Pharmacokinetic-Pharmacodynamic Modeling of Metformin for the Treatment of Type II Diabetes Mellitus

Metformin: A Prospective Alternative for the Treatment of Chronic Pain

Metformin (biguanide) is a drug widely used for the treatment of type 2 diabetes. This drug has been used for 60 years as a highly effective antihyperglycemic agent. The search for the mechanism of action of metformin has produced an enormous amount of research to explain its effects on gluconeogenesis, protein metabolism, fatty acid oxidation, oxidative stress, glucose uptake, autophagy and pain, among others. It was only up the end of the 1990s and beginning of this century that some of its mechanisms were revealed. Metformin induces its beneficial effects in diabetes through the activation of a master switch kinase named AMP-activated protein kinase (AMPK). Two upstream kinases account for the physiological activation of AMPK: liver kinase B1 and calcium/calmodulin-dependent protein kinase kinase 2. Once activated, AMPK inhibits the mechanistic target of rapamycin complex 1 (mTORC1), which in turn avoids the phosphorylation of p70 ribosomal protein S6 kinase 1 and phosphatidylinositol 3-kinase/protein kinase B signaling pathways and reduces cap-dependent translation initiation. Since metformin is a disease-modifying drug in type 2 diabetes, which reduces the mTORC1 signaling to induce its effects on neuronal plasticity, it was proposed that these mechanisms could also explain the antinociceptive effect of this drug in several models of chronic pain. These studies have highlighted the efficacy of this drug in chronic pain, such as that from neuropathy, insulin resistance, diabetic neuropathy, and fibromyalgia-type pain. Mounting evidence indicates that chronic pain may induce anxiety, depression and cognitive impairment in rodents and humans. Interestingly, metformin is able to reverse some of these consequences of pathological pain in rodents. The purpose of this review was to analyze the current evidence about the effects of metformin in chronic pain and three of its comorbidities (anxiety, depression and cognitive impairment).

Mathematical model of blood glucose dynamics by emulating the pathophysiology of glucose metabolism in type 2 diabetes mellitus

Mathematical modelling has established itself as a theoretical tool to understand fundamental aspects of a variety of medical-biological phenomena. The predictive power of mathematical models on some chronic conditions has been helpful in its proper prevention, diagnosis, and treatment. Such is the case of the modelling of glycaemic dynamics in type 2 diabetes mellitus (T2DM), whose physiology-based mathematical models have captured the metabolic abnormalities of this disease. Through a physiology-based pharmacokinetic-pharmacodynamic approach, this work addresses a mathematical model whose structure starts from a model of blood glucose dynamics in healthy humans. This proposal is capable of emulating the pathophysiology of T2DM metabolism, including the effect of gastric emptying and insulin enhancing effect due to incretin hormones. The incorporation of these effects lies in the implemented methodology since the mathematical functions that represent metabolic rates, with a relevant contribution to hyperglycaemia, are adjusting individually to the clinical data of patients with T2DM. Numerically, the resulting model successfully simulates a scheduled graded intravenous glucose test and oral glucose tolerance tests at different doses. The comparison between simulations and clinical data shows an acceptable description of the blood glucose dynamics in T2DM. It opens the possibility of using this model to develop model-based controllers for the regulation of blood glucose in T2DM.

Therapeutic effect of the epidermal growth factor on diabetic foot ulcer and the underlying mechanisms

The present study aimed to explore the therapeutic effect and underlying mechanism of epidermal growth factor (EGF) on the wound healing of diabetic foot ulcers (DFU). A total of 48 rabbits with DFU were randomly divided into 2 groups, comprising the treatment and control groups. Full-thickness skin (10×10 mm) was excised from the thigh of each rabbit. The wounds in the treatment group were treated with 100 mg/l EGF once a day for 1 month. The control group received no treatment. At 20 days following treatment, new granulation tissues that formed beyond the edge of the wound were collected for subsequent analysis. Tissues from rabbits in the treatment group produced a greater number of fibroblasts, which exhibited a fibroblastic morphology when compared with those in the control group. In the treatment group, a larger number of these fibroblasts were observed as clusters, and there were numerous blood vessels when compared with the control group. The fibroblasts in the control group exhibited an irregular morphology, contained fewer organelles and the surrounding collagenous fibers were sparse. These fibroblasts also demonstrated a disordered arrangement and it was revealed that the wound healed at a slower rate compared with the treatment group. Endogenous EGF mRNA detection revealed that there was a significant difference (P<0.05) in the relative gray value of EGF mRNA between the treatment (103.27±4.27) and control (63.88±4.36) groups. In conclusion, EGF may accelerate the healing of DFU, and exogenous EGF treatment may upregulate the expression of EGF mRNA in newly generated tissues.

Metformin Impairs Glutamine Metabolism and Autophagy in Tumour Cells

Metformin has been shown to inhibit glutaminase (GLS) activity and ammonia accumulation thereby reducing the risk of hepatic encephalopathy in type 2 diabetic patients. Since tumour cells are addicted to glutamine and often show an overexpression of glutaminase, we hypothesize that the antitumoral mechanism of metformin could be ascribed to inhibition of GLS and reduction of ammonia and ammonia-induced autophagy. Our results show that, in different tumour cell lines, micromolar doses of metformin prevent cell growth by reducing glutamate, ammonia accumulation, autophagy markers such as MAP1LC3B-II and GABARAP as well as degradation of long-lived proteins. Reduced autophagy is then accompanied by increased BECN1/BCL2 binding and apoptotic cell death. Interestingly, GLS-silenced cells reproduce the effect of metformin treatment showing reduced MAP1LC3B-II and GABARAP as well as ammonia accumulation. Since metformin is used as adjuvant drug to increase the efficacy of cisplatin-based neoadjuvant chemotherapy, we co-treated tumour cells with micromolar doses of metformin in the presence of cisplatin observing a marked reduction of MAP1LC3B-II and an increase of caspase 3 cleavage. In conclusion, our work demonstrates that the anti-tumoral action of metformin is due to the inhibition of glutaminase and autophagy and could be used to improve the efficacy of chemotherapy.

Limited sampling strategy for determining metformin area under the plasma concentration-time curve

AIM

The aim was to develop and validate limited sampling strategy (LSS) models to predict the area under the plasma concentration-time curve (AUC) for metformin.

METHODS

Metformin plasma concentrations (n = 627) at 0-24 h after a single 500 mg dose were used for LSS development, based on all subsets linear regression analysis. The LSS-derived AUC(0,24 h) was compared with the parameter 'best estimate' obtained by non-compartmental analysis using all plasma concentration data points. Correlation between the LSS-derived and the best estimated AUC(0,24 h) (r(2) ), bias and precision of the LSS estimates were quantified. The LSS models were validated in independent cohorts.

RESULTS

A two-point (3 h and 10 h) regression equation with no intercept estimated accurately the individual AUC(0,24 h) in the development cohort: r(2)  = 0.927, bias (mean, 95% CI) -0.5, -2.7-1.8% and precision 6.3, 4.9-7.7%. The accuracy of the two point LSS model was verified in study cohorts of individuals receiving single 500 or 1000 mg (r(2)  = -0.933-0.934) or seven 1000 mg daily doses (r(2)  = 0.918), as well as using data from 16 published studies covering a wide range of metformin doses, demographics, clinical and experimental conditions (r(2)  = 0.976). The LSS model reproduced previously reported results for effects of polymorphisms in OCT2 and MATE1 genes on AUC(0,24 h) and renal clearance of metformin.

CONCLUSIONS

The two point LSS algorithm may be used to assess the systemic exposure to metformin under diverse conditions, with reduced costs of sampling and analysis, and saving time for both subjects and investigators.

Ambient and Unobtrusive Cardiorespiratory Monitoring Techniques

Monitoring vital signs through unobtrusive means is a goal which has attracted a lot of attention in the past decade. This review provides a systematic and comprehensive review over the current state of the field of ambient and unobtrusive cardiorespiratory monitoring. To this end, nine different sensing modalities which have been in the focus of current research activities are covered: capacitive electrocardiography, seismo- and ballistocardiography, reflective photoplethysmography (PPG) and PPG imaging, thermography, methods relying on laser or radar for distance-based measurements, video motion analysis, as well as methods using high-frequency electromagnetic fields. Current trends in these subfields are reviewed. Moreover, we systematically analyze similarities and differences between these methods with respect to the physiological and physical effects they sense as well as the resulting implications. Finally, future research trends for the field as a whole are identified.