Orwa Albitar (1)*, Sabariah Noor Harun (1), Rama Ballouze (2), Dzul Azri Mohamed Noor (1), Siti Maisharah Sheikh Ghadzi (1)*.
(1) School of Pharmaceutical Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia. (2) Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, 13200 USM, Penang, Malaysia.
Objectives: Cyclosporine (CsA) is a calcineurin inhibitor that is still one of the main drugs of choice in renal transplant immunosuppression therapy [1]. It is characterized by large pharmacokinetic variability, a narrow therapeutic window, and numerous covariates affecting its pharmacokinetic parameters [2]. The gold standard for assessing CsA exposure was known to be the area under the concentration-time curve (AUC). AUC levels of 1130-8400 ng.h/mL were found not to be associated with the hazard of acute rejection in pediatric renal transplant patients [3]. Higher AUC levels more than 10000 ng.h/mL protected from graft versus host disease stage I and II in allogeneic hematopoietic stem cell transplantation [4]. However, CsA pharmacokinetic and pharmacodynamic (PKPD) relationship in terms of renal graft function response towards the CsA exposure has not been yet investigated. This study aims to develop a PKPD model of cyclosporine in Malaysian renal transplant patients.
Methods: Renal transplant patient records at Penang General Hospital were retrospectively analyzed. A PKPD model with covariate effects was developed using non-linear mixed-effect modeling NONMEM software [5]. The pharmacodynamic response of CsA was quantified based on the patient’s estimated glomerular filtration rate (eGFR), calculated using the 4 variable modification of diet in renal disease (MDRD) formula [6]. The population PK parameters were fixed to the published values in the same sample population [2], while including PK observations in the dataset. The total daily dose was inserted in the dataset as a time-varying covariate and AUC was calculated in the control stream using the total daily dose, bioavailability and patients individualized clearance. The eGFR response towards CsA exposure was assumed to equal a baseline that improves in a drug effect phase and then declines in a drug-related nephrotoxicity phase. The final model was chosen based on the lowest objective function value, prediction corrected visual predictive check (pcVPC), relative standard errors (RSE) produced from the sampling-importance resampling (SIR) method [7], and scientific plausibility. The individual eGFR predictions were evaluated based on the clinical response of acute rejection/nephrotoxicity events.
Results: A total of 1256 eGFR readings with 2473 drug concentrations were obtained from 107 renal transplant patients on cyclosporine. The majority of patients were males 60.7% with a median (range) age of 45 years (17-77), and weight of 62 Kg (23-106). The data were best described by an Emax drug effect with a linear drug toxicity model. The baseline renal graft function (E0) was estimated as 12.9 mL/min/1.73 m2, maximum effect (Emax) 50.7 mL/min/1.73 m2, AUC achieving 50% of the maximum effect (EAUC50) 1740 ng.h/mL, and nephrotoxicity slope 0.00033. An elevation in Hemoglobin of 1 g/dL above the median of 12.5 g/dL reflected a 5.3% improvement in E0. The model’s estimated inter-individual variability was 80.2% and 158.1% for E0 and EAUC50, respectively, with a shrinkage of 26.7%. Based on the pcVPC, the 95% confidence interval of the median, 10th, and 90th percentiles of the simulated data have overlaid the median, 10th, and 90th percentile of the observed data, implying the good predictive performance of the final model and all the models’ parameters have shown an RSE of not more than 25% based on the SIR results. The final model was integrated into an online free tool to predict the potential outcome:
(https://www.calconic.com/calculator-widgets/cyclosporine-response-in-kidney-transplant-recipients-copy/6001413546d9f50029e34391?layouts=true)
An AUC of around 14500 ng.h/mL was required to achieve the maximum response in eGFR of 54 mL/min/1.73 m2; AUC values beyond this led to nephrotoxicity. The model discriminated biopsy-proven acute rejections from nephrotoxicity in 7/9 (77.8%) of the cases as well as overall proven and clinically suspected acute rejections from nephrotoxicity in 19/24 (79.2%). The tool’s recommendations were built based on the current study population values and can be enhanced using the targeted population’s [8] or individualized clearance and covariates.
Conclusions: A PKPD model was successfully developed and implemented in an online tool to predict renal graft response. This may help discriminate acute rejection from nephrotoxicity, especially for patients not keen for biopsy or those waiting for biopsy results.
References:
[1] Kidney Disease: Improving Global Outcomes (KDIGO) Transplant Work Group. Special Issue: KDIGO Clinical Practice Guideline for the Care of Kidney Transplant Recipients. Am J Transplant. 2009;9:S1–155.
[2] Albitar O, Ballouze R, Harun SN, Mohamed Noor DA, Sheikh Ghadzi SM. Population Pharmacokinetic Modeling of Cyclosporine Among Malaysian Renal Transplant Patients: An Evaluation of Methods to Handle Missing Doses in Conventional Drug-Monitoring Data. J Clin Pharmacol. 2020;60:1474–82.
[3] Frobel AK, Karlsson MO, Backman JT, Hoppu K, Qvist E, Seikku P, et al. A time-to-event model for acute rejections in paediatric renal transplant recipients treated with ciclosporin A. Br J Clin Pharmacol. 2013;76:603–15.
[4] Inoue Y, Saito T, Ogawa K, Nishio Y, Kosugi S, Suzuki Y, et al. Pharmacokinetics of cyclosporine A at a high-peak concentration of twice-daily infusion and oral administration in allogeneic haematopoietic stem cell transplantation. J Clin Pharm Ther. 2011;36:518–24.
[5] Beal SL, Shiener LB, Boeckman AJ. NONMEM Users Guides (1989-2008). 2008
[6] Levey AS, Coresh J, Greene T, Stevens LA, Zhang Y, Hendriksen S, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145:247–54.
[7] Dosne AG, Bergstrand M, Harling K, Karlsson MO. Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling. J Pharmacokinet Pharmacodyn. 2016;43:583–96.
[8] Mao J-J, Jiao Z, Yun H-Y, Zhao C-Y, Chen H-C, Qiu X-Y, et al. External evaluation of population pharmacokinetic models for ciclosporin in adult renal transplant recipients. Br J Clin Pharmacol. 2018;84:153–71.
Reference: PAGE 29 (2021) Abstr 9863 [www.page-meeting.org/?abstract=9863]
Poster: Drug/Disease Modelling - Other Topics