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Computational models for studying physical instabilities in high concentration biotherapeutic formulations

Marco A. Blanco

2022mAbs43 citationsDOIOpen Access PDF

Abstract

Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.

Topics & Concepts

Context (archaeology)Computer scienceComputational modelStability (learning theory)Biochemical engineeringSimulationMachine learningEngineeringBiologyPaleontologyProtein purification and stabilityProtein Structure and DynamicsMonoclonal and Polyclonal Antibodies Research
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