Application of a PK-PD Modeling and Simulation-Based Strategy for Clinical Translation of Antibody-Drug Conjugates: a Case Study with Trastuzumab Emtansine (T-DM1)

Quantitative evaluation of trastuzumab deruxtecan pharmacokinetics and pharmacodynamics in mouse models of varying degrees of HER2 expression

Trastuzumab deruxtecan (T-DXd; DS-8201; ENHERTU®) is a human epithelial growth factor receptor 2 (HER2)-directed antibody drug conjugate (ADC) with demonstrated antitumor activity against a range of tumor types. Aiming to understand the relationship between antigen expression and downstream efficacy outcomes, T-DXd was administered in tumor-bearing mice carrying NCI-N87, Capan-1, JIMT-1, and MDA-MB-468 xenografts, characterized by varying HER2 levels. Plasma pharmacokinetics (PK) of total antibody, T-DXd, and released DXd and tumor concentrations of released DXd were evaluated, in addition to monitoring γΗ2AX and pRAD50 pharmacodynamic (PD) response. A positive relationship was observed between released DXd concentrations in tumor and HER2 expression, with NCI-N87 xenografts characterized by the highest exposures compared to the remaining cell lines. γΗ2AX and pRAD50 demonstrated a sustained increase over several days occurring with a time delay relative to tumoral-released DXd concentrations. In vitro investigations of cell-based DXd disposition facilitated the characterization of DXd kinetics across tumor cells. These outputs were incorporated into a mechanistic mathematical model, utilized to describe PK/PD trends. The model captured plasma PK across dosing arms as well as tumor PK in NCI-N87, Capan-1, and MDA-MB-468 models; tumor concentrations in JIMT-1 xenografts required additional parameter adjustments reflective of complex receptor dynamics. γΗ2AX longitudinal trends were well characterized via a unified PD model implemented across xenografts demonstrating the robustness of measured PD trends. This work supports the application of a mechanistic model as a quantitative tool, reliably projecting tumor payload concentrations upon T-DXd administration, as the first step towards preclinical-to-clinical translation.

Quantitative systems pharmacology modeling of HER2-positive metastatic breast cancer for translational efficacy evaluation and combination assessment across therapeutic modalities

HER2-positive (HER2+) metastatic breast cancer (mBC) is highly aggressive and a major threat to human health. Despite the significant improvement in patients' prognosis given the drug development efforts during the past several decades, many clinical questions still remain to be addressed such as efficacy when combining different therapeutic modalities, best treatment sequences, interindividual variability as well as resistance and potential coping strategies. To better answer these questions, we developed a mechanistic quantitative systems pharmacology model of the pathophysiology of HER2+ mBC that was extensively calibrated and validated against multiscale data to quantitatively predict and characterize the signal transduction and preclinical tumor growth kinetics under different therapeutic interventions. Focusing on the second-line treatment for HER2+ mBC, e.g., antibody-drug conjugates (ADC), small molecule inhibitors/TKI and chemotherapy, the model accurately predicted the efficacy of various drug combinations and dosing regimens at the in vitro and in vivo levels. Sensitivity analyses and subsequent heterogeneous phenotype simulations revealed important insights into the design of new drug combinations to effectively overcome various resistance scenarios in HER2+ mBC treatments. In addition, the model predicted a better efficacy of the new TKI plus ADC combination which can potentially reduce drug dosage and toxicity, while it also shed light on the optimal treatment ordering of ADC versus TKI plus capecitabine regimens, and these findings were validated by new in vivo experiments. Our model is the first that mechanistically integrates multiple key drug modalities in HER2+ mBC research and it can serve as a high-throughput computational platform to guide future model-informed drug development and clinical translation.

An In Silico Platform to Predict Cardiotoxicity Risk of Anti-tumor Drug Combination with hiPSC-CMs Based In Vitro Study

OBJECTIVE

Antineoplastic agent-induced systolic dysfunction is a major reason for interruption of anticancer treatment. Although targeted anticancer agents infrequently cause systolic dysfunction, their combinations with chemotherapies remarkably increase the incidence. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide a potent in vitro model to assess cardiovascular safety. However, quantitatively predicting the reduction of ejection fraction based on hiPSC-CMs is challenging due to the absence of the body's regulatory response to cardiomyocyte injury.

METHODS

Here, we developed and validated an in vitro-in vivo translational platform to assess the reduction of ejection fraction induced by antineoplastic drugs based on hiPSC-CMs. The translational platform integrates drug exposure, drug-cardiomyocyte interaction, and systemic response. The drug-cardiomyocyte interaction was implemented as a mechanism-based toxicodynamic (TD) model, which was then integrated into a quantitative system pharmacology-physiological-based pharmacokinetics (QSP-PBPK) model to form a complete translational platform. The platform was validated by comparing the model-predicted and clinically observed incidence of doxorubicin and trastuzumab-induced systolic dysfunction.

RESULTS

A total of 33,418 virtual patients were incorporated to receive doxorubicin and trastuzumab alone or in combination. For doxorubicin, the QSP-PBPK-TD model successfully captured the overall trend of systolic dysfunction incidences against the cumulative doses. For trastuzumab, the predicted incidence interval was 0.31-2.7% for single-agent treatment and 0.15-10% for trastuzumab-doxorubicin sequential treatment, covering the observations in clinical reports (0.50-1.0% and 1.5-8.3%, respectively).

CONCLUSIONS

In conclusion, the in vitro-in vivo translational platform is capable of predicting systolic dysfunction incidence almost merely depend on hiPSC-CMs, which could facilitate optimizing the treatment protocol of antineoplastic agents.

Towards a platform quantitative systems pharmacology (QSP) model for preclinical to clinical translation of antibody drug conjugates (ADCs)

A next generation multiscale quantitative systems pharmacology (QSP) model for antibody drug conjugates (ADCs) is presented, for preclinical to clinical translation of ADC efficacy. Two HER2 ADCs (trastuzumab-DM1 and trastuzumab-DXd) were used for model development, calibration, and validation. The model integrates drug specific experimental data including in vitro cellular disposition data, pharmacokinetic (PK) and tumor growth inhibition (TGI) data for T-DM1 and T-DXd, as well as system specific data such as properties of HER2, tumor growth rates, and volumes. The model incorporates mechanistic detail at the intracellular level, to account for different mechanisms of ADC processing and payload release. It describes the disposition of the ADC, antibody, and payload inside and outside of the tumor, including binding to off-tumor, on-target sinks. The resulting multiscale PK model predicts plasma and tumor concentrations of ADC and payload. Tumor payload concentrations predicted by the model were linked to a TGI model and used to describe responses following ADC administration to xenograft mice. The model was translated to humans and virtual clinical trial simulations were performed that successfully predicted progression free survival response for T-DM1 and T-DXd for the treatment of HER2+ metastatic breast cancer, including differential efficacy based upon HER2 expression status. In conclusion, the presented model is a step toward a platform QSP model and strategy for ADCs, integrating multiple types of data and knowledge to predict ADC efficacy. The model has potential application to facilitate ADC design, lead candidate selection, and clinical dosing schedule optimization.

A PKPD Case Study: Achieving Clinically Relevant Exposures of AZD5991 in Oncology Mouse Models

Capturing human equivalent drug exposures preclinically is a key challenge in the translational process. Motivated by the need to recapitulate the pharmacokinetic (PK) profile of the clinical stage Mcl-1 inhibitor AZD5991 in mice, we describe the methodology used to develop a refined mathematical model relating clinically relevant concentration profiles to efficacy. Administration routes were explored to achieve target exposures matching the clinical exposure of AZD5991. Intravenous infusion using vascular access button (VAB) technology was found to best reproduce clinical target exposures of AZD5991 in mice. Exposure-efficacy relationships were evaluated, demonstrating that dissimilar PK profiles result in differences in target engagement and efficacy outcomes. Thus, these data underscore the importance of accurately ascribing key PK metrics in the translational process to enable clinically meaningful predictions of efficacy.

Development of and insights from systems pharmacology models of antibody‐drug conjugates

Antibody‐drug conjugates (ADCs) have gained traction in the oncology space in the past few decades, with significant progress being made in recent years. Although the use of pharmacometric modeling is well‐established in the drug development process, there is an increasing need for a better quantitative biological understanding of the pharmacokinetic and pharmacodynamic relationships of these complex molecules. Quantitative systems pharmacology (QSP) approaches can assist in this endeavor; recent computational QSP models incorporate ADC‐specific mechanisms and use data‐driven simulations to predict experimental outcomes. Various modeling approaches and platforms have been developed at the in vitro, in vivo, and clinical scales, and can be further integrated to facilitate preclinical to clinical translation. These new tools can help researchers better understand the nature and mechanisms of these targeted therapies to help achieve a more favorable therapeutic window. This review delves into the world of systems pharmacology modeling of ADCs, discussing various modeling efforts in the field thus far.

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.

Simulating the Selection of Resistant Cells with Bystander Killing and Antibody Coadministration in Heterogeneous Human Epidermal Growth Factor Receptor 2-Positive Tumors

Intratumoral heterogeneity is a leading cause of treatment failure resulting in tumor recurrence. For the antibody-drug conjugate (ADC) ado-trastuzumab emtansine (T-DM1), two major types of resistance include changes in human epidermal growth factor receptor 2 (HER2) expression and reduced payload sensitivity, which is often exacerbated by heterogenous HER2 expression and ADC distribution during treatment. ADCs with bystander payloads, such as trastuzumab-monomethyl auristatin E (T-MMAE), can reach and kill adjacent cells with lower receptor expression that cannot be targeted directly with the ADC. Additionally, coadministration of T-DM1 with its unconjugated antibody, trastuzumab, can improve distribution and minimize heterogeneous delivery. However, the effectiveness of trastuzumab coadministration and ADC bystander killing in heterogenous tumors in reducing the selection of resistant cells is not well understood. Here, we use an agent-based model to predict outcomes with these different regimens. The simulations demonstrate that both T-DM1 and T-MMAE benefit from trastuzumab coadministration for tumors with high average receptor expression (up to 70% and 40% decrease in average tumor volume, respectively), with greater benefit for nonbystander payloads. However, the benefit decreases as receptor expression is reduced, reversing at low concentrations (up to 360% and 430% increase in average tumor volume for T-DM1 and T-MMAE, respectively) for this mechanism that impacts both ADC distribution and efficacy. For tumors with intrinsic payload resistance, coadministration uniformly exhibits better efficacy than ADC monotherapy (50%-70% and 19%-36% decrease in average tumor volume for T-DM1 and T-MMAE, respectively). Finally, we demonstrate that several regimens select for resistant cells at clinical tolerable doses, which highlights the need to pursue other mechanisms of action for durable treatment responses. SIGNIFICANCE STATEMENT: Experimental evidence demonstrates heterogeneity in the distribution of both the antibody-drug conjugate and the target receptor in the tumor microenvironment, which can promote the selection of resistant cells and lead to recurrence. This study quantifies the impact of increasing the antibody dose and utilizing bystander payloads in heterogeneous tumors. Alternative cell-killing mechanisms are needed to avoid enriching resistant cell populations.

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.

Unlocking the potential of antibody-drug conjugates for cancer therapy

Nine different antibody-drug conjugates (ADCs) are currently approved as cancer treatments, with dozens more in preclinical and clinical development. The primary goal of ADCs is to improve the therapeutic index of antineoplastic agents by restricting their systemic delivery to cells that express the target antigen of interest. Advances in synthetic biochemistry have ushered in a new generation of ADCs, which promise to improve upon the tissue specificity and cytotoxicity of their predecessors. Many of these drugs have impressive activity against treatment-refractory cancers, although hurdles impeding their broader use remain, including systemic toxicity, inadequate biomarkers for patient selection, acquired resistance and unknown benefit in combination with other cancer therapies. Emerging evidence indicates that the efficacy of a given ADC depends on the intricacies of how the antibody, linker and payload components interact with the tumour and its microenvironment, all of which have important clinical implications. In this Review, we discuss the current state of knowledge regarding the design, mechanism of action and clinical efficacy of ADCs as well as the apparent limitations of this treatment class. We then propose a path forward by highlighting several hypotheses and novel strategies to maximize the potential benefit that ADCs can provide to patients with cancer.

A meta-analysis of tumour response and relapse kinetics based on 34,881 patients: A question of cancer type, treatment and line of treatment

PURPOSE

Cancer disease burden is commonly assessed radiologically in solid tumours in support of response assessment via the RECIST criteria. These longitudinal data are amenable to mathematical modelling and these models characterise the initial tumour size, initial tumour shrinkage in responding patients and rate of regrowth as patient's disease progresses. Knowing how these parameters vary between patient populations and treatments would inform translational modelling approaches from non-clinical data as well as clinical trial design.

EXPERIMENTAL DESIGN

Here a meta-analysis of reported model parameter values is reported. Appropriate literature was identified via a PubMed search and the application of text-based clustering approaches. The resulting parameter estimates are examined graphically and with ANOVA.

RESULTS

Parameter values from a total of 80 treatment arms were identified based on 80 trial arms containing a total of 34,881 patients. Parameter estimates are generally consistent. It is found that a significant proportion of the variation in rates of tumour shrinkage and regrowth are explained by differing cancer and treatment: cancer type accounts for 66% of the variation in shrinkage rate and 71% of the variation in reported regrowth rates. Mean average parameter values by cancer and treatment are also reported.

CONCLUSIONS

Mathematical modelling of longitudinal data is most often reported on a per clinical trial basis. However, the results reported here suggest that a more integrative approach would benefit the development of new treatments as well as the further optimisation of those currently used.

Quantitative Evaluation of the Effect of Antigen Expression Level on Antibody-Drug Conjugate Exposure in Solid Tumor

Antibody-drug conjugates (ADCs) rely on high expression of target antigens on cancer cells to effectively enter the cell and release a cytotoxic payload. Previous studies have shown that ADC efficacy is not always tied to antigen expression. However, our recent in vitro study suggests a linear relationship between antigen expression and the intracellular levels of the ADC payload. In this study, we have explored the relationship between antigen expression and intratumoral ADC exposure in vivo. Using trastuzumab-vc-MMAE (T-vc-MMAE) and four cell lines with varying expression of human epithelial growth factor receptor 2 (HER2), the pharmacokinetics of total trastuzumab, released ("free") MMAE, and total MMAE were evaluated in a tumor xenograft model. Nude mice were implanted with tumors originating from BT-474, MDA-MB-453, MCF-7, and MDA-MB-468 cell lines and dosed with 10 mg/kg or 1 mg/kg of ADC. Observed data were mathematically characterized using a mechanism-based PK model. A strong positive correlation was observed between antigen expression levels and free/total MMAE exposure (R² ≥ 0.91) (total MMAE being the sum of released and conjugated MMAE) within the tumor, but not for total trastuzumab exposure. The PK model was able to recapitulate plasma PK through simulation; however, the tumor PK was overpredicted or underpredicted in some cases potentially due to differences in tumor vasculature or extracellular matrix conditions. Our results indicate a linear relationship between antigen expression and tumor exposure of free/total ADC payload in vivo, validating our previous finding in vitro, while also revealing the need to understand complex physiology of the tumor to predict tumor PK of ADC and its components. Our findings also support the concept of antigen expression screening in patients for targeted therapies like ADCs to achieve the maximum therapeutic benefit of the treatment.

Allometric scaling of therapeutic monoclonal antibodies in preclinical and clinical settings

Constant technological advancement enabled the production of therapeutic monoclonal antibodies (mAbs) and will continue to contribute to their rapid expansion. Compared to small-molecule drugs, mAbs have favorable characteristics, but also more complex pharmacokinetics (PK), e.g., target-mediated nonlinear elimination and recycling by neonatal Fc-receptor. This review briefly discusses mAb biology, similarities and differences in PK processes across species and within human, and provides a detailed overview of allometric scaling approaches for translating mAb PK from preclinical species to human and extrapolating from adults to children. The approaches described here will remain vital in mAb drug development, although more data are needed, for example, from very young patients and mAbs with nonlinear PK, to allow for more confident conclusions and contribute to further growth of this field. Improving mAb PK predictions will facilitate better planning of (pediatric) clinical studies and enable progression toward the ultimate goal of expediting drug development.

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.

Entering the era of computationally driven drug development

Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy. However, the increasing quality and standardization of experimental methods in preclinical stages of testing has created valuable data sets within companies that can be leveraged to further improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of Quantitative structure-activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integration of traditional computational methods with machine-learning approaches and existing internal pharma databases stands to make a fundamental impact on the speed and accuracy of predictions during the process of drug development and approval.

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.

Antibody Coadministration as a Strategy to Overcome Binding-Site Barrier for ADCs: a Quantitative Investigation

It has been proposed that the binding-site barrier (BSB) for antibody-drug conjugates (ADCs) can be overcome with the help of antibody coadministration. However, broad utility of this strategy remains in question. Consequently, here, we have conducted in vivo experiments and pharmacokinetics-pharmacodynamics (PK-PD) modeling and simulation (M&S) to further evaluate the antibody coadministration hypothesis in a quantitative manner. Two different Trastuzumab-based ADCs, T-DM1 (no bystander effect) and T-vc-MMAE (with a bystander effect), were evaluated in high-HER2 (N87) and low-HER2 (MDA-MB-453) expressing tumors, with or without the coadministration of 1, 3, or 8-fold higher Trastuzumab. The tumor growth inhibition (TGI) data was quantitatively characterized using a semi-mechanistic PK-PD model to determine the nature of drug interaction for each coadministration regimen, by estimating the interaction parameter ψ. It was found that the coadministration strategy improved ADC efficacy under certain conditions and had no impact on ADC efficacy in others. The benefit was more pronounced for N87 tumors with very high antigen expression levels where the effect on treatment was synergistic (a synergistic drug interaction, ψ = 2.86 [2.6-3.12]). The benefit was diminished in tumor with lower antigen expression (MDA-MB-453) and payload with bystander effect. Under these conditions, the coadministration regimens resulted in an additive or even less than additive benefit (ψ ≤ 1). As such, our results suggest that while antibody coadministration may be helpful for ADCs in certain circumstances, one should not broadly apply this strategy to all the scenarios without first identifying the costs and benefits of this approach.

Antibody-Drug Conjugates: Pharmacokinetic/Pharmacodynamic Modeling, Preclinical Characterization, Clinical Studies, and Lessons Learned

Antibody-drug conjugates are an emerging class of biopharmaceuticals changing the landscape of targeted chemotherapy. These conjugates combine the target specificity of monoclonal antibodies with the anti-cancer activity of small-molecule therapeutics. Several antibody-drug conjugates have received approval for the treatment of various types of cancer including gemtuzumab ozogamicin (Mylotarg^®), brentuximab vedotin (Adcetris^®), trastuzumab emtansine (Kadcyla^®), and inotuzumab ozogamicin, which recently received approval (Besponsa^®). In addition to these approved therapies, there are many antibody-drug conjugates in the drug development pipeline and in clinical trials, although these fall outside the scope of this article. Understanding the pharmacokinetics and pharmacodynamics of antibody-drug conjugates and the development of pharmacokinetic/pharmacodynamic models is indispensable, albeit challenging as there are many parameters to incorporate including the disposition of the intact antibody-drug conjugate complex, the antibody, and the drug agents following their dissociation in the body. In this review, we discuss how antibody-drug conjugates progressed over time, the challenges in their development, and how our understanding of their pharmacokinetics/pharmacodynamics led to greater strides towards successful targeted therapy programs.

A Cell-Level Systems PK-PD Model to Characterize In Vivo Efficacy of ADCs

Here, we have presented the development of a systems pharmacokinetics-pharmacodynamics (PK-PD) model for antibody-drug conjugates (ADCs), which uses intracellular target occupancy to drive in-vivo efficacy. The model is built based on PK and efficacy data generated using Trastuzumab-Valine-Citrulline-Monomethyl Auristatin E (T-vc-MMAE) ADC in N87 (high-HER2) and GFP-MCF7 (low-HER2) tumor bearing mice. It was observed that plasma PK of all ADC analytes was similar between the two tumor models; however, total trastuzumab, unconjugated MMAE, and total MMAE exposures were >10-fold, ~1.6-fold, and ~1.8-fold higher in N87 tumors. In addition, a prolonged retention of MMAE was observed within the tumors of both the mouse models, suggesting intracellular binding of MMAE to tubulin. A systems PK model, developed by integrating single-cell PK model with tumor distribution model, was able to capture all in vivo PK data reasonably well. Intracellular occupancy of tubulin predicted by the PK model was used to drive the efficacy of ADC using a novel PK-PD model. It was found that the same set of PD parameters was able to capture MMAE induced killing of GFP-MCF7 and N87 cells in vivo. These observations highlight the benefit of adopting a systems approach for ADC and provide a robust and predictive framework for successful clinical translation of ADCs.

A "Dual" Cell-Level Systems PK-PD Model to Characterize the Bystander Effect of ADC

Here, we have developed a cell-level systems PK-PD model to characterize the bystander effect of antibody-drug conjugates (ADCs). Cytotoxicity data generated following incubation of Trastuzumab-vc-MMAE in cocultures of high HER2-expressing N87 and low HER2-expressing GFP-MCF7 cells were used to build the model. Single-cell PK model for ADC was used to characterize the PK of trastuzumab-vc-MMAE and released MMAE in N87 and GFP-MCF7 cells. The 2 cell-level PK models were mechanistically integrated to mimic the coculture condition. MMAE-induced intracellular occupancy of tubulin was used to drive the efficacy of ADC, and improvement in the tubulin occupancy of GFP-MCF7 cells in the presence of N87 cells was used to drive the bystander effect of trastuzumab-vc-MMAE. The "dual" cell-level PK-PD model was able to capture the observed data reasonably well. It was found that similar and high occupancy of tubulin by MMAE was required to achieve the cytotoxic effect in each cell line. In addition, estimated model parameters suggested that ∼60% improvement in the tubulin occupancy was required to attain half of the maximum bystander killing effect by the ADC. The presented model provides foundation for in vivo systems PK-PD model to characterize and predict the bystander effect of ADCs.

Estimation of Solid Tumor Doubling Times from Progression-Free Survival Plots Using a Novel Statistical Approach

Tumor doubling time can significantly affect the outcome of anticancer therapy, but it is very challenging to determine. Here, we present a statistical approach that extracts doubling times from progression-free survival (PFS) plots, which inherently contains information regarding the growth of solid tumors. Twelve cancers were investigated and multiple PFS plots were evaluated for each type. The PFS plot showing fastest tumor growth was deemed to best represent the inherent growth kinetics of the solid tumor, and selected for further analysis. The exponential tumor growth rates were extracted from each PFS plot, along with associated variabilities, which ultimately allowed for the estimation of solid tumor doubling times. The mean simulated doubling times for pancreatic cancer, melanoma, hepatocellular carcinoma (HCC), renal cell carcinoma, triple negative breast cancer, non-small cell lung cancer, hormone receptor positive (HR+) breast cancer, human epidermal growth factor receptor-2 positive (HER-2+) breast cancer, gastric cancer, glioblastoma multiforme, colorectal cancer, and prostate cancer were 5.06, 3.78, 3.06, 2.67, 2.38, 2.40, 4.31, 4.12, and 3.84 months, respectively. For all cancers, clinically reported doubling times were within the estimated ranges. For all cancers, except HCC, the growth rates were best characterized by a log-normal distribution. For HCC, the gamma distribution best described the data. The statistical approach presented here provides a qualified method for extracting tumor growth rates and doubling times from PFS plots. It also allows estimation of the distributional characteristics for tumor growth rates and doubling times in a given patient population.

Pharmacokinetics of protein and peptide conjugates

Protein and peptide conjugates have become an important component of therapeutic and diagnostic medicine. These conjugates are primarily designed to improve pharmacokinetics (PK) of those therapeutic or imaging agents, which do not possess optimal disposition characteristics. In this review we have summarized preclinical and clinical PK of diverse protein and peptide conjugates, and have showcased how different conjugation approaches are used to obtain the desired PK. We have classified the conjugates into peptide conjugates, non-targeted protein conjugates, and targeted protein conjugates, and have highlighted diagnostic and therapeutic applications of these conjugates. In general, peptide conjugates demonstrate very short half-life and rapid renal elimination, and they are mainly designed to achieve high contrast ratio for imaging agents or to deliver therapeutic agents at sites not reachable by bulky or non-targeted proteins. Conjugates made from non-targeted proteins like albumin are designed to increase the half-life of rapidly eliminating therapeutic or imaging agents, and improve their delivery to tissues like solid tumors and inflamed joints. Targeted protein conjugates are mainly developed from antibodies, antibody derivatives, or endogenous proteins, and they are designed to improve the contrast ratio of imaging agents or therapeutic index of therapeutic agents, by enhancing their delivery to the site-of-action.

Pharmacokinetic and Immunological Considerations for Expanding the Therapeutic Window of Next-Generation Antibody-Drug Conjugates

Antibody-drug conjugate (ADC) development has evolved greatly over the last 3 decades, including the Food and Drug Administration (FDA) approval of several new drugs. However, translating ADCs from the design stage and preclinical promise to clinical success has been a major hurdle for the field, particularly for solid tumors. The challenge in clinical development can be attributed to the difficulty in connecting the design of these multifaceted agents with the impact on clinical efficacy, especially with the accelerated development of 'next-generation' ADCs containing a variety of innovative biophysical developments. Given their complex nature, there is an urgent need to integrate holistic ADC characterization approaches. This includes comprehensive in vivo assessment of systemic, intratumoral and cellular pharmacokinetics, pharmacodynamics, toxicodynamics, and interactions with the immune system, with the aim of optimizing the ADC therapeutic window. Pharmacokinetic/pharmacodynamic factors influencing the ADC therapeutic window include (1) selecting optimal target and ADC components for prolonged and stable plasma circulation to increase tumoral uptake with minimal non-specific systemic toxicity, (2) balancing homogeneous intratumoral distribution with efficient cellular uptake, and (3) translating improved ADC potency to better clinical efficacy. Balancing beneficial immunological effects such as Fc-mediated and payload-mediated immune cell activation against harmful immunogenic/toxic effects is also an emerging concern for ADCs. Here, we review practical considerations for tracking ADC efficacy and toxicity, as aided by high-resolution biomolecular and immunological tools, quantitative pharmacology, and mathematical models, all of which can elucidate the relative contributions of the multitude of interactions governing the ADC therapeutic window.

Survival Prolongation Index as a Novel Metric to Assess Anti-Tumor Activity in Xenograft Models

A single efficacy metric quantifying anti-tumor activity in xenograft models is useful in evaluating different tumors' drug sensitivity and dose-response of an anti-tumor agent. Commonly used metrics include the ratio of tumor volume in treated vs. control mice (T/C), tumor growth inhibition (TGI), ratio of area under the curve (AUC), and growth rate inhibition (GRI). However, these metrics have some limitations. In particular, for biologics with long half-lives, tumor volume (TV) of treated xenografts displays a delay in volume reduction (and in some cases, complete regression) followed by a growth rebound. These observed data cannot be described by exponential functions, which is the underlying assumption of TGI and GRI, and the fit depends on how long the tumor volumes are monitored. On the other hand, T/C and TGI only utilizes information from one chosen time point. Here, we propose a new metric called Survival Prolongation Index (SPI), calculated as the time for drug-treated TV to reach a certain size (e.g., 600 mm³) divided by the time for control TV to reach 600mm³ and therefore not dependent on the chosen final time point t_f. Simulations were conducted under different scenarios (i.e., exponential vs. saturable growth, linear vs. nonlinear kill function). For all cases, SPI is the most linear and growth-rate independent metric. Subsequently, a literature analysis was conducted using 11 drugs to evaluate the correlation between pre-clinically obtained SPI and clinical overall response. This retrospective analysis of approved drugs suggests that a predicted SPI of 2 is necessary for clinical response.

Pediatric physiology in relation to the pharmacokinetics of monoclonal antibodies

INTRODUCTION

Dose design for pediatric trials with monoclonal antibodies (mAbs) is often extrapolated from the adult dose according to weight, age, or body surface area. While these methods account for the size differences between adults and children, they do not account for the maturation of processes that may play a key role in the pharmacokinetics and/or pharmacodynamics of mAbs. With the same weight-based dose, infants and young children typically receive lower plasma exposures when compared to adults. Areas covered: The mechanistic features of mAb distribution, elimination, and absorption are explored in detail and literature-based hypotheses are generated to describe their age-dependence. This knowledge can be incorporated into a physiologically based pharmacokinetic (PBPK) modeling approach to pediatric dose determination. Expert opinion: As data from pediatric clinical trials become increasingly available, we have the opportunity to reflect on the physiologic drivers of pharmacokinetics, safety, and efficacy in children with mathematical models. A modeling approach that accounts for the age-related features of mAb disposition can be used to derive first-in-pediatric doses, design optimal sampling schemes for children in clinical trials and even explore new pharmacokinetic end-points as predictors of safety and efficacy in children.

Pharmacokinetic Considerations for Antibody-Drug Conjugates against Cancer

Antibody-drug conjugates (ADCs) are ushering in the next era of targeted therapy against cancer. An ADC for cancer therapy consists of a potent cytotoxic payload that is attached to a tumour-targeted antibody by a chemical linker, usually with an average drug-to-antibody ratio (DAR) of 3.5-4. The theory is to deliver potent cytotoxic payloads directly to tumour cells while sparing healthy cells. However, practical application has proven to be more difficult. At present there are only two ADCs approved for clinical use. Nevertheless, in the last decade there has been an explosion of options for ADC engineering to optimize target selection, Fc receptor interactions, linker, payload and more. Evaluation of these strategies requires an understanding of the mechanistic underpinnings of ADC pharmacokinetics. Development of ADCs for use in cancer further requires an understanding of tumour properties and kinetics within the tumour environment, and how the presence of cancer as a disease will impact distribution and elimination. Key pharmacokinetic considerations for the successful design and clinical application of ADCs in oncology are explored in this review, with a focus on the mechanistic determinants of distribution and elimination.

Measurement and Mathematical Characterization of Cell-Level Pharmacokinetics of Antibody-Drug Conjugates: A Case Study with Trastuzumab-vc-MMAE

The main objective of this work was to understand and mathematically characterize the cellular disposition of a tool antibody-drug conjugate (ADC), trastuzumab-valine-citrulline-monomethyl auristatin E (T-vc-MMAE). Toward this goal, three different analytical methods were developed to measure the concentrations of different ADC-related analytes in the media and cell lysate. A liquid chromatography-tandem mass spectrometry method was developed to quantify unconjugated drug (i.e., MMAE) concentrations, a forced deconjugation method was developed to quantify total drug concentrations, and an enzyme-linked immunosorbent assay method was developed to quantify total antibody (i.e., trastuzumab) concentrations. Cellular disposition studies were conducted in low-HER2-(GFP-MCF7) and high-HER2-expressing (N87) cell lines, following continuous or 2-hour exposure to MMAE and T-vc-MMAE. Similar intracellular accumulation of MMAE was observed between two cell lines following incubation with plain MMAE. However, when incubated with T-vc-MMAE, much higher intracellular exposures of unconjugated drug, total drug, and total antibody were observed in N87 cells compared with GFP-MCF7 cells. A novel single-cell disposition model was developed to simultaneously characterize in vitro pharmacokinetics of all three analytes of the ADC in the media and cellular space. The model was able to characterize all the data well and provided robust estimates of MMAE influx rate, MMAE efflux rate, and intracellular degradation rate for T-vc-MMAE. ADC internalization and degradation rates, HER2 expression, and MMAE efflux rate were found to be the key parameters responsible for intracellular exposure to MMAE, on the basis of a global sensitivity analysis. The single-cell pharmacokinetics model for ADCs presented here is expected to provide a better framework for characterizing bystander effect of ADCs.

Gemcitabine–camptothecin conjugates: a hybrid prodrug for controlled drug release and synergistic therapeutics

Self-assembled small molecule prodrug loaded with gemcitabine and camptothecin and responsive to reductive tumour microenvironment for combination cancer chemotherapy.