(T1230-02-08) Physiologically Based Pharmacokinetic Modelling of Risperidone and Its Metabolite Paliperidone PLGA Based In Situ Gel Forming Long-Acting Injectable Formulations In Vivo

Purpose: To predict in vivo pharmacokinetics (PK) of PERSERIS™ risperidone formulation which is a Poly lactic-co-glycolic acid (PLGA) based in situ gel-forming implants using the “Long acting injectable (LAI) PLGA based absorption model (LAI-PLGA) within the Simcyp Simulator (V23). The LAI-PLGA absorption model considers formulation parameters such as the mean molecular weight of the polymer, lactide to glycolide molar ratio and solubility of the compound. A physiologically based pharmacokinetic (PBPK) model describing the distribution and elimination parameters of risperidone and its metabolite paliperidone palmitate after intravenous and oral administration were developed and verified using published data. The model development for systemic absorption of risperidone in situ gel was carried out using the 60 mg dose injected subcutaneously. Then the developed model was verified by predicting the in vivo PK of 60 mg, 90 mg, and 120 mg doses of risperidone in situ gel forming implant injected subcutaneously in a separate clinical trial.

Methods: The PBPK models of risperidone and its metabolite paliperidone were developed characterizing the distribution and elimination of both molecules using the pharmacokinetic (PK) data of intravenous (IV) [1] and oral dosing [2, 3]. The developed models were verified using an external oral dosing dataset [4]. The formulation parameters and in vivo PK profiles of the in situ forming gel of risperidone were collected from literature[5, 6]. For simulating in situ gel PK of risperidone, formulation-related parameters were incorporated into the LAI – PLGA absorption model [6]. The simulations of a 60 mg dose of in situ gel were conducted by matching the experimental details and optimizing the polymer parameter's deionization and ionization rate constants. In addition to polymer parameters, formulation parameters such as initial releasable fraction and porosity are also optimized to predict the data as they are not available in the literature. For the in-situ gel PLGA formulations, an approximation of the length of the implant after injection was determined using the volume of the formulation and injection gauge diameter as the radius of the implant. The drug partition- and diffusion coefficients were kept as the default values. The model performance was evaluated by comparing the reported total active (risperidone + paliperidone) profile with the predicted total active profile i.e. adding plasma concentrations of both risperidone and paliperidone correcting for molecular weight differences. The PK parameter of total active maximum plasma concentration (C_max) [ng/mL] and Area Under the Concentration versus time Curve extrapolated to infinity (AUC_inf) [ng×h/mL] was assessed using the non-compartmental analysis module in Phoenix WinNonLin version 8.3 (Certara USA Inc.). The developed model was verified using external data for 60, 90 and 120 mg dose data and their respective formulation details such as the mass and volume of the formulation from a different clinical study [5].

Results: Figure 1a shows that using the optimized parameters, the predicted systemic profile of the 60 mg dose matches the observed profile. In addition, the model was able to predict the dual peak characteristic of the in situ gel system due to the burst effect of the formulation. The predictability of the model was further assessed by predicting plasma profiles of total of both moieties for 90 mg dose (Figure 1b) and 120 mg dose (Figure 1c). The predicted plasma PK parameters C_max and AUC_inf for all three doses using NCA analysis were within a 2-fold error range (Predicted/Observed) as shown in Table 1.

Conclusion: The results of this study| show that the developed LAI-PLGA model was able to predict the systemic plasma data of in-situ gel-forming implant formulation of risperidone. The model was able to account for both the polymer and formulation characteristics in combination with the physiology parameters of the local site to predict the systemic absorption of both risperidone and its metabolite paliperidone. A current limitation of the model, caused by the lack of experimental data on polymer precipitation kinetics and the pore formation in the in-situ gel formulation type, is the initial releasable fraction and fractional porosity which were optimized to describe the burst effect. The results show the utility of the LAI-PLGA model to predict systemic absorption of risperidone. It can be further utilized to investigate and understand the influence of polymer and formulation properties on systemic absorption.

References: 1. Huang, M.L., et al., Pharmacokinetics of the novel antipsychotic agent risperidone and the prolactin response in healthy subjects. Clin Pharmacol Ther, 1993. 54(3): p. 257-68.
2. Mahdy, W.Y.B., et al., Physiologically-based pharmacokinetic model to investigate the effect of pregnancy on risperidone and paliperidone pharmacokinetics: Application to a pregnant woman and her neonate. Clin Transl Sci, 2023. 16(4): p. 618-630.
3. Novalbos, J., et al., Effects of CYP2D6 genotype on the pharmacokinetics, pharmacodynamics, and safety of risperidone in healthy volunteers. J Clin Psychopharmacol, 2010. 30(5): p. 504-11.
4. Borison, R.L., et al., Pharmacokinetics of risperidone in chronic schizophrenic patients. Psychopharmacol Bull, 1994. 30(2): p. 193-7.
5. Praveen Balimane, K.K., Hao Zhu, Clinical Pharmacology Review C.f.D.E.a. Research, Editor. 2018.
6. Wang, X., et al., Reverse engineering of Perseris and development of compositionally equivalent formulations. Int J Pharm, 2023. 639: p. 122948.


a) Observed versus Predicted systemic concentrations of total active of study RB-US-09-0007 60 mg dose; b) 60 mg dose from other clinical trial; c) 90 mg dose and d) 120 mg dose. Yellow solid dots represent observed data, solid green lines represent mean systemic data, and top and bottom grey lines represent the 5th and 95th percentiles of the mean predicted data respectively.


Observed versus Predicted PK parameters of total active following subcutaneous injection of in situ gel forming Perseris formulation of Risperidone.