Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling

Metabolomic study combined with the low-level light therapy of Chinese acupuncture points and combined oral contraceptives in treatment of primary dysmenorrhea: A prospective, multicenter, randomized controlled study

Objective

To compare the changes of metabolites between Low-level light therapy (LLLT) and combined oral contraceptive (COC) after treatment of primary dysmenorrhea (PD), and to compare and analyze the biological and biochemical effects of the two treatments by analyzing the differences in metabolite profiles.

Methods

A multicenter, double-blind, prospective, parallel, randomized controlled study was conducted on 69 women aged 16-35 years old with PD who were randomly divided into COC treatment group or LLLT treatment group. Low-level light therapy with light-emitting diodes (LED) was applied on two acupoints named "Guanyuan" (CV4) and "Qihai" (CV6). After 12 weeks of treatment intervention, blood samples were collected before and after treatment for metabolomic analysis. We used UPLC-MS/MS analysis to compare the differences in metabolite changes between LLLT and COC before and after treatment.

Results

76 differential metabolites were detected in the LLLT group, and 92 differential metabolites were detected in the COC group, which were up-regulated or down-regulated (p < 0.001). Prostaglandin D2 (PG D2) was down-regulated and biliverdin was up-regulated after LLLT treatment, 4a-Hydroxytetrahydrobiopterin, Prostaglandin D2, 5-Hydroxy-l-tryptophan, Cholic acid were down-regulated and cortisol was up-regulated after COC treatment, and the differences were statistically significant. Cortisol and testosterone glucuronide in LLLT group were significantly lower than those in COC group. The metabolic pathways affected were glycerophospholipid metabolism, linoleic acid metabolism and arachidonic acid metabolism in the LLLT group, and glycerophospholipid metabolism, folate biosynthesis, arachidonic-acid-metabolism, and tryptophan metabolism in the COC group. The differential metabolic pathway were linoleic acid metabolism, steroid hormone biosynthesis, and alpha-Linolenic acid metabolism after the comparison of LLLT with COC.

Conclusion

LLLT and COC might relieve dysmenorrhea by down-regulating PGD2, and LLLT might also relieve dysmenorrhea by up-regulating biliverdin. The level of cortisol and testosterone glucuronide after LLLT treatment was lower than that after COC treatment, which might lead to the difference in the clinical efficacy of the two treatments for dysmenorrhea.

Chemotherapy-Induced Peripheral Neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is a debilitating side effect of many common anti-cancer agents that can lead to dose reduction or treatment discontinuation, which decrease chemotherapy efficacy. Long-term CIPN can interfere with activities of daily living and diminish the quality of life. The mechanism of CIPN is not yet fully understood, and biomarkers are needed to identify patients at high risk and potential treatment targets. Metabolomics can capture the complex behavioral and pathophysiological processes involved in CIPN. This chapter is to review the CIPN metabolomics studies to find metabolic pathways potentially involved in CIPN. These potential CIPN metabolites are then investigated to determine whether there is evidence from studies of other neuropathy etiologies such as diabetic neuropathy and Leber hereditary optic neuropathy to support the importance of these pathways in peripheral neuropathy. Six potential biomarkers and their putative mechanisms in peripheral neuropathy were reviewed. Among these biomarkers, histidine and phenylalanine have clear roles in neurotransmission or neuroinflammation in peripheral neuropathy. Further research is needed to discover and validate CIPN metabolomics biomarkers in large clinical studies.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean

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.

Cerebrospinal Fluid Metabolome in Parkinson's Disease and Multiple System Atrophy

Parkinson’s disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers.

How paediatric drug development and use could benefit from OMICs: a c4c expert group white paper

The safety and efficacy of pharmacotherapy in children, particularly preterms, neonates, and infants, is limited by a paucity of good quality data from prospective clinical drug trials. A specific challenge is the establishment of valid biomarkers. OMICs technologies may support these efforts, by complementary information about targeted and non-targeted molecules through systematic characterization and quantitation of biological samples. OMICs technologies comprise at least genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics in addition to the patient's phenotype. OMICs technologies are in part hypothesis-generating allowing an in depth understanding of disease pathophysiology and pharmacological mechanisms. Application of OMICs technologies in paediatrics faces major challenges before routine adoption. First, developmental processes need to be considered, including a sub-division into specific age groups as developmental changes clearly impact OMICs data. Second, compared to the adult population, the number of patients is limited as well as type and amount of necessary biomaterial, especially in neonates and preterms. Thus, advanced trial designs and biostatistical methods, non-invasive biomarkers, innovative biobanking concepts including data and samples from healthy children, as well as analytical approaches (e.g. liquid biopsies) should be addressed to overcome these obstacles. The ultimate goal is to link OMICs technologies with innovative analysis tools, like artificial intelligence at an early stage. The use of OMICs data based on a feasible approach will contribute to identify complex phenotypes and subpopulations of patients to improve development of medicines for children with potential economic advantages.

Self-Reported Medication Use and Urinary Drug Metabolites in the German Chronic Kidney Disease (GCKD) Study

BACKGROUND

Polypharmacy is common among patients with CKD, but little is known about the urinary excretion of many drugs and their metabolites among patients with CKD.

METHODS

To evaluate self-reported medication use in relation to urine drug metabolite levels in a large cohort of patients with CKD, the German Chronic Kidney Disease study, we ascertained self-reported use of 158 substances and 41 medication groups, and coded active ingredients according to the Anatomical Therapeutic Chemical Classification System. We used a nontargeted mass spectrometry-based approach to quantify metabolites in urine; calculated specificity, sensitivity, and accuracy of medication use and corresponding metabolite measurements; and used multivariable regression models to evaluate associations and prescription patterns.

RESULTS

Among 4885 participants, there were 108 medication-drug metabolite pairs on the basis of reported medication use and 78 drug metabolites. Accuracy was excellent for measurements of 36 individual substances in which the unchanged drug was measured in urine (median, 98.5%; range, 61.1%-100%). For 66 pairs of substances and their related drug metabolites, median measurement-based specificity and sensitivity were 99.2% (range, 84.0%-100%) and 71.7% (range, 1.2%-100%), respectively. Commonly prescribed medications for hypertension and cardiovascular risk reduction-including angiotensin II receptor blockers, calcium channel blockers, and metoprolol-showed high sensitivity and specificity. Although self-reported use of prescribed analgesics (acetaminophen, ibuprofen) was <3% each, drug metabolite levels indicated higher usage (acetaminophen, 10%-26%; ibuprofen, 10%-18%).

CONCLUSIONS

This comprehensive screen of associations between urine drug metabolite levels and self-reported medication use supports the use of pharmacometabolomics to assess medication adherence and prescription patterns in persons with CKD, and indicates under-reported use of medications available over the counter, such as analgesics.

The Pharmacometabodynamics of Gefitinib after Intravenous Administration to Mice: A Preliminary UPLC-IM-MS Study

The effects of intravenous gefitinib (10 mg/kg), an anilinoquinazoline thymidylate kinase inhibitor (TKI), selective for the epidermal growth factor receptor (EGFR), on the urinary metabotypes of mice were studied. We hypothesized that, in response to the administration of gefitinib, there might be significant changes in the excretion of many endogenous metabolites in the urine, which could be correlated with the plasma pharmacokinetics (PK) of the drug. In order to investigate this conjecture, urine from male C57 BL6 mice was collected before IV dosing (10 mg/kg) and at 0–3, 3–8, and 8–24 h post-dose. The samples were profiled by UPLC/IM/MS and compared with the profiles obtained from undosed control mice with the data analyzed using multivariate statistical analysis (MVA). This process identified changes in endogenous metabolites over time and these were compared with drug and drug metabolite PK and excretion. While the MVA of these UPLC/IM/MS data did indeed reveal time-related changes for endogenous metabolites that appeared to be linked to drug administration, this analysis did not highlight the presence of either the drug or its metabolites in urine. Endogenous metabolites affected by gefitinib administration were identified by comparison of mass spectral, retention time and ion mobility-derived collision cross section data (compared to authentic standards wherever possible). The changes in endogenous metabolites resulting from gefitinib administration showed both increases (e.g., tryptophan, taurocholic acid, and the dipeptide lysyl-arginine) and decreases (e.g., deoxyguanosine, 8-hydroxydeoxyguanosine, and asparaginyl-histidine) relative to the control animals. By 8–24 h, the post-dose concentrations of most metabolites had returned to near control values. From these studies, we conclude that changes in the amounts of endogenous metabolites excreted in the urine mirrored, to some extent, the plasma pharmacokinetics of the drug. This phenomenon is similar to pharmacodynamics, where the pharmacological effects are related to the drug concentrations, and by analogy, we have termed this effect “pharmacometabodynamics”.

Applying metabolomics to veterinary pharmacology and therapeutics

Metabolomics is the large-scale study of low-molecular-weight substances in a biological system in a given physiological state at a given time point. Metabolomics can be applied to identify predictors of inter-individual variability in drug response, provide clinicians with data useful for decision-making processes in drug selection, and inform about the pharmacokinetics and pharmacodynamics of a drug. It is, therefore, an exceptional approach for gaining new understanding effects in the field of comparative veterinary pharmacology. However, the incorporation of metabolomics into veterinary pharmacology and toxicology is not yet widespread, and this is probably, at least in part, a result of its highly multidisciplinary nature. This article reviews the potential applications of metabolomics in veterinary pharmacology and therapeutics. It integrates key concepts for designing metabolomics studies and analyzing and interpreting metabolomics data, providing solid foundations for applying metabolomics to the study of drugs in all veterinary species.

Perspectives of Pharmacology over the Past 100 Years

It is fitting that the 100th anniversary of the Handbook of Experimental Pharmacology celebrates not only its founding but also the founding of experimental pharmacology as both had their beginnings in Germany. Founded in 1919 by Arthur Heffter (1859-1925) as the "Handbuch der Experimentellen Pharmakologie" and renamed to its current title in 1937, the Handbook has continued to capture the emergence and developments of experimental pharmacology since the initial systematic work of Rudolf Buchheim and his student Oswald Schmiedeberg. Heffter, the first Chairman of the German Society of Pharmacology, was also responsible for isolating mescaline as the active psychedelic component from the peyote cactus, thereby initiating a series of studies along with an Institute that, much like the Handbook and the discipline of pharmacology, continues to discover and disseminate new findings to this day. These early endeavors to establish pharmacology as a viable and valuable contributor to the medical sciences met with considerable resistance and challenges. However, the persistence and dedication of these early pharmacologists placed pharmacology on a firm foundation from which to spread this discipline globally, leading ultimately to our current understanding of the principles of drug action and with an impact likely unanticipated by these founding scientists. Summarizing the beginnings of these efforts and their early spread to other countries provides an appropriate context in which to document the many contributions pharmacological research has made over the past 100 years and provide an opportunity to anticipate expectations around its future developments.