Developing psychiatric medications is a long and complex process. Candidate drugs are evaluated and assessed based on their effects on the behavior of animals, usually rats or mice. Each class of drugs, from antidepressants to antipsychotics, is tested differently often in a labor-intensive process that leaves plenty of room for human error. And there is a growing consensus that current procedures fail to effectively produce new medications.
Now, using a computational method called data mining, Dr. Neri Kafkafi of Tel Aviv University's Department of Zoology has discovered a small number of mouse behaviors that can be used to categorize psychiatric drugs more quickly and easily than standard tests. The research, conducted in collaboration with Greg Elmer of the University of Maryland and published in Psychopharmacology, could improve the drug-testing process and identify new uses for existing medications.
"For pharma companies, psychiatric drugs carry the highest risk. Some are getting out of the business because so few drugs make it through the development process," says Dr. Kafkafi. "Our data-mining algorithm can quickly predict which drugs are most effective for which disorders schizophrenia, psychosis, or depression, for example and eliminate the need for a lot of testing, potentially saving significant time and money."
Staking a new claim
Data mining which involves using computers to identify patterns in large amounts of information has already provided valuable insights into the human body. In recent years, it has been used to find gene expressions that predict cancer and drug responses, and to classify drugs. Behavior, though, has proven a less fertile ground for data mining. The problem is that the data has not yet been divided into meaningful units of analysis that can serve a role similar to genes in physiology.
To address this problem, Dr. Kafkafi recorded the movements of mice in a small
|Contact: George Hunka|
American Friends of Tel Aviv University