Mechanistic and predictive formulation development for viscosity mitigation of high-concentration biotherapeutics

Area of Science:

• Biopharmaceutical formulation
• Protein therapeutics delivery
• Rheology of biologics

Background:

• Protein therapeutics are crucial for disease treatment.
• Subcutaneous delivery offers patient convenience but requires high protein concentrations (>100 mg/mL).
• High protein concentrations lead to elevated viscosity, causing injectability and manufacturing issues.

Purpose of the Study:

• To review current experimental and computational formulation development approaches for viscosity mitigation in high-concentration protein solutions.
• To suggest new directions for applying these approaches beyond monoclonal antibodies.
• To facilitate the development of patient-centric biotherapeutics.

Main Methods:

• Review of state-of-the-art experimental formulation development.
• Review of computationally predictive approaches for viscosity reduction.
• Integration of machine learning and artificial intelligence in formulation development.

Main Results:

• High protein concentrations (>100 mg/mL) are necessary for subcutaneous delivery but increase viscosity.
• Viscosity mitigation is critical for manufacturing and injectability of protein therapeutics.
• Combined experimental and computational methods, including AI, can accelerate viscosity reduction.

Conclusions:

• Novel formulation strategies are needed to overcome viscosity challenges in high-concentration protein therapeutics.
• Leveraging AI and machine learning can expedite the identification of viscosity-reducing formulations.
• These advancements will enable the development of more patient-centric subcutaneous biotherapeutics.