“…The thin grey lines represent simulated individual trials (ten trials of ten subjects) and the thick black lines are the simulated mean of the healthy volunteer population ( n = 100) without ( solid lines ) and with interaction ( dashed lines ). The circles denote mean values from the clinical studies for rosuvastatin [21] and for cyclosporine [86]Fig. 4Simulated unbound liver concentrations in a extracellular (Cu EW ) and b intracellular (Cu IW ) compartments of rosuvastatin on day 10 in the absence and presence of cyclosporine (200 mg twice daily) in healthy volunteers following the oral administration of rosuvastatin 10 mg (multiple dose).…”
Section: Resultsmentioning
confidence: 99%
“…The thin grey lines represent simulated individual trials (ten trials of ten subjects) and the thick black lines are the simulated mean of the healthy volunteer population ( n = 100) without ( solid lines ) and with interaction ( dashed lines ). The circles denote mean values from the clinical studies for rosuvastatin [21] and for cyclosporine [86]…”
Background and ObjectivesThe interplay between liver metabolising enzymes and transporters is a complex process involving system-related parameters such as liver blood perfusion as well as drug attributes including protein and lipid binding, ionisation, relative magnitude of passive and active permeation. Metabolism- and/or transporter-mediated drug–drug interactions (mDDIs and tDDIs) add to the complexity of this interplay. Thus, gaining meaningful insight into the impact of each element on the disposition of a drug and accurately predicting drug–drug interactions becomes very challenging. To address this, an in vitro–in vivo extrapolation (IVIVE)-linked mechanistic physiologically based pharmacokinetic (PBPK) framework for modelling liver transporters and their interplay with liver metabolising enzymes has been developed and implemented within the Simcyp Simulator®.MethodsIn this article an IVIVE technique for liver transporters is described and a full-body PBPK model is developed. Passive and active (saturable) transport at both liver sinusoidal and canalicular membranes are accounted for and the impact of binding and ionisation processes is considered. The model also accommodates tDDIs involving inhibition of multiple transporters. Integrating prior in vitro information on the metabolism and transporter kinetics of rosuvastatin (organic-anion transporting polypeptides OATP1B1, OAT1B3 and OATP2B1, sodium-dependent taurocholate co-transporting polypeptide [NTCP] and breast cancer resistance protein [BCRP]) with one clinical dataset, the PBPK model was used to simulate the drug disposition of rosuvastatin for 11 reported studies that had not been used for development of the rosuvastatin model.ResultsThe simulated area under the plasma concentration–time curve (AUC), maximum concentration (Cmax) and the time to reach Cmax (tmax) values of rosuvastatin over the dose range of 10–80 mg, were within 2-fold of the observed data. Subsequently, the validated model was used to investigate the impact of coadministration of cyclosporine (ciclosporin), an inhibitor of OATPs, BCRP and NTCP, on the exposure of rosuvastatin in healthy volunteers.ConclusionThe results show the utility of the model to integrate a wide range of in vitro and in vivo data and simulate the outcome of clinical studies, with implications for their design.Electronic supplementary materialThe online version of this article (doi:10.1007/s40262-013-0097-y) contains supplementary material, which is available to authorized users.
“…The thin grey lines represent simulated individual trials (ten trials of ten subjects) and the thick black lines are the simulated mean of the healthy volunteer population ( n = 100) without ( solid lines ) and with interaction ( dashed lines ). The circles denote mean values from the clinical studies for rosuvastatin [21] and for cyclosporine [86]Fig. 4Simulated unbound liver concentrations in a extracellular (Cu EW ) and b intracellular (Cu IW ) compartments of rosuvastatin on day 10 in the absence and presence of cyclosporine (200 mg twice daily) in healthy volunteers following the oral administration of rosuvastatin 10 mg (multiple dose).…”
Section: Resultsmentioning
confidence: 99%
“…The thin grey lines represent simulated individual trials (ten trials of ten subjects) and the thick black lines are the simulated mean of the healthy volunteer population ( n = 100) without ( solid lines ) and with interaction ( dashed lines ). The circles denote mean values from the clinical studies for rosuvastatin [21] and for cyclosporine [86]…”
Background and ObjectivesThe interplay between liver metabolising enzymes and transporters is a complex process involving system-related parameters such as liver blood perfusion as well as drug attributes including protein and lipid binding, ionisation, relative magnitude of passive and active permeation. Metabolism- and/or transporter-mediated drug–drug interactions (mDDIs and tDDIs) add to the complexity of this interplay. Thus, gaining meaningful insight into the impact of each element on the disposition of a drug and accurately predicting drug–drug interactions becomes very challenging. To address this, an in vitro–in vivo extrapolation (IVIVE)-linked mechanistic physiologically based pharmacokinetic (PBPK) framework for modelling liver transporters and their interplay with liver metabolising enzymes has been developed and implemented within the Simcyp Simulator®.MethodsIn this article an IVIVE technique for liver transporters is described and a full-body PBPK model is developed. Passive and active (saturable) transport at both liver sinusoidal and canalicular membranes are accounted for and the impact of binding and ionisation processes is considered. The model also accommodates tDDIs involving inhibition of multiple transporters. Integrating prior in vitro information on the metabolism and transporter kinetics of rosuvastatin (organic-anion transporting polypeptides OATP1B1, OAT1B3 and OATP2B1, sodium-dependent taurocholate co-transporting polypeptide [NTCP] and breast cancer resistance protein [BCRP]) with one clinical dataset, the PBPK model was used to simulate the drug disposition of rosuvastatin for 11 reported studies that had not been used for development of the rosuvastatin model.ResultsThe simulated area under the plasma concentration–time curve (AUC), maximum concentration (Cmax) and the time to reach Cmax (tmax) values of rosuvastatin over the dose range of 10–80 mg, were within 2-fold of the observed data. Subsequently, the validated model was used to investigate the impact of coadministration of cyclosporine (ciclosporin), an inhibitor of OATPs, BCRP and NTCP, on the exposure of rosuvastatin in healthy volunteers.ConclusionThe results show the utility of the model to integrate a wide range of in vitro and in vivo data and simulate the outcome of clinical studies, with implications for their design.Electronic supplementary materialThe online version of this article (doi:10.1007/s40262-013-0097-y) contains supplementary material, which is available to authorized users.
“…The cyclosporine model reported by Jamei et al [ 4 ] was used ( Table 1 ). We validated the model using the data digitized from two different human studies [ 34 35 ] (Plot Digitizer, version 2.6.6, http://plotdigitizer.sourceforge.net ). Clinical trials in 100 virtual healthy subjects (10 trials×10 subjects per trial, aged 20–50, male:female=1:1) were simulated for the evaluation of the effects of cyclosporine on rosuvastatin PK.…”
Section: Methodsmentioning
confidence: 99%
“… The dashed lines represent the upper (95%) and lower (5%) percentile concentrations of rosuvastatin (A) and cyclosporine (B). Dots are observed data from references [ 34 35 ]. …”
It was recently reported that the Cmax and AUC of rosuvastatin increases when it is coadministered with telmisartan and cyclosporine. Rosuvastatin is known to be a substrate of OATP1B1, OATP1B3, NTCP, and BCRP transporters. The aim of this study was to explore the mechanism of the interactions between rosuvastatin and two perpetrators, telmisartan and cyclosporine. Published (cyclosporine) or newly developed (telmisartan) PBPK models were used to this end. The rosuvastatin model in Simcyp (version 15)'s drug library was modified to reflect racial differences in rosuvastatin exposure. In the telmisartan–rosuvastatin case, simulated rosuvastatin CmaxI/Cmax and AUCI/AUC (with/without telmisartan) ratios were 1.92 and 1.14, respectively, and the Tmax changed from 3.35 h to 1.40 h with coadministration of telmisartan, which were consistent with the aforementioned report (CmaxI/Cmax: 2.01, AUCI/AUC:1.18, Tmax: 5 h → 0.75 h). In the next case of cyclosporine–rosuvastatin, the simulated rosuvastatin CmaxI/Cmax and AUCI/AUC (with/without cyclosporine) ratios were 3.29 and 1.30, respectively. The decrease in the CLint,BCRP,intestine of rosuvastatin by telmisartan and cyclosporine in the PBPK model was pivotal to reproducing this finding in Simcyp. Our PBPK model demonstrated that the major causes of increase in rosuvastatin exposure are mediated by intestinal BCRP (rosuvastatin–telmisartan interaction) or by both of BCRP and OATP1B1/3 (rosuvastatin–cyclosporine interaction).
“…It is well known the influence of grapefruit in modifying the CsA biodisponibility, due to inhibiting pre-systemic metabolism by CYP3A4 located in the enteric mucosa, and/or via the P-glycoprotein mediated decreased transport of CsA back from enterocytes into the gut lumen, with augmentation of AUC [38,40]. In the opposite way, the red wine and red grape juice producing inhibition of P-glycoprotein intestinal efflux by polyphenolic compounds present in red grape juices, reducing CsA-ME absorption with considerable decrease of Cmax and AUC [41]. Even some diseases can affect metabolism (e.g., virus C hepatitis decreasing activity of CYP3A4 and suppressive inflammatory cytokines activity over the uptake and efflux hepatic transporters, which leads to lower metabolism and higher CsA through serum levels) [38,42].…”
Section: Pharmacodynamic Monitoring: Is It the Future?mentioning
Calcineurin inhibitors have been the mainstay of immunosuppression for the last decades and nowadays still remain as relevant drugs in the setting of solid organ transplantation. Nevertheless, the balance between excess of immunosuppression with cyclosporine (leading to increased risk of metabolic complications, nephrotoxicity or cancer), or its insufficiency (with augmented risk of rejection), is even so a challenge in the clinical practice. The aim of this paper is to review the pharmacokinetic and pharmacodynamic evolution of this landmark drug. We also discuss what could we expect on this CNI drug in this decade.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
