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Antibodies, Monoclonal

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Assessing subvisible particle risks in monoclonal antibodies: insights from quartz crystal microbalance with dissipation, machine learning, and in silico analysis.

mAbs
Throughout the lifecycle of biopharmaceutical development and manufacturing, monoclonal antibodies (mAbs) are subjected to diverse interfacial stresses and encounter various container surfaces. These interactions can cause the formation of subvisible...

Machine Learning-Powered Optimization of a CHO Cell Cultivation Process.

Biotechnology and bioengineering
Chinese Hamster Ovary (CHO) cells are the most widely used cell lines to produce recombinant therapeutic proteins such as monoclonal antibodies (mAbs). However, the optimization of the CHO cell culture process is very complex and influenced by variou...

Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics.

Scientific reports
Development of antibodies often begins with the assessment and optimization of their physicochemical properties, and their efficient engagement with the target of interest. Decisions at the early optimization stage are critical for the success of the...

PROPERMAB: an integrative framework for prediction of antibody developability using machine learning.

mAbs
Selection of lead therapeutic molecules is often driven predominantly by pharmacological efficacy and safety. Candidate developability, such as biophysical properties that affect the formulation of the molecule into a product, is usually evaluated on...

An antibody developability triaging pipeline exploiting protein language models.

mAbs
Therapeutic monoclonal antibodies (mAbs) are a successful class of biologic drugs that are frequently selected from phage display libraries and transgenic mice that produce fully human antibodies. However, binding affinity to the correct epitope is n...

Accelerating mechanistic model calibration in protein chromatography using artificial neural networks.

Journal of chromatography. A
In the manufacturing of therapeutic monoclonal antibodies (mAbs), mechanistic models can aid the evaluation and selection of suitable chromatography operating conditions during process development. However, model calibration remains a common bottlene...

Leveraging multi-modal feature learning for predictions of antibody viscosity.

mAbs
The shift toward subcutaneous administration for biologic therapeutics has gained momentum due to its patient-friendly nature, convenience, reduced healthcare burden, and improved compliance compared to traditional intravenous infusions. However, a s...

Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning.

mAbs
Highly concentrated antibody solutions are necessary for developing subcutaneous injections but often exhibit high viscosities, posing challenges in antibody-drug development, manufacturing, and administration. Previous computational models were only...

AI-designed antibody candidates hit a crucial target.

Science (New York, N.Y.)
Companies find enticing drug leads that bind to tricky cell membrane proteins.