Understanding Flip-Flop Pharmacokinetics: Challenges and Strategies in Drug Development

Pharmacokinetics (PK) modeling has been a useful tool thorough from pre-clinic to clinical (from Phase 1 to 3) in drug development, clinical research, and therapeutic optimization for centuries. The PK model used quantitative parameters to describe the complex absorption – distribution – metabolism – elimination (ADME) in the body.

However, the presence of flip-flop pharmacokinetics introduces complexities that can challenge traditional modeling approaches, potentially leading to inaccurate predictions and suboptimal therapeutic outcomes. Identifying, understanding and developing suitable strategies for handling these dynamics in the study design as well as during the PK modeling is a challenging but interesting journey for many pharmacokinetic scientists.

Understanding Flip-flop Pharmacokinetics

The term flip-flop pharmacokinetics is often defined as the phenomenon when the absorption rate constants (ka) is less than the elimination rate constants (k). This understanding is not wrong but somewhat misleading that the root cause of the flip-flop is mainly due to low/limited absorption. To be more accurate, the flip-flop pharmacokinetics should be defined as the scenario where the rate constants for multiexponential models appear to be switched, i.e. the absorption rate of a drug differs from its elimination rate, leading to fluctuating concentrations over time.

This can manifest in two main scenarios:

• The “flip” – delayed/limited absorption and normal/rapid elimination: Some drugs exhibit delayed or limited absorption, resulting in a slower rise in plasma concentrations after administration. However, once absorbed, these drugs are eliminated from the body rapidly, causing concentrations to decline quickly after reaching peak levels. The elimination phase of the drug profile reflects the input ka, rather than the actual output k.
• The “flop” – normal/rapid absorption and prolonged/limited elimination: Conversely, certain drugs may be rapidly absorbed into the bloodstream, leading to a rapid increase in plasma concentrations. However, these drugs may have a prolonged or limited elimination phase, resulting in a slow decline in concentrations over time, i.e. much higher k.  

Generally, there is only the “flip” or the “flop” occurring but not both, but in specific cases when a drug has a limited absorption from the gastrointestinal track and significant differences in elimination rate between sub-population, they can appear for the same drug. Metformin is hypothesized to demonstrate such behavior where it shows saturated absorption at high dose and the elimination rate depends strongly on the renal functions. Subjects with normal renal function have the ka < k, while subjects with impaired renal function may exhibit ka > k.

More interestingly, flip-flop kinetics can also be found during individual parameter estimation during the population PK modeling, where the calculated parameters may seem switched between the different models with identical prediction characteristics.

Why and How?

Flip-flop pharmacokinetics mostly occurred with extravascular drugs which can be observed in both animals and human. There are many contribution factors that result in this phenomenon, which can be divided into 4 major categories.

• Drug Formulation and Route of Administration: Differences in drug formulation, such as particle size, solubility, and excipients, can influence absorption rates. Such characteristics might appear in the unfavorable nature of the active ingredients/excipients (i.e. carvedilol) or an intended formulation manipulation (i.e. prolong-, controlled-release formulation). Along with the formulation, different route of administration will also impact not only the dissolution but permeability rate of the drug.
• Physiological Factors: Variations in gastrointestinal physiology, including gastric pH, transit time, and enzyme activity, can affect drug absorption rates. For example, metformin which has been known for dose-responsive absorption via gastrointestinal transporter protein that is saturated with dose higher than 1000 mg. Additionally, as may drugs have the metabolism and elimination process primarily depend on the renal and hepatic function. Impaired subjects might results in significantly different k comparing to the regular subjects.
• Dosage and Dosing Regimen: The total administration dose as well as the timing and frequency of drug administration can impact plasma concentrations and the extent of flip-flop kinetics, especially for drug with dose-concentration dependence. Variations in dosing intervals may alter drug absorption and elimination profiles.
• Flip–flop in population PK modeling: occurring in the estimate process of the individual PK parameters.

Implications in Study Design

When flip-flop pharmacokinetics occur, the terminal phase is generally prolonged. A longer duration of sampling may be necessary in order to avoid overestimation of fraction of dose absorbed and how long should you sampling could be a tricky question, especially when developing a new controlled-release formulation (CR) and flip-flop kinetics is expected. This will also critically impact the PK parameters estimation. Prolonged half-life should also be considered for washout duration and post-study restrictions.

In case there is limited data on the human PK, adaptive trial designs that incorporate real-time PK data analysis may allow for dynamic adjustments based on emerging pharmacokinetic insights, including flip-flop kinetics, especially for the Phase 1 clinical trial.

Implications in Pharmacokinetics Modeling

Challenges in Modeling Flip-flop Pharmacokinetics

Fail to identify and address the presence of flip-flop kinetics could lead to false pharmacokinetics interpretation and translation.

1. Parameter Estimation and Model Fitting: Traditional compartmental PK models may struggle to accurately capture the nonlinear absorption and elimination kinetics observed with flip-flop drugs. Parameter estimation techniques must account for the time-dependent changes in drug concentrations to avoid underestimating or overestimating key PK parameters. Truncated AUC should not be used in replacement for AUCt and AUCinf to evaluate the exposure of the product.
2. Prediction of Drug Behavior: when flip-flop kinetics are involved, simple extrapolation based on PK parameters could lead to false interpretation of drug behavior, including peak concentrations, time to reach steady-state, and effective half-life. This further results in suboptimal dosing regimens and compromised therapeutic outcomes.
3. Clinical Translation: Translating PK model predictions into clinical practice requires robust and validated models that accurately reflect the complex dynamics of flip-flop pharmacokinetics. Models must account for interindividual variability, dosing variability, and potential drug-drug interactions that may influence pharmacokinetic profiles. This is even more important when using the PK model to translate the PK data from pre-clinic to first-in-human study and phase 1 study, as the flip-flop phenomenon are more commonly observed in animal study due to the generally high exposure of drug in animal.  

Strategies for Handling Flip-flop Pharmacokinetics in PK Modeling

1. Mechanistic Modeling: Utilizing physiologically-based pharmacokinetic (PBPK) models or semi-mechanistic models can provide a more comprehensive understanding of drug absorption, distribution, metabolism, and elimination. These models integrate physiological parameters with drug-specific characteristics to simulate complex pharmacokinetic profiles, including the flip-flop kinetics.
2. Population PK Modeling: Incorporating population Pharmacokinetic modeling techniques allows for the analysis of variability in pharmacokinetic parameters across diverse patient populations. Population PK models can account for within-subject and between subject variability, increasing the predictive accuracy of PK simulations. Although the pop PK model itself might introduce the flip-flop situation with individual data, they can be considered a mathematical abstraction and a special case of a local identifiability problem in that it is not just a finite set of parameter values but a partial permutation of the set.
3. Model Validation and Sensitivity Analysis: Rigorous model validation using clinical data and sensitivity analysis to assess the impact of parameter uncertainty are essential steps in ensuring the robustness of PK models. Sensitivity analysis identifies key parameters that influence model predictions and informs strategies for refining model structure and parameter estimation.
4. Clinical Trial Design: Optimizing clinical trial design by integrating PK modeling and simulation facilitates dose selection, regimen optimization, and protocol development.
5. Integration of Real-world Data: Incorporating real-world data from clinical practice, including therapeutic drug monitoring data and pharmacogenomic information, enhances the applicability and predictive power of PK models. Real-world data provide insights into patient-specific factors that influence drug pharmacokinetics and guide personalized treatment strategies.

Why Choose BioPharma Services for Your Next Drug Development Project?

In conclusion, underlying flip-flop pharmacokinetics can result in PK interpretation errors. Understanding the mechanistic underpinnings of flip-flop kinetics, optimizing study design, employing appropriate PK calculation method, and integrating advent PK model with real-world data are essential strategies for navigating the complexities of flip-flop pharmacokinetics.

Engaging a partner such as BioPharma Services for your drug development process ensures that you are working with professionals who are well-versed in these challenges. We have a team with comprehensive understanding of the mathematical and physiological attributes of flip-flop pharmacokinetics that can aid in the avoidance of pharmacokinetic parameter interpretation errors and equip you with scientific advice that benefit for your coming phase 1 clinical study and your drug development plan.

Sunny Le

Director, Pharmacokinetics

BioPharma Services, Inc., a HEALWELL AI and clinical trial services company, is a full-service Contract Clinical Research Organization (CRO) based in Toronto, Canada, specializing in Phase 1 clinical trials 1/2a, Human Abuse Liability(HAL) and Bioequivalence clinical trials for international pharmaceutical companies worldwide. BioPharma Services conducts clinical research operations from its Canadian facility, with access to healthy volunteers and special populations.