When predicting a drug’s fate and interactions in the body, the most commonly applied metric is its concentration in the circulation to estimate systemic exposure and deduce downstream mechanisms such as tissue-specific permeability and elimination processes. The systemic cmax, or – if available – the unbound fraction (plasma fu) is also applied as a reference when selecting concentration ranges to test in various in vivo and in vitro interactions, as only the unbound fraction (fu) of a compound can interact with transport proteins and metabolic enzymes. This approach, however, does not take into account the possible accumulation of the drug into specific organs, where, consequently, the local intracellular drug concentrations may exceed its plasma levels even by magnitudes. Determination of tissue-specific unbound blood-to-organ partitioning coefficients – Kp,uu values can provide a more complex understanding of a compound’s systemic distribution and offer a more realistic basis for predicting (or explaining) downstream effects.
While Kp,uu values may be established for multiple organs of interest, hepatic partitioning is one of the most commonly studied aspects due to the central role of the liver in drug disposition and excretion, including its function in first-pass metabolism, in the overall metabolic and biliary clearance processes but also as a target organ for multiple indications. Hepatic Kp,uu of a compound is influenced by multiple factors and processes, including its active and/or passive transport mechanisms, its metabolism, binding to various proteins and components, and the cell’s membrane potential and pH gradient. In case a compound is subject to active sinusoidal uptake transport, this often becomes the rate determining step in its hepatic disposition and leads to a difference in compound plasma and intracellular unbound concentrations (Kp,uu ≠ 1). This also affects the rate of “downstream” hepatic processes, including metabolism and biliary excretion, so ultimately, is also a determining factor in the rate of hepatic overall clearance as well.
Figure 1. Mechanisms of drug disposition in hepatocytes which significantly affect unbound cellular concentration and thus Kp,uu. Schema modified from reference .
The Extended Clearance Model (ECM) serves as a reference to classify drugs into four classes (Figure 2) based on their in vitro determined clearance mechanisms  which can be used for predicting the rate determining step of their elimination. Hepatic overall clearance of a compound is the outcome of a complex interplay between sinusoidal uptake, metabolism, canalicular secretion and sinusoidal efflux (Figure 1), which are taken into account (directly or indirectly) when determining a compound’s ECM class .
As part of the classification, ECM also provides indirect estimates of unbound intrahepatic drug concentration i.e., unbound liver-to-blood partition coefficient (Kp,uu) , described using the below equation that also summarizes the main processes that influence hepatic partitioning :
As illustrated above, Kp,uu can be described using the extended clearance equation, which came from the ECM approach of hepatic clearance determination, where CLint,pass is passive intrinsic clearance, CLint,uptake is intrinsic uptake clearance, CLint,met is intrinsic metabolic clearance and CLint,efflux is intrinsic efflux clearance.
Most important mechanisms driving the Kp,uu proposed to be the below:
The impact of these processes can be deduced from the Kp,uu and subsequent ECM classification of a compound, based on which rate limiting processes for clearance proposed for each class - as illustrated on Figure 2 with relation to the compound’s clearance characteristics [2, 4].
Figure 2. Pre-requisites and rate-determining hepatic clearance processes based on the ECM model. Assumptions: PSeff,pas = PSinf,pas, and PSeff,act = 0. Figure based on references  and .
While in its execution, a Kp,uu determination experiment is largely similar, to the standard hepatocyte uptake measurements, the additional sampling of the supernatant for Kp calculation and fu determination allows the generation of a more complex dataset that allows estimation of several additional processes driving the compound’s pharmacokinetics.
Depending on compound Kp,uu < > = 1, the most likely rate limiting steps and clearance mechanisms can be predicted, which can inform next steps for pharmacokinetic and toxicity investigations further down the pipeline.
Assay description and technical considerations
Similar to our “standard” hepatocyte uptake assay, primary (human) hepatocytes seeded to collagen-coated plates are used. Determination of Kp,uu requires steady-state incubation with the substrate/test article (TA) and sampling of supernatant before washing the cells. Appropriate attachment of the cells allows for easy wash-off of substrate molecules not associated with the cells with the supernatant, while molecules subject to active transport remained trapped inside the cells.
Hepatocytes are then lysed, and uptake of the test article is quantified using adequate methods – for example by LC/MS, or depending on its potential labeling, fluorescent detection, or liquid scintillation. The same method would be applied to determine compound concentrations in the suprenatant as well. As previously described, hepatic Kp,uu is modelled in vitro as the ratio of cell-to-media concentrations, based on test compound quantification in the cell lysates and supernatant, Kp is determined using the C/M (intracellular/medium concentration ratio), while Kp,uu is calculated as: Kp,uu = Kp x fu.
Unbound fraction (fu) values were determined at 4°C, where compound permeation into the cells is considered to be driven by passive permeability only, and it is assumed that the unbound concentrations are equal in the medium and in the cells, with PSinf = PSeff at 4°C.
The assay was optimized and characterized in steady-state using a selection of relevant ECM Class 1-4 compounds to obtain a robust signal and a maximal signal-to-background ratio and to ensure accurate prediction of C/M, Kp,uu and fu values. Kp, fu and Kp,uu values were determined for Rosuvastatin (RSV), Pitavastatin (PTV), Atorvastatin (ATV), Fluvastatin (FLV), Pravastatin (PRV), Lovastatin, Simvastatin, Verapamil, Ketoconazole, and Midazolam, a selection of compounds with different plasma-binding from distinct classes of ECM groups and thus with different expected Kp.uu values (ECM class 1-2: Kp.uu ~ 1, ECM class 3-4: Kp.uu > 1), and results were compared to literature data where available. We identified the relative order of fu values as FLV ≈ PTV < ATV < RSV < PRV which, as well as their respective Kp,uu values (Figure 3), correlated well with literature findings, confirming that our assay system is suitable for reliable in vitro Kp,uu prediction. For further information, consult our poster on the Kp,uu assay development and characterisation.
Figure 3. Kp,uu, and fu values obtained with radiolabeled ([3H] LS) and bioanalytical (LC-MS) methods compared to literature data. Results are obtained at 60 minutes incubation at 37 °C with the compounds RSV and PTV. Figures show the average of three independent measurements ± SD.
Based on these initial results, as positive controls compounds were also selected that are run in parallel with unknown test articles to ensure assay functionality. When selecting controls as well as interpreting results, potential compound metabolism by hepatic enzymes, especially CYP450s also needs to be considered. In case the test article is expected to undergo CYP450-mediated hepatic metabolism, its intracellular accumulation may be masked by the metabolic depletion of the intracellular compound pool. For such compounds, addition of ABT (1-aminobenztriazole), a pan-CYP450 inhibitor, to allows for addressing hepatic partitioning and Kp,uu calculation without compound loss to metabolism, as illustrated by results obtained with Midazolam (Figure 4).
Figure 4. Comparison of time-dependent changes in extra-, and intracellular concentration of Midazolam in the presence or absence of ABT (1-aminobenztriazole), a pan-CYP450 inhibitor, and of its metabolite OH-midazolam. Figures show the average of two independent measurements ± SD.
In line with these considerations and our assay characterisation results, Rosuvastatin was selected as recommended positive control compound when testing compounds with no considerable CYP450-driven metabolism. For metabolized compounds, the assay is conducted with added ABT in the assay medium to inhibit compound loss due to CYP450 activity, and the positive control applied in the assay is Midazolam. Figure 5 summarizes the main considerations for the choice of the appropriate assay conditions and controls in the form of a decision tree. As, however, each compound and project is different, our scientists and expert team is at your disposal to discuss which setup could work best for you to ensure the obtained output is meaningful and can be interpreted in the context of your existing dataset.
Figure 5. Decision tree for choosing setup and positive control (PC) for the assay based on the properties of the test article (TA).
Webinar (2023): Evolving Approaches on Measurements and Applications of Intracellular Free Drug Concentration and Kp,uu in Drug Discovery – presented by DI, Li, PhD | Research Fellow, Pfizer Worldwide Research and Development, Groton, CT
 Li Di, Keith Riccardi & David Tess. Evolving approaches on measurements and applications of intracellular free drug concentration and Kp,uu in drug discovery. EXPERT OPINION ON DRUG METABOLISM & TOXICOLOGY 2021, VOL. 17, NO. 7, 733–746
 Riede J, Camenisch G, Huwyler J, Poller B. Current In Vitro Methods to Determine Hepatic Kp,uu: A Comparison of Their Usefulness and Limitations. Journal of Pharmaceutical Sciences 106 (2017) 2805-2814
 Camenisch G, Riede J, Kunze A, Huwyler J, Poller B, Umehara K. The extended clearance model and its use for the interpretation of hepatobiliary elimination data. ADMET & DMPK 3(1) (2015) 1-14