ArticleThe AAPS journal2022
In Vitro Assessment of Transporter Mediated Perpetrator DDIs for Several Hepatitis C Virus Direct-Acting Antiviral Drugs and Prediction of DDIs with Statins Using Static Models.
Article in The AAPS journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Molecular chameleons adaptability in target binding.Structural dynamics (Melville, N.Y.) · 2025Article
- Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges.Clinical pharmacokinetics · 2024Review
- The Role of CYPs and Transporters in the Biotransformation and Transport of the Anti-hepatitis C Antiviral Agents Asunaprevir, Daclatasvir, and Beclabuvir: Impact of Liver Disease, Race and Drug-drug Interactions on Safety and Efficacy.Current drug metabolism · 2024Review
- Mechanistic Static Model based Prediction of Transporter Substrate Drug-Drug Interactions Utilizing Atorvastatin and Rifampicin.Pharmaceutical research · 2023Article
- Cryo-EM structures of human organic anion transporting polypeptide OATP1B1.Cell research · 2023Article
- Protein Abundance of Drug Transporters in Human Hepatitis C Livers.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inhibitory effects of asunaprevir, daclatasvir, grazoprevir, paritaprevir, simeprevir, and voxilaprevir, direct-acting antiviral (DAA) drugs for the treatment of chronic hepatitis C virus (HCV) infection, were evaluated in vitro against a range of clinically important drug transporters. In vitro inhibition studies were conducted using transporter transfected cells and membrane vesicles. The risk of clinical drug-drug interactions (DDIs) was assessed using simplified static models recommended by regulatory agencies. Furthermore, we refined and developed static models to predict complex DDIs with several statins (pitavastatin, rosuvastatin, atorvastatin, and pravastatin) by mechanistically assessing differential inhibitory effects of perpetrator drugs on multiple transporters, such as organic anion transporting polypeptides (OATP1B), breast cancer resistance protein (BCRP), multidrug resistance protein 2 (MRP2), organic anion transporter 3 (OAT3), and cytochrome P450 CYP3A enzyme, as they are known to contribute to absorption, distribution, metabolism and excretion (ADME) of above statins. These models successfully predicted a total of 46 statin DDIs, including above DAA drugs and their fix-dose combination regimens. Predicted plasma area under curve ratio (AUCR) with and without perpetrator drugs was within ~ 2-fold of observed values. In contrast, simplified static R-value model resulted in increased false negative and false positive predictions when different prediction cut-off values were applied. Our studies suggest that mechanistic static model is a promising and useful tool to provide more accurate prediction of the risk and magnitude of DDIs with statins in early drug development and may help to improve the management of clinical DDIs for HCV drugs to ensure effective and safe HCV therapy. GRAPHICAL ABSTRACT.
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