PhD candidate University at Buffalo Tonwanda, New York, United States
Disclosure(s):
Krutika Patidar, n/a: No financial relationships to disclose
Objective: Protein therapeutics have revolutionized the treatment of a wide range of diseases. While they have distinct physicochemical characteristics that influence their absorption, distribution, metabolism, and excretion (ADME) properties, the relationship between the physicochemical properties and PK is still largely unknown. In this work, we present a minimal physiologically-based pharmacokinetic (mPBPK) model that incorporates a multivariate quantitative relation between a therapeutic's physicochemical parameters and its corresponding ADME properties and PK.
Method: The mPBPK model consists of plasma, lymph, and two lumped tissue compartments. The tissues with similar characteristics are lumped into respective tight and leaky tissue compartments. Tight tissue represents muscle, fat, brain, and skin, whereas other tissues are represented as leaky tissue. The model accounts for monoclonal antibody properties - molecular weight, molecular size (Stoke’s radius), molecular charge, binding affinity to FcRn, and specific antigen affinity. A multi-start non-linear least squares estimation for sensitive model parameters was performed where necessary as most physiology-based and kinetic parameters were known a priori. Through derived and fitted empirical relationships, the model demonstrates the effect of these antibody properties on its distribution and disposition in both plasma and peripheral tissues using observed PK data in mice and humans.
Results: The mPBPK model used the two-pore theory to predict MW and size-based clearance and exposure of full-length antibodies (150 kDa) and antibody fragments (50–100 kDa) within a 1-fold error. We derived quantitative relations between antibody charge and PK parameters (uptake rate, non-specific binding affinity, and volume of distribution) to capture the relatively faster clearance of positively charged mAb as compared to negatively charged mAb. The model predicts the terminal plasma clearance of slightly positively and negatively charged antibody in humans within a 1-fold error. The mPBPK model presented in this work also predicts the target-mediated disposition of a drug when antibody and target-specific properties are known. To our knowledge, a combined effect of antibody weight, size, charge, FcRn, and antigen has not been incorporated and studied in a single mPBPK model previously.
Conclusion: By conclusively incorporating and relating a multitude of protein’s physicochemical properties to observed PK, the mPBPK model aims to contribute as a platform approach in the early stages of drug development where many of these properties can be optimized to improve a molecule’s PK and ultimately its efficacy. Acknowledgement: This study was funded by Sanofi.
Citations: [1] Patidar, K., Pillai, N., Dhakal, S. et al. J Pharmacokinet Pharmacodyn (2024). [2] Cao, Y and Jusko, WJ. J Pharmacokinet Pharmacodyn (2012). [3] Li, Z and Shah, DK. J Pharmacokinet Pharmacodyn. (2019).
