A Mechanistic Tumor Penetration Model to Guide Antibody Drug Conjugate Design

Vasalou, Christina; Helmlinger, Gabriel; Gomes, Bruce

doi:10.1371/journal.pone.0118977

Cited by 76 publications

(59 citation statements)

References 38 publications

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“…1C, 1D). Currently, the endosomal processing and escape of the metabolite are not included but could be added to the modeling framework as others have done(33, 48). The higher vascular density in healthy tissue (resulting in shorter diffusion distances between vessels) and lack of specific binding in most tissue results in a homogeneous antibody distribution(49).…”

Section: Resultsmentioning

confidence: 99%

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Multiscale Modeling of Antibody-Drug Conjugates: Connecting Tissue and Cellular Distribution to Whole Animal Pharmacokinetics and Potential Implications for Efficacy

Cilliers

Guo

Liao

et al. 2016

AAPS J

100

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Antibody drug conjugates exhibit complex pharmacokinetics due to their combination of macromolecular and small molecule properties. These issues range from systemic concerns, such as deconjugation of the small molecule drug during the long antibody circulation time or rapid clearance from non-specific interactions, to local tumor tissue heterogeneity, cell bystander effects, and endosomal escape. Mathematical models can be used to study the impact of these processes on overall distribution in an efficient manner, and several types of models have been used to analyze varying aspects of antibody distribution including physiologically based pharmacokinetic (PBPK) models and tissue-level simulations. However, these processes are quantitative in nature and cannot be handled qualitatively in isolation. For example, free antibody from deconjugation of the small molecule will impact the distribution of conjugated antibodies within the tumor. To incorporate these effects into a unified framework, we have coupled the systemic and organ-level distribution of a PBPK model with the tissue-level detail of a distributed parameter tumor model. We used this mathematical model to analyze new experimental results on the distribution of the clinical antibody drug conjugate Kadcyla in HER2 positive mouse xenografts. This model is able to capture the impact of the drug antibody ratio (DAR) on tumor penetration, the net result of drug deconjugation, and the effect of using unconjugated antibody to drive ADC penetration deeper into the tumor tissue. This modeling approach will provide quantitative and mechanistic support to experimental studies trying to parse the impact of multiple mechanisms of action for these complex drugs.

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Section: Resultsmentioning

confidence: 99%

“…1C, 1D). This modeling approach has been validated for the tissue distribution of antibodies and ADCs by our group and others(27, 28, 32, 33, 35). Briefly, this model is based on the Krogh cylinder geometry of tumor blood vessels.…”

Section: Methodsmentioning

confidence: 97%

Multiscale Modeling of Antibody-Drug Conjugates: Connecting Tissue and Cellular Distribution to Whole Animal Pharmacokinetics and Potential Implications for Efficacy

Cilliers

Guo

Liao

et al. 2016

AAPS J

100

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show abstract

“…Several models have been proposed to mechanistically model ADC efficacy (43)(44)(45)(46)(47)(48)(49)(50)(51)(52), many of which have been previously reviewed (41). Mechanistic models have often been applied to investigate antibodies as a modality, although modeling studies focused on immunotoxins have also been reported (43,52).…”

Section: Antibody Drug Conjugate (Adc) Platformmentioning

confidence: 99%

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

Cheng

Thalhauser

Smithline

et al. 2017

AAPS J

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Abstract. 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.

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“…Another study used a semimechanistic PK/PD model to predict that the platelet nadir following adotrastuzumab emtansine 3.6 mg/kg every 3 weeks would occur after the first dose, thus supporting this as a tolerable dose requiring minimal dose delays or dose reductions for thrombocytopenia. 75 Application of Quantitative Systems Pharmacology Modeling Quantitative systems pharmacology modeling was previously used for ADC design optimization, 76,77 prediction of tumor ADC concentrations, and increasing the understanding of intracellular ADC activation in preclinical tumor models. This information was then used to guide dose selection 78,79 and translate data from preclinical species to humans to predict clinically efficacious doses.…”

Section: Immunogenicity Assessmentmentioning

confidence: 99%

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

Vezina

Cotreau

Han

et al. 2017

The Journal of Clinical Pharma

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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.

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A Mechanistic Tumor Penetration Model to Guide Antibody Drug Conjugate Design

Cited by 76 publications

References 38 publications

Multiscale Modeling of Antibody-Drug Conjugates: Connecting Tissue and Cellular Distribution to Whole Animal Pharmacokinetics and Potential Implications for Efficacy

Multiscale Modeling of Antibody-Drug Conjugates: Connecting Tissue and Cellular Distribution to Whole Animal Pharmacokinetics and Potential Implications for Efficacy

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

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

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