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.

     

Pharmacometabolomic signature links simvastatin therapy and insulin resistance

Introduction

Statins, widely prescribed drugs for treatment of cardiovascular disease, inhibit the biosynthesis of low density lipoprotein cholesterol (LDL-C). Despite providing major benefits, sub populations of patients experience adverse effects, including muscle myopathy and development of type II diabetes mellitus (T2DM) that may result in premature discontinuation of treatment. There are no reliable biomarkers for predicting clinical side effects in vulnerable individuals. Pharmacometabolomics provides powerful tools for identifying global biochemical changes induced by statin treatment, providing insights about drug mechanism of action, development of side effects and basis of variation of response.

Objective

To determine whether statin-induced changes in intermediary metabolism correlated with statin-induced hyperglycemia and insulin resistance; to identify pre-drug treatment metabolites predictive of post-drug treatment increased diabetic risk.

Methods

Drug-naïve patients were treated with 40 mg/day simvastatin for 6 weeks in the Cholesterol and Pharmacogenetics (CAP) study; metabolomics by gas chromatography-time-of-flight mass-spectrometry (GC–TOF–MS) was performed on plasma pre and post treatment on 148 of the 944 participants.

Results

Six weeks of simvastatin treatment resulted in 6.9% of patients developing hyperglycemia and 25% developing changes consistent with development of pre-diabetes. Altered beta cell function was observed in 53% of patients following simvastatin therapy and insulin resistance was observed in 54% of patients. We identified initial signature of simvastatin-induced insulin resistance, including ethanolamine, hydroxylamine, hydroxycarbamate and isoleucine which, upon further replication and expansion, could be predictive biomarkers of individual susceptibility to simvastatin-induced new onset pre-type II diabetes mellitus. No patients were clinically diagnosed with T2DM.

Conclusion

Within this short 6 weeks study, some patients became hyperglycemic and/or insulin resistant. Diabetic markers were associated with decarboxylated small aminated metabolites as well as a branched chain amino acid directly linked to glucose metabolism and fatty acid biosynthesis. Pharmacometabolomics provides powerful tools for precision medicine by predicting development of drug adverse effects in sub populations of patients. Metabolic profiling prior to start of drug therapy may empower physicians with critical information when prescribing medication and determining prognosis.

     

Predicting adverse drug reactions through interpretable deep learning framework

Background

Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.

Methods

In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.

Results

We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis.

Conclusions

The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.

     

Prediction of intravenous busulfan clearance by endogenous plasma biomarkers using global pharmacometabolomics

Introduction

High-dose busulfan (busulfan) is an integral part of the majority of hematopoietic cell transplantation conditioning regimens. Intravenous (IV) busulfan doses are personalized using pharmacokinetics (PK)-guided dosing where the patient’s IV busulfan clearance is calculated after the first dose and is used to personalize subsequent doses to a target plasma exposure. PK-guided dosing has improved patient outcomes and is clinically accepted but highly resource-intensive.

Objective

We sought to discover endogenous plasma biomarkers predictive of IV busulfan clearance using a global pharmacometabolomics-based approach

Methods

Using LC-QTOF, we analyzed 59 (discovery) and 88 (validation) plasma samples obtained before IV busulfan administration.

Results

In the discovery dataset, we evaluated the association of the relative abundance of 1885 ions with IV busulfan clearance and found 21 ions that were associated with IV busulfan clearance tertiles (r² ≥ 0.3). Identified compounds were deoxycholic acid and/or chenodeoxycholic acid, and linoleic acid. We used these 21 ions to develop a parsimonious seven-ion linear predictive model that accurately predicted IV busulfan clearance in 93 % (discovery) and 78 % (validation) of samples.

Conclusion

IV busulfan clearance was significantly correlated with the relative abundance of 21 ions, seven of which were included in a predictive model that accurately predicted IV busulfan clearance in the majority of the validation samples. These results reinforce the potential of pharmacometabolomics as a critical tool in personalized medicine, with the potential to improve the personalized dosing of drugs with a narrow therapeutic index such as busulfan.

     

Update on Therapeutic Drug Monitoring of Antifungals for the Prophylaxis and Treatment of Invasive Fungal Infections

Purpose of Review

Certain antifungals used as therapy for invasive fungal disease, including the extended-spectrum triazoles, may be limited by variable pharmacokinetics and drug interactions. This is especially important when drug exposure, as measured by trough concentrations, may be linked to either efficacy or toxicity. We review the rationale, indications, and controversies in TDM of antifungals.

Recent Findings

The monitoring of voriconazole drug levels is often practiced in patients that receive this triazole based on clinical data. Posaconazole delayed-release tablets achieve higher drug exposure more consistently, necessitating reconsideration of a role for therapeutic drug monitoring. Isavuconazole has predictable population kinetics, although exposure appears to be reduced in certain groups. However, the utility of isavuconazole therapeutic drug monitoring is unknown.

Summary

Therapeutic drug monitoring is warranted for certain antifungals, while its utility is being reconsidered for others.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Urinary antihypertensive drug metabolite screening using molecular networking coupled to high-resolution mass spectrometry fragmentation

Introduction

Mass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry in combination with MS/MS gas-phase experiments has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectral files pose challenges to downstream analysis, given their complexity and size.

Objectives

This study aims to detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments based on the spectral similarity of their fragmentation spectra. Furthermore, spectral clusters of endogenous metabolites were also examined.

Methods

Here we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. The mass spectrometry data was collected on a Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments.

Results

In total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlodipine. The molecular networking approach also generated clusters of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline.

Conclusions

The approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome.

     

Identification of a urine metabolite constellation characteristic for kidney allograft rejection

Introduction

Allograft rejection is still an important complication after kidney transplantation. Currently, monitoring of these patients mostly relies on the measurement of serum creatinine and clinical evaluation. The gold standard for diagnosing allograft rejection, i.e. performing a renal biopsy is invasive and expensive. So far no adequate biomarkers are available for routine use.

Objectives

We aimed to develop a urine metabolite constellation that is characteristic for acute renal allograft rejection.

Methods

NMR-Spectroscopy was applied to a training cohort of transplant recipients with and without acute rejection.

Results

We obtained a metabolite constellation of four metabolites that shows promising performance to detect renal allograft rejection in the cohorts used (AUC of 0.72 and 0.74, respectively).

Conclusion

A metabolite constellation was defined with the potential for further development of an in-vitro diagnostic test that can support physicians in their clinical assessment of a kidney transplant patient.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Quantum dots: a new tool for anti-malarial drug assays

Background

Malaria infects over 300 million people every year and one of the major obstacles for the eradication of the disease is parasite's resistance to current chemotherapy, thus new drugs are urgently needed. Quantum dot (QD) is a fluorescent nanocrystal that has been in the spotlight as a robust tool for visualization of live cell processes in real time. Here, a simple and efficient method using QD to directly label Plasmodium falciparum-infected erythrocytes (iRBCs) was searched in order to use the QD as a probe in an anti-malarial drug-screening assay.

Methods

A range of QDs with different chemical coatings were tested for their ability to specifically bind iRBCs by immunofluorescence assay (IFA). One QD was selected and used to detect parasite growth and drug sensitivity by flow cytometry.

Results

PEGylated-cationic QD (PCQD) was found to specifically label infected erythrocytes preferentially with late stage parasites. The detection of QD-labelled infected erythrocytes by flow cytometry was sensitive enough to monitor chloroquine anti-malarial toxicity with a drug incubation period as short as 24 h (EC₅₀ = 113nM). A comparison of our assay with another widely used anti-malarial drug screening assay, the pLDH assay, showed that PCQD-based assay had 50% improved sensitivity in detecting drug efficacy within a parasite life cycle. An excellent Z-factor of 0.8 shows that the QD assay is suitable for high-throughput screening.

Conclusions

This new assay can offer a rapid and robust platform to screen novel classes of anti-malarial drugs.

     

Three-dimensional perfused tumour spheroid model for anti-cancer drug screening

Objective

To build an in vitro-perfused, three-dimensional (3D) spheroid model based on the TissueFlex system for anti-cancer drug efficacy testing in order to mimic avascular micro-tissues with inherent O₂, nutrient and metabolite gradients, and to provide a more accurate prediction of drug toxicity and efficacy than traditional in vitro tumour models in conventional static culture well plates.

Results

The perfused cancer spheroid model showed higher cell viability and increased diameter of spheroids over a relatively long culture period (17 days). Three anti-cancer drugs with different cytotoxic mechanisms were tested. In perfusion, lower cytotoxicity was observed for traditional cytotoxic drug 5-fluorouracil and microtubule-interfering, paclitaxel, showed greater interruption of spheroid integrity. For the hypoxic-dependent drug, tirapazamine, there was no significant difference observed between static and perfusion cultures.

Conclusion

The perfusion culture provides a better homeostasis for cancer cell growth in a more controllable working platform for long-term drug testing.

     

Impaired mitochondrial calcium uptake caused by tacrolimus underlies beta-cell failure

Background

One of the most common side effects of the immunosuppressive drug tacrolimus (FK506) is the increased risk of new-onset diabetes mellitus. However, the molecular mechanisms underlying this association have not been fully clarified.

Methods

We studied the effects of the therapeutic dose of tacrolimus on mitochondrial fitness in beta-cells.

Results

We demonstrate that tacrolimus impairs glucose-stimulated insulin secretion (GSIS) in beta-cells through a previously unidentified mechanism. Indeed, tacrolimus causes a decrease in mitochondrial Ca²⁺ uptake, accompanied by altered mitochondrial respiration and reduced ATP production, eventually leading to impaired GSIS.

Conclusion

Our observations individuate a new fundamental mechanism responsible for the augmented incidence of diabetes following tacrolimus treatment. Indeed, this drug alters Ca²⁺ fluxes in mitochondria, thereby compromising metabolism-secretion coupling in beta-cells.

     

Phosphodiesterase 4 inhibitors and drugs of abuse: current knowledge and therapeutic opportunities

Background

Long-term exposure to drugs of abuse causes an upregulation of the cAMP-signaling pathway in the nucleus accumbens and other forebrain regions, this common neuroadaptation is thought to underlie aspects of drug tolerance and dependence. Phosphodiesterase 4 (PDE4) is an enzyme that the selective hydrolyzes intracellular cAMP. It is expressed in several brain regions that regulate the reinforcing effects of drugs of abuse.

Objective

Here, we review the current knowledge about central nervous system (CNS) distribution of PDE4 isoforms and the effects of systemic and brain-region specific inhibition of PDE4 on behavioral models of drug addiction.

Methods

A systematic literature search was performed using the Pubmed.

Results

Using behavioral sensitization, conditioned place preference and drug self-administration as behavioral models, a large number of studies have shown that local or systemic administration of PDE4 inhibitors reduce drug intake and/or drug seeking for psychostimulants, alcohol, and opioids in rats or mice.

Conclusions

Preclinical studies suggest that PDE4 could be a therapeutic target for several classes of substance use disorder. We conclude by identifying opportunities for the development of subtype-selective PDE4 inhibitors that may reduce addiction liability and minimize the side effects that limit the clinical potential of non-selective PDE4 inhibitors. Several PDE4 inhibitors have been clinically approved for other diseases. There is a promising possibility to repurpose these PDE4 inhibitors for the treatment of drug addiction as they are safe and well-tolerated in patients.

     

Transient decrease of hepatic NAD<Superscript>+</Superscript> and amino acid alterations during treatment with valproate: new insights on drug-induced effects in vivo using targeted MS-based metabolomics

Background

Therapeutic administration of the drug valproate (VPA) results in metabolic changes at the hepatic level that have not been fully characterized. Interference of this branched-chain fatty acid with the oxidative metabolism of amino acids may have consequences for the downstream biosynthesis of essential cofactors.

Objectives

We aimed to evaluate the effect of VPA on amino acid and NAD⁺ metabolism using targeted MS-based metabolite profiling.

Methods

Plasma samples from patients under chronic treatment with VPA were analyzed. VPA was administered to Wistar rats mimicking prolonged and acute treatment, the latter with two different doses. Plasma and liver samples were collected for targeted metabolomics studies using UPLC-MS/MS and GC-FID.

Results

Analysis of amino acids in rat plasma and liver and in human plasma demonstrated that drug intake is associated with a particularly significant drop in the levels of tryptophan, and increased levels of glycine and lysine. The lowered plasma tryptophan levels prompted us to study the intracellular content of tryptophan and various nicotinamide adenine dinucleotides. A significant decrease of NAD⁺ and NADP⁺ was observed in the liver of rats after the single administration of VPA at two different doses, but not after repeated administration.

Conclusion

The observed accumulation of kynurenine intermediates in rat liver tissue suggests a drug-induced interference with the de novo pathway of NAD⁺ biosynthesis. These findings provide novel insights into the mechanisms of VPA associated hepatocellular dysfunction and/or toxicity, but with possible major relevance to the anticancer effects of the drug.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Serum metabolomic study for detecting biomarkers of non-traumatic osteonecrosis of the femoral head

Introduction

Non-traumatic osteonecrosis of the femoral head (NTONFH) is a progressive disease, always leading to hip dysfunction if no early intervention was applied. The difficulty for early diagnosis of NTONFH is due to the slight symptoms at early stages as well as the high cost for screening patients by using magnetic resonance imaging.

Objective

The aim was to detect biomarkers of early-stage NTONFH, which was beneficial to the exploration of a cost-effective approach for the early diagnose of the disease.

Methods

Metabolomic approaches were employed in this study to detect biomarkers of early-stage NTONFH (22 patients, 23 controls), based on the platform of ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and the uses of multivariate statistic analysis, putative metabolite identification, metabolic pathway analysis and biomarker analysis.

Results

In total, 33 serum metabolites were found altered between NTONFH group and control group. In addition, glycerophospholipid metabolism and pyruvate metabolism were highly associated with the disease.

Conclusion

The combination of LysoPC (18:3), l-tyrosine and l-leucine proved to have a high diagnostic value for early-stage NTONFH. Our findings may contribute to the protocol for early diagnosis of NTONFH and further elucidate the underlying mechanisms of the disease.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

Quantitative analysis of tetrahydrofolate metabolites from <Emphasis Type="Italic">clostridium autoethanogenum</Emphasis>

Introduction

Quantification of tetrahydrofolates (THFs), important metabolites in the Wood–Ljungdahl pathway (WLP) of acetogens, is challenging given their sensitivity to oxygen.

Objective

To develop a simple anaerobic protocol to enable reliable THFs quantification from bioreactors.

Methods

Anaerobic cultures were mixed with anaerobic acetonitrile for extraction. Targeted LC–MS/MS was used for quantification.

Results

Tetrahydrofolates can only be quantified if sampled anaerobically. THF levels showed a strong correlation to acetyl-CoA, the end product of the WLP.

Conclusion

Our method is useful for relative quantification of THFs across different growth conditions. Absolute quantification of THFs requires the use of labelled standards.