Significant impact of time-of-day variation on metformin pharmacokinetics.

AIMS/HYPOTHESIS: The objective was to investigate if metformin pharmacokinetics is modulated by time-of-day in humans using empirical and mechanistic pharmacokinetic modelling techniques on a large clinical dataset. This study also aimed to generate and test hypotheses on the underlying mechanisms, including evidence for chronotype-dependent interindividual differences in metformin plasma and efficacy-related tissue concentrations. METHODS: A large clinical dataset consisting of individual metformin plasma and urine measurements was analysed using a newly developed empirical pharmacokinetic model. Causes of daily variation of metformin pharmacokinetics and interindividual variability were further investigated by a literature-informed mechanistic modelling analysis. RESULTS: A significant effect of time-of-day on metformin pharmacokinetics was found. Daily rhythms of gastrointestinal, hepatic and renal processes are described in the literature, possibly affecting drug pharmacokinetics. Observed metformin plasma levels were best described by a combination of a rhythm in GFR, renal plasma flow (RPF) and organic cation transporter (OCT) 2 activity. Furthermore, the large interindividual differences in measured metformin concentrations were best explained by individual chronotypes affecting metformin clearance, with impact on plasma and tissue concentrations that may have implications for metformin efficacy. CONCLUSIONS/INTERPRETATION: Metformin's pharmacology significantly depends on time-of-day in humans, determined with the help of empirical and mechanistic pharmacokinetic modelling, and rhythmic GFR, RPF and OCT2 were found to govern intraday variation. Interindividual variation was found to be partly dependent on individual chronotype, suggesting diurnal preference as an interesting, but so-far underappreciated, topic with regard to future personalised chronomodulated therapy in people with type 2 diabetes.

Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions.

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).

Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions.

PURPOSE: To 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. METHODS: PBPK 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. RESULTS: The 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 Cmax ratios within 1.25-fold of the observed values, and all predicted DDI AUC and Cmax ratios within 2.0-fold. CONCLUSIONS: Whole-body PBPK models of probenecid and furosemide were built and evaluated, providing useful tools to support the investigation of transporter mediated DDIs.

Primary skeletal muscle cells cultured on gelatin bead microcarriers develop structural and biochemical features characteristic of adult skeletal muscle.

A primary skeletal muscle cell culture, in which myoblasts derived from newborn rabbit hindlimb muscles grow on gelatin bead microcarriers in suspension and differentiate into myotubes, has been established previously. In the course of differentiation and beginning spontaneous contractions, these multinucleated myotubes do not detach from their support. Here, we describe the development of the primary myotubes with respect to their ultrastructural differentiation. Scanning electron microscopy reveals that myotubes not only grow around the surface of one carrier bead but also attach themselves to neighboring carriers, forming bridges between carriers. Transmission electron microscopy demonstrates highly ordered myofibrils, T-tubules, and sarcoplasmic reticulum. The functionality of the contractile apparatus is evidenced by contractile activity that occurs spontaneously or can be elicited by electrostimulation. Creatine kinase activity increases steadily until day 20 of culture. Regarding the expression of isoforms of myosin heavy chains (MHC), we could demonstrate that from day 16 on, no non-adult MHC isoform mRNAs are present. Instead, on day 28 the myotubes express predominantly adult fast MHCIId/x mRNA and protein. This MHC pattern resembles that of fast muscles of adult rabbits. In contrast, primary myotubes grown on matrigel-covered culture dishes express substantial amounts of non-adult MHC protein even on day 21. To conclude, primary myotubes grown on microcarriers in their later stages exhibit many features of adult skeletal muscle and characteristics of fast type II fibers. Thus, the culture represents an excellent model of adult fast skeletal muscle, for example, when investigating molecular mechanisms of fast-to-slow fiber-type transformation.

Inhibition of aldosterone synthase (CYP11B2) by torasemide prevents atrial fibrosis and atrial fibrillation in mice.

Loop diuretics are used for fluid control in patients with heart failure. Furosemide and torasemide may exert differential effects on myocardial fibrosis. Here, we studied the effects of torasemide and furosemide on atrial fibrosis and remodeling during atrial fibrillation. In primary neonatal cardiac fibroblasts, torasemide (50µM, 24h) but not furosemide (50µM, 24h) reduced the expression of connective tissue growth factor (CTGF; 65±6%) and the pro-fibrotic miR-21 (44±23%), as well as the expression of lysyl oxidase (LOX; 57±8%), a regulator of collagen crosslinking. Mineralocorticoid receptor (MR) expression and activity were not altered. Torasemide but not furosemide inhibited human aldosterone synthase (CYP11B2) activity in transfected lung fibroblasts (V79MZ cells) by 75±1.8%. The selective CYP11B2 inhibitor SL242 mimicked the torasemide effects. Mice with cardiac overexpression of Rac1 GTPase (RacET), which develop atrial fibrosis and spontaneous AF with aging, were treated long-term (8months) with torasemide (10mg/kg/day), furosemide (40mg/kg/day) or vehicle. Treatment with torasemide but not furosemide prevented atrial fibrosis in RacET as well as the up-regulation of CTGF, LOX, and miR-2, whereas MR expression and activity remained unaffected. These effects correlated with a reduced prevalence of atrial fibrillation (33% RacET+Tora vs. 80% RacET). Torasemide but not furosemide inhibits CYP11B2 activity and reduces the expression of CTGF, LOX, and miR-21. These effects are associated with prevention of atrial fibrosis and a reduced prevalence of atrial fibrillation in mice.

Gene regulation mediating fiber-type transformation in skeletal muscle cells is partly glucose- and ChREBP-dependent.

Adaptations in the oxidative capacity of skeletal muscle cells can occur under several physiological or pathological conditions. We investigated the effect of increasing extracellular glucose concentration on the expression of markers of energy metabolism in primary skeletal muscle cells and the C2C12 muscle cell line. Growth of myotubes in 25mM glucose (high glucose, HG) compared with 5.55mM led to increases in the expression and activity of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a marker of glycolytic energy metabolism, while oxidative markers peroxisome proliferator-activated receptor γ coactivator 1α and citrate synthase decreased. HG induced metabolic adaptations as are seen during a slow-to-fast fiber transformation. Furthermore, HG increased fast myosin heavy chain (MHC) IId/x but did not change slow MHCI/ß expression. Protein phosphatase 2A (PP2A) was shown to mediate the effects of HG on GAPDH and MHCIId/x. Carbohydrate response element-binding protein (ChREBP), a glucose-dependent transcription factor downstream of PP2A, partially mediated the effects of glucose on metabolic markers. The glucose-induced increase in PP2A activity was associated with an increase in p38 mitogen-activated protein kinase activity, which presumably mediates the increase in MHCIId/x promoter activity. Liver X receptor, another possible mediator of glucose effects, induced only an incomplete metabolic shift, mainly increasing the expression of the glycolytic marker. Taken together, HG induces a partial slow-to-fast transformation comprising metabolic enzymes together with an increased expression of MHCIId/x. This work demonstrates a functional role for ChREBP in determining the metabolic type of muscle fibers and highlights the importance of glucose as a signaling molecule in muscle.

Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network.

The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6 and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drug-drug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify the model using clinical drug-gene interaction (DGI) and DDI data. The model was built in PK-Sim® applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion, metabolism via CYP2C19 and 11ß-HSD, as well as binding to pharmacological targets. The impact of CYP2B6 polymorphisms is described for normal, poor, intermediate, and rapid metabolizers, with various allele combinations of the genetic variants CYP2B6*1, *4, *5 and *6. DDI model performance was evaluated by prediction of clinical studies with rifampicin (CYP2B6 and CYP2C19 inducer), fluvoxamine (CYP2C19 inhibitor) and voriconazole (CYP2B6 and CYP2C19 inhibitor). Model performance quantification showed 20/20 DGI ratios of hydroxybupropion to bupropion AUC ratios (DGI AUCHBup/Bup ratios), 12/13 DDI AUCHBup/Bup ratios, and 7/7 DDGI AUCHBup/Bup ratios within 2-fold of observed values. The developed model is freely available in the Open Systems Pharmacology model repository.

Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug-Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach.

The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine induces the metabolism of various drugs (including its own); on the other hand, its metabolism can be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parent-metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug-drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration-time profiles (dosing range 50-800 mg), as well as fractions excreted unchanged in urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase (UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and simvastatin, where 14/15 DDI AUClast ratios and 11/15 DDI Cmax ratios were within the prediction success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in the Open Systems Pharmacology model repository.

Passive mechanical forces upregulate the fast myosin heavy chain IId/x via integrin and p38 MAP kinase activation in a primary muscle cell culture.

We have studied the mechanism by which a previously described primary muscle culture growing on microcarriers predominantly expresses fast myosin heavy chain (MHC) IId/x. We have measured MHC IId/x mRNA and protein levels, mRNA of MHC I and markers of muscle metabolism, insulin-like growth factor (IGF)-1 and mechano-growth factor (MGF) transcripts, indicators of the activation of the Akt-mammalian target of rapamycin (mTOR) axis, the p38-, ERK1/2-, and JNK-mitogen-activated protein kinase (MAP) kinase pathways, and of protein phosphatase PP2A, and we have assessed the involvement of integrin. By placing the culture flasks on a rotary shaker, we induce a continuous motion of the culture medium in which the carrier-myotube aggregates are suspended. This motion exerts passive forces on the myotubes that are decisive for the predominance of MHC II expression. These forces act via integrin, which transduces the mechanical signal into activation of PP2A and of p38 MAP-Kinase. The latter presumably is directly responsible for a drastic upregulation of MHC IId/x, whereas MHC I and metabolic markers remain unaffected. At the same time, despite an elevated level of IGF-1 transcription under passive forces, the IGF-1 receptor-Akt-mTOR axis is switched off as evident from the lack of an effect of inhibition of the IGF-1 receptor and from the PP2A-mediated low degree of phosphorylation of Akt and 4E-BP1. Similarly, the ERK1/2- and JNK-MAP kinase pathways are repressed. We conclude that passive stretch exerted on the myotubes by the rotary fluid motion induces a rather selective upregulation of fast MHC II, which goes along with a mild muscle hypertrophy as judged from the amount of protein per cell and is caused by p38 MAP kinase activity elevated via integrin sensing. The direct link between passive stretch and MHC II expression constitutes a novel mechanism, which is expected to become effective physiologically under passive stretch and eccentric contractions of skeletal muscles.

A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug-Drug-Gene Interaction Predictions.

Trimethoprim is a frequently-prescribed antibiotic and therefore likely to be co-administered with other medications, but it is also a potent inhibitor of multidrug and toxin extrusion protein (MATE) and a weak inhibitor of cytochrome P450 (CYP) 2C8. The aim of this work was to develop a physiologically-based pharmacokinetic (PBPK) model of trimethoprim to investigate and predict its drug-drug interactions (DDIs). The model was developed in PK-Sim®, using a large number of clinical studies (66 plasma concentration-time profiles with 36 corresponding fractions excreted in urine) to describe the trimethoprim pharmacokinetics over the entire published dosing range (40 to 960 mg). The key features of the model include intestinal efflux via P-glycoprotein (P-gp), metabolism by CYP3A4, an unspecific hepatic clearance process, and a renal clearance consisting of glomerular filtration and tubular secretion. The DDI performance of this new model was demonstrated by prediction of DDIs and drug-drug-gene interactions (DDGIs) of trimethoprim with metformin, repaglinide, pioglitazone, and rifampicin, with all predicted DDI and DDGI AUClast and Cmax ratios within 1.5-fold of the clinically-observed values. The model will be freely available in the Open Systems Pharmacology model repository, to support DDI studies during drug development.

Effective Removal of Dabigatran by Idarucizumab or Hemodialysis: A Physiologically Based Pharmacokinetic Modeling Analysis.

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.

Physiologically Based Pharmacokinetic Modeling of Metoprolol Enantiomers and α-Hydroxymetoprolol to Describe CYP2D6 Drug-Gene Interactions.

The beta-blocker metoprolol (the sixth most commonly prescribed drug in the USA in 2017) is subject to considerable drug-gene interaction (DGI) effects caused by genetic variations of the CYP2D6 gene. CYP2D6 poor metabolizers (5.7% of US population) show approximately five-fold higher metoprolol exposure compared to CYP2D6 normal metabolizers. This study aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model to predict CYP2D6 DGIs with metoprolol. The metoprolol (R)- and (S)-enantiomers as well as the active metabolite α-hydroxymetoprolol were implemented as model compounds, employing data of 48 different clinical studies (dosing range 5-200 mg). To mechanistically describe the effect of CYP2D6 polymorphisms, two separate metabolic CYP2D6 pathways (α-hydroxylation and O-demethylation) were incorporated for both metoprolol enantiomers. The good model performance is demonstrated in predicted plasma concentration-time profiles compared to observed data, goodness-of-fit plots, and low geometric mean fold errors of the predicted AUClast (1.27) and Cmax values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUClast ratios and 18 out of 18 DGI Cmax ratios were within two-fold of the observed ratios. The newly developed and carefully validated model was applied to calculate dose recommendations for CYP2D6 polymorphic patients and will be freely available in the Open Systems Pharmacology repository.

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug-Drug-Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals.

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.

A Mechanistic, Enantioselective, Physiologically Based Pharmacokinetic Model of Verapamil and Norverapamil, Built and Evaluated for Drug-Drug Interaction Studies.

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 Ctrough 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.

Metabolic transformation of rabbit skeletal muscle cells in primary culture in response to low glucose.

We have investigated the mechanism of the changes in the profile of metabolic enzyme expression that occur in association with fast-to-slow transformation of rabbit skeletal muscle. The hypotheses assessed are: do 1) lowered intracellular ATP concentration or 2) reduction of the muscular glycogen stores act as triggers of metabolic transformation? We find that 3 days of decreased cytosolic ATP content have no impact on the investigated metabolic markers, whereas incubation of the cells with little or no glucose leads to decreases in glycogen in conjunction with decreases in glyceraldehyde-3-phosphate dehydrogenase (GAPDH) promoter activity, GAPDH mRNA and specific GAPDH enzyme activity (indicators of the anaerobic glycolytic pathway), and furthermore to increases in mitochondrial acetoacetyl-CoA thiolase (MAT, also known as ACAT) promoter activity, peroxisome proliferator-activated receptor gamma coactivator 1alpha (PGC-1alpha) expression and citrate synthase (CS) specific enzyme activity (all indicators of oxidative metabolic pathways). The AMP-activated protein kinase (AMPK) activity under these conditions is reduced compared to controls. In experiments with two inhibitors of glycogen degradation we show that the observed metabolic transformation caused by low glucose takes place even if intracellular glycogen content is high. These findings for the first time provide evidence that metabolic adaptation of skeletal muscle cells from rabbit in primary culture can be induced not only by elevation of intracellular calcium concentration or by a rise of AMPK activity, but also by reduction of glucose supply. Contrary to expectations, neither an increase in phospho-AMPK nor a reduction of muscular glycogen content are crucial events in the glucose-dependent induction of metabolic transformation in the muscle cell culture system studied.

Translational PBPK Modeling of the Protein Therapeutic and CD95L Inhibitor Asunercept to Develop Dose Recommendations for Its First Use in Pediatric Glioblastoma Patients.

The protein therapeutic and CD95L inhibitor asunercept is currently under clinical investigation for the treatment of glioblastoma and myelodysplastic syndrome. The purpose of this study was to predict the asunercept pharmacokinetics in children and to give dose recommendations for its first use in pediatric glioblastoma patients. A physiologically-based pharmacokinetic (PBPK) model of asunercept in healthy and diseased adults was successfully developed using the available clinical Phase I and Phase II study data. This model was then extrapolated to different pediatric populations, to predict the asunercept exposure in children and to find equivalent starting doses. Simulation of the asunercept serum concentration-time curves in children between 1⁻18 years of age shows that a dosing regimen based on body weight results in a similar asunercept steady-state exposure in all patients (pediatric or adult) above 12 years of age. For children between 1⁻12 years, higher doses per kg body weight are recommended, with the highest dose for the very young patients. Translational PBPK modeling is strongly encouraged by regulatory agencies to help with the initial dose selection for pediatric trials. To our knowledge, this is the first report of pediatric PBPK to support the dose selection of a therapeutic protein before its administration to children.

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Model of Dabigatran Etexilate, Dabigatran and Dabigatran Glucuronide in Healthy Adults and Renally Impaired Patients.

BACKGROUND AND OBJECTIVES: The thrombin inhibitor dabigatran is administered as the prodrug dabigatran etexilate, which is a substrate of esterases and P-glycoprotein (P-gp). Dabigatran is eliminated via renal excretion but is also a substrate of uridine 5'-diphospho (UDP)-glucuronosyltransferases (UGTs). The objective of this study was to build a physiologically based pharmacokinetic (PBPK) model comprising dabigatran etexilate, dabigatran, and dabigatran 1-O-acylglucuronide to describe the pharmacokinetics in healthy adults and renally impaired patients mechanistically. METHODS: Model development and evaluation were carried out using (i) physicochemical and absorption, distribution, metabolism, and excretion (ADME) parameter values of all three analytes; (ii) concentration-time profiles from 13 studies of healthy and renally impaired individuals after varying doses (0.1-300 mg), intravenous (dabigatran) and oral (dabigatran etexilate) administration, and different formulations of dabigatran etexilate (capsule, solution); and (iii) drug-drug interaction studies of dabigatran with the P-gp perpetrators rifampin (inducer) and clarithromycin (inhibitor). RESULTS: A PBPK model of dabigatran was successfully developed. The predicted area under the plasma concentration-time curve, trough concentration, and half-life values of the assessed clinical studies satisfied the two-fold acceptance criterion. Metabolic clearances of dabigatran etexilate and dabigatran were implemented using data on carboxylesterase 1/2 enzymes and UGT subtype 2B15. In severe renal impairment, the UGT2B15 metabolism and the P-gp transport in the model were reduced to 67% and 65% of the rates in healthy adults. CONCLUSION: This is the first implementation of a PBPK model for dabigatran to distinguish between the prodrug, active moiety, and main active metabolite. Following adjustment of the UGT2B15 metabolism and P-gp transport rates, the PBPK model accurately predicts the pharmacokinetics in renally impaired patients.

Physiologically-Based Pharmacokinetic Models for CYP1A2 Drug-Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam.

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.

Physiologically Based Pharmacokinetic Models for Prediction of Complex CYP2C8 and OATP1B1 (SLCO1B1) Drug-Drug-Gene Interactions: A Modeling Network of Gemfibrozil, Repaglinide, Pioglitazone, Rifampicin, Clarithromycin and Itraconazole.

BACKGROUND: Drug-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. OBJECTIVES: The 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. METHODS: PBPK 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. RESULTS: The 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. CONCLUSIONS: Whole-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.

PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin.

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.