Pharmacokinetic interaction between regorafenib and atorvastatin in rats.

BACKGROUND: Regorafenib is used in the treatment of colorectal cancer and hepatocellular carcinoma. Due to the co-morbidity of hyperlipidemia in these conditions, statins, including atorvastatin, are used as potential adjuvant therapy agents. Both regorafenib and atorvastatin are metabolized by CYP3A4. In addition, atorvastatin is a P-gp and BCRP substrate, whereas regorafenib and its active metabolites M-2 and M-5 are inhibitors of these transporters. Hence, the concomitant use of both drugs may increase the risk of a clinically significant drug-drug interaction. Therefore, the present study aimed to assess the pharmacokinetic interactions of atorvastatin and regorafenib and their active metabolites. METHODS: Male Wistar rats were assigned to three groups (eight animals in each) and were orally administered: regorafenib and atorvastatin (IREG+ATO), a carrier with regorafenib (IIREG), and atorvastatin with a carrier (IIIATO). Blood samples were collected for 72 h. UPLC-MS/MS was the method of measurement of regorafenib and atorvastatin concentrations. The pharmacokinetic parameters were calculated with a non-compartmental model. RESULTS: A single administration of atorvastatin increased the exposure to regorafenib and its active metabolites. In the IREG+ATO group, the Cmax, AUC0-t, and AUC0-∞ of regorafenib increased 2.7, 3.2, and 3.2-fold, respectively. Atorvastatin also significantly increased the Cmax, AUC0-t, and AUC0-∞ of both regorafenib metabolites. Regorafenib, in turn, decreased the AUC0-t and AUC0-∞ of 2-OH atorvastatin by 86.9% and 67.3%, and the same parameters of 4-OH atorvastatin by 45.0% and 46.8%, respectively. CONCLUSIONS: This animal model study showed a significant pharmacokinetic interaction between regorafenib and atorvastatin. While this interaction may be clinically significant, this needs to be confirmed in clinical trials involving cancer patients.

The Chemometric Evaluation of the Factors Influencing Cloud Point Extraction for Fluoroquinolones.

This study aimed to analyze the factors that impact the cloud point extraction of ciprofloxacin, levofloxacin, and moxifloxacin. The following independent variables were analyzed: Triton X-114 concentration, NaCl concentration, pH, and incubation temperature. The dependent variable studied was recovery. A central composite design model was used. The applied quantitation method was HPLC. The method was validated for linearity, precision, and accuracy. The results underwent ANOVA® analysis. The polynomial equations were generated for each analyte. The response surface methodology graphs visualized them. The analysis showed that the factor most affecting the recovery of levofloxacin is the concentration of Triton X-114, while the recovery of ciprofloxacin and moxifloxacin is most affected by pH value. However, the concentration of Triton X-114 also plays an important role. The optimization resulted in the following recoveries: for ciprofloxacin, 60%; for levofloxacin, 75%; and for moxifloxacin, 84%, which are identical to those estimated with regression equations-59%, 74% and 81% for ciprofloxacin, levofloxacin, and moxifloxacin, respectively. The research confirms the validity of using the model to analyze factors affecting the recovery of the analyzed compounds. The model allows for a thorough analysis of variables and their optimization.

The spectrophotometric determination of lipophilicity and dissociation constants of ciprofloxacin and levofloxacin.

Lipophilicity plays a significant role in the permeability of the drugs through cell membranes and impacts the drug activity in the human body. In this paper, the spectrophotometric method was used to determine the apparent partition coefficients of two amphoteric drugs: ciprofloxacin and levofloxacin. The apparent partition coefficient was determined with the classic shake-flask method with n-octanol according to OECD guidelines. The lipophilicity profiles in a wide range of pH were determined and described quantitatively with the quadratic function. Basing on the macro- and microdissociation constants, the true partition coefficient for both drugs was calculated. Both levofloxacin and ciprofloxacin were lipophilic. The neutral forms, i.e., zwitterionic and uncharged, dominate in the pH relevant to the one in the intestines, the place from which they are absorbed.

Recent Advances in Therapeutic Drug Monitoring of Voriconazole, Mycophenolic Acid, and Vancomycin: A Literature Review of Pediatric Studies.

The review includes studies dated 2011-2021 presenting the newest information on voriconazole (VCZ), mycophenolic acid (MPA), and vancomycin (VAN) therapeutic drug monitoring (TDM) in children. The need of TDM in pediatric patients has been emphasized by providing the information on the differences in the drugs pharmacokinetics. TDM of VCZ should be mandatory for all pediatric patients with invasive fungal infections (IFIs). Wide inter- and intrapatient variability in VCZ pharmacokinetics cause achieving and maintaining therapeutic concentration during therapy challenging in this population. Demonstrated studies showed, in most cases, VCZ plasma concentrations to be subtherapeutic, despite the updated dosages recommendations. Only repeated TDM can predict drug exposure and individualizing dosing in antifungal therapy in children. In children treated with mycophenolate mofetil (MMF), similarly as in adult patients, the role of TDM for MMF active form, MPA, has not been well established and is undergoing continued debate. Studies on the MPA TDM have been carried out in children after renal transplantation, other organ transplantation such as heart, liver, or intestine, in children after hematopoietic stem cell transplantation or cord blood transplantation, and in children with lupus, nephrotic syndrome, Henoch-Schönlein purpura, and other autoimmune diseases. MPA TDM is based on the area under the concentration-time curve; however, the proposed values differ according to the treatment indication, and other approaches such as pharmacodynamic and pharmacogenetic biomarkers have been proposed. VAN is a bactericidal agent that requires TDM to prevent an acute kidney disease. The particular group of patients is the pediatric one. For this group, the general recommendations of the dosing may not be valid due to the change of the elimination rate and volume of distribution between the subjects. The other factor is the variability among patients that concerns the free fraction of the drug. It may be caused by both the patients' population and sample preconditioning. Although VCZ, MMF, and VAN have been applied in pediatric patients for many years, there are still few issues to be solve regarding TDM of these drugs to ensure safe and effective treatment. Except for pharmacokinetic approach, pharmacodynamics and pharmacogenetics have been more often proposed for TDM.

The Overview on the Pharmacokinetic and Pharmacodynamic Interactions of Triazoles.

Second generation triazoles are widely used as first-line drugs for the treatment of invasive fungal infections, including aspergillosis and candidiasis. This class, along with itraconazole, voriconazole, posaconazole, and isavuconazole, is characterized by a broad range of activity, however, individual drugs vary considerably in safety, tolerability, pharmacokinetics profiles, and interactions with concomitant medications. The interaction may be encountered on the absorption, distribution, metabolism, and elimination (ADME) step. All triazoles as inhibitors or substrates of CYP isoenzymes can often interact with many drugs, which may result in the change of the activity of the drug and cause serious side effects. Drugs of this class should be used with caution with other agents, and an understanding of their pharmacokinetic profile, safety, and drug-drug interaction profiles is important to provide effective antifungal therapy. The manuscript reviews significant drug interactions of azoles with other medications, as well as with food. The PubMed and Google Scholar bases were searched to collect the literature data. The interactions with anticonvulsants, antibiotics, statins, kinase inhibitors, proton pump inhibitors, non-nucleoside reverse transcriptase inhibitors, opioid analgesics, benzodiazepines, cardiac glycosides, nonsteroidal anti-inflammatory drugs, immunosuppressants, antipsychotics, corticosteroids, biguanides, and anticoagulants are presented. We also paid attention to possible interactions with drugs during experimental therapies for the treatment of COVID-19.

Pharmacokinetic Interaction between Sorafenib and Atorvastatin, and Sorafenib and Metformin in Rats.

The tyrosine kinase inhibitor sorafenib is the first-line treatment for patients with hepatocellular carcinoma (HCC), in which hyperlipidemia and type 2 diabetes mellitus (T2DM) may often coexist. Protein transporters like organic cation (OCT) and multidrug and toxin extrusion (MATE) are involved in the response to sorafenib, as well as in that to the anti-diabetic drug metformin or atorvastatin, used in hyperlipidemia. Changes in the activity of these transporters may lead to pharmacokinetic interactions, which are of clinical significance. The study aimed to assess the sorafenib-metformin and sorafenib-atorvastatin interactions in rats. The rats were divided into five groups (eight animals in each) that received sorafenib and atorvastatin (ISOR+AT), sorafenib and metformin (IISOR+MET), sorafenib (IIISOR), atorvastatin (IVAT), and metformin (VMET). Atorvastatin significantly increased the maximum plasma concentration (Cmax) and the area under the plasma concentration-time curve (AUC) of sorafenib by 134.4% (p < 0.0001) and 66.6% (p < 0.0001), respectively. Sorafenib, in turn, caused a significant increase in the AUC of atorvastatin by 94.0% (p = 0.0038) and its metabolites 2-hydroxy atorvastatin (p = 0.0239) and 4-hydroxy atorvastatin (p = 0.0002) by 55.3% and 209.4%, respectively. Metformin significantly decreased the AUC of sorafenib (p = 0.0065). The AUC ratio (IISOR+MET group/IIISOR group) for sorafenib was equal to 0.6. Sorafenib did not statistically significantly influence the exposure to metformin. The pharmacokinetic interactions observed in this study may be of clinical relevance in HCC patients with coexistent hyperlipidemia or T2DM.

The application of Box-Behnken-Design in the optimization of HPLC separation of fluoroquinolones.

Box-Behnken Design is a useful tool for the optimization of the chromatographic analysis. The goal of this study was to select the most significant factors that influenced the following parameters of the chromatographic separation: retention time, relative retention time, symmetry of the peaks, tailing factor, a number of theoretical plates, Foley - Dorsey parameter, resolution factor, peak width at half height. The results underwent the ANOVA test to find the statistically significant variables and interactions between them. The level of significance was for p < 0.05. The polynomial equations described quantitatively the statistically significant parameters and the interactions between them. The statistical analysis indicated both the best conditions for the separation of the compounds and the variables that were most influential for peaks' parameters. The four-factor analysis performed for LEVO and MOXI indicated that ACN, TEA and pH are the most significant factors that influence the separation. The analysis for the pair CIPRO and LEVO required six factors. The statistical analysis proved that the most significant factors are ACN, MeOH and TEA. In the separation of these two compounds on the HPLC column, the interaction ACN × MeOH was also significant.

The degradation of levofloxacin in infusions exposed to daylight with an identification of a degradation product with HPLC-MS.

In this paper the decomposition product of levofloxacin was identified. Levofloxacin was dissolved in 0.9% NaCl, 5% glucose, and Ringer's solution. The solutions were divided into two batches: the first one was exposed to daylight and the second one was protected from it. The solutions were stored at the room temperature. The qualitative analysis of the degradation product was performed using MS and TOF detectors. The quantitative assay was done by a validated HPLC method. Visual inspection and pH assessment were done. Levofloxacin protected from daylight remained stable in 0.9% NaCl, 5% dextrose, and Ringer's solution. A slight decomposition of the analyte was observed in the solutions exposed to daylight with the fastest decomposition rate in Ringer's solution as compared with 0.9% NaCl and 5% dextrose solutions. The degradation product of levofloxacin detected with MS was levofloxacin N-oxide. Levofloxacin solutions should be protected from direct daylight to maintain drug stability. Levofloxacin N-oxide is formed regardless of the solvent used.

Determination of gliclazide minimum concentration in type 2 diabetes mellitus patients.

Type 2 diabetes mellitus is a worldwide health problem. Many drugs can be used in its treatment. One of them is gliclazide - the sulfonylurea derivative. It is dosed in modified release tablets. The study aimed to determine the minimum steady-state concentration of gliclazide at patients taking modified release tablets. Fasting plasma glucose, insulin level, and glycated hemoglobin were also assayed in this study. Ten patients of the primary care physician clinic took 30-90 mg of gliclazide daily. The statistical analysis proved that there is a statistically significant correlation between insulin level and body weight (p = 0.044) as well as between the dose and gliclazide concentrations (p = 0.015) and also between insulin level and minimum concentration of the drug (p = 0.0074). The linear correlation between dose and gliclazide's minimum steady state concentration proved its linear pharmacokinetics. The correlation between the minimum concentration of gliclazide and insulin level might be a potential predictor of patients compliance.

Analytical Methods for Determining Third and Fourth Generation Fluoroquinolones: A Review.

ABSTRACT: Fluoroquinolones of the third and fourth generation posses wide bactericidal activity. Monitoring concentrations of antibacterial agents provides effective therapy and prevents the increase of bacterial resistance to antibiotics. The pharmacodynamic parameters that best describe fluoroquinalone activity are AUC/MIC and Cmax/MIC. Determining the level of this type of drug is essential to reach the effective concentration that inhibits the growth of bacteria. Determining the pharmaceutical formulation confirms the purity of a substance. Many methods have been developed to determine the level of these substances. They involve mainly the following analytical techniques: chromatography, capillary electrophoresis, and spectroscopy. The separation techniques were combined with different measuring devices, such as ultraviolet (UV), fluorescence detector (FLD), diode array detector (DAD), and mass spectrometry (MS). The analytical procedures require proper sample pre-conditioning such as protein precipitation, extraction techniques, filtration, or dilution. This paper reviews the reported analytical methods for the determining representatives of the third and fourth generation of fluoroquinolones. Attention was paid to pre-conditioning of the samples and the applied mobile phase. This report might be helpful in the selection of the proper procedure in determining the abovementioned drugs in different matrices.

The pharmacokinetic interaction between levofloxacin and sunitinib.

BACKGROUND: The aim of this study was to evaluate the impact of sunitinib on pharmacokinetics of levofloxacin. The previous study proved that levofloxacin co-administered with sunitib changes the following pharmacokinetic parameters i.e. Cmax and AUC for both sunitinib and SU012662 (sunitinib metabolite). We will also investigate if the limited sample strategy can be applied for levofloxacin. METHODS: Rabbits were divided into two groups. In both groups there were six animals. In the control group levofloxacin was administered and in investigated group levofloxacin and sunitinib were co-administered. The dose of levofloxacin was 20mg/kg and the dose of sunitinib was 25mg. The concentration in plasma was determined by HPLC-FLD. The pharmacokinetic parameters were evaluated by WinNonLin software. The results were evaluated by the following statistical tests: Shapiro-Wilk, t-Student and Mann-Whitney test. RESULTS: Pharmacokinetics of levofloxacin obeys the two-compartment model. Sunitinib influences the following pharmacokinetic parameters of levofloxacin: half-life, elimination constant and volume of distribution. Statistical analysis proved that there is a correlation between AUC and the following five time-points: 0.25 h, 4h, 6h, 10h and 12h. CONCLUSIONS: The study proved that there is a potential pharmacokinetic interaction between sunitinib and levofloxacin. The statistical analysis proved that the limited sample strategy can be applied for levofloxacin.

The biological activity of G-quadruplex DNA binding papaverine-derived ligand in breast cancer cells.

It was shown previously that the papaverine oxidation products 6a,12a-diazadibenzo-[a,g]fluorenylium derivative (ligand 1) and 2,3,9,10-tetramethoxy-12-oxo-12H-indolo[2,1-a]isoquinolinium chloride (ligand 2) bind to guanine-quadruplexes (G4) of single stranded G-rich 3'-overhangs of mammalian telomeric DNA. Here we show the biological activity of ligand 1. This compound exhibit antiproliferative activity in MCF-7 cells (IC(50) for ligand 1 = 14.16 +/- 0.01 microM, 24 h, 1.158 +/- 0.056 microM, 72 h. PCNA levels were not altered after treatment of MCF-7 cells with concentrations of ligand 1 which, however, led to alterations in the cell cycle. 5 and 10 microM of the ligand 1 arrested cells in the G0/G1 phase of the cell cycle and this led to a decrease of cells in the S phase. Intracellular accumulation of ligand 1 was observed even after a cell passage and medium exchange in fluorescence microscopy while low concentrations of ligand 1 (0.001 to 0.1 microM) inhibited telomerase activity as shown by TRAP assay.