Quantitative Clinical Pharmacological Studies on Efavirenz and Atazanavir in the Treatment of HIV-1 Infection

There are 34 million people infected with the HIV-1 virus in the world today. Due to increased access to antiretroviral therapy, AIDS related death has dropped by 30% since 2005. Optimizing the pharmacotherapy of the HIV-1 infection is of great importance to reduce adverse effects, reduce viral resistance development and increase the patients’ survival as well as quality of life. This thesis presents pharmacometric applications to optimize pharmacotherapy of the HIV-1 infection as well as to expedite the clinical drug development of new drugs. Methods to extrapolate in vitro data to in vivo settings have been applied to predict the level of the drug-drug interaction between efavirenz and rifampicin as well as to evaluate the current dosage recommendations. Nonlinear mixed effects (NLME) models, as implemented in the software NONMEM, have been fitted to data from clinical studies to investigate the disease effect of HIV-1 on efavirenz pharmacokinetics. Further, NLME modeling and simulation was used to evaluate and validate bilirubin as a marker of exposure and adherence in HIV-1 infected patients. Simulation of a mechanistic viral dynamics model, describing the interplay between virus and CD4 cells, was used to optimize the design and analysis of clinical trials in antiretroviral drug development. Model based techniques for hypothesis testing were shown to be superior in terms of power compared to traditional statistical hypothesis testing. In conclusion, model based drug development techniques can be used to optimize HIV-1 therapy as well as expedite drug development of novel compounds

The myeloperoxidase inhibitor mitiperstat (AZD4831) does not prolong the QT interval at expected therapeutic doses

Abstract Mitiperstat is a myeloperoxidase inhibitor in clinical development for treatment of patients with heart failure and preserved or mildly reduced ejection fraction, non‐alcoholic steatohepatits and chronic obstructive pulmonary disease. We aimed to assess the risk of QT‐interval prolongation with mitiperstat using concentration–QT (C‐QT) modeling. Healthy male volunteers were randomized to receive single oral doses of mitiperstat 5, 15, 45, 135, or 405 mg (n = 6 per dose) or matching placebo (n = 10) in a phase 1 study (NCT02712372). Time‐matched pharmacokinetic and digital electrocardiogram data were collected at the baseline (pre‐dose) and at 11 time‐points up to 48 h post‐dose. C‐QT analysis was prespecified as an exploratory objective. The prespecified linear mixed effects model used baseline‐adjusted QT interval corrected for the heart rate by Fridericia's formula (ΔQTcF) as a dependent variable and plasma mitiperstat concentration as an independent variable. Initial exploratory analyses indicated that all model assumptions were met (no effect on heart rate; appropriate use of QTcF; no hysteresis; linear concentration–response relationship). Model‐predicted mean baseline‐corrected and placebo‐adjusted ΔΔQTcF was +0.73 ms (90% confidence interval [CI]: −1.73, +3.19) at the highest anticipated clinical exposure (0.093 μmol/L) during treatment with mitiperstat 5 mg once daily. The upper 90% CI was below the established threshold of regulatory concern. The 16‐fold margin to the highest observed exposure was high enough to mean that a positive control was not needed. Mitiperstat is not associated with risk of QT‐interval prolongation at expected therapeutic concentrations

A case‐study of model‐informed drug development of a novel PCSK9 anti sense oligonucleotide. Part 1: First time in man to phase II

Abstract Here, we show model‐informed drug development (MIDD) of a novel antisense oligonucleotide, targeting PCSK9 for treatment of hypocholesteremia. The case study exemplifies use of MIDD to analyze emerging data from an ongoing first‐in‐human study, utility of the US Food and Drug Administration MIDD pilot program to accelerate timelines, innovative use of competitor data to set biomarker targets, and use of MIDD to optimize sample size and dose selection, as well as to accelerate and de‐risk a phase IIb study. The focus of the case‐study is on the cross‐functional collaboration and other key MIDD enablers that are critical to maximize the value of MIDD, rather than the technical application of MIDD