Applying metabolomics to veterinary pharmacology and therapeutics

Analytical advances in horseracing medication and doping control from 2018 to 2023

The analytical approaches taken by laboratories to implement robust and efficient regulation of horseracing medication and doping control are complex and constantly evolving. Each laboratory's approach will be dictated by differences in regulatory, economic and scientific drivers specific to their local environment. However, in general, laboratories will all be undertaking developments and improvements to their screening strategies in order to meet new and emerging threats as well as provide improved service to their customers. In this paper, the published analytical advances in horseracing medication and doping control since the 22nd International Conference of Racing Analysts and Veterinarians will be reviewed. Due to the unprecedented impact of COVID-19 on the worldwide economy, the normal 2-year period of this review was extended to over 5 years. As such, there was considerable ground to cover, resulting in an increase in the number of relevant publications included from 107 to 307. Major trends in publications will be summarised and possible future directions highlighted. This will cover developments in the detection of 'small' and 'large' molecule drugs, sample preparation procedures and the use of alternative matrices, instrumental advances/applications, drug metabolism and pharmacokinetics, the detection and prevalence of 'endogenous' compounds and biomarker and OMICs approaches. Particular emphasis will be given to research into the potential threat of gene doping, which is a significant area of new and continued research for many laboratories. Furthermore, developments in analytical instrumentation relevant to equine medication and doping control will be discussed.

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BACKGROUND

Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed.

RESULTS

Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices).

SIGNIFICANCE

This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.

Nuclear Magnetic Resonance (NMR) Metabolomics: Current Applications in Equine Health Assessment

Metabolomics can allow for the comprehensive identification of metabolites within biological systems, at given time points, in physiological and pathological conditions. In the last few years, metabolomic analysis has gained popularity both in human and in veterinary medicine, showing great potential for novel applications in clinical activity. The aim of applying metabolomics in clinical practice is understanding the mechanisms underlying pathological conditions and the influence of certain stimuli (i.e., drugs, nutrition, exercise) on body systems, in the attempt of identifying biomarkers that can help in the diagnosis of diseases. Proton Nuclear Magnetic Resonance spectroscopy (1H-NMR) is well tailored to be used as an analytical platform for metabolites' detection at the base of metabolomics studies, due to minimal sample preparation and high reproducibility. In this mini-review article, the scientific production of NMR metabolomic applications to equine medicine is examined. The research works are very different in methodology and difficult to compare. Studies are mainly focused on exercise, reproduction, and nutrition, other than respiratory and musculoskeletal diseases. The available information on this topic is still scant, but a greater collection of data could allow researchers to define new reliable markers to be used in clinical practice for diagnostic and therapeutical purposes.

Uncovering the effects and mechanism of Danggui Shaoyao San intervention on primary dysmenorrhea by serum metabolomics approach

Danggui Shaoyao San (DSS) is a well-known prescription for relieving primary dysmenorrhea (PD) of women in China. However, its pharmacological mechanism has not been thoroughly uncovered. Here, an integrative UPLC-Q-TOF-MS-based serum metabolomics approach coupled with multivariate data analysis has been proposed to investigate the effects and mechanism of DSS on estradiol benzoate and oxytocin-induced PD rats. 31 potential biomarkers of PD were screened and identified, mainly involving phenylalanine, tyrosine and tryptophan biosynthesis, glycerophospholipid metabolism, primary bile acid biosynthesis, and the occurrence of PD could destroy biological homeostasis in vivo by monitoring these pathways. After DSS treatment, 18 identified different metabolites were restored to the nomal state in varying degrees and could be potential biomarkers contributing to the treatment of DSS. These findings implyed that DSS exhibited a therapeutic effect on PD rats through regulating multiple abnormal pathways. Of note, this study discovered some potential biomarkers related to PD for the first time, such as L-tyrosine, glycocholic acid, citric acid, palmitoylcarnitine, cholesterol. It preliminarily proved the pathophysiology of PD and action mechanisms of DSS on PD, and provided a novel insight into the effectiveness of DSS on PD.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.