An asymptotic description of a basic FcRn-regulated clearance mechanism and its implications for PBPK modelling of large antibodies.

A basic FcRn-regulated clearance mechanism is investigated using the method of matched asymptotic expansions. The broader aim of the work is to obtain further insight on the mechanism, thereby providing theoretical support for future pharmacologically-based pharmacokinetic modelling efforts. The corresponding governing equations are first non-dimensionalised and the order of magnitudes of the model parameters are assessed based on their values reported in the literature. Under the assumption of high FcRn-binding affinity, analytical approximations are derived that are valid over the characteristic phases of the problem. Additionally, relatively simple equations relating clearance and AUC to physiological model parameters are derived, which are valid over the longest characteristic time scale of the problem. For lower to moderate doses clearance is effectively linear, whereas for higher doses it is nonlinear. It is shown that for all doses sufficiently high the leading-order approximation for the IgG concentration in plasma, over the longest characteristic time scale, is independent of the initial dose. This is because IgG that is in 'excess' of FcRn is eliminated over a time scale much shorter than that of the terminal phase. In conclusion, analytical approximations of the basic FcRn mechanism have been derived using matched asymptotic expansions, leading to a simple equation relating clearance to FcRn binding affinity, the ratio of degradation and FcRn concentration, and the volumes of the system.

Assessing the performance of QSP models: biology as the driver for validation.

Validation of a quantitative model is a critical step in establishing confidence in the model's suitability for whatever analysis it was designed. While processes for validation are well-established in the statistical sciences, the field of quantitative systems pharmacology (QSP) has taken a more piecemeal approach to defining and demonstrating validation. Although classical statistical methods can be used in a QSP context, proper validation of a mechanistic systems model requires a more nuanced approach to what precisely is being validated, and what role said validation plays in the larger context of the analysis. In this review, we summarize current thoughts of QSP validation in the scientific community, contrast the aims of statistical validation from several contexts (including inference, pharmacometrics analysis, and machine learning) with the challenges faced in QSP analysis, and use examples from published QSP models to define different stages or levels of validation, any of which may be sufficient depending on the context at hand.

Targeted purification development enabled by computational biophysical modeling.

Chromatographic and non-chromatographic purification of biopharmaceuticals depend on the interactions between protein molecules and a solid-liquid interface. These interactions are dominated by the protein-surface properties, which are a function of protein sequence, structure, and dynamics. In addition, protein-surface properties are critical for in vivo recognition and activation, thus, purification strategies should strive to preserve structural integrity and retain desired pharmacological efficacy. Other factors such as surface diffusion, pore diffusion, and film mass transfer can impact chromatographic separation and resin design. The key factors that impact non-chromatographic separations (e.g., solubility, ligand affinity, charges and hydrophobic clusters, and molecular dynamics) are readily amenable to computational modeling and can enhance the understanding of protein chromatographic. Previously published studies have used computational methods such as quantitative structure-activity relationship (QSAR) or quantitative structure-property relationship (QSPR) to identify and rank order affinity ligands based on their potential to effectively bind and separate a desired biopharmaceutical from host cell protein (HCP) and other impurities. The challenge in the application of such an approach is to discern key yet subtle differences in ligands and proteins that influence biologics purification. Using a relatively small molecular weight protein (insulin), this research overcame limitations of previous modeling efforts by utilizing atomic level detail for the modeling of protein-ligand interactions, effectively leveraging and extending previous research on drug target discovery. These principles were applied to the purification of different commercially available insulin variants. The ability of these computational models to correlate directionally with empirical observation is demonstrated for several insulin systems over a range of purification challenges including resolution of subtle product variants (amino acid misincorporations). Broader application of this methodology in bioprocess development may enhance and speed the development of a robust purification platform.