We all want to find the next miracle drug—especially the one that will cure an ailing loved one. But it can take researchers a long time—decades, sometimes—to discover and develop new medicines. Not only do scientists and doctors hope to create a therapy that works well, but it can’t cause too many side-effects. And even then, after all that time and effort to move the therapy from the lab bench to the patient’s bedside, the drug might stop working after a few months if the patient develops resistance to it.
How can we overcome this final hurdle?
Sanford-Burnham faculty member Giovanni Paternostro, M.D., Ph.D. and his research group think Nature might have the answer to designing more effective therapies that are less likely to result in resistance: the many-to-many approach.
Most current drug regimens follow a one-to-one or one-to-many model. In other words, one drug is given to influence one particular target in the cell—usually a single protein that’s gone haywire. Or a single drug might affect several proteins (whether that was the intent or not). This method works well in many cases, but that’s not the way biology does things. Billions of years of evolution have perfected the way biological systems control the behavior of genes and proteins. And the solution is many-to-many.
See, in a cell, several regulatory proteins (“controllers”) might influence the behavior of several other proteins (“targets”). Paternostro and his team are wondering if Nature’s many-to-many approach might work for drug therapies, too. Let’s say a person has breast cancer. Instead of taking just one medication that’s aimed at one particular malfunctioning protein, why not design a cocktail of several different drugs (the controllers) that all address a handful of proteins that malfunction in cancer (the targets)?
“Combined drug interventions are an increasingly common therapeutic approach to complex diseases, such as cancer. However, drugs are usually developed individually and only later combined in the clinic based on their known successes or failures as single-therapy agents,” said Paternostro. “We are beginning to think that maybe scientists and doctors should start purposefully designing therapies based on the many-to-many concept.”
The biggest advantage to the many-to-many approach is redundancy. This means that if one drug in the cocktail stops working, it’s okay because there are several backups in the therapeutic cocktail and the therapy can continue to be effective.
What’s more, a many-to-many therapeutic approach might be more effective, too. Almost every cancer is different. One person’s breast cancer might be caused by an abnormal version of one protein, while someone else’s cancer is the result of a different mutant protein. A many-to-many-style cocktail of anti-cancer drugs would be more likely to help both of those patients instead of just one or the other.
“The many-to-many approach is a balance between redundancy and efficiency,” Paternostro said. “And Nature seems to have come up with this same balance again and again, in many different systems.”
This biomimetic approach—meaning “mimics biology”—doesn’t have to apply only to biological systems and therapeutics. Leonardo da Vinci’s famous drawing of the Vitruvian Man was based on biomimetics—he believed buildings could be designed based on the proportions of the human body. Likewise, engineers might be interested in considering the cell’s protein network and the complicated mechanisms that keep them operating smoothly, even when perturbed by outside forces such as infection or injury. So what about an electric grid? Or artificial intelligence systems? It’s easy to see how vulnerable these systems can be when a tree falls on a single node in the electric grid and the whole city goes dark. In that case, too, a balance between efficiency and redundancy would be a good thing.
Paternostro, postdoctoral researcher Jacob Feala, and their collaborators—physicists Carlo Piermarocchi and Phillip Duxbury from Michigan State University, along with bioengineer Andrew McCulloch and engineer Jorge Cortes at the University of California, San Diego—outlined a proof-of-principle for the many-to-many concept in a paper published January 3 in the journal PLoS ONE. Here, they developed a mathematical model to analyze and compare naturally occurring biological networks and a drug-target network. They found that both networks shared certain statistical properties that seem universal across systems and species—suggesting they might really be onto something here.
“If they were linked in a biomimetic way, therapeutic, electrical, or computer networks would be more redundant and therefore less fragile,” said Paternostro. “We view this paper not as an answer, but as a big challenge for people to think about—people in medicine, pharmacology, engineering, or any field where complex networks are involved.”
Feala, J., Cortes, J., Duxbury, P., McCulloch, A., Piermarocchi, C., & Paternostro, G. (2012). Statistical Properties and Robustness of Biological Controller-Target Networks PLoS ONE, 7 (1) DOI: 10.1371/journal.pone.0029374