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.

Therapeutic index improvement of antibody-drug conjugates.

The commentary by Colombo and Rich recently published in Cancer Cell provides a timely and comprehensive review of the clinical maximum tolerated doses (MTDs) of antibody-drug conjugates (ADCs) and their corresponding small molecules/chemotherapies. The authors identified similarities between their MTDs and therefore question the historic assumptions made for ADCs, namely, that they increase the MTDs of their corresponding cytotoxic molecules. However, the authors did not address the superior anti-tumor responses of ADCs compared to their corresponding chemotherapies, as reported in clinical trials. In this point of view, we propose a revised model wherein the anti-tumor activities of ADCs and consequently their therapeutic indexes (TIs) are not solely associated with changes not only in their MTDs but also in their minimal effective doses (MEDs). In addition, when using an exposure-based TI calculation method, the superior anti-tumor activities of ADCs relative to their corresponding chemotherapy can readily be explained. We discussed the clinical and preclinical data in support of lower MEDs of ADCs and generated a revised graph illustrating the TI improvements of ADCs vs chemotherapy more accurately. We believe that our revised model can provide a blueprint for future improvements in protein engineering and chemical engineering of toxins to further advance ADC research and development.

A next generation mathematical model for the in vitro to clinical translation of T-cell engagers.

T-cell engager (TCE) molecules activate the immune system and direct it to kill tumor cells. The key mechanism of action of TCEs is to crosslink CD3 on T cells and tumor associated antigens (TAAs) on tumor cells. The formation of this trimolecular complex (i.e. trimer) mimics the immune synapse, leading to therapeutic-dependent T-cell activation and killing of tumor cells. Computational models supporting TCE development must predict trimer formation accurately. Here, we present a next-generation two-step binding mathematical model for TCEs to describe trimer formation. Specifically, we propose to model the second binding step with trans-avidity and as a two-dimensional (2D) process where the reactants are modeled as the cell-surface density. Compared to the 3D binding model where the reactants are described in terms of concentration, the 2D model predicts less sensitivity of trimer formation to varying cell densities, which better matches changes in EC50 from in vitro cytotoxicity assay data with varying E:T ratios. In addition, when translating in vitro cytotoxicity data to predict in vivo active clinical dose for blinatumomab, the choice of model leads to a notable difference in dose prediction. The dose predicted by the 2D model aligns better with the approved clinical dose and the prediction is robust under variations in the in vitro to in vivo translation assumptions. In conclusion, the 2D model with trans-avidity to describe trimer formation is an improved approach for TCEs and is likely to produce more accurate predictions to support TCE development.

Quantitative systems pharmacology modeling provides insight into inter-mouse variability of Anti-CTLA4 response.

Clinical responses of immuno-oncology therapies are highly variable among patients. Similar response variability has been observed in syngeneic mouse models. Understanding of the variability in the mouse models may shed light on patient variability. Using a murine anti-CTLA4 antibody as a case study, we developed a quantitative systems pharmacology model to capture the molecular interactions of the antibody and relevant cellular interactions that lead to tumor cell killing. Nonlinear mixed effect modeling was incorporated to capture the inter-animal variability of tumor growth profiles in response to anti-CTLA4 treatment. The results suggested that intratumoral CD8+ T cell kinetics and tumor proliferation rate were the main drivers of the variability. In addition, simulations indicated that nonresponsive mice to anti-CTLA4 treatment could be converted to responders by increasing the number of intratumoral CD8+ T cells. The model provides a mechanistic starting point for translation of CTLA4 inhibitors from syngeneic mice to the clinic.

Quantitative modeling predicts competitive advantages of a next generation anti-NKG2A monoclonal antibody over monalizumab for the treatment of cancer.

A semimechanistic pharmacokinetic (PK)/receptor occupancy (RO) model was constructed to differentiate a next generation anti-NKG2A monoclonal antibody (KSQ mAb) from monalizumab, an immune checkpoint inhibitor in multiple clinical trials for the treatment of solid tumors. A three-compartment model incorporating drug PK, biodistribution, and NKG2A receptor interactions was parameterized using monalizumab PK, in vitro affinity measurements for both monalizumab and KSQ mAb, and receptor burden estimates from the literature. Following calibration against monalizumab PK data in patients with rheumatoid arthritis, the model successfully predicted the published PK and RO observed in gynecological tumors and in patients with squamous cell carcinoma of the head and neck. Simulations predicted that the KSQ mAb requires a 10-fold lower dose than monalizumab to achieve a similar RO over a 3-week period following q3w intravenous (i.v.) infusion dosing. A global sensitivity analysis of the model indicated that the drug-target binding affinity greatly affects the tumor RO and that an optimal affinity is needed to balance RO with enhanced drug clearance due to target mediated drug disposition. The model predicted that the KSQ mAb can be dosed over a less frequent regimen or at lower dose levels than the current monalizumab clinical dosing regimen of 10 mg/kg q2w. Either dosing strategy represents a competitive advantage over the current therapy. The results of this study demonstrate a key role for mechanistic modeling in identifying optimal drug parameters to inform and accelerate progression of mAb to clinical trials.

Discovery and optimization of a novel anti-GUCY2c x CD3 bispecific antibody for the treatment of solid tumors.

We report here the discovery and optimization of a novel T cell retargeting anti-GUCY2C x anti-CD3ε bispecific antibody for the treatment of solid tumors. Using a combination of hybridoma, phage display and rational design protein engineering, we have developed a fully humanized and manufacturable CD3 bispecific antibody that demonstrates favorable pharmacokinetic properties and potent in vivo efficacy. Anti-GUCY2C and anti-CD3ε antibodies derived from mouse hybridomas were first humanized into well-behaved human variable region frameworks with full retention of binding and T-cell mediated cytotoxic activity. To address potential manufacturability concerns, multiple approaches were taken in parallel to optimize and de-risk the two antibody variable regions. These approaches included structure-guided rational mutagenesis and phage display-based optimization, focusing on improving stability, reducing polyreactivity and self-association potential, removing chemical liabilities and proteolytic cleavage sites, and de-risking immunogenicity. Employing rapid library construction methods as well as automated phage display and high-throughput protein production workflows enabled efficient generation of an optimized bispecific antibody with desirable manufacturability properties, high stability, and low nonspecific binding. Proteolytic cleavage and deamidation in complementarity-determining regions were also successfully addressed. Collectively, these improvements translated to a molecule with potent single-agent in vivo efficacy in a tumor cell line adoptive transfer model and a cynomolgus monkey pharmacokinetic profile (half-life>4.5 days) suitable for clinical development. Clinical evaluation of PF-07062119 is ongoing.

Use of translational modeling and simulation for quantitative comparison of PF-06804103, a new generation HER2 ADC, with Trastuzumab-DM1.

A modeling and simulation approach was used for quantitative comparison of a new generation HER2 antibody drug conjugate (ADC, PF-06804103) with trastuzumab-DM1 (T-DM1). To compare preclinical efficacy, the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of PF-06804103 and T-DM1 was determined across a range of mouse tumor xenograft models, using a tumor growth inhibition model. The tumor static concentration was assigned as the minimal efficacious concentration. PF-06804103 was concluded to be more potent than T-DM1 across cell lines studied. TSCs ranged from 1.0 to 9.8 µg/mL (n = 7) for PF-06804103 and from 4.7 to 29 µg/mL (n = 5) for T-DM1. Two experimental models which were resistant to T-DM1, responded to PF-06804103 treatment. A mechanism-based target mediated drug disposition (TMDD) model was used to predict the human PK of PF-06804103. This model was constructed and validated based on T-DM1 which has non-linear PK at doses administered in the clinic, driven by binding to shed HER2. Non-linear PK is predicted for PF-06804103 in the clinic and is dependent upon circulating HER2 extracellular domain (ECD) concentrations. The models were translated to human and suggested greater efficacy for PF-06804103 compared to T-DM1. In conclusion, a fit-for-purpose translational PK/PD strategy for ADCs is presented and used to compare a new generation HER2 ADC with T-DM1.

Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development of Bispecific Antibodies in Oncology.

Bispecific antibodies (bsAbs) have become an integral component of the therapeutic research strategy to treat cancer. In addition to clinically validated immune cell re-targeting, bsAbs are being designed for tumor targeting and as dual immune modulators. Explorative preclinical and emerging clinical data indicate potential for enhanced efficacy and reduced systemic toxicity. However, bsAbs are a complex modality with challenges to overcome in early clinical trials, including selection of relevant starting doses using a minimal anticipated biological effect level approach, and predicting efficacious dose despite nonintuitive dose response relationships. Multiple factors can contribute to variability in the clinic, including differences in functional affinity due to avidity, receptor expression, effector to target cell ratio, and presence of soluble target. Mechanistic modeling approaches are a powerful integrative tool to understand the complexities and aid in clinical translation, trial design, and prediction of regimens and strategies to reduce dose limiting toxicities of bsAbs. In this tutorial, the use of mechanistic modeling to impact decision making for bsAbs is presented and illustrated using case study examples.

Correction to: A Translational Quantitative Systems Pharmacology Model for CD3 Bispecific Molecules: Application to Quantify T Cell-Mediated Tumor Cell Killing by P-Cadherin LP DART®.

Typesetting error occurred and Figure 1a and Figure 1b were altered during the uploading process. The original article has been corrected.

A Translational Quantitative Systems Pharmacology Model for CD3 Bispecific Molecules: Application to Quantify T Cell-Mediated Tumor Cell Killing by P-Cadherin LP DART®.

CD3 bispecific antibody constructs recruit cytolytic T cells to kill tumor cells, offering a potent approach to treat cancer. T cell activation is driven by the formation of a trimolecular complex (trimer) between drugs, T cells, and tumor cells, mimicking an immune synapse. A translational quantitative systems pharmacology (QSP) model is proposed for CD3 bispecific molecules capable of predicting trimer concentration and linking it to tumor cell killing. The model was used to quantify the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of a CD3 bispecific targeting P-cadherin (PF-06671008). It describes the disposition of PF-06671008 in the central compartment and tumor in mouse xenograft models, including binding to target and T cells in the tumor to form the trimer. The model incorporates T cell distribution to the tumor, proliferation, and contraction. PK/PD parameters were estimated for PF-06671008 and a tumor stasis concentration (TSC) was calculated as an estimate of minimum efficacious trimer concentration. TSC values ranged from 0.0092 to 0.064 pM across mouse tumor models. The model was translated to the clinic and used to predict the disposition of PF-06671008 in patients, including the impact of binding to soluble P-cadherin. The predicted terminal half-life of PF-06671008 in the clinic was approximately 1 day, and P-cadherin expression and number of T cells in the tumor were shown to be sensitive parameters impacting clinical efficacy. A translational QSP model is presented for CD3 bispecific molecules, which integrates in silico, in vitro and in vivo data in a mechanistic framework, to quantify and predict efficacy across species.

Establishing in vitro-in vivo correlation for antibody drug conjugate efficacy: a PK/PD modeling approach.

The objective of this manuscript was to establish in vitro-in vivo correlation (IVIVC) between the in vitro efficacy and in vivo efficacy of antibody drug conjugates (ADCs), using a PK/PD modeling approach. Nineteen different ADCs were used to develop IVIVC. In vitro efficacy of ADCs was evaluated using a kinetic cell cytotoxicity assay. The cytotoxicity data obtained from in vitro studies was characterized using a novel mathematical model, parameter estimates from which were used to derive an in vitro efficacy matrix for each ADC, termed as 'in vitro tumor static concentration' (TSCin vitro). TSCin vitro is a theoretical concentration at continuous exposure of which the number of cells will neither increase nor decrease, compared to the initial cell number in the experiment. The in vivo efficacy of ADCs was evaluated using tumor growth inhibition (TGI) studies performed on human tumor xenograft bearing mice. The TGI data obtained from in vivo studies was characterized using a PK/PD model, parameter estimates from which were used to derive an in vivo efficacy matrix for each ADC, termed as 'in vivo tumor static concentration' (TSCin vivo). TSCin vivo is a theoretical concentration if one were to maintain in the plasma of a tumor bearing mouse, the tumor volume will neither increase nor decrease compared to the initial tumor volume. Comparison of the TSCin vitro and TSCin vivo values from 19 ADCs provided a linear and positive IVIVC. The Spearman's rank correlation coefficient for TSCin vitro and TSCin vivo was found to be 0.82. On average TSCin vivo was found to be ~ 27 times higher than TSCin vitro. The reasonable IVIVC for ADCs suggests that in vitro efficacy data was correctly able to differentiate ADCs for their in vivo efficacy. Thus, IVIVC can be used as a tool to triage ADC molecules in the discovery stage, thereby preventing unnecessary scaling-up of ADCs and waste of time and resources. An ability to predict the concentration of ADC that is efficacious in vivo using the in vitro data can also help in optimizing the experimental design of preclinical efficacy studies. As such, the novel PK/PD modeling method presented here to establish IVIVC for ADCs holds promise, and should be evaluated further using diverse set of cell lines and anticancer agents.

A PTK7-targeted antibody-drug conjugate reduces tumor-initiating cells and induces sustained tumor regressions.

Disease relapse after treatment is common in triple-negative breast cancer (TNBC), ovarian cancer (OVCA), and non-small cell lung cancer (NSCLC). Therapies that target tumor-initiating cells (TICs) should improve patient survival by eliminating the cells that can drive tumor recurrence and metastasis. We demonstrate that protein tyrosine kinase 7 (PTK7), a highly conserved but catalytically inactive receptor tyrosine kinase in the Wnt signaling pathway, is enriched on TICs in low-passage TNBC, OVCA, and NSCLC patient-derived xenografts (PDXs). To deliver a potent anticancer drug to PTK7-expressing TICs, we generated a targeted antibody-drug conjugate (ADC) composed of a humanized anti-PTK7 monoclonal antibody, a cleavable valine-citrulline-based linker, and Aur0101, an auristatin microtubule inhibitor. The PTK7-targeted ADC induced sustained tumor regressions and outperformed standard-of-care chemotherapy. Moreover, the ADC specifically reduced the frequency of TICs, as determined by serial transplantation experiments. In addition to reducing the TIC frequency, the PTK7-targeted ADC may have additional antitumor mechanisms of action, including the inhibition of angiogenesis and the stimulation of immune cells. Together, these preclinical data demonstrate the potential for the PTK7-targeted ADC to improve the long-term survival of cancer patients.

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.

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.

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.

A Flow Cytometric Clonogenic Assay Reveals the Single-Cell Potency of Doxorubicin.

Standard cell proliferation assays use bulk media drug concentration to ascertain the potency of chemotherapeutic drugs; however, the relevant quantity is clearly the amount of drug actually taken up by the cell. To address this discrepancy, we have developed a flow cytometric clonogenic assay to correlate the amount of drug in a single cell with the cell's ability to proliferate using a cell tracing dye and doxorubicin, a naturally fluorescent chemotherapeutic drug. By varying doxorubicin concentration in the media, length of treatment time, and treatment with verapamil, an efflux pump inhibitor, we introduced 10(5) -10(10) doxorubicin molecules per cell; then used a dye-dilution assay to simultaneously assess the number of cell divisions. We find that a cell's ability to proliferate is a surprisingly conserved function of the number of intracellular doxorubicin molecules, resulting in single-cell IC50 values of 4-12 million intracellular doxorubicin molecules. The developed assay is a straightforward method for understanding a drug's single-cell potency and can be used for any fluorescent or fluorescently labeled drug, including nanoparticles or antibody-drug conjugates.

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

The objectives of this investigation were as follows: (a) to validate a mechanism-based pharmacokinetic (PK) model of ADC for its ability to a priori predict tumor concentrations of ADC and released payload, using anti-5T4 ADC A1mcMMAF, and (b) to analyze the PK model to find out main pathways and parameters model outputs are most sensitive to. Experiential data containing biomeasures, and plasma and tumor concentrations of ADC and payload, following A1mcMMAF administration in two different xenografts, were used to build and validate the model. The model performed reasonably well in terms of a priori predicting tumor exposure of total antibody, ADC, and released payload, and the exposure of released payload in plasma. Model predictions were within two fold of the observed exposures. Pathway analysis and local sensitivity analysis were conducted to investigate main pathways and set of parameters the model outputs are most sensitive to. It was discovered that payload dissociation from ADC and tumor size were important determinants of plasma and tumor payload exposure. It was also found that the sensitivity of the model output to certain parameters is dose-dependent, suggesting caution before generalizing the results from the sensitivity analysis. Model analysis also revealed the importance of understanding and quantifying the processes responsible for ADC and payload disposition within tumor cell, as tumor concentrations were sensitive to these parameters. Proposed ADC PK model provides a useful tool for a priori predicting tumor payload concentrations of novel ADCs preclinically, and possibly translating them to the clinic.

On translation of antibody drug conjugates efficacy from mouse experimental tumors to the clinic: a PK/PD approach.

Objectives of the present investigation were: (1) to compare three literature reported tumor growth inhibition (TGI) pharmacodynamic (PD) models and propose an optimal new model that best describes the xenograft TGI data for antibody drug conjugates (ADC), (2) to translate efficacy of the ADC Trastuzumab-emtansine (T-DM1) from mice to patients using the optimized PD model, and (3) to apply the translational strategy to predict clinically efficacious concentrations of a novel in-house anti-5T4 ADC, A1mcMMAF. First, the performance of all four of the PD models (i.e. 3 literature reported + 1 proposed) was evaluated using TGI data of T-DM1 obtained from four different xenografts. Based on the estimates of the pharmacodynamic/pharmacokinetic (PK/PD) modeling, a secondary parameter representing the efficacy index of the drug was calculated, which is termed as the tumor static concentration (TSC). TSC values derived from all four of the models were compared with each other, and with literature reported values, to assess the performance of these models. Subsequently, using the optimized PK/PD model, PD parameters obtained from different cell lines, human PK, and the proposed translational strategy, clinically efficacious doses of T-DM1 were projected. The accuracy of projected efficacious dose range for T-DM1 was verified by comparison with the clinical doses. Aforementioned strategy was then applied to A1mcMMAF for projecting its efficacious concentrations in clinic. TSC values for A1mcMMAF, obtained by fitting TGI data from 4 different xenografts with the proposed PK/PD model, were estimated to range from 0.6 to 11.5 µg mL⁻¹. Accordingly, the clinically efficacious doses for A1mcMMAF were projected retrospectively. All in all, the improved PD model and proposed translational strategy presented here suggest that appropriate correction for the clinical exposure and employing the TSC criterion can help translate mouse TGI data to predict first in human doses of ADCs.

Preclinical and clinical pharmacokinetic/pharmacodynamic considerations for antibody-drug conjugates.

Antibody-drug conjugates (ADCs) represent a promising therapeutic modality for the clinical management of cancer. Here we discuss the clinical pharmacology and safety of ADCs that are either approved or in late stages of clinical development. We have taken examples of ADCs employing either DNA damaging payloads (such as calicheamicin) or tubulin depolymerizing agents (such as auristatins and maytansinoids) to discuss the impact of dose and dosage intervals on pharmacokinetics/pharmacodynamics (PK/PD) and safety of ADCs. We also discuss the development of PK/PD models that were validated using preclinical and clinical data from two approved ADCs (ado-trastuzumab emtansine (T-DM1, Kadcyla™) and brentuximab vedotin (SGN-35, Adcetris™). These models could be used to predict clinical efficacious doses of ADCs.