The Parasite Reduction Ratio (PRR) Assay Version 2: Standardized Assessment of Plasmodium falciparum Viability after Antimalarial Treatment In Vitro

With artemisinin-resistant Plasmodium falciparum parasites emerging in Africa, the need for new antimalarial chemotypes is persistently high. The ideal pharmacodynamic parameters of a candidate drug are a rapid onset of action and a fast rate of parasite killing or clearance. To determine these parameters, it is essential to discriminate viable from nonviable parasites, which is complicated by the fact that viable parasites can be metabolically inactive, whilst dying parasites can still be metabolically active and morphologically unaffected. Standard growth inhibition assays, read out via microscopy or [3H] hypoxanthine incorporation, cannot reliably discriminate between viable and nonviable parasites. Conversely, the in vitro parasite reduction ratio (PRR) assay is able to measure viable parasites with high sensitivity. It provides valuable pharmacodynamic parameters, such as PRR, 99.9% parasite clearance time (PCT99.9%) and lag phase. Here we report the development of the PRR assay version 2 (V2), which comes with a shorter assay duration, optimized quality controls and an objective, automated analysis pipeline that systematically estimates PRR, PCT99.9% and lag time and returns meaningful secondary parameters such as the maximal killing rate of a drug (Emax) at the assayed concentration. These parameters can be fed directly into pharmacokinetic/pharmacodynamic models, hence aiding and standardizing lead selection, optimization, and dose prediction

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

The antimalarial MMV688533 provides potential for single-dose cures with a high barrier to

The emergence and spread of Plasmodium falciparum resistance to first-line antimalarials creates an imperative to identify and develop potent preclinical candidates with distinct modes of action. Here, we report the identification of MMV688533, an acylguanidine that was developed following a whole-cell screen with compounds known to hit high-value targets in human cells. MMV688533 displays fast parasite clearance in vitro and is not cross-resistant with known antimalarials. In a P. falciparum NSG mouse model, MMV688533 displays a long-lasting pharmacokinetic profile and excellent safety. Selection studies reveal a low propensity for resistance, with modest loss of potency mediated by point mutations in PfACG1 and PfEHD. These proteins are implicated in intracellular trafficking, lipid utilization, and endocytosis, suggesting interference with these pathways as a potential mode of action. This preclinical candidate may offer the potential for a single low-dose cure for malaria

The antimalarial MMV688533 provides potential for single-dose cures with a high barrier to

The emergence and spread of Plasmodium falciparum resistance to first-line antimalarials creates an imperative to identify and develop potent preclinical candidates with distinct modes of action. Here, we report the identification of MMV688533, an acylguanidine that was developed following a whole-cell screen with compounds known to hit high-value targets in human cells. MMV688533 displays fast parasite clearance in vitro and is not cross-resistant with known antimalarials. In a P. falciparum NSG mouse model, MMV688533 displays a long-lasting pharmacokinetic profile and excellent safety. Selection studies reveal a low propensity for resistance, with modest loss of potency mediated by point mutations in PfACG1 and PfEHD. These proteins are implicated in intracellular trafficking, lipid utilization, and endocytosis, suggesting interference with these pathways as a potential mode of action. This preclinical candidate may offer the potential for a single low-dose cure for malaria

Tissue-to-plasma partition coefficients of the drugs.

<p>Tissue-to-plasma partition coefficients of the drugs.</p

Summary of 10 <i>in vivo</i> clinical studies used in comparison to the simulations.

<p>Summary of 10 <i>in vivo</i> clinical studies used in comparison to the simulations.</p

Simulated enzyme levels as a function of time.

<p>The total enzyne level (free enzyne + enzyme-substrate complex) is represented as a fold change compared to the initial level. The color gradient indicates the positions within the lobule from blue (Entrance of the lobule) to red (Exit of the lobule): (A) Azithromycin (MBI inducer) (B) Cimetidine (Reversible inhibitor: No effect on enzyme level) (C) Ethinyl Estradiol (MBI inhibitor and inducer: It seems that in this case the effect cancels each other out) (D) Rifampin (Inducer).</p

Simulated PK profile for midazolam after an oral dose of 15 mg and comparison to clinical data.

<p>(⋆) Fee <i>et al</i>. 1987 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183794#pone.0183794.ref020" target="_blank">20</a>] (•) Zimmermann <i>et al</i>. 1996 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183794#pone.0183794.ref019" target="_blank">19</a>].</p

Lobule geometry and modelling.

<p>(A) The lobule cross section as represented displays an apparent elementary symmetry essential for its physiology primarily given by the blood vessels and the blood flow (Credit to Dr. Roger C. Wagner, University of Delaware). (B) This symmetry is used when lobule modeling or representation are involved. In general, a lobule is represented by a hexagon composed of hepatocyte plates. These plates are hierarchically organized to optimize exchanges. (C) To model the blood flow (and subsequent exchanges between the liver tissues and the blood), an algorithm was designed to automatically generate the length and radius of the sinusoids. The latter is used to estimate the changes in velocity within a sinusoid portion by assuming a constant blood flow and a constant velocity over the cross section.</p

Simulated PK profiles for midazolam with a placebo (blue) or a perpetrator (orange) and comparison to clinical data.

<p>The dots represent the clinical observations: (A) Azithromycin [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183794#pone.0183794.ref019" target="_blank">19</a>] (B) Cimetidine [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183794#pone.0183794.ref020" target="_blank">20</a>] (C) Ethinyl Estradiol [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183794#pone.0183794.ref023" target="_blank">23</a>] (D) Rifampin [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183794#pone.0183794.ref028" target="_blank">28</a>].</p