The efficacy and safety of psychopharmacotherapy with antidepressants is of great medical importance. The search for clinical and biological predictors for choosing the optimal psychopharmacotherapy with antidepressants is actively underway all over the world. Research is mainly devoted to searching for associations of polymorphic gene variants with the efficacy and safety of therapy. However, information about a patient\'s genetic polymorphism is often insufficient to predict the efficacy and safety of a drug. Modern research on the personalization of pharmacotherapy should include, in addition to genetic, phenotypic biomarkers. This is important because genotyping, for example, cannot accurately predict the actual metabolic activity of an isoenzyme. To personalize therapy, a combination of methods is required to obtain the most complete profile of the efficacy and safety of the drug. Successful treatment of depression remains a challenge, and inter-individual differences in response to antidepressants are common. About half of patients with depressive disorders do not respond to the first attempt at antidepressant therapy. Serious side-effects of antidepressant pharmacotherapy and discontinuation of treatment due to their intolerance are associated with ineffective therapy. This review presents the results of the latest studies of «omics» biomarkers of the efficacy and safety of antidepressants.
Эффективность и безопасность психофармакотерапии антидепрессантами имеет большое медицинское значение. Поиск клинических и биологических предикторов для подбора оптимальной психофармакотерапии антидепрессантами активно проводится во всем мире. В основном исследования посвящены поиску ассоциаций полиморфных вариантов генов с эффективностью и безопасностью терапии. Однако информации о генетическом полиморфизме у пациента часто недостаточно, чтобы спрогнозировать эффективность и безопасность лекарственного средства. Современное исследование персонализации фармакотерапии должно включать, помимо генетических, и фенотипические биомаркеры. Это важно, так как генотипирование, например, не может с точностью предсказать реальную метаболическую активность изофермента. Успешное лечение депрессии остается сложной задачей, межиндивидуальные различия в реакции на антидепрессанты являются обычным явлением. Около половины пациентов с депрессивными расстройствами не реагируют на первую попытку терапии антидепрессантами. Серьезные побочные эффекты при фармакотерапии антидепрессантами и прекращение лечения из-за их непереносимости связаны с неэффективностью терапии. В данном обзоре представлены результаты новейших исследований «омиксных» биомаркеров эффективности, безопасности антидепрессантов.

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.
CONCLUSIONS: 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.

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.

暂无摘要。

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.

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.

暂无摘要。

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: The enzyme inhibitory capacity of isoniazid is well-preservd in CCl4-induced liver injury.

UNASSIGNED: 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.
UNASSIGNED: Pre-treatment levels of 20 amino acid concentrations were measured via a targeted mass spectrometry assay in banked serum from the SWOG S0221 (NCT00070564) trial of patients with early-stage breast cancer receiving paclitaxel. The associations between amino acid levels and CIPN occurrence or severity were tested in regression analysis adjusted for paclitaxel schedule, age, self-reported race, and body mass index with Bonferroni correction for multiple comparisons. The network of metabolic pathways of amino acids was analyzed using over-representation analysis in MetaboAnalyst. The partial correlation network of amino acids was evaluated using a debiased sparse partial correlation algorithm and Cytoscape.
UNASSIGNED: 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 a secondary analysis, no amino acid was associated with CIPN occurrence (all p > 0.0025). 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.
UNASSIGNED: This analysis showed that pre-treatment serum amino acid concentrations are not strongly predictive of CIPN severity. Future prospectively designed studies that assess non-amino acid metabolomics predictors are encouraged.