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Pharmacokinetics-Pharmacodynamics Based Investigations to Support the Development of Antibody-Drug Conjugates.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Pharmacokinetics-Pharmacodynamics Based Investigations to Support the Development of Antibody-Drug Conjugates./
Author:	Singh, Aman P.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	553 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-09, Section: B.
Contained By:	Dissertations Abstracts International80-09B.
Subject:	Pharmaceutical sciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13426571
ISBN:	9780438944954

Pharmacokinetics-Pharmacodynamics Based Investigations to Support the Development of Antibody-Drug Conjugates.
Singh, Aman P.

Pharmacokinetics-Pharmacodynamics Based Investigations to Support the Development of Antibody-Drug Conjugates. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 553 p.

Source: Dissertations Abstracts International, Volume: 80-09, Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2019.

This item must not be sold to any third party vendors.

Antibody-drug Conjugates (ADCs) are one of the fastest growing class of anticancer therapeutics, which consists of monoclonal antibodies (mAbs) covalently bound to highly potent chemotherapeutic agents (payloads) via chemical linkers. Selective delivery of payloads to antigen-overexpressing tumor cells differentiates ADCs from conventional chemotherapy, by promising a wider therapeutic index. The mechanism-of-action of an ADC typically involves binding to overexpressed antigens on tumor cells followed by receptor-mediated internalization. Once internalized, the payloads are released in the endosomal/lysosomal space based on the chemistry of the linker. The released payload can either bind to its pharmacological target (microtubules or DNA) inside the targeted cell and elicit cytotoxic effect or can efflux out to bystanding tumor cells to exert their cytotoxicity via a phenomenon referred to as the bystander effect of ADCs. With continuous advancements in ADC research, the clinical portfolio of these compounds is exponentially increasing, with four FDA approved drugs and more than 80 molecules in the clinical development. However, development of these molecules can be challenging, as it requires simultaneous optimization of an antibody, linker and cytotoxic agents. We hypothesize that pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation (M&S) can serve as a valuable tool for optimizing the development of these complex therapeutic molecules. Preclinical-to-clinical translation of ADC molecules could be challenging due to complex PK of these molecules and differences between preclinical and clinical tumors. Within this dissertation, we have described a general PK-PD M&S based strategy for clinical translation of ADCs using Trastuzumab-DM1 (T-DM1) as a tool compound. First, in Chapter 2 a cellular disposition model for T-DM1 was developed, incorporating key mechanistic processes such as antigen-binding, internalization, intracellular degradation, and transport of released drug metabolites across tumor cells using active and passive routes. The developed cell model was later integrated with an in-vivo tumor distribution framework to a priori predict tumor pharmacokinetics of T-DM1. The tumor PK model was later employed to develop a mechanistic PK-PD relationship (Chapter 3), which was utilized to characterize tumor growth inhibition (TGI) datasets from 11 different HER2-expressing mouse models. Preclinical PK-PD model was then translated to clinic, by incorporating allometrically scaled plasma PK parameters, literature reported tumor growth and burden parameter estimates in HER2+ metastatic breast cancer patients, and tumor efficacy parameters along with inter-individual variability estimated from the xenograft studies. The translated PK-PD model was used to simulate progression-free survival (PFS) rates in the clinic, and model simulations were validated with the PFS rates reported from three different clinical trials conducted in sub-populations of low (1+) and high (3+) HER2-expressing patients. The clinical PK-PD model was also used to understand clinical pharmacology of the ADC, for example, utility of fractionated dosing regimen in improving the clinical efficacy of T-DM1. This dissertation also investigates the mechanistic determinants underlying the rate and extent of bystander effect by ADCs, using a multiscale systems PK-PD modeling approach. Towards this objective, we first measured in-vitro bystander effect of a tool ADC Trastuzumab-vc-MMAE (T-vc-MMAE) in cocultures of HER2-high N87 (Ag+) and HER2-low GFP-MCF7 cells (Chapter 4). It was observed that bystander effect of T-vc-MMAE increased with increasing fraction of Ag+ cells in a coculture, as well as with increasing level of HER2 expression on Ag+ cells. Subsequently, detailed cellular disposition studies were performed in N87 and GFP-MCF7 cells to characterize the intracellular processing of T-vc-MMAE. This was done by quantifying different bioanalytical measurements, such as unconjugated MMAE, total MMAE and total Trastuzumab in media and cellular spaces. A single cell disposition model was developed to characterize the cellular disposition of T-vc-MMAE by integrating different biomeasures and chemomeasures within a mathematical framework (Chapter 5). Single cell PK models of T-vc-MMAE developed for the two cell lines (i.e. GFP-MCF7 and N87) were mechanistically integrated to mimic the coculture condition (Chapter 6). In addition, a unique PK-PD relationship was developed, which utilized intracellular occupancy of tubulin (pharmacological target) by released MMAE molecules to drive the cytotoxicity. The final 'dual' cell systems PK-PD model accounted for the transport of released MMAE from N87 (Ag+) to GFP-MCF7 (Ag-) cells to characterize in-vitro bystander effect of T-vc-MMAE and was eventually translated to in-vivo scenario. In-vivo characterization of T-vc-MMAE PK-PD was first investigated in xenograft mouse models of N87 (Ag+) and GFP-MCF7 (Ag -) cells individually (Chapter 7). Later, the tumor PK and tumor growth inhibition (TGI) datasets were integrated within systems modeling framework, which incorporated cell-level PK-PD information of T-vc-MMAE from the in-vitro investigations. The model was able to characterize differential tumor exposures and TGI of T-vc-MMAE in N87 (Ag+) tumor bearing mice compared to GFP-MCF7 (Ag-) tumor bearing mice. (Abstract shortened by ProQuest.).

ISBN: 9780438944954Subjects--Topical Terms:

3173021
Pharmaceutical sciences.

Pharmacokinetics-Pharmacodynamics Based Investigations to Support the Development of Antibody-Drug Conjugates.
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520 $a Antibody-drug Conjugates (ADCs) are one of the fastest growing class of anticancer therapeutics, which consists of monoclonal antibodies (mAbs) covalently bound to highly potent chemotherapeutic agents (payloads) via chemical linkers. Selective delivery of payloads to antigen-overexpressing tumor cells differentiates ADCs from conventional chemotherapy, by promising a wider therapeutic index. The mechanism-of-action of an ADC typically involves binding to overexpressed antigens on tumor cells followed by receptor-mediated internalization. Once internalized, the payloads are released in the endosomal/lysosomal space based on the chemistry of the linker. The released payload can either bind to its pharmacological target (microtubules or DNA) inside the targeted cell and elicit cytotoxic effect or can efflux out to bystanding tumor cells to exert their cytotoxicity via a phenomenon referred to as the bystander effect of ADCs. With continuous advancements in ADC research, the clinical portfolio of these compounds is exponentially increasing, with four FDA approved drugs and more than 80 molecules in the clinical development. However, development of these molecules can be challenging, as it requires simultaneous optimization of an antibody, linker and cytotoxic agents. We hypothesize that pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation (M&S) can serve as a valuable tool for optimizing the development of these complex therapeutic molecules. Preclinical-to-clinical translation of ADC molecules could be challenging due to complex PK of these molecules and differences between preclinical and clinical tumors. Within this dissertation, we have described a general PK-PD M&S based strategy for clinical translation of ADCs using Trastuzumab-DM1 (T-DM1) as a tool compound. First, in Chapter 2 a cellular disposition model for T-DM1 was developed, incorporating key mechanistic processes such as antigen-binding, internalization, intracellular degradation, and transport of released drug metabolites across tumor cells using active and passive routes. The developed cell model was later integrated with an in-vivo tumor distribution framework to a priori predict tumor pharmacokinetics of T-DM1. The tumor PK model was later employed to develop a mechanistic PK-PD relationship (Chapter 3), which was utilized to characterize tumor growth inhibition (TGI) datasets from 11 different HER2-expressing mouse models. Preclinical PK-PD model was then translated to clinic, by incorporating allometrically scaled plasma PK parameters, literature reported tumor growth and burden parameter estimates in HER2+ metastatic breast cancer patients, and tumor efficacy parameters along with inter-individual variability estimated from the xenograft studies. The translated PK-PD model was used to simulate progression-free survival (PFS) rates in the clinic, and model simulations were validated with the PFS rates reported from three different clinical trials conducted in sub-populations of low (1+) and high (3+) HER2-expressing patients. The clinical PK-PD model was also used to understand clinical pharmacology of the ADC, for example, utility of fractionated dosing regimen in improving the clinical efficacy of T-DM1. This dissertation also investigates the mechanistic determinants underlying the rate and extent of bystander effect by ADCs, using a multiscale systems PK-PD modeling approach. Towards this objective, we first measured in-vitro bystander effect of a tool ADC Trastuzumab-vc-MMAE (T-vc-MMAE) in cocultures of HER2-high N87 (Ag+) and HER2-low GFP-MCF7 cells (Chapter 4). It was observed that bystander effect of T-vc-MMAE increased with increasing fraction of Ag+ cells in a coculture, as well as with increasing level of HER2 expression on Ag+ cells. Subsequently, detailed cellular disposition studies were performed in N87 and GFP-MCF7 cells to characterize the intracellular processing of T-vc-MMAE. This was done by quantifying different bioanalytical measurements, such as unconjugated MMAE, total MMAE and total Trastuzumab in media and cellular spaces. A single cell disposition model was developed to characterize the cellular disposition of T-vc-MMAE by integrating different biomeasures and chemomeasures within a mathematical framework (Chapter 5). Single cell PK models of T-vc-MMAE developed for the two cell lines (i.e. GFP-MCF7 and N87) were mechanistically integrated to mimic the coculture condition (Chapter 6). In addition, a unique PK-PD relationship was developed, which utilized intracellular occupancy of tubulin (pharmacological target) by released MMAE molecules to drive the cytotoxicity. The final 'dual' cell systems PK-PD model accounted for the transport of released MMAE from N87 (Ag+) to GFP-MCF7 (Ag-) cells to characterize in-vitro bystander effect of T-vc-MMAE and was eventually translated to in-vivo scenario. In-vivo characterization of T-vc-MMAE PK-PD was first investigated in xenograft mouse models of N87 (Ag+) and GFP-MCF7 (Ag -) cells individually (Chapter 7). Later, the tumor PK and tumor growth inhibition (TGI) datasets were integrated within systems modeling framework, which incorporated cell-level PK-PD information of T-vc-MMAE from the in-vitro investigations. The model was able to characterize differential tumor exposures and TGI of T-vc-MMAE in N87 (Ag+) tumor bearing mice compared to GFP-MCF7 (Ag-) tumor bearing mice. (Abstract shortened by ProQuest.).
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