A priori prediction of tumor payload concentrations: preclinical case study with an auristatin-based anti-5T4 antibody-drug conjugate

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.

Pharmacokinetics and Pharmacodynamics of Antibody-Drug Conjugates Administered via Subcutaneous and Intratumoral Routes

We hypothesize that different routes of administration may lead to altered pharmacokinetics/pharmacodynamics (PK/PD) behavior of antibody-drug conjugates (ADCs) and may help to improve their therapeutic index. To evaluate this hypothesis, here we performed PK/PD evaluation for an ADC administered via subcutaneous (SC) and intratumoral (IT) routes. Trastuzumab-vc-MMAE was used as the model ADC, and NCI-N87 tumor-bearing xenografts were used as the animal model. The PK of multiple ADC analytes in plasma and tumors, and the in vivo efficacy of ADC, after IV, SC, and IT administration were evaluated. A semi-mechanistic PK/PD model was developed to characterize all the PK/PD data simultaneously. In addition, local toxicity of SC-administered ADC was investigated in immunocompetent and immunodeficient mice. Intratumoral administration was found to significantly increase tumor exposure and anti-tumor activity of ADC. The PK/PD model suggested that the IT route may provide the same efficacy as the IV route at an increased dosing interval and reduced dose level. SC administration of ADC led to local toxicity and reduced efficacy, suggesting difficulty in switching from IV to SC route for some ADCs. As such, this manuscript provides unprecedented insight into the PK/PD behavior of ADCs after IT and SC administration and paves the way for clinical evaluation of these routes.

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.

Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation

Therapeutic monoclonal antibodies (mAbs) have been a promising therapeutic approach for several diseases and a wide variety of mAbs are being evaluated in clinical trials. To accelerate clinical development and improve the probability of success, pharmacokinetics and pharmacodynamics (PKPD) in humans must be predicted before clinical trials can begin. Traditionally, empirical-approach-based PKPD prediction has been applied for a long time. Recently, modeling and simulation (M&S) methods have also become valuable for quantitatively predicting PKPD in humans. Although several models (e.g., the compartment model, Michaelis–Menten model, target-mediated drug disposition model, and physiologically based pharmacokinetic model) have been established and used to predict the PKPD of mAbs in humans, more complex mechanistic models, such as the quantitative systemics pharmacology model, have been recently developed. This review summarizes the recent advances and future direction of M&S-based approaches to the quantitative prediction of human PKPD for mAbs.

Predictive Simulations in Preclinical Oncology to Guide the Translation of Biologics

Preclinical in vivo studies form the cornerstone of drug development and translation, bridging in vitro experiments with first-in-human trials. However, despite the utility of animal models, translation from the bench to bedside remains difficult, particularly for biologics and agents with unique mechanisms of action. The limitations of these animal models may advance agents that are ineffective in the clinic, or worse, screen out compounds that would be successful drugs. One reason for such failure is that animal models often allow clinically intolerable doses, which can undermine translation from otherwise promising efficacy studies. Other times, tolerability makes it challenging to identify the necessary dose range for clinical testing. With the ability to predict pharmacokinetic and pharmacodynamic responses, mechanistic simulations can help advance candidates from in vitro to in vivo and clinical studies. Here, we use basic insights into drug disposition to analyze the dosing of antibody drug conjugates (ADC) and checkpoint inhibitor dosing (PD-1 and PD-L1) in the clinic. The results demonstrate how simulations can identify the most promising clinical compounds rather than the most effective in vitro and preclinical in vivo agents. Likewise, the importance of quantifying absolute target expression and antibody internalization is critical to accurately scale dosing. These predictive models are capable of simulating clinical scenarios and providing results that can be validated and updated along the entire development pipeline starting in drug discovery. Combined with experimental approaches, simulations can guide the selection of compounds at early stages that are predicted to have the highest efficacy in the clinic.

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.

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.

Whole-Body Pharmacokinetics and Physiologically Based Pharmacokinetic Model for Monomethyl Auristatin E (MMAE)

Monomethyl auristatin E (MMAE) is one of the most commonly used payloads for developing antibody–drug conjugates (ADC). However, limited studies have comprehensively evaluated the whole-body disposition of MMAE. Consequently, here, we have investigated the whole-body pharmacokinetics (PK) of MMAE in tumor-bearing mice. We show that while MMAE is rapidly eliminated from the plasma, it shows prolonged and extensive distribution in tissues, blood cells, and tumor. Highly perfused tissues (e.g., lung, kidney, heart, liver, and spleen) demonstrated tissue-to-plasma area under the concentration curve (AUC) ratios > 20, and poorly perfused tissues (e.g., fat, pancreas, skin, bone, and muscle) had ratios from 1.3 to 2.4. MMAE distribution was limited in the brain, and tumor had 8-fold higher exposure than plasma. A physiological-based pharmacokinetic (PBPK) model was developed to characterize the whole-body PK of MMAE, which accounted for perfusion/permeability-limited transfer of drug in the tissue, blood cell distribution of the drug, tissue/tumor retention of the drug, and plasma protein binding. The model was able to characterize the PK of MMAE in plasma, tissues, and tumor simultaneously, and model parameters were estimated with good precision. The MMAE PBPK model presented here can facilitate the development of a platform PBPK model for MMAE containing ADCs and help with their preclinical-to-clinical translation and clinical dose optimization.

Pharmacokinetic-Pharmacodynamic Modeling of Tumor Targeted Drug Delivery Using Nano-Engineered Mesenchymal Stem Cells

Nano-engineered mesenchymal stem cells (nano-MSCs) are promising targeted drug delivery platforms for treating solid tumors. MSCs engineered with paclitaxel (PTX) loaded poly(lactide-co-glycolide) (PLGA) nanoparticles (NPs) are efficacious in treating lung and ovarian tumors in mouse models. The quantitative description of pharmacokinetics (PK) and pharmacodynamics (PD) of nano-MSCs is crucial for optimizing their therapeutic efficacy and clinical translatability. However, successful translation of nano-MSCs is challenging due to their complex composition and physiological mechanisms regulating their pharmacokinetic-pharmacodynamic relationship (PK-PD). Therefore, in this study, a mechanism-based preclinical PK-PD model was developed to characterize the PK-PD relationship of nano-MSCs in orthotopic A549 human lung tumors in SCID Beige mice. The developed model leveraged literature information on diffusivity and permeability of PTX and PLGA NPs, PTX release from PLGA NPs, exocytosis of NPs from MSCs as well as PK and PD profiles of nano-MSCs from previous in vitro and in vivo studies. The developed PK-PD model closely captured the reported tumor growth in animals receiving no treatment, PTX solution, PTX-PLGA NPs and nano-MSCs. Model simulations suggest that increasing the dosage of nano-MSCs and/or reducing the rate of PTX-PLGA NPs exocytosis from MSCs could result in improved anti-tumor efficacy in preclinical settings.

Mechanistic Modeling of Intra-Tumor Spatial Distribution of Antibody-Drug Conjugates: Insights into Dosing Strategies in Oncology

Antibody drug conjugates (ADCs) provide targeted delivery of cytotoxic agents directly inside tumor cells. However, many ADCs targeting solid tumors have exhibited limited clinical efficacy, in part, due to insufficient penetration within tumors. To better understand the relationship between ADC tumor penetration and efficacy, previously applied Krogh cylinder models that explore tumor growth dynamics following ADC administration in preclinical species were expanded to a clinical framework by integrating clinical pharmacokinetics, tumor penetration, and tumor growth inhibition. The objective of this framework is to link ADC tumor penetration and distribution to clinical efficacy. The model was validated by comparing virtual patient population simulations to observed overall response rates from trastuzumab‐DM1 treated patients with metastatic breast cancer. To capture clinical outcomes, we expanded upon previous Krogh cylinder models to include the additional mechanism of heterogeneous tumor growth inhibition spatially across the tumor. This expansion mechanistically captures clinical response rates by describing heterogeneous ADC binding and tumor cell killing; high binding and tumor cell death close to capillaries vs. low binding, and high tumor cell proliferation far from capillaries. Sensitivity analyses suggest that clinical efficacy could be optimized through dose fractionation, and that clinical efficacy is primarily dependent on the ADC‐target affinity, payload potency, and tumor growth rate. This work offers a mechanistic basis to predict and optimize ADC clinical efficacy for solid tumors, allowing dosing strategy optimization to improve patient outcomes.

A novel semi-mechanistic tumor growth fraction model for translation of preclinical efficacy of anti-glypican 3 antibody drug conjugate to human

The growing fraction (GF) of tumor has been reported as one of the predictive markers of the efficacy of chemotherapeutics. Therefore, a semi-mechanistic model has been developed that describes tumor growth on the basis of cell cycle, allowing the incorporation of the GF of a tumor in pharmacokinetic/pharmacodynamic (PK/PD) modeling. Efficacy data of anti-glypican 3 (GPC3) antibody drug conjugate (ADC) in a hepatocellular carcinoma (HCC) patient derived xenograft (PDX) model was used for evaluation of this proposed model. Our model was able to describe the kinetics of growth inhibition of HCC PDX models following treatment with anti-GPC3 ADC remarkably well. The estimated tumurostatic concentrations were used in tandem with human PKs translated from cynomolgus monkey for prediction of the efficacious dose. The projected efficacious human dose of anti-GPC3 ADC was in the range 0.20-0.63 mg/kg for the Q3W dosing regimen, with a median dose of 0.50 mg/kg. This publication is the first step in evaluating the applicability of GF in PK/PD modeling of ADCs. The authors are hopeful that incorporation of GF will result in an improved translation of the preclinical efficacy of ADCs to clinical settings and thereby better prediction of the efficacious human dose.

Capturing the Magic Bullet: Pharmacokinetic Principles and Modeling of Antibody-Drug Conjugates

Over the past two decades, antibody-drug conjugates (ADCs) have emerged as a promising class of drugs for cancer therapy and have expanded to nononcology fields such as inflammatory diseases, atherosclerosis, and bacteremia. Eight ADCs are currently approved by FDA for clinical applications, with more novel ADCs under clinical development. Compared with traditional chemotherapy, ADCs combine the target specificity of antibodies with chemotherapeutic capabilities of cytotoxic drugs. The benefits include reduced systemic toxicity and enhanced therapeutic index for patients. However, the heterogeneous structures of ADCs and their dynamic changes following administration create challenges in their development. The understanding of ADC pharmacokinetics (PK) is crucial for the optimization of clinical dosing regimens when translating from animal to human. In addition, it contributes to the optimization of dose selection and clinical monitoring with regard to safety and efficacy. This manuscript reviews the PK characteristics of ADCs and summarizes the diverse approaches for PK modeling that can be used to evaluate an ADC at the preclinical and clinical stages to support their successful development. Despite the numerous available options, fit-for-purpose modeling approaches for the PK and PD of ADCs should be critically planned and well-thought-out to adequately support the development of an ADC.

Application of Pharmacokinetic-Pharmacodynamic Modeling in Drug Delivery: Development and Challenges

With the advancement of technology, drug delivery systems and molecules with more complex architecture are developed. As a result, the drug absorption and disposition processes after administration of these drug delivery systems and engineered molecules become exceedingly complex. As the pharmacokinetic and pharmacodynamic (PK-PD) modeling allows for the separation of the drug-, carrier- and pharmacological system-specific parameters, it has been widely used to improve understanding of the in vivo behavior of these complex delivery systems and help their development. In this review, we summarized the basic PK-PD modeling theory in drug delivery and demonstrated how it had been applied to help the development of new delivery systems and modified large molecules. The linkage between PK and PD was highlighted. In particular, we exemplified the application of PK-PD modeling in the development of extended-release formulations, liposomal drugs, modified proteins, and antibody-drug conjugates. Furthermore, the model-based simulation using primary PD models for direct and indirect PD responses was conducted to explain the assertion of hypothetical minimal effective concentration or threshold in the exposure-response relationship of many drugs and its misconception. The limitations and challenges of the mechanism-based PK-PD model were also discussed.

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.

Combination Treatment with an Antibody-Drug Conjugate (A1mcMMAF) Targeting the Oncofetal Glycoprotein 5T4 and Carboplatin Improves Survival in a Xenograft Model of Ovarian Cancer

Background

Recurrence occurs in over 75% of women with epithelial ovarian cancer despite optimal treatment. Selectively killing tumour cells thought to initiate relapse using an antibody–drug conjugate could prolong progression-free survival and offer an improved side-effect profile. A1mcMMAF is an antibody–drug conjugate designed to target cells expressing the tumour-associated antigen 5T4. It has shown to be efficacious in various cell line models and have a greater impact when combined with routine chemotherapeutic regimes.

Objectives

This study aims to explore the potential for the use of a 5T4 antibody–drug conjugate in women with ovarian cancer both as a monotherapy and in combination with platinum-based chemotherapy.

Methods

Immunohistochemical analysis was used to assess 5T4 expression in tumours from patients with ovarian cancer. Effectiveness of A1mcMMAF therapy as a single agent and in combination with carboplatin was assessed in vitro in the ovarian cancer cell line SKOV3 and confirmed in vivo using a serial bioluminescence assay in a SKOV3 xenograft model of ovarian cancer.

Results

5T4 is confirmed as suitably expressed in epithelial ovarian cancers prior to adjuvant therapy and is an independent predictor of poor survival. A1mcMMAF showed specific activity, both in vitro and in vivo, against SKOV3 ovarian cancer cells. When used in combination with carboplatin, in vivo tumour growth was inhibited resulting in prolonged survival in a SKOV3 xenograft model.

Conclusions

These data support further investigation of A1mcMMAF in combination with platinum-based chemotherapy in ovarian and other cancer treatments.

Electronic supplementary material

The online version of this article (10.1007/s11523-019-00650-8) contains supplementary material, which is available to authorized users.

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.

Current status and future perspective on preclinical pharmacokinetic and pharmacodynamic (PK/PD) analysis: Survey in Japan pharmaceutical manufacturers association (JPMA)

Preclinical pharmacokinetic/pharmacodynamic (PK/PD) analysis is an efficient tool for the translational research and proof of mechanism/concept in animals. The questionnaire survey on the practice of preclinical PK/PD analysis was conducted in the member companies of the Japan Pharmaceutical Manufacturers Association (JPMA). According to the survey, 60% of companies conducted preclinical PK/PD analysis and its impact for drug development was different between each of the companies. The frequently analyzed therapeutic areas of preclinical PK/PD analysis were neurology, inflammation and metabolic disease, and those are different from the therapeutic area (infectious disease and oncology) in which PK/PD analysis was considered as effective by the present survey. Many companies which have used preclinical PK/PD analysis for the translation to human PK/PD and for the prediction of dose/regimen had good communication with other research & development (R&D) departments (e.g. pharmacology/clinical pharmacology). The increase in resources for preclinical PK/PD analysis including education was highly demanded. As a future perspective, the closer collaboration between pharmacokinetics scientists, pharmacologists, toxicologists and clinical pharmacologists and the increase in resources including upskilling and the comprehension of preclinical PK/PD analysis by the project team are considered to lead to efficient contributions to improve the success ratio of drug discovery and development.

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.

Prediction of non-linear pharmacokinetics in humans of an antibody-drug conjugate (ADC) when evaluation of higher doses in animals is limited by tolerability: Case study with an anti-CD33 ADC

For antibody-drug conjugates (ADCs) that carry a cytotoxic drug, doses that can be administered in preclinical studies are typically limited by tolerability, leading to a narrow dose range that can be tested. For molecules with non-linear pharmacokinetics (PK), this limited dose range may be insufficient to fully characterize the PK of the ADC and limits translation to humans. Mathematical PK models are frequently used for molecule selection during preclinical drug development and for translational predictions to guide clinical study design. Here, we present a practical approach that uses limited PK and receptor occupancy (RO) data of the corresponding unconjugated antibody to predict ADC PK when conjugation does not alter the non-specific clearance or the antibody-target interaction. We used a 2-compartment model incorporating non-specific and specific (target mediated) clearances, where the latter is a function of RO, to describe the PK of anti-CD33 ADC with dose-limiting neutropenia in cynomolgus monkeys. We tested our model by comparing PK predictions based on the unconjugated antibody to observed ADC PK data that was not utilized for model development. Prospective prediction of human PK was performed by incorporating in vitro binding affinity differences between species for varying levels of CD33 target expression. Additionally, this approach was used to predict human PK of other previously tested anti-CD33 molecules with published clinical data. The findings showed that, for a cytotoxic ADC with non-linear PK and limited preclinical PK data, incorporating RO in the PK model and using data from the corresponding unconjugated antibody at higher doses allowed the identification of parameters to characterize monkey PK and enabled human PK predictions.

Antibody-Drug Conjugates as Cancer Therapeutics: Past, Present, and Future

Antibody-drug conjugates (ADCs) represent an innovative therapeutic approach that provides novel treatment options and hope for patients with cancer. By coupling monoclonal antibodies (mAbs) to cytotoxic small-molecule payloads with a plasma-stable linker, ADCs offer the potential for increased drug specificity and fewer off-target effects than systemic chemotherapy. As evidence for the potential of these therapies, many new ADCs are in various stages of clinical development. Because their structure poses unique challenges to pharmacokinetic and pharmacodynamic characterization, it is critical to recognize the differences between ADCs and conventional chemotherapy in the design of ADC clinical development strategies. Although some properties may be determined mainly by either the mAb or the small-molecule portion, the behavior of these agents is not always predictable. Furthermore, because the absorption, distribution, metabolism, and excretion (ADME) of ADCs are influenced by all 3 of its components (mAb, linker, and payload), it is important to characterize the intact molecule, any target-mediated catabolic clearance of the mAb, and the ADME properties of the small-molecule payload. Here we describe key issues in the clinical development of ADCs, including considerations for designing first-in-human studies for ADCs. We discuss some difficulties of ADC pharmacokinetic characterization and current approaches to overcoming these challenges. Finally, we consider all aspects of clinical pharmacology assessment required during drug development, using examples from the literature to illustrate the discussion.

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.

Development of a Translational Physiologically Based Pharmacokinetic Model for Antibody-Drug Conjugates: a Case Study with T-DM1

Systems pharmacokinetic (PK) models that can characterize and predict whole body disposition of antibody-drug conjugates (ADCs) are needed to support (i) development of reliable exposure-response relationships for ADCs and (ii) selection of ADC targets with optimal tumor and tissue expression profiles. Towards this goal, we have developed a translational physiologically based PK (PBPK) model for ADCs, using T-DM1 as a tool compound. The preclinical PBPK model was developed using rat data. Biodistribution of DM1 in rats was used to develop the small molecule PBPK model, and the PK of conjugated trastuzumab (i.e., T-DM1) in rats was characterized using platform PBPK model for antibody. Both the PBPK models were combined via degradation and deconjugation processes. The degradation of conjugated antibody was assumed to be similar to a normal antibody, and the deconjugation of DM1 from T-DM1 in rats was estimated using plasma PK data. The rat PBPK model was translated to humans to predict clinical PK of T-DM1. The translation involved the use of human antibody PBPK model to characterize the PK of conjugated trastuzumab, use of allometric scaling to predict human clearance of DM1 catabolites, and use of monkey PK data to predict deconjugation of DM1 in the clinic. PBPK model-predicted clinical PK profiles were compared with clinically observed data. The PK of total trastuzumab and T-DM1 were predicted reasonably well, and slight systemic deviations were observed for the PK of DM1-containing catabolites. The ADC PBPK model presented here can serve as a platform to develop models for other ADCs.

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.

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)

Successful clinical translation of antibody-drug conjugates (ADCs) can be challenging due to complex pharmacokinetics and differences between preclinical and clinical tumors. To facilitate this translation, we have developed a general pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation (M&S)-based strategy for ADCs. Here we present the validation of this strategy using T-DM1 as a case study. A previously developed preclinical tumor disposition model for T-DM1 (Singh and Shah, AAPSJ. 2015; 18(4):861-875) was used to develop a PK-PD model that can characterize in vivo efficacy of T-DM1 in preclinical tumor models. The preclinical data was used to estimate the efficacy parameters for T-DM1. Human PK of T-DM1 was a priori predicted using allometric scaling of monkey PK parameters. The predicted human PK, preclinically estimated efficacy parameters, and clinically observed volume and growth parameters for breast cancer were combined to develop a translated clinical PK-PD model for T-DM1. Clinical trial simulations were performed using the translated PK-PD model to predict progression-free survival (PFS) and objective response rates (ORRs) for T-DM1. The model simulated PFS rates for HER2 1+ and 3+ populations were comparable to the rates observed in three different clinical trials. The model predicted only a modest improvement in ORR with an increase in clinically approved dose of T-DM1. However, the model suggested that a fractionated dosing regimen (e.g., front loading) may provide an improvement in the efficacy. In general, the PK-PD M&S-based strategy presented here is capable of a priori predicting the clinical efficacy of ADCs, and this strategy has been now retrospectively validated for all clinically approved ADCs.

Current Status of Marine-Derived Compounds as Warheads in Anti-Tumor Drug Candidates

In this review, we have attempted to describe all of the antibody–drug conjugates using a marine-derived compound as the “warhead”, that are currently in clinical trials as listed in the current version of the NIH clinical trials database (clinicaltrials.gov). In searching this database, we used the beta-test version currently available, as it permitted more specific search parameters, since the regular version did not always find trials that had been completed in the past with some agents. We also added small discussion sections on candidates that are still at the preclinical stage, including a derivative of diazonamide that has an unusual interaction with tubulin (DZ-23840), which may also be a potential warhead in the future.

Integrated Two-Analyte Population Pharmacokinetic Model for Antibody-Drug Conjugates in Patients: Implications for Reducing Pharmacokinetic Sampling

An integrated pharmacokinetics (PK) model that simultaneously describes concentrations of total antibody (Tab) and antibody‐conjugated monomethyl auristatin E (acMMAE) following administration of monomethyl auristatin E (MMAE)‐containing antibody–drug conjugates (ADCs) was developed based on phase I PK data with extensive sampling for two ADCs. Two linear two‐compartment models that shared all parameters were used to describe the PK of Tab and acMMAE, except that the deconjugation rate was an additional clearance pathway included in the acMMAE PK model compared to Tab. Further, the model demonstrated its ability to predict Tab concentrations and PK parameters based on observed acMMAE PK and various reduced or eliminated Tab PK sampling schemes of phase II data. Thus, this integrated model allows for the reduction of Tab PK sampling in late‐phase clinical development without compromising Tab PK characterization.

Biodistribution and Targeting of Anti-5T4 Antibody-Drug Conjugate Using Fluorescence Molecular Tomography

Understanding a drug's whole-body biodistribution and tumor targeting can provide important information regarding efficacy, safety, and dosing parameters. Current methods to evaluate biodistribution include in vivo imaging technologies like positron electron tomography and single-photon emission computed tomography or ex vivo quantitation of drug concentrations in tissues using autoradiography and standard biochemical assays. These methods use radioactive compounds or are cumbersome and do not give whole-body information. Here, for the first time, we show the utility of fluorescence molecular tomography (FMT) imaging to determine the biodistribution and targeting of an antibody-drug conjugate (ADC). An anti-5T4-antibody (5T4-Ab) and 5T4-ADC were conjugated with a near-infrared (NIR) fluorophore VivoTag 680XL (VT680). Both conjugated compounds were stable as determined by SEC-HPLC and plasma stability studies. Flow cytometry and fluorescence microscopy studies showed that VT680-conjugated 5T4-ADC specifically bound 5T4-expressing cells in vitro and also exhibited a similar cytotoxicity profile as the unconjugated 5T4-ADC. In vivo biodistribution and tumor targeting in an H1975 subcutaneous xenograft model demonstrated no significant differences between accumulation of VT680-conjugated 5T4-Ab or 5T4-ADC in either normal tissues or tumor. In addition, quantitation of heart signal from FMT imaging showed good correlation with the plasma pharmacokinetic profile suggesting that it (heart FMT imaging) may be a surrogate for plasma drug clearance. These results demonstrate that conjugation of VT680 to 5T4-Ab or 5T4-ADC does not change the behavior of native biologic, and FMT imaging can be a useful tool to understand biodistribution and tumor-targeting kinetics of antibodies, ADCs, and other biologics. Mol Cancer Ther; 15(10); 2530-40. ©2016 AACR.

Chemical Structure and Concentration of Intratumor Catabolites Determine Efficacy of Antibody Drug Conjugates

Despite recent technological advances in quantifying antibody drug conjugate (ADC) species, such as total antibody, conjugated antibody, conjugated drug, and payload drug in circulation, the correlation of their exposures with the efficacy of ADC outcomes in vivo remains challenging. Here, the chemical structures and concentrations of intratumor catabolites were investigated to better understand the drivers of ADC in vivo efficacy. Anti-CD22 disulfide-linked pyrrolobenzodiazepine (PBD-dimer) conjugates containing methyl- and cyclobutyl-substituted disulfide linkers exhibited strong efficacy in a WSU-DLCL2 xenograft mouse model, whereas an ADC derived from a cyclopropyl linker was inactive. Total ADC antibody concentrations and drug-to-antibody ratios (DAR) in circulation were similar between the cyclobutyl-containing ADC and the cyclopropyl-containing ADC; however, the former afforded the release of the PBD-dimer payload in the tumor, but the latter only generated a nonimmolating thiol-containing catabolite that did not bind to DNA. These results suggest that intratumor catabolite analysis rather than systemic pharmacokinetic analysis may be used to better explain and predict ADC in vivo efficacy. These are good examples to demonstrate that the chemical nature and concentration of intratumor catabolites depend on the linker type used for drug conjugation, and the potency of the released drug moiety ultimately determines the ADC in vivo efficacy.

Preclinical to Clinical Translation of Antibody-Drug Conjugates Using PK/PD Modeling: a Retrospective Analysis of Inotuzumab Ozogamicin

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model was used for preclinical to clinical translation of inotuzumab ozogamicin, a CD22-targeting antibody-drug conjugate (ADC) for B cell malignancies including non-Hodgkin's lymphoma (NHL) and acute lymphocytic leukemia (ALL). Preclinical data was integrated in a PK/PD model which included (1) a plasma PK model characterizing disposition and clearance of inotuzumab ozogamicin and its released payload N-Ac-γ-calicheamicin DMH, (2) a tumor disposition model describing ADC diffusion into the tumor extracellular environment, (3) a cellular model describing inotuzumab ozogamicin binding to CD22, internalization, intracellular N-Ac-γ-calicheamicin DMH release, binding to DNA, or efflux from the tumor cell, and (4) tumor growth and inhibition in mouse xenograft models. The preclinical model was translated to the clinic by incorporating human PK for inotuzumab ozogamicin and clinically relevant tumor volumes, tumor growth rates, and values for CD22 expression in the relevant patient populations. The resulting stochastic models predicted progression-free survival (PFS) rates for inotuzumab ozogamicin in patients comparable to the observed clinical results. The model suggested that a fractionated dosing regimen is superior to a conventional dosing regimen for ALL but not for NHL. Simulations indicated that tumor growth is a highly sensitive parameter and predictive of successful outcome. Inotuzumab ozogamicin PK and N-Ac-γ-calicheamicin DMH efflux are also sensitive parameters and would be considered more useful predictors of outcome than CD22 receptor expression. In summary, a multiscale, mechanism-based model has been developed for inotuzumab ozogamicin, which can integrate preclinical biomeasures and PK/PD data to predict clinical response.

Determination of Cellular Processing Rates for a Trastuzumab-Maytansinoid Antibody-Drug Conjugate (ADC) Highlights Key Parameters for ADC Design

Antibody-drug conjugates (ADCs) are a promising class of cancer therapeutics that combine the specificity of antibodies with the cytotoxic effects of payload drugs. A quantitative understanding of how ADCs are processed intracellularly can illustrate which processing steps most influence payload delivery, thus aiding the design of more effective ADCs. In this work, we develop a kinetic model for ADC cellular processing as well as generalizable methods based on flow cytometry and fluorescence imaging to parameterize this model. A number of key processing steps are included in the model: ADC binding to its target antigen, internalization via receptor-mediated endocytosis, proteolytic degradation of the ADC, efflux of the payload out of the cell, and payload binding to its intracellular target. The model was developed with a trastuzumab-maytansinoid ADC (TM-ADC) similar to trastuzumab-emtansine (T-DM1), which is used in the clinical treatment of HER2+ breast cancer. In three high-HER2-expressing cell lines (BT-474, NCI-N87, and SK-BR-3), we report for TM-ADC half-lives for internalization of 6-14 h, degradation of 18-25 h, and efflux rate of 44-73 h. Sensitivity analysis indicates that the internalization rate and efflux rate are key parameters for determining how much payload is delivered to a cell with TM-ADC. In addition, this model describing the cellular processing of ADCs can be incorporated into larger pharmacokinetics/pharmacodynamics models, as demonstrated in the associated companion paper.

Integration of bioanalytical measurements using PK-PD modeling and simulation: implications for antibody-drug conjugate development

Recent technological advances have enabled precise quantitation of various bioanalytical measurements pertaining to antibody-drug conjugates (ADCs). However, availability of bioanalytical data alone cannot guarantee the provision of correct go/no-go decisions at different stages of ADC development. Integration and comprehension of all the available data at each stage of ADC development is necessary to make a well informed and objective decision about moving the ADC forward to the clinic. In this manuscript, we have reviewed the application of PK-PD modeling and simulation for quantitative integration of diverse bioanalytical data available from different stages of ADC development. We have also elaborated on how similar bioanalytical data can be characterized using different models to gain distinct insights into ADC development.

Evolution of Antibody-Drug Conjugate Tumor Disposition Model to Predict Preclinical Tumor Pharmacokinetics of Trastuzumab-Emtansine (T-DM1)

A mathematical model capable of accurately characterizing intracellular disposition of ADCs is essential for a priori predicting unconjugated drug concentrations inside the tumor. Towards this goal, the objectives of this manuscript were to: (1) evolve previously published cellular disposition model of ADC with more intracellular details to characterize the disposition of T-DM1 in different HER2 expressing cell lines, (2) integrate the improved cellular model with the ADC tumor disposition model to a priori predict DM1 concentrations in a preclinical tumor model, and (3) identify prominent pathways and sensitive parameters associated with intracellular activation of ADCs. The cellular disposition model was augmented by incorporating intracellular ADC degradation and passive diffusion of unconjugated drug across tumor cells. Different biomeasures and chemomeasures for T-DM1, quantified in the companion manuscript, were incorporated into the modified model of ADC to characterize in vitro pharmacokinetics of T-DM1 in three HER2+ cell lines. When the cellular model was integrated with the tumor disposition model, the model was able to a priori predict tumor DM1 concentrations in xenograft mice. Pathway analysis suggested different contribution of antigen-mediated and passive diffusion pathways for intracellular unconjugated drug exposure between in vitro and in vivo systems. Global and local sensitivity analyses revealed that non-specific deconjugation and passive diffusion of the drug across tumor cell membrane are key parameters for drug exposure inside a cell. Finally, a systems pharmacokinetic model for intracellular processing of ADCs has been proposed to highlight our current understanding about the determinants of ADC activation inside a cell.

"Target-Site" Drug Metabolism and Transport

The recent symposium on "Target-Site" Drug Metabolism and Transport that was sponsored by the American Society for Pharmacology and Experimental Therapeutics at the 2014 Experimental Biology meeting in San Diego is summarized in this report. Emerging evidence has demonstrated that drug-metabolizing enzyme and transporter activity at the site of therapeutic action can affect the efficacy, safety, and metabolic properties of a given drug, with potential outcomes including altered dosing regimens, stricter exclusion criteria, or even the failure of a new chemical entity in clinical trials. Drug metabolism within the brain, for example, can contribute to metabolic activation of therapeutic drugs such as codeine as well as the elimination of potential neurotoxins in the brain. Similarly, the activity of oxidative and conjugative drug-metabolizing enzymes in the lung can have an effect on the efficacy of compounds such as resveratrol. In addition to metabolism, the active transport of compounds into or away from the site of action can also influence the outcome of a given therapeutic regimen or disease progression. For example, organic anion transporter 3 is involved in the initiation of pancreatic β-cell dysfunction and may have a role in how uremic toxins enter pancreatic β-cells and ultimately contribute to the pathogenesis of gestational diabetes. Finally, it is likely that a combination of target-specific metabolism and cellular internalization may have a significant role in determining the pharmacokinetics and efficacy of antibody-drug conjugates, a finding which has resulted in the development of a host of new analytical methods that are now used for characterizing the metabolism and disposition of antibody-drug conjugates. Taken together, the research summarized herein can provide for an increased understanding of potential barriers to drug efficacy and allow for a more rational approach for developing safe and effective therapeutics.

Preclinical Development of an anti-5T4 Antibody-Drug Conjugate: Pharmacokinetics in Mice, Rats, and NHP and Tumor/Tissue Distribution in Mice

The pharmacokinetics of an antibody (huA1)-drug (auristatin microtubule disrupting MMAF) conjugate, targeting 5T4-expressing cells, were characterized during the discovery and development phases in female nu/nu mice and cynomolgus monkeys after a single dose and in S-D rats and cynomolgus monkeys from multidose toxicity studies. Plasma/serum samples were analyzed using an ELISA-based method for antibody and conjugate (ADC) as well as for the released payload using an LC-MS/MS method. In addition, the distribution of the Ab, ADC, and released payload (cys-mcMMAF) was determined in a number of tissues (tumor, lung, liver, kidney, and heart) in two tumor mouse models (H1975 and MDA-MB-361-DYT2 models) using similar LBA and LC-MS/MS methods. Tissue distribution studies revealed preferential tumor distribution of cys-mcMMAF and its relative specificity to the 5T4 target containing tissue (tumor). Single dose studies suggests lower CL values at the higher doses in mice, although a linear relationship was seen in cynomolgus monkeys at doses from 0.3 to 10 mg/kg with no evidence of TMDD. Evaluation of DAR (drug-antibody ratio) in cynomolgus monkeys (at 3 mg/kg) indicated that at least half of the payload was still on the ADC 1 to 2 weeks after IV dosing. After multiple doses, the huA1 and conjugate data in rats and monkeys indicate that exposure (AUC) increases with increasing dose in a linear fashion. Systemic exposure (as assessed by Cmax and AUC) of the released payload increased with increasing dose, although exposure was very low and its pharmacokinetics appeared to be formation rate limited. The incidence of ADA was generally low in rats and monkeys. We will discuss cross species comparison, relationships between the Ab, ADC, and released payload exposure after multiple dosing, and insights into the distribution of this ADC with a focus on experimental design as a way to address or bypass apparent obstacles and its integration into predictive models.

Application of Pharmacokinetic-Pharmacodynamic Modeling and Simulation for Antibody-Drug Conjugate Development

Characterization and prediction of the pharmacokinetics (PK) and pharmacodynamics (PD) of Antibody-Drug Conjugates (ADCs) is challenging, since it requires simultaneous quantitative understanding about the PK-PD properties of three different molecular species i.e., the monoclonal antibody, the drug, and the conjugate. Mathematical modeling and simulation provides an excellent tool to overcome these challenges, as it can simultaneously integrate the PK-PD of ADCs and their components in a quantitative manner. Additionally, the computational PK-PD models can also serve as a cornerstone for the model-based drug development and preclinical-to-clinical translation of ADCs. To provide an overview of this subject matter, this manuscript reviews the PK-PD models applicable to ADCs. Additionally, the usage of these models during different drug development stages (i.e., discovery, preclinical development, and clinical development) is also emphasized. The importance of PK-PD modeling and simulation in making rationale go/no-go decisions throughout the drug development process is also highlighted. There is an array of PK-PD models available, ranging from the systems models specifically developed for ADCs to the empirical models applicable to all chemotherapeutic agents, which one can employ for ADCs. The decision about which model to choose depends on the questions to be answered, time at hand, and resources available.

Phenotype of TPBG Gene Replacement in the Mouse and Impact on the Pharmacokinetics of an Antibody-Drug Conjugate

The use of predictive preclinical models in drug discovery is critical for compound selection, optimization, preclinical to clinical translation, and strategic decision-making. Trophoblast glycoprotein (TPBG), also known as 5T4, is the therapeutic target of several anticancer agents currently in clinical development, largely due to its high expression in tumors and low expression in normal adult tissues. In this study, mice were engineered to express human TPBG under endogenous regulatory sequences by replacement of the murine Tpbg coding sequence. The gene replacement was considered functional since the hTPBG knockin (hTPBG-KI) mice did not exhibit clinical observations or histopathological phenotypes that are associated with Tpbg gene deletion, except in rare instances. The expression of hTPBG in certain epithelial cell types and in different microregions of the brain and spinal cord was consistent with previously reported phenotypes and expression patterns. In pharmacokinetic studies, the exposure of a clinical-stage anti-TPBG antibody-drug conjugate (ADC), A1mcMMAF, was lower in hTPBG-KI versus wild-type animals, which was evidence of target-related increased clearance in hTPBG-KI mice. Thus, the hTPBG-KI mice constitute an improved system for pharmacology studies with current and future TPBG-targeted therapies and can generate more precise pharmacokinetic and pharmacodynamic data. In general the strategy of employing gene replacement to improve pharmacokinetic assessments should be broadly applicable to the discovery and development of ADCs and other biotherapeutics.

Semi-mechanistic Multiple-Analyte Pharmacokinetic Model for an Antibody-Drug-Conjugate in Cynomolgus Monkeys

Purpose

A semi-mechanistic multiple-analyte population pharmacokinetics (PK) model was developed to describe the complex relationship between the different analytes of monomethyl auristatin E (MMAE) containing antibody-drug conjugates (ADCs) and to provide insight regarding the major pathways of conjugate elimination and unconjugated MMAE release in vivo.

Methods

For an anti-CD79b-MMAE ADC the PK of total antibody (Tab), conjugate (evaluated as antibody conjugated MMAE or acMMAE), and unconjugated MMAE were quantified in cynomolgus monkeys for single (0.3, 1, or 3 mg/kg), and multiple doses (3 or 5 mg/kg, every-three-weeks for 4 doses). The PK data of MMAE in cynomolgus monkeys, after intravenous administration of MMAE at single doses (0.03 or 0.063 mg/kg), was included in the analysis. A semi-mechanistic model was developed and parameter estimates were obtained by simultaneously fitting the model to all PK data using a hybrid ITS-MCPEM method.

Results

The final model well described the observed Tab, acMMAE and unconjugated MMAE concentration-time profiles. Analysis suggested that conjugate is lost via both proteolytic degradation and deconjugation, while unconjugated MMAE in systemic circulation appears to be mainly released via proteolytic degradation of the conjugate.

Conclusions

Our model improves the understanding of ADC catabolism, which may provide useful insights when designing future ADCs.

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-014-1585-y) contains supplementary material, which is available to authorized users.

Antibody-drug conjugates: an emerging modality for the treatment of cancer

Antibody-drug conjugates (ADCs) offer promise as a therapeutic modality that can potentially reduce the toxicities and poor therapeutic indices caused by the lack of specificity of conventional anticancer therapies. ADCs combine the potency of cytotoxic agents with the target selectivity of antibodies by chemically linking a cytotoxic payload to an antibody, potentially creating a synthetic molecule that will deliver targeted antitumor therapy that is both safe and efficacious. The ADC repertoire contains a range of payload molecules, antibodies, and linkers. Two ADC molecules, Kadcyla® and Adcetris®, have been approved by the FDA, and many more are currently in clinical development.