Dr. Strauss: Our goal is to move new science into the drug review process and close the gap between scientific innovation and drug review. We’re not just doing discovery basic science, but we’re trying to take those new scientific discoveries and change how we review new drugs.
David Strauss, M.D., Ph.D.
Division of Applied Regulatory Science
Office of Translational Sciences
Office of Clinical Pharmacology
Dr. Strauss: Our division is called the division of applied regulatory science, and we’re a diverse multi-disciplinary group, we have physicians, pharmacists, pharmacologists, chemists, and we bring multi-disciplinary teams together to address complex scientific problems.
Let me tell you about a few examples. The first one relates to analyzing the chemical structure of drugs to predict whether they can cause rare adverse events, such as cancer or liver failure, and this is important because clinical trials often don’t detect these rare adverse events. We develop, test, and implement new predictive models. And we can use the modeling data to decide if we need to collect additional study data before approving a drug.
We also use this chemical informatics modeling work to study the structure of synthetic street drugs and by doing that we can predict if drugs are going to become addictive.
The second example relates to the effects of drugs on the heart. A couple of decades ago, drugs were removed from the market, because they caused abnormal heart rhythms and sudden death.
And we now have a test to predict whether this can happen, however it’s not very specific. And so what that means is some drugs get flagged as having potential heart problems when they actually don’t and we end up potentially losing drugs that are in development. So what we’re doing is taking our laboratory data combining it together in computer models and predicting the effects of drug safety in patients. This will allow us to bring more new drugs to the market.
The third example relates to what’s called precision medicine or personalized medicine and the goal of personalized medicine is to have the right drug for the right patient at the right time at the right dose, and this is something that doctors have always tried to do, it’s not new but it’s gotten a lot of attention recently because we have lots of cool new genetic analysis tools.
And what we’ve done is test models that combine together information from multiple different genes in an individual patient to predict how that patient will respond to a drug. And if this is validated further, patients could have a blood draw before they get prescribed a medication, and it could predict whether that drug would be safe for them.
The work we do fills a gap that isn’t being done elsewhere. What we try to do is come up with new tools and models and approaches that can be applied across the drug development spectrum in multiple different types of drugs to better predict safety and effectiveness and it’s really something that others aren’t doing quite like we are.