UHPLC-MS/MS assay for simultaneous determination of amlodipine, metoprolol, pravastatin, rosuvastatin, atorvastatin with its active metabolites in human plasma, for population-scale drug-drug interactions studies in people living with HIV

Bioanalytical LC-MS/MS method for simultaneous estimation of atorvastatin, its major active metabolites and ezetimibe

Background: Hyperlipidemia is one of the most common chronic diseases encountered globally, and atorvastatin (ATV) is mainly metabolized into two major active metabolites. Methodology: Hence, we aimed to estimate ATV and ezetimibe (EZE) simultaneously in the presence of ATV major and active metabolites using a validated LC-MS/MS method. Conclusion: The proposed method was linear (r² >0.99), accurate (92.02-109.94%) and precise (CV% ≤14) over the concentration range of 0.50-120 ng/ml, 0.20-48 ng/ml, 0.50-120 ng/ml and 0.20-48 ng/ml for ATV, EZE, 2-hydroxy ATV and 4-hydroxy ATV, respectively. The applied liquid-liquid extraction gave rise to reliable extraction recoveries of 84.91 ± 1.14%, 85.20 ± 1.62%, 85.46 ± 0.41% and 105.46 ± 2.35% for ATV, EZE, 2-hydroxy ATV and 4-hydroxy ATV, respectively.

A review on liquid chromatographic methods for the bioanalysis of atorvastatin

Background

The unsatisfied clinical need has encouraged the development and validation of bioanalytical procedures for the quantification of drugs in biological samples because the monitoring of drug concentrations helps in personalizing the patient’s pharmacotherapy, assessing the adherence to therapy, and is also extensively useful for pharmacokinetics and drug-drug interactions studies.

Main Body

The present review aimed to provide insightful information about the various liquid chromatographic methods developed till 2019 for the analysis and quantification of atorvastatin, its metabolites, and co-administered drugs in the various biological matrices like the serum, plasma, and urine with special emphasis on sample preparation techniques applied before chromatographic analysis along with different chromatographic conditions and their validation data. A total of 88 published papers that have used liquid chromatography techniques to quantify atorvastatin in biological fluids are included in the study. Out of the total reported liquid chromatographic methods, 34% used UV spectrophotometer as a detector, and 55% used MS/MS as a detector. Whereas 38% of them used protein precipitation procedure, 33% applied liquid-liquid extraction approach, and 12% employed solid-phase extraction technique for sample preparation.

Conclusion

In the last decade, numerous bioanalytical procedures have been developed for the quantification of atorvastatin in different biological samples using liquid chromatographic techniques. Moreover, advancement in technology developed several new and advanced sample preparation approaches like dispersive liquid-liquid extraction, microextraction by packed sorbent, which have high recovery rates than conventional procedures. Thus, the summarized review may be consulted as an informative tool to support the optimization of new bioanalytical methods for the quantification of atorvastatin.

Real-life management of drug-drug interactions between antiretrovirals and statins

BACKGROUND

PIs cause drug-drug interactions (DDIs) with most statins due to inhibition of drug-metabolizing enzymes and/or the hepatic uptake transporter OATP1B1, which may alter the pharmacodynamic (PD) effect of statins.

OBJECTIVES

To assess the management of DDIs between antiretrovirals (ARVs) and statins in people living with HIV (PLWH) considering statin plasma concentrations, compliance with dosing recommendations and achievement of lipid targets.

METHODS

PLWH of the Swiss HIV Cohort Study were eligible if they received a statin concomitantly with ARVs. HDL, total cholesterol (TC) and statin plasma concentration were measured during follow-up visits. Individual non-HDL and TC target values were set using the Framingham score and the 2018 European AIDS Clinical Society recommendations.

RESULTS

Data were analysed for rosuvastatin (n = 99), atorvastatin (n = 92), pravastatin (n = 46) and pitavastatin (n = 21). Rosuvastatin and atorvastatin underdosing frequently led to suboptimal PD response. Insufficient lipid control was observed with PIs despite high atorvastatin concentrations, likely explained by inhibition of OATP1B1 resulting in less statin uptake in the liver. Target lipid values were more often achieved with unboosted integrase inhibitors due to both their favourable DDI profiles and neutral effect on lipids. Insufficient lipid control was common with pravastatin and pitavastatin regardless of co-administered ARVs and despite using maximal recommended statin doses. The latter suggests lower efficacy compared with rosuvastatin or atorvastatin.

CONCLUSIONS

Suboptimal management of DDIs with statin underdosing was observed in 29% of prescriptions. Integrase inhibitor-based regimens and/or treatment with rosuvastatin or atorvastatin should be favoured in patients with refractory dyslipidaemia.

Pharmacokinetic/Pharmacodynamic Modelling to Describe the Cholesterol Lowering Effect of Rosuvastatin in People Living with HIV

BACKGROUND

Rosuvastatin is a lipid-lowering agent widely prescribed in people living with HIV, which is actively transported into the liver, making it a potential victim of drug-drug interactions with antiretroviral agents.

OBJECTIVES

The aims of this study were to characterise the pharmacokinetic profile of rosuvastatin and to describe the relationship between rosuvastatin concentrations and non-high-density lipoprotein (HDL)-cholesterol levels in people living with HIV.

METHODS

A population pharmacokinetic model (NONMEM) was developed to quantify the influence of demographics, clinical characteristics and comedications on rosuvastatin pharmacokinetics. This model was combined with an indirect effect model to describe non-HDL-cholesterol measurements.

RESULTS

A two-compartment model with sequential zero- and first-order absorption best fitted the 154 rosuvastatin concentrations provided by 65 people living with HIV. None of the tested covariates significantly influenced rosuvastatin pharmacokinetics. A total of 403 non-HDL cholesterol values were available for pharmacokinetic-pharmacodynamic modelling. Baseline non-HDL cholesterol decreased by 14% and increased by 12% with etravirine and antiretroviral drugs with a known impact on the lipid profile (i.e. protease inhibitors, efavirenz, cobicistat), respectively. The baseline value was surprisingly 43% lower in people living with HIV aged 80 years compared with those aged 40 years. Simulations based on the covariate-free model predicted that, under standard rosuvastatin dosages of 5 mg and 20 mg once daily, 31% and 64% of people living with HIV would achieve non-HDL-cholesterol targets, respectively.

CONCLUSIONS

The high between-subject variability that characterises both rosuvastatin pharmacokinetic and pharmacodynamic profiles remained unexplained after the inclusion of usual covariates. Considering its limited potential for drug-drug interactions with antiretroviral agents and its potent lipid-lowering effect, rosuvastatin prescription appears safe and effective in people living with HIV with hypercholesterolaemia.

CLINICAL TRIAL REGISTRATION NO

NCT03515772.

Population pharmacokinetic modelling to quantify the magnitude of drug-drug interactions between amlodipine and antiretroviral drugs

PURPOSE

Drug-drug interactions (DDIs) with antiretroviral drugs (ARVs) represent an important issue in elderly people living with HIV (PLWH). Amlodipine is a commonly prescribed antihypertensive drug metabolized by CYP3A4, thus predisposed to a risk of DDIs. Guidance on the management of DDIs is mostly based on theoretical considerations derived from coadministration with other CYP3A4 inhibitors. This study aimed at characterizing the magnitude of DDIs between amlodipine and ARV drugs in order to establish dosing recommendations.

METHODS

A population pharmacokinetic analysis was developed using non-linear mixed effect modelling (NONMEM) and included 163 amlodipine concentrations from 55 PLWH. Various structural and error models were compared to characterize optimally the concentration-time profile of amlodipine. Demographic and clinical characteristics as well as comedications were tested as potential influential covariates. Model-based simulations were performed to compare amlodipine exposure (i.e. area under the curve, AUC) between coadministered ARV drugs.

RESULTS

Amlodipine concentration-time profile was best described using a one-compartment model with first-order absorption and a lag-time. Amlodipine apparent clearance was influenced by both CYP3A4 inhibitors and efavirenz (CYP3A4 inducer). Model-based simulations revealed that amlodipine AUC increased by 96% when coadministered with CYP3A4 inhibitors, while efavirenz decreased drug exposure by 59%.

CONCLUSION

Coadministered ARV drugs significantly impact amlodipine disposition in PLWH. Clinicians should adjust amlodipine dosage accordingly, by halving the dosage in PLWH receiving ARV with inhibitory properties (mainly ritonavir-boosted darunavir), whereas they should double amlodipine doses when coadministering it with efavirenz, under appropriate monitoring of clinical response and tolerance.

Aging does not impact drug--drug interaction magnitudes with antiretrovirals

: The risk of drug-drug interactions (DDIs) is elevated in aging people living with HIV (PLWH) because of highly prevalent age-related comorbidities leading to more comedications. To investigate the impact of aging on DDI magnitudes between comedications (amlodipine, atorvastatin, rosuvastatin) and boosted darunavir, we conducted a clinical trial in aging PLWH aged at least 55 years. DDI magnitudes were comparable with those reported in young individuals supporting that the clinical management of DDIs in aging PLWH can be similar.

Influence of Drug-Drug Interactions on the Pharmacokinetics of Atorvastatin and Its Major Active Metabolite ortho-OH-Atorvastatin in Aging People Living with HIV

Background

People living with HIV (PLWH) are aging and experience age-related physiological changes and comorbidities. Atorvastatin is a widely prescribed lipid-lowering agent metabolized by cytochrome P450 (CYP) 3A4, whose hepatocyte uptake is facilitated by organic anion transporting polypeptide (OATP) 1B1/1B3. Inhibition or induction of this enzyme and hepatic transporter can increase or decrease atorvastatin exposure, respectively.

Objective

This study aimed to describe the pharmacokinetic profile of atorvastatin and its major metabolite, and to evaluate drug–drug interactions (DDIs) with antiretrovirals (ARVs).

Methods

The atorvastatin pharmacokinetic profile was best described by a two-compartment model with first-order absorption and elimination. Metabolite concentrations were described by considering both linear metabolism from atorvastatin and presystemic metabolism. The influence of demographic and clinical covariates on drug and metabolite pharmacokinetics was assessed using NONMEM^®. Model-based simulations were performed to evaluate the magnitude of DDIs with ARVs.

Results

Full pharmacokinetic profiles (98 atorvastatin + 62 o-OH-atorvastatin concentrations) and sparse concentrations (78 and 53 for atorvastatin and o-OH-atorvastatin, respectively) were collected in 59 PLWH. Interindividual variability was high. The coadministration of boosted ARVs decreased atorvastatin clearance by 58% and slowed down o-OH-atorvastatin formation by 88%. Atorvastatin clearance increased by 78% when coadministered with CYP3A4 inducers. Simulations revealed a 180% increase and 44% decrease in atorvastatin exposure (area under the curve) in the presence of ARVs with inhibiting and inducing properties, respectively.

Conclusion

This study showed an important interindividual variability in atorvastatin pharmacokinetics that remains largely unexplained after the inclusion of covariates. Since boosted ARVs double atorvastatin exposure, the initial dosage might be reduced by half, and titrated based on individual clinical targets.

Electronic supplementary material

The online version of this article (10.1007/s40262-020-00876-0) contains supplementary material, which is available to authorized users.

Identification and determination of chemical constituents of Citrus reticulata semen through ultra high performance liquid chromatography combined with Q Exactive Orbitrap tandem mass spectrometry

Citrus reticulata semen, a traditional Chinese medicinal material, has desirable medicinal and dietary properties. In this study, a method combining ultra high performance liquid chromatography with Q Exactive Orbitrap tandem mass spectrometry was established and validated for the identification and analysis of the chemical components of C. reticulata semen for the first time. The evaluation of different retention times and fragmentation characteristics, as well as comparative analysis with the literature, resulted in the identification of 35 chemical constituents, including 21 flavonoids and 14 other compounds. The 21 flavonoids derived from C. reticulata semen were reported for the first time. Seven of the chemical components of C. reticulata semen were quantitatively analyzed using the developed method under the optimal conditions. The results showed that the content of limonin, hesperidin, nobiletin, synephrine, tangeretin, 3,5,6,7,8,3',4'-heptamethoxyflavone and 5-hydroxide-6,7,8,3',4'-pentamethoxyflavone in C. reticulata semen was 11.1666, 0.0404, 0.0092, 0.0255, 0.0087, 0.0010, and 0.0008 mg/g, respectively. This study demonstrated that the ultra high performance liquid chromatography Q Exactive Orbitrap mass spectrometry based method can be used to rapidly and reliably analyze the chemical constituents of C. reticulata semen. These results provide a scientific basis for further studies of C. reticulata semen.