Development of a physiologically based pharmacokinetic model of actinomycin D in children with cancer

Abstract

AimsUse of the anti‐tumour antibiotic actinomycin D is associated with development of hepatotoxicity, particularly in young children. A paucity of actinomycin D pharmacokinetic data make it challenging to develop a sound rationale for defining dosing regimens in younger patients. The study aim was to develop a physiologically based pharmacokinetic (PBPK) model using a combination of data from the literature and generated from experimental analyses.MethodsAssays to determine actinomycin D Log P, blood:plasma partition ratio and ABCB1 kinetics were conducted. These data were combined with physiochemical properties sourced from the literature to generate a compound file for use within the modelling‐simulation software Simcyp (version 14 release 1). For simulation, information was taken from two datasets, one from 117 patients under the age of 21 and one from 20 patients aged 16–48.ResultsThe final model incorporated clinical renal and biliary clearance data and an additional systemic clearance value. The mean AUC0‐26h of simulated subjects was within 1.25‐fold of the observed AUC0‐26h (84 ng h ml−1 simulated vs. 93 ng h ml−1 observed). For the younger age ranges, AUC predictions were within two‐fold of observed values, with simulated data from six of the eight age/dose ranges falling within 15% of observed data. Simulated values for actinomycin D AUC0‐26h and clearance in infants aged 0–12 months ranged from 104 to 115 ng h ml−1 and 3.5–3.8 l h−1, respectively.ConclusionsThe model has potential utility for prediction of actinomycin D exposure in younger patients and may help guide future dosing. However, additional independent data from neonates and infants is needed for further validation. Physiological differences between paediatric cancer patients and healthy children also need to be further characterized and incorporated into PBPK models.

A Comparative Study Between Allometric Scaling and Physiologically Based Pharmacokinetic Modeling for the Prediction of Drug Clearance From Neonates to Adolescents

“…These drugs were given intravenously. Because levofloxacin data from Edginton and Willmann 21 was not 31 1 IV; AUC was predicted 0.5-to 2-fold 6 6 (100%) 5 (83%) 0.5-to 1.5-fold 6 6 (100%) 5 (83%) Duan et al 31 1 oral; AUC was predicted 0.5-to 2-fold 6 6 (100%) 5 (83%) 0.5-to 1.5-fold 6 6 (100%) 5 (83%) Johnson et al 19 11 (4 IV and 8 oral); 1 drug by both routes; clearance was predicted 0.5-to 2-fold 46 41 (89%) 39 (85%) 0.5-to 1.5-fold 46 NA 37 (80%) Johnson and Rostami-Hodjegan 30 6 oral; AUC was predicted 0.5-to 2-fold 6 6 (100%) 6 (100%) 0.5-to 1.5-fold 6 6 (100%) 6 (100%) Kersting et al 32 1 IV; Clearance was predicted; <2 years to 14 years 0.5-to 2-fold 4 1 (25%) 4 (100%) 0.5-to 1.5-fold 4 0 (0%) 3 (75%) Leong et al 20 2 IV and 1 oral; Clearance was predicted 0.5-to 2-fold 13 11 (85%) 13 (100%) 0.5-to 1.5-fold 13 11 (85%) 11 (85%) Upreti and Wahlstrom 29 8 IV; Clearance was predicted 0.5-to 2-fold 36 36 (100%) 34 (94%) 0.5-to 1.5-fold 36 34 (94%) 33 (92%) Upreti and Wahlstrom 29 8 oral; Oral clearance was predicted 0.5-to 2-fold 44 43 (98%) 42 (96%) 0.5-to 1.5-fold 44 42 (96%) 40 (91%) Walsh et al 33 1 IV; AUC was predicted 0.5-to 2-fold 8 8 (100%) 8 (100%) 0.5-to 1.5-fold 8 8 (100%) 8 (100%) Zhou et al 27 7 IV; Clearance was predicted a 0.5-to 2-fold 28 26 (93%) 26 (93%) 0.5-to 1.5-fold 28 25 (89%) 24 (86%) Zhou et al 27 7 Oral; AUC was predicted a 0.5-to 2-fold 39 29 (74%) 34 (87%) 0.5-to 1.5-fold 39 20 (51%) 24 (62%) Zhou et al 28 9 IV; Clearance was predicted 0.5-to 2-fold 36 34 (94%) 33 (92%) 0.5-to 1.5-fold 36 33 (92%) 33 (92%) AUC indicates area under the concentration-time curve; IV, intravenous; PBPK, physiologically based pharmacokinetic method. a Four drugs were given by both oral and IV routes, whereas 3 drugs each were given by oral or by IV route.…”

“…Data Source for the PBPK Model From the literature, 10 studies 9,[19][20][21][27][28][29][30][31][32][33] were identified in which clearance or AUC of drugs in children ranging from neonates to adolescents was predicted using PBPK models. In these studies, drugs were given to subjects either by the intravenous or oral route.…”

“…The drug in this study was actinomycin D given intravenously (n = 8). 33 Subjects were divided into 3 age groups (10-20 years, 6 to <10 years, and 1 to <6 years). The mean clearance values from the 10-to 20-year age group were used as adult clearance value to develop the ADE model, and the AUC was predicted in subjects 6 to <10 years and 1 to <6 years of age.…”

Physiologically Based Pharmacokinetic Modeling for Olaparib Dosing Recommendations: Bridging Formulations, Drug Interactions, and Patient Populations

“…Validated PBPK models can also provide initial dosing recommendations for pediatric development plans. [16][17][18][19] In vitro enzymology data have suggested that olaparib metabolism is predominantly CYP3A-mediated and olaparib has the potential to cause CYP3A reversible and time-dependent inhibition (TDI) and induction. 20 The DDI potential of olaparib is therefore multivariate, and predicting the net effect on CYP3A in vivo is challenging.…”

“…Given the challenges in conducting pharmacokinetic (PK) studies in patients with hepatic or renal impairment, as well as numerous DDI studies of anticancer drugs, it can be beneficial to utilize physiologically based PK (PBPK) models to predict changes in drug exposure in various scenarios to guide dosing where clinical trials have not been conducted. Validated PBPK models can also provide initial dosing recommendations for pediatric development plans …”