Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies.

Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC0-672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL.

Resolving Liquid-Liquid Phase Separation for a Peptide Fused Monoclonal Antibody by Formulation Optimization.

Liquid-liquid phase separation (LLPS) of protein solutions has been usually related to strong protein-protein interactions (PPI) under certain conditions. For the first time, we observed the LLPS phenomenon for a novel protein modality, peptide-fused monoclonal antibody (pmAb). LLPS emerged within hours between pH 6.0 to 7.0 and disappeared when solution pH values decreased to pH 5.0 or lower. Negative values of interaction parameter (kD) and close to zero values of zeta potential (ζ) were correlated to LLPS appearance. However, between pH 6.0 to 7.0, a strong electrostatic repulsion force was expected to potentially avoid LLPS based on the sequence predicted pI value, 8.35. Surprisingly, this is significantly away from experimentally determined pI, 6.25, which readily attributes the LLPS appearances of pmAb to the attenuated electrostatic repulsion force. Such discrepancy between experiment and prediction reminds the necessity of actual measurement for a complicated modality like pmAb. Furthermore, significant protein degradation took place upon thermal stress at pH 5.0 or lower. Therefore, the effects of pH and selected excipients on the thermal stability of pmAb were further assessed. A formulation consisting of arginine at pH 6.5 successfully prevented the appearance of LLPS and enhanced its thermal stability at 40 °C for pmAb. In conclusion, we have reported LLPS for a pmAb and successfully resolved the issue by optimizing formulation with aids from PPI characterization.

High-Throughput Screening and Analysis of Charge Variants of Monoclonal Antibodies in Multiple Formulations.

Among different biopharmaceutical products, monoclonal antibodies (mAbs) show a high level of complexity, including heterogeneity due to differences in size, hydrophobicity, charge, and so forth. Such heterogeneity can be related to both cell-based production and any of the stages of purification, storage, and delivery that the mAb is subjected to. Choosing the right formulation composition providing both physical and chemical stabilities can be a very challenging process, especially when done in the limited time frame required for a typical drug development cycle. Charge variants, a common type of heterogeneity for mAbs, are easy to detect by ion exchange, specifically cation exchange chromatography (CEX). We have developed and implemented a high-throughput CEX-based approach for the rapid screening and analysis of charge modifications in multiple formulation conditions. In this work, 96 different formulations of antistreptavidin IgG1 and IgG2 molecules were automatically prepared and analyzed after incubation at high temperature. Design of experiment and statistical analysis tools have been utilized to determine the major formulation factors responsible for chemical stability of antibodies. Regression models were constructed to find the optimal formulation conditions. The methodology can be applied to different stages of preformulation and formulation development of mAbs.

High-throughput screening and stability optimization of anti-streptavidin IgG1 and IgG2 formulations.

Selection of a suitable formulation that provides adequate product stability is an important aspect of the development of biopharmaceutical products. Stability of proteins includes not only resistance to chemical modifications but also conformational and colloidal stabilities. While chemical degradation of antibodies is relatively easy to detect and control, propensity for conformational changes and/or aggregation during manufacturing or long-term storage is difficult to predict. In many cases, the formulation factors that increase one type of stability may significantly decrease another type under the same or different conditions. Often compromise is necessary to minimize the adverse effects of an antibody formulation by careful optimization of multiple factors responsible for overall stability. In this study, high-throughput stress and characterization techniques were applied to 96 formulations of anti-streptavidin antibodies (an IgG1 and an IgG2) to choose optimal formulations. Stress and analytical methods applied in this study were 96-well plate based using an automated liquid handling system to prepare the different formulations and sample plates. Aggregation and clipping propensity were evaluated by temperature and mechanical stresses. Multivariate regression analysis of high-throughput data was performed to find statistically significant formulation factors that alter measured parameters such as monomer percentage or unfolding temperature. The results of the regression models were used to maximize the stabilities of antibodies under different formulations and to find the optimal formulation space for each molecule. Comparison of the IgG1 and IgG2 data indicated an overall greater stability of the IgG1 molecule under the conditions studied. The described method can easily be applied to both initial preformulation screening and late-stage formulation development of biopharmaceutical products.