Mechanism-Based Pharmacokinetic/Pharmacodynamic Model for THIOMAB™ Drug Conjugates

Sukumaran, Siddharth; Gadkar, Kapil; Zhang, Crystal; Bhakta, Sunil; Liu, Luna; Xu, Keyang; Raab, Helga; Yu, Shang‐Fan; Mai, Elaine; Fourie-O’Donohue, Aimee; Kozak, Katherine R.; Ramanujan, Saroja; Junutula, Jagath R.; Lin, Kedan

doi:10.1007/s11095-014-1582-1

Cited by 38 publications

(21 citation statements)

References 22 publications

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“…The tumor distribution of antibody and small molecule drug is a function of the dose, DAR, systemic clearance, and in vivo kinetics (such as deconjugation depending on the linker(50, 51)). To capture the impact of the antibody dose relative to small molecule delivery, a constant dose of T-DM1-AF680 was given with increasing doses of trastuzumab.…”

Section: Resultsmentioning

confidence: 99%

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%

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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“…We have also not provided an analysis of cellular payload disposition in the examples, which would require additional data. We have developed the model providing equations with consistent clearance rates and DAR-proportional deconjugation and metabolism rates, and these equation forms may be refined with additional data to better support parameterizing DARdependent rates (56)(57)(58).…”

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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“…For ADCs carrying cytotoxic drugs, preclinically evaluable doses are restricted by tolerability, limiting concentration-time measurements to a range that may not be sufficient for robust characterization of the PK non-linearity. To date, various PK modeling efforts that support the drug development have been focused on complexities unique to ADCs, such as capturing the PK driven by the de-conjugation processes 10-12 or integrating the complex processes occurring at cellular, tissue and systemic levels using multi-scale models. 13-15 For newer generation ADCs, advances in conjugation technologies have reduced the rates of payload loss by improving linker stability compared to the first-generation ADCs.…”

Section: Introductionmentioning

confidence: 99%

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

Figueroa

Leipold

Leong

et al. 2018

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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.

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Mechanism-Based Pharmacokinetic/Pharmacodynamic Model for THIOMAB™ Drug Conjugates

Abstract: Results from the integrated model evaluated with two different TDCs highlight the impact of DAR and site of conjugation on pharmacokinetics and efficacy. The model can be used to guide future drug optimization and in-vivo studies.

Cited by 38 publications

References 22 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

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

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