A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions

Abstract

Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUC_last) and maximum plasma concentration (C_max) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUC_last and C_max ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.

FIGURE 1

Schematic overview of implemented metabolic processes in the dasatinib PBPK model (a) as well as investigated DDIs including enzyme‐mediated (b) and pH‐dependent DDIs (c). Drawings by Servier, licensed under CC BY 3.0. CL_hep., unspecific hepatic clearance; CYP, cytochrome P450; DDIs, drug–drug interactions; GET, gastric emptying time; OATP, organic anion transporting polypeptide.

FIGURE 2

Selection of predicted and observed dasatinib plasma concentration–time profiles of the training (a–d) and the test dataset (e–i) on a linear scale as well as goodness of fit plots of predicted versus observed AUC_last (j), C _max (k) and plasma concentrations (l). Blue and light blue solid lines show predicted geometric mean concentration–time profiles in healthy volunteers and cancer patients, respectively, with colored ribbons illustrating the corresponding geometric standard deviation of the population simulations (n = 100). Points demonstrate the mean observed data of dasatinib with the corresponding standard deviation (if depicted in the respective publication). Linear and semilogarithmic predicted and observed plasma concentration–time profiles of all studies are shown in Sections S2.1 and S2.2. In the goodness of fit plots, solid lines mark the lines of identity, dotted lines indicate 1.25‐fold and dashed lines twofold deviation. /, no information available; AUC_last, areas under the plasma concentration–time curves from the first to the last timepoint of measurement; bid, twice a day; C _max, maximum plasma concentration; md, multiple dose; n, number of participants; PFOS, powder for oral suspension; po, peroral; Q5D, five consecutive days once daily dosing followed by two nontreatment days; qd, once a day; sd, single dose; tab, tablet.

FIGURE 3

Predicted and observed plasma concentration–time profiles for enzyme‐mediated DDIs with dasatinib acting as victim (a, b) and perpetrator drug (c, d). The solid lines show predicted geometric mean concentration–time profiles with (colored) and without (gray) intake of the perpetrator drug and ribbons show the corresponding geometric standard deviation of the population simulations (n = 100). Points depict mean observed data with corresponding standard deviation of dasatinib, while squares and triangles depict the observed data with corresponding standard deviation of simvastatin lactone and simvastatin acid, respectively. Predicted and observed plasma concentration–time profiles of all studies on a semilogarithmic scale are shown in Section S3.2.1. DDIs, drug–drug interactions; n, number of participants.

FIGURE 4

Predicted and observed plasma concentration–time profiles of dasatinib for the pH‐dependent DDIs. The solid lines show predicted geometric mean concentration–time profiles with (colored) and without (gray) the intake of the perpetrator drug and ribbons show the corresponding geometric standard deviation of the population simulations (n = 100). Points depict mean observed data with corresponding standard deviation of dasatinib (if depicted in the respective publication). Predicted and observed plasma concentration–time profiles of all studies on a semilogarithmic scale are shown in Section S3.2.2. BHCl, betaine hydrochloride; DDIs, drug–drug interactions; n, number of participants.

FIGURE 5

Predicted versus observed DDI AUC_last ratios (a) and DDI C _max ratios (b) of dasatinib (circles), simvastatin lactone (squares) and simvastatin acid (triangles). The straight solid lines mark the lines of identity, the curved lines show the limits of the predictive measure proposed by Guest et al. with 1.25‐fold variability. Dotted lines indicate 1.25‐fold and dashed lines twofold deviation. AUC_last, area under the plasma concentration–time curve from the first to the last timepoint of measurement; BHCl, betaine hydrochloride; C _max, maximum plasma concentration; DDI, drug–drug interaction.

FIGURE 6

Model‐based dose adaptations for dasatinib within single (a–d) and multiple DDI scenarios (e–h) including moderate and strong CYP3A4 inhibitors and inducers. The first and second column represent the simulation results after administration of 100 mg and 140 mg dasatinib daily, respectively. Solid lines show the model predictions with (colored) and without (gray) intake of perpetrator drug. Colored dashed lines represent model predictions in the presence of perpetrator drug(s), using an adapted dasatinib dose. For the dosing simulations a virtual European male individual, 64 years of age and default values for body weight and height according to the International Commission on Radiological Protection (ICRP) database, was used. Bid, twice a day; CBZ, carbamazepine; DAS, dasatinib; DDI, drug–drug interaction; ERY, erythromycin; GFJ, grapefruit juice; ITZ, itraconazole; md, multiple dose; qd, once a day; tid, three times a day.

FIGURE 7

Overview of model‐based dose adaptations for dasatinib within single and multiple DDI scenarios based on the exposure matching principle, where points and squares show the percentage of the original dasatinib dose that match the PBPK simulated monotherapy AUC_ss at 100 mg and 140 mg, respectively. A virtual European male individual, 64 years of age and default values for body weight and height according to the International Commission on Radiological Protection (ICRP) database, was used for the dosing simulations. Red symbols depict dasatinib dose reductions and green symbols depict dasatinib dose elevations. The darker the color, the higher the magnitude of dasatinib dose adaptation. –, dose adaptations outside the simulated dose range; AUC_ss, steady‐state area under the concentration–time curve; bid, twice a day; D, day; md, multiple dose; qd, once daily; qid, four times a day; sd, single dose; tab, tablet; tid, three times a day.