A Whole-Body Physiologically Based Pharmacokinetic Model Characterizing Interplay of OCTs and MATEs in Intestine, Liver and Kidney to Predict Drug-Drug Interactions of Metformin with Perpetrators

No dose adjustment of metformin or substrates of organic cation transporters (OCT)1 and OCT2 and multidrug and toxin extrusion protein (MATE)1/2K with fostemsavir coadministration based on modeling approaches

Fostemsavir is an approved gp120-directed attachment inhibitor and prodrug for the treatment of human immunodeficiency virus type 1 infection in combination with other antiretrovirals (ARVs) in heavily treatment-experienced adults with multi-drug resistance, intolerance, or safety concerns with their current ARV regimen. Initial in vitro studies indicated that temsavir, the active moiety of fostemsavir, and its metabolites, inhibited organic cation transporter (OCT)1, OCT2, and multidrug and toxin extrusion transporters (MATEs) at tested concentration of 100 uM, although risk assessment based on the current Food and Drug Administration in vitro drug-drug interaction (DDI) guidance using the mechanistic static model did not reveal any clinically relevant inhibition on OCTs and MATEs. However, a DDI risk was flagged with EMA static model predictions. Hence, a physiologically based pharmacokinetic (PBPK) model of fostemsavir/temsavir was developed to further assess the DDI risk potential of OCT and MATEs inhibition by temsavir and predict changes in metformin (a sensitive OCT and MATEs substrate) exposure. No clinically relevant impact on metformin concentrations across a wide range of temsavir concentrations was predicted; therefore, no dose adjustment is recommended for metformin when co-administered with fostemsavir.

Assessment of Quinidine-Induced Torsades de Pointes Risks Using a Whole-Body Physiologically Based Pharmacokinetic Model Linked to Cardiac Ionic Current Inhibition

The lethality of torsades de pointes (TdP) by drugs is one of main reasons that some drugs were withdrawn from the market. In order to assess drug-induced TdP risks, a model of cardiac ionic current suppression in human ventricular myocytes (ToR-ORd model), combined with the maximum effective free therapeutic plasma concentration or the maximum effective free therapeutic myocyte concentration was often used, with the latter proved to be more relevant and more accurate. We aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model, incorporated with a human cardiomyocyte pharmacodynamic (PD) model, to provide a comprehensive assessment of drug-induced TdP risks in normal and specific scenarios. Quinidine served as an example to validate the PBPK-PD model via predicting plasma quinidine concentrations and quinidine-induced changes in QT interval (ΔQTc). The predicted plasma quinidine concentrations and ΔQTc values following oral administration or intravenous administration of quinidine were comparable to clinic observations. Visual predictive checks showed that most of the observed plasma concentrations and ΔQTc values fell within the 5th and 95th percentiles of simulations. The validated PBPK-PD model was further applied to assess the TdP risks using frequencies of early afterdepolarization and long-QT syndrome occurrence in 4 scenarios, such as therapeutic dose, supra-therapeutic dose, alkalosis, and hyperkalemia in 200 human subjects. In conclusion, the developed PBPK-PD model may be applied to predict the quinidine pharmacokinetics and quinidine-induced TdP risks in healthy subjects, but also simulate quinidine-induced TdP risks under disease conditions, such as hypokalemia and alkalosis.

Metformin does not slow cyst growth in the PCK rat model of polycystic kidney disease

Metformin (MET) has the potential to activate p-AMPK and block mTORC1-induced proliferation of tubular cells in PKD kidneys. The aim of this study was to determine the effects of MET on cyst growth, kidney function, AMPK and mTOR signaling, and lactate levels in male PCK rats, a Pkhd1 gene mutation model of human autosomal recessive polycystic kidney disease (ARPKD). MET 300 mg/kg/day IP from days 28 to 84 of age resulted in a mean serum metformin level that was 10 times the upper limit of therapeutic, no effect on cyst indices, nephrotoxicity, and increased serum lactate. MET 150 mg/kg resulted in a therapeutic serum metformin level but had no effect on kidney weight, cyst indices, kidney function, or mTOR and autophagy proteins. In summary, a standard dose of MET was ineffective in reducing PKD, did not activate p-AMPK or suppress mTOR and the higher dose resulted in increased lactate levels and nephrotoxicity. In conclusion, the study dampens enthusiasm for human studies of MET in PKD. Doubling the metformin dose resulted in a 10-fold increase in mean blood levels and toxicity suggesting that the dosage range between therapeutic and toxic is narrow.

Change of metformin concentrations in the liver as a pharmacological target site of metformin after long-term combined treatment with ginseng berry extract

Metformin as an oral glucose-lowering drug is used to treat type 2 diabetic mellitus. Considering the relatively high incidence of cardiovascular complications and other metabolic diseases in diabetic mellitus patients, a combination of metformin plus herbal supplements is a preferrable way to improve the therapeutic outcomes of metformin. Ginseng berry, the fruit of Panax ginseng Meyer, has investigated as a candidate in metformin combination mainly due to its anti-hyperglycemic, anti-hyperlipidemic, anti-obesity, anti-hepatic steatosis and anti-inflammatory effects. Moreover, the pharmacokinetic interaction of metformin via OCTs and MATEs leads to changes in the efficacy and/or toxicity of metformin. Thus, we assessed how ginseng berry extract (GB) affects metformin pharmacokinetics in mice, specially focusing on the effect of the treatment period (i.e., 1-day and 28-day) of GB on metformin pharmacokinetics. In 1-day and 28-day co-treatment of metformin and GB, GB did not affect renal excretion as a main elimination route of metformin and GB therefore did not change the systemic exposure of metformin. Interestingly, 28-day co-treatment of GB increased metformin concentration in the livers (i.e., 37.3, 59.3% and 60.9% increases versus 1-day metformin, 1-day metformin plus GB and 28-day metformin groups, respectively). This was probably due to the increased metformin uptake via OCT1 and decreased metformin biliary excretion via MATE1 in the livers. These results suggest that co-treatment of GB for 28 days (i.e., long-term combined treatment of GB) enhanced metformin concentration in the liver as a pharmacological target tissue of metformin. However, GB showed a negligible impact on the systemic exposure of metformin in relation to its toxicity (i.e., renal and plasma concentrations of metformin).

A Review of the Impact of Pharmacogenetics and Metabolomics on the Efficacy of Metformin in Type 2 Diabetes

Metformin is the most often prescribed drug for people with type 2 diabetes (T2D). More than 120 million patients with T2D use metformin worldwide. However, monotherapy fails to achieve glycemic control in a third of the treated patients. Genetics contribute to some of the inter-individual variations in glycemic response to metformin. Numerous pharmacogenetic studies have demonstrated that variations in genes related to pharmacokinetics and pharmacodynamics of metformin's encoding transporters are mainly associated with metformin response. The goal of this review is to evaluate the current state of metformin pharmacogenetics and metabolomics research, discuss the clinical and scientific issues that need to be resolved in order to increase our knowledge of patient response variability to metformin, and how to improve patient outcomes. Metformin's hydrophilic nature and absorption as well as its action mechanism and effectiveness on T2D initiation are discussed. The impacts of variations associated with various genes are analysed to identify and evaluate the effect of genetic polymorphisms on the therapeutic activity of metformin. The metabolic pattern of T2D and metformin is also indicated. This is to emphasise that studies of pharmacogenetics and metabolomics could expand our knowledge of metformin response in T2D.

Bile duct ligation differently regulates protein expressions of organic cation transporters in intestine, liver and kidney of rats through activation of farnesoid X receptor by cholate and bilirubin

Body is equipped with organic cation transporters (OCTs). These OCTs mediate drug transport and are also involved in some disease process. We aimed to investigate whether liver failure alters intestinal, hepatic and renal Oct expressions using bile duct ligation (BDL) rats. Pharmacokinetic analysis demonstrates that BDL decreases plasma metformin exposure, associated with decreased intestinal absorption and increased urinary excretion. Western blot shows that BDL significantly downregulates intestinal Oct2 and hepatic Oct1 but upregulates renal and hepatic Oct2. In vitro cell experiments show that chenodeoxycholic acid (CDCA), bilirubin and farnesoid X receptor (FXR) agonist GW4064 increase OCT2/Oct2 but decrease OCT1/Oct1, which are remarkably attenuated by glycine-β-muricholic acid and silencing FXR. Significantly lowered intestinal CDCA and increased plasma bilirubin levels contribute to different Octs regulation by BDL, which are confirmed using CDCA-treated and bilirubin-treated rats. A disease-based physiologically based pharmacokinetic model characterizing intestinal, hepatic and renal Octs was successfully developed to predict metformin pharmacokinetics in rats. In conclusion, BDL remarkably downregulates expressions of intestinal Oct2 and hepatic Oct1 protein while upregulates expressions of renal and hepatic Oct2 protein in rats, finally, decreasing plasma exposure and impairing hypoglycemic effects of metformin. BDL differently regulates Oct expressions via Fxr activation by CDCA and bilirubin.

Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward

The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug-drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.