Physiologically Based Modeling Approach to Predict Dopamine D2 Receptor Occupancy of Antipsychotics in Brain: Translation From Rat to Human

Abstract

Receptor occupancy (RO) is a translational biomarker for assessing drug efficacy and safety. We aimed to apply a physiologically based pharmacokinetic (PBPK) modeling approach to predict the brain dopamine D2 RO time profiles of antipsychotics. Clozapine and risperidone were modeled together with their active metabolites, norclozapine and paliperidone, First, in PK‐Sim a rat PBPK model was developed and optimized using literature plasma PK data. Then, blood‐brain barrier parameters including the expression and efflux transport kinetics of P‐glycoprotein were optimized using literature microdialysis data on brain extracellular fluid (brainECF), which were further adapted when translating the rat PBPK model into the human PBPK model. Based on the simulated drug and metabolite concentrations in brainECF, drug‐D2 receptor binding kinetics (association and dissociation rates) were incorporated in MoBi to predict RO. From an extensive literature search, 32 plasma PK data sets (16 from rat and 16 from human studies) and 23 striatum RO data sets (13 from rat and 10 from human studies) were prepared and compared with the model predictions. The rat PBPK‐RO model adequately predicted the plasma concentrations of the parent drugs and metabolites and the RO levels. The human PBPK‐RO model also captured the plasma PK and RO levels despite the large interindividual and interstudy variability, although it tended to underestimate the plasma concentrations and RO measured at late time points after risperidone dosing. The developed human PBPK‐RO model was successfully applied to predict the plasma PK and RO changes observed after risperidone dose reduction in a clinical trial in schizophrenic patients.

Pediatric Dose Selection for Therapeutic Proteins

“…The inherent strength of a PBPK model is that drug concentrations or receptor occupancy at the target site (eg, the brain, a tumor, or an inflamed bowel) can be feasibly extrapolated from animals through adults to pediatric patients with sufficient in vivo evidence 63 . The burden of an active disease state and its impact on PK can be mechanistically represented, as it has been done, for example, for the impact of inflammation on the PK of therapeutic proteins in animals and humans 56,64,65 .…”

Ligand with Two Modes of Interaction with the Dopamine D2 Receptor–An Induced-Fit Mechanism of Insurmountable Antagonism

“… 5 Ligand binding kinetics is presumed to play an important role in determining the time course of occupancy at the target receptor. 6 , 7 For example, the action of angiotensin-1 receptor antagonists is prolonged by their long-lasting, induced-fit binding to their target receptor, reflected by insurmountable antagonism in in vitro experiments. 8 Insurmountable antagonism is characterized by a decrease in the maximal agonist-induced response, which cannot be overcome by increasing the concentration of agonist.…”

“…D 2 R antagonism or weak partial agonism is the common denominator of antipsychotic drugs, and D 2 R agonism is a mainstay of Parkinson’s disease treatment. − The time course of receptor occupancy has been suggested to have an important impact on the clinical properties of therapeutic ligands, including antipsychotics, where transient rather than continuous D 2 R occupancy may be associated with a more favorable profile in terms of extrapyramidal side effects . Ligand binding kinetics is presumed to play an important role in determining the time course of occupancy at the target receptor. , For example, the action of angiotensin-1 receptor antagonists is prolonged by their long-lasting, induced-fit binding to their target receptor, reflected by insurmountable antagonism in in vitro experiments . Insurmountable antagonism is characterized by a decrease in the maximal agonist-induced response, which cannot be overcome by increasing the concentration of agonist …”

Physiologically‐based pharmacokinetic model for alectinib, ruxolitinib, and panobinostat in the presence of cancer, renal impairment, and hepatic impairment

“…The brain tissue in PK‐Sim is modeled as four compartments that represent blood cells, plasma, interstitial space, and cellular space with the assumption of permeability‐limited distribution at the capillary membrane (Bayer Technology Services, 2017). Brain tissue modeling as four compartments was adopted before in modeling brain exposure to drugs (Diestelhorst, 2013; Hughes, 2019; Wong, 2019). To simulate the alectinib levels in the CSF, it was assumed that the CSF level is equal to the alectinib free level in the interstitial fluid (Diestelhorst, 2013).…”