Molecular Assessment of Monoclonal Antibody-Based Therapeutics Enabling Lead Selection for Clinical Development

High concentration formulation developability approaches and considerations

The growing need for biologics to be administered subcutaneously and ocularly, coupled with certain indications requiring high doses, has resulted in an increase in drug substance (DS) and drug product (DP) protein concentrations. With this increase, more emphasis must be placed on identifying critical physico-chemical liabilities during drug development, including protein aggregation, precipitation, opalescence, particle formation, and high viscosity. Depending on the molecule, liabilities, and administration route, different formulation strategies can be used to overcome these challenges. However, due to the high material requirements, identifying optimal conditions can be slow, costly, and often prevent therapeutics from moving rapidly into the clinic/market. In order to accelerate and derisk development, new experimental and in-silico methods have emerged that can predict high concentration liabilities. Here, we review the challenges in developing high concentration formulations, the advances that have been made in establishing low mass and high-throughput predictive analytics, and advances in in-silico tools and algorithms aimed at identifying risks and understanding high concentration protein behavior.

Developability assessment for monoclonal antibody drug candidates: a case study

Various screening approaches are used by industry to evaluate development risks associated with discovery candidates. This process has become more complicated with biological therapeutics, a class dominated by monoclonal antibodies (mAb), and, increasingly, their derivative constructs. Effective early assessment for drug-like properties (DLP) can save time and costs by allowing a more complete consideration of issues that could impact the desired end result of a stable drug product. Here we report a case study of four IgG1 mAbs, with sequence variations in the variable domain region, screened as a set of possible drug candidates. Our comprehensive, tiered approach used a battery of analytical tools to assess molecular characteristics, conformational stability, colloidal stability, and short-term storage stability. While most DLP for the four candidates were developmentally acceptable and comparable, mAb-2 was associated with adverse colloidal properties. Further investigation of mAb-2 in an expanded pH range revealed a propensity for phase separation, indicating a need for the additional product development effort. Our results support that comprehensive DLP assessments in an expanded pH range are beneficial in identifying development options for promising molecules that show challenging stability trends. This adaptable approach may be especially useful in the development of increasingly complex antibody constructs.

Multi-criteria manufacturability indices for ranking high-concentration monoclonal antibody formulations

The need for high-concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high-concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high-concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high-throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi-criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi-criteria decision-making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. Biotechnol. Bioeng. 2017;114: 2043-2056. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc.