Interstitial IgG antibody pharmacokinetics assessed by combined in vivo- and physiologically-based pharmacokinetic modelling approaches.

KEY POINTS: For therapeutic antibodies, total tissue concentrations are frequently reported as a lump sum measure of the antibody in residual plasma, interstitial fluid and cells. In terms of correlating antibody exposure to a therapeutic effect, however, interstitial pharmacokinetics might be more relevant. In the present study, we collected total tissue and interstitial antibody biodistribution data in mice and assessed the composition of tissue samples aiming to correct total tissue measurements for plasma and cellular content. All data and parameters were integrated into a refined physiologically-based pharmacokinetic model for monoclonal antibodies to enable the tissue-specific description of antibody pharmacokinetics in the interstitial space. We found that antibody interstitial concentrations are highly tissue-specific and dependent on the underlying capillary structure but, in several tissues, they reach relatively high interstitial concentrations, contradicting the still-prevailing view that both the distribution to tissues and the interstitial concentrations for antibodies are generally low. ABSTRACT: For most therapeutic antibodies, the interstitium is the target space. Although experimental methods for measuring antibody pharmacokinetics (PK) in this space are not well established, thus making quantitative assessment difficult, the interstitial antibody concentration is assumed to be low. In the present study, we combined direct quantification of antibodies in the interstitial fluid with a physiologically-based PK (PBPK) modelling approach, with the aim of better describing the PK of monoclonal antibodies in the interstitial space of different tissues. We isolated interstitial fluid by tissue centrifugation and conducted an antibody biodistribution study in mice, measuring total tissue and interstitial concentrations in selected tissues. Residual plasma, interstitial volumes and lymph flows, which are important PBPK model parameters, were assessed in vivo. We could thereby refine the PBPK modelling of monoclonal antibodies, better interpret antibody biodistribution data and more accurately predict their PK in the different tissue spaces. Our results indicate that, in tissues with discontinuous capillaries (liver and spleen), interstitial concentrations are reflected by the plasma concentration. In tissues with continuous capillaries (e.g. skin and muscle), ∼50-60% of the plasma concentration is found in the interstitial space. In the brain and kidney, on the other hand, antibodies are restricted to the vascular space. Our data may significantly impact the interpretation of biodistribution data of monoclonal antibodies and might be important when relating measured concentrations to a therapeutic effect. By contrast to the view that the antibody distribution to the interstitial space is limited, using direct measurements and model-based data interpretation, we show that high antibody interstitial concentrations are reached in most tissues.

PKPD modeling of acquired resistance to anti-cancer drug treatment.

Non-small cell lung cancer (NSCLC) patients greatly benefit from the treatment with tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, emergence of acquired resistance inevitable occurs after long-term treatment in most patients and limits clinical improvement. In the present study, resistance to drug treatment in patient-derived NSCLC xenograft mice was assessed and modeling and simulation was applied to understand the dynamics of drug resistance as a basis to explore more beneficial drug regimen. Two semi-mechanistic models were fitted to tumor growth inhibition profiles during and after treatment with erlotinib or gefitinib. The base model proposes that as a result of drug treatment, tumor cells stop proliferating and undergo several stages of damage before they eventually die. The acquired resistance model adds a resistance term to the base model which assumes that resistant cells are emerging from the pool of damaged tumor cells. As a result, tumor cells sensitive to drug treatment will either die or be converted to a drug resistant cell population which is proliferating at a slower growth rate as compared to the sensitive cells. The observed tumor growth profiles were better described by the resistance model and emergence of resistance was concluded. In simulation studies, the selection of resistant cells was explored as well as the time-variant fraction of resistant over sensitive cells. The proposed model provides insight into the dynamic processes of emerging resistance. It predicts tumor regrowth during treatment driven by the selection of resistant cells and explains why faster tumor regrowth may occur after discontinuation of TKI treatment. Finally, it is shown how the semi-mechanistic model can be used to explore different scenarios and to identify optimal treatment schedules in clinical trials.

Predicting Tumor Killing and T-Cell Activation by T-Cell Bispecific Antibodies as a Function of Target Expression: Combining In Vitro Experiments with Systems Modeling.

Targeted T-cell redirection is a promising field in cancer immunotherapy. T-cell bispecific antibodies (TCB) are novel antibody constructs capable of binding simultaneously to T cells and tumor cells, allowing cross-linking and the formation of immunologic synapses. This in turn results in T-cell activation, expansion, and tumor killing. TCB activity depends on system-related properties such as tumor target antigen expression as well as antibody properties such as binding affinities to target and T cells. Here, we developed a systems model integrating in vitro data to elucidate further the mechanism of action and to quantify the cytotoxic effects as the relationship between targeted antigen expression and corresponding TCB activity. In the proposed model, we capture relevant processes, linking immune synapse formation to T-cell activation, expansion, and tumor killing for TCBs in vitro to differentiate the effect between tumor cells expressing high or low levels of the tumor antigen. We used cibisatamab, a TCB binding to carcinoembryonic antigen (CEA), to target different tumor cell lines with high and low CEA expression in vitro We developed a model to capture and predict our observations, as a learn-and-confirm cycle. Although full tumor killing and substantial T-cell activation was observed in high expressing tumor cells, the model correctly predicted partial tumor killing and minimal T-cell activation in low expressing tumor cells when exposed to cibisatamab. Furthermore, the model successfully predicted cytotoxicity across a wide range of tumor cell lines, spanning from very low to high CEA expression.

Quantification of IgG monoclonal antibody clearance in tissues.

Monoclonal antibodies are an important therapeutic entity, and knowledge of antibody pharmacokinetics has steadily increased over the years. Despite this effort, little is known about the extent of IgG antibody degradation in different tissues of the body. While studies have been published identifying sites of degradation with the use of residualizing and non-residualizing radiolabels, quantitative tissue clearances have not yet been derived. Here, we show that in physiologically-based pharmacokinetic (PBPK) models we can combine mouse data of Indium-111 and Iodine-125 labeled antibodies with prior physiologic knowledge to determine tissue-specific intrinsic clearances. Unspecific total tissue clearance (mL/day) in the mouse was estimated to be: liver = 4.75; brain = 0.02; gut = 0.40; heart = 0.07; kidney = 0.97; lung = 0.20; muscle = 3.02; skin = 3.89; spleen = 0.45; rest of body = 2.16. The highest catabolic activity (per g tissue) was in spleen for an FcRn wild-type antibody, but shifts to the liver for an antibody with reduced FcRn affinity. In the model developed, this shift can be explained by the liver having a greater FcRn-mediated protection capacity than the spleen. The quantification of tissue intrinsic clearances and FcRn salvage capacity increases our understanding of quantitative processes that drive the therapeutic responses of antibodies. This knowledge is critical, for instance to estimate the non-specific cellular uptake and degradation of antibodies used for targeted delivery of payloads.

Combining Nonclinical Experiments with Translational PKPD Modeling to Differentiate Erlotinib and Gefitinib.

We quantitatively compare the efficacy of two approved EGFR tyrosine kinase inhibitors, erlotinib and gefitinib, based on in vivo and in vitro data and show how a modeling approach can be used to scale from animal to humans. Gefitinib shows a higher tumor uptake in cancer patients, and we explored the potential impact on pharmacologic and antitumor activity in in vitro and in xenograft mice. Tumor growth inhibition was monitored, and the pharmacokinetics (PK) in plasma and tumor, as well as temporal changes of phospho-Erk (pErk) signals were examined in patient-derived tumor xenograft mice. These data were integrated in a translational PKPD model, allowing us to project an efficacious human dose, which we retrospectively compared with prescribed doses for cancer patients. In vitro experiments showed that cell-cycle arrest was similar for erlotinib and gefitinib. Similar pERK biomarker responses were obtained despite a 6.6-fold higher total tumor exposure for gefitinib. The PKPD model revealed a 3.7-fold higher in vivo potency for gefitinib, which did not translate into a lower anticipated efficacious dose in humans. The model-based dose prediction matched the recommended clinical doses well. These results suggest that despite having lower total tumor-to-plasma ratios, active drug exposure at target site is higher for erlotinib. Considering the PK properties, this translates in a 50% lower recommended daily dose of erlotinib in cancer patients. In summary, total exposure at target site is not suitable to rank compounds, and an integrated modeling and experimental approach can assess efficacy more accurately. Mol Cancer Ther; 15(12); 3110-9. ©2016 AACR.