Big Data Advances Personalized Medicine

Personalized medicine is much in the news today. What is it and why is it important?

Until recently, patient treatment has been characterized by a one size fits all approach. Patients with a given condition are given a set medication; many respond as expected, while others either do not respond or react unfavorably.

An important factor in variability in response to treatment is the underlying genetic makeup of individuals. Drug response is affected by the extent (variability) of drug delivery to the sites of drug action as well as by the effectiveness with which the drug interacts with specialized receptors or enzymes. The activity of these processes are influenced by genetics.

Roden, Dan & Alfred George. The Genetic Basis of Variability in Drug Responses. Nature Reviews Drug Discovery, Vol. 1, Jan. 2002, pp. 37-44.

Dramatic reductions in costs of genomic analyses have resulted in real advances in personalized medicine applications. A profile of each individual can be made by associating their unique genetic makeup with treatments that are most likely to respond favorably to that makeup. These personalized treatments (known as targeted therapies) result in more effective treatments at lower cost and time.

A Personalized Medicine World Conference is held each year providing the latest developments in the field. Past speaker videos can be viewed at:

Although the healthcare community recognizes that Big Data could aid in improving patient care, they often do not have the means to use it. This is where information technologies come into the picture. GNS Healthcare is an example.

their computational engine uses “ supercomputers to analyze relationships among multiple types of patient data, including patient population information, electronic medical records, images, clinical outcomes, and other data, learning as it goes.”  Personalized medicine, therefore, is going beyond application of genome analyses to include “real life” data.

Personalized medicine improves diagnostic capability and predictions of outcomes. It can aid investigators to select patients for clinical trials who are most likely to respond to the treatments. For trials in progress, biomarkers can be identified using genotype, gene expression, and patient outcome data. Big Data analytics can identify hidden drug interactions as well as patient characteristics and care processes that affect safety and efficacy.

Personalized medicine has come of age through an explosion of electronically available medical data and advances in computing technology to analyze it.