PROGRAM | Chemical Engineering

By: James Forder Chair: Christopher Roberts

ABSTRACT

Protein-based therapies are a prominent class of drug products used in the treatment of chronic illnesses such as cancer and immune-related disorders, and more recently infectious diseases. The development process for therapeutic proteins is uncertain, expensive, and resource-intensive, so there is interest in the biopharmaceutical industry in methods that can improve predictions of how likely a protein drug candidate is to be successfully developed into a commercial product or methods that can streamline the development process. Attractive protein-protein self-interactions are fundamentally associated with a host of challenging behaviors and properties faced during drug development such as reversible self-association, irreversible aggregation, elevated viscosity, and liquid-liquid phase separation. These issues are more severe at high protein concentration which is typically preferred for liquid protein-based therapeutics. Irreversible aggregation is a common concern and is especially problematic because methods to predict changes in aggregation rates or mechanisms between different proteins or solution conditions are not well-established. The presence of aggregates can be a liability in a number of manufacturing processes, reduce the efficacy and shelf-life of the drug product, and elicit a dangerous immunogenic response when administered to a patient.

This thesis is focused on the development of methods to characterize and predict self-interactions and aggregation rates for therapeutic proteins with emphasis on practical applications in streamlining industrial drug development. The experimental datasets span solution conditions and proteins similar to those in commercial protein-based therapies, are fairly diverse in the behaviors they represent, and are large compared to many other publicly available datasets. Coarse-grained (CG) molecular simulations were developed and applied to model self-interactions, predict self-interactions at high-concentration conditions, and identify specific problematic electrostatic interactions between charged residues. A range of CG models for therapeutic proteins were evaluated based on the tradeoffs between computational efficiency and accuracy in calculating net self-interactions. Net self-interactions and reversible self-association (a potential precursor to irreversible aggregation) were experimentally characterized for two Fc-fusion proteins and their corresponding fusion partner protein. CG molecular simulations were used to investigate the origins of attractive electrostatic self-interactions that were related to reversible self-association. Four monoclonal antibodies (MAbs) were systematically characterized by experimental measurements of net self-interactions over a range of representative formulation conditions. A previously developed method to combine experimental measurements of net self-interactions with CG molecular simulations to make predictions of high-concentration net self-interactions was improved by the integration of a hybrid CG model and by methods that represented charge equilibria more precisely. The four MAbs were also used in studies with the aim to measure and understand high-concentration MAb aggregation rates between different MAbs and solution conditions, and to evaluate experimental and statistical methods to predict those rates. Interpretable machine learning methods were developed to rigorously deconvolute the impacts of fundamental phenomena on aggregation rates, assess whether results from stability studies at higher incubation temperatures or lower protein concentrations could be useful for predicting aggregation rates, and to provide a robust platform for predicting aggregation rates with the vast datasets that are not publicly available but presumably exist in the archives of many pharmaceutical companies.

This thesis demonstrates how to 1) select a CG molecular model for a given application, 2) use CG molecular simulations in conjunction with experimental measurements to extract additional knowledge about self-interactions and predict net self-interactions at other conditions (e.g., higher protein concentrations), and 3) understand and predict MAb aggregation rates as a function of protein concentration, storage temperature, and solution conditions. These findings can be applied to various phases of industrial drug development for MAbs, Fc-fusion proteins, or other therapeutic proteins to improve selection of protein candidates and optimization of formulation conditions.

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