Minimal brain PBPK model to support the preclinical and clinical development of antibody therapeutics for CNS diseases

Abstract

There are several antibody therapeutics in preclinical and clinical development, industry-wide, for the treatment of central nervous system (CNS) disorders. Due to the limited permeability of antibodies across brain barriers, the quantitative understanding of antibody exposure in the CNS is important for the design of antibody drug characteristics and determining appropriate dosing regimens. We have developed a minimal physiologically-based pharmacokinetic (mPBPK) model of the brain for antibody therapeutics, which was reduced from an existing multi-species platform brain PBPK model. All non-brain compartments were combined into a single tissue compartment and cerebral spinal fluid (CSF) compartments were combined into a single CSF compartment. The mPBPK model contains 16 differential equations, compared to 100 in the original PBPK model, and improved simulation speed approximately 11-fold. Area under the curve ratios for minimal versus full PBPK models were close to 1 across species for both brain and plasma compartments, which indicates the reduced model simulations are similar to those of the original model. The minimal model retained detailed physiological processes of the brain while not significantly affecting model predictability, which supports the law of parsimony in the context of balancing model complexity with added predictive power. The minimal model has a variety of applications for supporting the preclinical development of antibody therapeutics and can be expanded to include target information for evaluating target engagement to inform clinical dose selection.

Keywords: Antibody; Drug development; Neuroscience; PBPK; Pharmacokinetics.

Fig. 1

Brain mPBPK model structure. The model contains 16 compartments and three regions: plasma (red), brain (blue) and non-brain tissues (green). Antibodies in plasma flow between brain and non-brain tissues. In tissue vascular, antibody travels transcellularly through the endosomal space or paracellularly by directly entering tissue interstitium. In the endosome, antibody binds FcRn to form an antibody-FcRn complex, which either is taken up into the tissue interstitial space or recycled back to the tissue vasculature. Unbound antibody in the endosome is cleared through lysosomal degradation. Antibody in tissue interstitial space leaves via lymphatic flow as well as via endosomal uptake. Antibody in brain vasculature crosses two brain barriers, BBB and BCSFB. Antibodies that cross the BBB and BCSFB enter the brain ISF and CSF, respectively. Paracellular transport across the BBB and BCSFB is governed by brain vascular reflection coefficients that represent the leakiness of the vasculature space. Transcellular transport across the brain via pinocytosis is described by uptake clearance processes. Antibody in brain endosomal spaces is either recycled via FcRn or eliminated via lysosomal degradation. Antibody in brain ISF and CSF can be cleared back to systemic circulation via the glymphatic system. Diagram was drawn using Inkscape (Color figure online)

Fig. 2

Brain mPBPK model equations. Antibody concentration in (1) plasma, (2) tissue vascular, (3) tissue endosome (unbound), (4) tissue endosome (FcRn-bound), (6) tissue interstitium, (7) brain vascular, (8) BBB endosome (unbound), (9) BBB endosome (FcRn-bound), (11) brain interstitium, (12) BCSFB endosome (unbound), (13) BCSFB endosome (FcRn-bound), (15) brain CSF, (16) lymph. FcRn concentration in (5) tissue endosome, (10) BBB endosome, and (14) BCSFB endosome. Equations written using Mathcha

Fig. 3

Minimal versus full PBPK model predictions. Antibody concentrations in human a serum, b brain interstitial fluid (ISF), c brain CSF in lateral ventricle (LV), and d brain CSF in subarachnoid space (SAS). Five IV doses were simulated 1 (red), 3 (green), 10 (blue), 30 (purple), and 100 (pink) mg/kg for a duration of 1000 h. Dotted and solid lines represent minimal and full PBPK model simulations, respectively (Color figure online)

Fig. 4

Computational speed improvement. Simulation time differences between the original/full (red) and reduced/minimal (blue) PBPK models. Simulations were performed for 5 doses over 1000 h for each species, using a solver step size of 0.01 h. The differential equation solver used was ode15s using a relative and absolute tolerance of 2.3E−14 and 1E−22, respectively. Each bar represents the average simulation time across four simulations, one simulation per species. Processor: Intel i7-8700K CPU @ 3.70 GHz (Color figure online)

Fig. 5

Sensitivity analysis of minimal brain PBPK model for antibody exposure in a plasma, b brain ISF, and c brain CSF. The change in AUC for each parameter is displayed as a box and whisker that represents 1000 simulations. Each parameter (one at a time) was multiplied by a value sampled from a log-normal distribution (µ = 0, σ = 0.25)