Effect of Size on Solid Tumor Disposition of Protein Therapeutics

Zhe Li1, Yingyi Li1, Hsuan-Ping Chang1

  • 1Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, New York.

Insights

Molecular size significantly impacts protein therapeutic delivery to tumors. Optimal sizes for maximum tumor uptake (~100 kDa) and tumor-to-plasma exposure ratio (~50 kDa) were identified for non-FcRn-binding agents.

Area of Science:

  • Pharmacokinetics and Drug Development
  • Biotechnology and Biologics Engineering
  • Oncology Therapeutics

Background:

  • Understanding protein therapeutic tumor disposition is crucial for effective cancer treatment.
  • Molecular size is a key factor influencing drug distribution within tumors.
  • Existing models require validation for diverse protein therapeutic formats.

Purpose of the Study:

  • To evaluate the impact of molecular size on the tumor disposition of various protein therapeutics.
  • To establish relationships between molecular weight and tumor uptake/exposure.
  • To validate a systems pharmacokinetic model for predicting tumor drug delivery.

Main Methods:

  • Administered trastuzumab and its fragments (27-150 kDa) to tumor-bearing mice (HER2-positive and negative).
  • Measured plasma and tumor pharmacokinetics (PK) to determine drug concentration over time.
  • Developed and validated a systems PK model using the experimental data.

Main Results:

  • Identified optimal molecular sizes for maximum tumor uptake (~100 kDa) and tumor-to-plasma AUC ratio (~50 kDa) for non-FcRn-binding agents.
  • The developed PK model accurately predicted drug disposition across different sizes and tumor types (within 2-3 fold).
  • Observed bell-shaped relationships between molecular weight and tumor uptake/exposure were captured by the model.

Conclusions:

  • Molecular size and target engagement are critical determinants of protein therapeutic tumor disposition.
  • The validated systems PK model can predict drug delivery, aiding in the development of novel protein therapeutics.
  • Findings support the rational design and preclinical-to-clinical translation of antibody-based cancer therapies.

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