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Pharmaceutics
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Drug–Drug Interactions
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Drug–Drug Interactions

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Pharmacokinetics and Pharmacodynamics".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 65596

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Dong Hyun Kim

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Guest Editor
Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Inje University, Busan 47392, Republic of Korea
Interests: drug–drug interaction; metabolomics; drug metabolism and pharmacokinetics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sangkyu Lee

E-Mail Website
Guest Editor
College of Pharmacy and Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu, Republic of Korea
Interests: proteomics; drug metabolism; mass spectrometry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Drug–drug interactions (DDIs) cause one drug to affect other drugs, leading to a reduced drug efficacy or an increased toxicity of the affected drugs. In some cases, drug interactions are reported to cause severe adverse drug reactions that are life-threatening to the patient. Traditionally, DDIs have been evaluated around the selective action of drugs on specific CYP enzymes. The interaction of drugs based on CYPs is still very important for drug interactions, but recently, other important mechanisms have also been studied as contributing to the drug interactions, such as drug transporter- or UDP-glucuronyltransferase-mediated DDI. In addition, the novel mechanism to regulate DDI can also be suggested. In the case of the substance to be interacted, not only the DDI, but also the herb– or food–drug interactions have been reported to be clinically relevant in terms of adverse side effects. Reporting examples of drug interactions on a marketed drug or a new mechanism study will be very helpful for preventing the side effects of the patient taking them. This Special Issue has the aim of highlighting the current progress in the clinical or non-clinical interactions of commercial drugs and the elucidation of the mechanism of drug interactions.

Prof. Dong Hyun Kim
Prof. Sangkyu Lee
Guest Editors

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Keywords

  • Drug–drug interaction
  • Cytochrome P450
  • UDP-glucuronyltransferase
  • Transporter
  • Pharmacokinetics
  • Pharmacogenetics
  • Adverse drug interaction

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Published Papers (15 papers)

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Research

Jump to: Review

7 pages, 968 KiB  
Article
In Silico Prediction of Drug–Drug Interactions Mediated by Cytochrome P450 Isoforms
by Alexander V. Dmitriev, Anastassia V. Rudik, Dmitry A. Karasev, Pavel V. Pogodin, Alexey A. Lagunin, Dmitry A. Filimonov and Vladimir V. Poroikov
Pharmaceutics 2021, 13(4), 538; https://doi.org/10.3390/pharmaceutics13040538 - 13 Apr 2021
Cited by 8 | Viewed by 2437
Abstract
Drug–drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated [...] Read more.
Drug–drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated by inhibition or induction of the most important drug-metabolizing cytochrome P450 isoforms. Our study was dedicated to creating a computer model for prediction of the DDIs mediated by the seven most important P450 cytochromes: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, and CYP3A4. For the creation of structure–activity relationship (SAR) models that predict metabolism-mediated DDIs for pairs of molecules, we applied the Prediction of Activity Spectra for Substances (PASS) software and Pairs of Substances Multilevel Neighborhoods of Atoms (PoSMNA) descriptors calculated based on structural formulas. About 2500 records on DDIs mediated by these cytochromes were used as a training set. Prediction can be carried out both for known drugs and for new, not-yet-synthesized substances. The average accuracy of the prediction of DDIs mediated by various isoforms of cytochrome P450 estimated by leave-one-out cross-validation (LOO CV) procedures was about 0.92. The SAR models created are publicly available as a web resource and provide predictions of DDIs mediated by the most important cytochromes P450. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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16 pages, 3309 KiB  
Article
Oral Proteasomal Inhibitors Ixazomib, Oprozomib, and Delanzomib Upregulate the Function of Organic Anion Transporter 3 (OAT3): Implications in OAT3-Mediated Drug-Drug Interactions
by Yunzhou Fan, Zhengxuan Liang, Jinghui Zhang and Guofeng You
Pharmaceutics 2021, 13(3), 314; https://doi.org/10.3390/pharmaceutics13030314 - 28 Feb 2021
Cited by 6 | Viewed by 2298
Abstract
Organic anion transporter 3 (OAT3) is mainly expressed at the basolateral membrane of kidney proximal tubules, and is involved in the renal elimination of various kinds of important drugs, potentially affecting drug efficacy or toxicity. Our laboratory previously reported that ubiquitin modification of [...] Read more.
Organic anion transporter 3 (OAT3) is mainly expressed at the basolateral membrane of kidney proximal tubules, and is involved in the renal elimination of various kinds of important drugs, potentially affecting drug efficacy or toxicity. Our laboratory previously reported that ubiquitin modification of OAT3 triggers the endocytosis of OAT3 from the plasma membrane to intracellular endosomes, followed by degradation. Oral anticancer drugs ixazomib, oprozomib, and delanzomib, as proteasomal inhibitors, target the ubiquitin–proteasome system in clinics. Therefore, this study investigated the effects of ixazomib, oprozomib, and delanzomib on the expression and transport activity of OAT3 and elucidated the underlying mechanisms. We showed that all three drugs significantly increased the accumulation of ubiquitinated OAT3, which was consistent with decreased intracellular 20S proteasomal activity; stimulated OAT3-mediated transport of estrone sulfate and p-aminohippuric acid; and increased OAT3 surface expression. The enhanced transport activity and OAT3 expression following drug treatment resulted from an increase in maximum transport velocity of OAT3 without altering the substrate binding affinity, and from a decreased OAT3 degradation. Together, our study discovered a novel role of anticancer agents ixazomib, oprozomib, and delanzomib in upregulating OAT3 function, unveiled the proteasome as a promising target for OAT3 regulation, and provided implication of OAT3-mediated drug–drug interactions, which should be warned against during combination therapies with proteasome inhibitor drugs. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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9 pages, 252 KiB  
Article
Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database
by Yoshihiro Noguchi, Tomoya Tachi and Hitomi Teramachi
Pharmaceutics 2020, 12(8), 762; https://doi.org/10.3390/pharmaceutics12080762 - 12 Aug 2020
Cited by 10 | Viewed by 3169
Abstract
Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. [...] Read more.
Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; “hypothetical” true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden’s index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug–drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug–drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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11 pages, 574 KiB  
Article
Prevalence of Potential Drug–Drug Interaction Risk among Chronic Kidney Disease Patients in a Spanish Hospital
by Gracia Santos-Díaz, Ana María Pérez-Pico, Miguel Ángel Suárez-Santisteban, Vanesa García-Bernalt, Raquel Mayordomo and Pedro Dorado
Pharmaceutics 2020, 12(8), 713; https://doi.org/10.3390/pharmaceutics12080713 - 30 Jul 2020
Cited by 20 | Viewed by 4441
Abstract
Chronic kidney disease (CKD) is a major health problem worldwide and, in Spain, it is present in 15.1% of individuals. CKD is frequently associated with some comorbidities and patients need to be prescribed multiple medications. Polypharmacy increases the risk of adverse drug reactions [...] Read more.
Chronic kidney disease (CKD) is a major health problem worldwide and, in Spain, it is present in 15.1% of individuals. CKD is frequently associated with some comorbidities and patients need to be prescribed multiple medications. Polypharmacy increases the risk of adverse drug reactions (ADRs). There are no published studies evaluating the prevalence of potential drug–drug interactions (pDDIs) among CKD patients in any European country. This study was aimed to determine the prevalence, pattern, and factors associated with pDDIs among CKD patients using a drug interactions program. An observational cross-sectional study was carried out at Plasencia Hospital, located in Spain. Data were collected among patients with CKD diagnoses and pDDIs were assessed by the Lexicomp® Drug Interactions platform. Data were obtained from 112 CKD patients. A total number of 957 prescribed medications were acknowledged, and 928 pDDIs were identified in 91% of patients. Age and concomitant drugs were significantly associated with the number of pDDIs (p < 0.05). According to the results, the use of programs for the determination of pDDIs (such as Lexicomp®) is recommended in the clinical practice of CKD patients in order to avoid serious adverse effects, as is paying attention to contraindicated drug combinations. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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14 pages, 1606 KiB  
Article
Effect of Rumex Acetosa Extract, a Herbal Drug, on the Absorption of Fexofenadine
by Jung Hwan Ahn, Junhyeong Kim, Naveed Ur Rehman, Hye-Jin Kim, Mi-Jeong Ahn and Hye Jin Chung
Pharmaceutics 2020, 12(6), 547; https://doi.org/10.3390/pharmaceutics12060547 - 12 Jun 2020
Cited by 8 | Viewed by 3059
Abstract
Herbal drugs are widely used for the auxiliary treatment of diseases. The pharmacokinetics of a drug may be altered when it is coadministered with herbal drugs that can affect drug absorption. The effects of herbal drugs on absorption must be evaluated. In this [...] Read more.
Herbal drugs are widely used for the auxiliary treatment of diseases. The pharmacokinetics of a drug may be altered when it is coadministered with herbal drugs that can affect drug absorption. The effects of herbal drugs on absorption must be evaluated. In this study, we investigated the effects of Rumex acetosa (R. acetosa) extract on fexofenadine absorption. Fexofenadine was selected as a model drug that is a substrate of P-glycoprotein (P-gp) and organic anion transporting polypeptide 1A2 (OATP1A2). Emodine—the major component of R. acetosa extract—showed P-gp inhibition in vitro and in vivo. Uptake of fexofenadine via OATP1A2 was inhibited by R. acetosa extract in OATP1A2 transfected cells. A pharmacokinetic study showed that the area under the plasma concentration–time curve (AUC) of fexofenadine was smaller in the R. acetosa extract coadministered group than in the control group. R. acetosa extract also decreased aqueous solubility of fexofenadine HCl. The results of this study suggest that R. acetosa extract could inhibit the absorption of certain drugs via intervention in the aqueous solubility and the drug transporters. Therefore, R. acetosa extract may cause drug interactions when coadministered with substrates of drug transporters and poorly water-soluble drugs, although further clinical studies are needed. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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13 pages, 1695 KiB  
Article
Strong and Selective Inhibitory Effects of the Biflavonoid Selamariscina A against CYP2C8 and CYP2C9 Enzyme Activities in Human Liver Microsomes
by So-Young Park, Phi-Hung Nguyen, Gahyun Kim, Su-Nyeong Jang, Ga-Hyun Lee, Nguyen Minh Phuc, Zhexue Wu and Kwang-Hyeon Liu
Pharmaceutics 2020, 12(4), 343; https://doi.org/10.3390/pharmaceutics12040343 - 10 Apr 2020
Cited by 19 | Viewed by 3249
Abstract
Like flavonoids, biflavonoids, dimeric flavonoids, and polyphenolic plant secondary metabolites have antioxidant, antibacterial, antiviral, anti-inflammatory, and anti-cancer properties. However, there is limited data on their effects on cytochrome P450 (P450) and uridine 5′-diphosphoglucuronosyl transferase (UGT) enzyme activities. In this study we evaluate the [...] Read more.
Like flavonoids, biflavonoids, dimeric flavonoids, and polyphenolic plant secondary metabolites have antioxidant, antibacterial, antiviral, anti-inflammatory, and anti-cancer properties. However, there is limited data on their effects on cytochrome P450 (P450) and uridine 5′-diphosphoglucuronosyl transferase (UGT) enzyme activities. In this study we evaluate the inhibitory potential of five biflavonoids against nine P450 activities (P450s1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A) in human liver microsomes (HLMs) using cocktail incubation and liquid chromatography-tandem mass spectrometry (LC–MS/MS). The most strongly inhibited P450 activity was CYP2C8-mediated amodiaquine N-dealkylation with IC50 ranges of 0.019~0.123 μM. In addition, the biflavonoids—selamariscina A, amentoflavone, robustaflavone, cupressuflavone, and taiwaniaflavone—noncompetitively inhibited CYP2C8 activity with respective Ki values of 0.018, 0.083, 0.084, 0.103, and 0.142 μM. As selamariscina A showed the strongest effects, we then evaluated it against six UGT isoforms, where it showed weaker inhibition (UGTs1A1, 1A3, 1A4, 1A6, 1A9, and 2B7, IC50 > 1.7 μM). Returning to the P450 activities, selamariscina A inhibited CYP2C9-mediated diclofenac hydroxylation and tolbutamide hydroxylation with respective Ki values of 0.032 and 0.065 μM in a competitive and noncompetitive manner. However, it only weakly inhibited CYP1A2, CYP2B6, and CYP3A with respective Ki values of 3.1, 7.9, and 4.5 μM. We conclude that selamariscina A has selective and strong inhibitory effects on the CYP2C8 and CYP2C9 isoforms. This information might be useful in predicting herb-drug interaction potential between biflavonoids and co-administered drugs mainly metabolized by CYP2C8 and CYP2C9. In addition, selamariscina A might be used as a strong CYP2C8 and CYP2C9 inhibitor in P450 reaction-phenotyping studies to identify drug-metabolizing enzymes responsible for the metabolism of new chemicals. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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17 pages, 2507 KiB  
Article
Lack of Correlation between In Vitro and In Vivo Studies on the Inhibitory Effects of (‒)-Sophoranone on CYP2C9 Is Attributable to Low Oral Absorption and Extensive Plasma Protein Binding of (‒)-Sophoranone
by Yu Fen Zheng, Soo Hyeon Bae, Zhouchi Huang, Soon Uk Chae, Seong Jun Jo, Hyung Joon Shim, Chae Bin Lee, Doyun Kim, Hunseung Yoo and Soo Kyung Bae
Pharmaceutics 2020, 12(4), 328; https://doi.org/10.3390/pharmaceutics12040328 - 07 Apr 2020
Cited by 8 | Viewed by 2848
Abstract
(‒)-Sophoranone (SPN) is a bioactive component of Sophora tonkinensis with various pharmacological activities. This study aims to evaluate its in vitro and in vivo inhibitory potential against the nine major CYP enzymes. Of the nine tested CYPs, it exerted the strongest inhibitory effect [...] Read more.
(‒)-Sophoranone (SPN) is a bioactive component of Sophora tonkinensis with various pharmacological activities. This study aims to evaluate its in vitro and in vivo inhibitory potential against the nine major CYP enzymes. Of the nine tested CYPs, it exerted the strongest inhibitory effect on CYP2C9-mediated tolbutamide 4-hydroxylation with the lowest IC50 (Ki) value of 0.966 ± 0.149 μM (0.503 ± 0.0383 μM), in a competitive manner. Additionally, it strongly inhibited other CYP2C9-catalyzed diclofenac 4′-hydroxylation and losartan oxidation activities. Upon 30 min pre-incubation of human liver microsomes with SPN in the presence of NADPH, no obvious shift in IC50 was observed, suggesting that SPN is not a time-dependent inactivator of the nine CYPs. However, oral co-administration of SPN had no significant effect on the pharmacokinetics of diclofenac and 4′-hydroxydiclofenac in rats. Overall, SPN is a potent inhibitor of CYP2C9 in vitro but not in vivo. The very low permeability of SPN in Caco-2 cells (Papp value of 0.115 × 10−6 cm/s), which suggests poor absorption in vivo, and its high degree of plasma protein binding (>99.9%) may lead to the lack of in vitro–in vivo correlation. These findings will be helpful for the safe and effective clinical use of SPN. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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16 pages, 2109 KiB  
Article
Evaluation of the Effect of CYP2D6 Genotypes on Tramadol and O-Desmethyltramadol Pharmacokinetic Profiles in a Korean Population Using Physiologically-Based Pharmacokinetic Modeling
by Hyeon-Cheol Jeong, Soo Hyeon Bae, Jung-Woo Bae, Sooyeun Lee, Anhye Kim, Yoojeong Jang and Kwang-Hee Shin
Pharmaceutics 2019, 11(11), 618; https://doi.org/10.3390/pharmaceutics11110618 - 17 Nov 2019
Cited by 4 | Viewed by 3782
Abstract
Tramadol is a μ-opioid receptor agonist and a monoamine reuptake inhibitor. O-desmethyltramadol (M1), the major active metabolite of tramadol, is produced by CYP2D6. A physiologically-based pharmacokinetic model was developed to predict changes in time-concentration profiles for tramadol and M1 according to dosage [...] Read more.
Tramadol is a μ-opioid receptor agonist and a monoamine reuptake inhibitor. O-desmethyltramadol (M1), the major active metabolite of tramadol, is produced by CYP2D6. A physiologically-based pharmacokinetic model was developed to predict changes in time-concentration profiles for tramadol and M1 according to dosage and CYP2D6 genotypes in the Korean population. Parallel artificial membrane permeation assay was performed to determine tramadol permeability, and the metabolic clearance of M1 was determined using human liver microsomes. Clinical study data were used to develop the model. Other physicochemical and pharmacokinetic parameters were obtained from the literature. Simulations for plasma concentrations of tramadol and M1 (after 100 mg tramadol was administered five times at 12-h intervals) were based on a total of 1000 virtual healthy Koreans using SimCYP® simulator. Geometric mean ratios (90% confidence intervals) (predicted/observed) for maximum plasma concentration at steady-state (Cmax,ss) and area under the curve at steady-state (AUClast,ss) were 0.79 (0.69–0.91) and 1.04 (0.85–1.28) for tramadol, and 0.63 (0.51–0.79) and 0.67 (0.54–0.84) for M1, respectively. The predicted time–concentration profiles of tramadol fitted well to observed profiles and those of M1 showed under-prediction. The developed model could be applied to predict concentration-dependent toxicities according to CYP2D6 genotypes and also, CYP2D6-related drug interactions. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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14 pages, 1647 KiB  
Article
Assessing Drug Interaction and Pharmacokinetics of Loxoprofen in Mice Treated with CYP3A Modulators
by Sanjita Paudel, Aarajana Shrestha, Piljoung Cho, Riya Shrestha, Younah Kim, Taeho Lee, Ju-Hyun Kim, Tae Cheon Jeong, Eung-Seok Lee and Sangkyu Lee
Pharmaceutics 2019, 11(9), 479; https://doi.org/10.3390/pharmaceutics11090479 - 16 Sep 2019
Cited by 4 | Viewed by 4316
Abstract
Loxoprofen (LOX) is a non-selective cyclooxygenase inhibitor that is widely used for the treatment of pain and inflammation caused by chronic and transitory conditions. Its alcoholic metabolites are formed by carbonyl reductase (CR) and they consist of trans-LOX, which is active, and cis-LOX, [...] Read more.
Loxoprofen (LOX) is a non-selective cyclooxygenase inhibitor that is widely used for the treatment of pain and inflammation caused by chronic and transitory conditions. Its alcoholic metabolites are formed by carbonyl reductase (CR) and they consist of trans-LOX, which is active, and cis-LOX, which is inactive. In addition, LOX can also be converted into an inactive hydroxylated metabolite (OH-LOXs) by cytochrome P450 (CYP). In a previous study, we reported that CYP3A4 is primarily responsible for the formation of OH-LOX in human liver microsomes. Although metabolism by CYP3A4 does not produce active metabolites, it can affect the conversion of LOX into trans-/cis-LOX, since CYP3A4 activity modulates the substrate LOX concentration. Although the pharmacokinetics (PK) and metabolism of LOX have been well defined, its CYP-related interactions have not been fully characterized. Therefore, we investigated the metabolism of LOX after pretreatment with dexamethasone (DEX) and ketoconazole (KTC), which induce and inhibit the activities of CYP3A, respectively. We monitored their effects on the PK parameters of LOX, cis-LOX, and trans-LOX in mice, and demonstrated that their PK parameters significantly changed in the presence of DEX or KTC pretreatment. Specifically, DEX significantly decreased the concentration of the LOX active metabolite formed by CR, which corresponded to an increased concentration of OH-LOX formed by CYP3A4. The opposite result occurred with KTC (a CYP3A inhibitor) pretreatment. Thus, we conclude that concomitant use of LOX with CYP3A modulators may lead to drug–drug interactions and result in minor to severe toxicity even though there is no direct change in the metabolic pathway that forms the LOX active metabolite. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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10 pages, 1021 KiB  
Article
Effect of Ticagrelor, a Cytochrome P450 3A4 Inhibitor, on the Pharmacokinetics of Tadalafil in Rats
by Young-Guk Na, Jin-Ju Byeon, Hyun Wook Huh, Min-Ki Kim, Young G. Shin, Hong-Ki Lee and Cheong-Weon Cho
Pharmaceutics 2019, 11(7), 354; https://doi.org/10.3390/pharmaceutics11070354 - 20 Jul 2019
Cited by 4 | Viewed by 5063
Abstract
Tadalafil is a cytochrome P450 (CYP) 3A4 substrate. Because there are few data on drug-drug interactions, it is advisable to take sufficient consideration when co-administering tadalafil with CYP3A4 inducers or inhibitors. This study was conducted to assess the effect of ticagrelor, a CYP3A4 [...] Read more.
Tadalafil is a cytochrome P450 (CYP) 3A4 substrate. Because there are few data on drug-drug interactions, it is advisable to take sufficient consideration when co-administering tadalafil with CYP3A4 inducers or inhibitors. This study was conducted to assess the effect of ticagrelor, a CYP3A4 inhibitor, on the pharmacokinetic properties of tadalafil after oral administration to rats. A total of 20 Sprague–Dawley male rats were randomly divided into the non-pretreated group and ticagrelor-pretreated group, and tadalafil was orally administered to each group after pretreatment with or without ticagrelor. Blood samples were collected at predetermined time points after oral administration of tadalafil. As a result, systemic exposure of tadalafil in the ticagrelor-pretreated group was significantly increased compared to the non-pretreated group (1.61-fold), and the clearance of tadalafil in the ticagrelor-pretreated group was significantly reduced than the non-pretreated group (37%). The prediction of the drug profile through the one-compartment model could explain the differences of pharmacokinetic properties of tadalafil in the non-pretreated and ticagrelor-pretreated groups. This study suggests that ticagrelor reduces a CYP3A-mediated tadalafil metabolism and that tadalafil and a combination regimen with tadalafil and ticagrelor requires dose control and specific pharmacotherapy. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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Review

Jump to: Research

25 pages, 676 KiB  
Review
Drug–Drug Interactions Involving Intestinal and Hepatic CYP1A Enzymes
by Florian Klomp, Christoph Wenzel, Marek Drozdzik and Stefan Oswald
Pharmaceutics 2020, 12(12), 1201; https://doi.org/10.3390/pharmaceutics12121201 - 11 Dec 2020
Cited by 33 | Viewed by 3714
Abstract
Cytochrome P450 (CYP) 1A enzymes are considerably expressed in the human intestine and liver and involved in the biotransformation of about 10% of marketed drugs. Despite this doubtless clinical relevance, CYP1A1 and CYP1A2 are still somewhat underestimated in terms of unwanted side effects [...] Read more.
Cytochrome P450 (CYP) 1A enzymes are considerably expressed in the human intestine and liver and involved in the biotransformation of about 10% of marketed drugs. Despite this doubtless clinical relevance, CYP1A1 and CYP1A2 are still somewhat underestimated in terms of unwanted side effects and drug–drug interactions of their respective substrates. In contrast to this, many frequently prescribed drugs that are subjected to extensive CYP1A-mediated metabolism show a narrow therapeutic index and serious adverse drug reactions. Consequently, those drugs are vulnerable to any kind of inhibition or induction in the expression and function of CYP1A. However, available in vitro data are not necessarily predictive for the occurrence of clinically relevant drug–drug interactions. Thus, this review aims to provide an up-to-date summary on the expression, regulation, function, and drug–drug interactions of CYP1A enzymes in humans. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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22 pages, 895 KiB  
Review
Pharmacokinetics, Pharmacodynamics and Drug–Drug Interactions of New Anti-Migraine Drugs—Lasmiditan, Gepants, and Calcitonin-Gene-Related Peptide (CGRP) Receptor Monoclonal Antibodies
by Danuta Szkutnik-Fiedler
Pharmaceutics 2020, 12(12), 1180; https://doi.org/10.3390/pharmaceutics12121180 - 03 Dec 2020
Cited by 59 | Viewed by 8431
Abstract
In the last few years, there have been significant advances in migraine management and prevention. Lasmiditan, ubrogepant, rimegepant and monoclonal antibodies (erenumab, fremanezumab, galcanezumab, and eptinezumab) are new drugs that were launched on the US pharmaceutical market; some of them also in Europe. [...] Read more.
In the last few years, there have been significant advances in migraine management and prevention. Lasmiditan, ubrogepant, rimegepant and monoclonal antibodies (erenumab, fremanezumab, galcanezumab, and eptinezumab) are new drugs that were launched on the US pharmaceutical market; some of them also in Europe. This publication reviews the available worldwide references on the safety of these anti-migraine drugs with a focus on the possible drug–drug (DDI) or drug–food interactions. As is known, bioavailability of a drug and, hence, its pharmacological efficacy depend on its pharmacokinetics and pharmacodynamics, which may be altered by drug interactions. This paper discusses the interactions of gepants and lasmiditan with, i.a., serotonergic drugs, CYP3A4 inhibitors, and inducers or breast cancer resistant protein (BCRP) and P-glycoprotein (P-gp) inhibitors. In the case of monoclonal antibodies, the issue of pharmacodynamic interactions related to the modulation of the immune system functions was addressed. It also focuses on the effect of monoclonal antibodies on expression of class Fc gamma receptors (FcγR). Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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18 pages, 695 KiB  
Review
Role of OATP1B1 and OATP1B3 in Drug-Drug Interactions Mediated by Tyrosine Kinase Inhibitors
by Dominique A. Garrison, Zahra Talebi, Eric D. Eisenmann, Alex Sparreboom and Sharyn D. Baker
Pharmaceutics 2020, 12(9), 856; https://doi.org/10.3390/pharmaceutics12090856 - 09 Sep 2020
Cited by 23 | Viewed by 3955
Abstract
Failure to recognize important features of a drug’s pharmacokinetic characteristics is a key cause of inappropriate dose and schedule selection, and can lead to reduced efficacy and increased rate of adverse drug reactions requiring medical intervention. As oral chemotherapeutic agents, tyrosine kinase inhibitors [...] Read more.
Failure to recognize important features of a drug’s pharmacokinetic characteristics is a key cause of inappropriate dose and schedule selection, and can lead to reduced efficacy and increased rate of adverse drug reactions requiring medical intervention. As oral chemotherapeutic agents, tyrosine kinase inhibitors (TKIs) are particularly prone to cause drug-drug interactions as many drugs in this class are known or suspected to potently inhibit the hepatic uptake transporters OATP1B1 and OATP1B3. In this article, we provide a comprehensive overview of the published literature and publicly-available regulatory documents in this rapidly emerging field. Our findings indicate that, while many TKIs can potentially inhibit the function of OATP1B1 and/or OATP1B3 and cause clinically-relevant drug-drug interactions, there are many inconsistencies between regulatory documents and the published literature. Potential explanations for these discrepant observations are provided in order to assist prescribing clinicians in designing safe and effective polypharmacy regimens, and to provide researchers with insights into refining experimental strategies to further predict and define the translational significance of TKI-mediated drug-drug interactions. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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18 pages, 2511 KiB  
Review
Mechanisms of CYP450 Inhibition: Understanding Drug-Drug Interactions Due to Mechanism-Based Inhibition in Clinical Practice
by Malavika Deodhar, Sweilem B Al Rihani, Meghan J. Arwood, Lucy Darakjian, Pamela Dow, Jacques Turgeon and Veronique Michaud
Pharmaceutics 2020, 12(9), 846; https://doi.org/10.3390/pharmaceutics12090846 - 04 Sep 2020
Cited by 79 | Viewed by 8588
Abstract
In an ageing society, polypharmacy has become a major public health and economic issue. Overuse of medications, especially in patients with chronic diseases, carries major health risks. One common consequence of polypharmacy is the increased emergence of adverse drug events, mainly from drug–drug [...] Read more.
In an ageing society, polypharmacy has become a major public health and economic issue. Overuse of medications, especially in patients with chronic diseases, carries major health risks. One common consequence of polypharmacy is the increased emergence of adverse drug events, mainly from drug–drug interactions. The majority of currently available drugs are metabolized by CYP450 enzymes. Interactions due to shared CYP450-mediated metabolic pathways for two or more drugs are frequent, especially through reversible or irreversible CYP450 inhibition. The magnitude of these interactions depends on several factors, including varying affinity and concentration of substrates, time delay between the administration of the drugs, and mechanisms of CYP450 inhibition. Various types of CYP450 inhibition (competitive, non-competitive, mechanism-based) have been observed clinically, and interactions of these types require a distinct clinical management strategy. This review focuses on mechanism-based inhibition, which occurs when a substrate forms a reactive intermediate, creating a stable enzyme–intermediate complex that irreversibly reduces enzyme activity. This type of inhibition can cause interactions with drugs such as omeprazole, paroxetine, macrolide antibiotics, or mirabegron. A good understanding of mechanism-based inhibition and proper clinical management is needed by clinicians when such drugs are prescribed. It is important to recognize mechanism-based inhibition since it cannot be prevented by separating the time of administration of the interacting drugs. Here, we provide a comprehensive overview of the different types of mechanism-based inhibition, along with illustrative examples of how mechanism-based inhibition might affect prescribing and clinical behaviors. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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20 pages, 954 KiB  
Review
Interpretation of Drug Interaction Using Systemic and Local Tissue Exposure Changes
by Young Hee Choi
Pharmaceutics 2020, 12(5), 417; https://doi.org/10.3390/pharmaceutics12050417 - 02 May 2020
Cited by 19 | Viewed by 4256
Abstract
Systemic exposure of a drug is generally associated with its pharmacodynamic (PD) effect (e.g., efficacy and toxicity). In this regard, the change in area under the plasma concentration-time curve (AUC) of a drug, representing its systemic exposure, has been mainly considered in evaluation [...] Read more.
Systemic exposure of a drug is generally associated with its pharmacodynamic (PD) effect (e.g., efficacy and toxicity). In this regard, the change in area under the plasma concentration-time curve (AUC) of a drug, representing its systemic exposure, has been mainly considered in evaluation of drug-drug interactions (DDIs). Besides the systemic exposure, the drug concentration in the tissues has emerged as a factor to alter the PD effects. In this review, the status of systemic exposure, and/or tissue exposure changes in DDIs, were discussed based on the recent reports dealing with transporters and/or metabolic enzymes mediating DDIs. Particularly, the tissue concentration in the intestine, liver and kidney were referred to as important factors of PK-based DDIs. Full article
(This article belongs to the Special Issue Drug–Drug Interactions)
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