Population pharmacokinetics (popPK) is the study of variability in drug concentrations within a patient population receiving clinically relevant doses of a drug of interest. PopPK methods use mathematical models to describe PK data and draw conclusions. PopPK models can help guide decision-making across all phases of drug development and can also provide critical support for efficacy and safety in marketing applications such as new drug applications (NDAs) and biologics license applications (BLAs). Not surprisingly, regulatory emphasis on popPK modeling continues to increase. Former FDA Commissioner, Dr. Scott Gottlieb, noted that almost 100% of all NDAs for new molecular entities (NMEs) include aspects of modeling and simulation. More than ever, both regulators and drug developers are realizing the power and utility of popPK modeling. So, let’s take a deeper look at what we mean by “population pharmacokinetics.”
Individual PK vs. Population PK
To understand popPK, it is important first to address pharmacokinetics (PK) as a discipline. In the simplest terms, PK is the study of what the body does to a drug. This means how the drug is absorbed, distributed, metabolized, and eliminated (ADME) from the body. These PK characteristics are determined by analyzing drug and metabolite concentrations over time in an appropriate matrix, typically plasma or blood. Using drug concentration data and the sampling schedule, a pharmacokineticist can determine how drug concentration changes with time and can calculate a number of key PK parameters. Different analysis approaches (e.g., noncompartmental versus compartmental analysis) can be used, but the end goal of any of these approaches is to determine what the body does to the drug. Now that we have defined PK, we are one step closer to a functional concept of popPK. For this, we need to think about a few key elements including timing, the purpose of the analysis, and the richness of subject-level data. Consider the following questions:
- Do you need to make quick but informed dosing decisions based on interim PK data between cohorts in a single ascending dose (SAD) or a multiple ascending dose (MAD) trial?
- Do you want detailed, subject-level PK profiles and calculated mean PK parameters within a single study?
- Do you want to understand potential sources of PK variability with the option to look across multiple studies?
- Are you interested in the influence of PK on pharmacodynamics (PD) (i.e., how the drug affects the body)?
- Did you (or will you) collect many samples from each subject (rich sampling) or only a few (sparse sampling)?
Individual PK approaches are the best to use when rapid turnaround of PK parameters is needed, or when you want to define complete individual PK profiles. These analyses require rich concentration-time data and typically employ noncompartmental methods. Noncompartmental PK analysis (NCA) is generally performed in early phase clinical pharmacology studies, although it can be useful across all phases of drug development.
In contrast to individual PK analysis, popPK modeling approaches rely on concentration-time data from multiple individuals and often utilize pooled data from more than one study. While popPK methods can be, and often are, applied to rich data, the real value of popPK lies in its ability to analyze clinical data collected in a setting where rich data are not practical, such as in Phase 2 and 3 trials. PopPK tends to use complex mathematical and compartmental methods to reach conclusions. Because of the technical complexities of popPK model development and optimization, popPK approaches typically require more time and effort compared to NCA. However, popPK methods also incorporate more than just concentration‑time data. By integrating covariate information (e.g., age, sex, weight, race, renal/hepatic function, concomitant medications), a popPK model can be used to explain sources of PK variability within a population. Understanding the sources of PK variability is vitally important because drug safety and efficacy can vary with changes in PK. Using the information provided by the popPK model, appropriate dosages can be selected for a given population or subgroup.
Population PK in Drug Development
PopPK modeling and simulation is a type of model-informed drug development (MIDD) that can help drive decision-making at all stages of drug development. Some applications of popPK models include:
- Allometric Scaling
- Clinical Trial Simulations
- Concentration-QT (C-QT)
- In Vitro In Vivo Correlation (IVIVC)
- Model Based Bioequivalence
Allometric Scaling: In allometric scaling, popPK models can be used to predict PK across species or across populations. For example, popPK models can be used across nonclinical species to help select doses for First-in-Human (FIH) studies or across populations by extrapolating PK data from adults to pediatrics. Exposure-Response: If there is a correlation between a safety or efficacy endpoint and drug exposure, then there is an exposure-response relationship. To determine the exposure-response relationship, popPK models can be used in combination with PD data to support evidence of safety and efficacy. Clinical Trial Simulations: Modeling and simulation with popPK can drive study design decisions by assessing the impact of variability on sample size, comparing the probability of success with various trial designs, determining optimal PK sampling schedules, and evaluating long‑term study scenarios. Clinical trial simulations are also used to optimize studies with adaptive designs. Concentration-QT: In C-QT analysis, a wide range of doses are used to characterize the potential for drug exposure to influence the QT interval (a measurement related to the electrical properties of the heart). C-QT analysis can sometimes serve as an alternative to conducting a standalone thorough QT study. In Vitro In Vivo Correlation: IVIVC is a predictive mathematical model that describes the relationship between an in vitro property of a dosage form and a relevant in vivo response. IVIVC models can be used as a surrogate for bioequivalence studies for formulation changes and to set dissolution specifications. Model Based Bioequivalence: In selected scenarios, when dense PK sampling is not feasible (e.g., pediatric studies or oncology studies), model based bioequivalence can be substituted for the traditional noncompartmental approach to bioequivalence. When this is done, attention must be paid to preserving the statistical rigor of the approach.
Population PK analyses are a crucial aspect of almost all NDAs and BLAs. They provide an integrated assessment of PK across studies and aim to explain variability in PK due to intrinsic and extrinsic factors. Understanding sources of variability in PK is important to guide optimal dosing in subpopulations and to support critical drug development decisions. Because of the growing emphasis placed on popPK analyses by regulatory authorities, not to mention the wealth of information that these analyses provide, it is more important than ever to consider how popPK fits into your own drug development program. Allucent has an expert team of pharmacometricians who build models tailored to guide your drug development program’s decisions. Contact us today to find out how Allucent’s popPK experts can help support your program’s modeling needs.