Researchers from the U.S. and China have developed statistical models that simulate a drug's reaction in a patient, an innovation that could help deliver treatments to specific disease targets.
In a study sponsored by the National Institutes of Health, Penn State University and Beijing Forestry University scientists used pharmacogenomics to determine differences in patients' reactions to drugs based on their genetic makeup, according to the university. By mapping both pharmacokinetics (how drugs are transported in the body) and pharmacodynamics (the response to the treatment) with a statistical analysis framework, the researchers were able to characterize many different properties of drugs that can play into their effectiveness.
The mathematical simulations provide a detailed view of a drug's absorption, distribution and elimination as a function of protein-DNA interactions in the patient. By collecting massive amounts of these data over time, the models can help predict what kind of drugs and what doses would be most beneficial.
"Traditional medicine doesn't consider mechanistic drug response," lead author and Center for Statistical Genetics Director Rongling Wu said in a statement. "We want to look at how an individual person responds to an individual drug by deriving and using sophisticated mathematical models, such as differential equations."
"If we know how genes control drug response, we can create a statistical model that shows us what will happen before using the drug," Wu said. "That is our final goal."
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