The Cancer Drug Fraction of Metabolism Database

Hua, Liyan; Chiang, Chun‐Pin; Wang, Cong; Li, Jin; Cheng, Lijun; Feng, Weixing; Quinney, Sara K.; Wang, Lei; Li, Lang

doi:10.1002/psp4.12417

Cited by 9 publications

(7 citation statements)

References 39 publications

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“…Bosutinib is primarily metabolised by CYP3A4 enzyme with a contribution of this pathway ( f m,CYP3A4 ) of approximately 0.9, estimated from a clinical interaction study with ketoconazole 74 based on the ratio of systemic exposure of bosutinib in the presence and absence of the CYP3A inhibitor 75 . This was in concordance with in vitro metabolic phenotyping which hinted at a significant role of CYP3A4 enzyme (but not CYP3A5) in bosutinib metabolism with minor contribution from flavin‐containing monooxygenase (FMO) and uridine 5′‐diphospho‐glucuronosyltransferase (UGT) enzymes 33,76 .…”

Section: Discussionmentioning

confidence: 99%

Potential for pharmacokinetic interactions between Schisandra sphenanthera and bosutinib, but not imatinib: in vitro metabolism study combined with a physiologically‐based pharmacokinetic modelling approach

Adiwidjaja

Boddy

McLachlan

2020

Brit J Clinical Pharma

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Aims This study aimed to investigate the potential interaction between Schisandra sphenanthera, imatinib and bosutinib combining in vitro and in silico methods. Methods In vitro metabolism of imatinib and bosutinib using recombinant enzymes and human liver microsomes were investigated in the presence and absence of Schisandra lignans. Physiologically‐based pharmacokinetic (PBPK) models for the lignans accounting for reversible and mechanism‐based inhibitions and induction of CYP3A enzymes were built in the Simcyp Simulator (version 17) and evaluated for their capability to predict interactions with midazolam and tacrolimus. Their potential effect on systemic exposures of imatinib and bosutinib were predicted using PBPK in silico simulations. Results Schisantherin A and schisandrol B, but not schisandrin A, potently inhibited CYP3A4‐mediated metabolism of imatinib and bosutinib. All three compounds showed a strong reversible inhibition on CYP2C8 enzyme with ki of less than 0.5 μmol L−1. The verified PBPK models were able to describe the increase in systemic exposure of midazolam and tacrolimus due to co‐administration of S. sphenanthera, consistent with the reported changes in the corresponding clinical interaction study (AUC ratio of 2.0 vs 2.1 and 2.4 vs 2.1, respectively). The PBPK simulation predicted that at recommended dosing regimens of S. sphenanthera, co‐administration would result in an increase in bosutinib exposure (AUC ratio 3.0) but not in imatinib exposure. Conclusion PBPK models for Schisandra lignans were successfully developed. Interaction between imatinib and Schisandra lignans was unlikely to be of clinical importance. Conversely, S. sphenanthera at a clinically‐relevant dose results in a predicted three‐fold increase in bosutinib systemic exposure.

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Section: Discussionmentioning

confidence: 99%

Potential for pharmacokinetic interactions between Schisandra sphenanthera and bosutinib, but not imatinib: in vitro metabolism study combined with a physiologically‐based pharmacokinetic modelling approach

Adiwidjaja

Boddy

McLachlan

2020

Brit J Clinical Pharma

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“…Target drug

f_{e}

values were collected from the Biopharmaceutics Drug Disposition Classification System 27 . The fraction of hepatic metabolism through CYP pathways (

f_{m}

) was estimated 28 with Eq. 2:

\begin{matrix} left \begin{matrix} left f_{m, CYP j} goodbreak= \sum_{i = 1}^{n} percentage of metabolite i goodbreak\times \frac{{inhibition}_{j}}{\sum_{j} {inhibition}_{j}} \end{matrix} \end{matrix}

where inhibition j refers to the percentage of inhibition for the j th enzyme.…”

Section: Methodsmentioning

confidence: 99%

“…Target drug f e values were collected from the Biopharmaceutics Drug Disposition Classification System. 27 The fraction of hepatic metabolism through CYP pathways ( f m) was estimated 28 with Eq. 2:…”

Section: Pharmacokinetic Screening: Input Parametersmentioning

confidence: 99%

Identifying Clinically Relevant Drug–Drug Interactions With Methadone and Buprenorphine: A Translational Approach to Signal Detection

Miano

Wang

Leonard

et al. 2022

Clin Pharma and Therapeutics

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Methadone and buprenorphine have pharmacologic properties that are concerning for a high risk of drugdrug interactions (DDIs). We performed high-throughput screening for clinically relevant DDIs with methadone or buprenorphine by combining pharmacoepidemiologic and pharmacokinetic approaches. We conducted pharmacoepidemiologic screening via a series of self-controlled case series studies (SCCS) in Optum claims data from 2000 to 2019. We included persons 18 years or older who experienced an outcome of interest during target drug treatment. Exposures were all overlapping medications (i.e., the candidate precipitants) during target drug treatment. Outcomes were opioid overdose, non-overdose adverse effects, and cardiac arrest. We used conditional Poisson regression to calculate rate ratios, accounting for multiple comparisons with semi-Bayes shrinkage. We explored the impact of key study design choices in analyses that varied the exposure definitions of the target drugs and the candidate precipitant drugs. Pharmacokinetic screening was conducted by incorporating published data on CYP enzyme metabolism into an equation-based static model. In SCCS analysis, 1,432 events were included from 248,069 new users of methadone or buprenorphine. In the primary analysis, statistically significant DDIs included gabapentinoids with either methadone or buprenorphine; baclofen with methadone; and benzodiazepines with methadone. In sensitivity analysis, additional statistically significant DDIs included methocarbamol, quetiapine, or simvastatin with methadone. Pharmacokinetic screening identified two moderate-to-strong potential DDIs (clonidine and fluconazole with buprenorphine). The combination of clonidine and buprenorphine was also associated with a significantly increased risk of opioid overdose in pharmacoepidemiologic screening. These DDI signals may be the most important targets for future confirmation studies.

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“…The mean dapaconazole C max of 9.5 µM in humans was obtained from simulations of 500 mg every 8 h and corrected by multiplying the ratio of unbound fraction in plasma (f u ) (0.037) [ 14 ] with the blood-to-plasma ratio (R b ) (6.08 predicted with Simcyp). Nifedipine, midazolam, phenacetin, S-mephenytoin, and bufuralol fm values were adapted from Simcyp; the paclitaxel fm value was extracted from Hua et al [ 19 ]; and the diclofenac fm value was extracted from Siu and Lai [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

Prospective Prediction of Dapaconazole Clinical Drug–Drug Interactions Using an In Vitro to In Vivo Extrapolation Equation and PBPK Modeling

et al. 2022

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This study predicted dapaconazole clinical drug–drug interactions (DDIs) over the main Cytochrome P450 (CYP) isoenzymes using static (in vitro to in vivo extrapolation equation, IVIVE) and dynamic (PBPK model) approaches. The in vitro inhibition of main CYP450 isoenzymes by dapaconazole in a human liver microsome incubation medium was evaluated. A dapaconazole PBPK model (Simcyp version 20) in dogs was developed and qualified using observed data and was scaled up for humans. Static and dynamic models to predict DDIs following current FDA guidelines were applied. The in vitro dapaconazole inhibition was observed for all isoforms investigated, including CYP1A2 (IC50 of 3.68 µM), CYP2A6 (20.7 µM), 2C8 (104.1 µM), 2C9 (0.22 µM), 2C19 (0.05 µM), 2D6 (0.87 µM), and 3A4 (0.008–0.03 µM). The dynamic (PBPK) and static DDI mechanistic model-based analyses suggest that dapaconazole is a weak inhibitor (AUCR > 1.25 and <2) of CYP1A2 and CYP2C9, a moderate inhibitor (AUCR > 2 and <5) of CYP2C8 and CYP2D6, and a strong inhibitor (AUCR ≥ 5) of CYP2C19 and CYP3A, considering a clinical scenario. The results presented may be a useful guide for future in vivo and clinical dapaconazole studies.

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The Cancer Drug Fraction of Metabolism Database

Cited by 9 publications

References 39 publications

Potential for pharmacokinetic interactions between Schisandra sphenanthera and bosutinib, but not imatinib: in vitro metabolism study combined with a physiologically‐based pharmacokinetic modelling approach

Potential for pharmacokinetic interactions between Schisandra sphenanthera and bosutinib, but not imatinib: in vitro metabolism study combined with a physiologically‐based pharmacokinetic modelling approach

Identifying Clinically Relevant Drug–Drug Interactions With Methadone and Buprenorphine: A Translational Approach to Signal Detection

Prospective Prediction of Dapaconazole Clinical Drug–Drug Interactions Using an In Vitro to In Vivo Extrapolation Equation and PBPK Modeling

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