Exploring the Applications of PBPK Modelling to Optimise HIV-1 Treatment

Human immunodeficiency virus (HIV-1) infection continues to be a significant public health concern, with 36.3 million lives being claimed by the infection thus far. Currently there is no cure for HIV. Antiretroviral (ARV) therapy has considerably increased life expectancy in people living with HIV (PLWH), however, several challenges remain. This thesis investigates the various ways in which physiologically based pharmacokinetic (PBPK) modelling can be developed and applied with the aim of optimising treatment for human immunodeficiency virus (HIV-1) infection. Neonatal patients are considered a vulnerable population as limited clinical studies are conducted in this population. Newborns born to mothers with HIV are at risk of receiving HIV. Lack of pharmacokinetic (PK) data means fewer treatment options are available. Chapters 2 & 3 focus on developing and applying a neonatal PBPK model to investigate the PK of integrase inhibitors, dolutegravir and bictegravir in neonates. Chapter 4 goes on to describe how modelling can be used to predict the PK of novel formulations by simulating long-acting, intramuscular, cabotegravir in neonates. Polypharmacy is routinely observed in PLWH, and drug-drug interactions (DDIs) prove an obstacle in HIV treatment, Chapter 5 involved developing an adult PBPK model to evaluate the magnitude of moderate inducers on novel ARVs. Residual levels of viraemia hinder the ability to develop a cure, Chapter 6 investigated the penetration of ARV drugs in lymphoid tissues using a mechanistic lymphatic PBPK model. Understanding the penetration of drugs in target tissues can help optimise ARV therapy. Collectively, this thesis evaluates the possible ways HIV treatment can be improved and optimised by investigating the potential of treatments in special populations, novel formulations of ARV drugs, management of drug-drug interactions and the penetration of therapy in target tissues

Predicting Drug-Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling

ObjectivesThe aim of this study was to simulate the drug-drug interaction (DDI) between ritonavir-boosted atazanavir (ATV/r) and rifampicin (RIF) using physiologically based pharmacokinetic (PBPK) modelling, and to predict suitable dose adjustments for ATV/r for the treatment of people living with HIV (PLWH) co-infected with tuberculosis.MethodsA whole-body DDI PBPK model was designed using Simbiology 9.6.0 (MATLAB R2019a) and verified against reported clinical data for all drugs administered alone and concomitantly. The model contained the induction mechanisms of RIF and ritonavir (RTV), the inhibition effect of RTV for the enzymes involved in the DDI, and the induction and inhibition mechanisms of RIF and RTV on the uptake and efflux hepatic transporters. The model was considered verified if the observed versus predicted pharmacokinetic values were within twofold. Alternative ATV/r dosing regimens were simulated to achieve the trough concentration (Ctrough) clinical cut-off of 150 ng/mL.ResultsThe PBPK model was successfully verified according to the criteria. Simulation of different dose adjustments predicted that a change in regimen to twice-daily ATV/r (300/100 or 300/200 mg) may alleviate the induction effect of RIF on ATV Ctrough, with > 95% of individuals predicted to achieve Ctrough above the clinical cut-off.ConclusionsThe developed PBPK model characterized the induction-mediated DDI between RIF and ATV/r, accurately predicting the reduction of ATV plasma concentrations in line with observed clinical data. A change in the ATV/r dosing regimen from once-daily to twice-daily was predicted to mitigate the effect of the DDI on the Ctrough of ATV, maintaining plasma concentration levels above the therapeutic threshold for most patients

Prediction of dolutegravir pharmacokinetics and dose optimization in neonates via physiologically based pharmacokinetic (PBPK) modelling

Background Only a few antiretroviral drugs (ARVs) are recommended for use during the neonatal period and there is a need for more to be approved to increase treatment and prophylaxis strategies. Dolutegravir, a selective integrase inhibitor, has potential for treatment of HIV infection and prophylaxis of transmission in neonates. Objectives To model the pharmacokinetics of dolutegravir in neonates and to simulate a theoretical optimal dosing regimen. Methods The physiologically based pharmacokinetic (PBPK) model was built incorporating the age-related changes observed in neonates. Virtual neonates between 0 and 28 days were simulated. The model was validated against observed clinical data for raltegravir and midazolam in neonates, prior to the prediction of dolutegravir pharmacokinetics. Results Both raltegravir and midazolam passed the criteria for model qualification, with simulated data within 1.8-fold of clinical data. The qualified model predicted the pharmacokinetics for several multidose regimens of dolutegravir. Regimen 6 involved 5 mg doses with a 48 h interval from Day 1–20, increasing to 5 mg once daily on Week 3, yielding AUC and Ctrough values of 37.2 mg·h/L and 1.3 mg/L, respectively. These exposures are consistent with those observed in paediatric patients receiving dolutegravir. Conclusions Dolutegravir pharmacokinetics were successfully simulated in the neonatal PBPK model. The predictions suggest that during the first 3 weeks of life a 5 mg dose administered every 48 h may achieve plasma exposures needed for therapy and prophylaxis

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