Mechanisms and Clinical Significance of Pharmacokinetic-Based Drug-Drug Interactions with Drugs Approved by the U.S. Food and Drug Administration in 2017

Abstract

Pharmacokinetic-based drug-drug interaction (DDI) data for drugs approved by the U.S. Food and Drug Administration in 2017 (N = 34) were analyzed using the University of Washington Drug Interaction Database. The mechanisms and clinical relevance of these interactions were characterized based on information from new drug application reviews. CYP3A inhibition and induction explained most of the observed drug interactions (new drugs as victims or as perpetrators), and transporters mediated about half of all DDIs, alone or with enzymes. Organic anion transporting polypeptide (OATP)1B1/1B3 played a significant role, mediating more than half of the drug interactions with area under the time-plasma curve (AUC) changes ‡5-fold. As victims, five new drugs were identified as sensitive substrates: abemeciclib, midostaurin, and neratinib for CYP3A and glecaprevir and voxilaprevir for OATP1B1/1B3. As perpetrators, three drugs were considered strong inhibitors: ribociclib for CYP3A, glecaprevir/pibrentasvir for OATP1B1/1B3, and sofosbuvir/velpatasvir/voxilaprevir for OATP1B1/1B3 and breast cancer resistance protein. No strong inducer of enzymes or transporters was identified. DDIs with AUC changes ‡5-fold and almost all DDIs with AUC changes 2-to 5-fold had dose recommendations in their respective drug labels. A small fraction of DDIs with exposure changes <2-fold had a labeling impact, mostly related to drugs with narrow therapeutic indices. As with drugs approved in recent years, all drugs found to be sensitive substrates or strong inhibitors of enzymes or transporters were among oncology or antiviral treatments, suggesting a serious risk of DDIs in these patient populations for whom effective therapy is already complex because of polytherapy. 136 Yu et al. 142 Yu et al.

In vitro and in vivo methods to assess pharmacokinetic drug– drug interactions in drug discovery and development

“…If CYP3A induction is positive, then induction of the CYP2C family, UGTs, and transporters such as P‐gp should be investigated in follow‐up studies. Since UGTs are generally low affinity and high capacity enzymes, clinical induction of UGTs is mostly within 2‐fold (Soars, Petullo, Eckstein, Kasper, & Wrighton, ; Yu, Petrie, Levy, & Ragueneau‐Majlessi, ). CYP inductions are species dependent, as evidenced by the proton pump inhibitor, omeprazole, which induces CYP1A in human but not in rat (Lu & Li, ).…”

“…Inhibition of uptake transporters can also lead to significant clinical DDI. Among the drugs approved by FDA in the last several years (2013–2017), inhibition of hepatic uptake transporters, OATP1B1 and OATP1B3, contribute to > 50% of the transporter‐mediated DDI studies and most of them show severe DDI with AUC changes of > 5‐fold (Yu et al, ; Yu, Zhou, Tay‐Sontheimer, Levy, & Ragueneau‐Majlessi, ). For example, rifampin caused an 8.37‐fold increase in systemic exposure of grazoprevir by inhibiting OATP1B1/1B3 mediated uptake (Yu et al, ).…”

“…For example, rifampin caused an 8.37‐fold increase in systemic exposure of grazoprevir by inhibiting OATP1B1/1B3 mediated uptake (Yu et al, ). Cyclosporine and rifampin significantly increased AUC of voxilaprevir and glecaprevir (both are OATP1B1/1B3 substrates) by approximately 8–10 fold and 5–8 fold, respectively (Yu et al, ). In vitro uptake assays are typically conducted using plated cells, either transfected cell lines (e.g., OAPT1B1‐HEK293, OCT2‐HEK293) or primary cells (e.g., HHEP).…”

Practical Assessment of Clinical Drug–Drug Interactions in Drug Development Using Physiologically Based Pharmacokinetics Modeling

“…Unfortunately, the results of PBPK modeling approaches may be considered less reliable or even ignored by some clinicians, who typically rely on empirical clinical evidence to inform dosing in patients and wrongfully argue that M&S is unreliable because it suffers from using assumptions and is a tedious approach that requires large amounts of physiological data, as well as intensive drug‐dependent input data . Despite these barriers, several successful examples have been reported where PBPK simulations were provided as evidence in lieu of clinical DDI studies to inform regulatory decisions and, ultimately, labeling . Considering “totality of the scientific evidence” from PBPK, exposure–response analyses and risk‐benefit evaluation enabled these successful examples to optimize drug development using a model‐informed framework.…”

The effect of apatinib on pharmacokinetic profile of buspirone both in vivo and in vitro

“…[2] It has a wide spectrum of anticancer effect, including advanced gastric cancer, breast cancer, non-small-cell lung cancer and hepatocellular carcinoma. [3][4][5][6][7] Pharmacokinetically, apatinib undergoes extensive metabolism which is mainly metabolized by CYP3A4/5 and by CYP2D6, CYP2C9 and CYP2E1 to a lesser extent. The major circulating metabolites are identified as M1-1, M1-2, M1-6 and M9-2.…”