Whether it is a drug-resistant strain of bacteria, or cancer cells that no longer react to the drugs intended to kill them, diverse mutations make cells resistant to chemicals, and “second generation” approaches are needed. Now, a team of Penn State engineers may have a way to predict which mutations will occur in people, creating an easier path to create 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.”
Standard practice to develop drugs is to model the structure of chemicals and their cellular targets to kill specific pathogens or cancer cells. Once mutations begin to change the cells, treatment requires new drugs. However, a variety of mutations may occur and drug developers need to target the appropriate mutation to kill the pathogen or the cancer cells.
The researchers wanted to discover what drives which mutations to grow out in the real world so that they could choose the most effective mutations to target. They reported in Cell Reports that they found that the most drug-resistant mutation was not necessarily the mutation that dominated. “Survival of the fittest” did not always hold and targeting should aim at the most probable mutation rather than the most resistant, at least for some cancers.
“We need to not just understand the biophysics,” said Pritchard. “We also need to understand the evolutionary dynamics.”
Drug resistance is a problem when treating diseases caused by bacteria, viruses and cancers, but the researchers chose to investigate mutations in cancers because understanding mutations in cancer cells is simpler. Mutations in bacteria and virus have two components — what happens within the cells and what happens when the bacteria or viruses spread from host to host. Because cancer is not, in humans, contagious, working with cancer cells removes a portion of the potential source of mutations.
“If we take out the community aspect of transmission, we can study just the de novo, or 'from nothing,' generation of mutations,” said Pritchard.
The researchers looked at existing data for leukemia and three other types of cancer. The leukemia database was the largest and most complete. They used algorithms similar to those used in modeling how chemical reactions in chemical physics take place. In this case, they used the simulations to model how evolution works.
“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.”