Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model

Almond, Lisa M.; Mukadam, Sophie; Gardner, Iain; Okialda, Krystle; Wong, Susan; Hatley, Oliver; Tay, Suzanne; Rowland-Yeo, Karen; Jamei, Masoud; Rostami-Hodjegan, Amin; Kenny, Jane R.

doi:10.1124/dmd.115.066845

Cited by 101Pharmacogenetics and Drug MetabolismDrug Transport and Resistance MechanismsDrug-Induced Hepatotoxicity and Protection

Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model

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Almond, Lisa M., Mukadam, Sophie, Gardner, Iain, Okialda, Krystle, Wong, Susan, Hatley, Oliver, Tay, Suzanne, Rowland-Yeo, Karen +3 more·2016·Drug Metabolism and Disposition

DOI 10.1124/dmd.115.066845PMC PMC4885489

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Abstract

Using physiologically based pharmacokinetic modeling, we predicted the magnitude of drug-drug interactions (DDIs) for studies with rifampicin and seven CYP3A4 probe substrates administered i.v. (10 studies) or orally (19 studies). The results showed a tendency to underpredict the DDI magnitude when the victim drug was administered orally. Possible sources of inaccuracy were investigated systematically to determine the most appropriate model refinement. When the maximal fold induction (Indmax) for rifampicin was increased (from 8 to 16) in both the liver and the gut, or when the Indmax was increased in the gut but not in liver, there was a decrease in bias and increased precision compared with the base model (Indmax = 8) [geometric mean fold error (GMFE) 2.12 vs. 1.48 and 1.77, respectively]. Induction parameters (mRNA and activity), determined for rifampicin, carbamazepine, phenytoin, and phenobarbital in hepatocytes from four donors, were then used to evaluate use of the refined rifampicin model for calibration. Calibration of mRNA and activity data for other inducers using the refined rifampicin model led to more accurate DDI predictions compared with the initial model (activity GMFE 1.49 vs. 1.68; mRNA GMFE 1.35 vs. 1.46), suggesting that robust in vivo reference values can be used to overcome interdonor and laboratory-to-laboratory variability. Use of uncalibrated data also performed well (GMFE 1.39 and 1.44 for activity and mRNA). As a result of experimental variability (i.e., in donors and protocols), it is prudent to fully characterize in vitro induction with prototypical inducers to give an understanding of how that particular system extrapolates to the in vivo situation when using an uncalibrated approach.

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A minor discrepancy was the overprediction of the induction effect mediated by rifampicin in Simcyp.
“A minor discrepancy was the overprediction of the induction effect mediated by rifampicin in Simcyp (Figure 6), which has also been reported previously for SVL and midazolam as victim drugs. 39”
From Does the choice of applied physiologically‐based pharmacokinetics platform matter? A case study on simvastatin disposition and drug–drug interaction (2022) · cites this paper as [39]
The Results section reports that the base rifampicin model underpredicted the magnitude of the interaction with orally administered midazolam, rather than overpredicting the induction effect. The Introduction likewise describes underprediction for rifampicin and oral midazolam; the Results figure caption identifies simvastatin among the victim drugs evaluated but does not reverse that reported direction. (LLM, conf=0.89)
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There are several previously published PBPK models for simvastatin: one in Simcyp, two in PK-Sim, and another semimechanistic PBPK model in NONMEM.
“There are several previously published PBPK models for simvastatin: one in Simcyp, 39 two in PK‐Sim 47 , 48 and another semimechanistic PBPK model in NONMEM. 49”
From Does the choice of applied physiologically‐based pharmacokinetics platform matter? A case study on simvastatin disposition and drug–drug interaction (2022) · cites this paper as [39]

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Porcine foetal and neonatal CYP3A liver expression
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Authors: Almond, L. M., Mukadam, S., Gardner, I., Okialda, K., Wong, S., Hatley, O., Tay, S., Rowland-Yeo, K., Jamei, M., Rostami-Hodjegan, A., & Kenny, J. R.
Affiliations: Genentech (US)
Corresponding author: Almond, Lisa M.
ORCID: Almond, Lisa M.
Hatley, Oliver
Jamei, Masoud
Venue: Drug Metabolism and Disposition
Publisher: Elsevier BV
Published: 2016-06
Volume: 44
Issue: 6
Pages: 821-832
ISSN: 0090-9556 (ISSN-L)
DOI: 10.1124/dmd.115.066845
PMC: PMC4885489
OpenAlex: W2323380120
Cited by: 101
References: 62
Citations by year: 2026: 2 · 2025: 4 · 2024: 16 · 2023: 9 · 2022: 12 · 2021: 17 · 2020: 10 · 2019: 11 · 2018: 8 · 2017: 9 · 2016: 3
FWCI: 12.92
Type: journal article
Language: en
Update notices: erratum (2016-06-01)
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor, 3136 d)
Locations: Drug Metabolism and Disposition (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Research Explorer (The University of Manchester) (submittedVersion)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
Keywords: drug-drug interactions, CYP3A4 induction, rifampicin, physiologically based pharmacokinetic modeling, model calibration, carbamazepine, phenytoin, phenobarbital, in vitro-in vivo extrapolation, oral administration
MeSH terms: Administration, Oral, Carbamazepine, Drug Interactions, Enzyme Induction, Humans, Liver, Models, Biological, Phenobarbital, Phenytoin, Rifampin, RNA, Messenger, Hepatocytes, Gastrointestinal Tract, Cytochrome P-450 CYP3A

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Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model

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