Evaluation of Pharmacokinetic Drug–Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches

Peng, Yongbo; Cheng, Zeneng; Xie, Feifan

doi:10.3390/metabo11020075

Cited by 43 publications

(38 citation statements)

References 85 publications

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“…Novel and more structurally diverse FQs will continue to be developed for application to challenging bacterial infections with increasing antibiotic resistance. Predicting potential drug–drug interactions and quantifying the extent is a significant hurdle as new FQs enter the drug development phase [ 85 ]. While spacing the administration of the multivalent metals and FQs serves as a general guide in clinical practice, the effect of chelation on FQ pharmacokinetics remains highly variable (e.g., rufloxacin, which only shows a 15% decrease in AUC upon aluminum coadministration, Table 2 ) and patient compliance cannot be guaranteed.…”

Section: Resultsmentioning

confidence: 99%

Effects of Magnesium, Calcium, and Aluminum Chelation on Fluoroquinolone Absorption Rate and Bioavailability: A Computational Study

et al. 2021

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Fluoroquinolones (FQs) are a widespread class of broad-spectrum antibiotics prescribed as a first line of defense, and, in some cases, as the only treatment against bacterial infection. However, when administered orally, reduced absorption and bioavailability can occur due to chelation in the gastrointestinal tract (GIT) with multivalent metal cations acquired from diet, coadministered compounds (sucralfate, didanosine), or drug formulation. Predicting the extent to which this interaction reduces in vivo antibiotic absorption and systemic exposure remains desirable yet challenging. In this study, we focus on quinolone interactions with magnesium, calcium and aluminum as found in dietary supplements, antacids (Maalox) orally administered therapies (sucralfate, didanosine). The effect of FQ–metal complexation on absorption rate was investigated through a combined molecular and pharmacokinetic (PK) modeling study. Quantum mechanical calculations elucidated FQ–metal binding energies, which were leveraged to predict the magnitude of reduced bioavailability via a quantitative structure–property relationship (QSPR). This work will help inform clinical FQ formulation design, alert to possible dietary effects, and shed light on drug–drug interactions resulting from coadministration at an earlier stage in the drug development pipeline.

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Section: Resultsmentioning

confidence: 99%

Effects of Magnesium, Calcium, and Aluminum Chelation on Fluoroquinolone Absorption Rate and Bioavailability: A Computational Study

et al. 2021

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“…Pharmacokinetic interactions of practical importance are mainly based on changes in drug absorption and metabolism [42,45,46]. Our study showed that the co-administration of morphine and sorafenib resulted in a higher exposure of sorafenib when compared to sorafenib alone.…”

Section: The Influence Of Morphine On the Pharmacokinetics Of Sorafen...mentioning

confidence: 65%

Pharmacokinetic Drug Interaction Study of Sorafenib and Morphine in Rats

et al. 2021

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A combination of the tyrosine kinase inhibitor—sorafenib—and the opioid analgesic—morphine—can be found in the treatment of cancer patients. Since both are substrates of P-glycoprotein (P-gp), and sorafenib is also an inhibitor of P-gp, their co-administration may affect their pharmacokinetics, and thus the safety and efficacy of cancer therapy. Therefore, the aim of this study was to evaluate the potential pharmacokinetic drug–drug interactions between sorafenib and morphine using an animal model. The rats were divided into three groups that Received: sorafenib and morphine (ISOR+MF), sorafenib (IISOR), and morphine (IIIMF). Morphine caused a significant increase in maximum plasma concentrations (Cmax) and the area under the plasma concentration–time curves (AUC0–t, and AUC0–∞) of sorafenib by 108.3 (p = 0.003), 55.9 (p = 0.0115), and 62.7% (p = 0.0115), respectively. Also, the Cmax and AUC0–t of its active metabolite—sorafenib N-oxide—was significantly increased in the presence of morphine (p = 0.0022 and p = 0.0268, respectively). Sorafenib, in turn, caused a significant increase in the Cmax of morphine (by 0.5-fold, p = 0.0018). Moreover, in the presence of sorafenib the Cmax, AUC0–t, and AUC0–∞ of the morphine metabolite M3G increased by 112.62 (p < 0.0001), 46.82 (p = 0.0124), and 46.78% (p = 0.0121), respectively. Observed changes in sorafenib and morphine may be of clinical significance. The increased exposure to both drugs may improve the response to therapy in cancer patients, but on the other hand, increase the risk of adverse effects.

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“…18 Several assessments (in vitro, clinical, or modeling and simulation) are used to assess the risk of transporters based DDI and to minimize the possibility of consequential safety/ efficacy concerns in later development phases. [19][20][21][22] Each of these assessments has its own advantages and limitations in terms of applications, throughput, resource requirement, and sample analysis. Many of these assays are complimentary and can be utilized at different times during drug development.…”

Section: Articlementioning

confidence: 99%

Trends in FDA Transporter‐Based Post‐Marketing Requirements and Commitments Over the Last Decade

Younis

Manchandani

Hassan

et al. 2022

Clin Pharma and Therapeutics

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Characterizing interactions between new molecular entities (NMEs) and drug transporters is a critical element of drug development that helps in assessing potential transporter‐based drug–drug interactions (DDIs). However, not all NME new drug applications (NDAs) include a full characterization of NMEs transporter‐based DDIs, which necessitates the issuance of post‐marketing requirement (PMR)/post‐marketing commitment (PMC) by the US Food and Drug Administration (FDA) to characterize these potential interactions. The objective of this analysis is to identify trends in transporter‐based PMRs/PMCs issued by the FDA between 2012 and 2021. A decrease in the number of transporter‐based PMRs/PMCs was observed from 2012 to 2016 and an increasing trend in the number of PMRs/PMCs was observed after 2017. The majority of these transporter‐based PMRs/PMCs requested clinical evaluation (48%), some requested in vitro assessment (38%), and 2.5% requested modeling and simulation assessment. Most of the PMRs/PMCs requested evaluation of NMEs as perpetrator with the efflux transporters, P‐gp and/or BCRP (53%). Forty‐eight percent of the PMRs/PMCs were fulfilled with 67% resulted in labeling updates. On average, 2.5 years were needed for the information related to PMRs/PMCs to show in NMEs labeling. In conclusion, this analysis highlights the increased emphasis from the FDA on proper characterization of transporter‐based DDI and call for the need of early characterization of NMEs‐transporters interaction before initial NDA approval.

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Evaluation of Pharmacokinetic Drug–Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches

Cited by 43 publications

References 85 publications

Effects of Magnesium, Calcium, and Aluminum Chelation on Fluoroquinolone Absorption Rate and Bioavailability: A Computational Study

Effects of Magnesium, Calcium, and Aluminum Chelation on Fluoroquinolone Absorption Rate and Bioavailability: A Computational Study

Pharmacokinetic Drug Interaction Study of Sorafenib and Morphine in Rats

Trends in FDA Transporter‐Based Post‐Marketing Requirements and Commitments Over the Last Decade

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