Evaluation of Methods to Assess CYP3A Induction Risk in Clinical Practice Using &lt;i&gt;in Vitro&lt;/i&gt; Induction Parameters

Tsutsui, Hironori; Kuramoto, Satoshi; Ozeki, Kazuhisa

doi:10.1248/bpb.b20-00578

Cited by 6 publications

(15 citation statements)

References 27 publications

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“…There have been numerous publications evaluating the translation of in vitro derived induction parameters to predict clinical risk [ 1 , 2 , 4 , 22 , 23 ]. Of note, despite a tendency for overprediction, there were still several false negative inducers when applying the basic models without consideration of in vitro binding or stability [ 4 ].…”

Section: Discussion and Conclusionmentioning

confidence: 99%

Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models

Ramsden

Fullenwider

2022

Eur J Drug Metab Pharmacokinet

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Background and Objective Induction of drug-metabolizing enzymes can lead to drug-drug interactions (DDIs); therefore, early assessment is often conducted. Previous reports focused on true positive cytochrome P450 3A (CYP3A) inducers leaving a gap in translation for in vitro inducers which do not manifest in clinical induction. The goal herein was to expand the in vitro induction dataset by including true negative clinical inducers to identify a correction factor to basic DDI models, which reduces false positives without impacting false negatives. Methods True negative clinical inducers were identified through a literature search, in vitro induction parameters were generated in three human hepatocyte donors, and the performance of basic induction models proposed by regulatory agencies, concentration producing twofold induction ( F 2), basic static model ( R 3) and relative induction score (RIS), was used to characterize clinical induction risk. Results The data demonstrated the importance of correcting for in vitro binding and metabolism to derive induction parameters. The aggregate analysis indicates that the RIS with a positive cut-off of < 0.7-fold area under the curve ratio (AUCR) provides the best quantitative prediction. Additionally, correction factors of ten and two times the unbound peak plasma concentration at steady state ( C max,ss,u ) can be confidently used to identify true positive inducers when referenced against the concentration resulting in twofold increase in messenger ribonucleic acid (mRNA) or using the R3 equation, respectively. Conclusions These iterative improvements, which reduce the number of false positives, could aid regulatory recommendations and limit unnecessary clinical explorations into CYP3A induction. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s13318-022-00763-y.

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Section: Discussion and Conclusionmentioning

confidence: 99%

Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models

Ramsden

Fullenwider

2022

Eur J Drug Metab Pharmacokinet

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“…For the 4 inducers used in this work, the induction data, including

E_{\max}

{EC}_{50}

I_{h}, and I_{g}

are shown in Table 1 . Specifically,

E_{\max}

and

{EC}_{50}

were collected from previous literature, 6,16 while the values for

I_{h}

and

I_{g}

were calculated based on FDA guidance:

I_{h} goodbreak= f_{normalu, normalp} goodbreak\times ()(, C_{\max} goodbreak+ \frac{f_{g} \times f_{a} \times k_{a} \times Dose}{Q_{h} \times R_{b} \times frequency}),

I_{g} goodbreak= \frac{f_{a} \times k_{a} \times Dose}{Q_{g} \times frequency} .

…”

Section: Methodsmentioning

confidence: 99%

“…For the 4 inducers used in this work, the induction data, including E max , EC 50 , I h , and I g are shown in Table 1. Specifically, E max and EC 50 were collected from previous literature, 6,16 while the values for I h and I g were calculated based on FDA guidance:…”

Section: In Vitro Data Collectionmentioning

confidence: 99%

“…AUCR = 1 a Median EC 50 and E max values of inducers were obtained from a previous study. 16 See the Supplementary Excel File for information on when mean EC 50 and E max values of inducers were used. b f u,hept is an unbound fraction of inducer in the liver and was collected from previous studies.…”

Section: Derivation Of the New Equation For Aucr Predictionmentioning

confidence: 99%

“…Such problems can be resolved by using the relative factor (RF) method. 16,48,49 Specifically, by using the RF method, which requires only the induction detection limit concentration, the E ind of a new drug candidate can be estimated without measuring E max and EC 50 . Importantly, because induction detection limit concentration is lower than EC 50 under the assumption that E max of different CYP3A inducers are the same, it is measurable even when a drug has cytotoxicity or poor solubility.…”

Section: Articlementioning

confidence: 99%

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Beyond the Michaelis–Menten: Accurate Prediction of Drug Interactions Through Cytochrome P450 3A4 Induction

Tran

Yun

et al. 2023

Clin Pharma and Therapeutics

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The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant (K m ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug (E T ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen (F a ) is also assumed to be one because F a is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, F a was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between K m and E T , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between K m and E T becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.Cytochrome P450 (CYP) is the most important superfamily of enzymes, playing a crucial role in the metabolic clearance of an enormous number of compounds in humans. Approximately 70-80% of marketed drugs are metabolized by this superfamily of enzymes, especially CYP1A2, CYP2C, CYP2D6, and CYP3A4. 1 Such CYP enzymes can be induced by xenobiotic substances, including drugs, resulting in the increased metabolic activity of these enzymes. For instance, rifampicin, a prototype CYP3A4 enzyme inducer, enhances the metabolic process of midazolam, a probe substrate of CYP3A4, decreasing its plasma concentration and thus its therapeutic efficacy. 2 Therefore, the induction of CYP enzymes is one of the major reasons for clinical drug-drug interactions (DDIs).

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Quantitative prediction of CYP3A induction-mediated drug-drug interactions in clinical practice

Tsutsui,

Kato,

Kuramoto

et al. 2024

Drug Metabolism and Pharmacokinetics

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Evaluation of Methods to Assess CYP3A Induction Risk in Clinical Practice Using <i>in Vitro</i> Induction Parameters

Cited by 6 publications

References 27 publications

Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models

Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models

Beyond the Michaelis–Menten: Accurate Prediction of Drug Interactions Through Cytochrome P450 3A4 Induction

Quantitative prediction of CYP3A induction-mediated drug-drug interactions in clinical practice

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