Utility of a Bayesian Mathematical Model to Predict the Impact of Immunogenicity on Pharmacokinetics of Therapeutic Proteins

Mathematical Models to Characterize the Absorption, Distribution, Metabolism, and Excretion of Protein Therapeutics

Therapeutic proteins (TPs) have ranked among the most important and fastest-growing classes of drugs in the clinic, yet the development of successful TPs is often limited by unsatisfactory efficacy. Understanding pharmacokinetic (PK) characteristics of TPs is key to achieving sufficient and prolonged exposure at the site of action, which is a prerequisite for eliciting desired pharmacological effects. PK modeling represents a powerful tool to investigate factors governing in vivo disposition of TPs. In this mini-review, we discuss many state-of-the-art models that recapitulate critical processes in each of the absorption, distribution, metabolism/catabolism, and excretion pathways of TPs, which can be integrated into the physiologically-based pharmacokinetic framework. Additionally, we provide our perspectives on current opportunities and challenges for evolving the PK models to accelerate the discovery and development of safe and efficacious TPs. SIGNIFICANCE STATEMENT: This minireview provides an overview of mechanistic pharmacokinetic (PK) models developed to characterize absorption, distribution, metabolism, and elimination (ADME) properties of therapeutic proteins (TPs), which can support model-informed discovery and development of TPs. As the next-generation of TPs with diverse physicochemical properties and mechanism-of-action are being developed rapidly, there is an urgent need to better understand the determinants for the ADME of TPs and evolve existing platform PK models to facilitate successful bench-to-bedside translation of these promising drug molecules.

Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity

Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

Failure Mode and Effects Analysis (FMEA) for Immunogenicity of Therapeutic Proteins

Biotherapeutic drugs made by cell-based systems are revolutionizing the practice of medicine. The next generation of biotherapeutics include recombinant proteins, monoclonal antibodies, viral vector expressed proteins, and cell therapies. Immunogenicity associated adverse events is one of the major risks for these biologics. Accurate and precise measurement of the immunogenicity of biologics is a critical component during all phases of drug development. We have utilized the principles of Failure Mode and Effects Analysis (FMEA) in performing assessment of risk of immunogenicity. The multi-dimensional approach involves: i) listing all the potential risks by likelihood of occurrence and severity as part of quality target product profile. ii) ascribing the causes by identifying the risks at each stage of development. iii) predicting the effects. iv) determining the risk mitigation strategy. v) implementing a monitoring process. vi) developing templates for data collection. vii) timely reporting and. viii) life cycle management. FMEA is a continuous process that works throughout the lifecycle of the product or the process and keeps on getting updated with new insights and knowledge.

Understanding Inter-Individual Variability in Monoclonal Antibody Disposition

Monoclonal antibodies (mAbs) are currently the largest and most dominant class of therapeutic proteins. Inter-individual variability has been observed for several mAbs; however, an understanding of the underlying mechanisms and factors contributing to inter-subject differences in mAb disposition is still lacking. In this review, we analyze the mechanisms of antibody disposition and the putative mechanistic determinants of inter-individual variability. Results from in vitro, preclinical, and clinical studies were reviewed evaluate the role of the neonatal Fc receptor and Fc gamma receptors (expression and polymorphism), target properties (expression, shedding, turnover, internalization, heterogeneity, polymorphism), and the influence of anti-drug antibodies. Particular attention is given to the influence of co-administered drugs and disease, and to the physiological relevance of covariates identified by population pharmacokinetic modeling, as determinants of variability in mAb pharmacokinetics.

A Model-Based Approach to Quantify the Time-Course of Anti-Drug Antibodies for Therapeutic Proteins

A mathematical antidrug antibody (ADA) model was developed to quantitatively assess immunogenicity for therapeutic proteins. The ADA model was built with antibody titer data in subjects from 10 clinical trials. The time course of the antibody titers was quantitatively characterized with a two-component semimechanistic model describing the double peaks of ADA titers. The relationship between antibody titer and incidence was also explored. The ADA incidences in subjects from 12 clinical trials were used for internal and external validations. The ADA titers reasonably predicted the incidence of antibody. The model-predicted elimination rate constant for antibody titer was 14.1 × 10^-3  day^-1 and 8.1 × 10^-3  day^-1 in healthy subjects and patients, respectively. This research provided a useful tool to quantitatively evaluate immunogenicity and its impact for therapeutic proteins.