Dose Response for ABC transporter inhibitors: specifically ABCB1 screen, ABCG2 counter-screen. Follow-up assay for Set 2
Dose Response for ABC transporter inhibitors: specifically ABCB1 screen, ABCG2 counter-screen. Follow-up assay. ..more
BioActive Compounds: 57
University of New Mexico Assay Overview:
Assay Support: 1R03MH081228-01A1
Dose Response for ABC transporter inhibitors: specifically ABCB1 screen, ABCG2 counter-screen. Follow-up assay.
PI: Richard Larson MD, PhD
Assay Development: Irena Ivnitski-Steele PhD, Susan M. Young, J. Jacob Strouse PhD
Assay Implementation: J. Jacob Strouse PhD, Anna Waller PhD, Annette Evangelisti PhD, Mark Carter MS
Target Team Leader for the Center: J. Jacob Strouse PhD
Assay Background and Significance:
The three major types of multidrug resistance (MDR) proteins in humans include members of the ABCB, the ABCC and the ABCG subfamilies. These proteins influence oral absorption and disposition of a wide variety of drugs. As a result, their expression levels have important consequences for susceptibility to drug-induced side effects, interactions, and treatment efficacy. One of the most widely studied MDR-ABC transporter is P-glycoprotein (MDR or ABC B1 transporter). This protein functions to remove lipids and drugs as they intercalate and diffuse through the cell membrane. In addition to T-lineage acute lymphoblastic leukemia, a variety of solid tumors, including those in breast and prostate and gastric cancer cells overexpress ABC B1. As a consequence of enhanced pump activity, these cells develop resistance to anthracyclins, vina-alkaloids, etoposide and paclitaxel [Kubota et al. 2001, Triller et al., 2006, Chuthapisith et al., 2007]. Another clinically important ABC transporter ABC G2, acts on a wide variety of anticancer agents, including the highly active transport of methotrexate (MTX). This high capacity high affinity pump transports newly developed antifolate agents and folate derivatives as well [Wielinga et al., 2005]. Individuals with high ABCG2 expression in leukemic blast cells have a higher probability of poor response to chemotherapy [Benderra et al. 2004, Steinbach et al. 2002, Suvannasankha et al. 2004, Uggla et al., 2005]. A third large drug transporter distantly related to P-glycoprotein and ABCG2 is MRP1 (ABCC1). Overexpression of MRP1, associated with poor treatment response, has been reported in a variety of hematological and solid tumors [Steinbach et al. 2002, Suvannasankha et al. 2004, Uggla et al. 2005, Larkin, et al. 2004] while other members of this protein family appear to participate in resistance to a narrower range of drugs [Ohno et al. 2001, Nooter et al. 1996]. All three of these transporters are expressed at relatively high levels in the blood-brain barrier, placenta, liver, gut, and kidney, and are increasingly recognized for their ability to modulate the absorption, distribution, metabolism, and elimination of xenobiotics in these tissues. Tumors are highly heterogenous and frequently develop multiple mechanisms of resistance. Multidrug efflux pumps provide the first line of defense.
The use of low toxicity ABC transporter inhibitors is a common treatment strategy to circumvent MDR in cancers [Gillet et al. 2007, O'Connor 2007]. The design of P-gp inhibitors has progressed through three generations. The second generation P-gp inhibitors (e.g. valspodar, biricodar) were associated with significant side effects and interaction with multiple ABC transporters. Though application of such inhibitors in combination with P-gp substrates showed therapeutic promise in animal models [Ozols et al. 1986, Horton et al. 1989, Arvello et al. 1993], follow up clinical studies have almost universally failed even with more specific third generation inhibitors [Perez-Thomas 2006, Szakacs et al. 2006]. A variety of modulators, such as cyclosporin A (CsA), quinine, trifluoperazine, droloxifene (Drol), tomoxifen (TmX), toremifene, desniuldipine (DNIG), verapamil, dexverapamil, PSC 833, and GF 120198, are now undergoing clinical trials.
Although definitive results for any of these compounds concerning their efficacy for multiple drug resistance has not been forthcoming, it is not surprising,given their specificity for other molecular targets, that many problems such as significant side effects, dose effects, and changes in chemotherapy pharmacokinetics are of constant concern and provide ample justification for identifying new classes of modulators and exploring the biology around them.
For the high-throughput screening a duplex assay was constructed in which ABCB1 and ABCG2 transporters were evaluated in parallel using flurosecent J-aggregate-forming lipophilic cation 5,5',6,6'-tetrachloro-1,1',3,3'-tetraethylbenzimidazolcarbocyanine iodide (JC-1) as substrate. However, for the dose response cells were tested separately in order to eliminate potential effects of cellular stain used for the color-coding. To account for compound fluorescence, cells were also measured without the addition of the pump substrate (JC-1).
The assay was a high throughput, singleplex no-wash procedure conducted in 384-well format micro plates in a total volume of 15.1 microliters, dipsensed sequentially as follows: 1) PBS buffer (10 microliter/well); 2) test compound (0.1 microliter/well); 3) drug-resistant Jurkat-DNR cells (ABCB1) in 10 percent fetal calf serum/PBS with 1 micromolar JC-1 (5 microliter/well). Final in-well concentration of cells was 3 million cells/mL. Nine point dilution series were made of the test compound and depending on the experimental day the concentration of the test compound in-well varied from 66 micromolar to 0.015 micromolar. The plate contents were mixed, the plate rotated end-over-end at 4 RPM and 25 degrees C for 10 minutes, and then cell samples were immediately analyzed. Sample analysis was conducted with the HyperCyt(R) high throughput flow cytometry platform [Kuckuck, et al., 2001; Ramirez, et al., 2003]. Approximately 2 microliter volumes from each well were collected at a rate of approximately 40 samples per minute. This resulted in analysis of approximately 1,000 cells of each cell type from each well. Flow cytometric data of light scatter and fluorescence emission at 530 +/- 20 nm (FL1) was collected via CyAn flow cytometer (Beckman Coulter). The resulting time-resolved single data file per plate was analyzed by IDLQuery software to determine the compound activity in each well.
The assay response range was defined by control wells containing Nicardipine (pump inhibitor, high fluorescence) or DMSO (no pump inhibition, low fluorescence).
Additional controls were run for evaluating the intrinsic fluorescence of the test compounds. Micro plates were made with cells with test compounds but without the presence of JC-1 substrate with the total volume of 15.1 microliters dispensed sequentially as follows: 1) PBS buffer (10 microliter/well); 2) test compound (0.1 microliter/well), 3) drug-resistant Jurkat-DNR cells (ABCB1) in 10 percent fetal calf serum/PBS (5 microliter/well).
Efflux pump activity was determined on the basis of the median fluorescence intensity (MFI) of JC-1 fluorescent substrate detected in the green fluorescence channel (530 +/- 20 nm). Innate fluorescence of test compound was subtracted out based on MFI of non-JC-1 cells in the green fluorescence channel before calculation of % inhibition of efflux pump activity. The following equations were used to calculate % inhibition;
FluorDelta = MFI_JC-1 - MFI_non-JC-1
in which MFI_JC-1 are the MFI of cells in wells in the presence of JC-1 and MFI_non-JC-1 are the MFI of cells in wells without JC-1.
%Inhibition = 100 x (FluorDelta_Sample - FluorDelta_NC)/(FluorDelta_PC - FluorDelta_NC)
in which FluorDelta_Sample are the differences of JC-1 minus non-JC-1 from wells with test compound, FluorDelta_NC are differences from negative control wells (cells with DMSO, minimum fluorescence intensity) and FluorDelta_PC are differences from positive control wells (cells with Nicardipine, maximum fluorescence intensity). In other words, % inhibition was normalized to the maximum effect of Nicardipine, i.e. 100% inhibition was the response of Nicardipine.
These dose response of %Inhibition data were fitted via GraphPad Prism to a sigmoidal dose response curve with variable hillslope:
%Inhibition = Bottom + (Top-Bottom)/(1+10^((LogEC50-LogCmpd)*HillSlope))
where LogCmpd is the log of compound concentration in micromolar and Top-Bottom is the FIT_PERCENT_SPAN. Prism reports estimated values and fitted statistics for the four parameters (Bottom, Top, Hillslope and EC50). Dose response fits assessed as decent were those with Residual square < 0.5, Hillslope < 5, Standard deviation of estimated LogEC50 <3, and standard deviation of top/estimate for top < 0.5. Only the fits that passed this filter are reported.
Activity Score was calculated based on two weighted criteria; EC50 < 10 micromolar and FIT_PERCENT_SPAN > 20% by the following equation:
Activity Score = 75 * (EC50Cutoff - EC50)/EC50Cutoff + 25*(Span - SpanCutoff)/SpanCutoff
If the calculated value for the Activity Score was greater than 100, then an Activity Score of 100 was assigned and if it was less than 0, then an Activity Score of 0 was assigned. Active compounds have Activity Scores greater than 52, inactive compounds have Activity Scores less than 52.
NIH Roadmap, NMMLSC, high-throughput flow cytometry, drug-resistance transporters, ABCB1, ABCG2, multiplex cell-based screening
Abbreviations: nm for nanometer, RPM for revolution per minute
Categorized Comment - additional comments and annotations
* Activity Concentration. ** Test Concentration.
Data Table (Concise)