Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation

Successful biologics must satisfy multiple properties including activity and particular physicochemical features that are globally defined as developability. These multiple properties must be simultaneously optimized in a very broad design space of protein sequences and buffer compositions. In this...

Full description

Saved in:
Bibliographic Details
Published in:Trends in pharmacological sciences (Regular ed.) 2021-03, Vol.42 (3), p.151-165
Main Authors: Narayanan, Harini, Dingfelder, Fabian, Butté, Alessandro, Lorenzen, Nikolai, Sokolov, Michael, Arosio, Paolo
Format: Article
Language:English
Subjects:
antibodies
biologics development
developability
formulation
machine learning
protein engineering
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Successful biologics must satisfy multiple properties including activity and particular physicochemical features that are globally defined as developability. These multiple properties must be simultaneously optimized in a very broad design space of protein sequences and buffer compositions. In this context, artificial intelligence (AI), and especially machine learning (ML), have great potential to accelerate and improve the optimization of protein properties, increasing their activity and safety as well as decreasing their development time and manufacturing costs. We highlight the emerging applications of ML in biologics discovery and development, focusing on protein engineering, early biophysical screening, and formulation. We discuss the power of ML in extracting information from complex datasets and in reducing the necessary experimental effort to simultaneously achieve multiple quality targets. We finally anticipate possible future interventions of AI in several steps of the biological landscape. Biologics are an important class of therapeutics due to their high specificity, efficacy, and safety.However, biomolecule discovery and optimal formulation development are time-and resource-intensive.The search space is highly complex and multidimensional because multiple physicochemical properties must be optimized.AI is emerging as a predictive and generative tool to aid in protein engineering for therapeutic applications. AI can also be employed to model multiple biophysical and chemical degradation properties.
ISSN:0165-6147
1873-3735
DOI:10.1016/j.tips.2020.12.004