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.

In a pilot single-blind study, gamma-vinyl GABA, an enzyme-activated irreversible inhibitor of GABA-transaminase (GABA-T), was administered orally to 10 epileptic patients who were refractory to conventional anticonvulsant therapy. Daily doses of 1 g and 2 g for 2 weeks each as add-on therapy were followed by 2 weeks of placebo treatment. CSF obtained from suboccipital and lumbar punctures demonstrated dose-related increases in concentrations of free and total GABA and homocarnosine with treatment, but no changes in 5-hydroxyindoleacetic acid or homovanillic acid levels, indicating effective and selective CNS GABA-T inhibition. These biochemical changes were associated with decreased seizure frequency in seven patients, decreased seizure severity in one, no change in one, and possible worsening in one. gamma-Vinyl GABA may be useful in the therapy of epilepsy.

The calcineurin-mediated signal transduction via nuclear factor of activated T cells (NFATc1) is involved in upregulating slow myosin heavy chain (MHC) gene expression during fast-to-slow transformation of skeletal muscle cells. This study aims to investigate the Ca2+ signal necessary to activate the calcineurin-NFATc1 cascade in skeletal muscle. Electrostimulation of primary myocytes from rabbit for 24 h induced a distinct fast-to-slow transformation at the MHC mRNA level and a full activation of the calcineurin-NFATc1 pathway, although resting Ca2+ concentration ([Ca2+]i) remained unaltered at 70 nM. During activation, the calcium transients of these myocytes reach a peak concentration of ∼500 nM. Although 70 nM [Ca2+]i does not activate calcineurin-NFAT, we show by the use of Ca2+ ionophore that the system is fully activated when [Ca2+]i is ≥150 nM in a sustained manner. We conclude that the calcineurin signal transduction pathway and the slow MHC gene in cultured skeletal muscle cells are activated by repetition of the rapid high-amplitude calcium transients that are associated with excitation-contraction coupling rather than by a sustained elevation of resting Ca2+ concentration.

Background Metformin is a widely prescribed antidiabetic BCS Class III drug (low permeability) that depends on active transport for its absorption and disposition. It is recommended by the US Food and Drug Administration as a clinical substrate of organic cation transporter 2/multidrug and toxin extrusion protein for drug-drug interaction studies. Cimetidine is a potent organic cation transporter 2/multidrug and toxin extrusion protein inhibitor. Objective The objective of this study was to provide mechanistic whole-body physiologically based pharmacokinetic models of metformin and cimetidine, built and evaluated to describe the metformin-SLC22A2 808G>T drug-gene interaction, the cimetidine-metformin drug-drug interaction, and the impact of renal impairment on metformin exposure. Methods Physiologically based pharmacokinetic models were developed in PK-Sim ® (version 8.0). Thirty-nine clinical studies (dosing range 0.001-2550 mg), providing metformin plasma and urine data, positron emission tomography measurements of tissue concentrations, studies in organic cation transporter 2 polymorphic volunteers, drug-drug interaction studies with cimetidine, and data from patients in different stages of chronic kidney disease, were used to develop the metformin model. Twenty-seven clinical studies (dosing range 100-800 mg), reporting cimetidine plasma and urine concentrations, were used for the cimetidine model development. Results The established physiologically based pharmacokinetic models adequately describe the available clinical data, including the investigated drug-gene interaction, drug-drug interaction, and drug-drug-gene interaction studies, as well as the metformin exposure during renal impairment. All modeled drug-drug interaction area under the curve and maximum concentration ratios are within 1.5-fold of the observed ratios. The clinical data of renally impaired patients shows the expected increase in metformin exposure with declining kidney function, but also indicates counter-regulatory mechanisms in severe renal disease; these mechanisms were implemented into the model based on findings in preclinical species. Conclusions Whole-body physiologically based pharmacokinetic models of metformin and cimetidine were built and qualified for the prediction of metformin pharmacokinetics during drug-gene interaction, drug-drug interaction, and different stages of renal disease. The model files will be freely available in the Open Systems Pharmacology model repository. Current guidelines for metformin treatment of renally impaired patients should be reviewed to avoid overdosing in CKD3 and to allow metformin therapy of CKD4 patients.

BackgroundDrug–drug interactions (DDIs) and drug–gene interactions (DGIs) pose a serious health risk that can be avoided by dose adaptation. These interactions are investigated in strictly controlled setups, quantifying the effect of one perpetrator drug or polymorphism at a time, but in real life patients frequently take more than two medications and are very heterogenous regarding their genetic background.ObjectivesThe first objective of this study was to provide whole-body physiologically based pharmacokinetic (PBPK) models of important cytochrome P450 (CYP) 2C8 perpetrator and victim drugs, built and evaluated for DDI and DGI studies. The second objective was to apply these models to describe complex interactions with more than two interacting partners.MethodsPBPK models of the CYP2C8 and organic-anion-transporting polypeptide (OATP) 1B1 perpetrator drug gemfibrozil (parent–metabolite model) and the CYP2C8 victim drugs repaglinide (also an OATP1B1 substrate) and pioglitazone were developed using a total of 103 clinical studies. For evaluation, these models were applied to predict 34 different DDI studies, establishing a CYP2C8 and OATP1B1 PBPK DDI modeling network.ResultsThe newly developed models show a good performance, accurately describing plasma concentration–time profiles, area under the plasma concentration–time curve (AUC) and maximum plasma concentration (Cmax) values, DDI studies as well as DGI studies. All 34 of the modeled DDI AUC ratios (AUC during DDI/AUC control) and DDI Cmax ratios (Cmax during DDI/Cmax control) are within twofold of the observed values.ConclusionsWhole-body PBPK models of gemfibrozil, repaglinide, and pioglitazone have been built and qualified for DDI and DGI prediction. PBPK modeling is applicable to investigate complex interactions between multiple drugs and genetic polymorphisms.Electronic supplementary materialThe online version of this article (10.1007/s40262-019-00777-x) 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 CYP 3A4 inhibitor fluvoxamine and of the sensitive CYP 1A2 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 CYP 1A2 substrate), rifampicin (moderate CYP 1A2 inducer), and midazolam (sensitive index CYP 3A4 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 (C max ) ratios (C max during DDI /C max 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.

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: NCT01360320

Potent and selective CYP11B1 inhibitors could be promising therapeutics for the treatment of Cushing's syndrome. Optimization of Ref 1 (5-((1H-imidazol-1-yl)methyl)-2-phenylpyridine) led to compound 44 (5-((5-methylpyridin-3-yl)methyl)-2-phenylpyridine) with a 50-fold improved IC50 value of 2 nM toward human CYP11B1 and an enhanced inhibition of the rat enzyme (IC50 = 2440 nM) compared to Ref 1 (IC50 > 10000 nM). Furthermore, selectivities over CYP11B2, CYP17, and CYP19 were observed, as well as satisfying metabolic stability not only in human and rat plasma but also in liver S9 fraction. Investigation of cytotoxicity and inhibition of hepatic CYP2A6 and CYP3A4 showed that 44 fulfills first safety criteria and can be considered for further in vivo evaluation in rats.