Development of a generalized pharmacokinetic model to characterize clinical pharmacokinetics of monomethyl auristatin E-based antibody-drug conjugates.

AIMS: This study aims to develop a generalized pharmacokinetic (PK) model for monomethyl auristatin E (MMAE)-based antibody-drug conjugates (ADCs) that can simultaneously capture the PK of multiple ADC analytes commonly measured in the clinic. METHODS: A comprehensive literature review was conducted to collect PK data on MMAE-based ADCs from clinical trials. From each study, PK profiles of total antibody, the ADC, conjugated MMAE, and unconjugated MMAE, were extracted. These data were pooled and dose-normalized to evaluate the generalizability of PK across various ADCs and dose levels. Upon confirming PK generalizability, a generalized PK model for MMAE-based ADCs was developed using the entire dataset. Furthermore, exposure metrics ( C max and AUC) reported across the range of doses were combined to establish linear relationships between dose and exposure metrics for MMAE-based ADCs. RESULTS: A total of 109 PK profiles from 18 distinct MMAE-based ADCs were gathered. The dose-normalized PK profiles supported the generalizability of PK for MMAE-based ADCs. A generalized PK model was developed, which enabled capturing the PK data for 4 ADC analytes across all collected MMAE-based ADCs. A linear relationship between dose and PK exposure metrics was established, enabling the prediction of typical exposure values across different doses for MMAE-based ADCs. CONCLUSIONS: This study comprehensively analysed clinical PK data from different valine-citrulline (vc)-MMAE-based ADCs. The generalized PK model developed here serves as an important tool for a priori prediction of the PK for multiple ADC analytes in clinical settings and lays the foundation for establishing generalized exposure-response and exposure-toxicity correlations for MMAE-based ADCs.

Translational two-pore PBPK model to characterize whole-body disposition of different-size endogenous and exogenous proteins.

Two-pore physiologically based pharmacokinetic (PBPK) modeling has demonstrated its potential in describing the pharmacokinetics (PK) of different-size proteins. However, all existing two-pore models lack either diverse proteins for validation or interspecies extrapolation. To fill the gap, here we have developed and optimized a translational two-pore PBPK model that can characterize plasma and tissue disposition of different-size proteins in mice, rats, monkeys, and humans. Datasets used for model development include more than 15 types of proteins: IgG (150 kDa), F(ab)2 (100 kDa), minibody (80 kDa), Fc-containing proteins (205, 200, 110, 105, 92, 84, 81, 65, or 60 kDa), albumin conjugate (85.7 kDa), albumin (67 kDa), Fab (50 kDa), diabody (50 kDa), scFv (27 kDa), dAb2 (23.5 kDa), proteins with an albumin-binding domain (26, 23.5, 22, 16, 14, or 13 kDa), nanobody (13 kDa), and other proteins (110, 65, or 60 kDa). The PBPK model incorporates: (i) molecular weight (MW)-dependent extravasation through large and small pores via diffusion and filtration, (ii) MW-dependent renal filtration, (iii) endosomal FcRn-mediated protection from catabolism for IgG and albumin-related modalities, and (iv) competition for FcRn binding from endogenous IgG and albumin. The finalized model can well characterize PK of most of these proteins, with area under the curve predicted within two-fold error. The model also provides insights into contribution of renal filtration and lysosomal degradation towards total elimination of proteins, and contribution of paracellular convection/diffusion and transcytosis towards extravasation. The PBPK model presented here represents a cross-modality, cross-species platform that can be used for development of novel biologics.

Assessment of AAV9 distribution and transduction in rats after administration through Intrastriatal, Intracisterna magna and Lumbar Intrathecal routes.

Challenges in obtaining efficient transduction of brain and spinal cord following systemic AAV delivery have led to alternative administration routes being used in clinical trials that directly infuse the virus into the CNS. However, data comparing different direct AAV injections into the brain remain limited making it difficult to choose optimal routes. Here we tested both AAV9-egfp and AAV9-fLuc delivery via intrastriatal (IST), intracisterna magna (ICM) and lumbar intrathecal (LIT) routes in adult rats and assessed vector distribution and transduction in brain, spinal cord and peripheral tissues. We find that IST infusion leads to robust transgene expression in the striatum, thalamus and cortex with lower peripheral tissue transduction and anti-AAV9 capsid titers compared to ICM or LIT. ICM delivery provided strong GFP and luciferase expression across more brain regions than the other routes and similar expression in the spinal cord to LIT injections, which itself largely failed to transduce the rat brain. Our data highlight the strengths and weaknesses of each direct CNS delivery route which will help with future clinical targeting.

Translational physiologically-based pharmacokinetic model for ocular disposition of monoclonal antibodies.

We have previously published a PBPK model comprising the ocular compartment to characterize the disposition of monoclonal antibodies (mAbs) in rabbits. While rabbits are commonly used preclinical species in ocular research, non-human primates (NHPs) have the most phylogenetic resemblance to humans including the presence of macula in the eyes as well as higher sequence homology. However, their use in ocular research is limited due to the strict ethical guidelines. Similarly, in humans the ocular samples cannot be collected except for the tapping of aqueous humor (AH). Therefore, we have translated this rabbit model to monkeys and human species using literature-reported datasets. Parameters describing the tissue volumes, physiological flows, and FcRn-binding were obtained from the literature, or estimated by fitting the model to the data. In the monkey model, the values for the rate of lysosomal degradation for antibodies (Kdeg), intraocular reflection coefficients (σaq, σret, σcho), bidirectional rate of fluid circulation between the vitreous chamber and the aqueous chamber (QVA), and permeability-surface area product of lens (PSlens) were estimated; and were found to be 31.5 h-1, 0.7629, 0.6982, 0.9999, 1.64 × 10-5 L/h, and 4.62 × 10-7 L/h, respectively. The monkey model could capture the data in plasma, aqueous humor, vitreous humor and retina reasonably well with the predictions being within twofold of the observed values. For the human model, only the value of Kdeg was estimated to fit the model to the plasma pharmacokinetics (PK) of mAbs and was found to be 24.4 h-1 (4.14%). The human model could also capture the ocular PK data reasonably well with the predictions being within two- to threefold of observed values for the plasma, aqueous and vitreous humor. Thus, the proposed framework can be used to characterize and predict the PK of mAbs in the eye of monkey and human species following systemic and intravitreal administration. The model can also facilitate the development of new antibody-based therapeutics for the treatment of ocular diseases as well as predict ocular toxicities of such molecules following systemic administration.

Expansion of platform physiologically-based pharmacokinetic model for monoclonal antibodies towards different preclinical species: cats, sheep, and dogs.

Monoclonal antibodies (mAbs) are becoming an important therapeutic option in veterinary medicine, and understanding the pharmacokinetic (PK) of mAbs in higher-order animal species is also important for human drug development. To better understand the PK of mAbs in these animals, here we have expanded a platform physiological-based pharmacokinetic (PBPK) model to characterize the disposition of mAbs in three different preclinical species: cats, sheep, and dogs. We obtained PK data for mAbs and physiological parameters for the three different species from the literature. We were able to describe the PK of mAbs following intravenous (IV) or subcutaneous administration in cats, IV administration in sheep, and IV administration dogs reasonably well by fixing the physiological parameters and just estimating the parameters related to the binding of mAbs to the neonatal Fc receptor. The platform PBPK model presented here provides a quantitative tool to predict the plasma PK of mAbs in dogs, cats, and sheep. The model can also predict mAb PK in different tissues where the site of action might be located. As such, the mAb PBPK model presented here can facilitate the discovery, development, and preclinical-to-clinical translation of mAbs for veterinary and human medicine. The model can also be modified in the future to account for more detailed compartments for certain organs, different pathophysiology in the animals, and target-mediated drug disposition.

Clinical validation of translational antibody PBPK model using tissue distribution data generated with 89Zr-immuno-PET imaging.

The main objective of this manuscript was to validate the ability of the monoclonal antibody physiologically-based pharmacokinetic (PBPK) model to predict tissue concentrations of antibodies in the human. To accomplish this goal, preclinical and clinical tissue distribution and positron emission tomography imaging data generated using zirconium-89 (89Zr) labeled antibodies were obtained from the literature. First, our previously published translational PBPK model for antibodies was expanded to describe the whole-body biodistribution of 89Zr labeled antibody and the free 89Zr, as well as residualization of free 89Zr. Subsequently, the model was optimized using mouse biodistribution data, where it was observed that free 89Zr mainly residualizes in the bone and the extent of antibody distribution in certain tissues (e.g., liver and spleen) may be altered by labeling with 89Zr. The mouse PBPK model was scaled to rat, monkey, and human by simply changing the physiological parameters, and a priori simulations performed by the model were compared with the observed PK data. It was found that model predicted antibody PK in majority of the tissues in all the species superimposed over the observed data, and the model was also able to predict the PK of antibody in human tissues reasonably well. As such, the work presented here provides unprecedented evaluation of the antibody PPBK model for its ability to predict tissue PK of antibodies in the clinic. This model can be used for preclinical-to-clinical translation of antibodies and for prediction of antibody concentrations at the site-of-action in the clinic.

International society of Pharmacometrics Mentorship Program (IMP): feedback survey from the first cohort of mentor-mentee pairs.

The International Society of Pharmacometrics (ISoP) Mentorship Program (IMP) aims to help professionals at all career stages to transition into the pharmacometrics field, move to a different role/area within pharmacometrics, or expand their skillsets. The program connects mentees at various stages of their careers with mentors based on established criteria for mentor-mentee matching. Pairing mentees with appropriate mentors ensures strong alignment between mentees' interests and mentors' expertise as this is critical to the success and continuation of the relationship between the mentor and mentee. Once mentors and mentees are connected, they are strongly encouraged to meet at least once per month for an hour. The mentor and mentee have the freedom to tailor their sessions to their liking, including frequency, duration, and topics they choose to focus on. Mentees are encouraged to clearly define their goals to help direct their mentor-mentee relationship and conversations. Mentees and mentors alike are given the opportunity to provide feedback about the program to the ISoP Education Committee through surveys and testimonials. Due to the program's infancy, structured guidelines for mentor-mentee sessions are still being developed and instituted using the program evaluation described in this paper.

Mathematical Models to Characterize the Absorption, Distribution, Metabolism, and Excretion of Protein Therapeutics.

Therapeutic proteins (TPs) have ranked among the most important and fastest-growing classes of drugs in the clinic, yet the development of successful TPs is often limited by unsatisfactory efficacy. Understanding pharmacokinetic (PK) characteristics of TPs is key to achieving sufficient and prolonged exposure at the site of action, which is a prerequisite for eliciting desired pharmacological effects. PK modeling represents a powerful tool to investigate factors governing in vivo disposition of TPs. In this mini-review, we discuss many state-of-the-art models that recapitulate critical processes in each of the absorption, distribution, metabolism/catabolism, and excretion pathways of TPs, which can be integrated into the physiologically-based pharmacokinetic framework. Additionally, we provide our perspectives on current opportunities and challenges for evolving the PK models to accelerate the discovery and development of safe and efficacious TPs. SIGNIFICANCE STATEMENT: This minireview provides an overview of mechanistic pharmacokinetic (PK) models developed to characterize absorption, distribution, metabolism, and elimination (ADME) properties of therapeutic proteins (TPs), which can support model-informed discovery and development of TPs. As the next-generation of TPs with diverse physicochemical properties and mechanism-of-action are being developed rapidly, there is an urgent need to better understand the determinants for the ADME of TPs and evolve existing platform PK models to facilitate successful bench-to-bedside translation of these promising drug molecules.

A population physiologically-based pharmacokinetic model to characterize antibody disposition in pediatrics and evaluation of the model using infliximab.

AIMS: In order to better predict the pharmacokinetics (PK) of antibodies in children, and to facilitate dose optimization of antibodies in paediatric patients, there is a need to develop systems PK models that integrate ontogeny-related changes in human physiological parameters. METHODS: A population-based physiological-based PK (PBPK) model to characterize antibody PK in paediatrics has been developed, by incorporating age-related changes in body weight, organ weight, organ blood flow rate and interstitial volumes in a previously published platform model. The model was further used to perform Monte Carlo simulations to investigate clearance vs. age and dose-exposure relationships for infliximab. RESULTS: By estimating only one parameter and associated interindividual variability, the model was able to characterize clinical PK of infliximab from two paediatric cohorts (n = 141, 4-19 years) reasonably well. Model simulations demonstrated that only 50% of children reached desired trough concentrations when receiving FDA-labelled dosing regimen for infliximab, suggesting that higher doses and/or more frequent dosing are needed to achieve target trough concentrations of this antibody. CONCLUSION: The paediatric PBPK model presented here can serve as a framework to characterize the PK of antibodies in paediatric patients. The model can also be applied to other protein therapeutics to advance precision medicine paradigm and optimize antibody dosing regimens in children.

Development of a Physiologically-Based Pharmacokinetic Model for Whole-Body Disposition of MMAE Containing Antibody-Drug Conjugate in Mice.

PURPOSE: To quantitate and mathematically characterize the whole-body pharmacokinetics (PK) of different ADC analytes following administration of an MMAE-conjugated ADC in tumor-bearing mice. METHODS: The PK of different ADC analytes (total antibody, total drug, unconjugated drug) was measured following administration of an MMAE-conjugated ADC in tumor-bearing mice. The PK of ADC analytes was compared with the whole-body PK of the antibody and drug obtained following administration of these molecules alone. An ADC PBPK model was developed by linking antibody PBPK model with small-molecule PBPK model, where the drug was assumed to deconjugate in DAR-dependent manner. RESULTS: Comparison of antibody biodistribution coefficient (ABC) values for total antibody suggests that conjugation of drug did not significantly affect the PK of antibody. Comparison of tissue:plasma AUC ratio (T/P) for the conjugated drug and total antibody suggests that in certain tissues (e.g., spleen) ADC may demonstrate higher deconjugation. It was observed that the tissue distribution profile of the drug can be altered following its conjugation to antibody. For example, MMAE distribution to the liver was found to increase while its distribution to the heart was found to decrease upon conjugation to antibody. MMAE exposure in the tumor was found to increase by ~20-fold following administration as conjugate (i.e., ADC). The PBPK model was able to a priori predict the PK of all three ADC analytes in plasma, tissues, and tumor reasonably well. CONCLUSIONS: The ADC PBPK model developed here serves as a platform for translational and clinical investigations of MMAE containing ADCs.

Physiologically-based pharmacokinetic model for pulmonary disposition of protein therapeutics in humans.

Lung related disorders like COPD and Asthma, as well as various infectious diseases, form a major therapeutic area which would benefit from a predictive and adaptable mathematical model for describing pulmonary disposition of biological modalities. In this study we fill that gap by extending the cross-species two-pore physiologically-based pharmacokinetic (PBPK) platform with more detailed respiratory tract that includes the airways and alveolar space with epithelial lining fluid. We parameterize the paracellular and FcRn-facilitated exchange pathways between the epithelial lining fluid and lung interstitial space by building a mechanistic model for the exchange between the two. The optimized two-pore PBPK model described pulmonary exposure of several systemically dosed mAbs for which data is available and is also in agreement with the observed levels of endogenous IgG and albumin. The proposed framework can be used to assess pharmacokinetics of new lung-targeting biologic therapies and guide their dosing to achieve desired exposure at the pulmonary site-of-action.

Physiologically-based pharmacokinetic model for 2,4-dinitrophenol.

New approaches in drug development are needed to address the growing epidemic of obesity as the prevalence of obesity increases worldwide. 2,4-Dinitrophenol (DNP) is an oxidative phosphorylation uncoupling agent that was widely used in the early 1930s for weight loss but was quickly banned by the FDA due to the severe toxicities associated with the compound. One of the limitations leading to the demise of DNP as a pharmaceutical was a lack of understanding about the pharmacokinetic-pharmacodynamic relationship. The purpose of this study was to investigate whole body disposition of DNP in order to understand the relationship between the pharmacokinetics, efficacy and toxicity in the C57BL/6J diet induced obese mouse model. Following intravenous administration of 1 mg/kg, and intraperitoneal administration of 5 mg/kg and 15 mg/kg of DNP, we found limited DNP distribution to tissues. Experimentally measured partition coefficients were found to be less than 1 for all analyzed tissues. In addition, DNP exhibits significant nonlinear pharmacokinetics, which we have attributed to nonlinear plasma protein binding and nonlinear partitioning into liver and kidney. By enhancing our understanding of the PK-PD relationship, we can develop new approaches to leverage oxidative phosphorylation uncoupling as a weight loss strategy.

Towards a translational physiologically-based pharmacokinetic (PBPK) model for receptor-mediated transcytosis of anti-transferrin receptor monoclonal antibodies in the central nervous system.

In this manuscript, we present a translational physiologically-based pharmacokinetic (PBPK) model to characterize receptor-mediated transcytosis (RMT) of anti-transferrin receptor (TfR) monoclonal antibodies (mAbs) in the central nervous system (CNS). The model accounts for the state-of-the-art knowledge of the brain's anatomy and physiology, and physiological parameters were fixed according to different species. By estimating a few parameters associated with the TfR concentration, the TfR turnover, and the internalization rate, the model simultaneously characterizes plasma, whole brain, interstitial fluid (ISF), and cerebrospinal fluid (CSF) PK of unbound and bound anti-TfR mAbs with different binding affinities in mice, rats, and monkeys obtained from various literature sources within a threefold prediction error. The final PBPK model was validated using external anti-TfR mAb PK data in mice and monkeys with different affinities and doses. The simulation reasonably predicted plasma and brain PK of monovalent/bivalent anti-TfR mAbs within a threefold prediction error and characterized a bell-shaped relationship between the brain ISF/plasma AUC ratio and the KD value. Although further refinements of the PBPK model and clinical validation are required, this PBPK model may provide physiologically-based translation of CNS disposition of anti-TfR mAbs by accounting for the physiological difference of the endogenous RMT system among different species. The PBPK model may also guide selection of other endogenous receptors, lead optimization, and clinical development of novel CNS-targeted mAbs.

PBPK model for antibody disposition in mouse brain: validation using large-pore microdialysis data.

The objective of this manuscript was to validate a physiologically-based pharmacokinetic (PBPK) model developed to characterize brain pharmacokinetics (PK) of monoclonal antibodies (mAbs) using novel large-pore microdialysis data generated in mice. To support this objective, brain, CSF, and ISF PK of a human anti-tetanus toxin (TeTx) antibody was measured in mice following intraperitoneal (IP) administration. This antibody has no binding in mice. In addition, our recently published mouse brain PK data generated following intravenous (IV) and IP administration of trastuzumab in mice, and other published PK data for brain disposition of antibody in mice, were used to evaluate the PBPK model. All the model parameters were obtained from literature or kept the same as in our previously published manuscript. The revised PBPK model was able to characterize the PK of antibodies in mice brain, CSF, and ISF reasonably well (i.e., within a three-fold error). However, a priori selected parameters led to underprediction of ISF PK during the initial phase of the profile. A local sensitivity analysis suggested that minor changes in several brain-related parameters can help overcome this discrepancy, where an increase in the convective flow of antibodies across BBB was found to be the most parsimonious way to capture all the PK profiles well. However, the presence of this pathway needs further validation. As such, here we have presented an improved PBPK model to characterize and predict the PK of mAbs in different regions of the mouse brain following systemic administration. This model can serve as a quantitative tool to facilitate the discovery, preclinical evaluation, and preclinical-to-clinical translation of novel antibodies targeted against CNS disorders.

Two-pore physiologically based pharmacokinetic model validation using whole-body biodistribution of trastuzumab and different-size fragments in mice.

In the past, our lab proposed a two-pore PBPK model for different-size protein therapeutics using de novo derived parameters and the model was validated using plasma PK data of different-size antibody fragments digitized from the literature (Li Z, Shah DK, J Pharmacokinet Pharmacodynam 46(3):305-318, 2009). To further validate the model using tissue distribution data, whole-body biodistribution study of 6 different-size proteins in mice were conducted. Studied molecules covered a wide MW range (13-150 kDa). Plasma PK and tissue distribution profiles is 9 tissues were measured, including heart, lung, liver, spleen, kidney, skin, muscle, small intestine, large intestine. Tumor exposure of different-size proteins were also evaluated. The PBPK model was validated by comparing percentage predictive errors (%PE) between observed and model predicted results for each type of molecule in each tissue. Model validation showed that the two-pore PBPK model was able to predict plasma, tissues and tumor PK of all studied molecules relatively well. This model could serve as a platform for developing a generic PBPK model for protein therapeutics in the future.

Evolution of the Systems Pharmacokinetics-Pharmacodynamics Model for Antibody-Drug Conjugates to Characterize Tumor Heterogeneity and In Vivo Bystander Effect.

The objective of this work was to develop a systems pharmacokinetics-pharmacodynamics (PK-PD) model that can characterize in vivo bystander effect of antibody-drug conjugate (ADC) in a heterogeneous tumor. To accomplish this goal, a coculture xenograft tumor with 50% GFP-MCF7 (HER2-low) and 50% N87 (HER2-high) cells was developed. The relative composition of a heterogeneous tumor for each cell type was experimentally determined by immunohistochemistry analysis. Trastuzumab-vc-MMAE (T-vc-MMAE) was used as a tool ADC. Plasma and tumor PK of T-vc-MMAE was analyzed in N87, GFP-MCF7, and coculture tumor-bearing mice. In addition, tumor growth inhibition (TGI) studies were conducted in all three xenografts at different T-vc-MMAE dose levels. To characterize the PK of ADC in coculture tumors, our previously published tumor distribution model was evolved to account for different cell populations. The evolved tumor PK model was able to a priori predict the PK of all ADC analytes in the coculture tumors reasonably well. The tumor PK model was subsequently integrated with a PD model that used intracellular tubulin occupancy to drive ADC efficacy in each cell type. The final systems PK-PD model was able to simultaneously characterize all the TGI data reasonably well, with a common set of parameters for MMAE-induced cytotoxicity. The model was later used to simulate the effect of different dosing regimens and tumor compositions on the bystander effect of ADC. The model simulations suggested that dose-fractionation regimen may further improve overall efficacy and bystander effect of ADCs by prolonging the tubulin occupancy in each cell type. SIGNIFICANCE STATEMENT: A PK-PD analysis is presented to understand bystander effect of Trastuzumab-vc-MMAE ADC in antigen (Ag)-low, Ag-high, and coculture (i.e., Ag-high + Ag-low) xenograft mice. This study also describes a novel single cell-level systems PK-PD model to characterize in vivo bystander effect of ADCs. The proposed model can serve as a platform to mathematically characterize multiple cell populations and their interactions in tumor tissues. Our analysis also suggests that fractionated dosing regimen may help improve the bystander effect of ADCs.

Evaluation of Quantitative Relationship Between Target Expression and Antibody-Drug Conjugate Exposure Inside Cancer Cells.

Antibody-drug conjugates (ADCs) employ overexpressed cell surface antigens to deliver cytotoxic payloads inside cancer cells. However, the relationship between target expression and ADC efficacy remains ambiguous. In this manuscript, we have addressed a part of this ambiguity by quantitatively investigating the effect of antigen expression levels on ADC exposure within cancer cells. Trastuzumab-valine-citrulline-monomethyl auristatin E was used as a model ADC, and four different cell lines with diverse levels of human epidermal growth factor receptor 2 (HER2) expression were used as model cells. The pharmacokinetics (PK) of total trastuzumab, released monomethyl auristatin E (MMAE), and total MMAE were measured inside the cells and in the cell culture media following incubation with two different concentrations of ADC. In addition, target expression levels, target internalization rate, and cathepsin B and MDR1 protein concentrations were determined for each cell line. All the PK data were mathematically characterized using a cell-level systems PK model for ADC. It was found that SKBR-3, MDA-MB-453, MCF-7, and MDA-MB-468 cells had ∼800,000, ∼250,000, ∼50,000, and ∼10,000 HER2 receptors per cell, respectively. A strong linear relationship (R 2 > 0.9) was observed between HER2 receptor count and released MMAE exposure inside the cancer cells. There was an inverse relationship found between HER2 expression level and internalization rate, and cathepsin B and multidrug resistance protein 1 (MDR1) expression level varied slightly among the cell lines. The PK model was able to simultaneously capture all the PK profiles reasonably well while estimating only two parameters. Our results demonstrate a strong quantitative relationship between antigen expression level and intracellular exposure of ADCs in cancer cells. SIGNIFICANCE STATEMENT: In this manuscript, we have demonstrated a strong linear relationship between target expression level and antibody-drug conjugate (ADC) exposure inside cancer cells. We have also shown that this relationship can be accurately captured using the cell-level systems pharmacokinetics model developed for ADCs. Our results indirectly suggest that the lack of relationship between target expression and efficacy of ADC may stem from differences in the pharmacodynamic properties of cancer cells.

Measurement and Quantitative Characterization of Whole-Body Pharmacokinetics of Exogenously Administered T Cells in Mice.

Here we have investigated whole-body pharmacokinetics (PK) of exogenously administered T cells in a mouse model of melanoma and have developed a physiologically based pharmacokinetic (PBPK) model to quantitatively characterize the data. T cells were isolated from the spleen of tumor-bearing mice, activated, and labeled with chromium-51 to facilitate the quantification. Labeled T cells were injected in the tumor-bearing mice, and PK was measured in 19 different tissues. It was found that T cells disappear from the blood rapidly after administration and accumulate in the tissues to various extents. Spleen, liver, lung, kidney, bone, and lymph nodes accounted for more than 90% of T cells in the body. The distribution of T cells in solid tumors was found to be very low, hovering below 1%ID/g (percent of injected dose per gram of tissue) during the entire study. However, this observation may differ for targeted TCR-T and CAR-T cells. Observed PK profiles also suggest that T-cell-based therapies may be more successful in treating cancers of the lymphatic system and bone marrow metastases compared to solid tumors. A PBPK model was developed to characterize the whole-body PK of T cells, which incorporated key processes such as extravasation, elimination, and recirculation of T cells via lymph flow. Retention factors were incorporated into the spleen, liver, and kidney compartment to adequately capture the PK profiles. The model was able to characterize observed PK profiles reasonably well, and parameters were estimated with good confidence. The PK data and PBPK model presented here provide unprecedented insight into the biodistribution of exogenously administered T cells.

Effect of Size on Solid Tumor Disposition of Protein Therapeutics.

In this study, we evaluated the effect of size on tumor disposition of protein therapeutics, including the plasma and tumor pharmacokinetics (PK) of trastuzumab (∼150 kDa), FcRn-nonbinding trastuzumab (∼150 kDa), F(ab)2 fragment of trastuzumab (∼100 kDa), Fab fragment of trastuzumab (∼50 kDa), and trastuzumab scFv (∼27 kDa) in both antigen (i.e., HER2)-overexpressing (N87) and antigen-nonexpressing (MDA-MB-468) tumor-bearing mice. The observed data were used to develop the maximum tumor uptake versus molecular weight and tumor-to-plasma area under the curve (AUC) ratio versus molecular weight relationships. Comparison of the PK of different sizes of FcRn nonbinding molecules in target-expressing tumor showed that ∼100 kDa is an optimal size to achieve maximum tumor uptake and ∼50 kDa is an optimal size to achieve maximum tumor-to-plasma exposure ratio of protein therapeutics. The PK data were also used to validate a systems PK model for tumor disposition of different-sized protein therapeutics. The PK model was able to predict a priori the PK of all five molecules in both tumor types reasonably well (within 2- to 3-fold). In addition, the model captured the bell-shaped relationships observed between maximum tumor uptake and molecular weight and between tumor-to-plasma AUC ratio and molecular weight. Our results provide an unprecedented insight into the effect of size and target engagement on the tumor PK of protein therapeutics. Our results also provide further validation of the tumor disposition model, which can be used to support discovery, development, and preclinical-to-clinical translation of different sizes of protein therapeutics. SIGNIFICANCE STATEMENT: This article highlights the importance of molecular size and target engagement on the tumor disposition of protein therapeutics. Our results suggest that ∼100 kDa is an optimal size to achieve maximum tumor uptake and ∼50 kDa is an optimal size to achieve maximum tumor-to-plasma exposure ratio for non-FcRn-binding targeted protein therapeutics. We also demonstrate that a systems pharmacokinetics model developed to characterize tumor disposition of protein therapeutics can predict a priori the disposition of different-sized protein therapeutics in target-expressing and target-nonexpressing solid tumors.

Two-pore physiologically based pharmacokinetic model with de novo derived parameters for predicting plasma PK of different size protein therapeutics.

Two-pore PBPK models have been used for characterizing the PK of protein therapeutics since 1990s. However, widespread utilization of these models is hampered by the lack of a priori parameter values, which are typically estimated using the observed data. To overcome this hurdle, here we have presented the development of a two-pore PBPK model using de novo derived parameters. The PBPK model was validated using plasma PK data for different size proteins in mice. Using the "two pore theory" we were able to establish the relationship between protein size and key model parameters, such as: permeability-surface area product (PS), vascular reflection coefficient (σ), peclet number (Pe), and glomerular sieving coefficient (θ). The model accounted for size dependent changes in tissue extravasation and glomerular filtration. The model was able to a priori predict the PK of 8 different proteins: IgG (150 kDa), scFv-Fc (105 kDa), F(ab)2 (100 kDa, minibody (80 kDa), scFv2 (55 kDa), Fab (50 kDa), diabody (50 kDa), scFv (27 kDa), and nanobody (13 kDa). In addition, the model was able to provide unprecedented quantitative insight into the relative contribution of convective and diffusive pathway towards trans-capillary mass transportation of different size proteins. The two-pore PBPK model was also able to predict systemic clearance (CL) versus Molecular Weight relationship for different size proteins reasonably well. As such, the PBPK model proposed here represents a bottom-up systems PK model for protein therapeutics, which can serve as a generalized platform for the development of truly translational PBPK model for protein therapeutics.