Goal is to apply differential equations with pharmacogenomics to better predict a patient’s response to prescription drugs, a development that could create role for pathologists to help clinicians interpret the data from such medical lab testing
New approaches to mathematical modeling are poised to transform pharmaceutical drug research and development—and create new opportunities in clinical laboratory testing down the road. U.S. and Chinese scientists have developed statistical models that more accurately simulate a drug’s reaction in a patient.
Using differential equations, these researchers seek to integrate mathematical modeling of drug reactions into pharmacogenomics. Their goal is to better predict interpersonal differences in drug response based on genetic information. This will help clinicians to develop a strategy for personalized drug delivery.
Researchers Use Pharmocogenomics and Differential Equations
In one study, researchers combined pharmacogenomics and newly developed differential equations to determine differences in patients’ reactions to drugs based on their genetic makeup, according to a story published at fiercedrugdelivery.com. This research could allow clinicians to predict a specific patient’s response to a specific drug. Additionally, the innovation could help deliver treatments to specific disease targets.
Moving Medicine from a Stratified to a “Virtual Patient” Approach
“Traditional medicine doesn’t consider mechanistic drug response,” stated lead author Rongling Wu, Ph.D., Director of the Center for Statistical Genetics and Professor of Public Health Sciences at the Penn State College of Medicine. Instead, drugs were traditionally prescribed according to a ‘one size fits all’ model,” suggested Munir Pirmohamed, Ph.D..
He is Professor, Department of Pharmacology and Therapeutics, University of Liverpool and was quoted in a paper published in The British Journal of Clinical Pharmacology.
In recent years, pharmacogenomics research has shown the diversity of patients’ responses to drugs. However, researchers recognize the current limitations of the existing pharmacogenetic-based method that is used to predict a response to a particular drug and dosage combination, according to a story published by ehealthserver.com.
“[W]ith increasing knowledge in this area it becomes obvious that the stratified medicine approach is no longer sufficient,” observed the authors of “Future of Medicine: Models in Predictive Diagnostics and Personalized Medicine”, published last year in Advances in Biochemical Engineering/Biotechnology. Instead, a “virtual patient” approach model will make medical treatment safer, more efficient and more cost-effective, they asserted.
Identifying How the Individual Patient Responds to a Therapeutic Drug
Wu and his colleagues created an innovative statistical analysis framework to take the field of pharmacogenomics one step further. “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,” stated Wu in the ehealthserver.com story. (A differential equation is a mathematical equation that relates some function of one or more variables with its derivatives, according to Wikipedia.)
The scientists expect this framework will help physicians and pharmacists by simulating different variables a drug has in a patient, such as protein-protein or protein-DNA interactions. Specifically, the framework characterizes a drug’s absorption, along with its distribution and elimination properties. This yields information on pharmacological targets, physiological pathways and, ultimately disease systems in patients. In turn, this allows the treatment’s effectiveness to be predicted.
“The results from this framework will facilitate the quantitative prediction of the responses of individual subjects, as well as the design of optimal drug treatments,” the researchers noted in a recent special issue of Advanced Drug Delivery Reviews.
Statistical Analysis Framework Helps Predict Treatment Effectiveness
“In the last years, pharmacokinetic, pharmacodynamic, and recently pharmacogenomic data allow conceptual and mathematical models building” stated Alexandru G. Floares, M.D., Ph.D.. Floares is President of SAIA (Solutions of Artificial Intelligence Applications) and Chair, Artificial Intelligence Department, Oncological Institute, Cluj-Napoca, Romania. “The most realistic models are systems of nonlinear coupled ordinary differential equations.” Floares wrote the observations in a paper published on the SAIA website.
Wu and his team focused on studying drug response and drug reaction. Specifically, they looked at pharmacokinetics and pharmacodynamics. Pharmacokinetics influences the concentration of a drug reaching its target. Pharmacodynamics determines the drug response.
“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 stated. “That is our final goal.”
These technologies of data-intensive analysis and computer-intensive modeling will form the foundation for a more sophisticated way to predict the individual patient’s response to specific therapeutic drugs. In turn, clinical validation of these complex modeling systems can be expected to broaden clinical diagnostics and laboratory medicine in ways that more actively engage pathologists pathologists and medical laboratory scientists.
—Pamela Scherer McLeod