Pharmacokinetics under the COVID-19 storm

Abstract

Aims: The storm-like nature of the health crises caused by COVID-19 has led to unconventional clinical trial practices such as the relaxation of exclusion criteria. The question remains: how can we conduct diverse trials without exposing subgroups of populations to potentially harmful drug exposure levels? The aim of this study was to build a knowledge base of the effect of intrinsic/extrinsic factors on the disposition of several repurposed COVID-19 drugs.

Methods: Physiologically based pharmacokinetic (PBPK) models were used to study the change in the pharmacokinetics (PK) of drugs repurposed for COVID-19 in geriatric patients, different race groups, organ impairment and drug-drug interactions (DDIs) risks. These models were also used to predict epithelial lining fluid (ELF) exposure, which is relevant for COVID-19 patients under elevated cytokine levels.

Results: The simulated PK profiles suggest no dose adjustments are required based on age and race for COVID-19 drugs, but dose adjustments may be warranted for COVID-19 patients also exhibiting hepatic/renal impairment. PBPK model simulations suggest ELF exposure to attain a target concentration was adequate for most drugs, except for hydroxychloroquine, azithromycin, atazanavir and lopinavir/ritonavir.

Conclusion: We demonstrate that systematically collated data on absorption, distribution, metabolism and excretion, human PK parameters, DDIs and organ impairment can be used to verify simulated plasma and lung tissue exposure for drugs repurposed for COVID-19, justifying broader patient recruitment criteria. In addition, the PBPK model developed was used to study the effect of age and ethnicity on the PK of repurposed drugs, and to assess the correlation between lung exposure and relevant potency values from in vitro studies for SARS-CoV-2.

Keywords: ADME; COVID-19; Drug-Drug Interactions; M&S; PBPK; PKPD; cytokine.

FIGURE 1

Symptoms and grades of cytokine release syndrome, also known as cytokine storm, and its impact on drug exposure

FIGURE 2

Effect of age (A) and race (B) on unbound plasma concentration‐time profiles of COVID‐19 drugs. (A) The black continuous line represents the median prediction using the PBPK model for 18‐40 years of age, the green line for 40‐65 years and the red line for 65‐98 years. The shaded area represents the 95% prediction intervals of Caucasian healthy volunteers. (B) The black continuous line represents the median prediction using the PBPK model for Caucasians subjects, the green line for Japanese subjects and the red line for Chinese patients. Different race simulations were run using 40‐65 years of age with 50% of females. Doses used: acalabrutinib 100 mg single dose, azithromycin 500 mg single dose, chloroquine 600 mg single dose, dapagliflozin 10 mg single dose, darunavir 800 mg single dose, hydroxychloroquine (HCQ) 200 mg single dose, ibrutinib 140 mg single dose, lopinavir 400 mg single dose (with 100 mg ritonavir concomitant interaction), rifampicin 600 mg single dose, baricitinib 2 mg single dose, ritonavir 600 mg single dose, ruxolitinib 20 mg single dose

FIGURE 3

Effect of hepatic (A) and renal (B) impairments on total plasma concentration‐time profiles of COVID‐19 drugs. The black continuous line represents the median prediction using the PBPK model for healthy population. The shaded area represents the 95% prediction intervals of the healthy population. The blue line represents mild hepatic impairment. The green line represents moderate renal or hepatic impairment. The red line represents severe renal or hepatic impairment. Doses used for hepatic impairment: acalabrutinib 50 mg single dose, azithromycin 500 mg single dose, atazanavir 400 mg single dose, chloroquine 300 mg single dose, dapagliflozin 10 mg single dose, hydroxychloroquine base (HCQ) 155 mg single dose, baricitinib 4 mg single dose, ritonavir 600 mg single dose, ruxolitinib 25mg single dose, lopinavir 400 mg single dosing interval (with 100 mg ritonavir concomitant interaction), darunavir 600 mg single dosing interval (with 100 mg ritonavir concomitant interaction), ibrutinib 140 mg single dose, dexamethasone8 mg single dose. Only the parent acalabrutinib was measured in organ dysfunction or DDI studies of acalabrutinib. Doses used for renal impairment: azithromycin 500 mg single dose, atazanavir 400 mg single dose, hydroxychloroquine base 155 mg, baricitinib 4 mg single dose, chloroquine 300 mg single dose, dapagliflozin 50 mg single dose, acalabrutinib 50 mg single dose, ruxolitinib 25mg single dose, ritonavir 600 mg single dose, lopinavir 400 mg single dose (with 100 mg ritonavir concomitant interaction), darunavir 600 mg single dosing interval (with 100 mg ritonavir concomitant interaction), dexamethasone 8 mg single dose

FIGURE 4

PBPK model‐based simulations of unbound drug concentration‐profiles in lung tissue using a multiple‐compartmental lung model in geriatric patients after verification of models using adult data for drugs that are being tested in COVID‐19 trials. Dashed lines represent a relevant potency value for either IC₅₀ or IC₉₀. Doses used for simulating lung exposure: multiple doses 14 days of dosing to steady‐state azithromycin 500 mg single dose, atazanavir 400 mg, hydroxychloroquine base 155 mg, baricitinib 4 mg, chloroquine 300 mg, dapagliflozin 10 mg, acalabrutinib 100 mg, ruxolitinib 25 mg, ritonavir 600 mg, lopinavir 400 mg, darunavir 800 mg, dexamethasone 8 mg

FIGURE 5

Sensitivity analysis on the possible effect of cytokines and consequent CYP3A4 abundance suppression in both liver and the gut on acalabrutinib (A) and ibrutinib (B) exposures