Prediction for Plasma Trough Concentration and Optimal Dosing of Imatinib under Multiple Clinical Situations Using Physiologically Based Pharmacokinetic Modeling

Gao, Dongmei; Wang, Guopeng; Wu, Honghai; Jin-hua, WU; Zhao, Xiaoang

doi:10.1021/acsomega.2c07967

Cited by 7 publications

(6 citation statements)

References 53 publications

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“…These models address aspects such as interethnic differences in imatinib PK as well as dose optimizations for children and adults in DDI scenarios with imatinib as the victim drug. 23 , 24 , 25 Contrasting with these, our whole‐body PBPK model of imatinib encompasses the formation and biotransformation of both imatinib and its main metabolite NDMI. This approach is crucial as NDMI not only contributes to imatinib's pharmacodynamic effects but also plays an important role in inhibiting enzymes such as CYP2C8, CYP2D6, and CYP3A4.…”

Section: Discussionmentioning

confidence: 99%

“…Numerous PBPK models of imatinib have been developed to explore various research inquiries. 23 , 24 , 25 However, our approach uniquely extends this body of work by providing a comprehensive whole‐body PBPK model that incorporates imatinib's main metabolite NDMI, and examines imatinib as both a victim and a perpetrator drug in DDI scenarios. To promote widespread access and encourage further research, the finalized model files will be made available to the public at http://models.clinicalpharmacy.me/ .…”

Section: Introductionmentioning

confidence: 99%

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Physiologically based pharmacokinetic modeling of imatinib and N‐desmethyl imatinib for drug–drug interaction predictions

Loer,

Kovar,

Rüdesheim

et al. 2024

CPT Pharmacom & Syst Pharma

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The first‐generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug–drug interactions (DDIs) when co‐administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N‐desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co‐medications. This work presents the development of a parent–metabolite whole‐body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK‐Sim® using a total of 60 plasma concentration–time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P‐glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUClast) ratios and 12/12 predicted DDI maximum plasma concentration (Cmax) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model‐informed drug development or the support of model‐informed precision dosing.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Physiologically based pharmacokinetic modeling of imatinib and N‐desmethyl imatinib for drug–drug interaction predictions

Loer,

Kovar,

Rüdesheim

et al. 2024

CPT Pharmacom & Syst Pharma

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“…Hence, to ensure the accuracy of CYP3A4 metabolism contribution in the PBPK model, the PK interactions between OSI and itraconazole (ITR) as well as rifampicin (RIF) were simulated in Asian populations. The PBPK model and interaction parameters for ITR and RIF were obtained from published papers ( Gao et al, 2023 ). Based on the clinical drug-drug interaction (DDI) study ( Vishwanathan et al, 2018b ), The DDI simulation between OSI and ITR involved administering OSI at a dose of 80 mg once daily (OD) on day 1 and 10, while ITR was administered at a dose of 200 mg twice daily from day 7 to day 19.…”

Section: Methodsmentioning

confidence: 99%

“…Frontiers in Pharmacology frontiersin.org contribution in the PBPK model, the PK interactions between OSI and itraconazole (ITR) as well as rifampicin (RIF) were simulated in Asian populations. The PBPK model and interaction parameters for ITR and RIF were obtained from published papers (Gao et al, 2023).…”

Section: Parameters (Units)mentioning

confidence: 99%

Exploring inter-ethnic and inter-patient variability and optimal dosing of osimertinib: a physiologically based pharmacokinetic modeling approach

Liang,

Zhang,

Xue

et al. 2024

Front. Pharmacol.

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Purpose: This study aimed to develop and validate a physiologically based pharmacokinetic (PBPK) model for osimertinib (OSI) to predict plasma trough concentration (Ctrough) and pulmonary EGFRm+ (T790M and L858R mutants) inhibition in Caucasian, Japanese, and Chinese populations. The PBPK model was also utilized to investigate inter-ethnic and inter-patient differences in OSI pharmacokinetics (PK) and determine optimal dosing regimens.Methods: Population PBPK models of OSI for healthy and disease populations were developed using physicochemical and biochemical properties of OSI and physiological parameters of different groups. And then the PBPK models were validated using the multiple clinical PK and drug-drug interaction (DDI) study data.Results: The model demonstrated good consistency with the observed data, with most of prediction-to-observation ratios of 0.8–1.25 for AUC, Cmax, and Ctrough. The PBPK model revealed that plasma exposure of OSI was approximately 2-fold higher in patients compared to healthy individuals, and higher exposure observed in Caucasians compared to other ethnic groups. This was primarily attributed to a lower CL/F of OSI in patients and Caucasian. The PBPK model displayed that key factors influencing PK and EGFRm+ inhibition differences included genetic polymorphism of CYP3A4, CYP1A2 expression, plasma free concentration (fup), albumin level, and auto-inhibition/induction on CYP3A4. Inter-patient PK variability was most influenced by CYP3A4 variants, fup, and albumin level. The PBPK simulations indicated that the optimal dosing regimen for patients across the three populations of European, Japanese, and Chinese ancestry was OSI 80 mg once daily (OD) to achieve the desired range of plasma Ctrough (328–677 nmol/L), as well as 80 mg and 160 mg OD for desirable pulmonary EGFRm+ inhibition (>80%).Conclusion: In conclusion, this study’s PBPK simulations highlighted potential ethnic and inter-patient variability in OSI PK and EGFRm+ inhibition between Caucasian, Japanese, and Chinese populations, while also providing insights into optimal dosing regimens of OSI.

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“…To incorporate the effect of plasma albumin levels on OSI concentration in patients, the model utilizes the plasma protein scale factor (PPSF) in PK-Sim software. The PPSF is estimated using a specific equation 38 : where f up is fraction of plasma free OSI. The albumin f is the fractional value of plasma albumin in healthy subjects than that in patients.…”

Section: Methodsmentioning

confidence: 99%

Integrated PBPK-EO modeling of osimertinib to predict plasma concentrations and intracranial EGFR engagement in patients with brain metastases

Liang,

Zhang,

Xue

et al. 2024

Sci Rep

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The purpose of this study was to develop and validate a physiologically based pharmacokinetic (PBPK) model combined with an EGFR occupancy (EO) model for osimertinib (OSI) to predict plasma trough concentration (Ctrough) and the intracranial time-course of EGFR (T790M and L858R mutants) engagement in patient populations. The PBPK model was also used to investigate the key factors affecting OSI pharmacokinetics (PK) and intracranial EGFR engagement, analyze resistance to the target mutation C797S, and determine optimal dosing regimens when used alone and in drug-drug interactions (DDIs). A population PBPK-EO model of OSI was developed using physicochemical, biochemical, binding kinetic, and physiological properties, and then validated using nine clinical PK studies, observed EO study, and two clinical DDI studies. The PBPK-EO model demonstrated good consistency with observed data, with most prediction-to-observation ratios falling within the range of 0.7 to 1.3 for plasma AUC, Cmax, Ctrough and intracranial free concentration. The simulated time-course of C797S occupancy by the PBPK model was much lower than T790M and L858R occupancy, providing an explanation for OSI on-target resistance to the C797S mutation. The PBPK model identified ABCB1 CLint,u, albumin level, and EGFR expression as key factors affecting plasma Ctrough and intracranial EO for OSI. Additionally, PBPK-EO simulations indicated that the optimal dosing regimen for OSI in patients with brain metastases is either 80 mg once daily (OD) or 160 mg OD, or 40 mg or 80 mg twice daily (BID). When used concomitantly with CYP enzyme perpetrators, the PBPK-EO model suggested appropriate dosing regimens of 80 mg OD with fluvoxamine (FLUV) itraconazole (ITR) or fluvoxamine (FLUC) for co-administration and an increase to 160 mg OD with rifampicin (RIF) or efavirenz (EFA). In conclusion, the PBPK-EO model has been shown to be capable of simulating the pharmacokinetic concentration–time profiles and the time-course of EGFR engagement for OSI, as well as determining the optimum dosing in various clinical situations.

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Prediction for Plasma Trough Concentration and Optimal Dosing of Imatinib under Multiple Clinical Situations Using Physiologically Based Pharmacokinetic Modeling

Cited by 7 publications

References 53 publications

Physiologically based pharmacokinetic modeling of imatinib and N‐desmethyl imatinib for drug–drug interaction predictions

Physiologically based pharmacokinetic modeling of imatinib and N‐desmethyl imatinib for drug–drug interaction predictions

Exploring inter-ethnic and inter-patient variability and optimal dosing of osimertinib: a physiologically based pharmacokinetic modeling approach

Integrated PBPK-EO modeling of osimertinib to predict plasma concentrations and intracranial EGFR engagement in patients with brain metastases

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