Population Pharmacokinetics of Hydroxychloroquine and 3 Metabolites in COVID-19 Patients and Pharmacokinetic/Pharmacodynamic Application.

We develop a population pharmacokinetic model for hydroxychloroquine (HCQ) and three of its metabolites (desethylhydroxychloroquine, Des HCQ; desethylchloroquine, DesCQ; and didesethylchloroquine, didesCQ) in COVID-19 patients in order to determine whether a pharmacokinetic (PK)/pharmacodynamic (PD) relationship was present. The population PK of HCQ was described using non-linear mixed effects modelling. The duration of hospitalization, the number of deaths, and poor clinical outcomes (death, transfer to ICU, or hospitalization ≥ 10 d) were evaluated as PD parameters. From 100 hospitalized patients (age = 60.7 ± 16 y), 333 BHCQ and M were available for analysis. The data for BHCQ were best described by a four-compartment model with a first-order input (KA) and a first-order output. For M, the better model of the data used one compartment for each metabolite with a first-order input from HCQ and a first-order output. The fraction of HCQ converted to the metabolites was 75%. A significant relationship was observed between the duration of hospitalization and BHCQ at 48 h (r2 = 0.12; p = 0.0052) or 72 h (r2 = 0.16; p = 0.0012). At 48 h or 72 h, 87% or 91% of patients vs. 63% or 62% had a duration < 25 d with a BHCQ higher or below 200 µg/L, respectively. Clinical outcome was significantly related to BHCQ at 48 h (good outcome 369 +/- 181 µg/L vs. poor 285 +/- 144 µg/L; p = 0.0441) but not at 72 h (407 +/- 207 µg/L vs. 311 +/- 174 µg/L; p = 0.0502). The number of deaths was not significantly different according to the trough concentration (p = 0.972 and 0.836 for 48 h and 72 h, respectively).

Impact of medical care, including use of anti-infective agents, on prognosis of COVID-19 hospitalized patients over time.

INTRODUCTION: The effect of anti-infective agents in COVID-19 is unclear. The impact of changes in practice on prognosis over time has not been evaluated. METHODS: Single center, retrospective study in adults hospitalized in a medicine ward for COVID-19 from March 5th to April 25th 2020. Patient characteristics were compared between two periods (before/after March 19th) considering French guidelines. The aim of the study was to evaluate how medical care impacted unfavorable outcome, namely admission to intensive care unit (ICU) and/or death. RESULTS: A total of 132 patients were admitted: mean age 59.0±16.3 years; mean C-reactive protein (CRP) level 84.0±71.1 mg/L; 46% had a lymphocyte count <1000/mm3. Prescribed anti-infective agents were lopinavir-ritonavir (n=12), azithromycin (AZI) (n=28) and AZI combined with hydroxychloroquine (HCQ) (n=52). There was a significant decrease in ICU admission, from 43% to 12%, between the two periods (P<0.0001). Delays until transfer to ICU were similar between periods (P=0.86). Pulmonary computerized tomography (CT)-scans were performed significantly more often with time (from 50% to 90%, P<0.0001), and oxygen-dependency (53% vs 80%, P=0.001) and prescription of AZI±HCQ (from 25% to 76%, P<0.0001) were also greater over time. Multivariate analyses showed a reduction of unfavorable outcome in patients receiving AZI±HCQ (hazard ratio [HR]=0.45, 95% confidence interval [CI: 0.21-0.97], P=0.04), particularly among an identified category of individuals (lymphocyte ≥1000/mm3 or CRP ≥100 mg/L). CONCLUSION: The present study showed a significant decrease in admission to ICU over time, which was probably related to multiple factors, including a better indication of pulmonary CT-scan, oxygen therapy, and a suitable prescription of anti-infective agents.

Population pharmacokinetics of lopinavir/ritonavir in Covid-19 patients.

OBJECTIVE: To develop a population pharmacokinetic model for lopinavir boosted by ritonavir in coronavirus disease 2019 (Covid-19) patients. METHODS: Concentrations of lopinavir/ritonavir were assayed by an accredited LC-MS/MS method. The population pharmacokinetics of lopinavir was described using non-linear mixed-effects modeling (NONMEM version 7.4). After determination of the base model that better described the data set, the influence of covariates (age, body weight, height, body mass index (BMI), gender, creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), C reactive protein (CRP), and trough ritonavir concentrations) was tested on the model. RESULTS: From 13 hospitalized patients (4 females, 9 males, age = 64 ± 16 years), 70 lopinavir/ritonavir plasma concentrations were available for analysis. The data were best described by a one-compartment model with a first-order input (KA). Among the covariates tested on the PK parameters, only the ritonavir trough concentrations had a significant effect on CL/F and improved the fit. Model-based simulations with the final parameter estimates under a regimen lopinavir/ritonavir 400/100 mg b.i.d. showed a high variability with median concentration between 20 and 30 mg/L (Cmin/Cmax) and the 90% prediction intervals within the range 1-100 mg/L. CONCLUSION: According to the estimated 50% effective concentration of lopinavir against SARS-CoV-2 virus in Vero E6 cells (16.7 mg/L), our model showed that at steady state, a dose of 400 mg b.i.d. led to 40% of patients below the minimum effective concentration while a dose of 1200 mg b.i.d. will reduce this proportion to 22%.

Quantification of plasma remdesivir and its metabolite GS-441524 using liquid chromatography coupled to tandem mass spectrometry. Application to a Covid-19 treated patient.

Objectives: A method based on liquid chromatography coupled to triple quadrupole mass spectrometry detection using 50 µL of plasma was developed and fully validated for quantification of remdesivir and its active metabolites GS-441524. Methods: A simple protein precipitation was carried out using 75 µL of methanol containing the internal standard (IS) remdesivir-13C6 and 5 µL ZnSO4 1 M. After separation on Kinetex® 2.6 µm Polar C18 100A LC column (100 × 2.1 mm i.d.), both compounds were detected by a mass spectrometer with electrospray ionization in positive mode. The ion transitions used were m/z 603.3 â†’ m/z 200.0 and m/z 229.0 for remdesivir, m/z 292.2 â†’ m/z 173.1 and m/z 147.1 for GS-441524 and m/z 609.3 â†’ m/z 206.0 for remdesivir-13C6. Results: Calibration curves were linear in the 1-5000 µg/L range for remdesivir and 5-2500 for GS-441524, with limit of detection set at 0.5 and 2 µg/L and limit of quantification at 1 and 5 µg/L, respectively. Precisions evaluated at 2.5, 400 and 4000 µg/L for remdesivir and 12.5, 125, 2000 µg/L for GS-441524 were lower than 14.7% and accuracy was in the [89.6-110.2%] range. A slight matrix effect was observed, compensated by IS. Higher stability of remdesivir and metabolite was observed on NaF-plasma. After 200 mg IV single administration, remdesivir concentration decrease rapidly with a half-life less than 1 h while GS-441524 appeared rapidly and decreased slowly until H24 with a half-life around 12 h. Conclusions: This method would be useful for therapeutic drug monitoring of these compounds in Covid-19 pandemic.