Metabolomic Approaches to Investigate the Effect of Metformin: An Overview

Combined untargeted metabolomics and network pharmacology approaches to reveal the therapeutic role of withanolide B in psoriasis

Psoriasis is a refractory inflammatory skin disorder in which keratinocyte hyperproliferation is a crucial pathogenic factor. Up to now, it is commonly acknowledged that psoriasis has a tight connection with metabolic disorders. Withanolides from Datura metel L. (DML) have been proved to possess anti-inflammatory and anti-proliferative properties in multiple diseases including psoriasis. Withanolide B (WB) is one of the abundant molecular components in DML. However, existing experimental studies regarding the potential effects and mechanisms of WB on psoriasis still remain lacking. Present study aimed to integrate network pharmacology and untargeted metabolomics strategies to investigate the therapeutic effects and mechanisms of WB on metabolic disorders in psoriasis. In our study, we observed that WB might effectively improve the symptoms of psoriasis and alleviate the epidermal hyperplasia in imiquimod (IMQ)-induced psoriasis-like mice. Both network pharmacology and untargeted metabolomics results suggested that arachidonic acid metabolism and arginine and proline metabolism pathways were linked to the treatment of psoriasis with WB. Meanwhile, we also found that WB may affect the expression of regulated enzymes 5-lipoxygenase (5-LOX), 12-LOX, ornithine decarboxylase 1 (ODC1) and arginase 1 (ARG1) in the arachidonic acid metabolism and arginine and proline metabolism pathways. In summary, this paper showed the potential metabolic mechanisms of WB against psoriasis and suggested that WB would have greater potential in psoriasis treatment.

Region-Specific Effects of Metformin on Gut Microbiome and Metabolome in High-Fat Diet-Induced Type 2 Diabetes Mouse Model

The glucose-lowering drug metformin alters the composition of the gut microbiome in patients with type 2 diabetes mellitus (T2DM) and other diseases. Nevertheless, most studies on the effects of this drug have relied on fecal samples, which provide limited insights into its local effects on different regions of the gut. Using a high-fat diet (HFD)-induced mouse model of T2DM, we characterize the spatial variability of the gut microbiome and associated metabolome in response to metformin treatment. Four parts of the gut as well as the feces were analyzed using full-length sequencing of 16S rRNA genes and targeted metabolomic analyses, thus providing insights into the composition of the microbiome and associated metabolome. We found significant differences in the gut microbiome and metabolome in each gut region, with the most pronounced effects on the microbiomes of the cecum, colon, and feces, with a significant increase in a variety of species belonging to Akkermansiaceae, Lactobacillaceae, Tannerellaceae, and Erysipelotrichaceae. Metabolomics analysis showed that metformin had the most pronounced effect on microbiome-derived metabolites in the cecum and colon, with several metabolites, such as carbohydrates, fatty acids, and benzenoids, having elevated levels in the colon; however, most of the metabolites were reduced in the cecum. Thus, a wide range of beneficial metabolites derived from the microbiome after metformin treatment were produced mainly in the colon. Our study highlights the importance of considering gut regions when understanding the effects of metformin on the gut microbiome and metabolome.

The association between hydration state and the metabolism of phospholipids and amino acids among young adults: a metabolomic analysis

Background

Water is vital for humans' survival and general health, which is involved in various metabolic activities.

Objectives

The aim of this study was to investigate the variation in urine metabolome and associated metabolic pathways among people with different hydration states.

Methods

A metabolomic analysis was conducted using 24-h urine samples collected during a cross-sectional study on fluid intake behavior from December 9 to 11, 2021, in Hebei, China. Subjects were divided into the optimal hydration (OH, ≤500 mOsm/kg, n = 21), middle hydration (500-800 mOsm/kg, n = 33), and hypohydration groups (HH, >800 mOsm/kg, n = 13) based on the 3-d average 24-h urine osmolality. Collected 24-h urine samples from 67 subjects (43 males and 34 females) were analyzed for urine metabolome using liquid chromatography-MS.

Results

The untargeted metabolomic analysis yielded 1055 metabolites by peak intensities. Integrating the results of the orthogonal projections to latent structures discriminant analysis and fold change test, 115 differential metabolites between the OH and HH groups, including phospholipids (PLs) and lysophospholipids, were identified. Among the 115 metabolites identified as differential metabolites, 85 were recorded by the Human Metabolome Database and uploaded to the Kyoto Encyclopedia of Genes and Genomes databases for pathway analysis. Twenty-one metabolic pathways were recognized. Phenylalanine metabolism (0.50, P = 0.007), phenylalanine, tyrosine, and tryptophan biosynthesis (0.50, P = 0.051), glycerophospholipid metabolism (0.31, P < 0.001), sphingolipid metabolism (0.27, P = 0.029), and cysteine and methionine metabolism (0.10, P = 0.066) had the leading pathway impacts.

Conclusions

We found variations in the urinary PLs and amino acids among subjects with different hydration states. Pathways associated with these differential metabolites could further impact various physiologic and pathologic functions. A more comprehensive and in-depth investigation of the physiologic and pathologic impact of the hydration state and the underlying mechanisms to elucidate and advocate optimal fluid intake habits is needed.This trial was registered at Chinese Clinical Trial Registry as ChiCTR2100045268.

Identification of glucose-independent and reversible metabolic pathways associated with anti-proliferative effect of metformin in liver cancer cells

INTRODUCTION

Despite the ability of cancer cells to survive glucose deprivation, most studies on anti-cancer effect of metformin explored its impact on glucose metabolism. No study ever examined whether its anti-cancer effect is reversible. Existing evidences warrant understanding of glucose-independent non-cytotoxic anti-proliferative effect of metformin to rationalize its role in liver cancer.

OBJECTIVES

Characterization of glucose-independent anti-proliferative metabolic effects of metformin as well as analysis of their reversibility in liver cancer cells.

METHODOLOGY

The dose-dependent effects of metformin on HepG2 cells were examined in presence and absence of glucose. The longitudinal evolution of metabolome was analyzed along with gene and protein expression as well as their correlations with and reversibility of cellular phenotype and metabolic signatures.

RESULTS

Metformin concentrations up to 2.5 mM were found to be anti-proliferative irrespective of presence of glucose without significant increase in cytotoxicity. Apart from mitochondrial impairment, derangement of fatty acid desaturation, one-carbon, glutathione, and polyamine metabolism were associated with metformin treatment irrespective of glucose supplementation. Depletion of pantothenic acid, downregulation of essential amino acid uptake and metabolism alongside purine salvage were identified as novel glucose-independent effects of metformin. These were significantly correlated with cMyc expression and reduction in proliferation. Rescue experiments established reversibility upon metformin withdrawal and tight association between proliferation, metabotype, and cMyc expression.

CONCLUSIONS

The derangement of multiple glucose-independent metabolic pathways, which are often upregulated in therapy-resistant cancer, and concomitant cMyc downregulation coordinately contribute to the anti-proliferative effect of metformin in liver cancer cells. These are reversible and may influence its therapeutic utility.

Gut microbiome encoded purine and amino acid pathways present prospective biomarkers for predicting metformin therapy efficacy in newly diagnosed T2D patients

Metformin is widely used for treating type 2 diabetes mellitus (T2D). However, the efficacy of metformin monotherapy is highly variable within the human population. Understanding the potential indirect or synergistic effects of metformin on gut microbiota composition and encoded functions could potentially offer new insights into predicting treatment efficacy and designing more personalized treatments in the future. We combined targeted metabolomics and metagenomic profiling of gut microbiomes in newly diagnosed T2D patients before and after metformin therapy to identify potential pre-treatment biomarkers and functional signatures for metformin efficacy and induced changes in metformin therapy responders. Our sequencing data were largely corroborated by our metabolic profiling and identified that pre-treatment enrichment of gut microbial functions encoding purine degradation and glutamate biosynthesis was associated with good therapy response. Furthermore, we identified changes in glutamine-associated amino acid (arginine, ornithine, putrescine) metabolism that characterize differences in metformin efficacy before and after the therapy. Moreover, metformin Responders' microbiota displayed a shifted balance between bacterial lipidA synthesis and degradation as well as alterations in glutamate-dependent metabolism of N-acetyl-galactosamine and its derivatives (e.g. CMP-pseudaminate) which suggest potential modulation of bacterial cell walls and human gut barrier, thus mediating changes in microbiome composition. Together, our data suggest that glutamine and associated amino acid metabolism as well as purine degradation products may potentially condition metformin activity via its multiple effects on microbiome functional composition and therefore serve as important biomarkers for predicting metformin efficacy.

Effect of Metformin Treatment on Serum Metabolic Profile Changes in Lean and Obese Zucker Rat Model for Fatty Liver Disease

Excessive weight and obesity are the leading risk factors for the development of chronic diseases, including diabetes. Metformin is capable of significantly improving coexisting complications of diabetes. We used a metabolomics approach to examine the effects of metformin administration on lean and obese (fa/fa) Zucker rats. After 1 week of acclimation, twenty-eight 5-week-old female lean and obese rats were randomly assigned to and maintained in the following four groups (seven rats/group) for 10 weeks: (1) lean control (LC); (2) obese control (OC); (3) lean metformin (LM); and (4) obese metformin (OM). At the end of 10 weeks, serum was collected and analyzed using HPLC with electrochemical detection, HPLC with UV detection, and liquid chromatography mass spectrometry. We selected 50 metabolites' peaks that were shared by all four groups of rats. Peak heights, as a defining factor, generally decreased in metformin-treated lean rats vs. untreated lean controls (3 LM:16 LC). Peak heights generally increased in metformin-treated obese rats vs. untreated obese controls (14 OM:5 OC). Overall, individual peaks were distributed as 11 that represented only lean rats, 11 that represented only obese rats, and 8 that were common among both lean and obese rats. In future studies, we will use a targeted metabolomics approach to identify those metabolites, map them to biochemical pathways and create a list of biomarkers. In summary, the current study contributed to a better understanding of the basic metabolic changes of lean and obese rats and demonstrated that both obesity and metformin make a significant impact on the metabolome of Zucker rats.

Exploring Metabolomic Patterns in Type 2 Diabetes Mellitus and Response to Glucose-Lowering Medications-Review

The spectrum of information related to precision medicine in diabetes generally includes clinical data, genetics, and omics-based biomarkers that can guide personalized decisions on diabetes care. Given the remarkable progress in patient risk characterization, there is particular interest in using molecular biomarkers to guide diabetes management. Metabolomics is an emerging molecular approach that helps better understand the etiology and promises the identification of novel biomarkers for complex diseases. Both targeted or untargeted metabolites extracted from cells, biofluids, or tissues can be investigated by established high-throughput platforms, like nuclear magnetic resonance (NMR) and mass spectrometry (MS) techniques. Metabolomics is proposed as a valuable tool in precision diabetes medicine to discover biomarkers for diagnosis, prognosis, and management of the progress of diabetes through personalized phenotyping and individualized drug-response monitoring. This review offers an overview of metabolomics knowledge as potential biomarkers in type 2 diabetes mellitus (T2D) diagnosis and the response to glucose-lowering medications.

Integration of Vitreous Lipidomics and Metabolomics for Comprehensive Understanding of the Pathogenesis of Proliferative Diabetic Retinopathy

As a vision-threatening complication of diabetes mellitus (DM), proliferative diabetic retinopathy (PDR) is associated with sustained metabolic disorders. Herein, we collected the vitreous cavity fluid of 49 patients with PDR and 23 control subjects without DM for metabolomics and lipidomics analyses. Multivariate statistical methods were performed to explore relationships between samples. For each group of metabolites, gene set variation analysis scores were generated, and we constructed a lipid network by using weighted gene co-expression network analysis. The association between lipid co-expression modules and metabolite set scores was investigated using the two-way orthogonal partial least squares (O2PLS) model. A total of 390 lipids and 314 metabolites were identified. Multivariate statistical analysis revealed significant vitreous metabolic and lipid differences between PDR and controls. Pathway analysis showed that 8 metabolic processes might be associated with the development of PDR, and 14 lipid species were found to be altered in PDR patients. Combining metabolomics and lipidomics, we identified fatty acid desaturase 2 (FADS2) as an important potential contributor to the pathogenesis of PDR. Collectively, this study integrates vitreous metabolomics and lipidomics to comprehensively unravel metabolic dysregulation and identifies genetic variants associated with altered lipid species in the mechanistic pathways for PDR.

A pilot metabolomic study of drug interaction with the immune response to seasonal influenza vaccination

Many human diseases, including metabolic diseases, are intertwined with the immune system. The understanding of how the human immune system interacts with pharmaceutical drugs is still limited, and epidemiological studies only start to emerge. As the metabolomics technology matures, both drug metabolites and biological responses can be measured in the same global profiling data. Therefore, a new opportunity presents itself to study the interactions between pharmaceutical drugs and immune system in the high-resolution mass spectrometry data. We report here a double-blinded pilot study of seasonal influenza vaccination, where half of the participants received daily metformin administration. Global metabolomics was measured in the plasma samples at six timepoints. Metformin signatures were successfully identified in the metabolomics data. Statistically significant metabolite features were found both for the vaccination effect and for the drug-vaccine interactions. This study demonstrates the concept of using metabolomics to investigate drug interaction with the immune response in human samples directly at molecular levels.

Metformin May Alter the Metabolic Reprogramming in Cancer Cells by Disrupting the L-Arginine Metabolism: A Preliminary Computational Study

Metabolic reprogramming in cancer is considered to be one of the most important hallmarks to drive proliferation, angiogenesis, and invasion. AMP-activated protein kinase activation is one of the established mechanisms for metformin’s anti-cancer actions. However, it has been suggested that metformin may exert antitumoral effects by the modulation of other master regulators of cellular energy. Here, based on structural and physicochemical criteria, we tested the hypothesis that metformin may act as an antagonist of L-arginine metabolism and other related metabolic pathways. First, we created a database containing different L-arginine-related metabolites and biguanides. After that, comparisons of structural and physicochemical properties were performed employing different cheminformatic tools. Finally, we performed molecular docking simulations using AutoDock 4.2 to compare the affinities and binding modes of biguanides and L-arginine-related metabolites against their corresponding targets. Our results showed that biguanides, especially metformin and buformin, exhibited a moderate-to-high similarity to the metabolites belonging to the urea cycle, polyamine metabolism, and creatine biosynthesis. The predicted affinities and binding modes for biguanides displayed good concordance with those obtained for some L-arginine-related metabolites, including L-arginine and creatine. In conclusion, metabolic reprogramming in cancer cells by metformin and biguanides may be also driven by metabolic disruption of L-arginine and structurally related compounds.

Metformin counters oxidative stress and mitigates adverse effects of radiation exposure: An overview

Metformin (1,1-dimethylbiguanidine hydrochloride) (MF) is a drug that has long been in use for the treatment of type 2 diabetes mellitus and recently is coming into use in the radiation therapy of cancer and other conditions. Exposure to ionizing radiation disturbs the redox homeostasis of cells and causes damage to proteins, membranes, and mitochondria, destroying a number of biological processes. After irradiation, MF activates cellular antioxidant and repair systems by signaling to eliminate the harmful consequences of disruption of redox homeostasis. The use of MF in the treatment of the negative effects of irradiation has great potential in medical patients after radiotherapy and in victims of nuclear accidents or radiologic terrorism.

Molecular Responses of Daphnids to Chronic Exposures to Pharmaceuticals

Pharmaceutical compounds are among several classes of contaminants of emerging concern, such as pesticides, heavy metals and personal care products, all of which are a major concern for aquatic ecosystems. The hazards posed by the presence of pharmaceutical is one which affects both freshwater organisms and human health-via non-target effects and by the contamination of drinking water sources. The molecular and phenotypic alterations of five pharmaceuticals which are commonly present in the aquatic environment were explored in daphnids under chronic exposures. Markers of physiology such as enzyme activities were combined with metabolic perturbations to assess the impact of metformin, diclofenac, gabapentin, carbamazepine and gemfibrozil on daphnids. Enzyme activity of markers of physiology included phosphatases, lipase, peptidase, β-galactosidase, lactate dehydrogenase, glutathione-S-transferase and glutathione reductase activities. Furthermore, targeted LC-MS/MS analysis focusing on glycolysis, the pentose phosphate pathway and the TCA cycle intermediates was performed to assess metabolic alterations. Exposure to pharmaceuticals resulted in the changes in activity for several enzymes of metabolism and the detoxification enzyme glutathione-S-transferase. Metabolic perturbations on key pathways revealed distinct groups and metabolic fingerprints for the different exposures and their mixtures. Chronic exposure to pharmaceuticals at low concentrations revealed significant alterations of metabolic and physiological endpoints.

Glucose Metabolism and Metabolomic Changes in Response to Prolonged Fasting in Individuals with Obesity, Type 2 Diabetes and Non-Obese People-A Cohort Trial

Metabolic regulation of glucose can be altered by fasting periods. We examined glucose metabolism and metabolomics profiles after 12 h and 36 h fasting in non-obese and obese participants and people with type 2 diabetes using oral glucose tolerance (OGTT) and intravenous glucose tolerance testing (IVGTT). Insulin sensitivity was estimated by established indices and mass spectrometric metabolomics was performed on fasting serum samples. Participants had a mean age of 43 ± 16 years (62% women). Fasting levels of glucose, insulin and C-peptide were significantly lower in all cohorts after 36 h compared to 12 h fasting (p < 0.05). In non-obese participants, glucose levels were significantly higher after 36 h compared to 12 h fasting at 120 min of OGTT (109 ± 31 mg/dL vs. 79 ± 18 mg/dL; p = 0.001) but insulin levels were lower after 36 h of fasting at 30 min of OGTT (41.2 ± 34.1 mU/L after 36 h vs. 56.1 ± 29.7 mU/L; p < 0.05). In contrast, no significant differences were observed in obese participants or people with diabetes. Insulin sensitivity improved in all cohorts after 36 h fasting. In line, metabolomics revealed subtle baseline differences and an attenuated metabolic response to fasting in obese participants and people with diabetes. Our data demonstrate an improved insulin sensitivity after 36 h of fasting with higher glucose variations and reduced early insulin response in non-obese people only.

Insulin resistance in Alzheimer's disease: The genetics and metabolomics links

Alzheimer's disease (AD) is a neurodegenerative disease with significant socioeconomic burden worldwide. Although genetics and environmental factors play a role, AD is highly associated with insulin resistance (IR) disorders such as metabolic syndrome (MS), obesity, and type two diabetes mellitus (T2DM). These findings highlight a shared pathogenesis. The use of metabolomics as a downstream systems' biology (omics) approach can help to identify these shared metabolic traits and assist in the early identification of at-risk groups and potentially guide therapy. Targeting the shared AD-IR metabolic trait with lifestyle interventions and pharmacological treatments may offer promising AD therapeutic approach. In this narrative review, we reviewed the literature on the AD-IR pathogenic link, the shared genetics and metabolomics biomarkers between AD and IR disorders, as well as the lifestyle interventions and pharmacological treatments which target this pathogenic link.

Several Metabolite Families Display Inflexibility during Glucose Challenge in Patients with Type 2 Diabetes: An Untargeted Metabolomics Study

Metabolic inflexibility is a hallmark of insulin resistance and can be extensively explored with high-throughput metabolomics techniques. However, the dynamic regulation of the metabolome during an oral glucose tolerance test (OGTT) in subjects with type 2 diabetes (T2D) is largely unknown. We aimed to identify alterations in metabolite responses to OGTT in subjects with T2D using untargeted metabolomics of both plasma and subcutaneous adipose tissue (SAT) samples. Twenty subjects with T2D and twenty healthy controls matched for sex, age, and body mass index (BMI) were profiled with untargeted metabolomics both in plasma (755 metabolites) and in the SAT (588) during an OGTT. We assessed metabolite concentration changes 90 min after the glucose load, and those responses were compared between patients with T2D and controls. Post-hoc analyses were performed to explore the associations between glucose-induced metabolite responses and markers of obesity and glucose metabolism, sex, and age. During the OGTT, T2D subjects had an impaired reduction in plasma levels of several metabolite families, including acylcarnitines, amino acids, acyl ethanolamines, and fatty acid derivates (p < 0.05), compared to controls. Additionally, patients with T2D had a greater increase in plasma glucose and fructose levels during the OGTT compared to controls (p < 0.05). The plasma concentration change of most metabolites after the glucose load was mainly associated with indices of hyperglycemia rather than insulin resistance, insulin secretion, or BMI. In multiple linear regression analyses, hyperglycemia indices (glucose area under the curve (AUC) during OGTT and glycosylated hemoglobin (HbA1c)) were the strongest predictors of plasma metabolite changes during the OGTT. No differences were found in the adipose tissue metabolome in response to the glucose challenge between T2D and controls. Using a metabolomics approach, we show that T2D patients display attenuated responses in several circulating metabolite families during an OGTT. Besides the well-known increase in monosaccharides, the glucose-induced lowering of amino acids, acylcarnitines, and fatty acid derivatives was attenuated in T2D subjects compared to controls. These data support the hypothesis of inflexibility in several metabolic pathways, which may contribute to dysregulated substrate partitioning and turnover in T2D. These findings are not directly associated with changes in adipose tissue metabolism; therefore, other tissues, such as muscle and liver, are probably of greater importance.

Pharmacokinetics and Metabolomic Profiling of Metformin and Andrographis paniculata: A Protocol for a Crossover Randomised Controlled Trial

This protocol aims to profile the pharmacokinetics of metformin and Andrographis paniculata (AP) and continue with untargeted pharmacometabolomics analysis on pre-dose and post-dose samples to characterise the metabolomics profiling associated with the human metabolic pathways. This is a single-centre, open-labelled, three periods, crossover, randomised-controlled, single-dose oral administration pharmacokinetics and metabolomics trial of metformin 1000 mg (n = 18), AP 1000 mg (n = 18), or AP 2000 mg (n = 18) in healthy volunteers under the fasting condition. Subjects will be screened according to a list of inclusion and exclusion criteria. Investigational products will be administered according to the scheduled timeline. Vital signs and adverse events will be monitor periodically, and standardized meals will be provided to the subjects. Fifteen blood samples will be collected over 24 h, and four urine samples will be collected within a 12 h period. Onsite safety monitoring throughout the study and seven-day phone call safety follow-up will be compiled after the last dose of administration. The plasma samples will be analysed for the pharmacokinetics parameters to estimate the drug maximum plasma concentration. Untargeted metabolomic analysis between pre-dose and maximum plasma concentration (Cmax) samples will be performed for metabolomic profiling to identify the dysregulation of human metabolic pathways that link to the pharmacodynamics effects. The metformin arm will focus on the individualised Cmax plasma concentration for metabolomics study and used as a model drug. After this, an investigation of the dose-dependent effects will be performed between pre-dose samples and median Cmax concentration samples in the AP 1000 mg and AP 2000 mg arms for metabolomics study. The study protocol utilises a crossover study design to incorporate a metabolomics-based study into pharmacokinetics trial in the drug development program. The combination analyses will complement the interpretation of pharmacological effects according to the bioavailability of the drug.

Human Serum Metabolites as Potential Mediators from Type 2 Diabetes and Obesity to COVID-19 Severity and Susceptibility: Evidence from Mendelian Randomization Study

Obesity, type 2 diabetes (T2D), and severe coronavirus disease 2019 (COVID-19) are closely associated. The aim of this study was to elucidate the casual and mediating relationships of human serum metabolites on the pathways from obesity/T2D to COVID-19 using Mendelian randomization (MR) techniques. We performed two-sample MR to study the causal effects of 309 metabolites on COVID-19 severity and susceptibility, based on summary statistics from genome-wide association studies (GWAS) of metabolites (n = 7824), COVID-19 phenotypes (n = 2,586,691), and obesity (n = 322,154)/T2D traits (n = 898,130). We conducted two-sample network MR analysis to determine the mediating metabolites on the causal path from obesity/T2D to COVID-19 phenotypes. We used multivariable MR analysis (MVMR) to discover causal metabolites independent of body mass index (BMI). Our MR analysis yielded four causal metabolites that increased the risk of severe COVID-19, including 2-stearoylglycerophosphocholine (OR 2.15; 95% CI 1.48–3.11), decanoylcarnitine (OR 1.32; 95% CI 1.17–1.50), thymol sulfate (OR 1.20; 95% CI 1.10–1.30), and bradykinin-des-arg(9) (OR 1.09; 95% CI 1.05–1.13). One significant mediator, gamma-glutamyltyrosine, lay on the causal path from T2D/obesity to severe COVID-19, with 16.67% (0.64%, 32.70%) and 6.32% (1.76%, 10.87%) increased risk, respectively, per one-standard deviation increment of genetically predicted T2D and BMI. Our comprehensive MR analyses identified credible causative metabolites, mediators of T2D and obesity, and obesity-independent causative metabolites for severe COVID-19. These biomarkers provide a novel basis for mechanistic studies for risk assessment, prognostication, and therapeutic purposes in COVID-19.

Pharmacokinetic-Pharmacometabolomic Approach in Early-Phase Clinical Trials: A Way Forward for Targeted Therapy in Type 2 Diabetes

Pharmacometabolomics in early phase clinical trials demonstrate the metabolic profiles of a subject responding to a drug treatment in a controlled environment, whereas pharmacokinetics measure the drug plasma concentration in human circulation. Application of the personalized peak plasma concentration from pharmacokinetics in pharmacometabolomic studies provides insights into drugs’ pharmacological effects through dysregulation of metabolic pathways or pharmacodynamic biomarkers. This proof-of-concept study integrates personalized pharmacokinetic and pharmacometabolomic approaches to determine the predictive pharmacodynamic response of human metabolic pathways for type 2 diabetes. In this study, we use metformin as a model drug. Metformin is a first-line glucose-lowering agent; however, the variation of metabolites that potentially affect the efficacy and safety profile remains inconclusive. Seventeen healthy subjects were given a single dose of 1000 mg of metformin under fasting conditions. Fifteen sampling time-points were collected and analyzed using the validated bioanalytical LCMS method for metformin quantification in plasma. The individualized peak-concentration plasma samples determined from the pharmacokinetic parameters calculated using Matlab Simbiology were further analyzed with pre-dose plasma samples using an untargeted metabolomic approach. Pharmacometabolomic data processing and statistical analysis were performed using MetaboAnalyst with a functional meta-analysis peaks-to-pathway approach to identify dysregulated human metabolic pathways. The validated metformin calibration ranged from 80.4 to 2010 ng/mL for accuracy, precision, stability and others. The median and IQR for Cmax was 1248 (849–1391) ng/mL; AUC_0-infinity was 9510 (7314–10,411) ng·h/mL, and Tmax was 2.5 (2.5–3.0) h. The individualized Cmax pharmacokinetics guided the untargeted pharmacometabolomics of metformin, suggesting a series of provisional predictive human metabolic pathways, which include arginine and proline metabolism, branched-chain amino acid (BCAA) metabolism, glutathione metabolism and others that are associated with metformin’s pharmacological effects of increasing insulin sensitivity and lipid metabolism. Integration of pharmacokinetic and pharmacometabolomic approaches in early-phase clinical trials may pave a pathway for developing targeted therapy. This could further reduce variability in a controlled trial environment and aid in identifying surrogates for drug response pathways, increasing the prediction of responders for dose selection in phase II clinical trials.

Metformin Protects against Spinal Cord Injury and Cell Pyroptosis via AMPK/NLRP3 Inflammasome Pathway

Spinal cord injury (SCI) is an extreme neurological impairment with few effective drug treatments. Pyroptosis is a recently found and proven type of programmed cell death that is characterized by a reliance on inflammatory caspases and the release of a large number of proinflammatory chemicals. Pyroptosis differs from other cell death mechanisms such as apoptosis and necrosis in terms of morphological traits, incidence, and regulatory mechanism. Pyroptosis is widely involved in the occurrence and development of SCI. In-depth research on pyroptosis will help researchers better understand its involvement in the onset, progression, and prognosis of SCI, as well as provide new therapeutic prevention and treatment options. Herein, we investigated the role of AMPK-mediated activation of the NLRP3 inflammasome in the neuroprotection of MET-regulated pyroptosis. We found that MET treatment reduced NLRP3 inflammasome activation by activating phosphorylated AMPK and reduced proinflammatory cytokine (IL-1β, IL-6, and TNF-α) release. At the same time, MET improved motor function recovery in rats after SCI by reducing motor neuron loss in the anterior horn of the spinal cord. Taken together, our study confirmed that MET inhibits neuronal pyroptosis after SCI via the AMPK/NLRP3 signaling pathway, which is mostly dependent on the AMPK pathway increase, hence decreasing NLRP3 inflammasome activation.