A targeted neurotransmitter quantification and nontargeted metabolic profiling method for pharmacometabolomics analysis of olanzapine by using UPLC-HRMS

Finding the best column for polar basic analytes across reversed-phase and hydrophilic interaction liquid chromatography

Background

The chromatographic measurement of polar molecules is often surprisingly complicated. On the one hand, the standard experiments use C18 columns, which is usually unsuitable. On the other hand, these types of molecules can behave vastly differently despite their similarities. Thus, finding the right chromatographic conditions is challenging. HILIC can be an obvious choice, but C18 is still used with suitable analytes. Comparing the two methods would be advantageous, but a numerical comparison can be problematic. Thus, a simple comparing and ranking system is needed and put to use to find the best method for the separation of polar basic molecules.

Results

In this paper, nine columns with different stationary phases, from reversed-phase to hydrophilic interaction, are compared and ranked in gradient elution. The effect of pH was also considered. The measurements were repeated in acidic, near-to-neutral, and basic conditions. A straightforward system is developed to rank different stationary phases. Its foundation was peak shapes and resolutions. Every measurement condition with all the columns on the available pHs is evaluated by each property. The testing solution consisted of ten components, which aimed to cover a wide range of polar basic molecules. The comparison also focuses on these analytes. We highlighted which analyte is adaptable to different methods or which needed exclusive conditions. The resulting best column with the optimal conditions is presented and proven highly efficient for their separation.

Significance

A straightforward comparison and ranking system is developed to test multiple chromatographic columns with different stationary phases, from reversed-phase to hydrophilic interaction in gradient elution, seeking the best method for separating polar basic molecules. Nine columns were compared in multiple conditions. The best setup resulting in the competition is presented in detail, which can be applied to a wide range of analytes.

Pharmaco-Multiomics: A New Frontier in Precision Psychiatry

The landscape of psychiatric care is poised for transformation through the integration of pharmaco-multiomics, encompassing genomics, proteomics, metabolomics, transcriptomics, epigenomics, and microbiomics. This review discusses how these approaches can revolutionize personalized treatment strategies in psychiatry by providing a nuanced understanding of the molecular bases of psychiatric disorders and individual pharmacotherapy responses. With nearly one billion affected individuals globally, the shortcomings of traditional treatments, characterized by inconsistent efficacy and frequent adverse effects, are increasingly evident. Advanced computational technologies such as artificial intelligence (AI) and machine learning (ML) play crucial roles in processing and integrating complex omics data, enhancing predictive accuracy, and creating tailored therapeutic strategies. To effectively harness the potential of pharmaco-multiomics approaches in psychiatry, it is crucial to address challenges such as high costs, technological demands, and disparate healthcare systems. Additionally, navigating stringent ethical considerations, including data security, potential discrimination, and ensuring equitable access, is essential for the full realization of this approach. This process requires ongoing validation and comprehensive integration efforts. By analyzing recent advances and elucidating how different omic dimensions contribute to therapeutic customization, this review aims to highlight the promising role of pharmaco-multiomics in enhancing patient outcomes and shifting psychiatric treatments from a one-size-fits-all approach towards a more precise and patient-centered model of care.

Gamma-aminobutyric acid supplementation improves olanzapine-induced insulin resistance by inhibiting macrophage infiltration in mice subcutaneous adipose tissue

AIMS

To investigate whether gamma-aminobutyric acid (GABA) supplementation improves insulin resistance during olanzapine treatment in mice and to explore the underlying mechanisms.

MATERIALS AND METHODS

Insulin resistance and body weight gain were induced in mice by 10 weeks of olanzapine treatment. Simultaneously, the mice were administered GABA after 4 weeks of olanzapine administration.

RESULTS

We found that mice treated with olanzapine had lower GABA levels in serum and subcutaneous white adipose tissue (sWAT). GABA supplementation restored GABA levels and improved olanzapine-induced lipid metabolism disorders and insulin resistance. Chronic inflammation in adipose tissue is one of the main contributors to insulin resistance. We found that GABA supplementation inhibited olanzapine-induced adipose tissue macrophage infiltration and M1-like polarization, especially in sWAT. In vitro studies showed that stromal vascular cells, rather than adipocytes, were sensitive to GABA. Furthermore, the results suggested that GABA improves olanzapine-induced insulin resistance at least in part through a GABAB receptor-dependent pathway.

CONCLUSIONS

These findings suggest that targeting GABA may be a potential therapeutic approach for olanzapine-induced metabolic disorders.

Metabolomics, Lipidomics, and Antipsychotics: A Systematic Review

Antipsychotics are an important pharmacotherapy option for the treatment of many mental illnesses. Unfortunately, selecting antipsychotics is often a trial-and-error process due to a lack of understanding as to which medications an individual patient will find most effective and best tolerated. Metabolomics, or the study of small molecules in a biosample, is an increasingly used omics platform that has the potential to identify biomarkers for medication efficacy and toxicity. This systematic review was conducted to identify metabolites and metabolomic pathways associated with antipsychotic use in humans. Ultimately, 42 studies were identified for inclusion in this review, with all but three studies being performed in blood sources such as plasma or serum. A total of 14 metabolite classes and 12 lipid classes were assessed across studies. Although the studies were highly heterogeneous in approach and mixed in their findings, increases in phosphatidylcholines, decreases in carboxylic acids, and decreases in acylcarnitines were most consistently noted as perturbed in patients exposed to antipsychotics. Furthermore, for the targeted metabolomic and lipidomic studies, seven metabolites and three lipid species had findings that were replicated. The most consistent finding for targeted studies was an identification of a decrease in aspartate with antipsychotic treatment. Studies varied in depth of detail provided for their study participants and in study design. For example, in some cases, there was a lack of detail on specific antipsychotics used or concomitant medications, and the depth of detail on sample handling and analysis varied widely. The conclusions here demonstrate that there is a large foundation of metabolomic work with antipsychotics that requires more complete reporting so that an objective synthesis such as a meta-analysis can take place. This will then allow for validation and clinical application of the most robust findings to move the field forward. Future studies should be carefully controlled to take advantage of the sensitivity of metabolomics while limiting potential confounders that may result from participant heterogeneity and varied analysis approaches.

Recent Advances in Mass Spectrometry-Based Spatially Resolved Molecular Imaging of Drug Disposition and Metabolomics

Mass spectrometric imaging is a nontargeted, tag-free, high-throughput, and highly responsive analytical approach. The highly accurate molecular visualization detection technology enables qualitative and quantitative analyses of biologic tissues or cells scanned by mass spectrometry in situ, extracting known and unknown multiple compounds, and simultaneously assessing relative contents of targeting molecules by monitoring their molecular ions and pinpointing the spatial locations of those molecules distributed. Five mass spectrometric imaging techniques and their characteristics are introduced in the review, including matrix-assisted laser desorption ionization mass spectrometry, secondary ion mass spectrometry, desorption electrospray ionization mass spectrometry, laser ablation electrospray ionization mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry. The mass spectrometry-based techniques provide the possibility for spatial metabolomics with the capability of high throughput and precision detection. The approaches have been widely employed to spatially image not only metabolome of endogenous amino acids, peptides, proteins, neurotransmitters, and lipids but also the disposition of exogenous chemicals, such as pharmaceutical agents, environmental pollutants, toxicants, natural products, and heavy metals. The techniques also provide us with spatial distribution imaging of analytes in single cells, tissue microregions, organs, and whole animals. SIGNIFICANCE STATEMENT: The review article includes an overview of five commonly used mass spectrometers for spatial imaging and describes the advantages and disadvantages of each. Examples of the technology applications cover drug disposition, diseases, and omics. Technical aspects of relative and absolute quantification by mass spectrometric imaging and challenges for future new applications are discussed as well. The reviewed knowledge may benefit the development of new drugs and provide a better understanding of biochemical processes related to physiology and diseases.

Determination of Multiple Neurotransmitters through LC-MS/MS to Confirm the Therapeutic Effects of Althaea rosea Flower on TTX-Intoxicated Rats

Tetrodotoxin (TTX) inhibits neurotransmission in animals, and there is no specific antidote. In clinical practice in China, Althaea rosea (A. rosea flower) extract has been used to treat TTX poisoning. In this work, the efficacy of the ethyl acetate fraction extract of A. rosea flower in treating TTX poisoning in rats was investigated. A high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed to determine nine neurotransmitters in rat brain tissue, including γ-aminobutyric acid (GABA), dopamine (DA), 5-hydroxytryptamine (5-HT), noradrenaline (NE), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxyindole-3-acetic acid (5-HIAA), epinephrine (E), and tyramine (Tyn). The detoxifying effect of A. rosea flower was verified by comparing the changes in neurotransmitters' content in brain tissue before and after poisoning in rats. The assay was performed in multiple reaction monitoring mode. The quantification method was performed by plotting an internal-standard working curve with good linearity (R² > 0.9941) and sensitivity. Analyte recoveries were 94.04-107.53% (RSD < 4.21%). Results indicated that the levels of 5-HT, DA, E, and NE in the brains of TTX-intoxicated rats decreased, whereas the levels of GABA, Tyn, and 5-HIAA showed an opposite trend, and HVA and DOPAC were not detected. The levels of all seven neurotransmitters returned to normal after the gavage administration of ethyl acetate extract of A. rosea flower to prove that the ethyl acetate extract of A. rosea flower had a therapeutic effect on TTX poisoning. The work provided new ideas for studies on TTX detoxification.

Metabolic, genetic, and pharmacokinetic parameters for the prediction of olanzapine efficacy

Clinical use of the a olanzapine has significantly different individual-to-individual outcomes. Accordingly, this study aimed to develop a means of predicting response to olanzapine using a combined approach based on pharmacokinetics, pharmacometabonomics, and genetic polymorphism. The olanzapine pharmacokinetics of 19 healthy volunteers treated with orally disintegrating tablets were determined using high-performance liquid chromatography-tandem mass spectrometry. Metabolic profiling and phenotyping were performed on the blood samples that remained after pharmacokinetic analysis using ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry. Uridine diphosphate-glucuronosyltransferase (UGT), tyrosine hydroxylase (TH), γ-aminobutyric acid transaminase (GABA-T), and succinic semialdehyde dehydrogenase (SSADH) were identified as key genes. The single nucleotide polymorphism genotypes most related to drug metabolism were investigated by polymerase chain reaction and Sanger sequencing. Forty-one metabolites (p < 0.05) are increased or decreased after treatment with olanzapine. Tryptophan metabolism, norepinephrine metabolism, and γ-aminobutyric acid metabolism were identified as being related to the effects of olanzapine. Subjects carrying rs1641031 AC and CC exhibited a 59.2% increase in the mean peak concentration (C_max) value and a 25.33% decrease in the mean oral clearance rate (CL/F) value, compared to that in subjects with the GABA-T rs1641031 AA genotype (p < 0.05). Moreover, polymorphism of the GABA-T gene has an impact on the metabolism of 5-hydroxytryptamine. Lysophosphatidylethanolamine (0:0/18:3), lysophosphatidylethanolamine (0:0/22:5), and octadecatrienoic acid distinguish subjects with high and low olanzapine drug oral clearance and are thus identified as biomarkers for predicting its efficacy.

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.