Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Exploring electromembrane extraction coupled to fast LC-MS/MS as a high-throughput platform for determination of 12 polar endogenous metabolites in human plasma

High efficiency in the analytical workflow, including fast sample preparation and LC-MS/MS analysis, is an advantage when analyzing a high number of samples. It can however be a challenge when determining polar analytes in complex, biological samples, and one must expect to make a compromise between a simple sample preparation followed by a long chromatographic separation, or vice versa, to limit matrix effects. In this proof-of-concept work, a one-step 96-well (parallel extraction) electromembrane extraction (EME) method was coupled to flow injection-MS/MS of 0.7 min per sample, allowing a very high-throughput analysis of 12 polar, endogenous metabolites from unprecipitated plasma of limited dilution. The throughput of the EME method matched the subsequent analysis. Recoveries ranged from 6 to 93 %, and repeatability and linearity were 2-15 % and R2 ≥ 0.9949, respectively, for all but two compounds. Matrix effects were approximately 50 % after EME and varied <11 % between 6 plasma donors, which represented a major improvement relative to a simple protein precipitation where signals were entirely suppressed. The work demonstrates a potential for EME coupled to flow injection-MS/MS to serve as a high-throughput platform for bioanalysis, not just of polar analytes, but also hydrophobic drugs both basic and acidic.

Mechanism of Action and Pharmacokinetics of Approved Bispecific Antibodies

Bispecific antibodies represent a significant advancement in therapeutic antibody engineering, offering the ability to simultaneously target two distinct antigens. This dual-targeting capability enhances therapeutic efficacy, especially in complex diseases, such as cancer and autoimmune disorders, where drug resistance and incomplete target coverage are prevalent challenges. Bispecific antibodies facilitate immune cell engagement and disrupt multiple signaling pathways, providing a more comprehensive treatment approach than traditional monoclonal antibodies. However, the intricate structure of bispecific antibodies introduces unique pharmacokinetic challenges, including issues related to their absorption, distribution, metabolism, and excretion, which can significantly affect their efficacy and safety. This review provides an in-depth analysis of the structural design, mechanisms of action, and pharmacokinetics of the currently approved bispecific antibodies. It also highlights the engineering innovations that have been implemented to overcome these challenges, such as Fc modifications and advanced dimerization techniques, which enhance the stability and half-life of bispecific antibodies. Significant progress has been made in bispecific antibody technology; however, further research is necessary to broaden their clinical applications, enhance their safety profiles, and optimize their incorporation into combination therapies. Continuous advancements in this field are expected to enable bispecific antibodies to provide more precise and effective therapeutic strategies for a range of complex diseases, ultimately improving patient outcomes and advancing precision medicine.

Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics

Adjuvant chemotherapy improves the survival outlook for patients undergoing operations for lung metastases caused by colorectal cancer (CRC). However, a multidisciplinary approach that evaluates several factors related to patient and tumor characteristics is necessary for managing chemotherapy treatment in metastatic CRC patients with lung disease, as such factors dictate the timing and drug regimen, which may affect treatment response and prognosis. In this study, we explore the potential of spatial metabolomics for evaluating metabolic phenotypes and therapy outcomes during the local delivery of the anticancer drug, oxaliplatin, to the lung. 12 male Yorkshire pigs underwent a 3 h left lung in vivo lung perfusion (IVLP) with various doses of oxaliplatin (7.5, 10, 20, 40, and 80 mg/L), which were administered to the perfusion circuit reservoir as a bolus. Biocompatible solid-phase microextraction (SPME) microprobes were combined with global metabolite profiling to obtain spatiotemporal information about the activity of the drug, determine toxic doses that exceed therapeutic efficacy, and conduct a mechanistic exploration of associated lung injury. Mild and subclinical lung injury was observed at 40 mg/L of oxaliplatin, and significant compromise of the hemodynamic lung function was found at 80 mg/L. This result was associated with massive alterations in metabolic patterns of lung tissue and perfusate, resulting in a total of 139 discriminant compounds. Uncontrolled inflammatory response, abnormalities in energy metabolism, and mitochondrial dysfunction next to accelerated kynurenine and aldosterone production were recognized as distinct features of dysregulated metabolipidome. Spatial pharmacometabolomics may be a promising tool for identifying pathological responses to chemotherapy.

Of Mice and Men: The Inter-individual Variability of the Brain's Response to Drugs

Biological variation is ubiquitous in nature. Despite highly standardized breeding and husbandry under controlled environmental conditions, phenotypic diversity exists in laboratory mice and rats just as it does in humans. The resulting inter-individual variability affects various characteristics of animal disease models, including the responsiveness to drugs. Thus, the common practice of averaging data within an experimental group can lead to misinterpretations in neuroscience and other research fields. In this commentary, the impact of inter-individual variation in drug responsiveness is illustrated by examples from the testing of antiseizure medications in rodent temporal lobe epilepsy models. Individual mice and rats rendered epileptic by treatment according to standardized protocols fall into groups that either do or do not respond to antiseizure medications, thus mimicking the clinical situation in patients with epilepsy. Population responses are not normally distributed, and divergent responding is concealed in averages subjected to parametric statistical tests. Genetic, epigenetic, and environmental factors are believed to contribute to inter-individual variation in drug response but the specific molecular and physiological causes are not well understood. Being aware of inter-individual variability in rodents allows an improved interpretation of both behavioral phenotypes and drug effects in a pharmacological experiment.

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

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.

Integration of metabolomics and machine learning revealed tryptophan metabolites are sensitive biomarkers of pemetrexed efficacy in non-small cell lung cancer

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.

Pharmacometabolomic Approach to Investigate the Response to Metformin in Patients with Type 2 Diabetes: A Cross-Sectional Study

Metformin constitutes the foundation therapy in type 2 diabetes (T2D). Despite its multiple beneficial effects and widespread use, there is considerable inter-individual variability in response to metformin. Our objective is to identify metabolic signatures associated with poor and good responses to metformin, which may improve our ability to predict outcomes for metformin treatment. In this cross-sectional study, clinical and metabolic data for 119 patients with type 2 diabetes taking metformin were collected from the Qatar Biobank. Patients were empirically dichotomized according to their HbA1C levels into good and poor responders. Differences in the level of metabolites between these two groups were compared using orthogonal partial least square discriminate analysis (OPLS-DA) and linear models. Good responders showed increased levels of sphingomyelins, acylcholines, and glutathione metabolites. On the other hand, poor responders showed increased levels of metabolites resulting from glucose metabolism and gut microbiota metabolites. The results of this study have the potential to increase our knowledge of patient response variability to metformin and carry significant implications for enabling personalized medicine.

Integrated pharmacokinetics and pharmacometabolomics to reveal the synergistic mechanism of a multicomponent Chinese patent medicine, Mailuo Shutong pills against thromboangiitis obliterans

BACKGROUND

Mailuo Shutong Pills (MLST) have displayed pharmacological activity against thromboangiitis obliterans (TAO). However, the active ingredients and therapeutic mechanism of MLST against TAO remained to be further clarified.

PURPOSE

The aim of this study was to explore the active components of MLST and their synergistic mechanism against TAO by integrating pharmacokinetics (PK) and pharmacometabolomics (PM).

METHODS

TAO model rats were established by sodium laurate solution. Firstly, the efficacy of MLST was evaluated by gangrene score, blood flow velocity, and hematoxylin-eosin (H&E) staining. Secondly, PK research was conducted on bioavailable components to characterize their dynamic behaviors under TAO. Thirdly, multiple plasma and urine metabolic biomarkers for sodium laurate-induced TAO rats were found by untargeted metabolomics, and then variations in TAO-altered metabolites following MLST treatment were analyzed utilizing multivariate and bioinformatic analysis. Additionally, metabolic pathway analysis was performed using MetaboAnalyst. Finally, the dynamic link between absorbed MLST-compounds and TAO-associated endogenous metabolites was established by correlation analysis.

RESULTS

MLST significantly alleviated gangrene symptoms by improving the infiltration of inflammatory cells and blood supply in TAO rats. Significant differences in metabolic profiles were found in 17 differential metabolites in plasma and 24 in urine between Sham and TAO rats. The 10 bioavailable MLST-compounds, such as chlorogenic acid and paeoniflorin, showed positive or negative correlations with various TAO-altered metabolites related to glutamate metabolism, histidine metabolism, arachidonic acid metabolism and so on.

CONCLUSION

This study originally investigated the dynamic interaction between MLST and the biosystem, providing unique insight for disclosing the active components of MLST and their synergistic mechanisms against TAO, which also shed light on new therapeutic targets for TAO and treatment.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

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.

Metabolomic and Lipidomic Profiling of Gliomas-A New Direction in Personalized Therapies

Simple Summary

Gliomas comprise an extremely diverse category of brain tumors that are difficult to diagnose and treat. As a result, scientists continue to search for new treatment solutions, with personalized medicine having emerged as a particularly promising therapeutic approach. Metabolomics and its sub-discipline, lipidomics, are two scientific fields well-suited to support this search. Metabolomics focuses on the physicochemical changes in the metabolome, which include all of the small endogenous and exogenous compounds in a biological system. As such, metabolic analysis can help identify important biochemical pathways which could be the targets for new therapeutic approaches. This review examines the new directions of personalized therapies for gliomas and how metabolomic and lipidomic analysis assists in developing these strategies and monitoring their effectiveness. The discussion of new strategies is preceded by a brief overview of the current “gold standard” treatment for gliomas and the obstacles that new treatment approaches must overcome.

Abstract

In addition to being the most common primary brain tumor, gliomas are also among the most difficult to diagnose and treat. At present, the “gold standard” in glioma treatment entails the surgical resection of the largest possible portion of the tumor, followed by temozolomide therapy and radiation. However, this approach does not always yield the desired results. Additionally, the ability to cross the blood-brain barrier remains a major challenge for new potential drugs. Thus, researchers continue to search for targeted therapies that can be individualized based on the specific characteristics of each case. Metabolic and lipidomic research may represent two of the best ways to achieve this goal, as they enable detailed insights into the changes in the profile of small molecules in a biological system/specimen. This article reviews the new approaches to glioma therapy based on the analysis of alterations to biochemical pathways, and it provides an overview of the clinical results that may support personalized therapies in the future.

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.

Isolation and Identification of 3,4-Seco-Solanidine-3,4-dioic acid (SSDA) as a Urinary Biomarker of Cytochrome P450 2D6 (CYP2D6) Activity

Cytochrome P450 2D6 (CYP2D6), is responsible for the metabolism and elimination of approximately 25% of clinically used drugs, including antidepressants and antipsychotics, and its activity varies considerably on a population basis primary due to genetic variation. CYP2D6 phenotype can be assessed in vivo following administration of an exogenous probe compound, such as dextromethorphan or debrisoquine, but use of a biomarker that does not require administration of an exogenous compound (i.e., drug) has considerable appeal for assessing CYP2D6 activity in vulnerable populations, such as children. The goal of this study was to isolate, purify and identify an "endogenous" urinary biomarker (M1; m/z 444.3102) of CYP2D6 activity reported previously. Several chromatographic separation techniques (reverse phase HPLC, cation exchange and analytical reverse phase UPLC) were used to isolate and purify 96 μg of M1 from 40 L of urine. Subsequently, 1D and 2D NMR, and functional group modification reactions were used to elucidate its structure. Analysis of mass spectrometry and NMR data revealed M1 to have similar spectroscopic features to the nitrogen-containing steroidal alkaloid, solanidine. 2D NMR characterization by HMBC, COSY, TOCSY, and HSQC-TOCSY proved to be invaluable in the structural elucidation of M1; derivatization of M1 revealed the presence of two carboxylic acid moieties. M1 was determined to be a steroidal alkaloid with a solanidine backbone that had undergone C-C bond scission to yield 3,4-seco-solanidine-3,4-dioic acid (SSDA). SSDA may have value as a dietary biomarker of CYP2D6 activity in populations where potato consumption is common. Significance Statement Endogenous biomarkers of processes involved in drug disposition and response may allow improved individualization of drug treatment, especially in vulnerable populations, such as children. Given that several CYP2D6 substrates are commonly used in pediatrics and the ubiquitous nature of potato consumption in western diets, SSDA has considerable appeal as non-invasive biomarker of CYP2D6 activity to guide treatment with CYP2D6 substrates in children and adults.

Pharmacokinetics and Metabolomic Profiling of Metformin and Andrographis paniculata: A Protocol for a Crossover Randomised Controlled Trial

This protocol aims to profile the pharmacokinetics of metformin and Andrographis paniculata (AP) and continue with untargeted pharmacometabolomics analysis on pre-dose and post-dose samples to characterise the metabolomics profiling associated with the human metabolic pathways. This is a single-centre, open-labelled, three periods, crossover, randomised-controlled, single-dose oral administration pharmacokinetics and metabolomics trial of metformin 1000 mg (n = 18), AP 1000 mg (n = 18), or AP 2000 mg (n = 18) in healthy volunteers under the fasting condition. Subjects will be screened according to a list of inclusion and exclusion criteria. Investigational products will be administered according to the scheduled timeline. Vital signs and adverse events will be monitor periodically, and standardized meals will be provided to the subjects. Fifteen blood samples will be collected over 24 h, and four urine samples will be collected within a 12 h period. Onsite safety monitoring throughout the study and seven-day phone call safety follow-up will be compiled after the last dose of administration. The plasma samples will be analysed for the pharmacokinetics parameters to estimate the drug maximum plasma concentration. Untargeted metabolomic analysis between pre-dose and maximum plasma concentration (Cmax) samples will be performed for metabolomic profiling to identify the dysregulation of human metabolic pathways that link to the pharmacodynamics effects. The metformin arm will focus on the individualised Cmax plasma concentration for metabolomics study and used as a model drug. After this, an investigation of the dose-dependent effects will be performed between pre-dose samples and median Cmax concentration samples in the AP 1000 mg and AP 2000 mg arms for metabolomics study. The study protocol utilises a crossover study design to incorporate a metabolomics-based study into pharmacokinetics trial in the drug development program. The combination analyses will complement the interpretation of pharmacological effects according to the bioavailability of the drug.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Pharmacokinetic-Pharmacometabolomic Approach in Early-Phase Clinical Trials: A Way Forward for Targeted Therapy in Type 2 Diabetes

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; AUC_0-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.

Sex-Specific Catabolic Metabolism Alterations in the Critically Ill following High Dose Vitamin D

Pharmacological interventions are essential for the treatment and management of critical illness. Although women comprise a large proportion of the critically ill, sex-specific pharmacological properties are poorly described in critical care. The sex-specific effects of vitamin D₃ treatment in the critically ill are not known. Therefore, we performed a metabolomics cohort study with 1215 plasma samples from 428 patients from the VITdAL-ICU trial to study sex-specific differences in the metabolic response to critical illness following high-dose oral vitamin D₃ intervention. In women, despite the dose of vitamin D₃ being higher, pharmacokinetics demonstrated a lower extent of vitamin D₃ absorption compared to men. Metabolic response to high-dose oral vitamin D₃ is sex-specific. Sex-stratified individual metabolite associations with elevations in 25(OH)D following intervention showed female-specific positive associations in long-chain acylcarnitines and male-specific positive associations in free fatty acids. In subjects who responded to vitamin D₃ intervention, significant negative associations were observed in short-chain acylcarnitines and branched chain amino acid metabolites in women as compared to men. Acylcarnitines and branched chain amino acids are reflective of fatty acid B oxidation, and bioenergesis may represent notable metabolic signatures of the sex-specific response to vitamin D. Demonstrating sex-specific pharmacometabolomics differences following intervention is an important movement towards the understanding of personalized medicine.

LCMD: Lung Cancer Metabolome Database

Lung cancer, one of the most common causes of cancer-related death worldwide, has been associated with high treatment cost and imposed great burdens. The 5-year postoperative survival rate of lung cancer (13%) is lower than many other leading cancers indicating the urgent needs to dissect its pathogenic mechanisms and discover specific biomarkers. Although several proteins have been proposed to be potential candidates for the diagnosis of lung cancer, they present low accuracy in clinical settings. Metabolomics has thus emerged as a very promising tool for biomarker discovery. To date, many lung cancer-related metabolites have been highlighted in the literature but no database is available for scientists to retrieve this information. Herein, we construct and introduce the first Lung Cancer Metabolome Database (LCMD), a freely available online database depositing 2013 lung cancer-related metabolites identified from 65 mass spectrometry-based lung cancer metabolomics studies. Researchers are able to explore LCMD via two ways. Firstly, by applying various filters in the “Browse Metabolites” mode, users can access a list of lung cancer-related metabolites that satisfy the filter specifications. For each metabolite, users can acquire the value of the fold change (cancer/normal), statistical significance (p-value) of the fold change, and the comparative research designs of all the mass spectrometry-based lung cancer metabolomics studies that identify this metabolite. Secondly, by applying various filters in the “Browse Studies” mode, users can obtain a list of mass spectrometry-based lung cancer metabolomics studies that satisfy the filter specifications. For each study, users can view the type of studied specimen, mass spectrometry (MS) method, MS data processing software, and differential analysis method, as well as all the identified lung cancer-related metabolites. Furthermore, the overview of each study is clearly illustrated by a graphical summary. The LCMD (http://cosbi7.ee.ncku.edu.tw/LCMD/) is the first database that brings together the meaningful information of lung cancer-related metabolites. The development of the LCMD is envisioned to promote the biomarker discovery of lung cancer.

Serum Predose Metabolic Profiling for Prediction of Rosuvastatin Pharmacokinetic Parameters in Healthy Volunteers

Rosuvastatin is a well-known lipid-lowering agent generally used for hypercholesterolemia treatment and coronary artery disease prevention. There is a substantial inter-individual variability in the absorption of statins usually caused by genetic polymorphisms leading to a variation in the corresponding pharmacokinetic parameters, which may affect drug therapy safety and efficacy. Therefore, the investigation of metabolic markers associated with rosuvastatin inter-individual variability is exceedingly relevant for drug therapy optimization and minimizing side effects. This work describes the application of pharmacometabolomic strategies using liquid chromatography coupled to mass spectrometry to investigate endogenous plasma metabolites capable of predicting pharmacokinetic parameters in predose samples. First, a targeted method for the determination of plasma concentration levels of rosuvastatin was validated and applied to obtain the pharmacokinetic parameters from 40 enrolled individuals; then, predose samples were analyzed using a metabolomic approach to search for associations between endogenous metabolites and the corresponding pharmacokinetic parameters. Data processing using machine learning revealed some candidates including sterols and bile acids, carboxylated metabolites, and lipids, suggesting the approach herein described as promising for personalized drug therapy.

Pharmaco-metabolomics opportunities in drug development and clinical research

Pharmaco-metabolomics uses metabolic phenotypes for the prediction of inter-individual variations in drug response and helps in understanding the mechanisms of drug action. The field has made significant progress over the last 14 years with numerous studies providing clinical evidence for personalised medicine. However, discovered pharmaco-metabolomic biomarkers are not yet translated into clinics due to a lack of large-scale validation. Integration of targeted and untargeted metabolomics workflows into pharmacokinetic analysis and drug development can advance the field from bench to bedside. Also, Indian pharmaceutical research and its bioanalytical infrastructure are in a position to take on these opportunities by addressing challenges such as appropriate training and regulatory compliance.

Review of Metabolomics-Based Biomarker Research for Parkinson's Disease

Parkinson's disease (PD), as the second most common neurodegenerative disease, is seriously affecting the life quality of the elderly. However, there is still a lack of efficient medical methods to diagnosis PD before apparent symptoms occur. In recent years, clinical biomarkers including genetic, imaging, and tissue markers have exhibited remarkable benefits in assisting PD diagnoses. Due to the advantages of high-throughput detection of metabolites and almost non-invasive sample collection, metabolomics research of PD is widely used for diagnostic biomarker discovery. However, there are also a few shortages for those identified biomarkers, such as the scarcity of verifications regarding the sensitivity and specificity. Thus, reviewing the research progress of PD biomarkers based on metabolomics techniques is of great significance for developing PD diagnosis. To comprehensively clarify the progress of current metabolic biomarker studies in PD, we reviewed 20 research articles regarding the discovery and validation of biomarkers for PD diagnosis from three mainstream academic databases (NIH PubMed, ISI Web of Science, and Elsevier ScienceDirect). By analyzing those materials, we summarized the metabolic biomarkers identified by those metabolomics studies and discussed the potential approaches used for biomarker verifications. In conclusion, this review provides a comprehensive and updated overview of PD metabolomics research in the past two decades and particularly discusses the validation of disease biomarkers. We hope those discussions might provide inspiration for PD biomarker discovery and verification in the future.

A novel canine nuclear magnetic resonance spectroscopy-based metabolomics platform: Validation and sample handling

BACKGROUND

Metabolomics has been proven to be an invaluable research tool by providing comprehensive insight into systemic metabolism. However, the lack of scalable and quantitative methods with known reference intervals (RIs) and documented reproducibility has prevented the use of metabolomics in the clinical setting.

OBJECTIVE

The objective of this study was to validate the developed quantitative nuclear magnetic resonance (NMR) spectroscopy-based metabolomics platform for canine serum and plasma samples and determine optimal sample handling conditions for its use.

METHODS

Altogether, 8247 canine samples were analyzed using a Bruker's 500 MHz NMR spectrometer. Using statistical approaches derived from international guidelines, we studied method precision, measurand stability in various long- and short-term storage conditions, as well as the effect of prolonged contact with red blood cells (RBCs), and differences among blood collection tubes. We also screened interferences with lipemia, hemolysis, and bilirubinemia. The results were compared against routine clinical chemistry methods, and RIs were defined for all measurands.

RESULTS

We determined RIs for 123 measurands, most of which were previously unpublished. The reproducibility of the results of the NMR platform appeared generally outstanding, and the integrity of the results can be ensured by following standard blood drawing and processing guidelines.

CONCLUSIONS

Owing to the advantages of quantitative results, high reproducibility, and scalability, this canine metabolomics platform holds great potential for numerous clinical and research applications to improve canine health and well-being.

Mass spectrometry based high-throughput bioanalysis of low molecular weight compounds: are we ready to support personalized medicine?

Liquid chromatography coupled to mass spectrometry (LC-MS) is the gold standard in bioanalysis for the development of quantitative assays to support drug development or therapeutic drug monitoring. High-throughput and low-cost gene sequencing have enabled a paradigm shift from one treatment fits all to personalized medicine (PM). However, gene monitoring provides only partial information about the health state. The full picture requires the combination of gene monitoring with the screening of exogenous compounds, metabolites, lipids, and proteins. This critical review discusses how mass spectrometry–based technologies and approaches including separation sciences, ambient ionization, and ion mobility are/could be used to support high-throughput bioanalysis of endogenous end exogenous low molecular weight compounds. It includes also various biological sample types (from blood to expired air), and various sample preparation techniques.

Real-time pharmacokinetics via online analysis of exhaled breath

The advantages that on-line breath analysis has shown in different fields have already made it stand as an interesting tool for pharmacokinetic studies. This review summarizes recent progress in the field, diving into the different analytical methods and the different advantages and hurdles encountered. We conclude that there is a wealth of limitations in the application of this technique, and key aspects like standardization are still outstanding. Nevertheless, this is an experimental field that has not yet been fully explored; and the advantages it offers for animal welfare, decrease in the amount of drug needed in experimental studies, and complementary insights to current pharmacological studies, warrant further exploration. Further studies are needed to overcome current limitations and incorporate this technique into the toolbox of pharmacological studies, both at an industrial and academic level.

Personalised therapeutic management of epileptic patients guided by pathway-driven breath metabolomics

BACKGROUND

Therapeutic management of epilepsy remains a challenge, since optimal systemic antiseizure medication (ASM) concentrations do not always correlate with improved clinical outcome and minimal side effects. We tested the feasibility of noninvasive real-time breath metabolomics as an extension of traditional therapeutic drug monitoring for patient stratification by simultaneously monitoring drug-related and drug-modulated metabolites.

METHODS

This proof-of-principle observational study involved 93 breath measurements of 54 paediatric patients monitored over a period of 2.5 years, along with an adult's cohort of 37 patients measured in two different hospitals. Exhaled breath metabolome of epileptic patients was measured in real time using secondary electrospray ionisation-high-resolution mass spectrometry (SESI-HRMS).

RESULTS

We show that systemic ASM concentrations could be predicted by the breath test. Total and free valproic acid (VPA, an ASM) is predicted with concordance correlation coefficient (CCC) of 0.63 and 0.66, respectively. We also find (i) high between- and within-subject heterogeneity in VPA metabolism; (ii) several amino acid metabolic pathways are significantly enriched (p < 0.01) in patients suffering from side effects; (iii) tyrosine metabolism is significantly enriched (p < 0.001), with downregulated pathway compounds in non-responders.

CONCLUSIONS

These results show that real-time breath analysis of epileptic patients provides reliable estimations of systemic drug concentrations along with risk estimates for drug response and side effects.

Metabolomics in asthma: A platform for discovery

Asthma, characterized by airway hyperresponsiveness, inflammation and remodeling, is a chronic airway disease with complex etiology. Severe asthma is characterized by frequent exacerbations and poor therapeutic response to conventional asthma therapy. A clear understanding of cellular and molecular mechanisms of asthma is critical for the discovery of novel targets for optimal therapeutic control of asthma. Metabolomics is emerging as a powerful tool to elucidate novel disease mechanisms in a variety of diseases. In this review, we summarize the current status of knowledge in asthma metabolomics at systemic and cellular levels. The findings demonstrate that various metabolic pathways, related to energy metabolism, macromolecular biosynthesis and redox signaling, are differentially modulated in asthma. Airway smooth muscle cell plays pivotal roles in asthma by contributing to airway hyperreactivity, inflammatory mediator release and remodeling. We posit that metabolomic profiling of airway structural cells, including airway smooth muscle cells, will shed light on molecular mechanisms of asthma and airway hyperresponsiveness and help identify novel therapeutic targets.

Serum 5-Hydroxyindoleacetic Acid and Ratio of 5-Hydroxyindoleacetic Acid to Serotonin as Metabolomics Indicators for Acute Oxidative Stress and Inflammation in Vancomycin-Associated Acute Kidney Injury

The incidence of vancomycin-associated acute kidney injury (VAKI) varies from 5–43%, and early detection of VAKI is important in deciding whether to discontinue nephrotoxic agents. Oxidative stress is the main mechanism of VAKI, and serotonin (5-HT) and its metabolite 5-hydroxyindoleacetic acid (5-HIAA) have been examined with respect to their involvement in ischemia/reperfusion damage in experimental animal models. In the current study, we assessed 5-HT and 5-HIAA as novel biomarkers for detecting VAKI in patients who have infections or compromised renal function, using a mass spectrometry–based metabolomics approach. We conducted amino acid profiling analysis and measurements of 5-HT and 5-HIAA using serum from subjects with VAKI (n = 28) and non-VAKI control subjects (n = 69), consisting of the infection subgroup (n = 23), CKD subgroup (n = 23), and healthy controls (HCs, n = 23). 5-HT was significantly lower in the VAKI group than in the non-VAKI groups, and the concentration of 5-HIAA and the ratio of 5-HIAA to 5-HT (5-HIAA/5-HT) showed higher values in the VAKI group. The infection subgroup presented a significantly greater 5-HIAA/5-HT ratio compared with the HC subgroup. Our study revealed that increased 5-HIAA/5-HT ratio has the potential to act as a VAKI surrogate marker, reflecting acute oxidative stress and inflammation.

Pharmacokinetics/pharmacometabolomics-pharmacodynamics reveals the synergistic mechanism of a multicomponent herbal formula, Baoyuan decoction against cardiac hypertrophy

Multicomponent herbal formulas (MCHFs) have earned a wide reputation for their definite efficacy in preventing or treating chronic complex diseases. However, holistic elucidation of the causal relationship between the bioavailable ingredients of MCHFs and their multitarget interactions is very challenging. To solve this problem, pharmacokinetics/pharmacometabolomics-pharmacodynamics (PK/PM-PD) combined with a multivariate biological correlation-network strategy was developed and applied to a classic MCHF, Baoyuan decoction (BYD), to clarify its active components and synergistic mechanism against cardiac hypertrophy (CH). First, multiple plasma metabolic biomarkers for β-adrenergic agonist-induced CH rats were identified by using untargeted metabolomic profiling, and then, these CH-associated endogenous metabolites and the absorbed BYD-compounds in plasma at different treatment stages after oral administration of BYD were analyzed by using targeted PK and PM. Second, the dynamic relationship of BYD-related compounds and CH-associated endogenous metabolites and signaling pathways was built by using multivariate and bioinformatic correlation analysis. Finally, metabolic-related PD indicators were predicted and further verified by biological tests. The results demonstrated that the bioavailable BYD-compounds, such as saponins and flavonoids, presented differentiated and distinctive metabolic features and showed positive or negative correlations with various CH-altered metabolites and PD-indicators related to gut microbiota metabolism, amino acid metabolism, lipid metabolism, energy homeostasis, and oxidative stress at different treatment stages. This study provides a novel strategy for investigating the dynamic interaction between BYD and the biosystem, providing unique insight for disclosing the active components and synergistic mechanisms of BYD against CH, which also supplies a reference for other MCHF related research.

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.

Nutritional Aspects of Ecologically Relevant Phytochemicals in Ruminant Production

This review provides an update of ecologically relevant phytochemicals for ruminant production, focusing on their contribution to advancing nutrition. Phytochemicals embody a broad spectrum of chemical components that influence resource competence and biological advantage in determining plant species' distribution and density in different ecosystems. These natural compounds also often act as plant defensive chemicals against predatorial microbes, insects, and herbivores. They may modulate or exacerbate microbial transactions in the gastrointestinal tract and physiological responses in ruminant microbiomes. To harness their production-enhancing characteristics, phytochemicals have been actively researched as feed additives to manipulate ruminal fermentation and establish other phytochemoprophylactic (prevent animal diseases) and phytochemotherapeutic (treat animal diseases) roles. However, phytochemical-host interactions, the exact mechanism of action, and their effects require more profound elucidation to provide definitive recommendations for ruminant production. The majority of phytochemicals of nutritional and pharmacological interest are typically classified as flavonoids (9%), terpenoids (55%), and alkaloids (36%). Within flavonoids, polyphenolics (e.g., hydrolyzable and condensed tannins) have many benefits to ruminants, including reducing methane (CH4) emission, gastrointestinal nematode parasitism, and ruminal proteolysis. Within terpenoids, saponins and essential oils also mitigate CH4 emission, but triterpenoid saponins have rich biochemical structures with many clinical benefits in humans. The anti-methanogenic property in ruminants is variable because of the simultaneous targeting of several physiological pathways. This may explain saponin-containing forages' relative safety for long-term use and describe associated molecular interactions on all ruminant metabolism phases. Alkaloids are N-containing compounds with vast pharmacological properties currently used to treat humans, but their phytochemical usage as feed additives in ruminants has yet to be exploited as they may act as ghost compounds alongside other phytochemicals of known importance. We discussed strategic recommendations for phytochemicals to support sustainable ruminant production, such as replacements for antibiotics and anthelmintics. Topics that merit further examination are discussed and include the role of fresh forages vis-à-vis processed feeds in confined ruminant operations. Applications and benefits of phytochemicals to humankind are yet to be fully understood or utilized. Scientific explorations have provided promising results, pending thorough vetting before primetime use, such that academic and commercial interests in the technology are fully adopted.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Applications and challenges in therapeutic drug monitoring of cancer treatment: A review

Most anticancer agents show wide variability in pharmacokinetics (PK) and have a narrow therapeutic index which makes fixed dosing suboptimal. To achieve the best therapeutic outcomes with these agents, many studies have postulated using PK or therapeutic drug monitoring (TDM)-guided dosing. However, multiple factors contribute to the variability in PKs making the application of TDM in practice challenging. Also, despite the known association with clinical outcomes, standard guidelines on PK-guided dosing are lacking for most agents. Understanding the factors that contribute to PK variability and their impact is essential for dose individualization. The purpose of this review is to discuss the factors that contribute to the PK variability of anticancer agents and the challenges faced in practice when individualizing doses for certain widely used agents. Searching the literature has identified several gaps and efforts are needed to ensure better targeting of cancer therapeutics.

Arginine and Arginine/ADMA Ratio Predict 90-Day Mortality in Patients with Out-of-Hospital Cardiac Arrest-Results from the Prospective, Observational COMMUNICATE Trial

(1) Background: In patients with shock, the L-arginine nitric oxide pathway is activated, causing an elevation of nitric oxide, asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA) levels. Whether these metabolites provide prognostic information in patients after out-of-hospital cardiac arrest (OHCA) remains unclear. (2) Methods: We prospectively included OHCA patients, recorded clinical parameters and measured plasma ADMA, SDMA and Arginine levels by liquid chromatography tandem mass spectrometry (LC-MS). The primary endpoint was 90-day mortality. (3) Results: Of 263 patients, 130 (49.4%) died within 90 days after OHCA. Compared to survivors, non-survivors had significantly higher levels of ADMA and lower Arginine and Arginine/ADMA ratios in univariable regression analyses. Arginine levels and Arginine/ADMA ratio were significantly associated with 90-day mortality (OR 0.51 (95%CI 0.34 to 0.76), p < 0.01 and OR 0.40 (95%CI 0.26 to 0.61), p < 0.001, respectively). These associations remained significant in several multivariable models. Arginine/ADMA ratio had the highest predictive value with an area under the curve (AUC) of 0.67 for 90-day mortality. Results for secondary outcomes were similar with significant associations with in-hospital mortality and neurological outcome. (4) Conclusion: Arginine and Arginine/ADMA ratio were independently associated with 90-day mortality and other adverse outcomes in patients after OHCA. Whether therapeutic modification of the L-arginine-nitric oxide pathway has the potential to improve outcome should be evaluated.

Computational profiling of the gut-brain axis: microflora dysbiosis insights to neurological disorders

Almost 2500 years after Hippocrates' observations on health and its direct association to the gastrointestinal tract, a paradigm shift has recently occurred, making the gut and its symbionts (bacteria, fungi, archaea and viruses) a point of convergence for studies. It is nowadays well established that the gut microflora's compositional diversity regulates via its genes (the microbiome) the host's health and provides preliminary insights into disease progression and regulation. The microbiome's involvement is evident in immunological and physiological studies that link changes in its biodiversity to its contributions to the host's phenotype but also in neurological investigations, substantiating the aptly named gut-brain axis. The definitive mechanisms of this last bidirectional interaction will be our main focus because it presents researchers with a new conundrum. In this review, we prospect current literature for computational analysis methodologies that accommodate the need for better understanding of the microbiome-gut-brain interactions and neurological disorder onset and progression, through cross-disciplinary systems biology applications. We will present bioinformatics tools used in exploring these synergies that help build and interpret microbial 16S ribosomal RNA data sets, produced by shotgun and high-throughput sequencing of healthy and neurological disorder samples stored in biological databases. These approaches provide alternative means for researchers to form hypotheses to their inquests faster, cheaper and swith precision. The goal of these studies relies on the integration of combined metagenomics and metabolomics assessments. An accurate characterization of the microbiome and its functionality can support new diagnostic, prognostic and therapeutic strategies for neurological disorders, customized for each individual host.

Current Concepts in Pharmacometabolomics, Biomarker Discovery, and Precision Medicine

Pharmacometabolomics (PMx) studies use information contained in metabolic profiles (or metabolome) to inform about how a subject will respond to drug treatment. Genome, gut microbiome, sex, nutrition, age, stress, health status, and other factors can impact the metabolic profile of an individual. Some of these factors are known to influence the individual response to pharmaceutical compounds. An individual’s metabolic profile has been referred to as his or her “metabotype.” As such, metabolomic profiles obtained prior to, during, or after drug treatment could provide insights about drug mechanism of action and variation of response to treatment. Furthermore, there are several types of PMx studies that are used to discover and inform patterns associated with varied drug responses (i.e., responders vs. non-responders; slow or fast metabolizers). The PMx efforts could simultaneously provide information related to an individual’s pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine. PMx biomarkers can also be discovered and validated during FDA clinical trials. Using biomarkers during medical development is described in US Law under the 21st Century Cures Act. Information on how to submit biomarkers to the FDA and their context of use is defined herein.

A Feasibility Study of an Extrusion-Based Fabrication Process for Personalized Drugs

Developing a high-efficiency manufacturing system for personalized medicine plays an important role in increasing the feasibility of personalized medication. The purpose of this study is to investigate the feasibility of a new extrusion-based fabrication process for personalized drugs with a faster production rate. This process uses two syringe pumps with a coaxial needle as an extruder, which extrudes two materials with varying ratios into a capsule. The mixture of hydrogel, polyethylene glycol (PEG), hydroxypropyl methylcellulose, poly acrylic acid and the simulated active pharmaceutical ingredient, Aspirin, was used. To validate the method, samples with different ratios of immediate release (IR) and sustained release (SR) mixtures were fabricated. The results of a dissolution test show that it is feasible to control the release profile by changing the IR and SR ratio using this fabrication setup. The fabrication time for each capsule is about 20 seconds, which is significantly faster than the current 3D printing methods. In conclusion, the proposed fabrication method shows a clear potential to step toward the feasibility of personalized medication.

Metabolomics in pharmacology - a delve into the novel field of pharmacometabolomics

Introduction: Pharmacometabolomics is an emerging science pursuing the application of precision medicine. Combining both genetic and environmental factors, the so-called pharmacometabolomic approach guides patient selection and stratification in clinical trials and optimizes personalized drug dosage, improving efficacy and safety.Areas covered: This review illustrates the progressive introduction of pharmacometabolomics as an innovative solution for enhancing the discovery of novel drugs and improving research and development (R&D) productivity of the pharmaceutical industry. An extended analysis on published pharmacometabolomics studies both in animal models and humans includes results obtained in several areas such as hepatology, gastroenterology, nephrology, neuropsychiatry, oncology, drug addiction, embryonic cells, neonatology, and microbiomics.Expert opinion: a tailored, individualized therapy based on the optimization of pharmacokinetics and pharmacodynamics, the improvement of drug efficacy, and the abolition of drug toxicity and adverse drug reactions is a key issue in precision medicine. Genetics alone has become insufficient for deciphring intra- and inter-individual variations in drug-response, since they originate both from genetic and environmental factors, including human microbiota composition. The association between pharmacogenomics and pharmacometabolomics may be considered the new strategy for an in-deep knowledge on changes and alterations in human and microbial metabolic pathways due to the action of a drug.

The Influence of Ionic Liquids on the Effectiveness of Analytical Methods Used in the Monitoring of Human and Veterinary Pharmaceuticals in Biological and Environmental Samples-Trends and Perspectives

Recent years have seen the increased utilization of ionic liquids (ILs) in the development and optimization of analytical methods. Their unique and eco-friendly properties and the ability to modify their structure allows them to be useful both at the sample preparation stage and at the separation stage of the analytes. The use of ILs for the analysis of pharmaceuticals seems particularly interesting because of their systematic delivery to the environment. Nowadays, they are commonly detected in many countries at very low concentration levels. However, due to their specific physiological activity, pharmaceuticals are responsible for bioaccumulation and toxic effects in aquatic and terrestrial ecosystems as well as possibly upsetting the body's equilibrium, leading to the dangerous phenomenon of drug resistance. This review will provide a comprehensive summary of the use of ILs in various sample preparation procedures and separation methods for the determination of pharmaceuticals in environmental and biological matrices based on liquid-based chromatography (LC, SFC, TLC), gas chromatography (GC) and electromigration techniques (e.g., capillary electrophoresis (CE)). Moreover, the advantages and disadvantages of ILs, which can appear during extraction and separation, will be presented and attention will be given to the criteria to be followed during the selection of ILs for specific applications.

Population Pharmacokinetics of Clozapine: A Systematic Review

BACKGROUND AND OBJECTIVE

Clozapine is a second-generation antipsychotic drug that is considered the most effective treatment for refractory schizophrenia. Several clozapine population pharmacokinetic models have been introduced in the last decades. Thus, a systematic review was performed (i) to compare published pharmacokinetics models and (ii) to summarize and explore identified covariates influencing the clozapine pharmacokinetics models.

METHODS

A search of publications for population pharmacokinetic analyses of clozapine either in healthy volunteers or patients from inception to April 2019 was conducted in PubMed and SCOPUS databases. Reviews, methodology articles, in vitro and animal studies, and noncompartmental analysis were excluded.

RESULTS

Twelve studies were included in this review. Clozapine pharmacokinetics was described as one-compartment with first-order absorption and elimination in most of the studies. Significant interindividual variations of clozapine pharmacokinetic parameters were found in most of the included studies. Age, sex, smoking status, and cytochrome P450 1A2 were found to be the most common identified covariates affecting these parameters. External validation was only performed in one study to determine the predictive performance of the models.

CONCLUSIONS

Large pharmacokinetic variability remains despite the inclusion of several covariates. This can be improved by including other potential factors such as genetic polymorphisms, metabolic factors, and significant drug-drug interactions in a well-designed population pharmacokinetic model in the future, taking into account the incorporation of larger sample size and more stringent sampling strategy. External validation should also be performed to the previously published models to compare their predictive performances.

Steroid-resistant nephrotic syndrome: pharmacogenetics and epigenetic points and views

Introduction: Glucocorticoids (GCs) are the first-line therapy for patients with nephrotic syndrome (NS), a common glomerular disease, that cause complete remission in most of the cases. In response to the treatment, NS patients are divided into glucocorticoid-sensitive and -resistant. This variation is due to the differences in pharmacokinetics and pharmacodynamics of GCs in each patient that affect the response to the treatment modality. Since the genetic variations in drug-metabolizing enzymes and transporter proteins significantly impact the pharmacokinetics, efficacy and safety of the applied medications, this review highlights the basic mechanisms of genetic variations involved in GCs metabolism in drug-resistant NS patients.Areas covered: This review explains the pharmacogenetic variations that influence the profile of GCs responses and their pharmacokinetics in NS patients. Moreover, the epigenetic variations including histone modifications and miRNA gene regulation that have an influence on GCs responses will review. A comprehensive literature search was performed using different keywords to the reviewed topics.Expert opinion: The accumulative data suggest the importance of pharmacogenetic studies to develop personalized therapies and increase the GCs responsiveness in these patients. It is imperative to know that genetic testing does not give absolute answers to all existing questions in steroid resistance.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Spatial and molecular changes of mouse brain metabolism in response to immunomodulatory treatment with teriflunomide as visualized by MALDI-MSI

Multiple sclerosis (MS) is an immune-mediated neurodegenerative disease of the central nervous system (CNS). One of the most promising recent medications for MS is teriflunomide. Its primary mechanism of action is linked to effects on the peripheral immune system by inhibiting dihydroorotate dehydrogenase (DHODH)-catalyzed de novo pyrimidine synthesis and reducing the expansion of lymphocytes in the peripheral immune system. Some in vitro studies suggested, however, that it can also have a direct effect on the CNS compartment. This potential alternative mode of action depends on the drug's capacity to traverse the blood-brain barrier (BBB) and to exert an effect on the complex network of brain biochemical pathways. In this paper, we demonstrate the application of high-resolution/high-accuracy matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry for molecular imaging of the mouse brain coronal sections from animals treated with teriflunomide. Specifically, in order to assess the effect of teriflunomide on the mouse CNS compartment, we investigated the feasibility of teriflunomide to traverse the BBB. Secondly, we systematically evaluated the spatial and semi-quantitative brain metabolic profiles of 24 different endogenous compounds after 4-day teriflunomide administration. Even though the drug was not detected in the examined cerebral sections (despite the high detection sensitivity of the developed method), in-depth study of the endogenous metabolic compartment revealed noticeable alterations as a result of teriflunomide administration compared to the control animals. The observed differences, particularly for purine and pyrimidine nucleotides as well as for glutathione and carbohydrate metabolism intermediates, shed some light on the potential impact of teriflunomide on the mouse brain metabolic networks. Graphical Abstract.

Beta-3 Adrenoceptor Signaling Pathways in Urothelial and Smooth Muscle Cells in the Presence of Succinate

Succinate, an intermediate metabolite of the Krebs cycle, can alter the metabolomics response to certain drugs and controls an array of molecular responses in the urothelium through activation of its receptor, G-protein coupled receptor 91 (GPR91). Mirabegron, a β3-adrenergic receptor (β3-AR) agonist used to treat overactive bladder syndrome (OAB), increases intracellular cAMP in the detrusor smooth muscle cells (SMC), leading to relaxation. We have previously shown that succinate inhibits forskolin-stimulated cAMP production in urothelium. To determine whether succinate interferes with mirabegron-mediated bladder relaxation, we examined their individual and synergistic effect in urothelial-cell and SMC signaling. We first confirmed β3-AR involvement in the mirabegron response by quantifying receptor abundance by immunoblotting in cultured urothelial cells and SMC and cellular localization by immunohistochemistry in rat bladder tissue. Mirabegron increased cAMP levels in SMC but not in urothelial cells, an increase that was inhibited by succinate, suggesting that it impairs cAMP-mediated bladder relaxation by mirabegron. Succinate and mirabegron increased inducible nitric oxide synthesis and nitric oxide secretion only in urothelial cells, suggesting that its release can indirectly induces SMC relaxation. Succinate exposure decreased the expression of β3-AR protein in whole bladder in vivo and in SMC in vitro, indicating that this metabolite may lead to impaired pharmacodynamics of the bladder. Together, our results demonstrate that increased levels of succinate in settings of metabolic stress (e.g., the metabolic syndrome) may lead to impaired mirabegron and β3-AR interaction, inhibition of cAMP production, and ultimately requiring mirabegron dose adjustment for its treatment of OAB related to these conditions.

Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery

This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.

Genetic polymorphisms of CYP2C9, CYP2C19, and CYP3A5 in Kosovar population

Cytochrome P450 genetic polymorphisms are responsible for individual variations in drug metabolism and drug-drug interactions. They are very important for pharmacogenetics, and their frequency varies across different populations. There is a big gap in the knowledge about the CYP gene family polymorphisms in the population of Kosovo, and the aim of our study was to fill that gap by determining the frequency of the most important variant alleles of CYP2C9, CYP2C19, and CYP3A5 in 234 nonrelated Kosovars. The allele frequencies of CYP2C9*2 and 2C9*3 were 17.52 %, and 10.89 %, respectively. Sixteen participants (6.81 %) were CYP2C9 poor metabolisers. The CYP2C19*2 and *17 variant frequencies were 13.03 % and 19.01 %, respectively. There were 2.13 % CYP2C19 poor and 4.27 % ultra-rapid metabolisers (homozygous carriers of the *17 allele). With regard to CYP3A5, the frequency of the *3 variant allele was 98.29 % (non-expressors), while the remaining participants (1.70 %) were expressors of CYP3A5. These findings are comparable with other European ethnicities, specifically those of Southeast Europe.

A review of metabolism-associated biomarkers in lung cancer diagnosis and treatment

INTRODUCTION

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection has proven essential to extend survival. Genomic and proteomic advances have provided impetus to the effort dedicated to detect and diagnose the disease at an earlier stage. Recently, the study of metabolites associated with tumor formation and progression has inaugurated the era of cancer metabolomics to aid in this effort.

OBJECTIVES

This review summarizes recent work regarding novel metabolites with the potential to serve as biomarkers for early lung tumor detection, evaluation of disease progression, and prediction of patient outcomes.

METHOD

We compare the metabolite profiling of cancer patients with that of healthy individuals, and the metabolites identified in tissue and biofluid samples and their usefulness as lung cancer biomarkers. We discuss metabolite alterations in tumor versus paired non-tumor lung tissues, as well as metabolite alterations in different stages of lung cancers and their usefulness as indicators of disease progression and overall survival. We evaluate metabolite dysregulation in different types of lung cancers, and those associated with lung cancer versus other lung diseases. We also examine metabolite differences between lung cancer patients and smokers/risk-factor individuals.

RESULT

Although an extensive list of metabolites has been evaluated to distinguish between these cases, refinement of methods is further required for adequate patient diagnosis.

CONCLUSION

We conclude that with technological advancement, metabolomics may be able to replace more invasive and costly diagnostic procedures while also providing the means to more effectively tailor treatment to patient-specific tumors.

Pharmacometabolomics applied to zonisamide pharmacokinetic parameter prediction

INTRODUCTION

Zonisamide is a new-generation anticonvulsant antiepileptic drug metabolized primarily in the liver, with subsequent elimination via the renal route.

OBJECTIVES

Our objective was to evaluate the utility of pharmacometabolomics in the detection of zonisamide metabolites that could be related to its disposition and therefore, to its efficacy and toxicity.

METHODS

This study was nested to a bioequivalence clinical trial with 28 healthy volunteers. Each participant received a single dose of zonisamide on two separate occasions (period 1 and period 2), with a washout period between them. Blood samples of zonisamide were obtained from all patients at baseline for each period, before volunteers were administered any medication, for metabolomics analysis.

RESULTS

After a Lasso regression was applied, age, height, branched-chain amino acids, steroids, triacylglycerols, diacyl glycerophosphoethanolamine, glycerophospholipids susceptible to methylation, phosphatidylcholines with 20:4 FA (arachidonic acid) and cholesterol ester and lysophosphatidylcholine were obtained in both periods.

CONCLUSION

To our knowledge, this is the only research study to date that has attempted to link basal metabolomic status with pharmacokinetic parameters of zonisamide.