March 22, 2021
By Julie Gould
We recently spoke with George R Aronoff, MD, MS, FACP, FASN, former chief of Nephrology and Hypertension at The University of Louisville School of Medicine, and currently the chief medical officer and co-inventor of Dosis, Inc, a cloud-based clinical decision support platform that uses artificial intelligence and control algorithms to personalize drug dosing. George and Dosis’s founding team have created a dosing platform that, in the short term, will leverage artificial intelligence to move away from paper-based dosing protocols and make a generational leap in personalizing drug dosing to treat chronic anemia for patients on dialysis. Their current solution, Strategic Anemia Advisor, has been used across the country by leading dialysis centers and has generated over 2 million dosing recommendations to date.
Dosing anemia drugs is a single example of the impact AI can have on the process of prescribing medications—something Dosis says will likely be the standard of care in the future for nearly all chronic disease. They see potential for exponential growth in the use of AI to drive many different dosing applications. Dosis has already begun a trial of an AI-based intravenous iron dosing protocol and developed a first of its kind tool that informs the simultaneous dosing of three different types of medications used to manage mineral and bone disorders.
George, can you tell us about the situation with drug dosing in the US today?
Problems related to preventable adverse drug reactions in the United States account for costs of approximately $20 billion annually, and 30–50 percent of these preventable side effects are due to dosing errors. This is one of many reasons why the movement from a one-size-fits-all approach to medication dosing toward a personalized, precision approach is such an important development in care delivery today. Many factors have come together to make now the right time for AI-powered dosing and realize the significant benefits it offers.
What is the current problem that must be fixed with regard to drug dosing?
The process of prescribing drugs has become increasingly complex for health care providers and the enormous costs are simply reflecting that fact. Medications often have both intended and unintended effects on the body, and drug doses need to be precise to achieve optimal outcomes while avoiding side effects.
To inform dosing decisions, doctors have relied primarily on their clinical experience—their knowledge of the medications they are prescribing, and the paper-based dosing recommendations from drug manufacturers and regulatory agencies. However, these recommendations are often imprecise since they draw from clinical studies that may not accurately reflect an individual patient’s response to the medication. As a result, there is an upper limit to the precision with which medication can be dosed using these traditional methods.
Why is artificial intelligence a good solution for drug dosing?
Today, the most compelling approach to solving this important problem is with the application of artificial intelligence to enable precision dosing.
Precision dosing is an umbrella term that refers to the process of transforming a “one-size-fits-all” therapeutic approach into one that is targeted, based on an individual patient’s demonstrated response to medication. It has been identified as a way to maximize therapeutic safety and efficacy with significant potential benefits for patients and health care providers, and AI-powered solutions have so far proven to be among the most powerful tools to actualize precision dosing.
In 2008, Dr. Donald M. Berwick, former Administrator of the Centers for Medicare and Medicaid Services, articulated the triple aim of the US health care system: to improve the experience of care, improve the health of populations, and reduce the per capita costs of health care. An October 2020 report by KLAS and the Center for Connected Medicine found artificial intelligence to be one of the most promising emerging health care technologies for clinical decision support to help the U.S. health care system achieve these aims.
How does AI-powered dosing perform at scale?
Despite significant promise, precision dosing applications have tended to be difficult to scale due to factors that make precision dosing challenging to generalize while maintaining efficacy. In fact, even some of the highest-profile precision medicine efforts have had challenges demonstrating efficacy at scale.
Effectively dosing a drug is a multifactorial problem because it is difficult to create a series of rules that comprehensively accounts for all the variables impacting a particular observed response. Past approaches have relied on clinicians’ professional expertise to identify as many of those variables as possible, but not even the most skilled clinicians can incorporate all relevant factors.
Without technology, decisions are made based on associative reasoning driven by the clinician’s training and interaction with the data, a mix of opinion, training, and experience. The quality of these factors varies enormously among individuals—a prescriber may be tired, distracted, or pressed for time—creating increased variability in how decisions are made.
On the other hand, an AI-powered algorithm is very consistent and primarily considers two factors in the decision-making process: the input and the outcome. Once an effective control algorithm is defined to achieve a certain outcome, it will consistently drive towards that outcome. By simulating the best of human intelligence with a mathematical algorithm, the results are consistent regardless of environmental factors.
What factors have come together to make the timing right?
As I mentioned earlier, several factors have come together to create the necessary conditions to begin realizing the potential for AI-powered precision dosing:
- Technological advancements in computing allow us to process large, complex datasets quickly, making AI solutions practical.
- Public familiarity with artificial intelligence as an effective tool for solving complex problems makes physicians comfortable incorporating such tools in clinical settings.
- Reliable data is now available in electronic medical records and is standardized in a manner that is much more ingestible by algorithms as compared to free-form paper medical records.
- Big data analytics techniques have also made applying artificial intelligence and control algorithms to complex datasets much more practical and efficient. Data is available from millions of patients to design and test algorithms in silico to predict effectiveness and iterate quickly. This is a vast improvement on expert systems that are based on a clinician’s smaller number of patients, only in the thousands or hundreds, that are usually only possible to test in much more costly and risky clinical trials.
- Increasingly complex and powerful drugs have been developed that impact basic physiologic processes. Drugs that impact multiple physiologic processes and have a narrow therapeutic window (the “sweet spot” between toxicity and ineffective therapy) have become more prevalent. These are the types of drugs for which AI-powered drug dosing can provide the most benefit.
Why is AI-powered dosing important to chronic anemia?
There are more than 550,000 dialysis patients suffering from End-Stage Kidney Disease (ESKD) in the United States. Dialysis patients are at high risk for adverse outcomes, have complex medical problems, and are typically receiving multiple medications that can interact with one another. As a result, they need innovative approaches to manage the medications that they receive.
Almost 90% of dialysis patients experience chronic anemia and are treated with Erythropoiesis Stimulating Agents (ESAs). However, exposure to high doses of ESAs is associated with an increase in adverse cardiovascular events, so the primary clinical intent is to use the minimum amount of ESA necessary to prevent patients from requiring blood transfusions while avoiding potentially serious or fatal adverse cardiovascular events.
Before Dosis developed Strategic Anemia Advisor, dosing recommendations for ESAs were protocol-based directives implemented in dialysis units as a one-size-fits-many approach. The ability to make dosing highly personalized using AI translates into significantly improved patient outcomes with far less harmful drug exposure for patients and reduced costs for dialysis units, insurance providers, and the US health care system.
Is there a future for AI-powered dosing? Can you give us an idea of what’s next?
Artificial intelligence is a valuable tool that can enhance a physician’s ability to practice and make the best judgements possible, improving the cost of care and the quality of care itself, and AI-powered precision dosing will likely be the standard of care for chronic disease management in the future.
Dosing anemia drugs is only one specific example of the impact that AI can have on medication prescribing. Dosis has already begun a trial of an AI-based intravenous iron dosing protocol, as an adjunct to Strategic Anemia Advisor. In addition, Dosis has developed a tool that informs the simultaneous dosing of three different types of medication that are used to manage mineral and bone disorder, a common comorbidity in kidney disease patients. This application will be the first of its kind, modeling three interdependent biological variables and three medications simultaneously that impact these values to return them to normal levels.
In 10 years, AI-driven dosing models will likely be the standard of care across the health care spectrum, used for a wide variety of drugs like warfarin, insulin, and immunosuppressives. Any drug that is administered chronically and has a narrow therapeutic range is a good candidate for AI-driven dosing.
Once AI for precision drug dosing is widely adopted, it will be extremely unlikely for the industry to revert to previous dosing methods. In addition, as more tools are developed and more opportunities to use those tools are identified, there will be exponential growth in the use of AI to drive therapies.
Dr George Aronoff is Chief Medical Officer of Dosis, Inc and co-inventor of Strategic Anemia Advisor, now commercialized and made available for use by Dosis Inc. He has over 30 years of experience in nephrology. He was previously Chief of Nephrology & Hypertension at the University of Louisville, where his research with Drs. Brier and Gaweda focused on using AI to dose ESAs in dialysis patients. He received his M.S. in Pharmacology and M.D. from Indiana University.