PBPK modelling of dexamethasone in patients with COVID-19 and liver disease

The aim of the study was to apply Physiologically-Based Pharmacokinetic (PBPK) modelling to predict the effect of liver disease (LD) on the pharmacokinetics (PK) of dexamethasone (DEX) in the treatment of COVID-19. A whole-body PBPK model was created to simulate 100 adult individuals aged 18-60 years. Physiological changes (e.g., plasma protein concentration, liver size, CP450 expression, hepatic blood flow) and portal vein shunt were incorporated into the LD model. The changes were implemented by using the Child-Pugh (CP) classification system. DEX was qualified using clinical data in healthy adults for both oral (PO) and intravenous (IV) administrations and similarly propranolol (PRO) and midazolam (MDZ) were qualified with PO and IV clinical data in healthy and LD adults. The qualified model was subsequently used to simulate a 6 mg PO and 20 mg IV dose of DEX in patients with varying degrees of LD, with and without shunting. The PBPK model was successfully qualified across DEX, MDZ and PRO. In contrast to healthy adults, the simulated systemic clearance of DEX decreased (35% - 60%) and the plasma concentrations increased (170% - 400%) in patients with LD. Moreover, at higher doses of DEX, the AUC ratio between healthy/LD individuals remained comparable to lower doses. The exposure of DEX in different stages of LD was predicted through PBPK modelling, providing a rational framework to predict PK in complex clinical scenarios related to COVID-19. Model simulations suggest dose adjustments of DEX in LD patients are not necessary considering the low dose administered in the COVID-19 protocol.

Leveraging translational approaches for accelerated clinical development of vericiguat

Background/Introduction Vericiguat is a soluble guanylate cyclase (sGC) stimulator, like riociguat and nelociguat, and entered clinical development in 2012. Before entering Phase 2, pharmacokinetics (PK) and pharmacodynamics (PD) of vericiguat had been studied in healthy volunteers only, whereas riociguat and nelociguat had also been studied in patients with pulmonary hypertension (PH) and left ventricular dysfunction (LVD) or biventricular chronic heart failure (HF). We hypothesised that integrating all PK/PD data from these compounds into population PK/PD (popPK/PD) and physiology-based PK (PBPK) models could be used to predict optimal and safe dose ranges of vericiguat for Phase 2b studies in patients with worsening chronic HF. This novel bridging approach was applied in one of several translational stages to accelerate the development of vericiguat (Figure 1). Purpose We used prior knowledge from other sGC stimulators in a combined PK/PD and PBPK modelling approach to directly initiate Phase 2b studies of vericiguat in patients after Phase 1 studies in healthy volunteers. Methods PK, heart rate (HR) and systemic vascular resistance (SVR) data for vericiguat, nelociguat and riociguat were used to calculate PK/PD slopes of linear models, corrected with fraction unbound percentages (2.2%, 3.6% and 3.9%, respectively), to compare potency relative to riociguat based on unbound concentrations. PK estimates for nelociguat and riociguat were derived using population PK modelling (NONMEM) from patient studies with sparse PK sampling. PBPK models informed by preclinical physicochemical and PK data as well as clinical data for vericiguat were used to predict vericiguat PK in patients with HF (PK-Sim). Exposure–response data for riociguat in patients indicated the optimal range of PD responses for vericiguat (blood pressure for safety and cardiac index for efficacy). Results Vericiguat and nelociguat had lower potency than riociguat when comparing PK/PD slopes for HR and SVR (slope ratios of 0.23–0.32 for vericiguat and 0.33–0.47 for nelociguat). Plasma concentrations of vericiguat would need to be ∼3.6 times that of riociguat for equivalent responses. In patients with PH and LVD the optimal plasma concentration range for riociguat was ∼10–100 μg/l in exposure–response and safety studies, which translates to a target exposure range of ∼90–900 μg/l for vericiguat in patients with HF. PBPK modelling showed that vericiguat 2.5 mg and 10 mg would cover the target exposure range and that 1.25 mg would be a “non-effective” dose level with respect to haemodynamics. Conclusions Our novel translational approach combining popPK/PD analyses of other sGC stimulators with PBPK modelling enabled vericiguat to move directly from Phase 1 to Phase 2b, reducing development time by ∼2 years. PK and safety results from Phase 2b (SOCRATES-REDUCED) and Phase 3 (VICTORIA) trials confirmed that use of this translational approach to predict dose ranges of vericiguat was successful. FUNDunding Acknowledgement Type of funding sources: Private company. Main funding source(s): Funding for this research was provided by Bayer AG, Berlin, Germany Figure 1

An open label, adaptive, phase 1 trial of high-dose oral nitazoxanide in healthy volunteers: an antiviral candidate for SARS-CoV-2

Repurposing approved drugs may rapidly establish effective interventions during a public health crisis. This has yielded immunomodulatory treatments for severe COVID-19, but repurposed antivirals have not been successful to date because of redundancy of the target in vivo or suboptimal exposures at studied doses. Nitazoxanide is an FDA approved antiparasitic medicine, that physiologically-based pharmacokinetic (PBPK) modelling has indicated may provide antiviral concentrations across the dosing interval, when repurposed at higher than approved doses. Within the AGILE trial platform (NCT04746183) an open label, adaptive, phase 1 trial in healthy adult participants was undertaken with high dose nitazoxanide. Participants received 1500mg nitazoxanide orally twice-daily with food for 7 days. Primary outcomes were safety, tolerability, optimum dose and schedule. Intensive pharmacokinetic sampling was undertaken day 1 and 5 with Cmin sampling on day 3 and 7. Fourteen healthy participants were enrolled between 18th February and 11th May 2021. All 14 doses were completed by 10/14 participants. Nitazoxanide was safe and well tolerated with no significant adverse events. Moderate gastrointestinal disturbance (loose stools) occurred in 8 participants (57.1%), with urine and sclera discolouration in 12 (85.7%) and 9 (64.3%) participants, respectively, without clinically significant bilirubin elevation. This was self-limiting and resolved upon drug discontinuation. PBPK predictions were confirmed on day 1 but with underprediction at day 5. Median Cmin was above the in vitro target concentration on first dose and maintained throughout. Nitazoxanide administered at 1500mg BID with food was safe and well tolerated and a phase 1b/2a study is now being initiated in COVID-19 patients.

Physiologically based pharmacokinetic modelling to predict the clinical effect of CYP3A inhibitors/inducers on esaxerenone pharmacokinetics in healthy subjects and subjects with hepatic impairment

Purpose Esaxerenone is a novel, oral, nonsteroidal treatment for hypertension. Physiologically based pharmacokinetic (PBPK) modelling was performed to predict the drug–drug interaction (DDI) effect of cytochrome P450 (CYP)3A modulators on esaxerenone pharmacokinetics in healthy subjects and subjects with hepatic impairment. Methods In our PBPK model, the fraction of esaxerenone metabolised by CYP3A was estimated from mass-balance data and verified and optimised by clinical DDI study results with strong CYP3A modulators. The model was also verified by the observed pharmacokinetics after multiple oral dosing and by the effect of hepatic impairment on esaxerenone pharmacokinetics. The model was applied to predict the DDI effects on esaxerenone pharmacokinetics with untested CYP3A modulators in healthy subjects and with strong CYP3A modulators in subjects with hepatic impairment. Results The PBPK model well described esaxerenone pharmacokinetics after multiple oral dosing. The predicted fold changes in esaxerenone plasma exposure after coadministration with strong CYP3A modulators were comparable with the observed data (1.53-fold with itraconazole and 0.31-fold with rifampicin). Predicted DDIs with untested moderate CYP3A modulators were less than the observed DDI with strong CYP3A modulators. The PBPK model also described the effect of hepatic impairment on esaxerenone plasma exposure. The predicted DDI results with strong CYP3A modulators in subjects with hepatic impairment indicate that, for concomitant use of CYP3A modulators, caution is advised for subjects with hepatic impairment, as is for healthy subjects. Conclusion The PBPK model developed predicted esaxerenone pharmacokinetics and DDIs and informed concurrent use of esaxerenone with CYP3A modulators.

Modelling Tools to Characterize Acetaminophen Pharmacokinetics in the Pregnant Population

This review describes acetaminophen pharmacokinetics (PK) throughout pregnancy, as analyzed by three methods (non-compartmental analyses (NCA), population PK, and physiologically based PK (PBPK) modelling). Eighteen studies using NCA were reported in the scientific literature. These studies reported an increase in the volume of distribution (3.5–60.7%) and an increase in the clearance (36.8–84.4%) of acetaminophen in pregnant women compared to non-pregnant women. Only two studies using population PK modelling as a technique were available in the literature. The largest difference in acetaminophen clearance (203%) was observed in women at delivery compared to non-pregnant women. One study using the PBPK technique was found in the literature. This study focused on the formation of metabolites, and the toxic metabolite N-acetyl-p-benzoquinone imine was the highest in the first trimester, followed by the second and third trimester, compared with non-pregnant women. In conclusion, this review gave an overview on acetaminophen PK changes in pregnancy. Also, knowledge gaps, such as fetal and placenta PK parameters, have been identified, which should be explored further before dosing adjustments can be suggested on an evidence-based basis.

Mechanistic PBPK Modelling to Predict the Advantage of the Salt Form of a Drug When Dosed with Acid Reducing Agents

Acid reducing agents (ARAs) reduce the dissolution rate of weakly basic drugs in the stomach potentially leading to lower bioavailability. Formulating the API as a rapidly dissolving salt is one strategy employed to reduce the impact of ARAs on dissolution of such drugs. In the present work, a model drug was selected with an immediate release formulation of the free base dosed in both the absence and presence of the ARA famotidine. In the latter case, bioavailability is restricted and several salt formulations were investigated. To simulate these drug products a mechanistic physiologically based pharmacokinetic (PBPK) model was built using the Simcyp Simulator, which illustrates the advantage of formulating an API as a salt compared to the free base form. The simulations use a mechanistic salt model utilising knowledge of the solubility product which was applied to predict the salt advantage. The developed PBPK model exemplifies that it can be critical to account for the surface pH and solubility when modelling the dissolution of low pKa bases and their salts in the gastric environment. In particular, the mechanistic salt model can be used to aid in screening and salt form selection where the aim is to mitigate effects of ARAs.