Biopharmaceuticals Research

Machine Learning Guided Stable Biophamaceutical Formulation Design

Advance AI/ML-based models to optimize monoclonal antibody selection and formulation conditions, accelerating pre-formulation and formulation stages in biopharmaceutical drug development. Support the growth of the biopharmaceutical sector by enhancing the efficiency of monoclonal antibody therapeutics, improving targeted treatment for critical diseases such as cancer, rheumatoid arthritis, autoimmune disorders, and asthma. 

Techniques

  • Rotational Rheometry
  • Microfluidic Analysis 
  • Spectroscopy

Optimizing Monoclonal Antibody Structure and Dynamics Through Formulation Variables

Evaluate the effects of formulation factors on enhancing the temperature stability of highly concentrated monoclonal antibody formulations. Identify parameters from diluted monoclonal antibody solutions that can be used to predict viscosity and stability properties in highly concentrated solutions. 

Techniques 

  • Microfluidic Analysis
  • Spectroscopy

Next-Generation Plant-Based Food Formulations

Optimizing protein structure and formulation proprties (rheology and tribology) to improve texture and stability.

Techniques 

  • Rotational Rheometry
  • Tribology
  • Microfluidic Analysis
  • Spectroscopy

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