Kinetics for Drug Discovery
Why invest €21 million to understand how potential drugs interact with their disease target?
People take a drug to cure a disease or to alleviate one or more health-related problems they are suffering from. To that end a drug should exert a certain effect on the body. In principle, a drug molecule exerts its clinical effect by blocking or activating a disease-related target within the human body for a certain period of time. Imagine a drug to treat sleeplessness; preferably the drug helps you sleep at night and it doesn't make you sleepy during the day. Or, imagine a drug to treat hay fever; you wish the drug provides relief for a substantial time and not for only a couple of minutes.
The nature of the disease dictates a drug interacts with its target for a relatively short period of time or for a more prolonged duration.
The discovery and development of new drugs normally involves the screening of hundreds of thousands of different molecules to find out how well they bind to the target protein of interest. Apart from how well a drug binds to a given target, there is mounting evidence that knowing how fast or slow a drug binds (association), but also how fast or slow it comes off again (dissociation), represents an important predictor for a potential drug molecule's efficacy and/or safety.
The key question for the pharmaceutical sector is how to discover and develop drugs having the right properties to exert the right effect for the right time.
Unfortunately, currently available methodology to investigate the binding kinetics of a drug with its disease target is time-consuming, labor intensive and costly. In addition, it often involves a "test tube" situation that does not fully mimic the actual clinical situation. With support from the Innovative Medicines Initiative (IMI), K4DD brings together a range of world class scientists from the academic and private sector within Europe. It focuses on enhancing understanding drug-target binding kinetics from the "test tube" to the human body and delivering a set of new, robust and improved methodologies. Ultimately this should enable early and improved prediction of the efficacy of a candidate drug in the clinic, and thereby facilitate the translation from bench to bed.
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