08 Dec GenXys is Taking the Guesswork out of Prescribing Medications: Is Your Practice Ready for personalized medicine for everyone?
MedicalResearch.com Interview with:
Bernard Esquivel Zavala, MD, PhD, MHA
GenXys Chief Medical Officer
MedicalResearch.com: What is the mission of GenXys?
Response: Our mission at GenXys is to tailor the right treatment for each individual patient at the right time. GenXys founders, including Professors Pieter Cullis and Martin Dawes, were heavily involved in the precision medicine field from the very beginning, and they noticed a functional gap between the expectations and the actual clinical implementation of precision medicine Particularly, when it came to, at the time, the new field of pharmacogenetics. Their solution was to provide a comprehensive, user-friendly platform that organizes all patient data relevant to prescribing to provide the safest and most appropriate personalized prescribing options. Simply put, GenXys’ solutions were made by clinicians, for clinicians. The GenXys software suite collects patient information and categorizes that information, including pharmacogenetic data, based on clinical relevance and runs it through advanced condition -based algorithms to provide real time accurate prescribing options. It makes my life as a clinician easier and safer and gives me the confidence that I am not practicing ‘trial and error’ prescribing.
Ideally, every healthcare provider should be using a real time medication decision support solution like ours, and not just for pharmacogenetic test results. Pharmacogenomics is just one piece. In fact, our core product, TreatGx™ can run with or without pharmacogenomics. Let’s say that you’ve run it without pharmacogenomics, meaning that you are using this tool to organize and rapidly identify how biophysical factors, liver function, kidney function, comorbidities, and drug-drug interactions may impact the medication you’re about to prescribe to your patient. This functionality alone is incredibly helpful. In fact, the factors I just mentioned likely account for 95% of the reasons why a patient does not respond to a particular medication or might have an adverse drug reaction. But the TreatGx platform will also highlight when the evidence supports bringing pharmacogenomic information into the mix. The right approach is bringing all those relevant clinical, biochemical, and molecular factors closer to the provider which will ultimately foster personalization. We will start treating the individual instead of the disease(s).
As with any new technology, there are barriers to precision prescribing. This includes educational and emotional barriers. It’s important to educate providers and keep them up to date to help them understand the power that precision prescribing can bring into their practice—and the limitations—to set the right level of expectation. The Human Genome Project was finished in 2000, and there was a lot of buzz about pharmacogenomics even back in 2003. The field got a lot of traction in 2015. So, everyone thought, “Oh, this is going to be groundbreaking and quite disruptive. From now on my prescription is going to be a hundred percent accurate and safe.” But it’s not quite the whole story. Pharmacogenomics has to be considered as another piece of the puzzle. It’s like saying that by having an MRI, you’re curing cancer. It’s just another piece of the treatment puzzle. There are also emotional barriers, where ego can factor into a decision. It can be uncomfortable for a physician to say, “I don’t know this. Let me check it out. Let me explore it further, review, and come back to you.” It’s easier to say if I don’t know it, that it doesn’t work or isn’t relevant, rather than exposing yourself. And so that, in terms of the emotional piece, I would say is a big component. We can tackle the emotional component that element by fostering education and bringing education closer to providers.
MedicalResearch.com: How does Precision Prescribing reduce adverse drug effects and assist in deprescribing unnecessary medications?
Response: That’s a very interesting question. Adverse drug reactions are a combination of several factors that are working against the patient. Some of them can be, for example, the drug-drug interactions. Take, for example, something as simple as the combination of ibuprofen and warfarin. There is a significantly increased risk of bleeding with these medications when used together. And that risk can be avoided by knowing that the patient is under warfarin or perindopril therapy and instructing the patient to avoid ibuprofen for over-the-counter pain relief. Avoiding drug-drug interactions is one way to prevent potential adverse drug effects.
If you go one step further, you can prevent adverse drug reactions by predicting how a patient will or will not respond to a medication—antidepressants for instance. By bringing pharmacogenomic input into the mix more precise prediction is possible. For instance, if you’re treating a patient with gout, one of the first line of treatment is Allopurinol. Allopurinol is one the most common cause of severe cutaneous adverse drug reactions such as Stevens-Johnson syndrome and toxic epidermal necrolysis where the mortality rate can be as high as 30%. Nowadays, there are specific guidelines coming from the American College of Rheumatology in terms of pharmacogenetic testing, HLA specifically, prior to prescribing allopurinol in
a) African American and b) south or east Asian patients to avoid this potentially fatal drug reaction.
Abacavir, a medication that is normally used in combination with others for HIV, and fluorouracil (5FU), an anticancer medication, both have specific guidelines and recommendations for pharmacogenetic testing prior to prescribing. In Europe, the European Medicines Agency requires some testing prior to prescribing, and the FDA is leaning towards that as well. We now know that we can prevent catastrophic drug reactions in some patients. In fact, up to 7% of cancer patients treated with 5FU will experience cardiotoxicity due to secondary or adverse reactions to the medication rather than their cancer. So it’s a significant problem. The good news is that modern technology can help the clinicians with accurate prescribing choices.
Let me go one step back. Normally, let’s say that you come to my office for a medical evaluation. Within 20 minutes, I need to remember, even though I have my EHR (electronic health record) at my side, why you’re here, what is happening to you right now, what happened to you in the past, which medications I have used with you, which medications you didn’t like, which medications you didn’t approve of, which medications your insurance didn’t pay for, which medications caused an adverse reaction, which medications you are allergic to, and so on. There are many data sets, many data points.
Don’t get me wrong, I honestly think providers do a great job of prescribing, but it’s still not good enough, because overall effectiveness in terms of prescribing in the U.S. is less than 50%. We can and must be more accurate and precise. So having said that, I need information about what makes you you, and that’s your genetic makeup. That’s why we brought pharmacogenomics in as part of this comprehensive solution. GenXys built an algorithm that evolved into a software platform that, in a nutshell, provides clinicians with a recommendation on what to prescribe for you right now, based on all of the evidence and relevant data sets that I previously mentioned.
I think another problem that we have nowadays is polypharmacy. Polypharmacy is defined by having five or more medications, and it has become a major problem in terms of older patients. There’s a lot of drug-drug interactions just by themselves. On top of that, with older patients’ organs and systems aren’t working properly. In geriatric medicine, they are very focused on diminished liver and kidney function, and they know how to work that out. But if you also add into the mix diet changes, social-emotional determinants of health, and pharmacogenetic factors, it is clear we have a very interesting combination of factors impacting our older population. And in fact, it has been shown that when you start implementing a precision medicine approach in the geriatric population, what you start doing is deprescribing, because most of the time you would give medication A, and to avoid or control the secondary effects of medication A, you will prescribe medication B. And then medication B may be affecting medication A, so you will prescribe medication F. On top of that, the patient has seven conditions, which is why they can be on 15 medications.
I think the pharmacists are the ones most suited for championing precision prescribing implementation. I think they have been trained for this, as they understand this better than anyone. They also have more time with the patients. Pharmacists do fantastic medication reviews, but they can take hours to complete, as you can imagine. It takes a lot of time to put this information together in order to provide a more accurate, personalized recommendation. So that’s what our software ReviewGX™, a complement to our TreatGX™ software, does—streamline that time and facilitate their job by compiling, assessing, and organizing that information, in a more friendly, structured, evidence-based fashion. These digital automation tools provide accuracy and significant time savings.
It is estimated that 90% of patients have at least one actionable pharmacogenomic variant within their genome. So in other words, almost everyone can benefit from pharmacogenetic testing. And that’s why the way we are providing healthcare, specifically in the U.S., is changing. Now it’s patient-based, and value based, because the previous trial-and-error method of prescribing we know, even from an economic standpoint, is not working at all. The vast majority of healthcare systems in the U.S. are leaning towards patient-centered healthcare.
MedicalResearch.com: Can the ReviewGx™ tool be integrated into current widely used medical record systems, i.e., EPIC?
Response: Yes. We have the capabilities in place to have a full integration and we have a number of customers that have our application integrated into the systems they use. The ideal scenario is where we are fully integrated within a clinical medical record, so we can pull the data to include in the prescribing algorithms. We can pull the labs from our patient. We can pull the diagnosis of the patient. We can pull the comorbidities. We will be pulling the other medications from different specialties, facilitating communication between specialties. You don’t know what the urologist is sending, and that may be interacting with what the cardiologist prescribed. So ideally, we are able to pull all that data, organize that data, including if the patient has undergone pharmacogenetic testing, for instance, and provide an accurate and reliable recommendation, based on all this evidence, to the provider on which medication may benefit the patient most.; everything within a HIPAA/ PIPEDA/GDPR compliant environment.
So, it’s not about us finding something new because basically we are relying entirely on evidence, on clinical guidelines, on drug monographs, on specific regulatory recommendations. It’s just about organizing and compiling that data in a very intuitive, user friendly and ergonomic platform and the presenting to the clinician something that is actionable. That’s why we are focusing on this comprehensive approach. However, unless you find a way to bring this technology in as part of the normal workflow that we use in clinical practice day to day, it’s not going to work. It is important to be platform agnostic. This allows the technology to be integrated into a number of data sources; to integrate with laboratory information management systems (LIMS) and as an input either NGS or PCR based in order to bring our solutions closer to molecular laboratories. Interoperability is a key industry word that is very important for the use of our applications across the journey of care.
MedicalResearch.com: Is there anything else you would like to add?
Response: The first would be that reactive medicine doesn’t work, and we all know that. There are a lot of healthcare economic studies that support that reactive medicine isn’t working. Pre-emptive care is crucial. Therefore, we should start preventing adverse drug reactions, and ideally with predictive medicine and modeling based on patient data. This will inform how a patient will respond to medication X or Y with a very high accuracy. So that’s the trend, and that’s where we should be moving.
Second, is that we should stop treating diseases and start treating the individual person. And it may sound obvious, but even the way we are creating medicines, we are studying the effects of medications based on the average response of a large cohort of maybe 20,000 people. You have a mean dosing recommendation, and that’s it. To be fair, the FDA has done a fantastic job pushing the industry to be as inclusive as possible in terms of diversity and different ethnical backgrounds. But in the reality of clinical life, the mean dosing recommendation isn’t always correct. Every single patient is unique, and you need to adapt your practice to every single person. So, we need to start treating each person rather than diseases. Genetics utilization gets past skin color and will improve treatment racial bias.
Our starting point at GenXys is how we clinicians think. That’s been one of the biggest barriers in terms of precision medicine: rather than trying to bring clinicians into your workflow, you need to find ways to integrate into theirs. We’re coming from that position—by clinicians for clinicians. And then we’re embedding into our normal practice all the solutions that make large amounts of patient data easier to understand, because they are aligned with the way we are trained, with the way we practice, and with the evidence that we normally rely upon to make the best clinical decision possible. We are focused on the nextgen of prescribing to make every prescribing choice, globally, better.
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