The aim of this study was to determine the acid dissociation constant (pKa) of piroxicam using high performance liquid chromatography (HPLC) and ultraviolet-visible (UV-Vis) spectrophotometry, and to determine the partition coefficient (Log P), distribution coefficient (Log D), and “Log kw” values of piroxicam using HPLC. The HPLC studies were performed on a reversed-phase ACE C18 (150x4.6 mm ID, 5 μm) column at a flow rate of 1.0 mL min-1. The detector was set to 360 nm. Log D at different pH values (3.0-6.5) was examined with a phosphate buffer (20 mM) and acetonitrile (30:70 v/v) mixture as the mobile phase. For pKa determination, HPLC studies were performed with a mixture of phosphate buffer (20 mM) and methanol within the pH range of 3.50-6.00. Log kw measurements were performed with phosphate buffer (20 mM) and MeOH (from 20:80 v/v to 10:90 v/v) mixtures within the pH range of 3.50-6.00. UV-Vis spectrophotometric pKa measurements were performed at 285 nm wavelength. The pKa value of piroxicam was found to be 5.3 by HPLC and 5.7 by UV-Vis spectrophotometry. Log P of piroxicam was determined as 1.58 in our experimental conditions. Log D values were 1.57, 1.57, 1.44, 1.13, and 0.46 for pH values of 3.17, 3.79, 4.44, 5.42, and 6.56, respectively. In the literature, different Log P (3.1, 2.2, and 0.6) and pKa (6.3 and 4.8) values were reported for piroxicam. The Log P (1.58) and pKa (5.3 and 5.7) values obtained for piroxicam in our study were within the range of the literature values. All these results indicate that different experimental approaches used for the determination of physicochemical properties could provide different values. Although UV spectrophotometry is easy to apply, HPLC is a unique technique for simultaneous determination of pKa, Log D, and Log P values of compounds.

Background/aim: Metabolomics has the potential to provide putative biomarkers and insights into the pathophysiology and diagnosis of pediatric multiple sclerosis (pMS), which is an inflammatory demyelinating disorder of the central nervous system with a broad spectrum of clinical manifestations. In this study, we aimed to investigate serum metabolomics in pMS to help elucidate the pathophysiology of MS. Materials and methods: An untargeted approach was applied using the quadrupole time-of-flight liquid chromatography/mass spectrometry (Q-TOF LC/MS) method to study plasma metabolites in patients with pMS (n = 33), patients with unclassified central nervous system demyelinating diseases (n = 6), and age-matched healthy control subjects (n = 40). The patient and control groups were compared for metabolites and the normalized peak areas differed statistically (p < 0.05), showing at least a 1.25-fold change between groups. Bioinformatic tools combined with a clinical perspective were employed for the identification of the putative metabolites. In addition to the untargeted metabolomics approach, targeted LC-MS/MS metabolite analysis was employed to compare the pMS group with the control group. Results: Significant differences between the patient and control groups were noted for tyramine, 4-hydroxyphenylacetaldehyde, sphingosine/3-dehydrosphinganine, prostaglandins/thromboxane A2, 20-hydroxy-leukotriene E4, 3∝,7∝,12∝-trihydroxy-5ß-cholestan26-al/calcitriol, pantetheine, ketoleucine/3-methyl-2-oxovaleric acid, L-arginine/D-arginine, coproporphyrinogen III, (S)-reticuline, carnosine, cytidine, and phosphoribosyl pyrophosphate. Additional tests for sphingosine 1-phosphate, sphingophosphocholines, ceramides, oxysterols, and calcitriol levels yielded significant metabolomic differences for the pMS group compared to the control group. The metabolomic data of 3/6 patients with unclassified demyelinating disorders matched the pMS group; their follow-up verified the diagnosis of pMS. Conclusion: In general, plasma metabolites related to sphingolipid metabolism, myelin products, inflammatory pathways, mitochondrial dysfunction, and oxidative stress were found to be altered in cases of pMS. The method applied in this study, combining untargeted analysis with a targeted approach, can be applied to larger series of cases of pMS and other demyelinating disorders for further validation.

Background: Including Chronic Myeloid Leukemia (CML) patients with deep molecular responses (MR4.5) and those with suboptimal responses provides valuable insights into treatment-associated metabolic changes. This study aimed to characterize the metabolomic alterations associated with CML and identify potential biomarkers for treatment response, particularly in patients achieving a deeper molecular response versus those with poorer responses. Methods: Plasma samples were collected from 51 chronic-phase CML patients and 24 healthy controls. CML patients were classified into two groups based on molecular responses: T1 (BCR-ABL1 IS ≤ 0.0032%) and T2 (BCR-ABL1 IS > 0.0032%, 1.5 included glutamate, hypoxanthine, and D-galactonic acid. In the T2 group, significant increases in malate and 5-aminoimidazole-4-carboxamide ribonucleotide were observed, reflecting disruptions in purine metabolism, the tricarboxylic acid cycle, and amino acid metabolism. The pathway enrichment analysis highlighted significant alterations in CML energy metabolism, nucleotide synthesis, and amino acid biosynthesis. Conclusions: CML patients exhibit pronounced metabolic changes, particularly in energy and nucleotide metabolism, which are linked to treatment response. These findings provide novel insights into CML biology and suggest potential biomarkers for monitoring treatment efficacy and predicting outcomes and therapeutic targets for improving treatment outcomes and overcoming tyrosine kinase inhibitor resistance.

Background: Platelet-rich plasma (PRP) is a widely used therapeutic product in musculoskeletal treatments due to its regenerative and anti-inflammatory properties. However, the lack of standardization in PRP preparation protocols hampers clinical consistency. Methods: In this study, the metabolic profiles of 10 different PRP types were compared using untargeted metabolomics via Q-TOF LC–MS. PRP-G and PRP-S were prepared from six donors to assess inter-individual variability, while the remaining types were obtained from a single donor to isolate the impact of preparation method alone. Multivariate analyses, VIP scores, and pathway enrichment analyses were conducted. Results: PRP formulations exhibited distinct metabolic differences associated with inflammatory signaling, redox homeostasis, steroid metabolism, energy production, and platelet activation. Samples from both single- and multi-donor groups showed high intra-group similarity, indicating that preparation method is a major determinant of PRP’s biochemical composition. Conclusion: Metabolomic profiling reveals that even minor differences in PRP preparation protocols can lead to significant biochemical changes that may affect therapeutic outcomes. This study highlights the need for standardized, indication-specific PRP products and underscores the value of metabolomic analysis in guiding optimal formulation selection in clinical practice.