Peter Stopfer

Background: Pharmacokinetic drug–drug interactions (DDIs) can be caused by the effect of a pharmaceutical compound on the activity of one or more subtypes of the Cytochrome P450 (CYP) family, UDP-glucuronosyltransferases (UGTs), and/or transporters. As the number of therapeutic areas with polypharmacy has increased, interest has grown in assessing the risk of DDIs during the early phases of drug development. Various lines of research have led to improved mathematical models to predict DDIs, culminating in the Food and Drug Administration’s (FDA) guidelines on evaluating pharmacokinetic DDI risks. However, the recommended static models are highly conservative and often result in false positive predictions. The current research aims to improve the workflow for assessing CYP-mediated DDI risk using Boehringer Ingelheim (BI) proprietary compounds. Methods: The Drug–drug Interaction Risk Calculator (PharmaPendium) was used to evaluate the mechanistic static model, and predictions were correlated with human pharmacokinetic studies from Phase I clinical trials. Results: The results demonstrated that the FDA formula performed well in predicting DDIs for BI proprietary compounds. Furthermore, the integration of either human renal excretion or preclinical species total excretion data into the mechanistic static model enhanced the predictive performance for candidate drugs as victims in DDIs. Conclusions: The basic static models (BSMs) for drug interactions should be used in early drug discovery to “rule out” DDI risks because of the minimal inputs required and the low rate of false negative predictions. Mechanistic static models (MSMs) can then be implemented for compounds that require additional evaluation.

The inhibition of renal transport proteins organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATE1, MATE2‐K), and organic anion transporters (OAT1, OAT3) causes clinically relevant drug–drug interactions (DDI). Endogenous biomarkers could be used to improve risk prediction of such renal DDIs. While a number of biomarkers for renal DDIs have been described so far, multiple criteria for valid biomarkers have frequently not been investigated, for example, specificity, metabolism, or food effects. Therefore, there is a need for novel biomarkers of renal DDIs. Here, we investigated the global metabolomic effects following the administration of two classical inhibitors of renal transport proteins [cimetidine (OCT2/MATEs), probenecid (OATs)] in human plasma and urine of healthy volunteers. Additionally, we investigated metabolomic effects of two inhibitors of other transporters [verapamil (P‐glycoprotein), rifampin (organic anion transporting polypeptides)] as controls. This analysis shows that both cimetidine and probenecid affect compounds involved in caffeine metabolism, carnitines, and sulfates. Hierarchical cluster analysis of the effects of all four inhibitors on endogenous compounds identified multiple promising new sensitive and specific biomarker candidates for OCT2/MATE‐ or OAT‐mediated DDIs. For OCT2/MATEs, 5‐amino valeric acid betaine (median log₂‐fold change of estimated renal elimination: −3.62) presented itself as a promising candidate. For OATs, estimated renal elimination of 7‐methyluric acid and cinnamoylglycine (median log₂‐fold changes −3.10 and −1.92, respectively) was both sensitive and specific. This study provides comprehensive information on metabolomic effects of transport protein inhibition in humans and identifies putative new sensitive and specific biomarkers for renal transporter‐mediated DDIs.

Endogenous biomarkers are discussed as tools for detection of drug‐drug interactions (DDIs) mediated by renal transport proteins, such as organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATE1 and MATE2‐K) and organic anion transporters (OAT1 and OAT3). Whereas sensitivity of some endogenous biomarkers against at least one clinical transporter inhibitor has frequently been shown, intra‐study comparisons of the extent of effects of inhibitors on different biomarkers are frequently lacking. Moreover, in vivo specificity of such discussed biomarkers has frequently not been studied. We therefore investigated changes of 10 previously described putative biomarkers for inhibition of OCT2/MATEs, as well as 15 previously described putative biomarkers for OATs in human plasma and urine samples of healthy volunteers in response to treatment with 4 inhibitors of transport proteins [verapamil (P‐glycoprotein), rifampin (organic anion transporting polypeptides), cimetidine (OCT2/MATEs), and probenecid (OATs)]. Two of the putative biomarkers had been suggested for both OCT2/MATEs and OATs. All substances were unequivocally identified in an untargeted metabolomics assay. The OCT2/MATE biomarkers choline and trimethylamine N‐oxide were both sensitive and specific (median log2‐fold changes −1.18 in estimated renal elimination and −0.85 in urinary excretion, respectively). For renal OATs, indoleacetyl glutamine and indoleacetic acid (median log2‐fold changes −3.77 and −2.85 in estimated renal elimination, respectively) were the candidates for sensitive and specific biomarkers with the most extensive change, followed by taurine, indolelactic acid, and hypoxanthine. This comprehensive study adds further knowledge on sensitivity and specificity of 23 previously described biomarkers of renal OCT2/MATE‐ and OAT‐mediated DDIs.

N¹‐methylnicotinamide (NMN) has been proposed as endogenous biomarker for drug–drug interactions mediated by inhibition of multidrug and toxin extrusion proteins (MATEs) at the renal proximal tubule. We analyzed NMN in plasma and urine samples of two clinical trials investigating a new probe drug cocktail (consisting of digoxin, metformin, furosemide, and rosuvastatin) dedicated to clinically relevant drug transporters. In trial 1, NMN was investigated after single‐dose treatment with individual cocktail components or after cocktail treatment. In trial 2, NMN was investigated after treatment with cocktail alone or with cocktail + inhibitor (cimetidine, a MATE inhibitor; or rifampin, verapamil, or probenecid, inhibitors of other transporters). In trial 1, NMN kinetics in plasma and urine were essentially not affected by individual cocktail components or after cocktail treatment. In trial 2, NMN renal clearance from 0 to 12 hours (CL_R,0–12) geometric mean ratio (GMR) after cocktail + cimetidine vs. cocktail alone was 75% (90% confidence interval (CI): 65–87%). NMN CL_R GMR after cocktail + verapamil, + rifampin, or + probenecid vs. cocktail alone was 99% (90% CI: 81–121%), 91% (90% CI: 75–111%), and 107% (90% CI: 91–126%), respectively. Compared with creatinine CL_R and creatinine area under the plasma‐concentration time curve, NMN CL_R was more specific and more sensitive for renal MATE inhibition. Absence of impact of the cocktail on NMN in trial 1 allows for utilization of NMN in studies using this transporter cocktail. Trial 2 data support that NMN CL_R is a specific and sensitive marker for MATE‐mediated renal drug–drug interactions.

The calcium channel blocker and antiarrhythmic agent verapamil is recommended by the FDA for drug–drug interaction (DDI) studies as a moderate clinical CYP3A4 index inhibitor and as a clinical Pgp inhibitor. The purpose of the presented work was to develop a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to investigate and predict DDIs with verapamil. The model was established in PK-Sim^®, using 45 clinical studies (dosing range 0.1–250 mg), including literature as well as unpublished Boehringer Ingelheim data. The verapamil R- and S-enantiomers and their main metabolites R- and S-norverapamil are represented in the model. The processes implemented to describe the pharmacokinetics of verapamil and norverapamil include enantioselective plasma protein binding, enantioselective metabolism by CYP3A4, non-stereospecific Pgp transport, and passive glomerular filtration. To describe the auto-inhibitory and DDI potential, mechanism-based inactivation of CYP3A4 and non-competitive inhibition of Pgp by the verapamil and norverapamil enantiomers were incorporated based on in vitro literature. The resulting DDI performance was demonstrated by prediction of DDIs with midazolam, digoxin, rifampicin, and cimetidine, with 21/22 predicted DDI AUC ratios or C_trough ratios within 1.5-fold of the observed values. The thoroughly built and qualified model will be freely available in the Open Systems Pharmacology model repository to support model-informed drug discovery and development.

Background and Objective A novel cocktail containing four substrates of key drug transporters was previously optimized to eliminate mutual drug–drug interactions between the probes digoxin (P-glycoprotein substrate), furosemide (organic anion transporter 1/3), metformin (organic cation transporter 2, multidrug and toxin extrusion protein 1/2-K), and rosuvastatin (organic anion transporting polypeptide 1B1/3, breast cancer resistance protein). This clinical trial investigated the effects of four commonly employed drug transporter inhibitors on cocktail drug pharmacokinetics. Methods In a randomized open-label crossover trial in 45 healthy male subjects, treatment groups received the cocktail with or without single oral doses of rifampin, verapamil, cimetidine or probenecid. Concentrations of the probe drugs in serial plasma samples and urine fractions were measured by validated liquid chromatography-tandem mass spectrometry assays to assess systemic exposure. Results The results were generally in accordance with known in vitro and/or clinical drug–drug interaction data. Single-dose rifampin increased rosuvastatin area under the plasma concentration–time curve up to the last quantifiable concentration (AUC_0–tz) by 248% and maximum plasma concentration (C_max) by 1025%. Probenecid increased furosemide AUC_0–tz by 172% and C_max by 23%. Cimetidine reduced metformin renal clearance by 26%. The effect of single-dose verapamil on digoxin systemic exposure was less than expected from multiple-dose studies (AUC_0–tz unaltered, C_max + 22%). Conclusions Taking all the interaction results together, the transporter cocktail is considered to be validated as a sensitive and specific tool for evaluating transporter-mediated drug–drug interactions in drug development. Clinical Trial Registration EudraCT number 2017-001549-29.

Nintedanib is an oral, small-molecule tyrosine kinase inhibitor approved for the treatment of idiopathic pulmonary fibrosis and patients with advanced non-small cell cancer of adenocarcinoma tumour histology. Nintedanib competitively binds to the kinase domains of vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF) and fibroblast growth factor (FGF). Studies in healthy volunteers and in patients with advanced cancer have shown that nintedanib has time-independent pharmacokinetic characteristics. Maximum plasma concentrations of nintedanib are reached approximately 2–4 h after oral administration and thereafter decline at least bi-exponentially. Over the investigated dose range of 50–450 mg once daily and 150–300 mg twice daily, nintedanib exposure increases are dose proportional. Nintedanib is metabolised via hydrolytic ester cleavage, resulting in the formation of the free acid moiety that is subsequently glucuronidated and excreted in the faeces. Less than 1% of drug-related radioactivity is eliminated in urine. The terminal elimination half-life of nintedanib is about 10–15 h. Accumulation after repeated twice-daily dosing is negligible. Sex and renal function have no influence on nintedanib pharmacokinetics, while effects of ethnicity, low body weight, older age and smoking are within the inter-patient variability range of nintedanib exposure and no dose adjustments are required. Administration of nintedanib in patients with moderate or severe hepatic impairment is not recommended, and patients with mild hepatic impairment should be monitored closely and the dose adjusted accordingly. Nintedanib has a low potential for drug–drug interactions, especially with drugs metabolised by cytochrome P450 enzymes. Concomitant treatment with potent inhibitors or inducers of the P-glycoprotein transporter can affect the pharmacokinetics of nintedanib. At an investigated dose of 200 mg twice daily, nintedanib does not have proarrhythmic potential.

Aims: Previous pharmacokinetic characterization of a transporter probe cocktail containing digoxin (P‐gp), furosemide (OAT1, OAT3), metformin (OCT2, MATE1, MATE2‐K) and rosuvastatin (OATP1B1, OATP1B3, BCRP) in healthy subjects showed increases in rosuvastatin systemic exposure compared to rosuvastatin alone. In this trial, the doses of metformin and furosemide as putative perpetrators were reduced to eliminate their drug–drug interaction (DDI) with rosuvastatin.Methods: In a randomized, open‐label, single‐centre, five‐treatment, five‐period crossover trial, 30 healthy male subjects received as reference treatments separately 0.25 mg digoxin, 1 mg furosemide, 10 mg metformin and 10 mg rosuvastatin, and as test treatment all four drugs administered together as a cocktail. Primary pharmacokinetic endpoints were AUC_0‐tz (area under the plasma concentration–time curve from time zero to the last quantifiable concentration) and C_max (maximum plasma concentration) of each probe drug.Results: Geometric mean ratios and 90% confidence intervals of test (cocktail) to reference (single drug) for AUC_0‐tz were 96.4% (88.2–105.3%) for digoxin, 102.6% (93.8–112.3%) for furosemide, 97.5% (93.5–101.6%) for metformin and 105.0% (96.4–114.4%) for rosuvastatin, indicating lack of interaction. The same analysis for C_max and for pharmacokinetic parameters of urinary excretion of all cocktail components also indicated no DDI.Conclusions: Digoxin (0.25 mg), furosemide (1 mg), metformin (10 mg) and rosuvastatin (10 mg) exhibit no mutual pharmacokinetic interactions and are well tolerated administered as a cocktail. The cocktail is thus optimized and has the potential to be used as a screening tool for clinical investigation of transporter‐mediated DDI.