Relationship between Drug/Metabolite Exposure and Impairment of Excretory Transport Function

The quantitative impact of excretory transport modulation on the systemic exposure to xenobiotics and derived metabolites is poorly understood. This article presents fundamental relationships between exposure and loss of a specific excretory process that contributes to overall clearance. The mathematical relationships presented herein were explored on the basis of hepatic excretory data for polar metabolites formed in the livers of various transporter-deficient rodents. Experimental data and theoretical relationships indicated that the fold change in exposure is governed by the relationship, 1/(1 – fe), where fe is the fraction excreted by a particular transport protein. Loss of function of a transport pathway associated with fe 0.5. These mathematical relationships may be extended to other organs, such as the intestine and kidney, as well as to systemic drug exposure. Finally, the relationship between exposure and fe is not only applicable to complete loss of function of a transport pathway but also can be extended to partial inhibition scenarios by modifying the equation with the ratio of the inhibitor concentration and inhibition constant

Proteasome inhibition for treatment of leishmaniasis, Chagas disease and sleeping sickness

Chagas disease, leishmaniasis and sleeping sickness affect 20 million people worldwide and lead to more than 50,000 deaths annually. The diseases are caused by infection with the kinetoplastid parasites Trypanosoma cruzi, Leishmania spp. and Trypanosoma brucei spp., respectively. These parasites have similar biology and genomic sequence, suggesting that all three diseases could be cured with drugs that modulate the activity of a conserved parasite target. However, no such molecular targets or broad spectrum drugs have been identified to date. Here we describe a selective inhibitor of the kinetoplastid proteasome (GNF6702) with unprecedented in vivo efficacy, which cleared parasites from mice in all three models of infection. GNF6702 inhibits the kinetoplastid proteasome through a non-competitive mechanism, does not inhibit the mammalian proteasome or growth of mammalian cells, and is well-tolerated in mice. Our data provide genetic and chemical validation of the parasite proteasome as a promising therapeutic target for treatment of kinetoplastid infections, and underscore the possibility of developing a single class of drugs for these neglected diseases

On The Rate and Extent of Drug Delivery to the Brain

To define and differentiate relevant aspects of blood–brain barrier transport and distribution in order to aid research methodology in brain drug delivery. Pharmacokinetic parameters relative to the rate and extent of brain drug delivery are described and illustrated with relevant data, with special emphasis on the unbound, pharmacologically active drug molecule. Drug delivery to the brain can be comprehensively described using three parameters: Kp,uu (concentration ratio of unbound drug in brain to blood), CLin (permeability clearance into the brain), and Vu,brain (intra-brain distribution). The permeability of the blood–brain barrier is less relevant to drug action within the CNS than the extent of drug delivery, as most drugs are administered on a continuous (repeated) basis. Kp,uu can differ between CNS-active drugs by a factor of up to 150-fold. This range is much smaller than that for log BB ratios (Kp), which can differ by up to at least 2,000-fold, or for BBB permeabilities, which span an even larger range (up to at least 20,000-fold difference). Methods that measure the three parameters Kp,uu, CLin, and Vu,brain can give clinically valuable estimates of brain drug delivery in early drug discovery programmes

Fitting the Elementary Rate Constants of the P-gp Transporter Network in the hMDR1-MDCK Confluent Cell Monolayer Using a Particle Swarm Algorithm

P-glycoprotein, a human multidrug resistance transporter, has been extensively studied due to its importance to human health and disease. In order to understand transport kinetics via P-gp, confluent cell monolayers overexpressing P-gp are widely used. The purpose of this study is to obtain the mass action elementary rate constants for P-gp's transport and to functionally characterize members of P-gp's network, i.e., other transporters that transport P-gp substrates in hMDR1-MDCKII confluent cell monolayers and are essential to the net substrate flux. Transport of a range of concentrations of amprenavir, loperamide, quinidine and digoxin across the confluent monolayer of cells was measured in both directions, apical to basolateral and basolateral to apical. We developed a global optimization algorithm using the Particle Swarm method that can simultaneously fit all datasets to yield accurate and exhaustive fits of these elementary rate constants. The statistical sensitivity of the fitted values was determined by using 24 identical replicate fits, yielding simple averages and standard deviations for all of the kinetic parameters, including the efflux active P-gp surface density. Digoxin required additional basolateral and apical transporters, while loperamide required just a basolateral tranporter. The data were better fit by assuming bidirectional transporters, rather than active importers, suggesting that they are not MRP or active OATP transporters. The P-gp efflux rate constants for quinidine and digoxin were about 3-fold smaller than reported ATP hydrolysis rate constants from P-gp proteoliposomes. This suggests a roughly 3∶1 stoichiometry between ATP hydrolysis and P-gp transport for these two drugs. The fitted values of the elementary rate constants for these P-gp substrates support the hypotheses that the selective pressures on P-gp are to maintain a broad substrate range and to keep xenobiotics out of the cytosol, but not out of the apical membrane

ITC recommendations for transporter kinetic parameter estimation and translational modeling of transport-mediated PK and DDIs in humans

This white paper provides a critical analysis of methods for estimating transporter kinetics and recommendations on proper parameter calculation in various experimental systems. Rational interpretation of transporter-knockout animal findings and application of static and dynamic physiologically based modeling approaches for prediction of human transporter-mediated pharmacokinetics and drug–drug interactions (DDIs) are presented. The objective is to provide appropriate guidance for the use of in vitro, in vivo, and modeling tools in translational transporter science