1H NMR based pharmacometabolomics analysis of metabolic phenotype on predicting metabolism characteristics of losartan in healthy volunteers

Understanding the side effects of chronic silodosin administration via untargeted metabolomics approach

BACKGROUND

Precision medicine, which looks for high efficacy and low toxicity in therapies, has increased in popularity with omics technology. This work aims to discover novel and low-toxicity therapy options by examining the complex relationship between silodosin-induced side effects and the metabolomic profiles associated with its administration.

MATERIALS AND METHODS

The plasma samples of the control group and silodosin-treated rats were analyzed by LC-Q-TOF-MS/MS. Employing XCMS and MetaboAnalyst software, MS/MS data processed to detect compounds and investigate metabolic pathways. MATLAB 2019b was used for data categorization and multivariate analysis. A thorough comparison of METLIN and HMDB databases revealed 41m/z values with significant differences between the drug-treated and control groups (p <0.01 and fold analysis≥1.5).

RESULTS

According to multivariate data analysis, 17-β-estradiol, taurocholic acid, L-kynurenine, N-formylkynurenine, D-glutamine, L-arginine, prostaglandin H2, prostaglandine G2, 15-keto-prostaglandin E2, calcidiol, thromboxane A2, 5'-methylthioadenosine, L-methionine and S-adenosylmethionine levels changed significantly compared to the control group. Differences in the metabolisms of glycerophospholipid, tyrosine, phenylalanine, arachidonic acid, cysteine and methionine, and biosynthesis of phenylalanine, tyrosine, and tryptophan, and aminoacyl-tRNA have been successfully demonstrated by metabolic pathway analysis. According to this study, vitamin D, D-glutamine, and L-arginine supplements can be recommended to prevent side effects such as fatigue, intraoperative floppy iris syndrome, blurred vision, and dizziness in the treatment of silodosin. Silodosin treatment negatively affected the immune system by affecting the kynurenine and tryptophan metabolism pathways.

CONCLUSIONS

The study is a guide for silodosin treatments that offer low side effects and high therapeutic effect within the scope of precision medicine.

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.

Metabolomic Profile, Plasmatic Levels of Losartan and EXP3174, Blood Pressure Control in Hypertensive Patients and Their Correlation with COVID-19

Systemic arterial hypertension (SAH) is one of the most prevalent chronic diseases worldwide and is related to serious health complications. It has been pointed out as a major risk factor for COVID-19. This study aimed to determine the impact of COVID-19 on the metabolomic profile, the correlation with the plasmatic levels of losartan and its active metabolite (EXP3174), biochemical markers, and blood pressure (BP) control in hypertensive patients. 1H NMR metabolomic profiles of hypertensive and normotensive patients with and without previous COVID-19 diagnosis were identified. Plasmatic levels of LOS and EXP3174 were correlated with BP, biochemical markers, and the metabolomic fingerprint of the groups. Biomarkers linked to important aspects of SAH and COVID-19 were identified, such as glucose, glutamine, arginine, creatinine, alanine, choline, erythritol, homogentisate, 0-tyrosine, and 2-hydroxybutyrate. Those metabolites are indicative of metabolic alterations, kidney damage, pulmonary dysfunction, and persistent inflammation, which can be found in both diseases. Some hypertensive patients did not reach the therapeutic levels of LOS and EXP3174, while the BP control was also limited among the normotensive patients with previous COVID-19 diagnoses. Metabolomics proved to be an important tool for assessing the effectiveness of losartan pharmacotherapy and the damage caused by SAH and COVID-19 in hypertensive patients.

Metabolomic Profile and Its Correlation with the Plasmatic Levels of Losartan, EXP3174 and Blood Pressure Control in Hypertensive and Chronic Kidney Disease Patients

Systemic arterial hypertension (SAH) is one of the most prevalent chronic diseases worldwide and, when dysregulated, may cause serious complications. Losartan (LOS) blocks relevant physiological aspects of hypertension, acting mainly on the reduction of peripheral vascular resistance. Complications of hypertension include nephropathy, in which diagnosis is based on the observation of functional or structural renal dysfunction. Therefore, blood pressure control is essential to attenuate the progression of chronic kidney disease (CKD). In this study, 1H NMR metabolomics were used to differentiate hypertensive and chronic renal patients. Plasmatic levels of LOS and EXP3174, obtained by liquid chromatography coupled with mass-mass spectroscopy, were correlated with blood pressure control, biochemical markers and the metabolomic fingerprint of the groups. Some biomarkers have been correlated with key aspects of hypertension and CKD progression. For instance, higher levels of trigonelline, urea and fumaric acid were found as characteristic markers of kidney failure. In the hypertensive group, the urea levels found could indicate the onset of kidney damage when associated with uncontrolled blood pressure. In this sense, the results point to a new approach to identify CKD in early stages and may contribute to improving pharmacotherapy and reducing morbidity and mortality associated with hypertension and CKD.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

A metabolomics study: Could plasma metabolites be a guide for the prevention of tamsulosin side effects?

BACKGROUND

The understanding of precision medicine, which aims for high efficacy and low toxicity in treatments, has gained more importance with omics technologies. In this study, it was aimed to reach new suggestions for low-toxicity treatment by clarifying the relationship between tamsulosin side effects and metabolome profiles.

MATERIALS AND METHODS

Plasma samples of control and tamsulosin-treated rats were analyzed by LC-Q-TOF/MS/MS. MS/MS data was processed in XCMS software for the identification of metabolite and metabolic pathway analysis. Data were classified with MATLAB 2019b for multivariate data analysis. 34m/z values were found to be significantly different between the drug and control groups (P≤0.01 and fold analysis≥1.5) and identified by comparing METLIN and HMDB databases.

RESULTS

According to multivariate data analysis, α-Linolenic Acid, Thiamine, Retinoic acid, 1.25-Dihydroxyvitamin D3-26.23-Lactone, L-Glutamine, L-Serine, Retinaldehyde, Sphingosine 1-phosphate, L-Lysine, 23S.25-Dihydroxyvitamin D3, Sphinganine, L-Cysteine, Uridine 5'-diphosphate, Calcidiol, L-Tryptophan, L-Alanine levels changed significantly compared to the control group. Differences in the metabolisms of Retinol, Sphingolipid, Alanine-Aspartate-Glutamate, Glutathione, Fatty Acid, Tryptophan, and biosynthesis of Aminoacyl-tRNA, and Unsaturated Fatty Acid have been successfully demonstrated by metabolic pathway analysis. According to our study, vitamin A and D supplements can be recommended to prevent side effects such as asthenia, rhinitis, nasal congestion, dizziness and IFIS in the treatment of tamsulosin. Alteration of aminoacyl-tRNA biosynthesis and sphingolipid metabolism pathways during tamsulosin treatment is effective in the occurrence of nasal congestion.

CONCLUSIONS

Our study provides important information for tamsulosin therapy with high efficacy and low side effects in precision medicine.

Feasibility of pharmacometabolomics to identify potential predictors of paclitaxel pharmacokinetic variability

PURPOSE

Paclitaxel is a commonly used chemotherapy drug with substantial variability in pharmacokinetics (PK) that affects treatment efficacy and toxicity. Pharmacometabolomic signatures that explain PK variability could be used to individualize dosing to improve therapeutic outcomes. The objective of this study was to identify pretreatment metabolites or metabolomic signatures that explain variability in paclitaxel PK.

METHODS

This analysis was conducted using data previously collected on a prospective observational study of 48 patients with breast cancer receiving weekly 80 mg/m² paclitaxel infusions. Paclitaxel plasma concentrations were measured during the first infusion to estimate paclitaxel time above threshold (T_c>0.05) and maximum concentration (C_max). Metabolites measured in pretreatment whole blood by nuclear magnetic resonance spectrometry were analyzed for an association with T_c>0.05 and C_max using Pearson correlation followed by stepwise linear regression.

RESULTS

Pretreatment creatinine, glucose, and lysine concentrations were positively correlated with T_c>0.05, while pretreatment betaine was negatively correlated and lactate was positively correlated with C_max (all uncorrected p < 0.05). After stepwise elimination, creatinine was associated with T_c>0.05, while betaine and lactate were associated with C_max (all p < 0.05).

CONCLUSION

This study identified pretreatment metabolites that may be associated with paclitaxel PK variability demonstrating feasibility of a pharmacometabolomics approach for understanding paclitaxel PK. However, identification of more robust pharmacometabolomic predictors will be required for broad and routine application for the clinical dosing of paclitaxel.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling

Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of twenty-first century healthcare.