Pharma faces an existential crisis. The cost and time lines of developing new medications have been growing exponentially for decades, with no end in sight. Could modeling and simulation approaches be the Next Great Hope for ending this madness and restoring sustainability to drug development? At the same time, can it deliver on the promise of making the dream of personalized medicine a reality? In this blog post, I’ll discuss how modeling and simulation (M&S) is changing drug development and some of the challenges the pharmacometrics community must overcome to make the greatest improvements in treatments for patients.
The shifting climate of drug development
The traditional approach to treating patients was the concept that “one dose fits all.” However, in many cases —pediatrics, organ impairment, obesity, and genetic variation— the use of doses for the “average patient” can cause significant safety and efficacy issues. How can we reduce the cost of healthcare while developing innovative treatments that help patients improve their quality of life?
The rise of personalized medicine is one of the biggest changes in our approach to healthcare in the last several decades. This paradigm uses genomic and proteomic information to predict patients’ medical risks, detect the presence of disease earlier, and manage their healthcare better. It’s also turning out to be an economic force to be reckoned with. Indeed, a report issued by PricewaterhouseCoopers valued this market in the United States alone at $232 billion in 2014 with 11% annual growth.
In an attempt to rein in the spiraling cost of clinical trials, many pharmaceutical companies have set up M&S departments to help optimize the design of clinical trials. As a result, the use of M&S, also known as biosimulation, in drug development has been steadily increasing over the past 20 years. The emerging discipline of M&S draws together diverse scientific domains including biomathematics, computer science, pharmacometrics, biostatistics, pharmacology, system theories, systems engineering, software engineering, artificial intelligence, and more. The diversity of this new discipline sometimes results in the challenge that people of different backgrounds do not share a common vocabulary in which to share ideas. To truly realize the potential of M&S will require building bridges between these different disciplines so that they can work together efficiently.
Personalized medicine and pharmacokinetic modeling and simulation
The emphasis on personalized medicine is a significant trend in pharmacokinetic modeling and simulation in drug development. I recently searched PubMed for the number of publications that used the keywords “Personalized Medicine” and/or “Pharmacokinetic.” In the first half of this year alone, the number of publications have already increased by over 50% from the previous year.
Further evidence for this trend is found in the increasing number of talks given at scientific meetings that focus on personalized medicine. For example, at this year’s ACOP meeting in Crystal City, Virginia, I had the opportunity to hear a talk by Dr. Yaning Wang, the Deputy Director in the Division of Pharmacometrics in the Office of Clinical Pharmacology at the US FDA. His talk focused on how modeling and simulation is impacting the issue of addressing specific patient subpopulations in regulatory submissions. I heard him elaborate on this talk at his presentation “Pharmacometrics in the Era of Personalized Medicine” at the International Symposium on Quantitative Pharmacology (ISQP) this past November in Shanghai, China. In both talks, Dr. Wang clearly indicated that regulatory agencies such as the FDA are considering clinical recommendations for treating patient subgroups in regulatory submissions.
Regulatory, clinical, and patient considerations
While the prospect of truly personalized medicine that is tailored to the individual patient seems to be coming into reach, making this dream a reality will require answering several key questions. How will regulatory agencies approve drug labels that are tailored to individuals based on a patient’s individual genetic, physiological, and environmental characteristics? In theory, individualized drug labels could lead to a practically infinite number of drug labels. How would the FDA even review and approve these labels? In the future, I could envision that personalized drug labels are in an electronic format that clinicians can transmit via smart phone. In the era of personalized medicine, how would clinicians match individual treatment plans to patients? And how would patients manage these new customized treatment plans? What would a patient do if he misses a dose or takes the dose at the wrong time? Would he take an extra dose to compensate for missing the dose? Building a technological infrastructure that supports personalized medicine will be a critical step in delivering precision medicine to patients.
In addition to my scientific interest in precision medicine, I also have personal ties to this issue. One of my family members has had hypertension for many years. In the future, perhaps she will be able to use a smart phone app that tells her when to take her medication and what dose she should take. In August, the FDA approved the first 3D-printed drug for epilepsy. Indeed, the smart phone app could potentially be coupled to 3D printing technology to enable real-time printing of pills with the optimal dose.
This smart phone app would also be able to advise her on what to do when she’s missed a dose or taken a short drug holiday. My relative was very interested in this application because she said that when she forgets to take her medication, she gets confused as what to do. Or, she would measure her blood pressure and get a high reading and would again be confused on what to do. New technologies such as smart phone apps and 3D printing may be important tools in improving the safety and efficacy of treatments for patients and their quality of life.
Leveraging biosimulation technology will help lower costs for pharmaceutical sponsors, clinicians, payers, and patients. Whether you are trying to determine first-in-human dosing or individualized dosing regimens, all M&S tools can share the same core computational engines. These engines can then be linked to different applications such as a desktop app, web app, smart phone app, etc. Likewise, the cost of drug development can be optimized using the same core engines with pharmacoeconomic techniques. Finally, the drug label description of clinical usage can be optimized with the same core engines using Bayesian optimization techniques, etc. Leveraging this integrated technology from the beginning of drug development all the way through patient care will reduce costs by streamlining the transitions between different phases of drug development and the transition from drug development to patient care.
Some progress in using pharmacometric models to provide insight into medication adherence
A good example of how M&S can provide insight into the drug development process is found in this recent paper in the Journal of Pharmacokinetics and Pharmacodynamics, “Development and application of an aggregate adherence metric derived from population pharmacokinetics to inform clinical trial enrichment.” The authors used a novel “reverse” application of a population PK (POPPK) model to assess the adherence of psychiatric patients to a commonly prescribed anti-psychotic medication. In a standard POPPK analysis, drug doses are given to patients and plasma sampling over time is used to build the POPPK model. In this paper, the authors used historical clinical trial data to build a PK model. Then, they conducted sparse plasma sampling from an independent population and used model outputs to generate a metric of observed vs. expected drug exposures.
Nonadherence to prescribed medications is a common and significant barrier to effective treatment. It is also very difficult for clinicians to detect using more traditional metrics such as subjective questionnaires. Critical decision making is hindered by having to rely on these subjective interpretations. Thus, quantitative, objective measures of systemic exposure yielded from pharmacometric models can help shed light on medication adherence. This novel use of pharmacometric analysis demonstrates one of the many ways that biosimulation technology is transforming the pharmaceutical industry.
All information presented derive from public source materials.