Data Mining as a Powerful Tool for Creating Novel Drugs in Cardiovascular Medicine: The Importance of a "Back-and-Forth Loop" Between Clinical Data and Basic Research.
Cardiovascular diseases, which lead to cardiovascular events including death, progress with many deleterious pathophysiological sequels. If a cause-and-effect relationship follows a one-to-one relation, we can focus on a cause to treat an effect, but such a relation cannot be applied in cardiovascular diseases. To identify novel drugs in the cardiovascular field, we generally adopt two different strategies: induction and deduction. In the cardiovascular field, it is difficult to use deduction because cardiovascular diseases are caused by many factors, leading us to use induction. In this method, we consider all clinical data, such as medical records or genetic data, and identify a few candidates. Recent computational and mathematical advances enable us to use data-mining methods to uncover hidden relationships between many parameters and clinical outcomes. However, because these candidates are not identified as promoting or inhibiting factors, or as causal or consequent factors of cardiovascular diseases, we need to test them in basic research, and bring them back to the clinical field to test their efficacy in clinical trials. With such a "back-and-forth loop" between clinical observation and basic research, data-mining methods may provide novel strategies leading to new tools for clinicians, basic findings for researchers, and better outcomes for patients.