AIMC Topic: Antibodies, Monoclonal

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Prediction of aggregation in monoclonal antibodies from molecular surface curvature.

Scientific reports
Protein aggregation is one of the key challenges in the biopharmaceutical industry as its control is crucial in achieving long-term stability and efficacy of biopharmaceuticals. Attempts have been made to develop regression models for predicting the ...

AlphaBind, a domain-specific model to predict and optimize antibody-antigen binding affinity.

mAbs
Antibodies are versatile therapeutic molecules that use combinatorial sequence diversity to cover a vast fitness landscape. Designing optimal antibody sequences, however, remains a major challenge. Recent advances in deep learning provide opportuniti...

Predicting the Effects of Charge Mutations on the Second Osmotic Virial Coefficient for Therapeutic Antibodies via Coarse-Grained Molecular Simulations and Deep Learning Methods.

Molecular pharmaceutics
The impact of various charge mutations on the second osmotic virial coefficient was examined for three model therapeutic monoclonal antibodies (MAbs) at representative formulation pH values by using coarse-grained (CG) molecular modeling. The wild-ty...

Patterns of calcitonin gene-related peptide monoclonal antibody use in people with migraine: Results of the OVERCOME (US) study.

Cephalalgia : an international journal of headache
BackgroundUnderstanding characteristics and reasons associated with using calcitonin gene-related peptide monoclonal antibodies (CGRP mAb) for migraine prevention may help clinicians individualize treatment plans and achieve better patient outcomes.M...

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...

Machine Learning for Predicting the Drug-to-Antibody Ratio (DAR) in the Synthesis of Antibody-Drug Conjugates (ADCs).

Journal of chemical information and modeling
The pharmaceutical industry faces challenges in developing efficient and cost-effective drug delivery systems. Among various applications, antibody-drug conjugates (ADCs) stand out by combining cytotoxic or bioactive agents with monoclonal antibodies...

A Novel Theranostic Strategy for Malignant Pulmonary Nodules by Targeted CECAM6 with Zr/I-Labeled Tinurilimab.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Lung adenocarcinoma (LUAD) constitutes a major cause of cancer-related fatalities worldwide. Early identification of malignant pulmonary nodules constitutes the most effective approach to reducing the mortality of LUAD. Despite the wide application o...

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...