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Zoptarelin doxorubicin is a fusion molecule of the chemotherapeutic doxorubicin and a luteinizing hormone-releasing hormone receptor (LHRHR) agonist, designed for drug targeting to LHRHR positive tumors. The aim of this study was to establish a physiologically based pharmacokinetic (PBPK) parent-metabolite model of zoptarelin doxorubicin and to apply it for drug–drug interaction (DDI) potential analysis. The PBPK model was built in a two-step procedure. First, a model for doxorubicin was developed, using clinical data of a doxorubicin study arm. Second, a parent-metabolite model for zoptarelin doxorubicin was built, using clinical data of three different zoptarelin doxorubicin studies with a dosing range of 10–267 mg/m2, integrating the established doxorubicin model. DDI parameters determined in vitro were implemented to predict the impact of zoptarelin doxorubicin on possible victim drugs. In vitro, zoptarelin doxorubicin inhibits the drug transporters organic anion-transporting polypeptide 1B3 (OATP1B3) and organic cation transporter 2 (OCT2). The model was applied to evaluate the in vivo inhibition of these transporters in a generic manner, predicting worst-case scenario decreases of 0.5% for OATP1B3 and of 2.5% for OCT2 transport rates. Specific DDI simulations using PBPK models of simvastatin (OATP1B3 substrate) and metformin (OCT2 substrate) predict no significant changes of the plasma concentrations of these two victim drugs during co-administration. The first whole-body PBPK model of zoptarelin doxorubicin and its active metabolite doxorubicin has been successfully established. Zoptarelin doxorubicin shows no potential for DDIs via OATP1B3 and OCT2.

Purpose To build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs). Methods The Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002–80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model. Results The carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUClast ratios (AUClast during DDI/AUClast without co-administration) and DDI Cmax ratios (Cmax during DDI/Cmax without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values. Conclusions A whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3).

Background Application of idarucizumab and hemodialysis are options to reverse the action of the oral anticoagulant dabigatran in emergency situations. Objectives The objectives of this study were to build and evaluate a mechanistic, whole-body physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of idarucizumab, including its effects on dabigatran plasma concentrations and blood coagulation, in healthy and renally impaired individuals, and to include the effect of hemodialysis on dabigatran exposure. Methods The idarucizumab model was built with the software packages PK-Sim® and MoBi® and evaluated using the full range of available clinical data. The default kidney structure in MoBi® was extended to mechanistically describe the renal reabsorption of idarucizumab and to correctly reproduce the reported fractions excreted into urine. To model the PD effects of idarucizumab on dabigatran plasma concentrations, and consequently also on blood coagulation, idarucizumab-dabigatran binding was implemented and a previously established PBPK model of dabigatran was expanded to a PBPK/PD model. The effect of hemodialysis on dabigatran was implemented by the addition of an extracorporeal dialyzer compartment with a clearance process governed by dialysate and blood flow rates. Results The established idarucizumab-dabigatran-hemodialysis PBPK/PD model shows a good descriptive and predictive performance. To capture the clinical data of patients with renal impairment, both glomerular filtration and tubular reabsorption were modeled as functions of the individual creatinine clearance. Conclusions A comprehensive and mechanistic PBPK/PD model to study dabigatran reversal has been established, which includes whole-body PBPK modeling of idarucizumab, the idarucizumab-dabigatran interaction, dabigatran hemodialysis, the pharmacodynamic effect of dabigatran on blood coagulation, and the impact of renal function in these different scenarios. The model was applied to explore different reversal scenarios for dabigatran therapy. Electronic supplementary material The online version of this article (10.1007/s40262-019-00857-y) contains supplementary material, which is available to authorized users.

This study provides whole‐body physiologically‐based pharmacokinetic models of the strong index cytochrome P450 (CYP)1A2 inhibitor and moderate CYP3A4 inhibitor fluvoxamine and of the sensitive CYP1A2 substrate theophylline. Both models were built and thoroughly evaluated for their application in drug–drug interaction (DDI) prediction in a network of perpetrator and victim drugs, combining them with previously developed models of caffeine (sensitive index CYP1A2 substrate), rifampicin (moderate CYP1A2 inducer), and midazolam (sensitive index CYP3A4 substrate). Simulation of all reported clinical DDI studies for combinations of these five drugs shows that the presented models reliably predict the observed drug concentrations, resulting in seven of eight of the predicted DDI area under the plasma curve (AUC) ratios (AUC during DDI/AUC control) and seven of seven of the predicted DDI peak plasma concentration (Cmax) ratios (Cmax during DDI/Cmax control) within twofold of the observed values. Therefore, the models are considered qualified for DDI prediction. All models are comprehensively documented and publicly available, as tools to support the drug development and clinical research community. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

PurposeTo provide whole-body physiologically based pharmacokinetic (PBPK) models of the potent clinical organic anion transporter (OAT) inhibitor probenecid and the clinical OAT victim drug furosemide for their application in transporter-based drug-drug interaction (DDI) modeling.MethodsPBPK models of probenecid and furosemide were developed in PK-Sim®. Drug-dependent parameters and plasma concentration-time profiles following intravenous and oral probenecid and furosemide administration were gathered from literature and used for model development. For model evaluation, plasma concentration-time profiles, areas under the plasma concentration–time curve (AUC) and peak plasma concentrations (Cmax) were predicted and compared to observed data. In addition, the models were applied to predict the outcome of clinical DDI studies.ResultsThe developed models accurately describe the reported plasma concentrations of 27 clinical probenecid studies and of 42 studies using furosemide. Furthermore, application of these models to predict the probenecid-furosemide and probenecid-rifampicin DDIs demonstrates their good performance, with 6/7 of the predicted DDI AUC ratios and 4/5 of the predicted DDI Cmaxratios within 1.25-fold of the observed values, and all predicted DDI AUC and Cmaxratios within 2.0-fold.ConclusionsWhole-body PBPK models of probenecid and furosemide were built and evaluated, providing useful tools to support the investigation of transporter mediated DDIs.

According to current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidance documents, physiologically based pharmacokinetic (PBPK) modeling is a powerful tool to explore and quantitatively predict drug-drug interactions (DDIs) and may offer an alternative to dedicated clinical trials. This study provides whole-body PBPK models of rifampicin, itraconazole, clarithromycin, midazolam, alfentanil, and digoxin within the Open Systems Pharmacology (OSP) Suite. All models were built independently, coupled using reported interaction parameters, and mutually evaluated to verify their predictive performance by simulating published clinical DDI studies. In total, 112 studies were used for model development and 57 studies for DDI prediction. 93% of the predicted area under the plasma concentration-time curve (AUC) ratios and 94% of the peak plasma concentration (Cmax ) ratios are within twofold of the observed values. This study lays a cornerstone for the qualification of the OSP platform with regard to reliable PBPK predictions of enzyme-mediated and transporter-mediated DDIs during model-informed drug development. All presented models are provided open-source and transparently documented.

Clarithromycin is a substrate and mechanism-based inhibitor of cytochrome P450 (CYP) 3A4 as well as a substrate and competitive inhibitor of P-glycoprotein (P-gp) and organic anion-transporting polypeptides (OATP) 1B1 and 1B3. Administered concomitantly, clarithromycin causes drug–drug interactions (DDI) with the victim drugs midazolam (CYP3A4 substrate) and digoxin (P-gp substrate). The objective of the presented study was to build a physiologically based pharmacokinetic (PBPK) DDI model for clarithromycin, midazolam, and digoxin and to exemplify dosing adjustments under clarithromycin co-treatment. The PBPK model development included an extensive literature search for representative PK studies and for compound characteristics of clarithromycin, midazolam, and digoxin. Published concentration-time profiles were used for model development (training dataset), and published and unpublished individual profiles were used for model evaluation (evaluation dataset). The developed single-compound PBPK models were linked for DDI predictions. The full clarithromycin DDI model successfully predicted the metabolic (midazolam) and transporter (digoxin) DDI, the acceptance criterion (0.5 ≤ AUCratio,predicted/AUCratio,observed ≤ 2) was met by all predictions. During co-treatment with 250 or 500 mg clarithromycin (bid), the midazolam and digoxin doses should be reduced by 74 to 88% and by 21 to 22%, respectively, to ensure constant midazolam and digoxin exposures (AUC). With these models, we provide highly mechanistic tools to help researchers understand and characterize the DDI potential of new molecular entities and inform the design of DDI studies with potential CYP3A4 and P-gp substrates.

Green tea polyphenols may contribute to the prevention of cancer and other diseases. To learn more about the pharmacokinetics and interindividual variation of green tea polyphenols after oral intake in humans we performed a population nutrikinetic study of standardized green tea extract. 84 healthy participants took green tea extract capsules standardized to 150 mg epigallocatechin-gallate (EGCG) twice a day for 5 days. On day 5 catechin plasma concentrations were analyzed using non-compartmental and population pharmacokinetic methods. A strong between-subject variability in catechin pharmacokinetics was found with maximum plasma concentrations varying more than 6-fold. The AUCs of EGCG, EGC and ECG were 877.9 (360.8–1576.5), 35.1 (8.0–87.4), and 183.6 (55.5–364.6) h*μg/L respectively, and the elimination half lives were 2.6 (1.8–3.8), 3.9 (0.9–10.7) and 1.8 (0.8–2.9) h, respectively. Genetic polymorphisms in genes of the drug transporters MRP2 and OATP1B1 could at least partly explain the high variability in pharmacokinetic parameters. The observed variability in catechin plasma levels might contribute to interindividual variation in benefical and adverse effects of green tea polyphenols. Our data could help to gain a better understanding of the causes of variability of green tea effects and to improve the design of studies on the effects of green tea polyphenols in different health conditions. Trial registration: ClinicalTrials.gov: {"type":"clinical-trial","attrs":{"text":"NCT01360320","term_id":"NCT01360320"}}NCT01360320

Topical application of CYP11B1 inhibitors to reduce cutaneous cortisol is a novel strategy to promote healing of chronic wounds. Pyridyl substituted arylsulfonyltetrahydroquinolines were designed and synthesized resulting in a strong inhibitor 34 (IC50 = 5 nM). It showed no inhibition of CYP17 and CYP19 and no mutagenic effects. It exhibited inverse metabolic stability in plasma (t1/2 ≫ 150 min), which is similar to wound fluid in composition, and in liver S9 fractions (t1/2 = 16 min).

The inhibition of 11β-hydroxylase is a promising strategy for the treatment of Cushing's syndrome, in particular for the recurrent and subclinical cases. To achieve proof of concept in rats, efforts were paid to identify novel lead compounds inhibiting both human and rat CYP11B1. Modifications on a potent promiscuous inhibitor of hCYP11B1, hCYP11B2 and hCYP19 (compound IV) that exhibited moderate rCYP11B1 inhibition led to compound 8 as a new promising lead compound. Significant improvements compared to starting point IV were achieved regarding inhibitory potency against both human and rat CYP11B1 (IC50 values of 2 and 163 nM, respectively) as well as selectivity over hCYP19 (IC50 = 1900 nM). Accordingly, compound 8 was around 7- and 28-fold more potent than metyrapone regarding the inhibition of human and rat CYP11B1 and exhibited a comparable selectivity over hCYP11B2 (SF of 3.5 vs 4.9). With further optimizations on this new lead compound 8, drug candidates with satisfying profiles are expected to be discovered.

Diseases triggered by an abnormally high level of cortisol (hypercortisolism), such as the Cushing's and metabolic syndromes, could be successfully tackled by inhibitors of CYP11B1, a steroidal cytochrome P450 enzyme that catalyzes the last hydroxylation step of the cortisol biosynthesis. Structural optimization of 7-(benzyloxy)-4-(1H-imidazol-1-ylmethyl)-2H-chromen-2-one 2, a selective aromatase inhibitor, afforded the 4-(1H-imidazol-1-ylmethyl)-7-{[3-(trifluoromethoxy)benzyl]oxy}-2H-chromen-2-one 7, with improved inhibitory potency at human CYP11B1 (IC50 = 5 nM) and an enhanced selectivity over human CYP11B2 (SIB = 25) compared to lead compound 2 (IC50 = 72 nM, SIB = 4.0) and metyrapone (IC50 = 15 nM, SIB = 4.8), a non-selective drug used in the therapy of the Cushing's syndrome. Structure–activity relationship studies allowed the design and optimization of a novel series of potent and selective compounds, that can be regarded as open analogues of 2H-chromen-2-one derivatives. Compound 23, 2-(1H-imidazol-1-yl)-1-(4-{[3(trifluoromethoxy)benzyl]oxy}phenyl) ethanone, was the most interesting inhibitor of the series displaying a high potency at CYP11B1 (IC50 = 15 nM), increased selectivities over CYP11B2 (SIB = 33), CYP19 (SIB = 390) and CYP17 (5% inhibition at 2.5 μM concentration).

17β-HSD2 is a promising new target for the treatment of osteoporosis. In this paper, a rational strategy to overcome the metabolic liability in the 2,5-thiophene amide class of 17β-HSD2 inhibitors is described, and the biological activity of the new inhibitors. Applying different strategies, as lowering the cLogP or modifying the structures of the molecules, compounds 27, 31 and 35 with strongly improved metabolic stability were obtained. For understanding biotransformation in the 2,5-thiophene amide class the main metabolic pathways of three properly selected compounds were elucidated.

Estradiol is the most potent estrogen in humans. It is known to be involved in the development and proliferation of estrogen dependent diseases such as breast cancer and endometriosis. The last step of its biosynthesis is catalyzed by 17β-hydroxysteroid dehydrogenase type 1 (17β- HSD1) which consequently is a promising target for the treatment of these diseases. Recently, we reported on bicyclic substituted hydroxyphenylmethanones as potent inhibitors of 17β-HSD1. The present study focuses on rational structural modifications in this compound class with the aim of gaining more insight into its structure-activity relationship (SAR). (4-Hydroxyphenyl)-(5-(3-hydroxyphenylsulfanyl)-thiophen-2-yl)methanone (25) was discovered as a member of a novel potent class of human 17β-HSD1 inhibitors. Computational methods were used to elucidate its interactions with the target protein. The compound showed activity also towards the murine 17β-HSD1 enzyme and thus is a starting point for the design of compounds suitable for evaluation in an animal disease model.

17β-Hydroxysteroid dehydrogenase type 2 (17β-HSD2) is responsible for the oxidation of the highly active estradiol (E2) and testosterone (T) into the less potent estrone (E1) and Δ4-androstene-3,17-dione (Δ4-AD), respectively. As 17β-HSD2 is present in bones and as estradiol and testosterone are able to induce bone formation and repress bone resorption, inhibition of this enzyme could be a new promising approach for the treatment of osteoporosis. Herein, we describe the design, the synthesis and the biological evaluation of 24 new 17β-HSD2 inhibitors in the 5-substituted thiophene-2-carboxamide class. Structure–activity and structure–selectivity relationships have been explored by variation of the sulfur atom position in the central core, exchange of the thiophene by a thiazole, substitution of the amide group with a larger moiety, exchange of the N-methylamide group with bioisosteres like N-methylsulfonamide, N-methylthioamide and ketone, and substitutions at positions 2 and 3 of the thiophene core with alkyl and phenyl groups leading to 2,3,5-trisubstituted thiophene derivatives. The compounds were evaluated on human and mouse enzymes. From this study, a novel highly potent and selective compound in both human and mouse 17β-HSD2 enzymes was identified, compound 21 (IC50(h17β-HSD2) = 235 nM, selectivity factor toward h17β-HSD1 = 95, IC50 (m17β-HSD2) = 54 nM). This new compound 21 could be used for an in vivo proof of principle to demonstrate the true therapeutic efficacy of 17β-HSD2 inhibitors in osteoporosis. New structural insights into the active sites of the human and mouse enzymes were gained.