Movatterモバイル変換

1. DETERMINATION OF EC50/IC₅₀
• Models and Curve Fitting Guidelines
• Examples
2. PRODUCTION MONITORING
• Control Compounds
• Examples
• Retrospective Studies
• Bridging Studies

Before fitting a dose-response curve to obtain the EC/IC₅₀, each well should be converted to either percent activity or percent inhibition with respect to positive and negative controls (note: for simplicity all text below is stated for determining IC₅₀’s; determining EC50’s is identical). Then all replicate wells from a given run (including multiple plates per run) for a given concentration should be averaged either by taking the mean, or preferably, taking the median. Outliers less influence the latter when there are 3 or more replicates. Thus only one point per concentration per run is used to fit the dose-response equation to the data. This is because replicate wells on either the same or different plates are often correlated with each other and, thus, do not provide true replication of the experiment.

The four parameter logistic model (4PL), also called the Hill-Slope model, is the most common equation fit to in vitro dose-response data. One form of the equation is

wherey is the percent activity andx is the corresponding concentration. The fitted IC₅₀ parameter is therelative IC₅₀, and is defined as the concentration giving a response half way between the fitted top and bottom of the curve. Some software, such as Activity/Base, also provides theabsolute IC₅₀, which is defined as the concentration giving exactly a 50% response. The relative IC₅₀ is recommended for most assays. The figures on the following page graphically demonstrate the principle of relative IC50 and relative EC50 for an inhibition and stimulation assay, respectively. A diagram for relative efficacy is also shown.

You should also report thefitting error, which is usually called the standard error by most software packages (we use the term fitting error to differentiate it from the standard error of the mean [SEM] derived from multiple determinations of a compound).

The 4PL model is the best model for dose-response data, but there are cases where it should not be used. In some cases, due to the potency of the compound falling outside the dosing range, the data may not fully describe the bottom or top asymptote of the curve. In those cases, respectively, the bottom (3PLFB) or top (3PLFT) can be fixed to improve the curve fit. If you observe a substantial reduction in the %Fitting Error, and a better dose-response plot of the fitted curve with respect to the actual data then you should switch to either the 3PLFB or 3PLFT model as appropriate.

Example 1 is a dose-response best fit by the 4PL model. Both asymptotes are defined by the data, and the fitting error is approximately the same with all 3 models. Note that even though the fitting error is smallest with the top fixed (8.63% versus 9.51%), the reduction is not small enough to warrant the fixed top model, nor is there any material change in the IC₅₀. The fixed bottom model is clearly inappropriate as the data clearly defines a bottom >0.

The fitting error is expressed here as a percentage of the fitted parameter value. For example, if the IC₅₀ is 0.061 and its fitting error is 0.058, then the %Fit Error is 9.51%.

Example 2 is best fit by the fixed top (3PLFT) model. The data does not define a top asymptote, and the fitted top (128.32) and slope (-0.58) from the 4PL model are inappropriate for this (binding) data. By fixing the top at 100% the fitting error is reduced from 57.54 to 21.55%, and the IC₅₀ increases by more than two-fold. Thus the 3PLFT model should be selected over the 4PL.

Example 3 is best fit by a fixed bottom (3PLFB) model. Note that the data does not define the bottom asymptote, and the fitted bottom (41.54) and fitted slope (-1.83) from the 4PL are inappropriate for binding data. The fixed bottom model reduces the fitting error from 80.19% to 20.85%, while the IC₅₀ increases by more than two-fold. The fitted IC₅₀ (20.88nM) is inside the dose-range (0.001-25nM), and so it is appropriate to report this value. Note in this case Activity Base was unable to fit a fixed top model.

Example 4 illustrates the definition and effect of outliers (left panel). Outliers are single, vertically isolated points that are clearly inappropriate. The point is “obviously” erroneous. The effect of the outlier in this case is to bias the estimate of the bottom upwards, pulling it away from the other points of the data. In general, outliers can bias either the top, bottom or slope parameter depending upon where they occur in the dose-response. It is appropriate to remove the outlier (right panel) and refit the points. Fixing top or bottom did not materially improve the curve fit (not shown).

Example 5 illustrates the effect of high assay variation. No single point stands out as “obviously erroneous”, and therefore it would be inappropriate to remove any points from the curve fit. Fixing top or bottom does not materially improve the curve fit, and so the 4PL model should be used. Note that the estimates themselves are not implausible, but the fitting error is 33.83%, which is caused by the relatively high assay variation.

1. This equation can be fit to the data using Activity/Base, JMP, Graphpad/Prism or Sigma/Plot. Note that the form of the equation varies from one software package to the next. Some, such as Graphpad/Prism, fit Log-IC₅₀ instead of IC₅₀, and the equation looks quite different, but the results are the same as that shown above.
2. The termsabsolute andrelative IC₅₀ are not universal. Both are usually just called the “IC₅₀”, and it’s left unstated which value is actually used.
3. If the software tool you are using reports Log-IC₅₀ then you must convert both the estimate and the % fitting error (%FE) according to the formulas
   
4. There should be at least one point on both sides of the reported IC₅₀, i.e. the reported IC₅₀ should lie inside the dose-range used in the assay. The intent of this rule is to make the IC₅₀ estimate an interpolation of generated data and not an extrapolation of generated data. Cases not satisfying this rule should not have an IC₅₀ reported or reported with a comment that indicates the value is extrapolated. If a value is reported, it should be “<X” or “>Y”, as appropriate, where X is the lowest concentration and Y is the largest concentration included in the analysis.
5. The fitting error of the IC₅₀ should be no more than 40% of the IC₅₀. Estimates not satisfying this rule should be flagged in the database. A fitting error of 40% of the IC₅₀ corresponds to an MSR of 3-fold.
6. It is a good idea to remove obvious outliers and then refit the curve without the outliers. Note that if it isn’t obvious, it isn’t an outlier. See examples 4 and 5 above to distinguish high variability from outliers.
7. For competition assays, such as radioligand binding assays and competitive inhibition assays, the fitted slope should be within 2 (slope) fitting errors of the value 1, and slope estimates outside this range indicate assay problems that need to be investigated.

The activity of the primary control should be at or near the most potent compound available, and ideally should be the Lead compound. There should also be sufficient stock of a single lot of the compound so that it can be run on a continuous basis for some period of time. Since the control compound is supposed to be representative of the test compounds, it should receive the same sample handling as all the test compounds, and not be specifically prepared and added to the assay outside of normal test compound procedures.

For Close Monitoring, the secondary control should be >100 fold less potent then the primary control. Otherwise it has the same requirements as the primary control. As the SAR develops the potency traditionally improves. So when the “best” compounds are more than 100-fold more potent than the primary control then select a new primary control. If the assay has a secondary control then the old primary control becomes the new secondary control, and the existing secondary control is dropped. If there is no secondary control then it is suggested to run both primary controls over the first 6 runs of the new primary control.

A scatter plot for control compound log-Ki/Kb/EC50 versus run date should be updated after every run and checked for problems. For assays with two control compounds the difference in log-Ki/Kb/EC50 versus run date should be plotted, and for agonist and non-competitive antagonist assays the efficacy versus run date should also be plotted. Outlier runs and trends either up or down (assay drift) should be checked visually, and problems investigated and corrected as they occur. Outlier runs should be repeated.

After 6 runs compute the overall MSR of the assay based on the control compounds according to formula,, wheres is the standard deviation of the log-Ki/Kb/EC50 values. This MSR is the total oroverall MSR (whereas the one computed in a test-retest study encompasses only the within-run variability), and should be less than or equal to 7.5. This standard comes from practical experience obtained thus far with assays in the company, and not theoretical statistical considerations. Note that this is a minimum standard that all assays should meet, and in practice chemistry requirements may indicate a smaller MSR (as low as 2-3) is required for some or all assays. The Project/Program Team should discuss this issue with a statistician to set appropriate MSR’s for their assays. A spreadsheet is availablehere for computation of single control per run and two controls per run protocols. See a statistician for other cases.

After each run, a running MSR plot should be maintained (i.e. computed from the last 6 runs) and checked to ensure the continued good reproducibility of the assay.

Example 2 illustrates an assay with two controls. In the left panel the red and blue lines represent the two compounds, and are positioned using the left axis. The green line is the potency ratio between the two compounds and is positioned using the right axis. The right panel shows the moving MSR values both within run and overall. The Overall-Overall MSR is the value to be reported. The within-run MSR’s are only for comparison backwards to the test-retest study results, and for times when compounds are compared within the same run of an assay. As with example 1, there are no apparent temporal problems, i.e. this is a stable assay with an overall MSR of 2.0. This assay is less variable than the assay in example 1.

Examples 3 and 4 illustrate problems with a shift in compound potency. Example 3 illustrates a steady degradation in potency over time, whereas Example 4 illustrates a more sudden shift in potency at a particular point in time. In Example 3 the assay variability appears to be shrinking, while in Example 4 it appears to be stationary. Repetitive freeze-thaw cycles of a compound may cause a slow degradation in potency whereas a change in lot of a key assay ingredient may result in a sudden potency shift. In both cases it is important to identify the cause and correct it as soon as possible.

Example 5 illustrates an assay with stable potency, but in June the assay variability increased. The moving MSR was stable around 3, but after June increased to over 10, and remained there. This also is most likely caused by a change in the assay process around that time. Again it is important to identify and correct the cause as soon as possible. Note however that a single outlier will cause the MSR chart to increase for the next 6 runs, and so it usually takes more time to correctly distinguish a change in assay variability from a single outlier result.

Dimethylsulfoxide: biological compatibility and compound storage Dimethylsulfoxide (DMSO) is a universal solvent for all compounds tested in high, medium and low throughput screens (HTS, MTS and LTS). Compounds are initially dissolved in 100% DMSO and further diluted into water and assay buffers in subsequent dilutions for screening and IC₅₀ or Ki determinations. It is extremely important that the DMSO compatibility of biological reagents such as enzymes, receptors, protein/peptide reagents and cells be established to ensure that the screening assays are not adversely affected. In general, the final DMSO concentrations in cell-based assays are <0.2% and are <1% in biochemical assays. It is highly recommended that the tolerable DMSO concentration be determined individually for each validated assay.

DMSO is also used as a cryoprotectant in the freezing of cell cultures at ATCC. The product is cell culture grade and has been tested to ensure cell viability. Each lot is also tested for the absence of bacteria, fungi, and endotoxin.

When solubilized compounds are stored in DMSO, it is important to understand the stability of these compounds under various storage conditions and freeze-thaw cycles. A detailed study of these effects was published recently (1). It is believed that the degradation of DMSO solubilized compounds is mainly due to moisture absorbed from the air. This can happen during frequent freeze-thaw cycles of compounds stored frozen in DMSO, or frequent exposure to air during repeated access for biological testing (cherry-picking).

• 96- well polypropylene plates.
• Storage temperature: 10 degree Cor room temperature.
• Inert gas atmosphere: argon flush.
• Minimal exposure to moist environments.

1. Cheng X., Hochlowski J, Tan H, Hepp, D, Beckner C, Kantor S, Schmitt R, Studies on Repository Compound Stability in DMSO Under Various Conditions. J Biomol Screening, 2003, 8(3), 292-304.
2. Kozikowski, BA, Burt, TM, Tirey, DA, Williams LE, . Kuzmak, BR, Stanton, DT, Morand, KL, and Nelson, SL The Effect of Room-Temperature Storage on the Stability of Compounds in DMSO J Biomol Screen 2003 8: 205-209.