Understanding the pharmacokinetics and target organ dosimetry properties of small molecular drugs and large molecular biologics is essential in the design of proper therapeutic regimens for disease treatments. To improve drug discovery, development and therapeutic designs, multiple pharmacokinetic methods have been developed, including classic pharmacokinetic modelling, allometry scaling, population pharmacokinetic modelling, pharmacokinetic/pharmacodynamic modelling, bioequivalence analysis and population physiologically based pharmacokinetic (PBPK) modelling. Among these methods, the former five are data‐driven empirical approaches, whereas PBPK modelling is unique in being a mechanism‐based approach that describes and predicts the absorption, distribution, metabolism and elimination of the modelled substance by considering the physicochemical properties, the pharmacological and toxicological mechanisms, and the anatomy and physiology of the organism. The advantages of PBPK modelling include the ability to predict target tissue dosimetry of the modelled compound and the robust extrapolation power from in vitro to in vivo, across species (e.g. animal to human), exposure routes, doses and duration. Consequently, PBPK modelling has been applied in a number of areas, including risk assessment of environmental chemicals (Reddy et al. 2005), drug tissue residue estimation and safety assessment of animal‐derived food products (Lin et al. 2016), as well as in the discovery and development of new drugs, biologics and nanoparticle‐based drug formulations (Rowland et al. 2011; Li et al. 2017).
One prerequisite for PBPK model development is the availability of accurate physiological parameters, comprehensive plasma and tissue concentration data for optimization of unknown parameters and for model calibration. PBPK models require lots of physiological parameters and not all of them are available experimentally for a particular animal species, strain or breed. Frequently, most physiological parameters are fixed in the model to parameter values taken from the literature for the same or a relevant species/strain/breed. The use of inaccurate or uncertain physiological parameters could result in flawed models and incorrect simulation results. Thus, the validity and accuracy of physiological parameters are essential for a PBPK model. All physiological parameters used in a PBPK model should be critically evaluated and assumptions made around such values should be clearly stated and scientifically sound, especially when conducting species extrapolation.
In PBPK modelling, it is a common practice that total tissue concentrations representing a lump sum measure of drug in residual plasma, interstitial fluid and cells are used to represent the target site concentration and to correlate with drug efficacy/toxicity. However, different therapeutic molecules have different target sites. For most therapeutic antibodies, the interstitium is the target space, whereas for drugs that target nuclear receptors (e.g. tamoxifen), the intracellular space is the target site. While the importance of simulating drug concentrations in different subcompartments (i.e. residual plasma, interstitial fluid and cells) within an organ is widely recognized, in part due to lack of granular data in individual subcompartments and the technical difficulties of collecting these data, drug concentrations in individual subcompartments are typically predicted based on a PBPK model that was calibrated with total tissue concentration data. This approach results in a great uncertainty in estimating target site dosimetry of drugs and has been a critical challenge in this field.
In this issue of The Journal of Physiology, the study by Eigenmann et al. (2017) significantly improves our understanding of the pharmacokinetics of therapeutic monoclonal antibodies, and represents an advancement of PBPK modelling with wide potential applications. The authors collected total tissue and interstitial anti‐interleukin 17 (anti‐IL17) IgG antibody time‐dependent biodistribution data in mice, measured the volume fractions of residual plasma and interstitial spaces of multiple organs, and determined the lymph flow rates in skin and muscle. These experimentally measured concentration data and physiological parameter values were used to calibrate a PBPK model that adequately simulated the distribution of the antibody in residual plasma, interstitium and intracellular spaces of different organs in mice. One unique aspect of this work is that the model was calibrated with experimentally determined tissue interstitial concentrations of the antibody and a number of other model parameters were measured experimentally, which increases the validity, accuracy and reliability of the model. One important finding of this study is that the antibody interstitial concentrations are highly organ‐specific (quite high in some organs) and dependent on the underlying capillary structures of individual organs. For example, the interstitial concentrations of the antibody in the skin and muscle were up to 50% of the plasma concentrations, while in organs with leaky discontinuous capillaries (e.g. liver and spleen), the interstitial concentrations were close to the plasma levels. In contrast to the still‐prevailing view that distribution to tissues and interstitial concentrations for antibodies are generally low, this new finding improves our understanding of the pharmacokinetics of antibodies and provides new insights into the design of new antibody‐based therapeutics. To be more specific, the results suggest that the total tissue concentrations of antibodies may not reflect how much a therapeutic antibody can reach its target in the interstitial space. Thus, in future studies, direct evaluation of the distribution of therapeutic antibodies to the interstitial spaces of different organs, especially the target organ, is encouraged.
From the perspective of PBPK modelling, this study suggests the importance of using measured interstitial concentrations of antibodies in the construction of PBPK models. However, it is important to note that currently there are technical limitations concerning available methods for direct measurements of antibody concentrations in the interstitial space. The centrifugation methodology used by Eigenmann et al. (2017) is a terminal approach and has only been validated for skin and muscle. Whether this method is applicable to other tissues remains to be tested. Therefore, further experimental methodological development is needed before such measurements become a standard approach in most of the organs and in multiple species.
This study is an excellent example of how experimental data and computational PBPK modelling can complement each other and be integrated to gain deeper insights into the physiological mechanisms, pharmacological and toxicological properties of drugs and biologics. The methodology, experimental data, parameter values, model design and model codes could potentially inform the development of PBPK models for other biologics, drugs, environmental chemicals, and nanoparticle‐based drug formulations. Therefore, this study represents a significant advancement of PBPK modelling and provides insights into the fields of risk assessment of environmental chemicals, drug tissue residue estimation and safety assessment of animal‐derived food products, as well as in the discovery and development of new drugs, biologics and nanomedicines.
Additional information
Competing interests
None declared.
Funding
The author would like to acknowledge funding support from the United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) for the Food Animal Residue Avoidance Databank (FARAD) Program (Grant no.: 2017‐41480‐27310), the United States National Institutes of Health (NIH) National Institute of Biomedical Imaging and Bioengineering (NIBIB) Small Research Grant Program (Grant no.: 1R03EB025566‐01), and the New Faculty Start‐up Funds from Kansas State University.
Linked articles This Perspective highlights an article by Eigenmann et al. To read this article, visit https://doi.org/10.1113/JP274819.
Edited by: Kim Barrett & Peying Fong
References
- Eigenmann MJ, Karlsen TV, Krippendorff BF, Tenstad O, Fronton L, Otteneder MB & Wiig H (2017). Interstitial IgG antibody pharmacokinetics assessed by combined in vivo‐ and physiologically‐based pharmacokinetic modelling approaches. J Physiol 595, 7311–7330. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li M, Zou P, Tyner K & Lee S (2017). Physiologically based pharmacokinetic (PBPK) modeling of pharmaceutical nanoparticles. AAPS J 19, 26–42. [DOI] [PubMed] [Google Scholar]
- Lin Z, Gehring R, Mochel JP, Lavé T & Riviere JE (2016). Mathematical modeling and simulation in animal health – Part II: principles, methods, applications, and value of physiologically based pharmacokinetic modeling in veterinary medicine and food safety assessment. J Vet Pharmacol Ther 39, 421–438. [DOI] [PubMed] [Google Scholar]
- Reddy M, Yang RS, Andersen ME. & Clewell HJ., III (eds) (2005). Physiologically Based Pharmacokinetic Modeling: Science and Applications. John Wiley & Sons, Inc, Hoboken, New Jersey, United States. [Google Scholar]
- Rowland M, Peck C & Tucker G (2011). Physiologically‐based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51, 45–73. [DOI] [PubMed] [Google Scholar]