Chasing Graphene-Based Anticancer Drugs: Where are We Now on the Biomedical Graphene Roadmap?

Graphene and graphene-based materials have attracted growing interest for potential applications in medicine because of their good biocompatibility, cargo capability and possible surface functionalizations. In parallel, prototypic graphene-based devices have been developed to diagnose, imaging and track tumor growth in cancer patients. There is a growing number of reports on the use of graphene and its functionalized derivatives in the design of innovative drugs delivery systems, photothermal and photodynamic cancer therapy, and as a platform to combine multiple therapies. The aim of this review is to introduce the latest scientific achievements in the field of innovative composite graphene materials as potentially applied in cancer therapy. The "Technology and Innovation Roadmap" published in the Graphene Flagship indicates, that the first anti-cancer drugs using graphene and graphene-derived materials will have appeared on the market by 2030. However, it is necessary to broaden understanding of graphene-based material interactions with cellular metabolism and signaling at the functional level, as well as toxicity. The main aspects of further research should elucidate how treatment methods (e.g., photothermal therapy, photodynamic therapy, combination therapy) and the physicochemical properties of graphene materials influence their ability to modulate autophagy and kill cancer cells. Interestingly, recent scientific reports also prove that graphene nanocomposites modulate cancer cell death by inducing precise autophagy dysfunctions caused by lysosome damage. It turns out as well that developing photothermal oncological treatments, it should be taken into account that near-infrared-II radiation (1000-1500 nm) is a better option than NIR-I (750-1000 nm) because it can penetrate deeper into tissues due to less scattering at longer wavelengths radiation.

Determination of urinary androgens in women with polycystic ovary syndrome using LC-QqQ/MS and the application of thin film solid-phase microextraction (TF-SPME).

Hyperandrogenism is one of the most pronounced symptoms of Polycystic Ovary Syndrome (PCOS) and seems to play a key role in the pathogenesis of this complex disorder. Nevertheless, there is still a lack of consistent results regarding common steroid predictors of PCOS. Therefore, a liquid chromatography tandem mass spectrometry (HPLC-QqQ/MS) method was developed and validated to determine the concentrations of four classic androgens: androstenedione (An-dione), testosterone (T), 5α-dihydrotestosterone (DHT) and androsterone (An) in urine samples obtained from women with PCOS and healthy controls. The limits of detection were between 0.04 and 0.09 ng/mL, while the limits of quantification ranged from 0.1 to 0.3 ng/mL respectively. As a pre-treatment procedure prior to analysis, hydrolysis using ß-glucuronidase and thin film solid-phase microextraction (TF-SPME) was applied. The methodology was employed to perform targeted metabolomics of urinary steroids in women with PCOS and healthy controls. All measured androgens: An-dione (p < 0.0001), T (p = 0.0001), DHT (p < 0.0001) and An (p = 0.0002) showed significantly higher concentrations in the urine of women with PCOS. The largest difference in the mean concentration was found for DHT, which was 2.8 times higher in the PCOS group (13.9 ± 14.1 ng/mg creatinine) in comparison to healthy controls (4.9 ± 3.4 ng/mg creatinine). The results of receiver operating characteristic curve indicated that determination of the panel of three urinary androgens: T+DHT+An-dione with, under the study assumptions, was the best predictor of PCOS diagnosis (AUC of ROC curve = 0.91 (95 % CI: 0.8212-0.9905). The application of an LC-MS/MS-based analysis, together with highly sensitive extraction techniques like TF-SPME, is a suitable approach to perform fast assays and obtain reliable results - crucial in the search for valuable and significant steroids predictors of PCOS.

Targeted quantitative metabolomics with a linear mixed-effect model for analysis of urinary nucleosides and deoxynucleosides from bladder cancer patients before and after tumor resection.

In the present study, we developed and validated a fast, simple, and sensitive quantitative method for the simultaneous determination of eleven nucleosides and deoxynucleosides from urine samples. The analyses were performed with the use of liquid chromatography coupled with triple quadrupole mass spectrometry. The sample pretreatment procedure was limited to centrifugation, vortex mixing of urine samples with a methanol/water solution (1:1, v/v), evaporation and dissolution steps. The analysis lasted 20 min and was performed in dynamic multiple reaction monitoring mode (dMRM) in positive polarity. Process validation was conducted to determine the linearity, precision, accuracy, limit of quantification, stability, recovery and matrix effect. All validation procedures were carried out in accordance with current FDA and EMA regulations. The validated method was applied for the analysis of 133 urine samples derived from bladder cancer patients before tumor resection and 24 h, 2 weeks, and 3, 6, 9, and 12 months after the surgery. The obtained data sets were analyzed using a linear mixed-effect model. The analysis revealed that concentration level of 2-methylthioadenosine was decreased, while for inosine, it was increased 24 h after tumor resection in comparison to the preoperative state. The presented quantitative longitudinal study of urine nucleosides and deoxynucleosides before and up to 12 months after bladder tumor resection brings additional prospective insight into the metabolite excretion pattern in bladder cancer disease. Moreover, incurred sample reanalysis was performed proving the robustness and repeatability of the developed targeted method.

Development of the thin film solid phase microextraction (TF-SPME) method for metabolomics profiling of steroidal hormones from urine samples using LC-QTOF/MS.

In the present study, the development and optimization of a thin film solid phase microextraction method (TF-SPME) was conducted for metabolomics profiling of eight steroid compounds (androsterone, dihydrotestosterone, dihydroepiandrosterone, estradiol, hydroxyprogesterone, pregnenolone, progesterone and testosterone) from urine samples. For optimization of extraction method, two extraction sorbents (PAN-C18 and PS-DVB) were used as they are known to be effective for isolation of low-polarity analytes. The stages of sample extraction and analyte desorption were considered as the most crucial steps in the process. Regarding the selection of the most suitable desorption solution, six different mixtures were analyzed. As a result, the mixture of ACN: MeOH (1:1, v/v) was chosen in terms of the highest analytes' abundances that were achieved using the chosen solvent. Besides other factors were examined such as the volume of desorption solvent and the time of both extraction and desorption processes. The analytical determination was carried out using the ultra-high performance liquid chromatography coupled with high resolution tandem mass spectrometry detection in electrospray ionization and positive polarity in a scan mode (UHPLC-ESI-QTOF/MS). The developed and optimized TF-SPME method was validated in terms of such parameters as extraction efficiency, recovery as well as matrix effect. As a result, the extraction efficiency and recovery were in a range from 79.3% to 99.2% and from 88.8% to 111.8%, respectively. Matrix effect, calculated as coefficient of variation was less than 15% and was in a range from 1.4% to 11.1%. The values of both validation parameters (recovery and matrix effect) were acceptable in terms of EMA criteria. The proposed TF-SPME method was used successfully for isolation of steroids hormones from pooled urine samples before and after enzymatic hydrolysis of analytes.

Toward the General Mechanistic Model of Liquid Chromatographic Retention.

Large datasets of chromatographic retention times are relatively easy to collect. This statement is particularly true when mixtures of compounds are analyzed under a series of gradient conditions using chromatographic techniques coupled with mass spectrometry detection. Such datasets carry much information about chromatographic retention that, if extracted, can provide useful predictive information. In this work, we proposed a mechanistic model that jointly explains the relationship between pH, organic modifier type, temperature, gradient duration, and analyte retention based on liquid chromatography retention data collected for 187 small molecules. The model was built utilizing a Bayesian multilevel framework. The model assumes (i) a deterministic Neue equation that describes the relationship between retention time and analyte-specific and instrument-specific parameters, (ii) the relationship between analyte-specific descriptors (log P, pKa, and functional groups) and analyte-specific chromatographic parameters, and (iii) stochastic components of between-analyte and residual variability. The model utilizes prior knowledge about model parameters to regularize predictions which is important as there is ample information about the retention behavior of analytes in various stationary phases in the literature. The usefulness of the proposed model in providing interpretable summaries of complex data and in decision making is discussed.

Untargeted Metabolomics Study of Three Matrices: Seminal Fluid, Urine, and Serum to Search the Potential Indicators of Prostate Cancer.

The simultaneous determination of metabolites from biological fluids may provide more accurate information about the current body condition. So far, the metabolomics approach has been successfully applied to study the mechanism of several disorders and to search for novel biomarkers. Urine and plasma are widely accepted matrices for the evaluation of several pathologies, while prostate cancer (CaP) development is still unknown. For this reason, an alternative matrix, the seminal fluid, was proposed to expand the knowledge about the CaP pathomechanism. The main aim of this study was to develop and optimize the sample preparation protocol to ensure the highest coverage of the metabolome of ejaculate samples. Parameters like the type and composition of the solvent mixture, time of extraction, and applied volume of the solvent were tested. The optimized method was applied for the untargeted metabolomics profiling of seminal fluid samples obtained from CaP patients. Moreover, urine and serum samples were also prepared for untargeted metabolomics analysis. Analyses were carried out with the use of two complementary analytical techniques: GC-EI-QqQ/MS and LC-ESI-TOF/MS. Finally, the metabolic signature of seminal fluid (n = 7), urine (n = 7), and plasma (n = 7) samples was compared. Furthermore, the hypothesis of the increased level of metabolites in ejaculate samples related to the CaP development was evaluated. The results indicated that the developed and optimized sample preparation protocol for seminal fluid may be successfully applied for metabolomics study. Untargeted analysis of ejaculate enabled to determine the following classes of compounds: fatty acids, sphingolipids, phospholipids, sugars, and their derivatives, as well as amino acids. Finally, a comparison of the three tested matrices was carried out. To our best knowledge, it is the first time when the metabolic profile of the three matrices, namely, urine, plasma, and seminal fluid, was compared. Based on the results, it can be pointed out that ejaculate comprises the metabolic signature of both matrices (polar compounds characteristic for urine, and non-polar ones present in plasma samples). Compared to plasma, semen samples revealed to have a similar profile; however, determined levels of metabolites were lower in case of ejaculate. In case of urine samples, compared to semen metabolic profiles, the levels of detected metabolites were decreased in the latter ones.

Comprehensive Metabolic Signature of Renal Dysplasia in Children. A Multiplatform Metabolomics Concept.

Renal dysplasia is a severe congenital abnormality of the kidney parenchyma, which is an important cause of end-stage renal failure in childhood and early adulthood. The diagnosis of renal dysplasia relies on prenatal or postnatal ultrasounds as children show no specific clinical symptoms before chronic kidney disease develops. Prompt diagnosis is important in terms of early introduction of nephroprotection therapy and improved long-term prognosis. Metabolomics was applied to study children with renal dysplasia to provide insight into the changes in biochemical pathways underlying its pathology and in search of early indicators for facilitated diagnosis. The studied cohort consisted of 72 children, 39 with dysplastic kidneys and 33 healthy controls. All subjects underwent comprehensive urine metabolic profiling with the use of gas chromatography and liquid chromatography coupled to mass spectrometry, with two complementary separation modes of the latter. Univariate and multivariate statistical calculations identified a total of nineteen metabolites, differentiating the compared cohorts, independent of their estimated glomerular filtration rate. Seven acylcarnitines, xanthine, and glutamine were downregulated in the urine of renal dysplasia patients. Conversely, renal dysplasia was associated with higher urinary levels of dimethylguanosine, threonic acid or glyceric acid. This is the first metabolomic study of subjects with renal dysplasia. The authors define a characteristic urine metabolic signature in children with dysplastic kidneys, irrespective of renal function, linking the condition with altered fatty acid oxidation, amino acid and purine metabolisms.

Effects of perineural administration of ropivacaine combined with perineural or intravenous administration of dexmedetomidine for sciatic and saphenous nerve blocks in dogs.

OBJECTIVE: To evaluate the effects of using ropivacaine combined with dexmedetomidine for sciatic and saphenous nerve blocks in dogs. ANIMALS: 7 healthy adult Beagles. PROCEDURES: In phase 1, dogs received each of the following 3 treatments in random order: perineural sciatic and saphenous nerve injections of 0.5% ropivacaine (0.4 mL/kg) mixed with saline (0.9% NaCl) solution (0.04 mL/kg; DEX0PN), 0.5% ropivacaine mixed with dexmedetomidine (1 µg/kg; DEX1PN), and 0.5% ropivacaine mixed with dexmedetomidine (2 µg/kg; DEX2PN). In phase 2, dogs received perineural sciatic and saphenous nerve injections of 0.5% ropivacaine and an IV injection of diluted dexmedetomidine (1 µg/kg; DEX1IV). For perineural injections, the dose was divided equally between the 2 sites. Duration of sensory blockade was evaluated, and plasma dexmedetomidine concentrations were measured. RESULTS: Duration of sensory blockade was significantly longer with DEX1PN and DEX2PN, compared with DEX0PN; DEX1IV did not prolong duration of sensory blockade, compared with DEX0PN. Peak plasma dexmedetomidine concentrations were reached after 15 minutes with DEX1PN (mean ± SD, 348 ± 200 pg/mL) and after 30 minutes DEX2PN (816 ± 607 pg/mL), and bioavailability was 54 ± 40% and 73 ± 43%, respectively. The highest plasma dexmedetomidine concentration was measured with DEX1IV (1,032 ± 415 pg/mL) 5 minutes after injection. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggested that perineural injection of 0.5% ropivacaine in combination with dexmedetomidine (1 µg/kg) for locoregional anesthesia in dogs seemed to balance the benefit of prolonging sensory nerve blockade while minimizing adverse effects.

Metabolic Evaluation of Urine from Patients Diagnosed with High Grade (HG) Bladder Cancer by SPME-LC-MS Method.

Bladder cancer (BC) is a common malignancy of the urinary system and a leading cause of death worldwide. In this work, untargeted metabolomic profiling of biological fluids is presented as a non-invasive tool for bladder cancer biomarker discovery as a first step towards developing superior methods for detection, treatment, and prevention well as to further our current understanding of this disease. In this study, urine samples from 24 healthy volunteers and 24 BC patients were subjected to metabolomic profiling using high throughput solid-phase microextraction (SPME) in thin-film format and reversed-phase high-performance liquid chromatography coupled with a Q Exactive Focus Orbitrap mass spectrometer. The chemometric analysis enabled the selection of metabolites contributing to the observed separation of BC patients from the control group. Relevant differences were demonstrated for phenylalanine metabolism compounds, i.e., benzoic acid, hippuric acid, and 4-hydroxycinnamic acid. Furthermore, compounds involved in the metabolism of histidine, beta-alanine, and glycerophospholipids were also identified. Thin-film SPME can be efficiently used as an alternative approach to other traditional urine sample preparation methods, demonstrating the SPME technique as a simple and efficient tool for urinary metabolomics research. Moreover, this study's results may support a better understanding of bladder cancer development and progression mechanisms.

An Overview of the Application of Systems Biology in an Understanding of Chronic Rhinosinusitis (CRS) Development.

Chronic rhinosinusitis (CRS) is an inflammatory disease of the paranasal sinuses. It is defined as the presence of a minimum of two out of four main symptoms such as hyposmia, facial pain, nasal blockage, and discharge, which last for 8-12 weeks. CRS significantly impairs a patient's quality of life. It needs special treatment mainly focusing on preventing local infection/inflammation with corticosteroid sprays or improving sinus drainage using nasal saline irrigation. When other treatments fail, endoscopic sinus surgery is considered an effective option. According to the state-of-the-art knowledge of CRS, there is more evidence suggesting that it is more of an inflammatory disease than an infectious one. This condition is also treated as a multifactorial inflammatory disorder as it may be triggered by various factors, such as bacterial or fungal infections, airborne irritants, defects in innate immunity, or the presence of concomitant diseases. Due to the incomplete understanding of the pathological processes of CRS, there is a continuous search for new indicators that are directly related to the pathogenesis of this disease-e.g., in the field of systems biology. The studies adopting systems biology search for possible factors responsible for the disease at genetic, transcriptomic, proteomic, and metabolomic levels. The analyses of the changes in the genome, transcriptome, proteome, and metabolome may reveal the dysfunctional pathways of inflammatory regulation and provide a clear insight into the pathogenesis of this disease. Therefore, in the present paper, we have summarized the state-of-the-art knowledge of the application of systems biology in the pathology and development of CRS.