Metformin's Intrinsic Blood-to-Plasma Partition Ratio (B/P): Reconciling the Perceived High In Vivo B/P > 10 with the In Vitro Equilibrium Value of Unity

Drug transport by red blood cells

This review focuses on the role of human red blood cells (RBCs) as drug carriers. First, a general introduction about RBC physiology is provided, followed by the presentation of several cases in which RBCs act as natural carriers of drugs. This is due to the presence of several binding sites within the same RBCs and is regulated by the diffusion of selected compounds through the RBC membrane and by the presence of influx and efflux transporters. The balance between the influx/efflux and the affinity for these binding sites will finally affect drug partitioning. Thereafter, a brief mention of the pharmacokinetic profile of drugs with such a partitioning is given. Finally, some examples in which these natural features of human RBCs can be further exploited to engineer RBCs by the encapsulation of drugs, metabolites, or target proteins are reported. For instance, metabolic pathways can be powered by increasing key metabolites (i.e., 2,3-bisphosphoglycerate) that affect oxygen release potentially useful in transfusion medicine. On the other hand, the RBC pre-loading of recombinant immunophilins permits increasing the binding and transport of immunosuppressive drugs. In conclusion, RBCs are natural carriers for different kinds of metabolites and several drugs. However, they can be opportunely further modified to optimize and improve their ability to perform as drug vehicles.

An SS31-rapamycin conjugate via RBC hitchhiking for reversing acute kidney injury

Mitochondrial dysfunction plays a major role in driving acute kidney injury (AKI) via alteration in energy and oxygen supply, which creates further ROS and inflammatory responses. However, mitochondrial targeting medicine in recovering AKI is challenging. Herein, we conjugated SS31, a mitochondria-targeted antioxidant tetrapeptide connecting a cleavable linker to rapamycin (Rapa), which provided specific interaction with FK506-binding protein (FKBP) in the RBCs. Once entering the bloodstream, SS31-Rapa could be directed to the intracellular space of RBCs, allowing the slow diffusion of the conjugate to tissues via the concentration gradient. The new RBC hitchhiking strategy enables the encapsulation of conjugate into RBC via a less traumatic and more natural and permissive manner, resulting in prolonging the t1/2 of SS31 by 6.9 folds. SS31-Rapa underwent the direct cellular uptake, instead of the lysosomal pathway, released SS31 in response to activated caspase-3 stimulation in apoptotic cells, favoring the mitochondrial accumulation of SS31. Combined with autophagy induction associated with Rapa, a single dose of SS31-Rapa can effectively reverse cisplatin and ischemia reperfusion-induced AKI. This work thus highlights a simple and effective RBC hitchhiking strategy and a clinically translatable platform technology to improve the outcome of other mitochondrial dysfunctional related diseases.

Design and Measurement of Drug Tissue Concentration Asymmetry and Tissue Exposure-Effect (Tissue PK-PD) Evaluation

The "free drug hypothesis" assumes that, in the absence of transporters, the steady state free plasma concentrations equal to that at the site of action that elicit pharmacologic effects. While it is important to utilize the free drug hypothesis, exceptions exist that the free plasma exposures, either at C_max, C_trough, and C_average, or at other time points, cannot represent the corresponding free tissue concentrations. This "drug concentration asymmetry" in both total and free form can influence drug disposition and pharmacological effects. In this review, we first discuss options to assess total and free drug concentrations in tissues. Then various drug design strategies to achieve concentration asymmetry are presented. Last, the utilities of tissue concentrations in understanding exposure-effect relationships and translational projections to humans are discussed for several therapeutic areas and modalities. A thorough understanding in plasma and tissue exposures correlation with pharmacologic effects can provide insightful guidance to aid drug discovery.

Metformin Transport Rates Between Plasma and Red Blood Cells in Humans

Background

Metformin has been used for the treatment of type 2 diabetes for over 60 years; however, its mechanism of pharmacological action is not fully clear. Different hypotheses exist regarding metformin distribution and redistribution mechanisms between plasma and erythrocytes/red blood cells (RBCs).

Objective

We aimed to test the hypothesis that the metformin distribution between plasma and RBC occurs via concentration difference-driven passive transport and estimated transport rate coefficient values based on metformin concentration time series in plasma and RBCs from in vivo studies.

Methods

An ordinary differential equation (ODE) system with two compartments was used to describe diffusion-based passive transport between plasma and RBCs. Metformin concentration time series in plasma and RBCs of 35 individuals were used for metformin transport parametrization. Plasma concentration has been approximated by biexponential decline.

Results

A single passive transport coefficient, k = 0.044 ± 0.014 (h^–1), can be applied, describing the uptake and release transport rate versus the linear equation v = k × (M_pl − M_RBC), where M_pl is the metformin concentration in plasma and M_RBC is the metformin concentration in RBCs.

Conclusions

Our research suggests that passive transport can explain metformin distribution dynamics between plasma and RBCs because transport speed is proportional to the metformin concentration difference and independent of the transport direction. Concentration difference-driven passive transport can explain the mechanism of faster metformin distribution to RBCs the first few hours after administration, and faster release and domination of the redistribution transport rate after metformin concentration in plasma becomes smaller than in RBCs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-021-01058-2.

Meta-Assessment of Metformin Absorption and Disposition Pharmacokinetics in Nine Species

The objective of this study was to systematically assess literature datasets and quantitatively analyze metformin PK in plasma and some tissues of nine species. The pharmacokinetic (PK) parameters and profiles of metformin in nine species were collected from the literature. Based on a simple allometric scaling, the systemic clearances (CL) of metformin in these species highly correlate with body weight (BW) (R2 = 0.85) and are comparable to renal plasma flow in most species except for rabbit and cat. Reported volumes of distribution (VSS) varied appreciably (0.32 to 10.1 L/kg) among species. Using the physiological and anatomical variables for each species, a minimal physiologically based pharmacokinetic (mPBPK) model consisting of blood and two tissue compartments (Tissues 1 and 2) was used for modeling metformin PK in the nine species. Permeability-limited distribution (low fd1 and fd2) and a single tissue-to-plasma partition coefficient (Kp) value for Tissues 1 and 2 were applied in the joint mPBPK fitting. Nonlinear regression analysis for common tissue distribution parameters along with species-specific CL values reasonably captured the plasma PK profiles of metformin across most species, except for rat and horse with later time deviations. In separate fittings of the mPBPK model to each species, Tissue 2 was considered as slowly-equilibrating compartment consisting of muscle and skin based on in silico calculations of the mean transit times through tissues. The well-fitted mPBPK model parameters for absorption and disposition PK of metformin for each species were compared with in vitro/in vivo results found in the literature with regard to the physiological details and physicochemical properties of metformin. Bioavailability and absorption rates decreased with the increased BW among the species. Tissues such as muscle dominate metformin distribution with low permeability and partitioning while actual tissue concentrations found in rats and mice show likely transporter-mediated uptake in liver, kidney, and gastrointestinal tissues. Metformin has diverse pharmacologic actions, and this assessment revealed allometric relationships in its absorption and renal clearance but considerable variability in actual and modeled tissue distribution probably caused by transporter differences.

Comparison of the gamma-Pareto convolution with conventional methods of characterising metformin pharmacokinetics in dogs

A model was developed for long term metformin tissue retention based upon temporally inclusive models of serum/plasma concentration (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ C $$\end{document}C) having power function tails called the gamma-Pareto type I convolution (GPC) model and was contrasted with biexponential (E2) and noncompartmental (NC) metformin models. 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The GPC, E2 and NC models were applied to a total of 148 serum samples drawn from 20 min to 72 h following bolus intravenous metformin in seven healthy mongrel dogs. The GPC model was used to calculate area under the curve (AUC), clearance (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ CL $$\end{document}CL), and functions of time, f(t), for drug mass remaining (M), apparent volume of distribution (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V_{d}$$\end{document}Vd), as well as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t_{1/2}\ f(t)$$\end{document}t1/2f(t) for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ C $$\end{document}C, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ M $$\end{document}M and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V_{d}$$\end{document}Vd. 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The GPC \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ CL $$\end{document}CL-values were significantly less than the corresponding NC and E2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ CL $$\end{document}CL-values of 104.7% and 123.7% of RPF, respectively. The GPC plasma/serum only model predicted 78.9% drug \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ M $$\end{document}M average urinary recovery at 72 h; similar to prior human urine drug \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ M $$\end{document}M collection results. The GPC model \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t_{1/2}$$\end{document}t1/2 of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ M $$\end{document}M, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ C $$\end{document}C and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V_d$$\end{document}Vd, were asymptotically proportional to elapsed time, with a constant limiting \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t_{1/2}$$\end{document}t1/2 ratio of M/C averaging 7.0 times, a result in keeping with prior simultaneous \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ C $$\end{document}C and urine \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ M $$\end{document}M collection studies and exhibiting a rate of apparent volume growth of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V_d$$\end{document}Vd that achieved limiting constant values. A simulated constant average drug mass multidosing protocol exhibited increased \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V_d$$\end{document}Vd and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t_{1/2}$$\end{document}t1/2 with elapsing time, effects that have been observed experimentally during same-dose multidosing. The GPC heavy-tailed models explained multiple documented phenomena that were unexplained with lighter-tailed models.

Abemaciclib Inhibits Renal Tubular Secretion Without Changing Glomerular Filtration Rate

Abemaciclib, an inhibitor of cyclin dependent kinases 4 and 6, is indicated for metastatic breast cancer treatment. Reversible increases in serum creatinine levels of ~15-40% over baseline have been observed following abemaciclib dosing. This study assessed the in vitro and clinical inhibition of renal transporters by abemaciclib and its metabolites using metformin (a clinically relevant transporter substrate), in a clinical study that quantified glomerular filtration and iohexol clearance. In vitro, abemaciclib inhibited metformin uptake by organic cation transporter 2, multidrug and toxin extrusion (MATE)1, and MATE2-K transporters with a half-maximal inhibitory concentration of 0.4-3.8 μM. Clinically, abemaciclib significantly increased metformin exposure but did not significantly affect measured glomerular filtration rate, serum neutrophil gelatinase-associated lipocalin (NGAL), serum cystatin-C, or the urinary markers of kidney tubular injury, NGAL and kidney injury molecule-1.

Physiologically-Based Pharmacokinetic Modeling Analysis for Quantitative Prediction of Renal Transporter-Mediated Interactions Between Metformin and Cimetidine

Metformin is an important antidiabetic drug and often used as a probe for drug–drug interactions (DDIs) mediated by renal transporters. Despite evidence supporting the inhibition of multidrug and toxin extrusion proteins as the likely DDI mechanism, the previously reported physiologically‐based pharmacokinetic (PBPK) model required the substantial lowering of the inhibition constant values of cimetidine for multidrug and toxin extrusion proteins from those obtained in vitro to capture the clinical DDI data between metformin and cimetidine.¹ We constructed new PBPK models in which the transporter‐mediated uptake of metformin is driven by a constant membrane potential. Our models successfully captured the clinical DDI data using in vitro inhibition constant values and supported the inhibition of multidrug and toxin extrusion proteins by cimetidine as the DDI mechanism upon sensitivity analysis and data fitting. Our refined PBPK models may facilitate prediction approaches for DDI involving metformin using in vitro inhibition constant values.

Sustained low-efficiency dialysis for metformin-associated lactic acidosis in patients with acute kidney injury

BACKGROUND

The choice of the specific modality and treatment duration of renal replacement therapy (RRT) to adopt in metformin-associated lactic acidosis (MALA) is still debated. We aimed to verify if sustained low-efficiency dialysis (SLED) is a rational choice in patients with MALA and acute kidney injury (AKI).

METHODS

We collected serial serum metformin measurements, clinical parameters, and outcome data in ten consecutive patients (mean age 77 years [range 58-88], 5 males) admitted to our renal intensive care unit for suspected MALA associated with AKI and hemodynamic instability. Patients underwent a 16-h SLED session performed with either conventional dialysis machines or machines for continuous RRT (CRRT). A 2-compartment open-infusion pharmacokinetic model with first-order elimination was fitted to each subject's serum concentration-time data to model post-SLED rebound and predict the need for further treatments.

RESULTS

Two patients died within 24 h after SLED start. Three patients needed one further dialysis session. Surviving patients (n = 8) were dialysis-free at discharge. Metformin levels were in the toxic range at baseline (median [range] 32.5 mg/l [13.6-75.6]) and decreased rapidly by the end of SLED (8.1 mg/l [4.5-15.8], p < 0.001 vs. baseline), without differences according to the dialysis machine used (p = 0.84). We observed a slight 4-h post-SLED rebound (9.7 mg/l [3.5-22.0]), which could be predicted by our pharmacokinetic model. Accordingly, we predicted that the majority of patients would need one additional dialysis session performed the following day to restore safe metformin levels.

CONCLUSIONS

A 16-h SLED session, performed with either conventional dialysis machines or CRRT machines, allows effective metformin removal in patients with MALA and AKI. However, due to possible post-SLED rebound in serum metformin levels, one additional dialysis treatment is required the following day in the majority of patients.

Treatment of Metformin Intoxication Complicated by Lactic Acidosis and Acute Kidney Injury: The Role of Prolonged Intermittent Hemodialysis

Metformin intoxication with lactic acidosis, a potentially lethal condition, may develop in diabetic patients when the drug dose is inappropriate and/or its clearance is reduced. Diagnosis and therapy may be delayed due to nonspecific symptoms at presentation, with severe anion gap metabolic acidosis and elevated serum creatinine values being the most prominent laboratory findings. Confirmation requires measurement of serum metformin by high-performance liquid chromatography-tandem mass spectrometry, but this technique is available only at specialized institutions and cannot be relied on as a guide to immediate treatment. Thus, based on strong clinical suspicion, renal replacement therapy must be started promptly to achieve efficient drug clearance and correct the metabolic acidosis. However, because metformin accumulates in the intracellular compartment with prolonged treatment, a rebound in serum concentrations due to redistribution is expected at the end of dialysis. We report a case of metformin intoxication, severe lactic acidosis, and acute kidney injury in a diabetic patient with pre-existing chronic kidney disease stage 3, treated effectively with sustained low-efficiency dialysis. We discuss the pathophysiology, differential diagnosis, and treatment options and highlight specific pharmacokinetic issues that should be considered in selecting the appropriate modality of renal replacement therapy.

Metformin and cimetidine: Physiologically based pharmacokinetic modelling to investigate transporter mediated drug-drug interactions

Metformin is used as a probe for OCT2 mediated transport when investigating possible DDIs with new chemical entities. The aim of the current study was to investigate the ability of physiologically-based pharmacokinetic (PBPK) models to simulate the effects of OCT and MATE inhibition by cimetidine on metformin kinetics. PBPK models were developed, incorporating mechanistic kidney and liver sub-models for metformin (OCT and MATE substrate) and a mechanistic kidney sub-model for cimetidine. The models were used to simulate inhibition of the MATE1, MATE2-K, OCT1 and OCT2 mediated transport of metformin by cimetidine. Assuming competitive inhibition and using cimetidine Ki values determined in vitro, the predicted metformin AUC ratio was 1.0 compared to an observed value of 1.46. The observed AUC ratio could only be recovered with this model when the cimetidine Ki for OCT2 was decreased 1000-fold or the Ki's for both OCT1 and OCT2 were decreased 500-fold. An alternative description of metformin renal transport by OCT1 and OCT2, incorporating electrochemical modulation of the rate of metformin uptake together with 8-18-fold decreases in cimetidine Ki's for OCTs and MATEs, allowed recovery of the extent of the observed effect of cimetidine on metformin AUC. While the final PBPK model has limitations, it demonstrates the benefit of allowing for the complexities of passive permeability combined with active cellular uptake modulated by an electrochemical gradient and active efflux.