Carreño, F1; Helfer, VE1; Staudt, KJ1; Paese, K1; Meyer, FS1; Herrmann, AP1; Guterres, S.S1, Rates, SMK1; Trocóniz, IF2, Dalla Costa, T1
(1) Pharmaceutical Sciences Graduate Program, Federal University of Rio Grande do Sul, Porto Alegre, Brazil, (2) Pharmacometrics & Systems Pharmacology, University of Navarra, Pamplona, Spain.
Introduction: High variability in chronic SCZ response to treatment may be partially related to blood-brain barrier (BBB) dysfunction caused by the disease and consequent alterations on antipsychotic drug transport to the central nervous system (CNS)1. Therefore, we developed lipid-core nanocapsules (LNC) aiming to improve drug targeting to the brain2.
Objectives: In the current study we aim developing a populational pharmacokinetic (popPK) model capable of describing changes in plasma and brain pharmacokinetics after administration of quetiapine (QTP) solution (FQ) or encapsulated in lipid core nanocapsules (QLNC) to naïve and schizophrenia-phenotyped (SPR) rats, increasing the understanding of the role of this drug delivery system in brain drug disposition.
Methods: Study approved by CEUA/UFRGS (#31001). QLNC (1 mg/mL) were obtained by nanoprecipitation and presented average size of 166 ± 39 nm, low polydispersity index (< 0.15) and high encapsulation efficiency (93.0 ± 1.4%). Wistar pregnant dams (GD15) received a single 4 mg/kg i.v. bolus dose of poly(i:c) or saline (naïve offspring) and SCZ-like deficits in the adult offspring (PND75) were accessed by pre-pulse inhibition of the startle response (PPI) in comparison to the naïve offspring. Model building was based on experimental data from venous blood (total plasma), venous microdialysis (unbound plasma) and hippocampus and medium prefrontal cortex microdialysis (unbound brain concentrations) obtained after the administration of single i.v. bolus dose of FQ (10 mg/Kg) or QLNC (5 mg/Kg) to naïve and SPR rats. Data were analyzed with nonlinear mixed effect modeling in NONMEM, version 7.4. The first order conditional estimation method with interaction (FOCE INTER) was used for all analysis. The overall aim was to estimate plasma and brain pharmacokinetic parameters, and the protein binding simultaneously.
Results: A two-compartment model was identifiable both in blood and in the brain after administration of FQ formulation to naïve and SPR rats. The in vivo unbound fraction of QTP was estimated to be 24% (RSE: 12%). Bi-directional transport of QTP across the BBB parametrized as CLin and CLout sufficiently described the data. Stepwise covariate model (SCM) revealed that the brain distribution of QTP was significantly affected by the disease status and is correlated with the PPI behavioral test results. SPR animals presented a significant reduction in the rate of BBB transport (CLin: 0.019 L/h/kg; RSE: 16 % and CLout: 0.017 L/h/kg; RSE: 7 %) in comparison to naïve animals (CLin: 0.045 L/h/kg; RSE: 10 % and CLout: 0.023 L/h/kg; RSE: 14 %). The model for FQ formulation was expanded to describe different features of the QLNC formulation (plasma and tissue release from the nanocarrier, including an estimate for the fraction of the dose associated to the interface of the polymeric shell that is released as a burst after the administration of the nanocarrier). The final model describes two in vivo QTP release processes from the nanocarrier in plasma (KrelBURST: 0.261 h-1; RSE: 4% and KrelSLOW: 0.47×10-3 h-1; RSE: 25%). The significant decrease in brain exposure in SPR rats was reverted by drug nanoencapsulation, showing that LNC facilitates QTP distribution to brain interstitial space by carrying the drug into the brain and other tissues (CLin,nano: 0.067 L/h/kg; RSE: 11%).
Conclusion: A simultaneous modeling of total and unbound plasma and unbound brain concentrations allowed the quantification of rate and extent of QTP brain distribution from FQ and QLNC formulations in naïve and SPR rats. The present model-based approach is useful to better understand the potential of LCN for drug delivery to the brain, opening the opportunity to use this approach to improve SCZ-treatment limited response rates.
Acknowledgments: Financial support from CNPq/Brazil (421767/2016-2), CAPES- PROEX 646/2014 and CAPES-PDSE 47/2017.
References:
[1] Millan, J. et al. Nat Rev Drug Discov. 15, 485, 2016.
[2] Carreño, F. et al. Mol. Pharm. 13, 1289, 2016.
Reference: PAGE 28 (2019) Abstr 8923 [www.page-meeting.org/?abstract=8923]
Poster: Drug/Disease Modelling - CNS