Skip to main content
Advertisement

Main menu

  • Home
  • Articles
    • Current Issue
    • Fast Forward
    • Latest Articles
    • Special Sections
    • Archive
  • Information
    • Instructions to Authors
    • Submit a Manuscript
    • FAQs
    • For Subscribers
    • Terms & Conditions of Use
    • Permissions
  • Editorial Board
  • Alerts
    • Alerts
    • RSS Feeds
  • Virtual Issues
  • Feedback
  • Submit
  • Other Publications
    • Drug Metabolism and Disposition
    • Journal of Pharmacology and Experimental Therapeutics
    • Molecular Pharmacology
    • Pharmacological Reviews
    • Pharmacology Research & Perspectives
    • ASPET

User menu

  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
Drug Metabolism & Disposition
  • Other Publications
    • Drug Metabolism and Disposition
    • Journal of Pharmacology and Experimental Therapeutics
    • Molecular Pharmacology
    • Pharmacological Reviews
    • Pharmacology Research & Perspectives
    • ASPET
  • My alerts
  • Log in
  • My Cart
Drug Metabolism & Disposition

Advanced Search

  • Home
  • Articles
    • Current Issue
    • Fast Forward
    • Latest Articles
    • Special Sections
    • Archive
  • Information
    • Instructions to Authors
    • Submit a Manuscript
    • FAQs
    • For Subscribers
    • Terms & Conditions of Use
    • Permissions
  • Editorial Board
  • Alerts
    • Alerts
    • RSS Feeds
  • Virtual Issues
  • Feedback
  • Submit
  • Visit dmd on Facebook
  • Follow dmd on Twitter
  • Follow ASPET on LinkedIn
Review ArticleMinireview

Pharmacokinetic Drug-Drug Interactions with Drugs Approved by the US Food and Drug Administration in 2020: Mechanistic Understanding and Clinical Recommendations

Jingjing Yu, Yan Wang and Isabelle Ragueneau-Majlessi
Drug Metabolism and Disposition January 2022, 50 (1) 1-7; DOI: https://doi.org/10.1124/dmd.121.000401
Jingjing Yu
UW Drug Interaction Solutions, Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yan Wang
UW Drug Interaction Solutions, Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Isabelle Ragueneau-Majlessi
UW Drug Interaction Solutions, Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF + SI
  • PDF
Loading

This article has corrections. Please see:

  • Correction to “Identification of 5-Hydroxycotinine in the Plasma of Nicotine-Treated Mice: Implications for Cotinine Metabolism and Disposition in Vivo” - January 01, 2023
  • Correction to “Dimethandrolone, a Potential Male Contraceptive Pill, is Primarily Metabolized by the Highly Polymorphic UDP-Glucuronosyltransferase 2B17 Enzyme in Human Intestine and Liver” - January 01, 2023

Abstract

Drug-drug interaction (DDI) data for small molecular drugs approved by the US Food and Drug Administration in 2020 (N = 40) were analyzed using the University of Washington Drug Interaction Database. The mechanism(s) and clinical relevance of these interactions were characterized based on information available in the new drug application reviews. About 180 positive clinical studies defined as mean area under the curve ratios (AUCRs) ≥1.25 for inhibition DDIs or pharmacogenetic studies and ≤0.8 for induction DDIs were then fully analyzed. Oncology was the most represented therapeutic area, including 30% of 2020 approvals. As victim drugs, inhibition and induction of CYP3A explained most of all observed clinical interactions. Three sensitive substrates were identified: avapritinib (CYP3A), lonafarnib (CYP3A), and relugolix (P-glycoprotein), with AUCRs of 7.00, 5.07, and 6.25 when coadministered with itraconazole, ketoconazole, and erythromycin, respectively. As precipitants, three drugs were considered strong inhibitors of enzymes (AUCR ≥ 5): cedazuridine for cytidine deaminase and lonafarnib and tucatinib for CYP3A. No drug showed strong inhibition of transporters. No strong inducer of enzymes or transporters was identified. As expected, all DDIs with AUCRs ≥5 or ≤0.2 and almost all those with AUCRs of 2–5 and 0.2–0.5 triggered dosing recommendations in the drug label. Overall, all 2020 drugs found to be either sensitive substrates or strong inhibitors of enzymes or transporters were oncology treatments, underscoring the need for effective DDI management strategies in patients with cancer often receiving polytherapy.

SIGNIFICANCE STATEMENT This minireview provides a thorough and specific overview of the most significant pharmacokinetic-based DDI data observed (or expected) with small molecular drugs approved by the US Food and Drug Administration in 2020. It will help to better understand mitigation strategies to manage the DDI risks in the clinic.

Introduction

Understanding the various processes involved in pharmacokinetic drug-drug interactions (DDIs) is critical to facilitate the optimal management of these DDIs in the clinic. For over 2 decades, the US Food and Drug Administration (FDA) has released several guidance documents on drug interactions (with the most recent version published last year); 2020 DDI guidance (FDA, 2020r,s) provided a strong framework for the evaluation of DDIs during drug development and identifying essential information to be communicated in labeling to enable the safe and effective use of new marketed products. A systematic, risk-based, integrated approach, including in vitro, in silico, and clinical evaluations, has been recommended by regulators to evaluate enzyme- and transporter-mediated drug interactions. In general, a new drug should be evaluated in vitro, and further clinical evaluations (or in silico predictions, when appropriate) with clinical index inhibitors, inducers, substrates, or likely concomitant medications in the indicated patient populations may be warranted (FDA, 2020r,s). This systematic, mechanistic, and quantitative approach recommended by the FDA is best expressed in new drug application (NDA) approval packages, and the analysis of relevant data in these documents offers a unique and detailed understanding of the risk of pharmacokinetic DDIs in the clinical context of the various represented therapeutic classes. The aim of the present review was to summarize the most significant clinical DDIs associated with the 2020 NDAs, briefly discuss their most likely mechanism(s), and highlight how to best manage the risk of DDI in the targeted patient populations using labeling recommendations.

Materials and Methods

Pharmacokinetic DDI data for small molecular drugs approved by the FDA in 2020 were analyzed using the University of Washington Drug Interaction Database (http://www.druginteractioninfo.org). This analysis was performed following the methodology previously described (Yu et al., 2019). The mechanism(s) and clinical relevance of these interactions were characterized based on information available in the NDA reviews. Clinical DDI study results were obtained from dedicated clinical trials, pharmacogenetic (PGx) studies, and physiologically based pharmacokinetics (PBPK) modeling and population pharmacokinetic analysis that were used as alternatives to dedicated clinical studies. Using available mean area under the time-plasma concentration curve ratios (AUCRs), all clinical studies with AUCRs ≥1.25 and ≤0.8 (i.e., positive DDI results) were analyzed. Applying the categorization recommended by the FDA, any drug interactions with area under the time-plasma concentration curve (AUC) changes ≥5-fold (i.e., AUCRs ≥5 or ≤0.2), 2- to 5-fold (2 ≤ AUCR < 5 or 0.2 < AUCR ≤ 0.5), or 1.25- to 2-fold (1.25 ≤ AUCR < 2 or 0.5 < AUCR ≤ 0.8) were considered strong, moderate, or weak drug interactions, respectively.

Results

A total of 40 new molecular entities (NMEs) were approved by the FDA in 2020, and their chemical structures are presented as Supplemental Data (Supplemental Table 1). Similar to what was observed with drugs approved from 2013 to 2017 (Yu et al., 2018, 2019), antineoplastic agents were found to be the most represented therapeutic area, comprising 30% of all approved drugs (Fig. 1). Among the 12 oncology drugs, 8 were kinase inhibitors, highlighting the continuous major role of this therapeutic class in cancer therapy. Interestingly, four new drugs were indicated for the treatment of non–small cell lung cancer, namely capmatinib, lurbinectedin, pralsetinib, and selpercatinib, bringing new therapeutic options for the treatment of a disease with often still poor outcome. Anti-infective agents (N = 4, including one antimalarial, two antiparasitics, and two antivirals) were the second represented class; however, their overall proportion was much smaller (12%) compared with previous years (20% and 23% for NDAs approved in 2013–2016 and 2017, respectively). Central nervous system agents comprised 12% of the approved drugs, which is followed by diagnostic agents (10%) and metabolism/gastrointestinal agents (10%). About one-third of the drugs (N = 14; 35%) were considered first-in-class, a strong indicator of the continuous innovation of the pharmaceutical industry, and more than half (N = 22; 55%) of all drugs were approved under the orphan disease approval process.

Fig. 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 1.

Therapeutic classes of drugs (small molecules) approved in 2020. Other refers to a new class of drug, indicated to reduce the risk of death due to rare genetic diseases that cause premature aging.

Metabolism- and Transport-Based DDIs

Except for two diagnostic agents and an osmotic laxative used to treat chronic idiopathic constipation, all NDAs (N = 37) included in vitro and clinical drug metabolism and transport interaction data. Among them, 25 NDAs had clinical drug interaction data available, 4 presented PGx information, 6 had PBPK simulation data, and 3 had population pharmacokinetic analysis. Following the regulatory recommended approach (FDA, 2020r), drug interaction studies using index or clinical substrates, inhibitors, and inducers were systemically performed when in vitro evaluations suggested a possible risk of clinical interactions. There were approximately 180 DDI studies with AUCRs meeting the criteria for positive interaction (AUCRs ≥1.25 or ≤0.8): 82 inhibition DDIs (plus 5 PGx studies) and 31 induction interaction studies in which NMEs were the substrates (or victim drugs) and 55 inhibition interaction studies and 7 induction DDIs in which NMEs were the precipitants (or perpetrators). Almost all clinical DDIs with an AUC change of at least 2-fold triggered label recommendations due to possible safety issues or lack of efficacy, and these interactions are discussed in detail in the following sections. For drug interactions with AUC changes less than 2-fold, most were not considered clinically relevant, and only those leading to specific clinical recommendations are briefly reviewed below.

Other Mechanism of DDIs

In addition to metabolism- and transporter-based DDIs, 12 new drugs were evaluated for gastric pH-dependent DDIs, with 9 using dedicated clinical trials and 3 evaluated with population pharmacokinetic analysis. Ten of them were class II or IV drugs according to the Biopharmaceutics Classification System, with pH-dependent solubility. Ten drugs were indicated for cancer treatment, including seven kinase inhibitors. Proton pump inhibitors (e.g., esomeprazole, omeprazole, pantoprazole, rabeprazole) and histamine receptor-2 antagonists (e.g., famotidine, ranitidine) were the acid-reducing agents used as perpetrators in the clinical studies. The greatest change in exposure was observed for selpercatinib, a kinase inhibitor indicated for the treatment of non–small cell lung cancer and thyroid cancer. Under fasted conditions, omeprazole coadministration decreased selpercatinib AUC and Cmax by 69% and 88%, respectively, and it is therefore recommended to avoid concomitant use of a proton pump inhibitor, a histamine receptor-2 receptor antagonist, or a locally acting antacid with selpercatinib. If concomitant use cannot be avoided, the risk of DDI can be mitigated by taking food or staggering dosing with acid-reducing agents (FDA, 2020a).

NMEs as Substrates

DDIs with AUC Changes ≥2-Fold.

There were approximately 60 moderate-to-strong drug interaction studies involving 15 NMEs as substrates, with more than half (N = 8) being oncology drugs. Inhibition and induction of CYP3A explained most of these interactions (about 90%). All drug interactions with AUC change ≥5-fold are presented in Table 1, with the maximum AUCR observed listed.

View this table:
  • View inline
  • View popup
TABLE 1

Drug interactions with AUC changes ≥5 in the victim drugs

Maximum AUCR is presented for the same mechanism; for each section, DDIs are arranged based on the AUCR; all studies were conducted in healthy subjects unless otherwise specified (this information was not available for elugolix in the NDA review or the drug label).

Based on the results of mechanistic studies with clinical index inhibitors, three drugs were identified as sensitive substrates, namely avapritinib and lonafarnib for CYP3A and relugolix for P-glycoprotein (P-gp). When concurrently administered with the strong CYP3A inhibitor itraconazole (200 mg once daily for 14 days) in healthy subjects, avapritinib, a kinase inhibitor indicated for the treatment of gastrointestinal stromal tumor (GIST), exhibited a 4.32-fold increase in AUC (after 200-mg single dose). A higher change of 7.00-fold in healthy subjects and 7.90-fold in patients with GIST in avapritinib steady-state AUC (after the clinical dose of 300 mg once daily) was predicted using PBPK modeling and simulations. The effect of moderate and weak CYP3A inhibitors was also predicted using the same approach. Erythromycin (500 mg three times daily), fluconazole (200 mg once daily), and verapamil (80 mg three times daily), all moderate CYP3A inhibitors, were predicted to increase avapritinib steady-state AUC 2- to 3-fold in patients with GIST, whereas the weak CYP3A inhibitor cimetidine (400 mg three times daily) was not predicted to affect avapritinib exposure (FDA, 2020b). Because higher plasma concentrations of avapritinib may increase the incidence and severity of adverse reactions, concomitant administration with strong and moderate CYP3A inhibitors should be avoided. If coadministration with a moderate CYP3A inhibitor cannot be avoided, the dose of avapritinib should be reduced (FDA, 2020b). Lonafarnib is an orphan drug used to reduce the risk of death due to Hutchinson-Gilford progeria syndrome and for the treatment of certain processing-deficient progeroid laminopathies in patients 12 months of age and older. Coadministration with ketoconazole (200 mg once daily for 5 days), a strong CYP3A inhibitor, resulted in a 5.07-fold increase in lonafarnib AUC in healthy subjects (FDA, 2020p). CYP3A inhibitors with lower potency were not evaluated but were expected to increase lonafarnib exposure to a clinically meaningful extent. Therefore, because of safety concerns, the use of lonafarnib with strong or moderate CYP3A inhibitors is contraindicated. Concomitant use of lonafarnib with weak CYP3A inhibitors should be avoided as well; however, if coadministration is unavoidable, the dose of lonafarnib should be reduced. Additionally, patients should be monitored for adverse reactions, such as arrhythmias and other cardiovascular events like syncope and heart palpitations, as the effect of lonafarnib on QT interval is unknown (FDA, 2020p). Finally, exposure to the oncology drug relugolix was found to increase when coadministered with erythromycin (6.25-fold increase in AUC and 6.18-fold increase in Cmax), primarily due to inhibition of intestinal P-gp. In vitro, relugolix was a P-gp substrate, with an efflux ratio of 16.4 in Caco-2 cells. Although contribution of CYP3A cannot be fully ruled out because relugolix was metabolized by CYP3A in vitro and erythromycin is also a moderate CYP3A inhibitor, CYP3A is expected to play a minimal role given that a much smaller increase (1.21- to 1.51-fold) in the exposure of relugolix was observed in the presence of moderate and strong CYP3A inhibitors (FDA, 2020i).

Compared with the inhibition results, more drugs (N = 6) were sensitive to induction. In addition to avapritinib and lonafarnib, fostemsavir (prodrug, active moiety temsavir), rimegepant, pemigatinib, and selpercatinib were all sensitive to CYP3A induction, with AUC decreases of 81%–87% after concomitant administration of multiple doses of rifampin, an index inducer of CYP3A. Notably, induction of P-gp may also be involved in these interactions, as all were P-gp substrates in vitro except avapritinib (FDA, 2020a,b,g,j,k,p). To avoid reduced efficacy associated with the significant decrease in drug exposure, concomitant use with strong CYP3A inducers is either contraindicated or should be avoided for these drugs (FDA, 2020a,b,g,j,k,p).

Regarding moderate inhibition, 10 drugs were found to be moderate sensitive substrates (AUCRs 2–5) based on inhibition or PGx results: fostemsavir (CYP3A), oliceridine (CYP2D6), ozanitib [breast cancer resistance protein (BCRP)], pemigatinib (CYP3A), pralsetinib (CYP3A), rimegepant (CYP3A), selpercatinib (CYP3A), tazemetostat (CYP3A), tucatinib (CYP2C8), and vibegron (P-gp). Oliceridine, an opioid agonist indicated for the management of severe acute pain, was primarily metabolized by CYP2D6 and CYP3A4 in vitro. The moderate sensitivity to CYP2D6 inhibition was identified through PGx studies, wherein CYP2D6 poor metabolizers showed approximately 2-fold higher exposure to oliceridine compared with normal metabolizers. The effect of concomitant administration of a CYP2D6 inhibitor on oliceridine pharmacokinetics has not been evaluated but is expected to be similar to what was observed in CYP2D6 poor metabolizers based on population pharmacokinetic analyses. An approximate 4-fold increase in oliceridine AUC and 70% reduction in clearance were expected in CYP2D6 normal metabolizers in the worst-case scenario of concomitant inhibition of both CYP3A4 and CYP2D6 enzymes, whereas coadministration with itraconazole, a strong CYP3A inhibitor, was estimated to reduce oliceridine clearance by 45% (FDA, 2020h). On the other hand, based on induction studies, three kinase inhibitors (capmatinib, pralsetinib, and selumetinib) were found to be moderate sensitive substrates of CYP3A, with coadministration of multiple doses of rifampin significantly reducing their exposure by 51%–68% (FDA, 2020c,f,l). Most of DDIs mentioned above led to specific label recommendations when concomitantly administered with known inhibitors or inducers. For instance, it is recommended to avoid concomitant use of selpercatinib with strong CYP3A inhibitors; if coadministration is unavoidable, the dose of selpercatinib should be reduced by half. In addition, because selpercatinib can cause QT prolongation, it is suggested to monitor the QT interval with ECGs more frequently (FDA, 2020a,o). For ozanimod, a drug indicated for the treatment of multiple sclerosis, coadministration with inhibitors of BCRP is not recommended (FDA, 2020o).

DDIs with AUC Changes <2-Fold but with Clinical Implications.

There were about 50 studies with AUCR 1.25–2 or 0.5–0.8 involving NMEs as substrates, but less than half led to clinical recommendations. Inhibition or induction of CYP3A explained more than 60% of these results, and the majority of studies were evaluations of sensitive substrates with less potent inhibitors or inducers. Additionally, the following seven drugs were found to be weak substrates based on studies with strong inhibitors (itraconazole for CYP3A, gemfibrozil for CYP2C8, and cyclosporine for P-gp and BCRP): berotralstat (P-gp, BCRP), capmatinib (CYP3A), oliceridine (CYP3A), ozanimod (CYP2C8), relugolix (CYP3A), rimegepant (CYP2C9; PGx study), and ripretinib (CYP3A). Given the smaller change in the drugs’ exposure, label recommendations are mostly to monitor for adverse events and reduce dose or dosing frequency as needed. However, a few drugs have stricter recommendations because of a narrower therapeutic range. For instance, it is recommended to avoid coadministration of strong or moderate CYP3A4 inhibitors or fluconazole with selumetinib, a kinase inhibitor to treat neurofibromatosis type 1. If coadministration cannot be avoided, the dose of selumetinib should be reduced (FDA, 2020f). Similarly, for ozanimod, “Coadministration of ZEPOSIA with strong CYP2C8 inhibitors (e.g., gemfibrozil) is not recommended” (FDA, 2020o).

NMEs as Precipitants

DDIs with AUC Changes ≥2-Fold.

There were only 10 moderate-to-strong drug interactions involving NMEs as precipitants, and all were related to inhibition, with no strong or moderate inducer of enzymes or transporters identified. Inhibition of CYP3A explained about half of the interactions. A total of seven drugs were involved, with more than half (N = 4) indicated for cancer treatment. All interaction results led to label recommendations to mitigate the risk of DDI in clinical settings.

Three drugs were considered strong inhibitors of enzymes (victim drug AUCR ≥5): cedazuridine for cytidine deaminase, lonafarnib for CYP3A, and tucatinib for CYP3A (Table 1). No drug exhibited strong inhibition of transporters. Notably, cedazuridine, indicated in combination with decitabine for the treatment of myelodysplastic syndromes, is used to prevent the rapid metabolism of decitabine in the gastrointestinal tract to allow for oral administration and therefore increase systemic exposure of decitabine. Following different dosing regimen, cedazuridine was found to increase the AUC of decitabine up to 12-fold (FDA, 2020d). Because cedazuridine is a strong inhibitor of cytidine deaminase, coadministration of the combination drug with drugs metabolized by cytidine deaminase may result in increased exposure with potential for increased toxicity of these drugs. Therefore, coadministration of cedazuridine and decitabine with drugs that are metabolized by cytidine deaminase should be avoided (FDA, 2020d). Regarding lonafarnib and tucatinib, both caused a significant increase in the AUC of midazolam, an index substrate of CYP3A (7.39- and 5.74-fold, respectively). The clinical trials were performed because in vitro results suggested that both drugs have the potential to inhibit CYP3A at clinically relevant concentrations. In vitro, both were mechanism-based inhibitors of CYP3A (KI = 0.54 µM and kinact = 0.011/min for tucatinib; values not provided for lonafarnib in the NDA review), and tucatinib was found to also reversibly inhibit CYP3A with a Ki value of 0.805 µM (FDA, 2020n,p).

Additionally, the following four drugs were found to be moderate inhibitors: berotralstat (CYP2D6 and CYP3A), capmatinib (CYP1A2 and BCRP), osilodrostat (CYP1A2 and CYP2C19), and selpercatinib (CYP2C8), with increases in the index substrates of 2- to 3-fold. Label recommendations are provided regarding concomitant medications that are substrates of these cytochrome P450 isoforms or BCRP transporter with a narrow therapeutic index (NTI). For example, both osilodrostat (indicated for the treatment of Cushing’s disease) and capmatinib (a kinase inhibitor to treat metastatic non–small cell lung cancer) increased the AUC of the CYP1A2 index substrate caffeine to a similar extent (AUCRs of 2.50 and 2.43, respectively), with no change in its Cmax. Both labels give recommendations regarding concomitant administration of CYP1A2 substrates with an NTI. For osilodrostat, it is recommended to use with caution or decrease dose of the substrate drug when coadministered with CYP1A2 substrates with an NTI (FDA, 2020e). If concomitant use is unavoidable between capmatinib and CYP1A2 substrates, in which minimal concentration changes may lead to serious adverse reactions, dose of the CYP1A2 substrates should be decreased in accordance with its approved prescription information (FDA, 2020l).

DDIs with AUC Changes <2-fold but with Clinical Implications.

There were 56 studies showing weak inhibition or induction. Similarly to the substrate studies, only 40% of these interactions were considered clinically relevant. Eight drugs weakly inhibited one or two specific cytochrome P450 enzymes, leading to label recommendations for only two of them, namely lonafarnib (CYP2C19) and selpercatinib (CYP3A). Regarding induction studies, three drugs exhibited weak induction, but only one drug, tazemetostat, had label recommendations based on the results. Indeed, tazemetostat, an orphan drug for epithelioid sarcoma, was found to decrease the AUC of midazolam by 37% when coadministered in patients with cancer at the dose of 800 mg twice daily, due to induction of CYP3A. It is noted in the label that “coadministration of CYP3A substrates, including hormonal contraceptives, with tazemetostat can result in decreased concentrations and reduced efficacy of CYP3A substrates” (FDA, 2020m).

About a third of these weak interactions were mediated by drug transporters, involving P-gp, BCRP, organic anion–transporting polypeptide (OATP) 1B1/1B3, organic cation transporter (OCT) 2, and multiantimicrobial extrusion protein (MATE) 1/2-K. Regarding inhibition of renal transporters, in vitro capmatinib inhibited MATE1 (Ki = 0.28 µM) and MATE2-K (Ki = 0.29 µM), pemigatinib inhibited MATE1 (IC50 = 0.075 µM) and OCT2 (IC50 = 1.1 µM), selpercatinib inhibited MATE1 (IC50 = 0.666 µM), and tucatinib inhibited MATE1 (IC50 = 0.0855 µM) and MATE2-K (IC50 = 0.135 µM) as well as OCT2 (IC50 = 0.107 µM). Because of the potential inhibition of MATEs and OCT2 in vivo by these four oncology drugs, endogenous creatinine was measured as the marker of renal function as well as activity of MATEs and OCT2. For capmatinib, all subjects showed transient grade 1 serum creatinine increase postdose during a 72-hour sampling period. According to the NDA review, these results indirectly suggest that reversible inhibition of renal transporters (i.e., MATE1 and MATE2-K) may explain the increase in serum creatinine. Based on this observation, the label states that “coadministration of capmatinib may increase the exposure of MATE1 and MATE2-K substrates, which may increase the adverse reactions of these substrates. If coadministration is unavoidable between capmatinib and MATE1 or MATE2-K substrates where minimal concentration changes may lead to serious adverse reactions, dose of MATE1 or MATE2K substrate should be decreased in accordance with the approved prescribing information” (FDA, 2020l). For pemigatinib, endogenous serum creatinine was found to increase an average of 0.2 mg/dl within the first 21-day cycle of treatment, whereas for selpercatinib and tucatinib, a mean increase of 1.18- to 1.32-fold in endogenous serum creatinine was observed in clinical studies, and their labels suggest that “alternative markers of renal function should be considered if persistent elevations in serum creatinine are observed” (FDA, 2020a,j,n).

Discussion

The systematic mechanistic framework of pharmacokinetic drug interactions evaluation during drug development continues to provide essential information for the management of DDIs in the clinic since the use of index substrates, inhibitors, and inducers of enzymes and transporters enables extrapolations with multiple comedications and guides the safe and effective use of new drug products. In the present analysis, pharmacokinetic-based DDI data from NDA reviews for drugs approved by the FDA in 2020 were thoroughly reviewed, and the clinical significance of the interactions was assessed based on label recommendations. As expected from previous similar evaluations (Yu et al., 2014, 2018, 2019), CYP3A mediated the majority of interactions, with NMEs either substrates or precipitants. Indeed, of the 10 largest interactions with AUC changes of the victim drug equal to or greater than 5-fold, eight were mediated by CYP3A (Table 1). Most of the clinical evaluations were performed using index drugs recommended by the FDA DDI guidance. Endogenous biomarkers were also used with seven drugs: 4-β-hydroxycholesterol for tazemetostat and creatinine for bempedoic acid, capmatinib, pemigatinib, rimegepant, selpercatinib, and tucatinib, highlighting the growing interest of using these biomarkers for the clinical evaluation of DDIs. The CYP3A4 endogenous marker 4-β-hydroxycholesterol was measured in patients with cancer after tazemetostat treatment. There was a 1.70-fold increase in the exposure of this marker, consistent with the midazolam study results (a 37% decrease in AUC), further confirming induction of CYP3A by tazemetostat. Endogenous creatinine was measured to assess the activity of the renal transporters MATE1/2-K and OCT2, and the observed changes in serum creatinine levels were used to guide label recommendations for four drugs (FDA, 2020a,j,l,n). This seems to be a relatively new approach, as this was not observed in previous years, in which drugs with potential to inhibit renal transporters were evaluated with substrates like metformin or cephalexin (Yu and Ragueneau-Majlessi, 2020). Similar to drugs approved between 2013 and 2019 though (Yu et al., 2014, 2018; Yu and Ragueneau-Majlessi, 2020), no drugs approved in 2020 exhibited greater than 2-fold change in exposure of the substrate drug transported by MATEs and OCT2, suggesting overall a relatively limited risk of overexposure when inhibiting these renal transporters. Endogenous biomarkers, such as coproporphyrins, have been proposed for assessing OATP1B1/1B3-mediated DDIs (Lai et al., 2016; Chu et al., 2017). However, none of these biomarkers were measured with the 2020 drugs. For example, bempedoic acid and fostemasavir inhibited OATP1B1/1B3 in vitro, and clinical studies with different statins were conducted to investigate the clinical relevance of this inhibition (FDA, 2020k,q).

Regarding labeling recommendations based on DDI study results, almost all drug interactions with AUC changes of at least 2-fold led to specific label recommendations, with all larger DDIs (AUC change ≥5-fold) leading to either contraindication or avoidance of concomitant administration. For DDIs with AUC changes less than 2-fold, labeling recommendations were primarily related to NTI drugs. Finally, it is worth noting the substantial contribution of oncology drugs to the largest clinical interactions involving NMEs identified as sensitive substrates and strong inhibitors. This highlights the significant risk of pharmacokinetic DDIs and the need for clear and assertive mitigation strategies in patients with cancer for whom therapeutic management is complex due to polytherapy and the coadministration of interacting drugs is often difficult to avoid.

Acknowledgments

The authors would like to thank Dr. Sophie Argon, Dr. Ichiko Petrie, Dr. Katie H. Owens, Dr. Jessica Sontheimer, and Dr. Cheryl Wu for their contributions to the NDA data curation.

Authorship Contributions

Participated in research design: Yu, Ragueneau-Majlessi.

Performed data analysis: Yu, Wang, Ragueneau-Majlessi.

Wrote or contributed to the writing of the manuscript: Yu, Wang, Ragueneau-Majlessi.

Footnotes

    • Received January 29, 2021.
    • Accepted October 5, 2021.
  • This work received no external funding.

  • The authors declare they have no conflicts of interest with the contents of this article.

  • https://dx.doi.org/10.1124/dmd.121.000401.

  • ↵Embedded ImageThis article has supplemental material available at dmd.aspetjournals.org.

Abbreviations

AUC
area under the time-plasma concentration curve
AUCR
area under the curve ratio
BCRP
breast cancer resistance protein
DDI
drug-drug interaction
FDA
Food and Drug Administration
GIST
gastrointestinal stromal tumor
MATE
multiantimicrobial extrusion protein
NDA
new drug application
NME
new molecular entity
NTI
narrow therapeutic index
OATP
organic anion–transporting polypeptide
OCT
organic cation transporter
PBPK
physiologically-based pharmacokinetics
P-gp
P-glycoprotein
PGx
pharmacogenetic(s)
  • Copyright © 2021 by The American Society for Pharmacology and Experimental Therapeutics

References

  1. ↵
    1. Chu X,
    2. Chan GH, and
    3. Evers R
    (2017) Identification of endogenous biomarkers to predict the propensity of drug candidates to cause hepatic or renal transporter-mediated drug-drug interactions. J Pharm Sci 106:2357–2367.
    OpenUrl
  2. ↵
    Food and Drug Administration (FDA) (2020a) Drug Approval Package: RETEVMO (selpercatinib). FDA Application NDA 213246, FDA, Silver Spring, MD.
  3. ↵
    Food and Drug Administration (FDA) (2020b) Drug Approval Package: AYVAKIT (avapritinib). FDA Application NDA 212608, FDA, Silver Spring, MD.
  4. ↵
    Food and Drug Administration (FDA) (2020c) Drug Approval Package: GAVRETO (pralsetinib). FDA Application NDA 213721, FDA, Silver Spring, MD.
  5. ↵
    Food and Drug Administration (FDA) (2020d) Drug Approval Package: INQOVI (cedazuridine; decitabine). FDA Application NDA 212576, FDA, Silver Spring, MD.
  6. ↵
    Food and Drug Administration (FDA) (2020e) Drug Approval Package: ISTURISA (osilodrostat). FDA Application NDA 212801, FDA, Silver Spring, MD.
  7. ↵
    Food and Drug Administration (FDA) (2020f) Drug Approval Package: KOSELUGO (selumetinib). FDA Application NDA 213756, FDA, Silver Spring, MD.
  8. Food and Drug Administration (FDA) (2020g) Drug Approval Package: NURTEC ODT (rimegepant). FDA Application NDA 212728, FDA, Silver Spring, MD.
  9. ↵
    Food and Drug Administration (FDA) (2020h) Drug Approval Package: OLINVYK (oliceridine). FDA Application NDA 210730, FDA, Silver Spring, MD.
  10. ↵
    Food and Drug Administration (FDA) (2020i) Drug Approval Package: ORGOVYX (relugolix). FDA Application NDA 214621, FDA, Silver Spring, MD.
  11. Food and Drug Administration (FDA) (2020j) Drug Approval Package: PEMAZYRE (pemigatinib). FDA Application NDA 213736, FDA, Silver Spring, MD.
  12. ↵
    Food and Drug Administration (FDA) (2020k) Drug Approval Package: RUKOBIA (fostemsavir). FDA Application NDA 212950, FDA, Silver Spring, MD.
  13. ↵
    Food and Drug Administration (FDA) (2020l) Drug Approval Package: TABRECTA (capmatinib). FDA Application NDA 213591, FDA, Silver Spring, MD.
  14. ↵
    Food and Drug Administration (FDA) (2020m) Drug Approval Package: TAZVERIK (tazemetostat). FDA Application NDA 211723, FDA, Silver Spring, MD.
  15. ↵
    Food and Drug Administration (FDA) (2020n) Drug Approval Package: TUKYSA (tucatinib). FDA Application NDA 213411, FDA, Silver Spring, MD.
  16. ↵
    Food and Drug Administration (FDA) (2020o) Drug Approval Package: ZEPOSIA (ozanimod). FDA Application NDA 209899, FDA, Silver Spring, MD.
  17. ↵
    Food and Drug Administration (FDA) (2020p) Drug Approval Package: ZOKINVY (lonafarnib). FDA Application NDA 213969, FDA, Silver Spring, MD.
  18. Food and Drug Administration (FDA) (2020q) Drug Approval Package: NEXLETOL (bempedoic acid). FDA Application NDA 211616, FDA, Silver Spring, MD.
  19. ↵
    Food and Drug Administration (FDA)(2020r). Guidance for Industry: Clinical Drug Interaction Studies — Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions, Center for Drug Evaluation and Research, FDA, Silver Spring, MD.
  20. Food and Drug Administration (FDA)(2020s). Guidance for Industry: In Vitro Drug Interaction Studies — Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions, Center for Drug Evaluation and Research, FDA, Silver Spring, MD
  21. ↵
    1. Lai Y,
    2. Mandlekar S,
    3. Shen H,
    4. Holenarsipur VK,
    5. Langish R,
    6. Rajanna P,
    7. Murugesan S,
    8. Gaud N,
    9. Selvam S,
    10. Date O, et al.
    (2016) Coproporphyrins in plasma and urine can be appropriate clinical biomarkers to recapitulate drug-drug interactions mediated by organic anion transporting polypeptide inhibition. J Pharmacol Exp Ther 358:397–404.
    OpenUrlAbstract/FREE Full Text
  22. ↵
    1. Yu J,
    2. Petrie ID,
    3. Levy RH, and
    4. Ragueneau-Majlessi I
    (2019) Mechanisms and clinical significance of pharmacokinetic-based drug-drug interactions with drugs approved by the U.S. Food and Drug Administration in 2017. Drug Metab Dispos 47:135–144.
    OpenUrlAbstract/FREE Full Text
  23. ↵
    1. Yu J and
    2. Ragueneau-Majlessi I
    (2020) In vitro-to-in vivo extrapolation of transporter inhibition data for drugs approved by the US Food and Drug Administration in 2018. Clin Transl Sci 13:693–699.
    OpenUrl
  24. ↵
    1. Yu J,
    2. Ritchie TK,
    3. Mulgaonkar A, and
    4. Ragueneau-Majlessi I
    (2014) Drug disposition and drug-drug interaction data in 2013 FDA new drug applications: a systematic review. Drug Metab Dispos 42:1991–2001.
    OpenUrlAbstract/FREE Full Text
  25. ↵
    1. Yu J,
    2. Zhou Z,
    3. Tay-Sontheimer J,
    4. Levy RH, and
    5. Ragueneau-Majlessi I
    (2018) Risk of clinically relevant pharmacokinetic-based drug-drug interactions with drugs approved by the U.S. Food and Drug Administration between 2013 and 2016. Drug Metab Dispos 46:835–845.
    OpenUrlAbstract/FREE Full Text
PreviousNext
Back to top

In this issue

Drug Metabolism and Disposition: 50 (1)
Drug Metabolism and Disposition
Vol. 50, Issue 1
1 Jan 2022
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by author
  • Editorial Board (PDF)
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Drug Metabolism & Disposition article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Pharmacokinetic Drug-Drug Interactions with Drugs Approved by the US Food and Drug Administration in 2020: Mechanistic Understanding and Clinical Recommendations
(Your Name) has forwarded a page to you from Drug Metabolism & Disposition
(Your Name) thought you would be interested in this article in Drug Metabolism & Disposition.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Review ArticleMinireview

A Review of Clinical DDIs in 2020 NDAs

Jingjing Yu, Yan Wang and Isabelle Ragueneau-Majlessi
Drug Metabolism and Disposition January 1, 2022, 50 (1) 1-7; DOI: https://doi.org/10.1124/dmd.121.000401

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero

Share
Review ArticleMinireview

A Review of Clinical DDIs in 2020 NDAs

Jingjing Yu, Yan Wang and Isabelle Ragueneau-Majlessi
Drug Metabolism and Disposition January 1, 2022, 50 (1) 1-7; DOI: https://doi.org/10.1124/dmd.121.000401
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Acknowledgments
    • Authorship Contributions
    • Footnotes
    • Abbreviations
    • References
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF + SI
  • PDF

Related Articles

Cited By...

More in this TOC Section

  • Activation/ inactivation of anticancer drugs by CYP3A4
  • Human Blood-Testis Barrier Transporters
  • Clinical Pharmacology of FDA-Approved Small Interfering RNAs
Show more Minireview

Similar Articles

Advertisement
  • Home
  • Alerts
Facebook   Twitter   LinkedIn   RSS

Navigate

  • Current Issue
  • Fast Forward by date
  • Fast Forward by section
  • Latest Articles
  • Archive
  • Search for Articles
  • Feedback
  • ASPET

More Information

  • About DMD
  • Editorial Board
  • Instructions to Authors
  • Submit a Manuscript
  • Customized Alerts
  • RSS Feeds
  • Subscriptions
  • Permissions
  • Terms & Conditions of Use

ASPET's Other Journals

  • Journal of Pharmacology and Experimental Therapeutics
  • Molecular Pharmacology
  • Pharmacological Reviews
  • Pharmacology Research & Perspectives
ISSN 1521-009X (Online)

Copyright © 2023 by the American Society for Pharmacology and Experimental Therapeutics