Population Pharmacokinetics of Ibrutinib and Its Dihydrodiol Metabolite in Patients with Lymphoid Malignancies

Gallais, Fernand; Ysebaert, Loïc; Despas, Fabien; Barros, Sandra De; Dupré, Loı̈c; Quillet‐Mary, Anne; Protin, Caroline; Thomas, Fabienne; Obéric, Lucie; Allal, Ben; Chatelut, Étienne; White-Koning, Mélanie

doi:10.1007/s40262-020-00884-0

Cited by 13 publications

(13 citation statements)

References 25 publications

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“…While this strategy appears to successfully achieve changes in pharmacodynamic markers associated with ibrutinib activity, it fails to address the problems surrounding the unpredictable pharmacokinetic profile associated with this drug, which in turn, could potentially result in nonresponders when levels are too low or contribute to idiosyncratic toxicities when certain threshold levels are exceeded. This supposition is consistent with recently reported data from a population pharmacokinetic modeling analysis suggesting that higher plasma concentrations of ibrutinib are associated with a higher rate of discontinuation due to adverse events (42). A properly designed and executed pharmacokinetic boosting strategy can offer significant advantages in this context, and could further decrease the overall cost by facilitating a decrease in the prescribed ibrutinib dose by >18-fold.…”

Section: Discussionsupporting

confidence: 89%

“…3B; Supplementary Table S3). Gallais and colleagues previously reported that first-pass metabolism of ibrutinib in humans can be modeled by a link between the dosing compartment and metabolite central compartment (42). However, our model was unable to link ibrutinib and metabolite levels due to the lack of detectable metabolite levels when CYP3A was genetically or pharmacologically inhibited.…”

Section: Computational Model-based Predictions Of Ibrutinib Pharmacokineticsmentioning

confidence: 94%

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Intentional Modulation of Ibrutinib Pharmacokinetics through CYP3A Inhibition

Eisenmann

Muhowski

et al. 2021

Cancer Research Communications

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Ibrutinib (Imbruvica; PCI-32765) is an orally administered inhibitor of Bruton's tyrosine kinase that has transformed the treatment of B-cell malignancies. However, ibrutinib has very low oral bioavailability that contributes to significant variability in systemic exposure between patients, and this has the potential to affect both efficacy and toxicity. We hypothesized that the oral bioavailability of ibrutinib is limited by CYP3A isoform–mediated metabolism, and that this pathway can be inhibited to improve the pharmacokinetic properties of ibrutinib. Pharmacokinetic studies were performed in wild-type mice and mice genetically engineered to lack all CYP3A isoforms (CYP3A−/−) that received ibrutinib alone or in combination with CYP3A inhibitors cobicistat or ketoconazole. Computational modeling was performed to derive doses of ibrutinib that, when given after a CYP3A inhibitor, results in therapeutically relevant drug levels. Deficiency of CYP3A in mice was associated with an approximately 10-fold increase in the AUC of ibrutinib. This result could be phenocopied by administration of cobicistat before ibrutinib in wild-type mice, but cobicistat did not influence levels of ibrutinib in CYP3A−/− mice. Population pharmacokinetic and prospectively validated physiologically based pharmacokinetic models established preclinical and clinical doses of ibrutinib that could be given safely in combination with cobicistat without negatively affecting antileukemic properties. These findings signify a dominant role for CYP3A-mediated metabolism in the elimination of ibrutinib, and suggest a role for pharmacologic inhibitors of this pathway to intentionally modulate the plasma levels and improve the therapeutic use of this clinically important agent. Significance: Ibrutinib has limited oral bioavailability, which contributes to significant interindividual pharmacokinetic variability. Using engineered mouse models, we here report a causal relationship between CYP3A-mediated metabolism and ibrutinib's bioavailability and drug–drug interaction with cobicistat. These results offer a mechanistic basis for reported pharmacokinetic interactions with ibrutinib, and in conjunction with a newly developed computational model, allow for the rational design of clinical trials aimed at improving the therapeutic use of ibrutinib.

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Section: Discussionsupporting

confidence: 89%

Section: Computational Model-based Predictions Of Ibrutinib Pharmacokineticsmentioning

confidence: 94%

Intentional Modulation of Ibrutinib Pharmacokinetics through CYP3A Inhibition

Eisenmann

Muhowski

et al. 2021

Cancer Research Communications

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“…For ibrutinib, while no exposure‐driven relationships were identified for individual toxicities (grade ≥3 neutropenia or grade ≥3 infections and infestations), a slight increase in the proportion of patients with any grade ≥3 AEs was noted with increasing ibrutinib mean steady‐state trough concentrations in patients with MCL or CLL in 2 pivotal trials 20 . Additionally, a population PK model of ibrutinib and its metabolite in lymphoid malignancies demonstrated significantly higher ibrutinib exposure, as measured by AUC, among patients who discontinued treatment due to toxicity 21 . For zanubrutinib, results from an exposure–response analysis in patients with MCL, CLL and Waldenström macroglobulinemia suggested no relationships between zanubrutinib exposure and the probability of experiencing AEs of interest including neutropenia, thrombocytopenia, anaemia, infections, second primary malignancies, atrial fibrillation/flutter and bleeding events 22 .…”

Section: Discussionmentioning

confidence: 99%

“…20 Additionally, a population PK model of ibrutinib and its metabolite in lymphoid malignancies demonstrated significantly higher ibrutinib exposure, as measured by AUC, among patients who discontinued treatment due to toxicity. 21 For zanubrutinib, results from an exposure–response analysis in patients with MCL, CLL and Waldenström macroglobulinemia suggested no relationships between zanubrutinib exposure and the probability of experiencing AEs of interest including neutropenia, thrombocytopenia, anaemia, infections, second primary malignancies, atrial fibrillation/flutter and bleeding events. 22 In general, fewer toxicities related to off‐target kinase inhibition are expected because of the increased kinase selectivity of acalabrutinib relative to ibrutinib or zanubrutinib.…”

Section: Discussionmentioning

confidence: 99%

Exposure–response analysis of acalabrutinib and its active metabolite, ACP‐5862, in patients with B‐cell malignancies

Edlund

Buil-Bruna

Vishwanathan

et al. 2021

Brit J Clinical Pharma

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Examine relationships between the systemic exposure of acalabrutinib, a highly selective, next-generation Bruton tyrosine kinase inhibitor, and its active metabolite (ACP-5862) vs. efficacy and safety responses in patients with B-cell malignancies who received acalabrutinib as monotherapy or in combination with obinutuzumab.Methods: For exposure-efficacy analyses, patients with untreated chronic lymphocytic leukaemia were assessed for best overall response, progression-free survival and tumour regression. For exposure-safety analyses, incidences of grade ≥2 adverse events (AEs), grade ≥3 AEs and grade ≥2 events of clinical interest were assessed in patients with B-cell malignancies. Acalabrutinib and ACP-5862 pharmacokinetic (PK) parameter estimates were obtained from population PK modelling. Exposure calculations were based on study dosing regimens. Total active moieties were calculated to account for contributions of ACP-5862 to overall efficacy/safety.Results: A total of 573 patients were included (exposure-efficacy analyses, n = 274; exposure-safety analyses, n = 573). Most patients (93%) received acalabrutinib 100 mg twice daily. Median total active area under the concentration-time curve (AUC 24h,ss ) and total active maximal concentration at steady-state (C max,ss ) were Helena Edlund and Núria Buil-Bruna: equal contribution.A principal investigator was not included in the author byline because the reported data are from a pooled exposure-response analysis of data from 8 different studies. The principal investigators of the original studies were not involved in performing these analyses or interpreting the data; therefore, they did not qualify for authorship according to ICMJE criteria.

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“…Data were generated in accordance with the drug administration schedules and sampling strategies used in the context of TDM with n = 1000 individuals. In addition, a variable corresponding to the routinely monitored PK outcome was computed with both NONMEM and mapbayr: last dose of a 3‐day schedule to reach a total AUC of 24 mg·min/ml for carboplatin 32 ; AUC at steady state between two doses (AUC τ,SS ) for ibrutinib 33,34 ; trough concentration at steady state for cabozantinib, 35 pazopanib, 36 and voriconazole 37 ; sum of parent drug and metabolite trough concentration for sunitinib and N‐desethyl‐sunitinib 38 ; and time to reach a concentration below 0.2 μmol/L for methotrexate 39 . A detailed description of these models and the related data are provided in Table 3.…”

Section: Methodsmentioning

confidence: 99%

Easy and reliable maximum a posteriori Bayesian estimation of pharmacokinetic parameters with the open‐source R package mapbayr

Louedec

Puisset

Thomas

et al. 2021

CPT Pharmacom & Syst Pharma

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Pharmacokinetic (PK) parameter estimation is a critical and complex step in the model‐informed precision dosing (MIPD) approach. The mapbayr package was developed to perform maximum a posteriori Bayesian estimation (MAP‐BE) in R from any population PK model coded in mrgsolve. The performances of mapbayr were assessed using two approaches. First, “test” models with different features were coded, for example, first‐order and zero‐order absorption, lag time, time‐varying covariates, Michaelis–Menten elimination, combined and exponential residual error, parent drug and metabolite, and small or large inter‐individual variability (IIV). A total of 4000 PK profiles (combining single/multiple dosing and rich/sparse sampling) were simulated from each test model, and MAP‐BE of parameters was performed in both mapbayr and NONMEM. Second, a similar procedure was conducted with seven “real” previously published models to compare mapbayr and NONMEM on a PK outcome used in MIPD. For the test models, 98% of mapbayr estimations were identical to those given by NONMEM. Some discordances could be observed when dose‐related parameters were estimated or when models with large IIV were used. The exploration of objective function values suggested that mapbayr might outdo NONMEM in specific cases. For the real models, a concordance close to 100% on PK outcomes was observed. The mapbayr package provides a reliable solution to perform MAP‐BE of PK parameters in R. It also includes functions dedicated to data formatting and reporting and enables the creation of standalone Shiny web applications dedicated to MIPD, whatever the model or the clinical protocol and without additional software other than R.

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Population Pharmacokinetics of Ibrutinib and Its Dihydrodiol Metabolite in Patients with Lymphoid Malignancies

Cited by 13 publications

References 25 publications

Intentional Modulation of Ibrutinib Pharmacokinetics through CYP3A Inhibition

Intentional Modulation of Ibrutinib Pharmacokinetics through CYP3A Inhibition

Exposure–response analysis of acalabrutinib and its active metabolite, ACP‐5862, in patients with B‐cell malignancies

Easy and reliable maximum a posteriori Bayesian estimation of pharmacokinetic parameters with the open‐source R package mapbayr

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