Ocular Half-Life of Intravitreal Biologics in Humans and Other Species: Meta-Analysis and Model-Based Prediction.
Antonello CarusoMatthias FüthRubén Alvarez-SánchezSara BelliCheikh DiackKatie F MaassDietmar SchwabHubert KettenbergerNorman A MazerPublished in: Molecular pharmaceutics (2020)
Therapeutic antibodies administered intravitreally are the current standard of care to treat retinal diseases. The ocular half-life (t1/2) is a key determinant of the duration of target suppression. To support the development of novel, longer-acting drugs, a reliable determination of t1/2 is needed together with an improved understanding of the factors that influence it. A model-based meta-analysis was conducted in humans and nonclinical species (rat, rabbit, monkey, and pig) to determine consensus values for the ocular t1/2 of IgG antibodies and Fab fragments. Results from multiple literature and in-house pharmacokinetic studies are presented within a mechanistic framework that assumes diffusion-controlled drug elimination from the vitreous. Our analysis shows, both theoretically and experimentally, that the ocular t1/2 increases in direct proportion to the product of the hydrodynamic radius of the macromolecule (3.0 nm for Fab and 5.0 nm for IgG) and the square of the radius of the vitreous globe, which varies approximately 24-fold from the rat to the human. Interspecies differences in the proportionality factors are observed and discussed in mechanistic terms. In addition, mathematical formulae are presented that allow prediction of the ocular t1/2 for molecules of interest. The utility of these formulae is successfully demonstrated in case studies of aflibercept, brolucizumab, and PEGylated Fabs, where the predicted ocular t1/2 values are found to be in reasonable agreement with the experimental data available for these molecules.
Keyphrases
- systematic review
- optic nerve
- endothelial cells
- case control
- optical coherence tomography
- diabetic retinopathy
- randomized controlled trial
- emergency department
- solid phase extraction
- deep learning
- clinical practice
- artificial intelligence
- data analysis
- health insurance
- adverse drug
- liquid chromatography
- tandem mass spectrometry