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.

Palaeoenvironmental proxies indicate long-term development of agro-pastoralist landscapes in Inner Asian mountains.

A growing body of archaeological research on agro-pastoralist populations of the Inner Asian mountains indicates that these groups adapted various systems of mobile herding and cultivation to ecotopes across the region from as early as 5000 BP. It has been argued that these adaptations allowed the development of flexible social-ecological systems well suited to the long-term management of these mountain landscapes. At present, less attention has been paid to examining the long-term ecological legacy of these adaptations within the sedimentary or palaeoenvironmental record. Here we present sediment, palynomorph and charcoal data that we interpret as indicating agro-pastoralist environmental perturbations, taken from three cores at middle and high altitudes in the Kashmir Valley at the southern end of the Inner Asian mountains. Our data indicate spatially and temporally discontinuous patterns of agro-pastoralist land use beginning close to 4000 BP. Periods of intensification of upland herding are often coincident with phases of regional social or environmental change, in particular we find the strongest signals for agro-pastoralism in the environmental record contemporary with regionally arid conditions. These patterns support previous arguments that specialised agro-pastoralist ecologies across the region are well placed to respond to past and future climate deteriorations. Our data indicating long-term co-evolution of humans and landscape in the study area also have implications for the ongoing management of environments generally perceived as "pristine" or "wilderness".

New evidence for supplementary crop production, foddering and fuel use by Bronze Age transhumant pastoralists in the Tianshan Mountains.

The nature of economies and the movement of agricultural crops across Eurasia in the Bronze Age have been the subject of significant research interest in recent years. This study presents and discusses new results of flotation, radiocarbon and carbon stable isotope analyses from the seed assemblage at the Adunqiaolu site (northwestern Xinjiang), in combination with archaeological evidence. Archaeobotanical evidence, including carbonized foxtail millet, broomcorn millet, and naked barley, documents the diversity of local cereal consumption during the mid-second millennium BC. Our results suggest that crops were not grown locally, however, but in the lower Boertala Valley, supporting the argument that Adunqiaolu was a winter camp. These new sets of data constitute an important contribution to the discussion on cereal dispersal across the Tianshan Mountains in the Bronze Age.

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.

Seasonal movements of Bronze Age transhumant pastoralists in western Xinjiang.

The paper explores seasonal movements of Bronze Age mobile pastoralists in the western Tianshan mountainous region of Xinjiang, China. Fieldwork by a team from the Institute of Archaeology of the Chinese Academy of Social Science (CASS) and the University of Sydney, Australia have identified cyclical land use practices associated with the Andronovo cultural complex. Their pattern of seasonal movements has been reconstructed through ethnographic studies and analysis of modern snow and grass cover. Using this detailed combination of data, the study defines requirements for seasonal pastures-winter, summer and spring/autumn-and shows a clear correlation between modern land use and seasonal patterns of movement in the Bronze Age.

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.

A Physiologically-Based Pharmacokinetic Model for the Prediction of Monoclonal Antibody Pharmacokinetics From In Vitro Data.

Monoclonal antibody (mAb) pharmacokinetics (PK) have largely been predicted via allometric scaling with little consideration for cross-species differences in neonatal Fc receptor (FcRn) affinity or clearance/distribution mechanisms. To address this, we developed a mAb physiologically-based PK model that describes the intracellular trafficking and FcRn recycling of mAbs in a human FcRn transgenic homozygous mouse and human. This model uses mAb-specific in vitro data together with species-specific FcRn tissue expression, tissue volume, and blood-flow physiology to predict mAb in vivo linear PK a priori. The model accurately predicts the terminal half-life of 90% of the mAbs investigated within a twofold error. The mechanistic nature of this model allows us to not only predict linear PK from in vitro data but also explore the PK and target binding of mAbs engineered to have pH-dependent binding to its target or FcRn and could aid in the selection of mAbs with optimal PK and pharmacodynamic properties.

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.

Linear pharmacokinetic parameters for monoclonal antibodies are similar within a species and across different pharmacological targets: A comparison between human, cynomolgus monkey and hFcRn Tg32 transgenic mouse using a population-modeling approach.

The linear pharmacokinetics (PK) of therapeutic monoclonal antibodies (mAbs) can be considered a class property with values that are similar to endogenous IgG. Knowledge of these parameters across species could be used to avoid unnecessary in vivo PK studies and to enable early PK predictions and pharmacokinetic/pharmacodynamic (PK/PD) simulations. In this work, population-pharmacokinetic (popPK) modeling was used to determine a single set of 'typical' popPK parameters describing the linear PK of mAbs in human, cynomolgus monkey and transgenic mice expressing the human neonatal Fc receptor (hFcRn Tg32), using a rich dataset of 27 mAbs. Non-linear PK was excluded from the datasets and a 2-compartment model was applied to describe mAb disposition. Typical human popPK estimates compared well with data from comparator mAbs with linear PK in the clinic. Outliers with higher than typical clearance were found to have non-specific interactions in an affinity-capture self-interaction nanoparticle spectroscopy assay, offering a potential tool to screen out these mAbs at an early stage. Translational strategies were investigated for prediction of human linear PK of mAbs, including use of typical human popPK parameters and allometric exponents from cynomolgus monkey and Tg32 mouse. Each method gave good prediction of human PK with parameters predicted within 2-fold. These strategies offer alternative options to the use of cynomolgus monkeys for human PK predictions of linear mAbs, based on in silico methods (typical human popPK parameters) or using a rodent species (Tg32 mouse), and call into question the value of completing extensive in vivo preclinical PK to inform linear mAb PK.

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.