Nils Bundgaard (1) , Daniel Kaschek (1), Stefan Wetzel (1), Alexandra Kropotova (2), Christoph Bausch (2)
(1) IntiQuan AG, Basel, Switzerland, (2) SAb Biotherapeutics Inc, Sioux Falls, United states of America
Objectives:
In the pursuit of advancing immunotherapy for immune and autoimmune disorders, SAb has developed fully human, multi-targeted immunoglobulins (IgGs) produced in transchromosomic bovines (TcB). Inherent difficulties in generating qualified data for fully human polyclonal multi-targeted immunoglobulin drugs pose challenges in informing dose selection for subsequent clinical phases. One key challenge is the quantification of human IgG drug pharmacokinetics (PK), compounded by the complexities of distinguishing exogenous, fully human IgGs from endogenous ones.
This abstract proposes a methodological framework to address some of these challenges. By integrating limited trial data with relevant literature data and leveraging pre-existing models, we aim to better inform human IgG trials. Our approach seeks to address the limitations in quantifying drug PK and optimizing dose selection for polyclonal antibody drugs, contributing to a more comprehensive understanding.
Methods:
The underlying idea is to integrate trial data with other sources of information. Such additional sources of information can be pre-existing data (in vitro and/or in-vivo pre-clinical data), or mechanistic models that allow to bridge between observations where empirical data is inherently difficult or impossible to obtain. Here, we exemplify the suggested framework through SAb’s research on IgGs targeting influenza:
One approach used in our analysis for antibody SAB-176 was a Titer-based PK model, which utilized microneutralization (MN) or hemagglutination (HAI) titers evaluated across a diverse panel of influenza viruses. This allowed for deriving a PK readout.
Complementing this, our investigation utilized mechanistic models of viral load from literature [1, 2, 3]. The selected viral load model was calibrated on both literature [1, 4, 5, 6] and healthy volunteer data from SAb’s Phase 2a study. This approach allowed us to integrate existing knowledge and trial data to enhance the understanding of the efficacy of SAB-176.
Finally, we applied a joint modeling approach to analyze viral load and time-to-alleviation of symptom data from patients at high risk of developing influenza complications and otherwise healthy individuals [7]. By utilizing a joint viral load / time-to-event model, we could effectively compare time-to-event data, providing valuable insights into the differences in viral load between these two populations.
The synergy of these three models – titer-based PK, mechanistic viral load models, and time-to-event modeling – as well as the combination of trial and literature data enabled a comprehensive simulation of the time to alleviation of symptoms in the targeted population of patients at risk of complications.
Hence, our framework involved the amalgamation of literature models and both study as well as literature data to address inherent deficiencies in study data systematically. By integrating study-specific data with literature-derived models, a cohesive model emerges, facilitating the simulation of untested scenarios.
Results:
The underlying idea is to integrate trial data with other sources of information. Such additional sources of information can be pre-existing data (in vitro and/or in-vivo pre-clinical data), or mechanistic models that allow to bridge between observations where empirical data is inherently difficult or impossible to obtain. Here, we exemplify the suggested framework through SAb’s research on IgGs targeting influenza:
One approach used in our analysis for antibody SAB-176 was a Titer-based PK model, which utilized microneutralization (MN) or hemagglutination (HAI) titers evaluated across a diverse panel of influenza viruses. This allowed for deriving a PK readout.
Complementing this, our investigation utilized mechanistic models of viral load from literature [1, 2, 3]. The selected viral load model was calibrated on both literature [1, 4, 5, 6] and healthy volunteer data from SAb’s Phase 2a study. This approach allowed us to integrate existing knowledge and trial data to enhance the understanding of the efficacy of SAB-176.
Finally, we applied a joint modeling approach to analyze viral load and time-to-alleviation of symptom data from patients at high risk of developing influenza complications and otherwise healthy individuals [7]. By utilizing a joint viral load / time-to-event model, we could effectively compare time-to-event data, providing valuable insights into the differences in viral load between these two populations.
The synergy of these three models – titer-based PK, mechanistic viral load models, and time-to-event modeling – as well as the combination of trial and literature data enabled a comprehensive simulation of the time to alleviation of symptoms in the targeted population of patients at risk of complications.
Hence, our framework involved the amalgamation of literature models and both study as well as literature data to address inherent deficiencies in study data systematically. By integrating study-specific data with literature-derived models, a cohesive model emerges, facilitating the simulation of untested scenarios.
References:
[1] Baccam P., Beauchemin C., Macken C.A., Hayden F.G., Perelson A.S., Kinetics of Influenza A Virus Infection., Journal of Virology, 2006.
[2] Beauchemin C.AA., Handel A., A review of mathematical models of influenza A infections within a host or cell culture: lessons learned and challenges ahead, BMC Public Health, 2011.
[3] Hadjichrysanthou C., Cauet E., Lawrence E., Vegvari C., de Wolf F., Anderson R.M., Understanding the within-host dynamics of influenza A virus: from theory to clinical implications, J. R. Soc. Interface, 2016.
[4] Hayden F.G., Treanor J.J., Betts R.F., Lobo M., Esinhart J.D., Hussey E.K., Safety and Efficacy of the Neuraminidase Inhibitor GG167 in Experimental Human Influenza., Journal of American Medical Association, 1996.
[5] Hayden F.G., Sugaya N., Hirotsu N., Lee N., de Jong M.D., Hurt A.C., Ishida T., Sekino H., Yamada K., Portsmouth S., Kawaguchi K., Shishido T., Arai M., Tsuchiya K., Uehara T., Watanabe A., Baloxavir Marboxil Investigators Group, Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents, The new england journal of medicine, 2018.
[6] Vegvari C., Hadjichrysanthou C., Cauet E., Lawrence E., Cori A., de Wolf F., Anderson R.M., How Can Viral Dynamics Models Inform Endpoint Measures in Clinical Trials of Therapies for Acute Viral Infections?, PLOS One, 2016.
[7] Retout S., De Buck S. Jolivet S., Duval S., Cosson V., A Pharmacokinetics–Time to Alleviation of Symptoms Model to Support Extrapolation of Baloxavir Marboxil Clinical Efficacy in Different Ethnic Groups with Influenza A or B, Clinical Pharmacology & Therapeutics, 2022.
Reference: PAGE 32 (2024) Abstr 10934 [www.page-meeting.org/?abstract=10934]
Poster: Drug/Disease Modelling - Infection