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Drug Combinations and Emerging Interactions

Published on 26/06/17 at 10:50am

Pamela Yeh, Assistant Professor at the Department of Ecology and Evolutionary Biology, University of California, Los Angeles, discusses how drug interactions may hold the key to developing new treatments, particularly in drug-resistant bugs. She outlines how when compound treatment approaches are trialed, there is the possibility for the compounds to work synergistically to improve outcomes

There is an age-old question about multiple factors working together: is the whole greater than the sum of its parts? For many species, like ant colonies, wolf packs or human societies, we can easily see how the sum is more than just its constituent elements; ants would not be able to build intricate tunnels for their homes and repel invaders without working together; wolves would not be able to hunt down prey much larger than themselves without joining forces; humans would not be able to create, teach, innovate and live the way we do without families, neighbourhoods, communities, states and countries. Yet, it’s clear that for other systems this would not be the case. For example, two adult tigers would not work together, but instead would spend time and energy fighting over territory – precious resources would be wasted on combat instead of finding food or mates.

In the case of drugs, people have long known that you could combine several different agents to get a better effect. Traditional medicinal practices from around the world have used medicines in combination to treat a variety of ailments. Within the last century, there has been concerted effort to quantify how combining drugs can enhance or diminish their effects.

For instance, if our goal is to slow the growth of pathogenic bacteria, and agent X can cause growth rates to be 70% of the rate of bacteria in no-drug environments, and drug Y can cause growth rates to be 50% of bacteria in the same manner, then if the two are operating independently of each other and do not interact, we would expect that X and Y together would cause growth rates to be at around 35% compared to bacteria not experiencing any drugs. This would be called an additive or no-interaction effect between them.

However, drugs can interact synergistically or antagonistically. A synergistic interaction means that two drugs together work better than expected compared with the effects in isolation. In the above scenario, if the growth rate of bacteria in the presence of both drugs was 10%, drugs X and Y would be considered synergistic. If, on the other hand, the growth of bacteria in the presence of both them yielded a growth of 50%, they would be considered antagonistic. Not surprisingly, clinicians, when they consider interactions at all, prefer synergistic combinations.

It gets a little more complicated, though, because with drugs we can vary their concentrations. The above examples of interaction, termed ‘Bliss Independence’, rely on definitions first proposed by the American biologist and statistician Chester Bliss in the earlier part of the 20th century and these assume fixed concentrations of drugs. Another method to measure interactions is termed ‘Loewe Additivity’, named after German scientist S. Loewe, which proposed that one could classify drugs based on their effects across an entire gradient of concentrations for each one.

While there have been substantial research efforts aimed at conceptualising, developing, and evaluating classification schemes for two drugs when used in conjunction, there has been extremely little attention paid to higher-order combinations – those involving more than two drugs. As more treatment strategies relying on higher-order combinations are being used in the clinic – the HIV cocktail of drugs – there is an urgent need to develop new conceptual tools to help us understand and categorise interactions.

This brings us back to the question of whether the whole is greater than the sum of its parts. In the context of drug interactions, what we want to ask is the following: can we predict how three-drug combinations will interact based only on the pairwise interactions? That is, can we tell what the interaction type of drugs X, Y and Z together will be, if we know the interaction type for X and Y, Y and Z, and X and Z? Are there patterns that we can find? For example, if all three pairwise interactions are synergistic, does that mean the interaction will always be synergistic? Essentially, is the whole equal to the sum of its parts? Our research indicates that the answer is no, meaning that there are emergent properties associated with higher-order combinations.

Emergent properties in these higher-order drug interactions can be understood as properties of a system that only become clear when all components of a system are present. That is, properties we could not have predicted from just the constituent parts. We would not be able to foresee the interaction type of a three-drug combination if we only knew all of its two-drug interactions.

Emergent synergies are cases where three or more drugs produce a synergistic interaction that would not be predictable based on the three two-drug interactions alone. These types of interactions are especially of interest to us because it indicates that we actually need all three drugs present to find a synergistic combination. Thus, identifying emergent synergies could be particularly helpful in finding novel treatment strategies.

Similar to two-drug interactions, there are many different ways we can conceptually understand, empirically test and classify higher-order interactions. Several labs, including ours at the University of California, Los Angeles, are actively working on understanding these interactions from a theoretical standpoint, and engaged in finding the best methods to experimentally measure these interactions. Though the different labs don’t always agree on the details, we do agree that this is both an attractive problem from a scientific standpoint and a pressing problem from a clinical perspective.

Ultimately, our lab is most interested in discovering fundamental properties of higher-order interactions so that we can find optimal treatment strategies to use against multidrug-resistant bacteria. Understanding what and how higher-order combinations yield emergent properties seems like a good place to start. In essence, we want to answer the question we started off with at the beginning of this piece: is the whole greater than the sum of its parts? When does this hold true? When does it not? What patterns can we discern to help us develop the most powerful drugs possible to combat deadly pathogens? If we and others are successful, then we may be able to help design treatment strategies in the clinic in a rational and systematic way to combat multidrug-resistant bugs.

Pamela Yeh

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