Establishment and application for simultaneous determination method of atorvastatin and its active/toxic metabolites in rat plasma / 中国药房

OBJECTIVE To establish a method for simultaneous determination of atorvastatin （ATV） and its active metabolites 2-hydroxy atorvastatin acid （2-HAT）， 4-hydroxy atorvastatin acid （4-HAT） and toxic metabolite atorvastatin lactone （ALT） in rat plasma and apply it for pharmacokinetic study. METHODS LC-MS/MS method was adopted for analysis. The one-step precipitation method was used for processing plasma samples （plasma samples were pretreated by acidification to adjust pH value so as to prevent inversion of configuration）， gradient elution was used to analyze the samples， and the analysis time was 5 min. Electrospray positive ionization was adopted， and positive ion scanning was performed in multi-reaction monitoring. The m/z of quantified ion pairs of ATV and its metabolites such as 2-HAT， 4-HAT and ATL， and internal standard pitavastatin were 559.3→ 440.2， 575.2→440.3， 575.0→440.2， 540.9→448.2 and 422.2→290.0， respectively. After conducting a comprehensive methodological investigation of the analytical method， the concentrations of ATV and its metabolites 2-HAT， 4-HAT，and ATL were determined， and the pharmacokinetic parameters of ATV and its metabolites were calculated using the non- compartment model of WinNonlin 6.1. RESULTS The results of methodological validation showed that endogenous substances in blank plasma did not interfere with the determination of the components to be tested， and the standard curve had a good linear relationship； the lower limits of quantification for ATV， 2-HAT， 4-HAT and ATL were 0.5， 0.5， 0.25 and 0.063 nmol/L， respectively. The precision， accuracy， recovery， matrix effect and stability investigation were all in line with the requirements of biological analysis. Pharmacokinetic analysis showed that after intragastric administration in rats， ATV calcium metabolized rapidly， and was mainly exposed to blood circulation in the form of ATV and 2-HAT， with the lowest concentration of lactone-type metabolites. CONCLUSIONS The established method is precise， rapid and accurate for plasma concentration analysis of ATV and its active/toxic metabolites. The application of the method could help to fully elucidate the pharmacokinetic characteristics of atorvastatin calcium in rats.

Sensitive, High-Throughput Liquid Chromatography-Tandem Mass Spectrometry Analysis of Atorvastatin and Its Pharmacologically Active Metabolites in Serum for Supporting Precision Pharmacotherapy.

The antihyerlipidemic drug atorvastatin (ATR) is used worldwide as part of the strategy to prevent cardiovascular events. The high prevalence of patient nonadherence remains an important challenge which could be addressed efficiently by precision pharmacotherapy based on therapeutic drug monitoring (TDM). ATR is metabolized to pharmacologically active metabolites, and evidence shows that the sums of ATR acid and lactone form concentrations (ATR + ATRL), or of ATR and hydroxylated metabolites (ATR + MET) should be assayed. A method is presented for the analysis of these substances in serum. Method validation included the estimation of the quantitative relationship between the concentrations and the standard deviations (SD), which supports the optimal incorporation of TDM results into nonparametric pharmacokinetic models. The concentrations of the analytes were evaluated in human subjects receiving ATR. The method's performance improved by taking the sums of acid and lactone concentrations into account. The concentration-SD relationship was linear, and we recommend applying Theil's regression for estimating the assay error. All analytes could be detected by 2 h post dose in the samples of human subjects. The changes in metabolite/parent drug concentration ratios in time depended on the dose. The method is suitable for the TDM of ATR with a focus on precision pharmacotherapy.

Prediction of pharmacokinetic drug-drug interactions causing atorvastatin-induced rhabdomyolysis using physiologically based pharmacokinetic modelling.

Atorvastatin and its lactone form metabolite are reported to be associated with statin-induced myopathy (SIM) such as myalgia and life-threatening rhabdomyolysis. Though the statin-induced rhabdomyolysis is not common during statin therapy, its incidence will significantly increase due to pharmacokinetic drug-drug interactions (DDIs) with inhibitor drugs which inhibit atorvastatin's and its lactone's metabolism and hepatic uptake. Thus, the quantitative analysis of DDIs of atorvastatin and its lactone with cytochrome P450 3A4 (CYP3A4) and organic anion-transporting polypeptide (OATP) inhibitors is of great importance. This study aimed to predict pharmacokinetic DDIs possibly causing atorvastatin-induced rhabdomyolysis using Physiologically Based Pharmacokinetic (PBPK) Modelling. Firstly, we refined the PBPK models of atorvastatin and atorvastatin lactone for predicting the DDIs with CYP3A4 and OATP inhibitors. Thereafter, we predicted the exposure changes of atorvastatin and atorvastatin lactone originating from the case reports of atorvastatin-induced rhabdomyolysis using the refined models. The simulation results show that pharmacokinetic DDIs of atorvastatin and its lactone with fluconazole, palbociclib diltiazem and cyclosporine are significant. Consequently, clinicians should be aware of necessary dose adjustment of atorvastatin being used with these four inhibitor drugs.

Physiologically based pharmacokinetic modeling of disposition and drug-drug interactions for atorvastatin and its metabolites.

Atorvastatin is the most commonly used of all statins to lower cholesterol. Atorvastatin is extensively metabolized in both gut and liver to produce several active metabolites. The purpose of the present study is to develop a physiologically based pharmacokinetic (PBPK) model for atorvastatin and its two primary metabolites, 2-hydroxy-atorvastatin acid and atorvastatin lactone, using in vitro and in vivo data. The model was used to predict the pharmacokinetic profiles and drug-drug interaction (DDI) effect for atorvastatin and its metabolites in different DDI scenarios. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministration of atorvastatin with different medications such as itraconazole, clarithromycin, cimetidine, rifampin and phenytoin. This population based PBPK model was able to describe the concentration-time profiles of atorvastatin and its two metabolites reasonably well in the absence or presence of those drugs at different dose regimens. The predicted maximum concentration (Cmax), area under the concentration-time curve (AUC) values and between-phase ratios were in good agreement with clinically observed data. The model has also revealed the importance of different metabolic pathways on the disposition of atorvastatin metabolites. This PBPK model can be utilized to assess the safety and efficacy of atorvastatin in the clinic. This study demonstrated the feasibility of applying PBPK approach to predict the DDI potential of drugs undergoing complex metabolism.

Paraoxonase (PON1 and PON3) Polymorphisms: Impact on Liver Expression and Atorvastatin-Lactone Hydrolysis.

Atorvastatin δ-lactone, a major, pharmacologically inactive metabolite, has been associated with toxicity. In a previous study we showed that polymorphisms of UGT1A3 influence atorvastatin δ-lactone formation. Here we investigated the reverse reaction, atorvastatin δ-lactone hydrolysis, in a human liver bank. Screening of microarray data revealed paraoxonases PON1 and PON3 among 17 candidate esterases. Microsomal δ-lactone hydrolysis was significantly correlated to PON1 and PON3 protein (r(s) = 0.60; r(s) = 0.62, respectively; P < 0.0001). PON1 and PON3 were strongly correlated to each other (r(s) = 0.60) but PON1 was shown to be more extensively glycosylated than PON3. In addition a novel splice-variant of PON3 was identified. Genotyping of 40 polymorphisms within the PON-locus identified PON1 promoter polymorphisms (-108T > C, -832G > A, -1741G > A) and a tightly linked group of PON3 polymorphisms (-4984A > G, -4105G > A, -1091A > G, -746C > T, and F21F) to be associated with changes in atorvastatin δ-lactone hydrolysis and expression of PON1 but not PON3. However, carriers of the common PON1 polymorphisms L55M or Q192R showed no difference in δ-lactone hydrolysis or PON expression. Haplotype analysis revealed decreased δ-lactone hydrolysis in carriers of the most common haplotype *1 compared to carriers of haplotypes *2, *3, *4, and *7. Analysis of non-genetic factors showed association of hepatocellular and cholangiocellular carcinoma with decreased PON1 and PON3 expression, respectively. Increased C-reactive protein and γ-glutamyl transferase levels were associated with decreased protein expression of both enzymes, and increased bilirubin levels, cholestasis, and presurgical exposure to omeprazole or pantoprazole were related to decreased PON3 protein. In conclusion, PON-locus polymorphisms affect PON1 expression whereas non-genetic factors have an effect on PON1 and PON3 expression. This may influence response to therapy or adverse events in statin treatment.