October 16-17, 2017  |  Orlando, Florida USA

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The Personalized/Precision Medicine Blog

The official blog of the Annual Personalized and Precision Medicine Conference provides readers with information, insight and analysis regarding the field of personalized and precision medicine, genomics, genomic interpretation and the evolution of healthcare in the post-genomic era.

The Elephant in the Room: Drug-Induced Disease Diagnosis

The Elephant in the Room: Drug-Induced Disease Diagnosis
 
Guest Blogger: Kristine Ashcraft, Chief Executive Officer at YouScript
 
 
 
 
One of our clinical pharmacists tells a story of his car breaking down in high school. He had it towed to a local mechanic as none of his attempts to get the car going were successful. The gas gauge was showing one quarter of a tank. After running numerous diagnostics to troubleshoot the issue, the mechanic finally determined that the vehicle was out of gas and the gas gauge was broken. Luckily, no needless repairs were done.
 
This story mimics a common occurrence in healthcare. We often treat the problems our patients describe to us without fully investigating the cause of those problems. For example, let’s say patient is newly diagnosed with depression and started on the antidepressant, citalopram (Celexa). That patient may or may not respond to that medication. Furthermore, that patient may experience, sleeplessness, nausea, anxiety or a myriad of other symptoms. When patients experience side effects of medications, clinicians, who are eager to do the right thing, often perform more diagnostic tests and prescribe additional medications to treat those side effects, attributing them to a new disease or condition rather than recognizing that they were caused by a new medication, such as citalopram in the example above. In a way, it is like trying to fix the car without actually knowing what it is that went wrong.
 
This is not the provider’s fault; the clinical decision support tools available are not great at warning when a medication is the probable cause of a new symptom. Providers are used to looking for allergies to medications as well as inappropriate combinations of drugs (e.g., drug interactions). Providers are also aware of physiologic conditions like poor renal or hepatic function which may alter the metabolism and clearance of certain medications. But, providers were likely never taught how common it is for genetic variations to impact drug response in their patients. As a result, at present, we are limited in looking for potential adverse drug events (ADEs) by systems and/or software that may miss the diagnosis simply because they are not designed to look for it.
 
 
Take Elise Astleford for example. An active, retired minister, she had to give up her weekly bridge games because she was experiencing memory problems.  She was frightened that she was in the early stages of Alzheimer’s Disease. Genetic testing was ordered and it was determined that levels of her allergy medication, chlorpheniramine (Clor-Trimeton), were 2-3 times that of what they were expected to be. Like 6% of Americans, she was not genetically able to produce the liver enzyme CYP (pronounced “sip”) 2D6. CYP2D6 is responsible for converting the allergy medication Elise was taking into a compound the body is able to flush out. This did not create a problem immediately, but the drug levels continued to build up over time. When she stopped this allergy medication, her memory problems resolved. But most patients like Elise do not have genetic testing ordered.
 
Another patient in Florida was experiencing dizziness, fainting, fatigue, and shortness of breath that landed her in the ER numerous times. She was missing a lot of work and her quality of life was suffering. She was referred to a pulmonologist for her breathing issues who learned that she had started an antidepressant, citalopram (Celexa), several weeks before the issues had started. Genetic testing was ordered and the YouScript software predicted a greater than 200% increase in her metoprolol levels, caused by a combination of her CYP2D6 genetics and the antidepressant, which is a CYP2D6 inhibitor. When she was switched from metoprolol (Lopressor, Toprol XL) to bisoprolol (Zebeta), all of her symptoms resolved.
 
Drug-drug interactions are not the only form of adverse drug events; patients may also experience drug-gene interactions.  But they cannot be found if they are not sought, much like failing to look at the gas gauge in the automobile example.  Another way of looking at the problem is to assume an automobile is traveling down a highway at a high rate of speed when a cloudburst occurs.  Sometimes, the windshield wipers cannot keep up with the volume of rain.  On other occasions, the wiper blades may be worn or otherwise inadequate.   In either of these examples the driver (clinician) cannot “see” the road (diagnosis) because the tools being used are not adequate.
 
Let’s examine what we already know about genetics and adverse drug events:
 
1. In 2009, we spent as much on medication related issues as we did on medication.[i]
 
 
2. About half of medications do not work as intended. [ii]
 
 
 
3. The FDA states in drug development guidance that, “Drug-gene interactions should be considered to be similar in scope to drug-drug interactions.”[iii]
 
4. The majority of commonly prescribed medications are metabolized by five main enzymes in the liver called Cytochromes or CYPs (pronounced “sips”) – CYP2D6, CYP2C19, CYP2C9, CYP3A4 and CYP3A5. Click here to see an alphabetical list of medications impacted by genetic variability.
 
5. 93% of patients have a variation in at least one of these five main CYPs. [iv]
 
 
6. A recent study showed that in patients over 65 taking multiple medications, one or more of which is known to be impacted by genetics, ER visits were 71% lower and hospitalizations 39% lower compared to patients on similar medications of similar health and age from a retrospective database.
 
 
7. Any healthcare provider you speak to will confirm that patients of the same age, health status, weight, height, and race do not respond to the same drug and dose in the same way. We know that some of that variability in response is caused by genetics and interactions with other medications. The issue is that the vast majority of the time we are treating these factors as an unknown when we actually have the tools today to treat it as a known.[v]
 
 
I was recently discussing this with Phil Dyer, the Senior Vice President of Healthcare Management Services at Kibble and Prentice, who has over 30 years of experience in medical professional liability for physicians, surgeons, group practices and hospitals. His response was “res ipsa loquitur”; Latin for “the thing speaks for itself.”
 
Genetic testing of cytochrome P450 pathways and interpretation with YouScript software is akin to replacing worn wiper blades, which allows the driver/clinician to see far more clearly and react more rationally to the issues at hand. We must not assume that new symptoms are necessarily a different or new disease or even another manifestation of the current disease, nor assume that the current “windshield wipers” are sufficient to clear the view to find the real problem.  Judiciously used, genetic testing can provide a much clearer view of the diagnosis.
 
The next time you or a patient you treat complains of new symptoms, I hope you will remember to first evaluate current medications as the culprit before prescribing an additional test or medication to treat that symptom, so that this prescribing cascade can be avoided.
 
 
 
 
 
 
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UC San Diego Research Team to Present Proof of Concept for Predicting Drug’s Side Effects Through Analyzing Red Blood Cells

Researchers at the University of California, San Diego have discovered proof of concept in determining whether a drug will produce adverse side effects based on assessing blood samples. This predictive model predicts how variations in an individual’s genetic backgrounds will affect how the drug is metabolized, and therefore if a side effect is probable.

This UCSD study focused specifically on red blood cells, as they are the simplest human cells. The study was conducted amongst 24 individuals to determine why some individuals experience side effects from ribavirin, a drug used to treat hepatitis C, and others did not. It was discovered that a side effect of ribavirin is that it can cause anemia, or a decrease in red blood cell levels, which occurred in around 8 to 10 percent of patients.

“A goal of our predictive model is to pinpoint specific regions in the red blood cell that might increase susceptibility to this side effect and predict what will potentially happen to any particular patient on this drug over time,” said UC San Diego alumnus Aarash Bordbar, who was part of the research team as a Ph.D. student.

Aarash Bordbar will be presenting this research at the 8th Annual Personalized & Precision Medicine Conference, taking place on October 12-13, 2016 in San Francisco, CA. For more information about conference, visit: http://personalizedmedicinepartnerships.com/.

By predicting how variations in patients’ genes impact how they metabolize the drug, pharmaceutical companies could soon have the ability to conduct predictive screenings before beginning clinical trials. Therefore, this model has the potential to revolutionize what is known about the variance in metabolizing drugs.

In the future, the UCSD research team strives to develop a predictive model for platelet cells, which are vastly more complex than red blood cells, as well as a predictive liver cell model. As this organ is where the majority of drugs are metabolized, and where many drug side effects are manifested, this finding would be revolutionary to pharmaceutical companies in clinical trials. To learn more about this study, visit: http://bit.ly/20oo7rp.

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