Limited-sampling strategy models for estimating the pharmacokinetic parameters of 4-methylaminoantipyrine, an active metabolite of dipyrone

Pharmacotherapeutic monitoring of dipyrone in northeastern Brazilian donkeys (Equus asinus)

This study aimed to monitor the effects of dipyrone following multiple administrations in northeastern donkeys. Ten castrated male donkeys, aged 6.4 ± 3 years and weighing 130.6 ± 9.8 kg, were administered dipyrone (25 mg/kg IV) every 12 h, resulting in six administrations (D1 to D6) per animal. Blood samples were collected over a 72 h monitoring period. A validated UHPLC-MS/MS method was employed to determine the plasma concentrations of the 4- methylaminoantipyrine (4-MAA) and 4-aminoantipyrine (4-AA). The calculated pharmacokinetic variables of 4-MAA after D1 and D6 were, respectively: Cmax (μg/mL) = 163.60 ± 179.72 and 178.79 ± 196.94; T1/2beta (h) = 2.65 ± 0.65 and 3.37 ± 1.03; and AUC0-t (μg/mL × h) = 240.38 ± 130.87 and 373.52 ± 78.85. The same variables for 4-AA were: Cmax, (μg/mL) = 0.44 ± 0.27 and 0.90 ± 0.31, T1/2beta (h) = 14.77 ± 13.13 and 35.97 and AUC0-t (μg/mL × h) = 3.20 ± 0.43 and 27.73 ± 11.99. Concentrations of 4-MAA exceeded the minimum concentration required for 50% inhibition of cyclooxygenases 1 and 2. However, an accumulation of 4-AA, was observed. Further clinical studies are necessary to ascertain the implications of these findings on the pharmacodynamic response to dipyrone in northeastern donkeys.

Rapid method for the determination of metamizole residues in bovine muscle by LC-MS/MS

Metamizole is a pyrazolone non-steroidal anti-inflammatory drug allowed for use in food-producing animals. According to Council Directive 96/23, residues of this drug have to be monitored because of the potential risk to consumers' health. Metamizole is hydrolysed to its marker residue 4-methylaminoantypyrine.This compound is further metabolised to three main metabolites: 4-formylaminoantipyrine, 4-aminoantipyrine and 4-acetylaminoantipyrine. The MRL of 4-methylaminoantipyrine in animal tissues is 100 µg kg(-1). Considering the above points, a method for the detection of four metamizole metabolites in bovine muscles was developed. Analytes were extracted from muscle by a mixture of acetonitrile and sodium acetate buffer. After centrifugation, the supernatant was passed through alumina cartridges, diluted with mobile phase and analysed by using LC-MS/MS. Four metamizole metabolites were separated on a C8 column in 23 min with a gradient of methanol:acetonitrile:ammonium formate solution and analysed by using positive ionisation. Validation of the method indicated a within-laboratory reproducibility in the range of 7-30% and recovery in the range of 45-95%. The method fulfils the criteria for confirmatory methods and, thanks to its labour efficiency, may also be used for screening purposes.

High on treatment platelet reactivity against aspirin by non-steroidal anti-inflammatory drugs--pharmacological mechanisms and clinical relevance

Inhibition of platelet function by aspirin results from irreversible inhibition of platelet cyclooxygenase (COX)-1. While sufficient inhibition is obtained at antiplatelet doses (75-325 mg/day) in most (≥95%) treated patients, the antiplatelet effect of aspirin and subsequent cardiovascular risk reduction is much less in clinical settings and disease-dependent. Several reasons for this "high on treatment platelet reactivity" are known. This paper reviews the evidence for an interaction between aspirin and other COX inhibitors, namely non-steroidal anti-inflammatory drugs (NSAIDs). Numerous experimental studies demonstrated a pharmacodynamic interaction between aspirin and NSAIDs. This likely occurs within the hydrophobic substrate channel of platelet COX-1 and might be explained by molecular competition between inhibitor drugs and substrate (arachidonic acid) at overlapping binding sites. This interaction is found with some compounds, notably ibuprofen and dipyrone (metamizole), but not with others, such as diclofenac and acetaminophen (paracetamol). Hence, this interaction is not a class effect of NSAIDs and/or non-steroidal analgesics but rather due to specific structural requirements which still remain to be defined. In vivo studies on healthy subjects and patients tend to confirm this type of interaction as well as large differences between NSAIDs and non-steroidal analgesics, respectively. These interactions may be clinically relevant and may increase the cardiovascular risk in long-term treatment for primary and secondary cardiovascular prevention in patients with chronic inflammation, such as rheumatoid arthritis. These patients have an elevated risk for myocardial infarctions and may require chronic antiplatelet treatment by aspirin in addition to treatment of inflammatory pain.

Spot-test identification and rapid quantitative sequential analysis of dipyrone

A qualitative spot-test and tandem quantitative analysis of dipyrone in the bulk drug and in pharmaceutical preparations is proposed. The formation of a reddish-violet color indicates a positive result. In sequence a quantitative procedure can be performed in the same flask. The quantitative results obtained were statistically compared with those obtained with the method indicated by the Brazilian Pharmacopoeia, using the Student's t and the F tests. Considering the concentration in a 100 µL aliquot, the qualitative visual limit of detection is about 5×10-6 g; instrumental LOD ≅ 1.4×10-4 mol L-1 ; LOQ ≅ 4.5×10-4 mol L-1.

A limited sampling strategy for tacrolimus in renal transplant patients

AIMS

To develop and validate limited sampling strategy (LSS) equations to estimate area under the curve (AUC(0-12)) in renal transplant patients.

METHODS

Twenty-nine renal transplant patients (3-6 months post transplant) who were at steady state with respect to tacrolimus kinetics were included in this study. The blood samples starting with the predose (trough) and collected at fixed time points for 12 h were analysed by microparticle enzyme immunoassay. Linear regression analysis estimated the correlations of tacrolimus concentrations at different sampling time points with the total measured AUC(0-12). By applying multiple stepwise linear regression analysis, LSS equations with acceptable correlation coefficients (R(2)), bias and precision were identified. The predictive performance of these models was validated by the jackknife technique.

RESULTS

Three models were identified, all with R(2) > or = 0.907. Two point models included one with trough (C(0)) and 1.5 h postdose (C(1.5)), another with trough and 4 h postdose. Increasing the number of sampling time points to more than two increased R(2) marginally (0.951 to 0.990). After jackknife validation, the two sampling time point (trough and 1.5 h postdose) model accurately predicted AUC(0-12). Regression coefficient R(2) = 0.951, intraclass correlation = 0.976, bias [95% confidence interval (CI)] 0.53% (-2.63, 3.69) and precision (95% CI) 6.35% (4.36, 8.35).

CONCLUSION

The two-point LSS equation [AUC(0-12) = 19.16 + (6.75.C(0)) + (3.33.C1.5)] can be used as a predictable and accurate measure of AUC(0-12) in stable renal transplant patients prescribed prednisolone and mycophenolate.

Influence of antipyretic drugs on the labeling of blood elements with technetium-99m

Acetaminophen (AAP), acetylsalicylic acid (ASA) and dipyrone (DIP) are antipyretic and analgesics drugs that have wide use in health sciences. Some drugs can modify the labeling of blood elements with technetium-99m (99mTc). This work has evaluated the effect of AAP, ASA and DIP on the labeling of the blood elements with 99mTc. Blood was incubated with different concentrations of the drugs before the 99mTc-labeled process. Plasma (P), blood cells (BC), insoluble (IF-P, IF-BC) and soluble (SF-P, SF-BC) fractions were separated and percentage of radioactivity (%ATI) in each fraction was determined. Data have shown that the antipyretic drugs used in this study did not significantly modify the fixation of 99mTc on the blood elements when the experiments were carried out with the doses usually used in human beings. Although the experiments were carried out with rats, it is possible to suggest that AAP, ASA or DIP should not interfere with the procedures in nuclear medicine involving the labeling of blood elements with 99mTc.

Bayesian estimation of cyclosporin exposure for routine therapeutic drug monitoring in kidney transplant patients

AIMS

AUC-based monitoring of cyclosporin A (CsA) is useful to optimize dose adaptation in difficult cases. We developed a population pharmacokinetic model to describe dose-exposure relationships for CsA in renal transplant patients and applied it to the Bayesian estimation of AUCs using three blood concentrations.

METHODS

A total of 84 renal graft recipients treated with CsA microemulsion were included in this study. Population pharmacokinetic analysis was conducted using NONMEM. A two-compartment model with zero-order absorption and a lag time best described the data. Bayesian estimation was based on CsA blood concentrations measured before dosing and 1 h and 2 h post dose. Predictive performance was evaluated using a cross-validation approach. Estimated AUCs were compared with AUCs calculated by the trapezoidal method. The Bayesian approach was also applied to an independent group of eight patients exhibiting unusual pharmacokinetic profiles.

RESULTS

Mean population pharmacokinetic parameters were apparent clearance 30 l h(-1), apparent volume of distribution 79.8 l, duration of absorption 52 min, absorption lag time 7 min. No significant relationships were found between any of the pharmacokinetic parameters and individual characteristics. A good correlation was obtained between Bayesian-estimated and experimental AUCs, with a mean prediction error of 2.8% (95% CI [-0.6, 6.2]) and an accuracy of 13.1% (95% CI [7.5, 17.2]). A good correlation was also obtained in the eight patients with unusual pharmacokinetic profiles (r(2) = 0.96, P < 0.01).

CONCLUSIONS

Our Bayesian approach enabled a good estimation of CsA exposure in a population of patients with variable pharmacokinetic profiles, showing its usefulness for routine AUC-based therapeutic drug monitoring.