Ipatasertib is a pan-AKT inhibitor in development for the treatment of cancer. Ipatasertib was metabolized by CYP3A4 to its major metabolite, M1 (G-037720), and was a P-gp substrate and OATP1B1/1B3 ...inhibitor in vitro. A phase I drug-drug interaction (DDI) study (
= 15) was conducted in healthy subjects to evaluate the effect of itraconazole (200-mg solution QD, 4 days), a strong CYP3A4 and P-gp inhibitor, on pharmacokinetics of ipatasertib (100-mg single dose). Itraconazole increased the C
and AUC
of ipatasertib by 2.3- and 5.5-fold, respectively, increased the half-life by 53%, and delayed the t
by 1 hour. The C
and AUC
of its metabolite M1 (G-037720) reduced by 91% and 68%, respectively. This study confirmed that CYP3A4 plays a major role in ipatasertib clearance. Furthermore, the interaction of ipatasertib with coproporphyrin (CP) I and CPIII, the two endogenous substrates of OATP1B1/1B3, was evaluated in this study. CPI and CPIII plasma levels were unchanged in the presence of ipatasertib, both at exposures of 100 mg and at higher exposures in combination with itraconazole. This indicated no in vivo inhibition of OATP1B1/1B3 by ipatasertib. Additionally, it was shown that CPI and CPIII were not P-gp substrates in vitro, and itraconazole had no effect on CPI and CPIII concentrations in vivo. The latter is an important finding because it will simplify interpretation of future DDI studies using CPI/CPIII as OATP1B1/1B3 biomarkers. SIGNIFICANCE STATEMENT: This drug-drug interaction study in healthy volunteers demonstrated that CYP3A4 plays a major role in ipatasertib clearance, and that ipatasertib is not an organic anion transporting polypeptide 1B1/1B3 inhibitor. Furthermore, it was demonstrated that itraconazole, an inhibitor of CYP3A4 and several transporters, did not affect CPI/CPIII levels in vivo. This increases the understanding and application of these endogenous substrates as well as itraconazole in complex drug interaction studies.
Purpose
Ipatasertib, a potent and highly selective small-molecule inhibitor of AKT, is currently under investigation for treatment of cancer. Ipatasertib is a substrate and a time-dependent inhibitor ...of CYP3A4. It exhibits non-linear pharmacokinetics at subclinical doses in the clinical dose escalation study. To assess the DDI risk of ipatasertib at the intended clinical dose of 400 mg with CYP3A4 inhibitors, inducers, and substrates, a fit-for-purpose physiologically based pharmacokinetic (PBPK) model of ipatasertib was developed.
Methods
The PBPK model was constructed in Simcyp using in silico, in vitro, and clinical data and was optimized and verified using clinical data.
Results
The PBPK model described non-linear pharmacokinetics of ipatasertib and captured the magnitude of the observed clinical DDIs. Following repeated doses of 400 mg ipatasertib once daily (QD), the PBPK model predicted a 3.3-fold increase of ipatasertib exposure with itraconazole; a 2–2.5-fold increase with moderate CYP3A4 inhibitors, erythromycin and diltiazem; and no change with a weak CYP3A4 inhibitor, fluvoxamine. Additionally, in the presence of strong or moderate CYP3A4 inducers, rifampicin and efavirenz, ipatasertib exposures were predicted to decrease by 86% and 74%, respectively. As a perpetrator, the model predicted that ipatasertib (400 mg) caused a 1.7-fold increase in midazolam exposure.
Conclusion
This study demonstrates the value of using a fit-for-purpose PBPK model to assess the clinical DDIs for ipatasertib and to provide dosing strategies for the concurrent use of other CYP3A4 perpetrators or victims.
Purpose
To examine the single- and multiple-dose pharmacokinetics (PK), CYP3A inhibition potential of ipatasertib, and effect of food on PK of ipatasertib in patients with refractory solid tumors and ...a dedicated food effect assessment in healthy subjects.
Methods
The Phase I dose-escalation study enrolled patients with solid tumors in a standard 3 + 3 design with a 1 week washout after the first dose, followed by once-daily dosing on a 3-week-on/1-week-off schedule. In the expansion cohort, the effect of ipatasertib on CYP3A substrate (midazolam) was assessed by examining the change in midazolam exposure when dosed in the absence and presence of steady-state ipatasertib at 600 mg. The effect of food on ipatasertib PK was studied with ipatasertib administered in fed or fasted state (6 patients from Phase I patient study and 18 healthy subjects from the dedicated food effect study).
Results
Ipatasertib was generally well tolerated at doses up to 600 mg given daily for 21 days. Ipatasertib showed rapid absorption (t
max
, 0.5–3 h), was dose-proportional over a range of 200–800 mg, had a median half-life (range) of 45.0 h (27.8–66.9 h), and had approximately two-fold accumulation following once-daily dosing. Midazolam exposure (AUC
0-∞
) increased by 2.2-fold in the presence of ipatasertib. PK was comparable in subjects administered ipatasertib in a fed or fasted state.
Conclusion
Ipatasertib exhibited rapid absorption and was dose-proportional over a broad dose range. Ipatasertib appeared to be a moderate CYP3A inhibitor when administered at 600 mg and could be administered with or without food in clinical studies.
Trail registration
NCT01090960 (registered March 23, 2010); NCT02536391 (registered August 31, 2015).
The drug-drug interaction (DDI) potential of deleobuvir, an hepatitis C virus (HCV) polymerase inhibitor, and its two major metabolites, CD 6168 (formed via reduction by gut bacteria) and ...deleobuvir-acyl glucuronide (AG), was assessed in vitro. Area-under-the-curve (AUC) ratios (AUCi/AUC) were predicted using a static model and compared with actual AUC ratios for probe substrates in a P450 cocktail of caffeine (CYP1A2), tolbutamide (CYP2C9), and midazolam (CYP3A4), administered before and after 8 days of deleobuvir administration to HCV-infected patients. In vitro studies assessed inhibition, inactivation and induction of P450s. Induction was assessed in a short-incubation (10 hours) hepatocyte assay, validated using positive controls, to circumvent cytotoxicity seen with deleobuvir and its metabolites. Overall, P450 isoforms were differentially affected by deleobuvir and its two metabolites. Of note was more potent CYP2C8 inactivation by deleobuvir-AG than deleobuvir and P450 induction by CD 6168 but not by deleobuvir. The predicted net AUC ratios for probe substrates were 2.92 (CYP1A2), 0.45 (CYP2C9), and 0.97 (CYP3A4) compared with clinically observed ratios of 1.64 (CYP1A2), 0.86 (CYP2C9), and 1.23 (CYP3A4). Predictions of DDI using deleobuvir alone would have significantly over-predicted the DDI potential for CYP3A4 inhibition (AUC ratio of 6.15). Including metabolite data brought the predicted net effect close to the observed DDI. However, the static model over-predicted the induction of CYP2C9 and inhibition/inactivation of CYP1A2. This multiple-perpetrator DDI scenario highlights the application of the static model for predicting complex DDI for CYP3A4 and exemplifies the importance of including key metabolites in an overall DDI assessment.
Purpose
The potent, selective phosphodiesterase-9A inhibitor BI 409306 may be beneficial for patients with attenuated psychosis syndrome and could prevent relapse in patients with schizophrenia. ...Transient BI 409306-dependent increases in heart rate (HR) demonstrated previously necessitated cardiac safety characterisation. We evaluated cardiac effects of BI 409306 in healthy volunteers during rest and exercise.
Methods
In this double-blind, three-way crossover study, volunteers received placebo, BI 409306 50 mg or 200 mg in randomised order (same treatment on Days 1 resting and 3 exercise). Cardiopulmonary exercise testing was performed twice post treatment on Day 3 of each period. BI 409306-mediated effects on placebo-corrected change from baseline in resting HR (ΔΔHR) were evaluated based on exposure–response analysis and a random coefficient model. Adverse events (AEs) were recorded.
Results
Overall, 19/20 volunteers completed. Resting ΔΔHR versus BI 409306 concentration yielded a slope of 0.0029 beats/min/nmol/L. At the geometric mean (gMean) maximum plasma concentration (
C
max
) for BI 409306 50 and 200 mg, predicted mean (90% CI) ΔΔHRs were 0.80 (− 0.76, 2.36) and 5.46 (2.44, 8.49) beats/min, respectively. Maximum adjusted mean differences from placebo (90% CI) in resting HR for BI 409306 50 and 200 mg were 3.85 (0.73, 6.97) and 4.93 (1.69, 8.16) beats/min. Maximum differences from placebo in resting HR occurred at/near gMean
C
max
and returned to baseline after approximately 4 h. The proportion of volunteers with AEs increased with BI 409306 dose.
Conclusion
Observed hemodynamic effects following BI 409306 administration were of low amplitude, transient, and followed the pharmacokinetic profile of BI 409306.
Ipatasertib, an AKT inhibitor, in combination with prednisone and abiraterone, is under evaluation for the treatment of metastatic castration‐resistant prostate cancer (mCRPC). Hyperglycemia is an ...on‐target effect of ipatasertib. An open‐label, single‐arm, single‐sequence, signal‐seeking study (n = 25 mCRPC patients) was conducted to evaluate the glucose changes across four different treatment periods: ipatasertib alone, ipatasertib‐prednisone combination, ipatasertib‐prednisone‐abiraterone combination (morning dose), and ipatasertib‐prednisone‐abiraterone combination (evening dose). Continuous glucose monitoring (CGM) was used in this study to compare the dynamic glucose changes across the different treatment periods. Four key parameters: average glucose, peak glucose and % time in range (70–180 and >180 mg/dl) were evaluated for this comparison. Ipatasertib‐prednisone‐abiraterone combination when administered in the morning after an overnight fast significantly increased average glucose, peak glucose and % time in range >180 mg/dl compared to ipatasertib monotherapy. Ipatasertib, when co‐administered with abiraterone, increased ipatasertib and M1 (G‐037720) metabolite exposures by approximately 1.5‐ and 2.2‐fold, respectively. Exposure–response analysis results show that increased exposures of ipatasertib in combination with abiraterone are associated with increased glucose levels. When ipatasertib‐prednisone‐abiraterone combination was administered as an evening dose compared to a morning dose, lowered peak glucose and improved % time in range was observed. The results from this study suggest that dosing ipatasertib after an evening meal followed by overnight fasting can be an effective strategy for managing increased glucose levels.
Deleobuvir is a potent inhibitor of the hepatitis C virus nonstructural protein 5B polymerase. In humans, deleobuvir underwent extensive reduction to form CD 6168. This metabolite was not formed in ...vitro in aerobic incubations with human liver microsomes or cytosol. Anaerobic incubations of deleobuvir with rat and human fecal homogenates produced CD 6168. Using these in vitro formation rates, a retrospective analysis was conducted to assess whether the fecal formation of CD 6168 could account for the in vivo levels of this metabolite. The formation of CD 6168 was also investigated using a pseudo-germ free (pGF) rat model, in which gut microbiota were largely eradicated by antibiotic treatment. Plasma exposure (area under the curve from 0 to ∞) of CD 6168 was approximately 9-fold lower in pGF rats (146 ± 64 ng·h/ml) compared with control rats (1,312 ± 649 ng·h/ml). Similarly, in pGF rats, lower levels of CD 6168 (1.5% of the deleobuvir dose) were excreted in feces compared with control rats (42% of the deleobuvir dose). In agreement with these findings, in pGF rats, approximately all of the deleobuvir dose was excreted as deleobuvir into feces (105% of dose), whereas only 26% of the deleobuvir dose was excreted as deleobuvir in control rats. These differences in plasma and excretion profiles between pGF and control rats confirm the role of gut bacteria in the formation of CD 6168. These results underline the importance of evaluating metabolism by gut bacteria and highlight experimental approaches for nonclinical assessment of bacterial metabolism in drug development.
Pralsetinib, a potent and selective inhibitor of oncogenic RET fusion and RET mutant proteins, is a substrate of the drug metabolizing enzyme CYP3A4 and a substrate of the efflux transporter P‐gp ...based on in vitro data. Therefore, its pharmacokinetics (PKs) may be affected by co‐administration of potent CYP3A4 inhibitors and inducers, P‐gp inhibitors, and combined CYP3A4 and P‐gp inhibitors. With the frequent overlap between CYP3A4 and P‐gp substrates/inhibitors, pralsetinib is a challenging and representative example of the need to more quantitatively characterize transporter‐enzyme interplay. A physiologically‐based PK (PBPK) model for pralsetinib was developed to understand the victim drug–drug interaction (DDI) risk for pralsetinib. The key parameters driving the magnitude of pralsetinib DDIs, the P‐gp intrinsic clearance and the fraction metabolized by CYP3A4, were determined from PBPK simulations that best captured observed DDIs from three clinical studies. Sensitivity analyses and scenario simulations were also conducted to ensure these key parameters were determined with sound mechanistic rationale based on current knowledge, including the worst‐case scenarios. The verified pralsetinib PBPK model was then applied to predict the effect of other inhibitors and inducers on the PKs of pralsetinib. This work highlights the challenges in understanding DDIs when enzyme‐transporter interplay occurs, and demonstrates an important strategy for differentiating enzyme/transporter contributions to enable PBPK predictions for untested scenarios and to inform labeling.
A study to determine the impact of cyclosporine (Neoral), an inhibitor of P‐gp, on the pharmacokinetics of pralsetinib (trade name GAVRETO®) was conducted in 15 healthy adult volunteers. A single 200 ...mg dose of pralsetinib was administered orally alone and in combination with cyclosporine with a 9‐day washout between treatments. Co‐administration with cyclosporine resulted in a clinically relevant increase in pralsetinib maximum plasma concentration (Cmax) and area under the plasma concentration–time curve extrapolated to infinity (AUC0–∞) with associated geometric mean ratios (GMRs) and 90% confidence intervals (CIs) of 148% (109, 201) and 181% (136, 241), respectively. These findings provide insight into concomitant dosing of pralsetinib with inhibitors of P‐gp given the increases in pralsetinib exposure observed when administered with cyclosporine. Based on these results, co‐administration of pralsetinib with P‐gp inhibitors is not recommended. In the event that co‐administration cannot be avoided, it is recommended that the dose of pralsetinib be reduced.
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