Comparison of the Static
            <i>In Vivo</i>
            Approach to a Physiologically Based Pharmacokinetic Approach for Metabolic Drug–Drug Interactions Prediction

Tod, Michel; Pierrillas, Philippe B.; Bourguignon, Laurent; Goutelle, Sylvain

doi:10.4155/ipk.16.2

Cited by 14 publications

(10 citation statements)

References 39 publications

(76 reference statements)

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“…DDI-Predictor’s algorithm is based on steady-state equations in a physiologically based pharmacokinetic model [ 30 ]. The model has been externally validation ([ 17 ] and on the tool’s web site). The parameters for substrates and interactors were estimated exclusively from clinical studies: no in vitro data were used [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

“…DDI-Predictor ( , accessed on 18 January 2021) is a recently developed, free, online decision-making tool for characterizing pharmacokinetic modifications that involve the main CYPs; it notably takes account of possible cirrhosis or gene polymorphisms in the patient [ 17 ]. Using a mathematical model based on the DDIs observed in humans, DDI-Predictor’s output is quantified as the ratio of the area under the drug concentration curve (AUC), relative to that of a “standard” patient (R AUC ) [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

Moreau¹,

Simon

Walther³

et al. 2021

Metabolites

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The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare the three tools’ respective abilities to detect DDIs in routine clinical practice and (ii) measure the pharmacist intervention rate (PIR) and physician acceptance rate (PAR) associated with the use of DDI-P. The three tools’ respective DDI detection rates (in %) were measured. The PIRs and PARs were compared by using the area under the curve ratio given by DDI-P (RAUC) and applying a chi-squared test. The DDI detection rates differed significantly: 40.0%, 76.5%, and 85.2% for ANSM, GUH and DDI-P, respectively (p < 0.0001). The PIR differed significantly according to the DDI-P’s RAUC: 90.0%, 44.2% and 75.0% for RAUC ≤ 0.5; RAUC 0.5–2 and RAUC > 2, respectively (p < 0.001). The overall PAR was 85.1% and did not appear to depend on the RAUC category (p = 0.729). Our results showed that more pharmacist interventions were issued when details of the strength of the DDI were available. The three tools can be used in a complementary manner, with a view to refining medication adjustments.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

Moreau¹,

Simon

Walther³

et al. 2021

Metabolites

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“…We used the in vivo mechanistic static model (IMSM) to derive quantitative predictions for DDI with UPA caused by CYP3A4 inducers. This approach has been previously applied for in vivo quantitative prediction of CYP‐mediated drug interactions in several publications from our group and others, and has shown good performance in validation studies [7,10–14]. Details on the physiological rationale and demonstration of the equations presented below are available in those publications.…”

Section: Methodsmentioning

confidence: 99%

Drug interactions between emergency contraceptive drugs and cytochrome inducers: literature review and quantitative prediction

Corvaisier

Capelle

France

et al. 2020

Fundamemntal Clinical Pharma

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The emergency contraceptive drugs (EC), levonorgestrel (LNG) and ulipristal acetate (UPA), are sensitive substrates of cytochrome P450 3A4 (CYP3A4). In 2016, the label of LNG was updated based on a drug–drug interaction (DDI) study showing a significant decrease in LNG exposure when co‐administered with efavirenz, a known CYP3A4 inducer. DDI between UPA and CYP3A4 inducers are poorly characterized. The aims of this study were to review quantitative data from the literature on DDI with EC, to provide quantitative predictions of DDI between UPA and CYP3A4 inducers, and to identify moderate and severe DDI that may require a dose adjustment. A literature search was performed on pharmacokinetic DDI of LNG and UPA. Quantitative prediction of DDI with UPA was carried out by using the in vivo mechanistic static model (IMSM). Limited information was available on DDI with emergency contraception drugs. For LNG, data from eleven studies were retrieved, including five known CYP3A4 inducers that confirmed a risk of underexposure to LNG when co‐administered with inducers. For UPA, only three studies were identified, including only one CYP3A4 inducer. The IMSM approach indicated that UPA is a sensitive substrate of CYP3A4, with an estimated contribution of 86% of CYP3A4 to oral clearance. Moderate to severe DDI were predicted in 17 cases with CYP3A4 inducers, and dosage adjustments were suggested. This study illustrates the ability of the IMSM approach to inform about the DDI profile of old and new drugs.

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“…In mathematical modelling, generally speaking, to compensate for its lesser interpretability and its higher risk of overfitting, a complex model should show a significant advantage over a simple model with respect to predictive power for the context of use [20,[64][65][66][67]. If simple and complex models show comparable predictive power with regard to the context of use, the simple model should be selected (cf.…”

Section: To Explain or To Predict? Which Is The Position Of A Pbpk Model?mentioning

confidence: 99%

Lost in modelling and simulation?

Sugano

2021

ADMET DMPK

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Over the past few decades, physiologically-based pharmacokinetic modelling (PBPK) has been anticipated to be a powerful tool to improve the productivity of drug discovery and development. However, recently, multiple systematic evaluation studies independently suggested that the predictive power of current oral absorption (OA) PBPK models needs significant improvement. There is some disagreement between the industry and regulators about the credibility of OA PBPK modelling. Recently, the editorial board of AMDET&DMPK has announced the policy for the articles related to PBPK modelling (Modelling and simulation ethics). In this feature article, the background of this policy is explained: (1) Requirements for scientific writing of PBPK modelling, (2) Scientific literacy for PBPK modelling, and (3) Middle-out approaches. PBPK models are a useful tool if used correctly. This article will hopefully help advance the science of OA PBPK models.

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Comparison of the Static In Vivo Approach to a Physiologically Based Pharmacokinetic Approach for Metabolic Drug–Drug Interactions Prediction

Cited by 14 publications

References 39 publications

Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

Drug interactions between emergency contraceptive drugs and cytochrome inducers: literature review and quantitative prediction

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