Finding the Secret to Individualized Medicine
What if we utilized a system that could comb through the anonymized information of hundreds of millions of patients? The AI system would take both subjective and objective data into consideration.
Massive advancements are being made both in AI and 3-D printing, but have you heard of these technologies working in tandem? I doubt it.
The thought of artificial intelligence has been at the forefront of innovation for the last twenty years, but the last six months alone have produced a cyber evolution that dwarfs any “digital revolution” of the past. From drones and mechanized deliveries, to in-depth analysis of COVID protein strands, artificial intelligence has shown itself invaluable to a world in need.
3-D printing has also made several giant leaps in recent years. We have seen a wide array of uses that include house construction, personalized orthopedic implants, and most important to our conversation, an FDA approved drug called Spritam. The possibilities being discovered are endless. And to be honest, the recent successes are only the tip of the iceberg.
To understand the perspective of this article, you’ll have to know a little more about me. My background has exposed me to the worlds of both biopharma management consulting and medical rehabilitation, so my outlook ignores the dogmatic boundaries of a siloed specialist or SME.
Let me speak first from my background as Doctor of Physiotherapy. Throughout my years of schooling and hands-on treatment, the theme of highest importance was always individualized medicine. No two people should ever be treated exactly the same. A cookie-cutter approach is lazy and lacks efficacy. True change to a patient population is accomplished by allowing treatments to be uniquely crafted for the betterment of those under your care.
Now, as a representative of PharmAllies, let me speak from a BioPharma perspective. Manufacturing lines cost a enormous amount of money. Pharmacokinetic properties are extensively dependent on the patient’s own body. Drug personalization is both overwhelmingly ignorant and financially impractical. There would be no way to facilitate a drug that could both be effective for the patient and monetarily fruitful for the BioPharma industry.
How could we ever satisfy both the biopharma industry and the practitioner providing individualized treatment? Let’s dream for a second.
Our medical science teams around the world have made impressive advances into producing a wide array of diagnostic indications. The range of testing available now makes it possible to identify pathologies or, at the very least, a predisposition toward certain conditions. Blood testing alone can produce thyroid and metabolic panels, complete blood counts, enzyme markers, and a countless number of analyses that allow for in-depth health inferences, in regards to a patient’s current and future condition. With the depth of technology currently making its way into the medical marketplace, we will see a focus on preventative medicine that will increase health and lower the prevalence of overall disease. But how does this relate to 3-D printing and individualized medicine?
Remember when we discussed the in-depth analysis for COVID proteins carried out by an array of super computers? This same type of program has taken these findings and began the search for a vaccine or drug treatment for COVID-19. By compiling mass amounts of past data, it can both identify the individual pathology and begin working on a unique course of treatment. What if we could take this exact same principle and apply it on a larger scale?
What if we utilized a system that could comb through the anonymized information of hundreds of millions of patients? The AI system would take both subjective and objective data into consideration. Patient outcomes, drug profile interaction, unique physiology, and best practice would be only a few of the pieces of information utilized to produce a 360-degree view of both diagnosis and treatment selection. This would then include the ability to prescribe a medication according to an exhaustive search of both past results and patient outcomes. And this is where we get into 3-D printing.
Four years ago, Aprecia, a pharmaceutical company based in the United States, produced the first FDA approved 3-D printed drug, Spritam. It is a medication that calms seizure activity in patients with epilepsy. The true breakthrough, in this case, does not lie within the actual medication, but in the route taken to manufacture it. By proving that 3-D printing is possible within Biopharma, the door to endless possibilities is opened. Instead of a pharmaceutical approach that relies on a “one size fits all” mentality, 3-D printing would now allow for a hyper-personalized drug prescription.
How would it affect the price tag? The AI portion of this would allow us to eliminate a portion of the cost of research and development. The individualized drug production is done with a printer that can change according to programming, so there are no large and costly manufacturing lines. Packaging is already a process that is largely automated. We have, essentially, handed over 90% of the work to a computer.
So, here is the theoretical process. We begin with patient testing. Those test results are then transferred into a system that uses AI to comb through data to produce an accurate diagnosis. This diagnosis, based on billions of pieces of information and hundreds of millions of patient profiles, is then used to produce the best approach. Any prescribed medication is then tailored to the patient’s individual makeup and profile. The drug is built uniquely using 3-D printing and provided to the patient. Efficacy increases and successful outcomes skyrocket due to the individualized nature of the entire process. Exciting, right?
Okay, okay…I’ll address what I know is on the tip of your tongue… “Justin, there is no way this is even close to possible in the near future.” You are correct. This process is 100% theoretical and entirely impractical, as previously mentioned. There are a lot more moving parts than I would like to admit. We aren’t even close to being able to utilize a system that resembles this health model. But do you really think we won’t see it in the next twenty to thirty years? I think we will.
Entirely theoretical? Yes.
Completely impossible right now? Yes.
Going to happen in the future? Oh, get ready.