The influence of telmisartan on metformin pharmacokinetics and pharmacodynamics

Modulator Effect of AT1 Receptor Knockdown on THP-1 Macrophage Proinflammatory Activity

Currently, it is known that angiotensin II (AngII) induces inflammation, and an AT1R blockade has anti-inflammatory effects. The use of an AT1 receptor antagonist promotes the inhibition of the secretion of multiple proinflammatory cytokines in macrophages, as well as a decrease in the concentration of reactive oxygen species. The aim of this study was to determine the effect of AT1 receptor gene silencing on the modulation of cytokines (e.g., IL-1β, TNF-α, and IL-10) in THP-1 macrophages and the relation to the gene expression of NF-κB.

MATERIALS AND METHODS

We evaluated the gene expression of PPAR-γ in THP-1 macrophages using PMA (60 ng/mL). For the silencing, cells were incubated with the siRNA for 72 h and telmisartan (10 µM) was added to the medium for 24 h. After that, cells were incubated during 1 and 24 h, respectively, with Ang II (1 µM). The gene expression levels of AT1R, NF-κB, and cytokines (IL-1β, TNF-α, and IL-10) were measured by RT-qPCR.

RESULTS

We observed that silencing of the AT1 receptor causes a decrease in the expression of mRNA of proinflammatory cytokines (IL-1β and TNF-α), NF-κB, and PPAR-γ.

CONCLUSIONS

We conclude that AT1R gene silencing is an alternative to modulating the production of proinflammatory cytokines such as TNF-α and IL-1β via NF-κB in macrophages and having high blood pressure decrease.

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

Transmembrane transport of metformin is highly controlled by transporters including organic cation transporters (OCTs), plasma membrane monoamine transporter (PMAT), and multidrug/toxin extrusions (MATEs). Hepatic OCT1, intestinal OCT3, renal OCT2 on tubule basolateral membrane, and MATE1/2-K on tubule apical membrane coordinately work to control metformin disposition. Drug–drug interactions (DDIs) of metformin occur when co-administrated with perpetrators via inhibiting OCTs or MATEs. We aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model characterizing interplay of OCTs and MATEs in the intestine, liver, and kidney to predict metformin DDIs with cimetidine, pyrimethamine, trimethoprim, ondansetron, rabeprazole, and verapamil. Simulations showed that co-administration of perpetrators increased plasma exposures to metformin, which were consistent with clinic observations. Sensitivity analysis demonstrated that contributions of the tested factors to metformin DDI with cimetidine are gastrointestinal transit rate > inhibition of renal OCT2 ≈ inhibition of renal MATEs > inhibition of intestinal OCT3 > intestinal pH > inhibition of hepatic OCT1. Individual contributions of transporters to metformin disposition are renal OCT2 ≈ renal MATEs > intestinal OCT3 > hepatic OCT1 > intestinal PMAT. In conclusion, DDIs of metformin with perpetrators are attributed to integrated effects of inhibitions of these transporters.

Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues

Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans’ known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T_1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T_1/2 and muscle, kidney, and red blood cells that have longer T_1/2. The highest maximal concentrations (C_max) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalized model can be used as a decision support tool for precision therapy development for individuals.