Stevens Institute of Technology
The convolutional neural network surrogate model can accelerate early-stage antibody drug development by screening for elevated viscosity in a fraction of a second.
Monoclonal antibody therapeutics are complicated to develop, complicated to manufacture and complicated to administer.
Beneficial in the treatment of a variety of diseases and conditions — including cancer, heart disease, multiple sclerosis, arthritis and COVID-19 — most traditional antibody treatments are injected intravenously (through a vein), requiring specialized medical staff, training and equipment.
Generally taking several hours, such treatments are available only in hospitals or other high-volume medical settings. In addition to the healthcare resources required, the process costs patients in the form of time, expenses and the potential loss of wages with every infusion needed.
A less costly, more convenient method of delivery would be if patients could self-administer antibody treatments subcutaneously (beneath the skin) using a simple syringe while at home.
But at concentrations high enough to reach therapeutic effect, antibodies — which are large proteins — tend to stick together. That stickiness, or high viscosity, can create a drug solution that is simply too thick to draw through a syringe needle.
This syringeability issue, however, is often only discoverable late in the drug development pipeline. Current methods for predicting its likelihood sooner are prohibitively slow and expensive, requiring the immense processing power of supercomputers running for days at a time.
To shortcut this process, Stevens Institute of Technology chemical engineering and materials science assistant professor Pin-Kuang Lai has developed DeepSCM, the first deep learning model specifically designed for predicting high concentration antibody viscosity. . . .
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