AI Medical Compendium Journal:
mAbs

Showing 1 to 10 of 19 articles

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...

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...

Predicting purification process fit of monoclonal antibodies using machine learning.

mAbs
In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensive experimentation. However, the work required for upstream protein expression and downstream purifi...

Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach.

mAbs
Subcutaneous injections are an increasingly prevalent route of administration for delivering biological therapies including monoclonal antibodies (mAbs). Compared with intravenous delivery, subcutaneous injections reduce administration costs, shorten...

Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions.

mAbs
The development of bispecific antibodies that bind at least two different targets relies on bringing together multiple binding domains with different binding properties and biophysical characteristics to produce a drug-like therapeutic. These buildin...

Effective binding to protein antigens by antibodies from antibody libraries designed with enhanced protein recognition propensities.

mAbs
Antibodies provide immune protection by recognizing antigens of diverse chemical properties, but elucidating the amino acid sequence-function relationships underlying the specificity and affinity of antibody-antigen interactions remains challenging. ...

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method.

mAbs
Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the ...

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...