Understanding FDA’s Perspective on Precision Dosing


Continued embrace of precision dosing will reduce costs and optimize clinical outcomes.

Yaning Wang, PhD

Yaning Wang, PhD

Sirj Goswami, PhD

Sirj Goswami, PhD

As the shift to value-based care drives increased interest in improving clinical outcomes through individualized care, precision medicine will take center stage, permanently impacting the way drugs are administered to patients. To understand how we got here, it’s important to examine the role of clinical pharmacology and its impact on drug approval and real-world dosing in patients.

The key pillars of clinical pharmacology, pharmacokinetics (PK), and pharmacodynamics (PD), play a pivotal role in precision medicine as it allows us to study drug response variability and determine dosing strategies at both the patient and population level. PK can be defined as the study of how the body processes a drug via the mechanisms of absorption, distribution, metabolism, and excretion. Conversely, PD can be defined as the study of the effects of the drug within the human body as it is processed.

Clinical pharmacology’s role in drug approval

Pharmaceutical companies leverage their understanding of pharmacokinetics and pharmacodynamics to assess drug efficacy, ensure patient safety, and ultimately determine the dosing strategy that goes into the drug label. As part of its regulatory oversight of the pharmaceutical industry, FDA’s Office of Clinical Pharmacology also applies clinical pharmacology principles to validate the efficacy and safety of a drug label’s dosing.

In the early years, FDA’s drug evaluation process focused almost exclusively on pharmacokinetic data. FDA used PK-matching principles—which assume a steady concentration/response relationship—to adjust a drug’s dosage for adult or pediatric populations using criteria such as body weight. However, FDA realized that PK data alone was insufficient. After all, patients who have achieved the same drug concentration levels can still experience varying effects based on their individual disease severity, genetics, or other characteristics.

In recent years, FDA started to link drug concentration to the patient response through PD data. FDA now relies on exposure-response (ER) data as the foundation for its work validating dose justification for the general population, as well as for those with special conditions.

Our understanding of exposure-response is a prerequisite to tailored dosing strategies in the drug label. To reduce patient harm, the approved dose must balance the risk and benefits for each patient. For example, the final approved label dose for one drug designed to reduce the risk of stroke, edoxaban, is individualized based on the patient’s current renal function for patients with mild to moderate renal impairment. For patients with severe renal impairment, it is considered too hazardous to administer.

Some commonly used drugs, such as warfarin, have product label dosage information that is predicated on both PK and PD data. In some instances, a drug’s sponsor requests the removal of an indication for a certain subpopulation based on its own retrospective subset analysis of clinical trial data. For example, a recent oncology drug, cetuximab, now recommends dose individualization based on genetic factors and the type of tumor mutations.

Precision dosing at the bedside

Following drug approval, clinicians may also attempt to leverage PK and PD knowledge to dose-adjust for an individual patient. Precision dosing at the bedside means that a drug’s dosage can be adjusted to patient-specific characteristics—such as body weight, age, gender, and genetics—as well as extrinsic patient-specific factors, such as drug interactions, diet, and behavior.

Surprisingly enough, the first model-informed precision dosing (MIPD) tool using both PK and PD data was developed in 1969, to ascertain the optimal dosing for patients on anticoagulation therapy. Over the past several decades, technological advances such as the ubiquity of EHRs, increased data accessibility, and the emergence of cloud-based infrastructure have enabled adoption of MIPD at scale.

Precision dosing can be implemented as a continuous learning cycle to update our understanding of PK and PD in subpopulations and improve the predictive accuracy of underlying models and algorithms. Practically speaking, clinicians may use precision dosing tools through an EHR-integrated application to determine optimal dosage regimens for their patients.

Over time, patient data can be aggregated and stored in a database and subsequently analyzed to provide new insights into a drug’s behavior in patient subpopulations that are not typically studied by drug manufacturers. By implementing precision dosing as a continuous learning cycle informed by analytics and model updates, we will be able to expand our cumulative clinical knowledge of the drug and democratize such knowledge for providers.

FDA’s guidance on overcoming precision dosing challenges

In 2019, FDA invited hospital representatives, software developers, and practicing precision-dosing physicians to a working session on how to best promote the widespread adoption of precision dosing. They discussed a variety of challenges to implementing an evidence-based dosing regimen in hospital or clinic setting, from current drug labeling practices to incomplete clinical trial populations, insufficient predictive models for major drug clearance mechanisms, and an overall lack of real-world data.

In two subsequent papers that resulted from the session, researchers and FDA scientists offered a prescription for catalyzing industry change around individualized medicine.

First, drug labels should be more dynamic, and should use broader language to encourage the use of exposure-response data within the studied range of safe, effective doses in the clinic. By enrolling more local patients and including adequate measures to characterize the patient, disease, and drug, global clinical trials can differentiate responders from non-responders and connect the trial results to local patient population characteristics. This will help improve the approval process and promote precise labeling of drugs.

Secondly, to combat the lack of data to inform precision dosing approaches, pharmaceutical companies should be encouraged to use multiple drug doses or titration-based dosing during clinical trials, as well as to enroll understudied and more diverse populations. FDA’s recent initiative in oncology with Project Optimus is a reflection of this broader trend. The goal of Project Optimus is to educate, innovate, and collaborate with companies, academia, professional societies, international regulatory authorities, and patients to move forward with a dose-finding and dose optimization paradigm across oncology that emphasizes selection of a dose or doses that maximizes not only the efficacy of a drug, but also its safety and tolerability as well.

To broaden our understanding of drug response, real-world data sources can also provide adequate safety and efficacy information to help researchers predict precise doses for certain patient subgroups not represented in trial data, such as children and pregnant women. FDA has already created a repository of deidentified subject-level data to help the industry advance its understanding of drug behavior in different patient sub-populations. In addition, researchers recommended that the FDA and health plans should assess and share known phase III trial-RWD drug efficacy-safety gaps for major diseases and treatments.

To increase industry support for precision dosing approaches, healthcare leaders also proposed increasing training on quantitative modeling and simulations to show clinicians and pharmacists how precision dosing improves patient outcomes. Precision dosing at the bedside can also be incentivized with increased reimbursement, particularly under value-based models that reward the provision of high-value, individualized care. In the meantime, FDA and pharmaceutical companies should develop better dosing for relevant older drugs—as well as newer drugs—and translate this knowledge to prescribers and patients.

FDA and industry leaders also proposed solutions for how to address the industry-wide lack of clarity regarding regulatory pathways for clinical decision support (CDS) tools and encouraged software companies to design drug dosing CDS systems to support the needs of patients, physicians, and prescribers.

There is no question that quantitative approaches such as pharmacometrics and machine learning techniques will serve as the quantitative basis for personalized drug dosing, although many questions still remain as to how these approaches will be incorporated into the bedside and in clinical trials. As we usher in this new era of drug development—the age of individualized medicine—many researchers are calling on clinical specialist societies to create a high-priority drug-disease target list to highlight when precision dosing is likely to result in improved patient outcomes.

One thing is certain: we can be sure that individualized medicine is here to stay. Over time, the embrace of precision dosing will reduce costs, empower clinicians, and optimize drug delivery for every population segment, helping patients to realize the best possible clinical outcomes.

Yaning Wang, PhD, CEO, Createrna Science and Technology, former Director of FDA’s Division of Pharmacometrics, and Sirj Goswami, PhD, CEO, co-founder of InsightRX

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