Computational transport analysis of antibody-drug conjugate bystander effects and payload tumoral distribution: implications for therapy

Khera, Eshita; Cilliers, Cornelius; Bhatnagar, Sumit; Thurber, Greg M.

doi:10.1039/c7me00093f

Cited by 44 publications

(50 citation statements)

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“…While there is a 'well-mixed' population of two cell-types in the examined coculture system, within a solid tumor there is a significant intra-tumoral heterogeneity and the composition of different cell types can significantly vary across different regions of the tissue (29). Additionally, based on our previous work that investigated the role of different pathways in bringing the drug inside a cell following in-vitro and in-vivo ADC administration, (13), the extent of in-vivo bystander effect can be dampened by diffusion of the released drug out of the tumor into the systemic circulation (7). In fact, Khera et al (7) have recently presented a theoretical analysis of ADC bystander effect with similar conclusion, suggesting payloads with physicochemical properties that allow them to be taken up by cells rapidly compared to the tumor washout rate would demonstrate the maximum in vivo bystander effect.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, based on our previous work that investigated the role of different pathways in bringing the drug inside a cell following in-vitro and in-vivo ADC administration, (13), the extent of in-vivo bystander effect can be dampened by diffusion of the released drug out of the tumor into the systemic circulation (7). In fact, Khera et al (7) have recently presented a theoretical analysis of ADC bystander effect with similar conclusion, suggesting payloads with physicochemical properties that allow them to be taken up by cells rapidly compared to the tumor washout rate would demonstrate the maximum in vivo bystander effect. As such, one should develop in vivo systems PK model by incorporating cell-level coculture models like the one developed here to obtain realistic insight into ADC induced in vivo bystander effect.…”

Section: Discussionmentioning

confidence: 99%

“…Antibody-Drug Conjugates (ADCs) are one of those targeted immunotherapies that leverages the targeting potential of a monoclonal antibody (mAb) to deliver highly potent cytotoxic agents (payloads) to antigenrich tumor cells (3), with the goal of achieving a wider therapeutic index (4). The last decade has witnessed an exponential growth in the clinical pipeline of ADCs (5,6), with more than 70 molecules currently in the development (7). With the recent approvals of Besponsa® (Inotuzumab Ozogamicin) (8) and Mylotarg® (Gemtuzumab Ozogamicin) (9), there are now four FDA-approved ADCs in the market.…”

Section: Introductionmentioning

confidence: 99%

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A “Dual” Cell-Level Systems PK-PD Model to Characterize the Bystander Effect of ADC

Singh¹,

Shah²

2019

Journal of Pharmaceutical Sciences

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Here we have developed a cell-level systems PK-PD model to characterize the bystander effect of 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 two 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A “Dual” Cell-Level Systems PK-PD Model to Characterize the Bystander Effect of ADC

Singh¹,

Shah²

2019

Journal of Pharmaceutical Sciences

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“…These parameter values are derived from mAbs, such as Herceptin, and ADCs, including brentuximab-vedotin and T-DM1, and may vary depending on the experimental environment [12–16]. Based on a particular study [16], the payload influx/efflux rate k in and k out were deemed to be 8.46·10 −2 and 4.122·10 −2 per minute, respectively. The values are at a day-scale of approximately 121.824 and 5.9357·10 4 .…”

Section: Methodsmentioning

confidence: 99%

Modeling to capture bystander-killing effect by released payload in target positive tumor cells

Byun

Jung

2019

BMC Cancer

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Background Antibody-drug conjugates (ADCs) are intended to bind to specific positive target antigens and eradicate only tumor cells from an intracellular released payload through the lysosomal protease. Payloads, such as MMAE, have the capacity to kill adjacent antigen-negative (Ag–) tumor cells, which is called the bystander-killing effect, as well as directly kill antigen-positive (Ag+) tumor cells. We propose that a dose-response curve should be independently considered to account for target antigen-positive/negative tumor cells. Methods A model was developed to account for the payload in Ag+/Ag– cells and the associated parameters were applied. A tumor growth inhibition (TGI) effect was explored based on an ordinary differential equation (ODE) after substituting the payload concentration in Ag+/Ag– cells into an Emax model, which accounts for the dose-response curve. To observe the bystander-killing effects based on the amount of Ag+/Ag– cells, the Emax model is used independently. TGI models based on ODE are unsuitable for describing the initial delay through a tumor–drug interaction. This was solved using an age-structured model based on the stochastic process. Results β ∈(0,1] is a fraction parameter that determines the proportion of cells that consist of Ag+/Ag– cells. The payload concentration decreases when the ratio of efflux to influx increases. The bystander-killing effect differs with varying amounts of Ag+ cells. The larger β is, the less bystander-killing effect. The decrease of the bystander-killing effect becomes stronger as Ag+ cells become larger than the Ag– cells. Overall, the ratio of efflux to influx, the amount of released payload, and the proportion of Ag+ cells determine the efficacy of the ADC. The tumor inhibition delay through a payload-tumor interaction, which goes through several stages, may be solved using an age-structured model. Conclusions The bystander-killing effect, one of the most important topics of ADCs, has been explored in several studies without the use of modeling. We propose that the bystander-killing effect can be captured through a mathematical model when considering the Ag+ and Ag– cells. In addition, the TGI model based on the age-structure can capture the initial delay through a drug interaction as well as the bystander-killing effect. Electronic supplementary material The online version of this article (10.1186/s12885-019-5336-7) contains supplementary material, which is available to authorized users.

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“…There are computational methods of estimating small-molecule delivery in tumor tissue (8), and these principles can be incorporated into ADC tissue models to provide precise predictions of tissue, cellular, and subcellular payload distribution. Theoretically, bystander payloads with optimal physicochemical properties can distribute homogeneously throughout the tumor (9). However, there are currently a lack of available experimental payload distribution data to validate or refute these computational predictions.…”

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confidence: 99%

“Standing by” for Bystander Effects: Dual-Isotope Imaging of Antibody–Drug Conjugate and Payload Distribution

Cilliers

Thurber

2018

J Nucl Med

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Computational transport analysis of antibody-drug conjugate bystander effects and payload tumoral distribution: implications for therapy

Abstract: A computational model predicting bystander payload distribution as a function of controllable design parameters for guiding efficient clinical ADC development.

Cited by 44 publications

References 73 publications

A “Dual” Cell-Level Systems PK-PD Model to Characterize the Bystander Effect of ADC

A “Dual” Cell-Level Systems PK-PD Model to Characterize the Bystander Effect of ADC

Modeling to capture bystander-killing effect by released payload in target positive tumor cells

“Standing by” for Bystander Effects: Dual-Isotope Imaging of Antibody–Drug Conjugate and Payload Distribution

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