Pharmacokinetics of quinacrine in the treatment of prion disease

BACKGROUND: Prion diseases are caused by the accumulation of an aberrantly folded isoform of the prion protein, designated PrP(Sc). In a cell-based assay, quinacrine inhibits the conversion of normal host prion protein (PrP(C)) to PrP(Sc )at a half-maximal concentration of 300 nM. While these data suggest that quinacrine may be beneficial in the treatment of prion disease, its penetration into brain tissue has not been extensively studied. If quinacrine penetrates brain tissue in concentrations exceeding that demonstrated for in vitro inhibition of PrP(Sc), it may be useful in the treatment of prion disease. METHODS: Oral quinacrine at doses of 37.5 mg/kg/D and 75 mg/kg/D was administered to mice for 4 consecutive weeks. Plasma and tissue (brain, liver, spleen) samples were taken over 8 weeks: 4 weeks with treatment, and 4 weeks after treatment ended. RESULTS: Quinacrine was demonstrated to penetrate rapidly into brain tissue, achieving concentrations up to 1500 ng/g, which is several-fold greater than that demonstrated to inhibit formation of PrP(Sc )in cell culture. Particularly extensive distribution was observed in spleen (maximum of 100 μg/g) and liver (maximum of 400 μg/g) tissue. CONCLUSIONS: The documented extensive brain tissue penetration is encouraging suggesting quinacrine might be useful in the treatment of prion disease. However, further clarification of the distribution of both intracellular and extracellular unbound quinacrine is needed. The relative importance of free quinacrine in these compartments upon the conversion of normal host prion protein (PrP(C)) to PrP(Sc )will be critical toward its potential benefit

A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions

All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies

Maturation and growth of renal function: Dosing renally cleared drugs in children

A model was developed that characterized the maturation and growth of the renal function parameters (RFPs) glomerular filtration rate (GF), active tubular secretion (AS), and renal plasma flow (QR). Published RFP values were obtained from 63 healthy children between the ages of 2 days and 12 years. Maturation over time was assumed to be exponential from an immature (RFPim) to a mature (RFPma) level; for growth, RFPim and RFPma were assumed to follow the allometric equation: RFP(age, W)=aWbe−kmat*age+cWb(1−e−kmat*age), where W is body weight, kmat is the maturation rate constant, b is the body weight exponent, and a and c are RFPim and RFPma at unit W. The model-based equation was fitted to the age-W, RFP values by a nonlinear least-squares method. For GF, the maturation half-life was 7.9 months (90% maturation, 26 months), the body weight exponent was 0.662, and the ratio c/a (which reflected the magnitude of the maturation influence) was 3.1. For AS and QR, the maturation half-lives were about 3.8 months and the ratio c/a was about 1.8. For renally eliminated drugs, the model can be used to estimate dosing regimens that are based on the adult dosing regimen and the age and weight of the child