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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.

Abstract Background Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. Methods Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. Results Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. Conclusion This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies.

Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a treatment-limiting and debilitating neurotoxicity of many commonly used anti-cancer agents, including paclitaxel. The objective of this study was to confirm the previously found inverse association between pre-treatment blood concentrations of histidine and CIPN occurrence and examine relationships of other amino acids with CIPN severity. Methods: Pre-treatment serum concentrations of 20 amino acids were measured in the SWOG S0221 (NCT00070564) trial of patients with early-stage breast cancer receiving paclitaxel. The associations between amino acids and CIPN severity were tested in regression analysis adjusted for paclitaxel schedule, age, self-reported race, and body mass index with Bonferroni correction. The network of metabolic pathways of amino acids was analyzed using over-representation analysis. The partial correlation network of amino acids was evaluated using a debiased sparse partial correlation algorithm. Results: In the primary analysis, histidine concentration was not associated with CIPN occurrence (odds ratio (OR) = 0.97 [0.83, 1.13], p = 0.72). In secondary analyses, higher concentrations of four amino acids, glutamate (β = 0.58 [0.23, 0.93], p = 0.001), phenylalanine (β = 0.54 [0.19, 0.89], p = 0.002), tyrosine (β = 0.57 [0.23, 0.91], p = 0.001), and valine (β = 0.58 [0.24, 0.92], p = 0.001) were associated with more severe CIPN, but none of these associations retained significance after adjustment. In the over-representation analysis, no amino acid metabolic pathways were significantly enriched (all FDR > 0.05). In the network of enriched pathways, glutamate metabolism had the highest centrality. Conclusions: This analysis showed that pre-treatment serum amino acid concentrations are not strongly predictive of CIPN severity. Prospectively designed studies that assess non-amino acid metabolomics predictors are encouraged. [ABSTRACT FROM AUTHOR]

Background: Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. Methods: Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. Results: Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. Conclusion: This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a devastating motor neuron disease. The immunosuppressive functions of regulatory T lymphocytes (Tregs) are impaired in ALS, and correlate to disease progression. The phase 2a IMODALS trial reported an increase in Treg number in ALS patients following the administration of low‐dose (ld) interleukin‐2 (IL‐2). We propose a pharmacometabolomics approach to decipher metabolic modifications occurring in patients treated with ld‐IL‐2 and its relationship with Treg response. Blood metabolomic profiles were determined on days D1, D64, and D85 from patients receiving 2 MIU of IL‐2 (n = 12) and patients receiving a placebo (n = 12). We discriminated the three time points for the treatment group (average error rate of 42%). Among the important metabolites, kynurenine increased between D1 and D64, followed by a reduction at D85. The percentage increase of Treg number from D1 to D64, as predicted by the metabolome at D1, was highly correlated with the observed value. This study provided a proof of concept for metabolic characterization of the effect of ld‐IL‐2 in ALS. These data could present advances toward a personalized medicine approach and present pharmacometabolomics as a key tool to complement genomic and transcriptional data for drug characterization, leading to systems pharmacology. [ABSTRACT FROM AUTHOR]

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Scientific relevance. Valproic acid (VPA) is a psychotropic medicinal product, which may be associated with serious adverse drug reactions (ADRs). While pharmacogenetics and pharmacometabolomics can significantly affect the safety of valproates, there are no unified approaches to predicting, preventing, and correcting VPA-induced ADRs.Aim. This study aimed to collate the results of national and international studies on toxic VPA metabolites and to develop a novel personalised approach to assessing the safety and risks of valproate therapy in real-world clinical practice.Discussion. This study analysed national and international publications reflecting the results of preclinical and clinical studies on toxic VPA metabolites submitted to e-Library, PubMed, Scopus, and Google Scholar in 2012–2022. The inclusion criteria were full-text original articles, systematic reviews, meta-analyses, Cochrane reviews, and clinical cases in Russian or English. According to the analysis results, VPA has 20 studied toxic metabolites, which result from hepatic VPA metabolism involving P-oxidation, acetylation (β-oxidation), and glucuronidation enzymes. The functional activity of these enzymes is genetically determined and associated with heterozygous or homozygous carriage of non-functional/low-function single-nucleotide variant alleles in genes encoding these enzymes. The safety of VPA and its compounds can be improved by transferring the results of preclinical and clinical studies into real-world clinical practice using pharmacogenetics-informed pharmacometabolomics. Pharmacogenetics-informed pharmacometabolomics is a novel and personalised approach that helps, based on pharmacogenetic profiling, identify patients at high risk of VPA-induced ADRs, individually select starting and target doses of VPA and its compounds, determine the timing and frequency for therapeutic drug monitoring and monitoring toxic VPA metabolites in biological fluids (blood, saliva, and urine), and select a strategy for the prevention and correction of VPA-induced ADRs, taking into account patients’ individual pharmacometabolic profiles.Conclusions. The quality of medical care for patients with neurological diseases and mental disorders will improve with proper monitoring of VPA-induced ADRs by all entities involved in the medicinal product life cycle; active involvement of neurologists and psychiatrists in the prediction, prevention, and monitoring of the safety of valproate treatment; and inclusion of specific sections on practical pharmacogenetics-informed pharmacometabolomics and pharmacovigilance in the professional training curricula for neurologists and psychiatrists.

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients. [Display omitted] • LC–MS facilitates routine TDM and pharmacometabolomics toward precision dosing. • LC–MS-based bioassay methods of anti-TB drugs and metabolites was summarized. • Perspectives on numerous advancements in TB management were highlighted. • Comprehensive approach was proposed to enhance clinical outcomes and eradicate TB. [ABSTRACT FROM AUTHOR]

Abstract Background Anti‐folate drug pemetrexed is a vital chemotherapy medication for non‐small cell lung cancer (NSCLC). Its response varies widely and often develops resistance to the treatment. Therefore, it is urgent to identify biomarkers and establish models for drug efficacy evaluation and prediction for rational drug use. Methods A total of 360 subjects were screened and 323 subjects were recruited. Using metabolomics in combination with machine learning methods, we are trying to select potential biomarkers to diagnose NSCLC and evaluate the efficacy of pemetrexed in treating NSCLC. Furtherly, we measured the concentration of eight metabolites in the tryptophan metabolism pathway in the validation set containing 201 subjects using a targeted metabolomics method with UPLC‐MS/MS. Results In the discovery set containing 122 subjects, the metabolic profile of healthy controls (H), newly diagnosed NSCLC patients (ND), patients who responded well to pemetrexed treatment (S) and pemetrexed‐resistant patients (R) differed significantly on the PLS‐DA scores plot. Pathway analysis showed that glycine, serine and threonine metabolism occurred in every two group comparisons. TCA cycle, pyruvate metabolism and glycerolipid metabolism are the most significantly changed pathways between ND and H group, pyruvate metabolism was the most altered pathway between S and ND group, and tryptophan metabolism was the most changed pathway between S and R group. We found Random forest method had the maximum area under the curve (AUC) and can be easily interpreted. The AUC is 0.981 for diagnosing patients with NSCLC and 0.954 for evaluating pemetrexed efficiency. Conclusion We compared eight mathematical models to evaluate pemetrexed efficiency for treating NSCLC. The Random forest model established with metabolic markers tryptophan, kynurenine and xanthurenic acidcan accurately diagnose NSCLC and evaluate the response of pemetrexed.

Background: Anti‐folate drug pemetrexed is a vital chemotherapy medication for non‐small cell lung cancer (NSCLC). Its response varies widely and often develops resistance to the treatment. Therefore, it is urgent to identify biomarkers and establish models for drug efficacy evaluation and prediction for rational drug use. Methods: A total of 360 subjects were screened and 323 subjects were recruited. Using metabolomics in combination with machine learning methods, we are trying to select potential biomarkers to diagnose NSCLC and evaluate the efficacy of pemetrexed in treating NSCLC. Furtherly, we measured the concentration of eight metabolites in the tryptophan metabolism pathway in the validation set containing 201 subjects using a targeted metabolomics method with UPLC‐MS/MS. Results: In the discovery set containing 122 subjects, the metabolic profile of healthy controls (H), newly diagnosed NSCLC patients (ND), patients who responded well to pemetrexed treatment (S) and pemetrexed‐resistant patients (R) differed significantly on the PLS‐DA scores plot. Pathway analysis showed that glycine, serine and threonine metabolism occurred in every two group comparisons. TCA cycle, pyruvate metabolism and glycerolipid metabolism are the most significantly changed pathways between ND and H group, pyruvate metabolism was the most altered pathway between S and ND group, and tryptophan metabolism was the most changed pathway between S and R group. We found Random forest method had the maximum area under the curve (AUC) and can be easily interpreted. The AUC is 0.981 for diagnosing patients with NSCLC and 0.954 for evaluating pemetrexed efficiency. Conclusion: We compared eight mathematical models to evaluate pemetrexed efficiency for treating NSCLC. The Random forest model established with metabolic markers tryptophan, kynurenine and xanthurenic acidcan accurately diagnose NSCLC and evaluate the response of pemetrexed. [ABSTRACT FROM AUTHOR]

Objective: Trabectedin is an anti-cancer drug commonly used for the treatment of patients with metastatic soft tissue sarcoma (mSTS). Despite its recognized efficacy, significant variability in pharmacological response has been observed among mSTS patients. To address this issue, this pharmacometabolomics study aimed to identify pre-dose plasma metabolomics signatures that can explain individual variations in trabectedin pharmacokinetics and overall clinical response to treatment. Methods: In this study, 40 mSTS patients treated with trabectedin administered by 24 h-intravenous infusion at a dose of 1.5 mg/m2 were enrolled. The patients' baseline plasma metabolomics profiles, which included derivatives of amino acids and bile acids, were analyzed using multiple reaction monitoring LC-MS/MS together with their pharmacokinetics profile of trabectedin. Multivariate Partial least squares regression and univariate statistical analyses were utilized to identify correlations between baseline metabolite concentrations and trabectedin pharmacokinetics, while Partial Least Squares-Discriminant Analysis was employed to evaluate associations with clinical response. Results: The multiple regression model, derived from the correlation between the AUC of trabectedin and pre-dose metabolomics, exhibited the best performance by incorporating cystathionine, hemoglobin, taurocholic acid, citrulline, and the phenylalanine/tyrosine ratio. This model demonstrated a bias of 4.6% and a precision of 17.4% in predicting drug AUC, effectively accounting for up to 70% of the inter-individual pharmacokinetic variability. Through the use of Partial least squares-Discriminant Analysis, cystathionine and hemoglobin were identified as specific metabolic signatures that effectively distinguish patients with stable disease from those with progressive disease. Conclusions: The findings from this study provide compelling evidence to support the utilization of pre-dose metabolomics in uncovering the underlying causes of pharmacokinetic variability of trabectedin, as well as facilitating the identification of patients who are most likely to benefit from this treatment. [ABSTRACT FROM AUTHOR]

Background: Haloperidol is commonly prescribed to patients with alcohol-induced psychotic disorder (AIPD). Notably however, individuals differ extensively with regards to therapeutic response and adverse drug reactions (ADRs). Previous studies have shown that haloperidol biotransformation is mainly metabolized by CYP2D6. Objective: The objective of our study was to investigate the use of pharmacogenetic (CYP2D6*4 genetic polymorphism) and pharmacometabolomic biomarkers to predict haloperidol efficacy and safety rates. Material and Methods: The study enrolled 150 patients with AIPD. Therapy included haloperidol in a daily dose of 5 to 10 mg/day by injections for 5 days. Efficacy and safety of treatment were evaluated using the validated psychometric scales PANSS, UKU, and SAS. Results: No association of the urinary 6-НО-ТНВС/pinoline ratio values which could be evidence of the CYP2D6 activity level with both the efficacy and safety rates of haloperidol was demonstrated. However, a statistically significant association between haloperidol safety profile and CYP2D6*4 genetic polymorphism was demonstrated (P <.001). Conclusion: To predict haloperidol efficacy and safety rates, utilization of pharmacogenetic testing that defines CYP2D6*4 genetic polymorphism is found preferable over the use of the pharmacometabolomic marker in a clinical setting. [ABSTRACT FROM AUTHOR]

Background: We investigated the pharmacokinetic behavior of pyrazinamide (PZA) and pyrazinoic acid (PA) in the presence of carbon-tetrachloride (CCl4) plus antitubercular treatment (ATT) drug-induced liver injury (DILI) in rats. Methods: Thirty rats utilized in the experiment were separated equally into five groups. Each rat was injected with 0.5 ml/kg CCl4 intra-peritoneal injection on day zero. Group, I rats did receive only CCl4 (single i.p. injection, 0.5 ml/Kg in olive oil in a 1:1 ratio). Groups II, III, IV, and V did receive daily oral PZA, PZA plus isoniazid (INH), rifampicin (RMP) plus pyrazinamide (PZA), and three drugs together, respectively, for 21-days. Pharmacokinetic sampling was performed at 0, 0.5,1,3,6,12 and 24 hours post-dosing on day-20. Liver function test (LFT) was assessed at days 0,1,7, and 21 days after CCl4 and ATT administration, and rats were sacrificed on the last experiment day. Results: ATT treatment maintained the liver function changes initiated by CCl4 administration. An evidential LFT rise was observed in groups administered with pyrazinamide. Co-administration of Isoniazid caused a 2.02 and 1.78 times increase in Area-under-the-curve (AUC) values of PZA and PA, respectively (p < 0.05). Histological and oxidative-stress changes supported the biochemical and pharmacokinetic observations. Conclusion: The enzyme inhibitory capacity of isoniazid is well-preservd in CCl4-induced liver injury. [ABSTRACT FROM AUTHOR]

Background: We investigated the pharmacokinetic behavior of pyrazinamide (PZA) and pyrazinoic acid (PA) in the presence of carbon-tetrachloride (CCl4) plus antitubercular treatment (ATT) drug-induced liver injury (DILI) in rats. Methods: Thirty rats utilized in the experiment were separated equally into five groups. Each rat was injected with 0.5 ml/kg CCl4 intra-peritoneal injection on day zero. Group, I rats did receive only CCl4 (single i.p. injection, 0.5 ml/Kg in olive oil in a 1:1 ratio). Groups II, III, IV, and V did receive daily oral PZA, PZA plus isoniazid (INH), rifampicin (RMP) plus pyrazinamide (PZA), and three drugs together, respectively, for 21-days. Pharmacokinetic sampling was performed at 0, 0.5,1,3,6,12 and 24 hours post-dosing on day-20. Liver function test (LFT) was assessed at days 0,1,7, and 21 days after CCl4 and ATT administration, and rats were sacrificed on the last experiment day. Results: ATT treatment maintained the liver function changes initiated by CCl4 administration. An evidential LFT rise was observed in groups administered with pyrazinamide. Co-administration of Isoniazid caused a 2.02 and 1.78 times increase in Area-under-the-curve (AUC) values of PZA and PA, respectively (p < 0.05). Histological and oxidative-stress changes supported the biochemical and pharmacokinetic observations. Conclusion: The enzyme inhibitory capacity of isoniazid is well-preservd in CCl4-induced liver injury.

The development of new methods for determining the concentration of drugs is an actual topic today. The article contains a detailed review on vibrational spectroscopy and nuclear magnetic resonance methods using for pharmacokinetic research. This study is devoted to the possibility of using vibrational spectroscopy and 1H nuclear magnetic resonance spectroscopy to determine the concentration of drugs and the use of these groups of techniques for therapeutic drug monitoring. The study was conducted by using scientific libraries (Scopus, Web of Science Core Collection, Medline, GoogleScholar, eLIBRARY, PubMed) and reference literature. A search was conducted for the period from 2011 to 2021 in Russian and English, by combinations of words: 1H nuclear magnetic resonance (1H NMR), vibrational spectroscopy, Surface-Enhanced Raman spectroscopy, drug concentration, therapeutic drug monitoring. These methods have a number of advantages and are devoid of some of the disadvantages of classical therapeutic drug monitoring (TDM) methods – high performance liquid chromatography and mass spectrometry. This review considers the possibility of using the methods of surface-enhanced Raman scattering (SERS) and 1H NMR-spectroscopy to assess the concentration of drugs in various biological media (blood, urine), as well as to study intracellular metabolism and the metabolism of ophthalmic drugs. 1Н NMR-spectroscopy can be chosen as a TDM method, since it allows analyzing the structure and identifying metabolites of various drugs. 1Н NMR-based metabolomics can provide information on the side effects of drugs, predict response to treatment, and provide key information on the mechanisms of action of known and new drug compounds. SERS and 1Н NMR-spectroscopy have great potential for further study and the possibility of introducing them into clinical practice, including for evaluating the efficacy and safety of drugs. [ABSTRACT FROM AUTHOR]

Objective: Trabectedin is an anti-cancer drug commonly used for the treatment of patients with metastatic soft tissue sarcoma (mSTS). Despite its recognized efficacy, significant variability in pharmacological response has been observed among mSTS patients. To address this issue, this pharmacometabolomics study aimed to identify pre-dose plasma metabolomics signatures that can explain individual variations in trabectedin pharmacokinetics and overall clinical response to treatment.Methods: In this study, 40 mSTS patients treated with trabectedin administered by 24 h-intravenous infusion at a dose of 1.5 mg/m2 were enrolled. The patients' baseline plasma metabolomics profiles, which included derivatives of amino acids and bile acids, were analyzed using multiple reaction monitoring LC-MS/MS together with their pharmacokinetics profile of trabectedin. Multivariate Partial least squares regression and univariate statistical analyses were utilized to identify correlations between baseline metabolite concentrations and trabectedin pharmacokinetics, while Partial Least Squares-Discriminant Analysis was employed to evaluate associations with clinical response.Results: The multiple regression model, derived from the correlation between the AUC of trabectedin and pre-dose metabolomics, exhibited the best performance by incorporating cystathionine, hemoglobin, taurocholic acid, citrulline, and the phenylalanine/tyrosine ratio. This model demonstrated a bias of 4.6% and a precision of 17.4% in predicting drug AUC, effectively accounting for up to 70% of the inter-individual pharmacokinetic variability. Through the use of Partial least squares-Discriminant Analysis, cystathionine and hemoglobin were identified as specific metabolic signatures that effectively distinguish patients with stable disease from those with progressive disease.Conclusions: The findings from this study provide compelling evidence to support the utilization of pre-dose metabolomics in uncovering the underlying causes of pharmacokinetic variability of trabectedin, as well as facilitating the identification of patients who are most likely to benefit from this treatment.

Currently, there is no effective treatment for the fragile X-associated tremor/ataxia syndrome (FXTAS), a late-onset neurodegenerative disorder. In this pilot study, we evaluated whether allopregnanolone, a natural neurosteroid that exerts beneficial effects in neurodegenerative diseases, nervous system injury, and peripheral neuropathies, could improve lymphocytic bioenergetics and plasma pharmacometabolomics in six males with FXTAS (68 ± 3 years old; FMR1 CGG repeats 94 ± 4; FXTAS stages ranging from 3 to 5) enrolled in a 12-week open-label intervention study conducted at the University of California Davis from December 2015 through July 2016. Plasma pharmacometabolomics and lymphocytic mitochondria function were assessed at baseline (on the day of the first infusion) and at follow-up (within 48 h from the last infusion). In parallel, quantitative measurements of tremor and ataxia and neuropsychological evaluations of mental state, executive function, learning, memory, and psychological symptoms were assessed at the same time points. Allopregnanolone treatment impacted significantly GABA metabolism, oxidative stress, and some of the mitochondria-related outcomes. Notably, the magnitude of the individual metabolic response, as well as the correlation with some of the behavioral tests, was overwhelmingly carrier-specific. Based on this pilot study, allopregnanolone treatment has the potential for improving cognitive and GABA metabolism in FXTAS aligned with the concept of precision medicine.

Currently, there is no effective treatment for the fragile X-associated tremor/ataxia syndrome (FXTAS), a late-onset neurodegenerative disorder. In this pilot study, we evaluated whether allopregnanolone, a natural neurosteroid that exerts beneficial effects in neurodegenerative diseases, nervous system injury, and peripheral neuropathies, could improve lymphocytic bioenergetics and plasma pharmacometabolomics in six males with FXTAS (68 ± 3 years old; FMR1 CGG repeats 94 ± 4; FXTAS stages ranging from 3 to 5) enrolled in a 12-week open-label intervention study conducted at the University of California Davis from December 2015 through July 2016. Plasma pharmacometabolomics and lymphocytic mitochondria function were assessed at baseline (on the day of the first infusion) and at follow-up (within 48 h from the last infusion). In parallel, quantitative measurements of tremor and ataxia and neuropsychological evaluations of mental state, executive function, learning, memory, and psychological symptoms were assessed at the same time points. Allopregnanolone treatment impacted significantly GABA metabolism, oxidative stress, and some of the mitochondria-related outcomes. Notably, the magnitude of the individual metabolic response, as well as the correlation with some of the behavioral tests, was overwhelmingly carrier-specific. Based on this pilot study, allopregnanolone treatment has the potential for improving cognitive and GABA metabolism in FXTAS aligned with the concept of precision medicine.

The brain is the most advanced organ with various complex structural and functional microregions. It is often challenging to understand what and where the molecular events would occur for a given drug treatment in the brain. Herein, a temporo-spatial pharmacometabolomics method was proposed based on ambient mass spectrometry imaging and was applied to evaluate the microregional effect of olanzapine (OLZ) on brain tissue and demonstrate its effectiveness in characterizing the microregional pharmacokinetics and pharmacodynamics of OLZ for improved understanding of the molecular mechanism of drugs acting on the microregions of the brain. It accurately and simultaneously illustrated the levels dynamics and microregional distribution of various substances, including exogenous drugs and its metabolites, as well as endogenous functional metabolites from complicated brain tissue. The targeted imaging analysis of the prototype drug and its metabolites presented the absorption, distribution, metabolism, and excretion characteristics of the drug itself. Moreover, the endogenous functional metabolites were identified along with the associated therapeutic and adverse effects of the drug, which can reflect the pharmacodynamics effect on the microregional brain. Therefore, this method is significant in elucidating and understanding the molecular mechanism of central nervous system drugs at the temporo and spatial metabolic level of system biology.

Glioblastoma (GB) is a primary malignancy of the central nervous system that is classified by the WHO as a grade IV astrocytoma. Despite decades of research, several aspects about the biology of GB are still unclear. Its pathogenesis and resistance mechanisms are poorly understood, and methods to optimize patient diagnosis and prognosis remain a bottle neck owing to the heterogeneity of the malignancy. The field of omics has recently gained traction, as it can aid in understanding the dynamic spatiotemporal regulatory network of enzymes and metabolites that allows cancer cells to adjust to their surroundings to promote tumor development. In combination with other omics techniques, proteomic and metabolomic investigations, which are a potent means for examining a variety of metabolic enzymes as well as intermediate metabolites, might offer crucial information in this area. Therefore, this review intends to stress the major contribution these tools have made in GB clinical and preclinical research and highlights the crucial impacts made by the integrative "omics" approach in reducing some of the therapeutic challenges associated with GB research and treatment. Thus, our study can purvey the use of these powerful tools in research by serving as a hub that particularly summarizes studies employing metabolomics and proteomics in the realm of GB diagnosis, treatment, and prognosis. [ABSTRACT FROM AUTHOR]

Valproic acid (VPA) and its salts are psychotropic drugs that are widely used in neurological diseases (epilepsy, neuropathic pain, migraine, etc.) and psychiatric disorders (schizophrenia, bipolar affective disorder, addiction diseases, etc.). In addition, the indications for the appointment of valproate have been expanding in recent years in connection with the study of new mechanisms of action of therapeutic and toxic metabolites of VPA in the human body. Thus, VPA is considered a component of disease-modifying therapy for multiple tumors, neurodegenerative diseases (Huntington's disease, Parkinson's disease, Duchenne progressive dystrophy, etc.), and human immunodeficiency syndrome. The metabolism of VPA is complex and continues to be studied. Known pathways of VPA metabolism include: β-oxidation in the tricarboxylic acid cycle (acetylation); oxidation with the participation of cytochrome P-450 isoenzymes (P-oxidation); and glucuronidation. The complex metabolism of VPA explains the diversity of its active and inactive metabolites, which have therapeutic, neutral, or toxic effects. It is known that some active metabolites of VPA may have a stronger clinical effect than VPA itself. These reasons explain the relevance of this narrative review, which summarizes the results of studies of blood (serum, plasma) and urinary metabolites of VPA from the standpoint of the pharmacogenomics and pharmacometabolomics. In addition, a new personalized approach to assessing the cumulative risk of developing VPA-induced adverse reactions is presented and ways for their correction are proposed depending on the patient's pharmacogenetic profile and the level of therapeutic and toxic VPA metabolites in the human body fluids (blood, urine). [ABSTRACT FROM AUTHOR]

β-thalassemia is one of the most common monogenic disorders and a life-threatening health issue in children. A cost-effective and safe therapeutic approach to treat this disease is to reactivate the γ-globin gene for fetal hemoglobin (HbF) production that has been silenced during infancy. Hydroxyurea (HU) is the only FDA approved HbF inducer. However, its cytotoxicity and inability to respond significantly in all patients pose a need for an HbF inducer with better efficacy. The study describes the serum metabolic alteration in β-YAC transgenic mice treated with Tenofovir disoproxil fumarate (TDF) (n = 5), a newly identified HbF inducer, and compared to the mice groups treated with HU (n = 5) and untreated control (n = 5) using gas chromatography-mass spectrometry. Various univariate and multivariate statistical analyses were performed to identify discriminant metabolites that altered the biological pathways encompassing galactose metabolism, lactose degradation, and inositol. Furthermore, the decreased concentrations of L-fucose and geraniol in TDF-treated mice help in recovering towards normal, decreasing oxidative stress even much better than the HU-treated mice. The proposed study suggested that TDF can reduce the deficiency of blood required for β-thalassemia and can be used for the preclinical study at phase I/II for fetal hemoglobin production. [ABSTRACT FROM AUTHOR]

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.

Various groups of antihypertensive drugs targeting different pathways have been developed; however, the pharmacometabolic responses to these drugs have rarely been compared to elucidate the common pathway of blood pressure regulation. Here, we performed a comparative multi-dimensional pharmacometabolic study on the four major lines of antihypertensive drugs, namely angiotensin-converting enzyme inhibitors (ACEis), angiotensin receptor blockers (ARBs), calcium channel blockers (CCBs), and diuretics (DIURs), through ultra-performance liquid chromatography coupled to quantum time-of-flight mass spectrometry. Two hundred fifty patients with young-onset hypertension, who were equally divided among five study groups: non-medicated, ACEi, ARB, CCB, and DIUR groups, were recruited. In a metabolome-wide association study conducted through analysis of covariance, 37 molecular features significantly associated with pharmacometabolic responses to antihypertensive drugs were identified. One-third of these features were shared by multiple medications. ACEis, ARBs, and DIURs shared more features than CCB, partially reflecting that ACEis, ARBs, and DIURs affect the renin-angiotensin-aldosterone system. Thirteen molecular features were consistently identified by all four models of the analysis of covariance. A tandem mass spectrometry (or MS/MS) experiment was performed to decipher the chemical structure of these 13 molecular features, including ARB-associated lysophosphatidylcholine (P4135), CCB-associated diacylglycerol(15:0/18:2) (P1175), and DIUR-associated oleamide (P1516). In addition, diacylglycerol(15:0/14:2) (P408) was significantly associated with the pharmacometabolic response to all four antihypertensive drugs. The identified metabolites provide insights into the mechanisms of blood pressure regulation and potential predictive markers of pharmacometabolic responses to antihypertensive drugs.

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance. [ABSTRACT FROM AUTHOR]

Early diagnosis and effective pharmacotherapy of arterial hypertension are urgent problems, a significant contribution to the solution of which can be made by metabolomics. The etiology of hypertension remains unknown for the majority of patients with high blood pressure; the diagnosis for 90% is defined as essential (primary) hypertension. This population is characterized by disturbance of the metabolic pathways of lipids, glucose, biogenic amines and amino acids, which may manifest with hyperlipidemia, hyperglycemia, and insulin resistance with the possible subsequent development of type II diabetes mellitus. The study of the metabolomic signature can provide a clue to the identification of biomarkers of hypertension and contribute to the effective development of preclinical diagnosis and identification of risk groups, as well as a more complete understanding of the etiological and pathogenetic mechanisms of increased blood pressure. Published studies indicate the existence of metabolome characteristic of hypertensive patients, distinguishing them from normotensive subjects. The most typical are changes involving amino acids, polyunsaturated fatty acids, carnitines, phosphatidylcholines, and acylglycerols.The variability of the response to antihypertensive therapy does not allow achieving effective control of blood pressure in a significant proportion of patients. The peculiarities of changes in the metabolome under the use of various pharmacological groups can be used to identify metabolite markers of the response to the main classes of antihypertensive drugs, as well as markers of the development of side effects of drug therapy. Thus, individualization of the pharmacotherapeutic approach based on pharmacometabolomics can significantly increase the efficacy and safety of antihypertensive therapy.This review aims to study the main groups of metabolites identified in published trials as predictors of the development of hypertension, as well as metabolite markers of response to antihypertensive therapy.

The brain is the most advanced organ with various complex structural and functional microregions. It is often challenging to understand what and where the molecular events would occur for a given drug treatment in the brain. Herein, a temporo-spatial pharmacometabolomics method was proposed based on ambient mass spectrometry imaging and was applied to evaluate the microregional effect of olanzapine (OLZ) on brain tissue and demonstrate its effectiveness in characterizing the microregional pharmacokinetics and pharmacodynamics of OLZ for improved understanding of the molecular mechanism of drugs acting on the microregions of the brain. It accurately and simultaneously illustrated the levels dynamics and microregional distribution of various substances, including exogenous drugs and its metabolites, as well as endogenous functional metabolites from complicated brain tissue. The targeted imaging analysis of the prototype drug and its metabolites presented the absorption, distribution, metabolism, and excretion characteristics of the drug itself. Moreover, the endogenous functional metabolites were identified along with the associated therapeutic and adverse effects of the drug, which can reflect the pharmacodynamics effect on the microregional brain. Therefore, this method is significant in elucidating and understanding the molecular mechanism of central nervous system drugs at the temporo and spatial metabolic level of system biology. An effective temporo-spatial pharmacometabolomics method based on mass spectrometry imaging has been proposed for pharmacokinetic and pharmacodynamic characterization of brain microregions to elucidate the molecular mechanism of central nervous system (CNS) drugs. [Display omitted] [ABSTRACT FROM AUTHOR]

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; AUC0-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. [ABSTRACT FROM AUTHOR]

IntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.MethodsFasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.ResultsMultivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.DiscussionMetabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

IntroductionStatins, widely prescribed drugs for treatment of cardiovascular disease, inhibit the biosynthesis of low density lipoprotein cholesterol (LDL-C). Despite providing major benefits, sub populations of patients experience adverse effects, including muscle myopathy and development of type II diabetes mellitus (T2DM) that may result in premature discontinuation of treatment. There are no reliable biomarkers for predicting clinical side effects in vulnerable individuals. Pharmacometabolomics provides powerful tools for identifying global biochemical changes induced by statin treatment, providing insights about drug mechanism of action, development of side effects and basis of variation of response.ObjectiveTo determine whether statin-induced changes in intermediary metabolism correlated with statin-induced hyperglycemia and insulin resistance; to identify pre-drug treatment metabolites predictive of post-drug treatment increased diabetic risk.MethodsDrug-naïve patients were treated with 40 mg/day simvastatin for 6 weeks in the Cholesterol and Pharmacogenetics (CAP) study; metabolomics by gas chromatography-time-of-flight mass-spectrometry (GC-TOF-MS) was performed on plasma pre and post treatment on 148 of the 944 participants.ResultsSix weeks of simvastatin treatment resulted in 6.9% of patients developing hyperglycemia and 25% developing changes consistent with development of pre-diabetes. Altered beta cell function was observed in 53% of patients following simvastatin therapy and insulin resistance was observed in 54% of patients. We identified initial signature of simvastatin-induced insulin resistance, including ethanolamine, hydroxylamine, hydroxycarbamate and isoleucine which, upon further replication and expansion, could be predictive biomarkers of individual susceptibility to simvastatin-induced new onset pre-type II diabetes mellitus. No patients were clinically diagnosed with T2DM.ConclusionWithin this short 6 weeks study, some patients became hyperglycemic and/or insulin resistant. Diabetic markers were associated with decarboxylated small aminated metabolites as well as a branched chain amino acid directly linked to glucose metabolism and fatty acid biosynthesis. Pharmacometabolomics provides powerful tools for precision medicine by predicting development of drug adverse effects in sub populations of patients. Metabolic profiling prior to start of drug therapy may empower physicians with critical information when prescribing medication and determining prognosis.

Bronchopulmonary dysplasia (BPD) is one of the most common health complications of premature birth. Corticosteroids are commonly used for treatment of BPD, but their use is challenging due to variability in treatment response. Previous pharmacometabolomics study has established patterns of metabolite levels with response to dexamethasone. We obtained additional patient samples for metabolomics analysis to find associations between the metabolome and dexamethasone response in a validation cohort. A total of 14 infants provided 15 plasma and 12 urine samples. The measure of treatment response was the calculated change in respiratory severity score (deltaRSS) from pre-to-post treatment. Each metabolite was assessed with paired analysis of pre and post-treatment samples using Wilcoxon signed rank test. Correlation analysis was conducted between deltaRSS and pre-to-post change in metabolite level. Paired association analysis identified 20 plasma and 26 urine metabolites with significant level difference comparing pre to post treatment samples (p < 0.05). 4 plasma and 4 urine metabolites were also significant in the original study. Pre-to-post treatment change in metabolite analysis identified 4 plasma and 8 urine metabolites significantly associated with deltaRSS (p < 0.05). Change in urine citrulline levels showed a similar correlation pattern with deltaRSS in the first study, with increasing level associated with improved drug response. These results help validate the first major findings from pharmacometabolomics of BPD including key metabolites within the urea cycle and trans-4-hydroxyproline as a potential marker for lung injury. Ultimately, this study furthers our understanding of the mechanisms of steroid response in BPD patients and helps to design future targeted metabolomics studies in this patient population.

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Sotorasib is a novel targeted inhibitor of Kirsten rat sarcoma (KRAS) (G12C) that has shown exciting tumor-suppressing effects not only for single targeted agents but also for combination with immune checkpoint inhibitors. However, no integrative analysis of the pharmacokinetics (PK) and pharmacometabolomics (PM) of sotorasib has been reported to date. In the present study, a sensitive and robust high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS) method was firstly developed and fully validated for the quantitation of sotorasib in rat plasma. After one-step protein precipitation, sotorasib and an internal standard (carbamazepine) were separated on a Waters XBrige C18 column (50 mm × 2.1 mm, 3.5 μm) and analyzed in electrospray ionization positive ion (ESI+) mode. The optimized method was fully validated according to guidance and was successfully applied for the PK study of sotorasib at a dose of 10 mg/kg. In addition, a longitudinal and transversal PM was employed and correlated with PK using partial least squares model and Pearson's analysis. With multivariate statistical analysis, the selected six (AUC model) and nine (C max model) metabolites completely distinguished the high- and low-exposure groups after sotorasib treatment, which indicates that these potential biomarkers can predict drug exposure or toxicity. The results of this study will not only shed light on how sotorasib disturbs the metabolic profiles and the relationship between PK and PM but also offer meaningful references for precision therapy in patients with the KRAS (G12C) mutation. [ABSTRACT FROM AUTHOR]

Fluxomics is an innovative -omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics fields, portraying the whole picture of molecular interactions. Recently, fluxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using 13C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main fluxomics approaches and experimental platforms. Moreover, we summarize recent fluxomic results in different biological systems. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most common urological cancers, and their incidence has been rising over time. Surgery is the standard treatment for these cancers, but this procedure is only effective when the disease is localized. For metastatic disease, PCa is typically treated with androgen deprivation therapy, while BCa is treated with chemotherapy, and RCC is managed primarily with targeted therapies. However, response rates to these therapeutic options remain unsatisfactory due to the development of resistance and treatment-related toxicity. Thus, the discovery of biomarkers with prognostic and predictive value is needed to stratify patients into different risk groups, minimizing overtreatment and the risk of drug resistance development. Pharmacometabolomics, a branch of metabolomics, is an attractive tool to predict drug response in an individual based on its own metabolic signature, which can be collected before, during, and after drug exposure. Hence, this review focuses on the application of pharmacometabolomic approaches to identify the metabolic responses to hormone therapy, targeted therapy, immunotherapy, and chemotherapy for the most prevalent urological cancers. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB), one of the oldest and deadliest bacterial diseases, continues to cause serious global economic, health, and social problems. Current TB treatments are lengthy, expensive, and routinely ineffective against emerging drug resistant strains. Thus, there is an urgent need for the identification and development of novel TB drugs possessing comprehensive and specific mechanisms of action (MoAs). Metabolomics is a valuable approach to elucidating the MoA, toxicity, and potency of promising chemical leads, which is a critical step of the drug discovery process. Recent advances in metabolomics methodologies for deciphering MoAs include high-throughput screening techniques, the integration of multiple omics methods, mass spectrometry imaging, and software for automated analysis. This review describes recently introduced metabolomics methodologies and techniques for drug discovery, highlighting specific applications to the discovery of new antitubercular drugs and the elucidation of their MoAs.

Sotorasib is a novel targeted inhibitor of Kirsten rat sarcoma (KRAS) (G12C) that has shown exciting tumor-suppressing effects not only for single targeted agents but also for combination with immune checkpoint inhibitors. However, no integrative analysis of the pharmacokinetics (PK) and pharmacometabolomics (PM) of sotorasib has been reported to date. In the present study, a sensitive and robust high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS) method was firstly developed and fully validated for the quantitation of sotorasib in rat plasma. After one-step protein precipitation, sotorasib and an internal standard (carbamazepine) were separated on a Waters XBrige C18 column (50 mm × 2.1 mm, 3.5 μm) and analyzed in electrospray ionization positive ion (ESI+) mode. The optimized method was fully validated according to guidance and was successfully applied for the PK study of sotorasib at a dose of 10 mg/kg. In addition, a longitudinal and transversal PM was employed and correlated with PK using partial least squares model and Pearson’s analysis. With multivariate statistical analysis, the selected six (AUC model) and nine (Cmax model) metabolites completely distinguished the high- and low-exposure groups after sotorasib treatment, which indicates that these potential biomarkers can predict drug exposure or toxicity. The results of this study will not only shed light on how sotorasib disturbs the metabolic profiles and the relationship between PK and PM but also offer meaningful references for precision therapy in patients with the KRAS (G12C) mutation.

Fluxomics is an innovative -omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics fields, portraying the whole picture of molecular interactions. Recently, fluxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using 13C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main fluxomics approaches and experimental platforms. Moreover, we summarize recent fluxomic results in different biological systems.

Introduction background to metabolomicsMetabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.Objectives of white paper—expected treatment outcomes and metabolomics enabling tool for precision medicineWe anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.Conclusions key scientific concepts and recommendations for precision medicineOur metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

While atenolol is an effective antihypertensive agent, its use is also associated with adverse events including hyperglycemia and incident diabetes that may offset the benefits of blood pressure lowering. By combining metabolomic and genomic data acquired from hypertensive individuals treated with atenolol, it may be possible to better understand the pathways that most impact the development of an adverse glycemic state. To identify biomarkers that can help predict susceptibility to blood glucose excursions during exposure to atenolol. Plasma samples acquired from 234 Caucasian participants treated with atenolol in the Pharmacogenomic Evaluation of Antihypertensive Responses trial were analyzed by gas chromatography Time-Of-Flight Mass Spectroscopy. Metabolomics and genomics data were integrated by first correlating participant's metabolomic profiles to change in glucose after treatment with atenolol, and then incorporating genotype information from genes involved in metabolite pathways associated with glucose response. Our findings indicate that the baseline level of β-alanine was associated with glucose change after treatment with atenolol (Q = 0.007, β = 2.97 mg/dL). Analysis of genomic data revealed that carriers of the G allele for SNP rs2669429 in gene DPYS, which codes for dihydropyrimidinase, an enzyme involved in β-alanine formation, had significantly higher glucose levels after treatment with atenolol when compared with non-carriers (Q = 0.05, β = 2.76 mg/dL). This finding was replicated in participants who received atenolol as an add-on therapy (P = 0.04, β = 1.86 mg/dL). These results suggest that β-alanine and rs2669429 may be predictors of atenolol-induced hyperglycemia in Caucasian individuals and further investigation is warranted.

Cytochrome P450 (CYP)-derived epoxyeicosatrienoic acids (EETs) exhibit potent cardiovascular protective effects in preclinical models, and promoting the effects of EETs has emerged as a potential therapeutic strategy for coronary artery disease (CAD). The relationship between circulating EET levels and CAD extent in humans, however, remains unknown. A panel of free (unesterified) plasma eicosanoid metabolites was quantified in 162 patients referred for coronary angiography, and associations with extent of CAD [no apparent CAD (N = 39), nonobstructive CAD (N = 51), and obstructive CAD (N = 72)] were evaluated. A significant relationship between free EET levels and CAD extent was observed (P = 0.003) such that the presence of obstructive CAD was associated with lower circulating EET levels. This relationship was confirmed in multiple regression analysis where CAD extent was inversely and significantly associated with EET levels (P = 0.013), and with a biomarker of EET biosynthesis (P < 0.001), independent of clinical and demographic factors. Furthermore, quantitative enrichment analysis revealed that these associations were the most pronounced compared with other eicosanoid metabolism pathways. Collectively, these findings suggest that the presence of obstructive CAD is associated with lower EET metabolite levels secondary to suppressed EET biosynthesis. Novel strategies that promote the effects of EETs may have therapeutic promise for patients with obstructive CAD.

Millions of individuals are diagnosed with type 2 diabetes mellitus (T2D), which increases the risk for a plethora of adverse outcomes including cardiovascular events and kidney disease. Metformin is the most widely prescribed medication for the treatment of T2D; however, its mechanism is not fully understood and individuals vary in their response to this therapy. Here, we use a non-targeted, pharmacometabolomics approach to measure 384 metabolites in 33 non-diabetic, African American subjects dosed with metformin. Three plasma samples were obtained from each subject, one before and two after metformin administration. Validation studies were performed in wildtype mice given metformin. Fifty-four metabolites (including 21 unknowns) were significantly altered upon metformin administration, and 12 metabolites (including six unknowns) were significantly associated with metformin-induced change in glucose (q < 0.2). Of note, indole-3-acetate, a metabolite produced by gut microbes, and 4-hydroxyproline were modulated following metformin exposure in both humans and mice. 2-Hydroxybutanoic acid, a metabolite previously associated with insulin resistance and an early biomarker of T2D, was positively correlated with fasting glucose levels as well as glucose levels following oral glucose tolerance tests after metformin administration. Pathway analysis revealed that metformin administration was associated with changes in a number of metabolites in the urea cycle and in purine metabolic pathways (q < 0.01). Further research is needed to validate the biomarkers of metformin exposure and response identified in this study, and to understand the role of metformin in ammonia detoxification, protein degradation and purine metabolic pathways.