ResearchPad - drug-metabolism https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Methamphetamine administration increases hepatic CYP1A2 but not CYP3A activity in female guinea pigs]]> https://www.researchpad.co/article/elastic_article_7848 Methamphetamine use has increased over the past decade and the first use of methamphetamine is most often when women are of reproductive age. Methamphetamine accumulates in the liver; however, little is known about the effect of methamphetamine use on hepatic drug metabolism. Methamphetamine was administered on 3 occassions to female Dunkin Hartley guinea pigs of reproductive age, mimicking recreational drug use. Low doses of test drugs caffeine and midazolam were administered after the third dose of methamphetamine to assess the functional activity of cytochrome P450 1A2 and 3A, respectively. Real-time quantitative polymerase chain reaction was used to quantify the mRNA expression of factors involved in glucocorticoid signalling, inflammation, oxidative stress and drug transporters. This study showed that methamphetamine administration decreased hepatic CYP1A2 mRNA expression, but increased CYP1A2 enzyme activity. Methamphetamine had no effect on CYP3A enzyme activity. In addition, we found that methamphetamine may also result in changes in glucocorticoid bioavailability, as we found a decrease in 11β-hydroxysteroid dehydrogenase 1 mRNA expression, which converts inactive cortisone into active cortisol. This study has shown that methamphetamine administration has the potential to alter drug metabolism via the CYP1A2 metabolic pathway in female guinea pigs. This may have clinical implications for drug dosing in female methamphetamine users of reproductive age.

]]>
<![CDATA[Abrogation of pathogenic attributes in drug resistant <i>Candida auris</i> strains by farnesol]]> https://www.researchpad.co/article/elastic_article_7651 Candida auris, a decade old Candida species, has been identified globally as a significant nosocomial multidrug resistant (MDR) pathogen responsible for causing invasive outbreaks. Biofilms and overexpression of efflux pumps such as Major Facilitator Superfamily and ATP Binding Cassette are known to cause multidrug resistance in Candida species, including C. auris. Therefore, targeting these factors may prove an effective approach to combat MDR in C. auris. In this study, 25 clinical isolates of C. auris from different hospitals of South Africa were used. All the isolates were found capable enough to form biofilms on 96-well flat bottom microtiter plate that was further confirmed by MTT reduction assay. In addition, these strains have active drug efflux mechanism which was supported by rhodamine-6-G extracellular efflux and intracellular accumulation assays. Antifungal susceptibility profile of all the isolates against commonly used drugs was determined following CLSI recommended guidelines. We further studied the role of farnesol, an endogenous quorum sensing molecule, in modulating development of biofilms and drug efflux in C. auris. The MIC for planktonic cells ranged from 62.5–125 mM, and for sessile cells was 125 mM (4h biofilm) and 500 mM (12h and 24h biofilm). Furthermore, farnesol (125 mM) also suppresses adherence and biofilm formation by C. auris. Farnesol inhibited biofilm formation, blocked efflux pumps and downregulated biofilm- and efflux pump- associated genes. Modulation of C. auris biofilm formation and efflux pump activity by farnesol represent a promising approach for controlling life threatening infections caused by this pathogen.

]]>
<![CDATA[Time-lapse imaging of HeLa spheroids in soft agar culture provides virtual inner proliferative activity]]> https://www.researchpad.co/article/Nceafa1bd-f75c-4e08-9c15-587118f668b1

Cancer is a complex disease caused by multiple types of interactions. To simplify and normalize the assessment of drug effects, spheroid microenvironments have been utilized. Research models that involve agent measurement with the examination of clonogenic survival by monitoring culture process with image analysis have been developed for spheroid-based screening. Meanwhile, computer simulations using various models have enabled better predictions for phenomena in cancer. However, user-based parameters that are specific to a researcher’s own experimental conditions must be inputted. In order to bridge the gap between experimental and simulated conditions, we have developed an in silico analysis method with virtual three-dimensional embodiment computed using the researcher’s own samples. The present work focused on HeLa spheroid growth in soft agar culture, with spheroids being modeled in silico based on time-lapse images capturing spheroid growth. The spheroids in silico were optimized by adjusting the growth curves to those obtained from time-lapse images of spheroids and were then assigned virtual inner proliferative activity by using generations assigned to each cellular particle. The ratio and distribution of the virtual inner proliferative activities were confirmed to be similar to the proliferation zone ratio and histochemical profiles of HeLa spheroids, which were also consistent with those identified in an earlier study. We validated that time-lapse images of HeLa spheroids provided virtual inner proliferative activity for spheroids in vitro. The present work has achieved the first step toward an in silico analysis method using computational simulation based on a researcher’s own samples, helping to bridge the gap between experiment and simulation.

]]>
<![CDATA[Identification of NUDT15 gene variants in Amazonian Amerindians and admixed individuals from northern Brazil]]> https://www.researchpad.co/article/N0a09703b-e69a-40d3-8ae4-dfe23e56b45d

Introduction

The nudix hydrolase 15 (NUDT15) gene acts in the metabolism of thiopurine, by catabolizing its active metabolite thioguanosine triphosphate into its inactivated form, thioguanosine monophosphate. The frequency of alternative NUDT15 alleles, in particular those that cause a drastic loss of gene function, varies widely among geographically distinct populations. In the general population of northern Brazilian, high toxicity rates (65%) have been recorded in patients treated with the standard protocol for acute lymphoblastic leukemia, which involves thiopurine-based drugs. The present study characterized the molecular profile of the coding region of the NUDT15 gene in two groups, non-admixed Amerindians and admixed individuals from the Amazon region of northern Brazil.

Methods

The entire NUDT15 gene was sequenced in 64 Amerindians from 12 Amazonian groups and 82 admixed individuals from northern Brazil. The DNA was extracted using phenol-chloroform. The exome libraries were prepared using the Nextera Rapid Capture Exome (Illumina) and SureSelect Human All Exon V6 (Agilent) kits. The allelic variants were annotated in the ViVa® (Viewer of Variants) software.

Results

Four NUDT15 variants were identified: rs374594155, rs1272632214, rs147390019, andrs116855232. The variants rs1272632214 and rs116855232 were in complete linkage disequilibrium, and were assigned to the NUDT15*2 genotype. These variants had high frequencies in both our study populations in comparison with other populations catalogued in the 1000 Genomes database. We also identified the NUDT15*4 haplotype in our study populations, at frequencies similar to those reported in other populations from around the world.

Conclusion

Our findings indicate that Amerindian and admixed populations from northern Brazil have high frequencies of the NUDT15 haplotypes that alter the metabolism profile of thiopurines.

]]>
<![CDATA[Toward precision prescribing for methadone: Determinants of methadone deposition]]> https://www.researchpad.co/article/N51499fe4-a854-40f2-ac0e-5bd2b114360f

Background

Despite the World Health Organization listing methadone as an essential medication, effective dose selection is challenging, especially in racial and ethnic minority populations. Subtherapeutic doses can result in withdrawal symptoms while supratherapeutic doses can result in overdose and death. Although CYP3A4 was conventionally considered the principal methadone metabolizing enzyme, more recent data have identified CYP2B6 as the principal enzyme. CYP2B6 has ethnically-associated polymorphisms that affect the metabolic rate. Our objective was to investigate the effects of genetic and nongenetic factors on methadone metabolism.

Methods

We measured trough plasma methadone levels in 100 participants with opioid use disorder. We assessed methadone metabolism by calculating the metabolite ratio (major metabolite: 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine [EDDP] divided by methadone concentration). We assessed hepatic fibrosis and steatosis by transient elastography and CYP2B6 alleles, principally responsible for methadone metabolism. Mixed effects models modeled the data in 97 participants.

Results

Participants were largely male (58%), minority (61% African American) and non-Hispanic (68%). Forty percent were HCV mono-infected, 40% were uninfected, and 20% were HCV/HIV co-infected. Female sex had significant effects on (R)- and (S)-methadone metabolism (p = 0.016 and p = 0.044, respectively). CYP2B6 loss of function (LOF) alleles significantly affected (S)-methadone metabolism (p = 0.012). Body mass index (BMI) significantly affected (R)-methadone metabolism (p = 0.034). Methadone metabolism appeared to be lower in males, in individuals with LOF alleles, and elevated BMI.

Conclusions

Genetic analysis, especially in minority populations, is essential to delivering individualized treatments. Although the principal methadone metabolizing enzyme remains controversial, our results suggest that sex, CYP2B6 genotype, and BMI should be incorporated into multivariate models to create methadone dosing algorithms. Methadone dosing algorithms should facilitate medication delivery, improve patient satisfaction, and diminish overdose potential.

]]>
<![CDATA[Preliminary results of identification and quantification of paclitaxel and its metabolites in human meconium from newborns with gestational chemotherapeutic exposure]]> https://www.researchpad.co/article/5c76fe5dd5eed0c484e5b96b

Objective

Cancer diagnosis during pregnancy occurs in 1 out of 1000 pregnancies with common malignancies including breast and hematological cancers. Fetal exposure to currently utilized agents is poorly described. We directly assessed fetal exposure by screening meconium from 23 newborns whose mothers had undergone treatment for cancer during pregnancy.

Study design

Meconium was collected from newborns whose mothers were diagnosed with cancer during pregnancy and underwent chemotherapy in the second or third trimester as part of the Cancer and Pregnancy Registry. We conducted screening of 23 meconium samples for chemotherapeutics and known metabolites of chemotherapeutics by liquid chromatography-high resolution mass spectrometry (LC-HRMS). Putative identification of paclitaxel and/or its metabolites was made in 8 screened samples. In positively screened samples, we quantified paclitaxel, 3’-p-hydroxypaclitaxel, and 6α-hydroxypaclitaxel by stable isotope dilution-LC-HRMS.

Results

Mean (standard deviation) levels of paclitaxel in positively screened samples were 399.9 (248.6) pg/mg in meconium samples from newborn born to mothers that underwent chemotherapy during pregnancy. 3’-p-hydroxypaclitaxel and 6α-hydroxypaclitaxel mean levels were 105.2 (54.6) and 113.4 (48.9) pg/mg meconium, respectively.

Conclusion

Intact paclitaxel, 3’-p-hydroxypaclitaxel, and 6α-hydroxypaclitaxel were detected in meconium, providing unambiguous confirmation of human fetal exposure. Variability in meconium levels between individuals may indicate a potential for reducing fetal exposure based on timing, dosing, and individual characteristics. This preliminary study may provide an approach for examining the effects of cancer diagnosis during pregnancy on other outcomes by providing a measure of direct fetal exposure.

]]>
<![CDATA[Pharmacokinetics of morphine in encephalopathic neonates treated with therapeutic hypothermia]]> https://www.researchpad.co/article/5c6f14b9d5eed0c48467a745

Objective

Morphine is a commonly used drug in encephalopathic neonates treated with therapeutic hypothermia after perinatal asphyxia. Pharmacokinetics and optimal dosing of morphine in this population are largely unknown. The objective of this study was to describe pharmacokinetics of morphine and its metabolites morphine-3-glucuronide and morphine-6-glucuronide in encephalopathic neonates treated with therapeutic hypothermia and to develop pharmacokinetics based dosing guidelines for this population.

Study design

Term and near-term encephalopathic neonates treated with therapeutic hypothermia and receiving morphine were included in two multicenter cohort studies between 2008–2010 (SHIVER) and 2010–2014 (PharmaCool). Data were collected during hypothermia and rewarming, including blood samples for quantification of morphine and its metabolites. Parental informed consent was obtained for all participants.

Results

244 patients (GA mean (sd) 39.8 (1.6) weeks, BW mean (sd) 3,428 (613) g, male 61.5%) were included. Morphine clearance was reduced under hypothermia (33.5°C) by 6.89%/°C (95% CI 5.37%/°C– 8.41%/°C, p<0.001) and metabolite clearance by 4.91%/°C (95% CI 3.53%/°C– 6.22%/°C, p<0.001) compared to normothermia (36.5°C). Simulations showed that a loading dose of 50 μg/kg followed by continuous infusion of 5 μg/kg/h resulted in morphine plasma concentrations in the desired range (between 10 and 40 μg/L) during hypothermia.

Conclusions

Clearance of morphine and its metabolites in neonates is affected by therapeutic hypothermia. The regimen suggested by the simulations will be sufficient in the majority of patients. However, due to the large interpatient variability a higher dose might be necessary in individual patients to achieve the desired effect.

Trial registration

www.trialregister.nl NTR2529.

]]>
<![CDATA[Biochemical metabolic levels and vitamin D receptor FokⅠ gene polymorphisms in Uyghur children with urolithiasis]]> https://www.researchpad.co/article/5c6b2681d5eed0c484289bc2

Because of lacking studies of urolithiasis in children, we detected the biochemical metabolic levels and FokⅠ polymorphisms in the vitamin D receptor (VDR) in Uyghur children with urolithiasis, and evaluated the associations of biochemical metabolic levels with FokⅠ genotypes. We included 142 Uyghur children (108 males) under age 14 years with a diagnosis of urolithiasis and 238 Uyghur children (154 males) under age 14 years without a history of urolithiasis as controls. Baseline information and data for serum and urine parameters were obtained from medical records. PCR-restriction fragment length polymorphism (PCR-RFLP) was used to analyze the VDR FokⅠ polymorphisms. In univariate analyses adjusting for age and sex, carbon dioxide combining power (CO2CP) (odds ratio [OR] = 1.13, 95% confidence interval [CI]: 1.07–1.19), serum magnesium (Mg) (OR = 1.27, 95% CI: 1.03–1.56) and serum chlorine (Cl) (OR = 0.93, 95% CI: 0.88–0.97) were related to Uyghur children urolithiasis risk. A multiple logistic regression model showed CO2CP (OR = 1.17, 95% CI: 1.09–1.26), levels of uric acid (OR = 1.01, 95% CI: 1.00–1.01) and serum sodium (Na) (OR = 0.90, 95% CI: 0.82–0.99) were associated with pediatric urolithiasis. The risk of urolithiasis was increased with the F versus f allele overall (OR = 1.42; 95% CI: 1.01–2.00) and for males (OR = 1.52, 95% CI: 1.02–2.27). However, metabolic levels did not differ by FokⅠ genotypes. In our population, CO2CP and levels of uric acid and serum Na as well as polymorphism of the F allele of the VDR FokⅠ may provide important clues to evaluate the risk of urolithiasis in Uyghur children.

]]>
<![CDATA[Predicting inadequate postoperative pain management in depressed patients: A machine learning approach]]> https://www.researchpad.co/article/5c648d07d5eed0c484c81d9a

Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity and therefore may reduce the effectiveness of prodrug opioids. We used a machine learning approach to identify patients prescribed a combination of SSRIs and prodrug opioids postoperatively and to examine the effect of this combination on postoperative pain control. Using EHR data from an academic medical center, we identified patients receiving surgery over a 9-year period. We developed and validated natural language processing (NLP) algorithms to extract depression-related information (diagnosis, SSRI use, symptoms) from structured and unstructured data elements. The primary outcome was the difference between preoperative pain score and postoperative pain at discharge, 3-week and 8-week time points. We developed computational models to predict the increase or decrease in the postoperative pain across the 3 time points by using the patient’s EHR data (e.g. medications, vitals, demographics) captured before surgery. We evaluate the generalizability of the model using 10-fold cross-validation method where the holdout test method is repeated 10 times and mean area-under-the-curve (AUC) is considered as evaluation metrics for the prediction performance. We identified 4,306 surgical patients with symptoms of depression. A total of 14.1% were prescribed both an SSRI and a prodrug opioid, 29.4% were prescribed an SSRI and a non-prodrug opioid, 18.6% were prescribed a prodrug opioid but were not on SSRIs, and 37.5% were prescribed a non-prodrug opioid but were not on SSRIs. Our NLP algorithm identified depression with a F1 score of 0.95 against manual annotation of 300 randomly sampled clinical notes. On average, patients receiving prodrug opioids had lower average pain scores (p<0.05), with the exception of the SSRI+ group at 3-weeks postoperative follow-up. However, SSRI+/Prodrug+ had significantly worse pain control at discharge, 3 and 8-week follow-up (p < .01) compared to SSRI+/Prodrug- patients, whereas there was no difference in pain control among the SSRI- patients by prodrug opioid (p>0.05). The machine learning algorithm accurately predicted the increase or decrease of the discharge, 3-week and 8-week follow-up pain scores when compared to the pre-operative pain score using 10-fold cross validation (mean area under the receiver operating characteristic curve 0.87, 0.81, and 0.69, respectively). Preoperative pain, surgery type, and opioid tolerance were the strongest predictors of postoperative pain control. We provide the first direct clinical evidence that the known ability of SSRIs to inhibit prodrug opioid effectiveness is associated with worse pain control among depressed patients. Current prescribing patterns indicate that prescribers may not account for this interaction when choosing an opioid. The study results imply that prescribers might instead choose direct acting opioids (e.g. oxycodone or morphine) in depressed patients on SSRIs.

]]>
<![CDATA[The Human Cytomegalovirus UL38 protein drives mTOR-independent metabolic flux reprogramming by inhibiting TSC2]]> https://www.researchpad.co/article/5c536b4cd5eed0c484a485ec

Human Cytomegalovirus (HCMV) infection induces several metabolic activities that are essential for viral replication. Despite the important role that this metabolic modulation plays during infection, the viral mechanisms involved are largely unclear. We find that the HCMV UL38 protein is responsible for many aspects of HCMV-mediated metabolic activation, with UL38 being necessary and sufficient to drive glycolytic activation and induce the catabolism of specific amino acids. UL38’s metabolic reprogramming role is dependent on its interaction with TSC2, a tumor suppressor that inhibits mTOR signaling. Further, shRNA-mediated knockdown of TSC2 recapitulates the metabolic phenotypes associated with UL38 expression. Notably, we find that in many cases the metabolic flux activation associated with UL38 expression is largely independent of mTOR activity, as broad spectrum mTOR inhibition does not impact UL38-mediated induction of glycolysis, glutamine consumption, or the secretion of proline or alanine. In contrast, the induction of metabolite concentrations observed with UL38 expression are largely dependent on active mTOR. Collectively, our results indicate that the HCMV UL38 protein induces a pro-viral metabolic environment via inhibition of TSC2.

]]>
<![CDATA[The red pepper’s spicy ingredient capsaicin activates AMPK in HepG2 cells through CaMKKβ]]> https://www.researchpad.co/article/5c59fed8d5eed0c484135716

Capsaicin is a natural compound present in chili and red peppers and the responsible of their spicy flavor. It has recently provoked interest because of its antitumoral effects in many cell types although its action mechanism is not clearly understood. As metabolic dysregulation is one of the hallmarks of cancer cells and the key metabolic sensor in the AMP-activated kinase (AMPK), in this study we explored the ability of capsaicin to modulate AMPK activity. We found that capsaicin activated AMPK in HepG2 cells by increasing AMPK phosphorylation and its downstream target ACC. Mechanistically, we determined that capsaicin activated AMPK through the calcium/calmodulin-dependent protein kinase kinase β, CaMKKβ as either the CaMKK inhibitor STO-609 or CaMKK knock down with siRNA abrogated the activation of AMPK. Moreover, capsaicin decreased cell viability, inhibited Akt/mTOR pathway and increased reactive oxygen species (ROS) in HepG2 cells. AMPK activation was involved in the underpinning mechanism of capsaicin-induced cell death.

]]>
<![CDATA[Lithium is able to minimize olanzapine oxidative-inflammatory induction on macrophage cells]]> https://www.researchpad.co/article/5c59ff0bd5eed0c4841359bf

Background

Olanzapine (OLZ) is a second-generation antipsychotic drug used for treatment of schizophrenia, bipolar disorder, and other neuropsychiatric conditions. Undesirable side effects of OLZ include metabolic alterations associated with chronic oxidative-inflammation events. It is possible that lithium (Li), a mood modulator that exhibits anti-inflammatory properties may attenuate OLZ-induced oxi-inflammatory effects.

Methodology

To test this hypothesis we activated RAW 264.7 immortalized macrophages with OLZ and evaluated oxidation and inflammation at the gene and protein levels. Li and OLZ concentrations were determined using estimated plasma therapeutic concentrations.

Results

OLZ triggered a significant increase in macrophage proliferation at 72 h. Higher levels of oxidative markers and proinflammatory cytokines, such as TNF-α, IL-1β, and IL-6, with a concomitant reduction in IL-10, were observed in OLZ-exposed macrophages. Lithium (Li) exposure triggered a short and attenuated inflammatory response demonstrated by elevation of superoxide anion (SA), reactive oxygen species (ROS), IL-1β, and cellular proliferation followed by elevation of anti-inflammatory IL-10 levels. Li treatment of OLZ-supplemented macrophages was able to reverse elevation of oxidative and inflammatory markers and increase IL-10 levels.

Conclusions

Despite methodological limitations related to in vitro protocols, results suggested that Li may attenuate OLZ-induced oxidative and inflammatory responses that result from metabolic side effects associated with OLZ.

]]>
<![CDATA[Cellular determinants of metabolite concentration ranges]]> https://www.researchpad.co/article/5c536be4d5eed0c484a4947e

Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component’s concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications.

]]>
<![CDATA[The influence of concomitant antiepileptic drugs on lamotrigine serum concentrations in Northwest Chinese Han population with epilepsy]]> https://www.researchpad.co/article/5c478ca5d5eed0c484bd3a7f

Objective

The aims of this study were to identify the influencing factors such as gender, age, dose and combinations of other antiepileptic drugs (AEDs), especially in triple combinations on the pharmacokinetic of Lamotrigine (LTG) in epilepsy patients of Northwest Chinese Han population.

Methods

Data of the LTG concentration and clinical information were analyzed retrospectively from a therapeutic drug monitoring (TDM) database at the Clinical Pharmacy Laboratory of Xi’an Central Hospital between January 1, 2016 and January 1, 2018. The independent-sample t-test, one-way ANOVA analysis and Bonferroni and Tamhane T3 post-hoc test, the stepwise multivariate regression analysis were adopted by IBM SPSS, version 22.0.

Results

226 serum samples met the inclusion criteria and were evaluated. The mean LTG serum concentration was 5.48±3.83 μg/mL. There were no gender differences (P = 0.64), and there were no significant effects by age on LTG serum concentration after age stratification (3–14 years old, 14-45 years old, 45–59 years old) (P = 0.05). Multiple regression analysis showed that the daily LTG dose and co-administration of other AEDs significantly affected LTG serum concentrations. Combination with enzyme-inducer AEDs, the mean steady-state LTG concentration could be decreased by 30.73% compared with LTG monotherapy. Among enzyme-inducer AEDs, particularly strong inducer Carbamazepine (CBZ) could decrease the mean LTG concentration by 53.65%, but weak inducer AEDs such as Oxcarbazepine (OXC) and Topiramate (TPM) had no effect, Valproic acid (VPA) could increase the mean LTG concentration by 93.95%, and the inducer only partially compensated for the inhibitory effect of VPA in triple combination.

Conclusions

There were no significant gender and age effects, but the LTG daily dose and co-administration of other AEDs significantly affected LTG serum concentration. Combination with enzyme-inducer AEDs, especially CBZ could significantly decrease LTG serum concentrations, VPA could significantly increase LTG serum concentrations, and the inducer only partially compensated for the inhibitory effect of VPA in triple combination. In the clinical setting, these findings can help to estimate LTG concentrations and adjust dosage and evaluate adverse drug reactions.

]]>
<![CDATA[Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment]]> https://www.researchpad.co/article/5c6059c7d5eed0c4847cbe16

Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational framework—Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment.

]]>
<![CDATA[Constraints-based analysis identifies NAD+ recycling through metabolic reprogramming in antibiotic resistant Chromobacterium violaceum]]> https://www.researchpad.co/article/5c390bbed5eed0c48491e192

In the post genomic era, high throughput data augment stoichiometric flux balance models to compute accurate metabolic flux states, growth and energy phenotypes. Investigating altered metabolism in the context of evolved resistant genotypes potentially provide simple strategies to overcome drug resistance and induce susceptibility to existing antibiotics. A genome-scale metabolic model (GSMM) for Chromobacterium violaceum, an opportunistic human pathogen, was reconstructed using legacy data. Experimental constraints were used to represent antibiotic susceptible and resistant populations. Model predictions were validated using growth and respiration data successfully. Differential flux distribution and metabolic reprogramming were identified as a response to antibiotics, chloramphenicol and streptomycin. Streptomycin resistant populations (StrpR) redirected tricarboxylic acid (TCA) cycle flux through the glyoxylate shunt. Chloramphenicol resistant populations (ChlR) resorted to overflow metabolism producing acetate and formate. This switch to fermentative metabolism is potentially through excess reducing equivalents and increased NADH/NAD ratios. Reduced proton gradients and changed Proton Motive Force (PMF) induced by antibiotics were also predicted and verified experimentally using flow cytometry based membrane potential measurements. Pareto analysis of NADH and ATP maintenance showed the decoupling of electron transfer and ATP synthesis in StrpR. Redox homeostasis and NAD+ cycling through rewiring metabolic flux was implicated in re-sensitizing antibiotic resistant C. violaceum. These approaches can be used to probe metabolic vulnerabilities of resistant pathogens. On the verge of a post-antibiotic era, we foresee a critical need for systems level understanding of pathogens and host interaction to extend shelf life of antibiotics and strategize novel therapies.

]]>
<![CDATA[Cross-sectional survey of off-label and unlicensed prescribing for inpatients at a paediatric teaching hospital in Western Australia]]> https://www.researchpad.co/article/5c3e4f70d5eed0c484d754d2

Objectives

To evaluate the prevalence of off-label and unlicensed prescribing in inpatients at a major paediatric teaching hospital in Western Australia and to identify which drugs are commonly prescribed off-label or unlicensed, including factors influencing such prescribing.

Methods

A retrospective cross-sectional study was conducted in June, 2013. Patient and prescribing data were collected from 190 inpatient medication chart records which had been randomly selected from all admissions during the second week of February 2013. Drugs were categorised as licensed, off-label or unlicensed, according to their approved Australian registration product information (PI). All drugs were classified according to the Anatomical Therapeutic Chemical (ATC) code.

Results

There were 120 male and 70 female inpatients. The average age was 6.0 years (± 4.7). The study included 1160 prescribed drugs suitable for analysis. The number of drugs prescribed per patient ranged from 1 to 25 with an average of 6.1 (± 4.3). More than half (54%) were prescribed off-label. Oxycodone, clonidine, parecoxib and midazolam were always prescribed off-label. The most common off-label drugs were ondansetron (18.5%), fentanyl (12.9%), oxycodone (8.8%) and paracetamol (6.1%). Many ATC classifications included high off-label proportions especially the genitourinary system and sex hormones, respiratory system drugs, systemic hormonal preparations and alimentary tract and metabolism drugs.

Conclusions

This study highlights that prescribing of paediatric drugs needs to be better supported by existing and new evidence. Incentives should be established to foster the conduct of evidence-based studies in the paediatric population. The current level of off-label prescribing raises issues of unexpected toxicity and adverse drug effects in children that are in some cases severely ill.

]]>
<![CDATA[Metabolomic investigations in cerebrospinal fluid of Parkinson's disease]]> https://www.researchpad.co/article/5c1813b6d5eed0c484775a2e

The underlying mechanisms of Parkinson´s disease are not completely revealed. Especially, early diagnostic biomarkers are lacking. To characterize early pathophysiological events, research is focusing on metabolomics. In this case-control study we investigated the metabolic profile of 31 Parkinson´s disease-patients in comparison to 95 neurologically healthy controls. The investigation of metabolites in CSF was performed by a 12 Tesla SolariX Fourier transform-ion cyclotron resonance-mass spectrometer (FT-ICR-MS). Multivariate statistical analysis sorted the most important biomarkers in relation to their ability to differentiate Parkinson versus control. The affected metabolites, their connection and their conversion pathways are described by means of network analysis. The metabolic profiling by FT-ICR-MS in CSF yielded in a good group separation, giving insights into the disease mechanisms. A total number of 243 metabolites showed an affected intensity in Parkinson´s disease, whereas 15 of these metabolites seem to be the main biological contributors. The network analysis showed a connection to the tricarboxylic cycle (TCA cycle) and therefore to mitochondrial dysfunction and increased oxidative stress within mitochondria. The metabolomic analysis of CSF in Parkinson´s disease showed an association to pathways which are involved in lipid/ fatty acid metabolism, energy metabolism, glutathione metabolism and mitochondrial dysfunction.

]]>
<![CDATA[Metabolic effects of an aspartate aminotransferase-inhibitor on two T-cell lines]]> https://www.researchpad.co/article/5c141f02d5eed0c484d2940f

An emerging method to help elucidate the mode of action of experimental drugs is to use untargeted metabolomics of cell-systems. The interpretations of such screens are however complex and more examples with inhibitors of known targets are needed. Here two T-cell lines were treated with an inhibitor of aspartate aminotransferase and analyzed with untargeted GC-MS. The interpretation of the data was enhanced by the use of two different cell-lines and supports aspartate aminotransferase as a target. In addition, the data suggest an unexpected off-target effect on glutamate decarboxylase. The results exemplify the potency of metabolomics to provide insight into both mode of action and off-target effects of drug candidates.

]]>
<![CDATA[An efficient proteome-wide strategy for discovery and characterization of cellular nucleotide-protein interactions]]> https://www.researchpad.co/article/5c12cef8d5eed0c484913c3b

Metabolite-protein interactions define the output of metabolic pathways and regulate many cellular processes. Although diseases are often characterized by distortions in metabolic processes, efficient means to discover and study such interactions directly in cells have been lacking. A stringent implementation of proteome-wide Cellular Thermal Shift Assay (CETSA) was developed and applied to key cellular nucleotides, where previously experimentally confirmed protein-nucleotide interactions were well recaptured. Many predicted, but never experimentally confirmed, as well as novel protein-nucleotide interactions were discovered. Interactions included a range of different protein families where nucleotides serve as substrates, products, co-factors or regulators. In cells exposed to thymidine, a limiting precursor for DNA synthesis, both dose- and time-dependence of the intracellular binding events for sequentially generated thymidine metabolites were revealed. Interactions included known cancer targets in deoxyribonucleotide metabolism as well as novel interacting proteins. This stringent CETSA based strategy will be applicable for a wide range of metabolites and will therefore greatly facilitate the discovery and studies of interactions and specificities of the many metabolites in human cells that remain uncharacterized.

]]>