers will need to perform after getting back the first screening results. Once these basic models can answer the basic questions regarding uniformity of treatment impact, researchers will have to move on to animal models.

Moving Combinations Into the Lab

Moving the drug combinations into in vivo studies requires a few more models to be generated, namely to account for any animal model specific isomorphs of the aging targets that the combination therapy seeks to treat. Once these isomorphs are accounted for and it’s clear that each of the drugs in the combination still behaves the same as in humans when binding to the quirky motifs of the model, it’s finally time to move into the laboratory.

Most labs aren’t currently capable of taking this workflow from the database screening stage and carrying it through to testing in animal models, however. Though most labs have access to some sort of database querying tool for ligands, making models of their own is quite uncommon. By the same token, robust molecular modeling software and the skills to use it are typically retained only by specialty groups or cores that are swamped with the number of aspiring collaborators who can’t make models on their own. It’s clear that if researchers want to test combinations of prior approved drugs in their own laboratories, they’ll need to pick up a molecular modeling suite of their own that packs a punch.

BIOVIA Biologics is the molecular and biological modeling suite that allow your lab to dig through prior approved drugs for gems to apply in new disease contexts like aging. With Biologics, you’ll be able to make ligand binding models quickly and easily such that you’ll never have to waste time following up your screening efforts with fruitless experiments.Contact us today to find out how you can use Biologics to start exploring combination therapies for senescence.

  1.  “Recent Research Into The Details Of Cellular Senescence.” June 2017, https://www.fightaging.org/archives/2017/06/recent-research-into-the-details-of-cellular-senescence.
  2. “Repurposing FDA-Approved Drugs For Anti-Aging Therapies.” November 2016, https://link.springer.com/article/10.1007/s10522-016-9660-x.
  3.  “The Repurposing Drugs In Oncology (ReDO) Project.” July 2014, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4096030/.
  4.  “How Many Drugs Has FDA Approved In Its Entire History? New Paper Explains.” October 2014, http://www.raps.org/Regulatory-Focus/News/2014/10/03/20488/How-Many-Drugs-has-FDA-Approved-in-its-Entire-History-New-Paper-Explains
  5.  “Experimental Validation of FINDSITEcomb Virtual Ligand Screening Results For Eight Proteins Yields Novel Nanomolar And Micromolar Binders.” April 2014, https://jcheminf.springeropen.com/articles/10.1186/1758-2946-6-16.
  6. “Virtual Screening In Drug Discovery – A Computational Perspective.” 2007, http://www.eurekaselect.com/78566/article
  7.  “Rejuvenation Of The Aging Arm: Multimodal Combination Therapy For Optimal Results.” May 2016, http://journals.lww.com/dermatologicsurgery/Abstract/2016/05001/Rejuvenation_of_the_Aging_Arm___Multimodal.8.aspx.