Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach.

Subcutaneous injections are an increasingly prevalent route of administration for delivering biological therapies including monoclonal antibodies (mAbs). Compared with intravenous delivery, subcutaneous injections reduce administration costs, shorten the administration time, and are strongly preferred from a patient experience point of view. An understanding of the absorption process of a mAb from the injection site to the systemic circulation is critical to the process of subcutaneous mAb formulation development. In this study, we built a model to predict the absorption rate constant (ka), which denotes how fast a mAb is absorbed from the site of administration. Once trained, our model (enabled by the XGBoost algorithm in machine learning) can predict the ka of a mAb following a subcutaneous injection using in silico molecular properties alone (generated from the primary sequence). Our model does not need clinically observed plasma concentration-time data; this is a novel capability not previously achieved in predictive pharmacokinetic models. The model also showed improved performance when benchmarked against a recently reported mechanistic model that relied on clinical data to predict subcutaneous absorption of mAbs. We further interpreted the model to understand which molecular properties affect the absorption rate and showed that our findings are consistent with previous studies evaluating subcutaneous absorption through direct experimentation. Taken altogether, this study reports the development, validation, benchmarking, and interpretation of a model that can predict the clinical ka of a mAb using its primary sequence as the only input.

Characterizing Protein-Protein Interactions and Viscosity of a Monoclonal Antibody from Low to High Concentration Using Small-Angle X-ray Scattering and Molecular Dynamics Simulations.

Understanding protein-protein interactions and formation of reversible oligomers (clusters) in concentrated monoclonal antibody (mAb) solutions is necessary for designing stable, low viscosity (η) concentrated formulations for processing and subcutaneous injection. Here we characterize the strength (K) of short-range anisotropic attractions (SRA) for 75-200 mg/mL mAb2 solutions at different pH and cosolute conditions by analyzing structure factors (Seff(q)) from small-angle X-ray scattering (SAXS) using coarse-grained molecular dynamics simulations. Best fit simulations additionally provide cluster size distributions, fractal dimensions, cluster occluded volume, and mAb coordination numbers. These equilibrium properties are utilized in a model to account for increases in viscosity caused by occluded volume in the clusters (packing effects) and dissipation of stress across lubricated fractal clusters. Seff(q) is highly sensitive to K at 75 mg/mL where mAbs can mutually align to form SRA contacts but becomes less sensitive at 200 mg/mL as steric repulsion due to packing becomes dominant. In contrast, η at 200 mg/mL is highly sensitive to SRA and the average cluster size from SAXS/simulation, which is observed to track the cluster relaxation time from shear thinning. By analyzing the distribution of sub-bead hot spots on the 3D mAb surface, we identify a strongly attractive hydrophobic patch in the complementarity determining region (CDR) at pH 4.5 that contributes to the high K and consequently large cluster sizes and high η. Adding NaCl screens electrostatic interactions and increases the impact of hydrophobic attraction on cluster size and raises η, whereas nonspecific binding of Arg attenuates all SRA, reducing η. The hydrophobic patch is absent at higher pH values, leading to smaller K, smaller clusters, and lower η. This work constitutes a first attempt to use SAXS and CG modeling to link both structural and rheological properties of concentrated mAb solutions to the energetics of specific hydrophobic patches on mAb surfaces. As such, our work opens an avenue for future research, including the possibility of designing coarse-grained models with physically meaningful interacting hot spots.

Subclass Effects on Self-Association and Viscosity of Monoclonal Antibodies at High Concentrations.

The effects of a subclass of monoclonal antibodies (mAbs) on protein-protein interactions, formation of reversible oligomers (clusters), and viscosity (η) are not well understood at high concentrations. Herein, we quantify a short-range anisotropic attraction between the complementarity-determining region (CDR) and CH3 domains (KCDR-CH3) for vedolizumab IgG1, IgG2, or IgG4 subclasses by fitting small-angle X-ray scattering (SAXS) structure factor Seff(q) data with an extensive library of 12-bead coarse-grained (CG) molecular dynamics simulations. The KCDR-CH3 bead attraction strength was isolated from the strength of long-range electrostatic repulsion for the full mAb, which was determined from the theoretical net charge and a scaling parameter ψ to account for solvent accessibility and ion pairing. At low ionic strength (IS), the strongest short-range attraction (KCDR-CH3) and consequently the largest clusters and highest η were observed with IgG1, the subclass with the most positively charged CH3 domain. Furthermore, the trend in KCDR-CH3 with the subclass followed the electrostatic interaction energy between the CDR and CH3 regions calculated with the BioLuminate software using the 3D mAb structure and molecular interaction potentials. Whereas the equilibrium cluster size distributions and fractal dimensions were determined from fits of SAXS with the MD simulations, the degree of cluster rigidity under flow was estimated from the experimental η with a phenomenological model. For the systems with the largest clusters, especially IgG1, the inefficient packing of mAbs in the clusters played the largest role in increasing η, whereas for other systems, the relative contribution from stress produced by the clusters was more significant. The ability to relate η to short-range attraction from SAXS measurements at high concentrations and to theoretical characterization of electrostatic patches on the 3D surface is not only of fundamental interest but also of practical value for mAb discovery, processing, formulation, and subcutaneous delivery.

Revisit PEG-Induced Precipitation Assay for Protein Solubility Assessment of Monoclonal Antibody Formulations.

PURPOSE: Protein solubility is an important attribute of pharmaceutical monoclonal antibody (MAb) formulations, particularly at high MAb concentrations. PEG-induced protein precipitation has been routinely used to assess protein solubility. To provide insights for better understanding and implementation of PEG-induced protein precipitation assay, this work compares different solubility measures and examines their relevance to loss of protein solubility in concentrated formulations. METHODS: Solubility of a MAb in 15 formulations was evaluated using PEG-induced precipitation assay. Three apparent protein solubility measures, the middle-point and onset PEG concentrations (cmid and conset) as well as the binding free energy (µB), were obtained from the PEG-induced protein precipitation assay and compared to the DLS protein interaction parameter (kD). Visual inspection of loss of protein solubility in concentrated formulations during storage was used to further examine the discrepancy of protein solubility ranking by these measures. RESULTS: PEG-induced precipitation assay predicted overall protein solubility ranking similar to that by DLS kD. However, for three formulations with ionic excipients NaCl, Arg·Cl, and Arg·Glu·Cl, PEG-induced precipitation assay yielded more accurate predictions compared to DLS kD measurements. Furthermore, µB showed superior ability in distinguishing protein solubility for these formulations. CONCLUSIONS: This study demonstrated good correlations between the protein solubility measures obtained from PEG-induced precipitation experiments and DLS kD measurement. It also provides one example in which protein solubility ranking by binding free energy is more accurate than the other measures. The results support the theoretical proposition that µB has a potential to serve as standard protein solubility measure.

Temperature Dependence of Protein Solution Viscosity and Protein-Protein Interactions: Insights into the Origins of High-Viscosity Protein Solutions.

Protein solution viscosity (η) as a function of temperature was measured at a series of protein concentrations under a range of formulation conditions for two monoclonal antibodies (MAbs) and a globular protein (aCgn). Based on theoretical arguments, a strong temperature dependence for protein-protein interactions (PPI) indicates highly anisotropic, short-ranged attractions that could lead to higher solution viscosities. The semi-empirical Ross-Minton model was used to determine the apparent intrinsic viscosity, shape, and "crowding" factors for each protein as a function of temperature and formulation conditions. The apparent intrinsic viscosity was independent of temperature for aCgn, while a slight decrease with increasing temperature was observed for the MAbs. The temperature dependence of solution viscosity was analyzed using the Andrade-Eyring equation to determine the effective activation energy of viscous flow (Ea,η). While Ea,η values were different for each protein, they were independent of formulation conditions for a given protein. PPI were quantified via the osmotic second virial coefficient (B22) and the protein diffusion interaction parameter (kD) as a function of temperature under the same formulation conditions as the viscosity measurements. Net interactions ranged from strongly attractive to repulsive by changing formulation pH and ionic strength for each protein. Overall, larger activation energies for PPI corresponded to larger activation energies for η, and those were predictive of the highest η values at higher protein concentrations.

Light Scattering to Quantify Protein-Protein Interactions at High Protein Concentrations.

Static and dynamic (laser) light scattering (SLS and DLS, respectively) can be used to measure the so-called weak or colloidal protein-protein interactions in solution from low to high protein concentrations (c2). This chapter describes a methodology to measure protein-protein self-interactions using SLS and DLS, with illustrative examples for monoclonal antibody solutions from low to high protein concentrations (c2 ~ 1-102 g/L).

How Well Do Low- and High-Concentration Protein Interactions Predict Solution Viscosities of Monoclonal Antibodies?

Protein-protein interactions (PPI) and solution viscosities were measured at low and high protein concentrations under a range of formulation conditions for 4 different monoclonal antibodies. Static light scattering was used to quantify the osmotic second virial coefficient (B22) and the zero-q limit static structure factor (Sq=0), versus protein concentration (c2) from low to high c2. Dynamic light scattering was used to measure the collective diffusion coefficient as a function of c2 and to determine the protein interaction parameter (kD). Static light scattering and dynamic light scattering were combined to determine the hydrodynamic factor (Hq=0), which accounts for changes in hydrodynamic PPI as a function of c2. The net PPI ranged from strongly repulsive to attractive interactions, via changes in buffer pH, ionic strength, and choice of monoclonal antibodies. Multiple-particle tracking microrheology and capillary viscometery were used to measure monoclonal antibodies solution viscosities under the same solution conditions. In most cases, even large and qualitative changes in PPI did not result in significant changes in protein solution viscosity. This highlights the complex nature of PPI and how they influence protein solution viscosity and raises questions as to the validity of using experimental PPI metrics such as kD or B22 as predictors of high viscosity.

Viscosities and Protein Interactions of Bispecific Antibodies and Their Monospecific Mixtures.

Solution viscosities (η) and protein-protein interactions (PPI) of three monoclonal antibodies (mAb-A, mAb-B, mAb-C), two bispecific antibodies (BsAb-A/B, BsAb-A/C), and two 1:1 binary mixtures (mAb-A + mAb-B and mAb-A + mAb-C) were measured. mAb-A and mAb-C have similar isoelectric point (pI) values but significantly different η versus protein concentration ( c2) profiles. The viscosity of the mAb-A + mAb-C mixture followed an Arrhenius mixing rule and was identical to viscosity of the bispecific BsAb-A/C. In contrast, mAb-A and mAb-B had similar η versus c2 profiles, but the Arrhenius mixing rule failed to predict the higher viscosities of their mixtures. The viscosity of the bispecific BsAb-A/B followed the 1:1 mAb-A + mAb-B mixture at all concentrations. The nature of the interactions for mAb-A, mAb-B, the BsAb-A/B bispecific, and the 1:1 mAb-A + mAb-B mixture were characterized by static and dynamic light scattering (SLS and DLS). mAb-A and mAb-B exhibited net-attractive and -repulsive electrostatic interactions, respectively. The bispecific antibody (BsAb-A/B) had short-ranged attractive interactions, suggesting that the increase in viscosity for this molecule and the mAb-A + mAb-B mixture was due to cross-interactions between Fab regions. At high and low ionic strengths and protein concentrations, the Rayleigh scattering profile, the collective diffusion coefficient, and viscosity for the mixture closely followed that for the bispecific antibody. These results highlight the possible anomalous viscosity increases of bispecific antibodies constructed from relatively low-viscosity mAbs but demonstrates a potentially fruitful approach of using mAb mixtures to predict the viscosity of candidate bispecific constructs.

Predicting Protein Interactions of Concentrated Globular Protein Solutions Using Colloidal Models.

Protein interactions of α-chymotrypsinogen A (aCgn) were quantified using light scattering from low to high protein concentrations. Static light scattering (SLS) was used to determine the excess Rayleigh ratio (Rex) and osmotic second virial coefficients (B22) as a function of pH and total ionic strength (TIS). Repulsive (attractive) protein-protein interactions (PPI) were observed at pH 5 (pH 7), with decreasing repulsions (attractions) upon increasing TIS. Simple colloidal potential of mean force models (PMF) that account for short-range nonelectrostatic attractions and screened electrostatic interactions were used to fit model parameters from data for B22 vs TIS at both pH values. The parameters and PMF models from low-concentration conditions were used as the sole input to transition matrix Monte Carlo simulations to predict high concentration Rex behavior. At conditions where PPI are repulsive to slightly attractive, experimental Rex data at high concentrations could be predicted quantitatively by the simulations. However, accurate predictions were challenging when PPI were strongly attractive due to strong sensitivity to changes in PMF parameter values. Additional simulations with higher-resolution coarse-grained molecular models suggest an approach to qualitatively predict cases when anisotropic surface charge distributions will lead to overall attractive PPI at low ionic strength, without assumptions regarding electrostatic "patches" or multipole expansions.

Gel-induced selective crystallization of polymorphs.

Although nanoporous materials have been explored for controlling crystallization of polymorphs in recent years, polymorphism in confined environments is still poorly understood, particularly from a kinetic perspective, and the role of the local structure of the substrate has largely been neglected. Herein, we report the use of a novel material, polymer microgels with tunable microstructure, for controlling polymorph crystallization from solution and for investigating systematically the effects of nanoconfinement and interfacial interactions on polymorphic outcomes. We show that the polymer microgels can improve polymorph selectivity significantly. The polymorphic outcomes correlate strongly with the gel-induced nucleation kinetics and are very sensitive to both the polymer microstructure and the chemical composition. Further mechanistic investigations suggest that the nucleation-templating effect and the spatial confinement imposed by the polymer network may be central to achieving polymorph selectivity. We demonstrate polymer microgels as promising materials for controlling crystal polymorphism. Moreover, our results help advance the fundamental understanding of polymorph crystallization at complex interfaces, particularly in confined environments.