Home
Resource
Mechanistic modeling for optimization of delivery of protein therapeutics
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(6.71 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Gao, Xizhe. Mechanistic modeling for optimization of delivery of protein therapeutics. Retrieved from https://doi.org/doi:10.7282/t3-cah3-1p66

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleMechanistic modeling for optimization of delivery of protein therapeutics
NameGao, Xizhe (author); Kagan, Leonid (chair); Hatefi, Arash (member); Minko, Tamara (member); Guo, Grace (member); Michniak-Kohn, Bozena (member); Rutgers University; School of Graduate Studies
Date Created2022
Other Date2022-01 (degree)
SubjectPharmaceutical sciences, Immunoglobulins -- Therapeutic use, Monoclonal antibodies -- Therapeutic use, Pharmacokinetics
Extent232 pages : illustrations
DescriptionProtein therapeutics has become an essential class for treatment of cancer and autoimmune disease. They have been highly successful in clinic since 1980s. Over 200 biotherapeutics and their variants have been approved by US-FDA and about 30% of them are antibody-based therapeutics. Antibody-based therapeutics have structure similar as full size antibody and account for the largest segment of global biologics market. They are versatile and highly specific to endogenous targets. They have few drug-drug interactions and generally have an extended half-life in systemic circulation via FcRn-mediated recycling. There are several challenges in delivery of antibody-based therapeutics. While IV administration had been the mostly used route of delivery for antibody-based therapeutics, SC delivery has been increasingly used due to its convenience. Lymphatics serve as the major route for SC absorption of large protein therapeutics but quantitative information on its role after IV and SC administration is limited. Comparing with IV administration, incomplete availability and low injection volume are important limitations for SC delivery. Hyaluronidase (rHuPH20) has been reported to facilitate SC delivery of protein therapeutics by overcoming these disadvantages. While the mechanism of rHuPH20’s action is known, a quantitative relationship to describe the kinetics of this action has not been established yet. Unlike small molecules, many mAbs are dosed based on body weight while obese population have been under-represented in the clinical trials. The effect of obesity on the pharmacokinetics of mAbs has not been fully understood yet.

The thesis focused on using quantitative approaches for understanding SC administration/absorption and optimizing delivery of antibody-based therapeutics. In the introductory Chapter 1, an overview of protein therapeutics, SC absorption, major types of protein therapeutics and typical modeling approaches are presented. Chapter 2 is focused on exploration of the role of lymphatic system in the pharmacokinetics of etanercept, a fusion protein, following both IV and SC dosing in a lymphatic-cannulated rat model. Refinement of the experimental technique allowed for collection of lymph samples for up to 7 days after surgery. A mechanistic pharmacokinetic model was developed to successfully describe systemic and lymphatic disposition of etanercept after IV and SC dosing in lymphatic-cannulated and control rats. Lymphatic system was found to play an essential role in systemic disposition and SC absorption of etanercept. Chapter 3 is focused on the quantification of the effect of hyaluronidase (rHuPH20) on SC absorption of protein therapeutics. A mechanistic pharmacokinetics model was developed to describe cetuximab pharmacokinetics with co-administration or pretreatment of rHuPH20. The model combined three major mechanistic components: kinetics of rHuPH20 at SC site; hyaluronan (HA) homeostasis and its disruption by rHuPH20; and cetuximab systemic disposition and the effect of HA disruption on cetuximab SC absorption. It provided good description of experimental data obtained in this study and previous literature data. Furthermore, the model can serve as a translational framework for modeling the effect of rHuPH20 across multiple preclinical species and human studies. In Chapter 4, a mechanistic pharmacokinetic model was developed to describe the obesity-induced changes in human IgG pharmacokinetics and endogenous rat IgG profiles following IV and SC administration of high-dose IVIG to Zucker rats. The model included several major mechanistic components: 1) homeostasis of endogenous rat IgG through endosomal recycling and with age-dependent synthesis rate; 2) competition of human IgG and endogenous rat IgG for endosomal FcRn binding and its effect on endogenous rat IgG concentrations following injection of a high dose of human IgG; 3) and the effect of body size and composition (changing over time and dependent on the obesity status) on pharmacokinetic parameters. The model provided good description of human and rat IgG serum data in this work and most of parameter estimates are consistent with previous literature data. Quantification of the effect of obesity on IVIG pharmacokinetics contributes to the development of more effective and safe dosing strategy for obese population. In Chapter 5, the model was evaluated by simulation using final estimates overlaid with human IgG and rat IgG serum data in Obese Resistant and Obese Prone rats following SC and IV administration of 0.5 g/kg human IgG.

Collectively, this thesis provided important quantitative insights into the mechanistic pharmacokinetics of antibody-based therapeutics. Mechanistic modeling approaches were utilized for understanding of SC absorption and optimization of delivery with antibody-based therapeutics.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
Persistent URLhttps://doi.org/doi:10.7282/t3-cah3-1p66
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024