MAR 21, 2020 10:28 AM PDT

Modeling Mutations to Beat Drug Resistance

WRITTEN BY: Nouran Amin

Mutations in bacteria or even cancer cells can make them resistant to drug treatments—that means they no longer respond to it and thus continue their harm.

To address this issue, engineers at Penn State have modeled a way of predicting which mutations will occur in people to create an easier path for effective pharmaceuticals.

"Structure-based drug design works very well," said Justin Pritchard, assistant professor of biomedical engineering and holder of the Dorothy Foehr Huck and J. Lloyd Huck Early Career Entrepreneurial Professorship. "It is an amazing ecosystem of technology, but you still have to point it at a set of resistance mutations."

How can this happen? Well researchers know the drugs are developed to the structure of chemicals and their cell targets—however, once a mutation occurs, the cells ‘change’ making them resistant to drugs.

"We need to not just understand the biophysics," said Pritchard. "We also need to understand the evolutionary dynamics."

"If we take out the community aspect of transmission, we can study just the de novo, or 'from nothing,' generation of mutations," said Pritchard.

"We are trying to create a generalized approach to getting the numbers that we use in the models," said Pritchard. "To do this we did not 'fit' the model, but used data obtained from experiments and scaling."

Drug resistance is a critical problem when treating diseases caused by pathogens and cancers. In the study, researchers were curious to know what drives these mutations that lead to resistance.

Learn more about drug resistance:

 

 

"We are trying to create a generalized approach to getting the numbers that we use in the models," said Pritchard. "To do this we did not 'fit' the model, but used data obtained from experiments and scaling."

"We ran the model and it matched clinical data to a degree much better than I ever expected," said Pritchard. "We did this from first principles (basic assumptions)."

Findings of the study was published in Cell Reports.

"We shouldn't always focus on the strongest resistance mutation because there are other evolutionary forces that dictate what happens in the real world," said Pritchard. "Sometimes drug resistance relies on biased random events."

"The data are not quite as strong in the prostate and breast cancer setting," said Pritchard. "In non-small cell lung cancer we didn't see this effect at all."

"If we take out the community aspect of transmission, we can study just the de novo, or 'from nothing,' generation of mutations," said Pritchard.

Source: Science Daily

About the Author
  • Nouran is a scientist, educator, and life-long learner with a passion for making science more communicable. When not busy in the lab isolating blood macrophages, she enjoys writing on various STEM topics.
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