Population Pharmacokinetics of Hydroxychloroquine in COVID-19 Patients: Implications for Dose Optimization

Hydroxychloroquine is Metabolized by Cytochrome P450 2D6, 3A4, and 2C8, and Inhibits Cytochrome P450 2D6, while its Metabolites also Inhibit Cytochrome P450 3A in vitro

This study aimed to explore the cytochrome P450 (CYP) metabolic and inhibitory profile of hydroxychloroquine (HCQ). Hydroxychloroquine metabolism was studied using human liver microsomes (HLMs) and recombinant CYP enzymes. The inhibitory effects of HCQ and its metabolites on nine CYPs were also determined in HLMs, using an automated substrate cocktail method. Our metabolism data indicated that CYP3A4, CYP2D6, and CYP2C8 are the key enzymes involved in HCQ metabolism. All three CYPs formed the primary metabolites desethylchloroquine (DCQ) and desethylhydroxychloroquine (DHCQ) to various degrees. Although the intrinsic clearance (CL_int) value of HCQ depletion by recombinant CYP2D6 was > 10-fold higher than that by CYP3A4 (0.87 versus 0.075 µl/min/pmol), scaling of recombinant CYP CL_int to HLM level resulted in almost equal HLM CL_int values for CYP2D6 and CYP3A4 (11 and 14 µl/min/mg, respectively). The scaled HLM CL_int of CYP2C8 was 5.7 µl/min/mg. Data from HLM experiments with CYP-selective inhibitors also suggested relatively equal roles for CYP2D6 and CYP3A4 in HCQ metabolism, with a smaller contribution by CYP2C8. In CYP inhibition experiments, HCQ, DCQ, DHCQ, and the secondary metabolite didesethylchloroquine were direct CYP2D6 inhibitors, with 50% inhibitory concentration (IC₅₀) values between 18 and 135 µM. HCQ did not inhibit other CYPs. Furthermore, all metabolites were time-dependent CYP3A inhibitors (IC₅₀ shift 2.2-3.4). To conclude, HCQ is metabolized by CYP3A4, CYP2D6, and CYP2C8 in vitro. HCQ and its metabolites are reversible CYP2D6 inhibitors, and HCQ metabolites are time-dependent CYP3A inhibitors. These data can be used to improve physiologically-based pharmacokinetic models and update drug-drug interaction risk estimations for HCQ. SIGNIFICANCE STATEMENT: While CYP2D6, CYP3A4, and CYP2C8 have been shown to mediate chloroquine biotransformation, it appears that the role of CYP enzymes in hydroxychloroquine (HCQ) metabolism has not been studied. In addition, little is known about the CYP inhibitory effects of HCQ. Here, we demonstrate that CYP2D6, CYP3A4, and CYP2C8 are the key enzymes involved in HCQ metabolism. Furthermore, our findings show that HCQ and its metabolites are inhibitors of CYP2D6, which likely explains the previously observed interaction between HCQ and metoprolol.

Population Pharmacokinetics of Hydroxychloroquine Sulfate in Healthcare Workers, Given for Prophylaxis Against Coronavirus Disease 2019 (COVID-19) in India

Healthcare workers (HCWs) and frontline workers were recommended hydroxychloroquine (HCQ) 400 mg twice a day on day 1, followed by 400 mg once weekly for the next 7 weeks, as prophylaxis against COVID-19. There was limited information on the population pharmacokinetics (popPK) of HCQ in an Indian setting when administered for prophylaxis against COVID-19, and hence this study was proposed. It was a multicentric prospective study conducted at 3 sites in India wherein HCWs who were already on HCQ prophylaxis, who were about to start prophylaxis or who had stopped the prophylaxis for any reason were enrolled. Each participant gave 2 to 6 blood samples at different time points and whole-blood HCQ concentrations were assayed using liquid chromatography with tandem mass spectrometry (LC MS/MS). popPK analysis was performed using PUMAS 1.1.0. A total of N = 338 blood samples from N = 121 participants were included in the popPK analysis. A 2-compartment structural model with linear elimination was able to explain the observed data. Body weight was found to be a significant covariate influencing drug clearance. The final model was assessed using goodness-of-fit plots, a visual predictive check and a bootstrap, all of which confirmed that the model was appropriate. Simulations based on the current regimen showed that trough values were below the half-maximal effective concentration (EC50) of 0.7 μmol against COVID-19. A new weight-based dosage regimen was proposed to maintain the trough concentration above the EC50 threshold.

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

Pharmacokinetics and pharmacodynamics of hydroxychloroquine in hospitalized patients with COVID-19

BACKGROUND

Hydroxychloroquine (HCQ) dosage required to reach circulating levels that inhibit SARS-Cov-2 are extrapolated from pharmacokinetic data in non-COVID-19 patients.

METHODS

We performed a population-pharmacokinetic analysis from 104 consecutive COVID-19 hospitalized patients (31 in intensive care units, 73 in medical wards, n=149 samples). Plasma HCQ concentration were measured using high performance liquid chromatography with fluorometric detection. Modelling used Monolix-2019R2.

RESULTS

HCQ doses ranged from 200 to 800mg/day administered for 1 to 11days and median HCQ plasma concentration was 151ng/mL. Among the tested covariates, only bodyweight influenced elimination oral clearance (CL) and apparent volume of distribution (Vd). CL/F (F for unknown bioavailability) and Vd/F (relative standard-error, %) estimates were 45.9L/h (21.2) and 6690L (16.1). The derived elimination half-life (t1/2) was 102h. These parameters in COVID-19 differed from those reported in patients with lupus, where CL/F, Vd/F and t1/2 are reported to be 68L/h, 2440 L and 19.5h, respectively. Within 72h of HCQ initiation, only 16/104 (15.4%) COVID-19 patients had HCQ plasma levels above the in vitro half maximal effective concentration of HCQ against SARS-CoV-2 (240ng/mL). HCQ did not influence inflammation status (assessed by C-reactive protein) or SARS-CoV-2 viral clearance (assessed by real-time reverse transcription-PCR nasopharyngeal swabs).

CONCLUSION

The interindividual variability of HCQ pharmacokinetic parameters in severe COVID-19 patients was important and differed from that previously reported in non-COVID-19 patients. Loading doses of 1600mg HCQ followed by 600mg daily doses are needed to reach concentrations relevant to SARS-CoV-2 inhibition within 72hours in≥60% (95% confidence interval: 49.5-69.0%) of COVID-19 patients.

Hydroxychloroquine as a Chemoprophylactic Agent for COVID-19: A Clinico-Pharmacological Review

Hydroxychloroquine has gained much attention as one of the candidate drugs that can be repurposed as a prophylactic agent against SARS-CoV-2, the agent responsible for the COVID-19 pandemic. Due to high transmissibility and presence of asymptomatic carriers and presymptomatic transmission, there is need for a chemoprophylactic agent to protect the high-risk population. In this review, we dissect the currently available evidence on hydroxychloroquine prophylaxis from a clinical and pharmacological point of view. In vitro studies on Vero cells show that hydroxychloroquine effectively inhibits SARS-CoV-2 by affecting viral entry and viral transport via endolysosomes. However, this efficacy has failed to replicate in in vivo animal models as well as in most clinical observational studies and clinical trials assessing pre-exposure prophylaxis and postexposure prophylaxis in healthcare workers. An analysis of the pharmacology of HCQ in COVID-19 reveals certain possible reasons for this failure-a pharmacokinetic failure due to failure to achieve adequate drug concentration at the target site and attenuation of its inhibitory effect due to the presence of TMPRSS2 in airway epithelial cells. Currently, many clinical trials on HCQ prophylaxis in HCW are ongoing; these factors should be taken into account. Using higher doses of HCQ for prophylaxis is likely to be associated with increased safety concerns; thus, it may be worthwhile to focus on other possible interventions.

Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?-The IDENTIFY Trial

Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydroxychloroquine was associated with improved survival; this population might be relevant for study in a clinical trial. A pragmatic trial was conducted at six United States hospitals. We enrolled COVID-19 patients that were admitted between 10 March and 4 June 2020. Treatment was not randomized. The study endpoint was mortality; discharge was a competing event. Hazard ratios were obtained on the entire population, and on the subpopulation indicated by the algorithm as suitable for treatment. A total of 290 patients were enrolled. In the subpopulation that was identified by the algorithm, hydroxychloroquine was associated with a statistically significant (p = 0.011) increase in survival (adjusted hazard ratio 0.29, 95% confidence interval (CI) 0.11–0.75). Adjusted survival among the algorithm indicated patients was 82.6% in the treated arm and 51.2% in the arm not treated. No association between treatment and mortality was observed in the general population. A 31% increase in survival at the end of the study was observed in a population of COVID-19 patients that were identified by a machine learning algorithm as having a better outcome with hydroxychloroquine treatment. Precision medicine approaches may be useful in identifying a subpopulation of COVID-19 patients more likely to be proven to benefit from hydroxychloroquine treatment in a clinical trial.