Susceptibility testing of pathogenic fungi with itraconazole: a process analysis of test variables

Bisoen Rambalia, Juan Antonio Fernandeza, Luc Van Nuffelb, Filip Woestenborghsb, Lieven Baertb, Desire L. Massarta and Frank C. Oddsc,*

a Farmaceutisch Instituut, Vrije Unversiteit Brussel, Laarbeeklaan 103, B-1090 Brussels; b Janssen Research Foundation, Beerse, Belgium; c Department of Molecular and Cell Biology, Insitute for Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
A 210–5 fractional factorial model was used to investigate the influence of 10 process variables in broth microdilution susceptibility tests with itraconazole against eight isolates of Candida species and six isolates of filamentous fungi in two growth media. An analysis of variance (ANOVA) indicated that glucose concentration and incubation time both significantly influenced control turbidity optical density (OD) values for most of the Candida spp. isolates, while incubation in >10% CO2 versus ambient air, incubation temperature and inoculum size significantly influenced these OD values for about half of the yeast isolates. Control OD values for the mould isolates were most influenced by incubation time and temperature, and by occlusion of the wells with an adhesive sticker. Three statistical approaches, ANOVA, rank transformation and Mann–Whitney U-test, were used to assess the influence of the variable combinations on MIC, determined with a 50% growth reduction end-point. Incubation temperature and time, glucose concentration and inoculum size were the variables that most often affected susceptibility results to the level of statistical significance; however, the supplier of RPMI 1640 medium, the use of adhesive stickers and the atmosphere of incubation significantly influenced the MIC for some isolates. The medium used to prepare the test inoculum, the solvent used to prepare the stock solution and the shape of the microdilution plate wells significantly affected outcome, but only sporadically. A principal component analysis of the data matrix confirmed this order of relative influence of the test variables on the MIC. Since each fungal isolate responded differently to combinations of process variables in the test, we conclude that any unified method for antifungal susceptibility determination represents a compromise, rather than an idealized system.


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
The objective of laboratory testing with antimicrobial agents is to obtain reproducible indications of the susceptibility of a microbial isolate to an agent in vitro. If the test is to have any clinical value, the result should have a reasonable predictive value for determining the outcome of therapy with the agent in vivo. However, in practice, clinical interpretation of susceptibility test results is not easy,1,2 and correlations between resistance of a microbe to an inhibitory agent in vitro and treatment failures are often poor.3,4

For determination of MICs of antifungal agents, approved reference method M27-A has been published by the National Committee for Clinical Laboratory Standards (NCCLS) for testing with yeasts,5 and proposed reference method M38-P is being developed for filamentous fungi.6 These methods embody the results of extensive, long-term, collaborative interlaboratory investigations of MIC test variables, including composition of the growth medium, temperature and time of incubation, concentration and method of preparation of the inoculum, and method of visual end-point determination.79

A broth microdilution adaptation of the original NCCLS M27-A broth macrodilution MIC test is currently employed in many laboratories. Despite the considerable effort that went into defining and standardizing the M27-A test parameters, many authors have indicated that, for tests with Candida spp., a 24 h end-point may improve reproducibility and correlate better with responses in vivo than the recommended 48 h reading time.10,11 They have also indicated that addition of extra glucose to the recommended RPMI 1640 broth medium improves end-point determination,11,1214 and that a 50% growth-reduction end-point in microdilution tests as assessed by spectrophotometer readings is more reproducible than the recommended 80% inhibition visual end-point with azole antifungal agents.12,14,15 The M38-P method for susceptibility testing of filamentous fungi is clearly derived from M27-A, since many of the test conditions, including the recommended growth medium, are the same for the filamentous fungi as for the yeasts.6

While many consider that method M27-A has led to improved interlaboratory agreement with respect to MIC determinations in Candida spp.,7,8,16 there are indications that this is not always the case. For example, published data for ketoconazole versus Candida albicans tested by method M27-A or a very near equivalent show considerable disparities in MIC ranges and summary statistics.1725 These differences indicate either considerable diversity in ketoconazole susceptibility between C. albicans populations from different sources, both anatomical and geographical, or that interlaboratory consistency with the M27-A method may yet leave something to be desired. The report of Still et al.,24 in which 90% of 142 C. albicans isolates from burns patients were inhibited only at or above 16 mg/L ketoconazole, particularly indicates that technical test problems may have confounded these results, since no other study has ever found such a remarkably high prevalence of azole resistance, certainly not in a group of isolates with <2% resistant to fluconazole.

Many sources of interlaboratory variation in antifungal MIC data can be postulated. There are studies indicating that the batch of growth medium,26 the pH27 and even the solvent used to prepare antifungal stock solutions28 can all affect the MIC, in addition to long-established sources of variation such as inoculum size, incubation time, end-point criterion and, in the case of azole antifungals, the ‘trailing growth’ effect.27,29 In the USA, most laboratories favour the use of microdilution plates with U-bottomed wells for antifungal susceptibility testing, and method M27-A stipulates the use of such plates, yet flat-bottomed wells are the common form used in Europe. Unpublished anecdotes of, for example, the use of CO2 versus air incubators and sealed versus unsealed microdilution plates, according to judgement, availability or laboratory habit, indicate further possible sources of variation in test outcomes. It is not unreasonable to suppose that many, if not most, laboratories claiming to follow method M27-A actually depart from the fine detail of the protocol with respect to those variables that are probably assumed to be unimportant.

Our aim was to reinvestigate systematically the impact of a wide range of test variables on the outcome of antifungal susceptibility tests otherwise performed according to the M27-A and M38-P microdilution criteria with yeasts and filamentous fungi. We chose to include some variables already known or suggested to affect MIC results, as well as some that have not been previously examined. The experimental design was a 210–5 fractional factorial model that allowed statistical evaluation of the differential effects of process variables in tests carried out with two growth media (RPMI 1640, a synthetic medium, versus CYG, a complex buffered medium containing casein hydrolysate, yeast extract and glucose). For each growth medium, binary variations were made in the supplier of the principal medium ingredient, the glucose concentration in the medium (0.2 versus 2%), the incubation temperature (30 versus 35°C), the incubation time, the shape of the microdilution plate well (U-bottomed versus flat-bottomed), the source of the inoculum, the inoculum concentration, the solvent used to prepare stock solutions, sealed versus unsealed microdilution plates and incubation in air or <10% CO2 in air.

The test antifungal chosen was itraconazole, which is a water-insoluble representative of the azole antifungal class, and the test isolates were eight Candida spp. and eight filamentous fungi, selected on the basis of pilot experimentation to include representatives that demonstrated either high or low itraconazole susceptibility, and also some isolates that regularly demonstrated the interpretation problem of ‘trailing growth’ effects. Results were determined by spectrophotometric readings of test plates, as this offers advantages over visual assessment of MIC end-points. The optical density (OD) readings for each isolate and concentration of test agent were archived for future retrieval and the data used to generate dose–response curves that provide an objective means of determination of MIC end-points. In this study, results were analysed separately for RPMI 1640 and CYG cultures, and the effects of test variables on control growth OD values were examined, as well as their effects on MIC. The MIC end-point used throughout was the IC50, i.e. the lowest itraconazole concentration that reduced growth of a fungus to <50% of the appropriate control.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Isolates

All fungi tested were stored at –80°C in the stock collection of the Department of Bacteriology and Mycology at the Janssen Research Foundation (Beerse, Belgium). Six of eight isolates of Candida spp. originally obtained from clinical or veterinary sources were included in the study on the basis of results from pilot experiments: they were C. albicans B59630 and B2630, Candida glabrata B63155, J931545 and J940839, and Candida tropicalis CDC44. The remaining two isolates were the strains recommended for quality control purposes with method M27-A,5 Candida krusei ATCC 6258 and Candida parapsilosis ATCC 22019, both purchased from the American Type Culture Collection. C. albicans B2630 is the strain used since the 1960s in routine antifungal screening tests at the Janssen Research Foundation, and C. albicans B59630 has been described previously as an itraconazole-resistant isolate.30 Eight isolates of filamentous fungi were chosen to represent the diversity of clinically important moulds, including fastand slow-growing types, and isolates with high or low itraconazole susceptibility in vitro. They were Aspergillus fumigatus NCPF7099 and J980617, Fusarium oxysporum J990081, Microsporum canis B68128, Paecilomyces lilacinus J980407, Scedosporium apiospermum J961338, Sporothrix schenckii B64284 and Trichophyton rubrum B68183. A. fumigatus NCPF7099 was first reported by Denning et al.31 as an isolate resistant to itraconazole in vitro and in animal tests, and was referred to in the same paper as AF72.

Candida spp. were maintained in culture on Sabouraud glucose agar (SAB; Oxoid, Basingstoke, UK) and moulds on potato-glucose agar (PGA; Difco, Detroit, MI, USA).

Yeast inocula were prepared by two methods. Candida spp. were either grown for 18–24 h to a constant yield of 4 x 107 yeasts/mL in CYGi broth at 30°C with rotation as described previously,32 or were grown on SAB for 18–24 h at 35°C, suspended in sterile water and the concentration adjusted by spectrophotometry to 4 x 107 cells/mL. Mould inocula were prepared from cultures either on SAB or PGA at 30°C. When the fungi were judged to have formed abundant conidia, sterile 0.05% sodium dodecyl sulphate (SDS) solution was poured on to the thallus and the surface of the culture was gently scraped with a sterile inoculating loop. Suspensions were allowed to stand for a few minutes to allow settling of large clumps and the remaining suspensions of fine particles were removed. Microscopic examination of these suspensions showed that they consisted almost entirely of conidia, with very few mycelial fragments. The concentrations of conidia were adjusted by spectrophotometry with reference to previously determined calibration graphs of OD versus measured cfu for each fungus type. All inoculum suspensions were adjusted to four times the desired starting concentration for the experiments.

Microdilution plates with itraconazole

The antifungal agent itraconazole was obtained from the Janssen Research Foundation. The pure powder was dissolved in either polyethylene glycol 200 (PEG 200) or dimethyl sulphoxide (DMSO) to a concentration of 3.2 mg/mL. From this stock solution, a series of nine further two-fold stock dilutions was prepared in PEG 200 or DMSO.

Plastic, 96-well microdilution plates with either Ubottomed (cat. no. 262162) or flat-bottomed (cat. no. 243656) wells were purchased from NUNC laboratories (Merck Eurolab, Leuven, Belgium). Aliquots of 100 µL of sterile water were added to all wells with the aid of a Labsystems Multi-Drop Dispenser, then 2 µL of the stock dilutions were added with a multi-channel pipette to provide eight rows of 12 wells in each plate that contained 10 serial itraconazole concentrations, at 32–0.063 mg/L, plus two itraconazole-free control wells with 2 µL of PEG 200 or DMSO added to the 100 µL of water. These plates were stored at –20°C for up to 1 week. Addition of culture media and inoculum to the wells of these plates diluted the final itraconazole concentrations to a two-fold dilution series from 16 to 0.032 mg/L, with 1% (v/v) PEG 200 or DMSO in all wells.

Culture media

Two broth media were examined for microdilution susceptibility testing. The first was RPMI 1640 with glutamine and without bicarbonate, buffered to pH 7.0 with 0.165 M MOPS (this is the medium recommended for use in NCCLS method M27-A).5 Unbuffered RPMI 1640 medium was purchased as a 10-fold concentrated, sterilized liquid product from ICN or Gibco and stored at 4°C. As a first step, RPMI 1640 media were prepared that contained all components at four times their final concentrations; 50 µL volumes of these were later added to the itraconazole solutions in the microdilution plates to dilute the media to their working strengths. The RPMI 1640 10x stock solutions were mixed with an equal volume of 1.65 M MOPS, pH 7.2 (final dilution of the medium alters the pH to the desired 7.0), then with either 0.5 vol. sterile water or with 0.5 vol. sterile 36% glucose solution, yielding glucose concentrations of 0.8 and 8%, respectively. These glucose concentrations were reduced to a final 0.2 and 2% by admixture with itraconazole solutions and inoculum.

The second culture medium investigated was CYG,30 which contains casein hydrolysate, yeast extract (Difco) and glucose, buffered to pH 7.0 with MOPS and Tris. Variables in the composition of the CYG medium were the two sources of casein hydrolysate, pancreatic casein digest (Merck) and Bacto-casitone (Difco), and a final glucose concentration of either 0.2 or 2%, as with the RPMI 1640 media. The CYG components, all powders, were weighed and dissolved in water at four times the desired final strength, as with RPMI 1640 medium, and the media were similarly inoculated and added in 50 µL volumes to the wells of the microdilution plates already containing 100 µL of itraconazole dilutions in water.

Microdilution plate test format

At the start of each experiment, microdilution plates containing the itraconazole dilution series were thawed, then 50 µL of the various growth media were added. Two initial concentrations of yeast cells or conidia were used: 103 cells/mL (NCCLS recommendation) or 4 x 104 cells/mL. Appropriately diluted inoculum suspensions in water were dispensed in 50 µL volumes into the microdilution plates as the final step in plate preparation. For Candida spp., all eight test isolates were added to each plate, i.e. each of the eight rows of wells contained one yeast isolate, exposed to 10 itraconazole concentrations plus two drug-free control wells. Four relatively fast-growing moulds (the two isolates of A. fumigatus, F. oxysporum J990081 and P. lilacinus J980407) were inoculated into every other row of the microdilution plates prepared for these fungi. The remaining four, slower-growing isolates of filamentous fungi were similarly inoculated into alternating rows in separate microdilution plates.

The inoculated plates were either sealed with adhesive transparent stickers or they were covered with loose-fitting plastic lids that maintained sterility but did not prevent atmospheric exchange. All plates were then stacked in plastic trays together with a glass beaker of water to provide humidity. The trays were incubated according to one of two protocols. In the first, the trays were placed in transparent polythene bags that were closed hermetically with electric heat-sealing apparatus. These were then placed in an air incubator set at either 30 or 35°C. In the second protocol, the trays were placed in humidified incubators set at either 30 or 35°C and provided with an atmosphere of 10% CO2 in air.

Plates inoculated with Candida spp. were incubated for 24 and 48 h. The fast-growing filamentous fungi were incubated for 2.5 and 5 days. The slower-growing moulds were incubated for 4 and 12 days.

The experimental variables described above were combined according to the randomization scheme shown in Table IGo. Thus, for each isolate tested, 32 process variable combinations were examined experimentally. Each of these 32 combinations per isolate was repeated in duplicate experiments, and the process was carried out separately for cultures based on CYG and RPMI 1640 media. This experimental design based on 10 parameters constituted a 210–5 fractional factorial design with 32 runs.


View this table:
[in this window]
[in a new window]
 
Table I. The 210–5 fractional factorial design with 32 runs designed to assess the effects of process variables on the outcome of microdilution susceptibility tests with itraconazole (ITZ) against 16 fungal isolates
 
In exploratory tests, where fewer process variables were studied, experiments were set up in quadruplicate to allow estimation of quantitative reproducibility.

Growth measurement and calculation of IC50 values

In all experiments with Candida spp., at the end of the incubation period, the microdilution plates were thoroughly agitated using a bench vortex mixer set at maximum speed to ensure resuspension of the yeasts in the culture wells. The plates were then left to settle for a minimum of 5 min before the OD405 of the contents of each well was measured with a microplate spectrophotometer. Preliminary tests showed that no variation in measured OD occurred after a 5 min settling period. The OD values of mould cultures were measured without preliminary agitation of the plates: several of the test isolates formed profuse aerial hyphae with conidia in some wells, and it was considered that agitation of these plates might lead to artefacts from cross-contamination of neighbouring wells.

The OD values of separate U-bottomed or flatbottomed plates containing all components except test fungi were measured to provide an indication of background OD readings of the culture media. The mean background OD for all 96 wells in these control plates was subtracted from the OD measured for inoculated, incubated plates to correct for background OD.

A computer spreadsheet template was used to facilitate analysis of the data. For each experimental system, i.e. fungus isolate and process variable combination, the mean growth control OD was calculated from the two itraconazole-free wells, corrected by subtraction of background OD, and used as the 100% growth reference OD for that system. Background-corrected OD values for fungal growth in wells containing itraconazole were then expressed as a percentage of the reference growth OD for each test system. These percentage control data were used to generate a dose–response graph for each fungus isolate/process variable combination. MICs were determined from the dose–response curves as the lowest itraconazole concentrations that reduced growth to <50% of the control. These results are referred to as IC50 values.

Statistical analysis of results

When growth >50% of control was measured at all itraconazole concentrations tested, the IC50 was recorded as 32 mg/L, i.e. the next concentration step above the highest actually tested. When growth over the whole itraconazole concentration range was <50% of control, the IC50 was recorded as 0.016 mg/L, i.e. the next concentration step below the lowest actually tested. The IC50 values subjected to statistical analysis were the geometric means of results from the duplicate experiments. In practice, examples of disparities greater than two dilution steps between the duplicates were exceptionally rare.

Background-corrected control growth OD values provide a measure of the overall level of growth of each fungal isolate achieved with each combination of process variables. The influence of the process variables on control OD values was assessed by analysis of variance (ANOVA) and by Principal Component Analysis.

For analysis of the effects of the process variables on IC50 values, three statistical approaches were used. The first was ANOVA. Since this procedure requires normally distributed data and the IC50 values were determined from a logarithmically distributed concentration series, the analysis was performed on a logarithmic transformation of the IC50 data. Fleming et al.33 showed that logarithmic transformation of IC50 values achieves a normal distribution. For some of the isolates tested, there were clear outliers to the distribution even after log transformation; these were identified with a normal probability plot and removed from the ANOVA analysis.

To overcome the drawbacks of parametric analysis of non-normally distributed, discrete variables with removal of outlying data, the IC50 results were also analysed by the Mann–Whitney U-test, a non-parametric test. This approach was likely to be less sensitive than ANOVA to significant effects, but it provided a robust second statistical viewpoint for erratically distributed data, and it permitted inclusion of the data treated as outliers for the ANOVA analysis. The third statistical analysis was a rank transformation analysis, whereby a parametric analysis was applied to the rank of the data rather than to the data themselves. This method provides a bridge between parametric and non-parametric statistical analyses.


    Results
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Reproducibility of the quantitative data

Figure 1Go illustrates the high level of reproducibility with which dose–response curves could be measured in microdilution susceptibility tests with itraconazole. The example in Figure 1Go shows low standard deviations determined for means of percentage control data from quadruplicate tests. The high level of reproducibility was typical for data in these experiments.



View larger version (18K):
[in this window]
[in a new window]
 
Figure 1. Reproducibility of dose–response curves in microdilution plate MIC tests with itraconazole. C. glabrata J940839 tested in CYG medium. Circles, 0.2% glucose; squares, 2% glucose; open symbols, initial yeast concentration 1000 cfu/mL; filled symbols, initial yeast concentration 40000 cfu/mL. (a) plates incubated at 30°C; (b) plates incubated at 35°C.

 
The preliminary experiments also indicated that the nature of differences in itraconazole dose–response curves resulting from different combinations of process variables was highly strain dependent. In the example shown in Figure 1Go, it is evident that the inhibition curves are very similar for C. glabrata J940839 grown in CYG, regardless of the initial yeast concentration or the glucose concentration in the medium. However, the incubation temperature profoundly affected the outcome for this isolate, with an IC50 of 1 mg/L determined at 30°C and >16 mg/L at 35°C. For other fungal isolates, similarly large alterations in dose– response curves were found with changes in inoculum size or glucose concentration, but the isolates were little affected by differences in incubation temperature (data not shown).

Control OD values and the impact of process variables

Itraconazole dose–response curves such as those shown in Figure 1Go depend on the precision with which control OD values can be determined, since all growth is expressed as a percentage relative to that control. From the preliminary experiments it was clear that low control OD values generated gross imprecisions in the dose–response curves. It was found that a control OD value that was <0.1 after subtraction of mean background OD frequently gave poorly reproducible dose–response data and therefore unreliable IC50 end-points. The high reproducibility illustrated in Figure 1Go was consistently associated with control OD values >=0.1. Two isolates of filamentous fungi, S. schenckii B64284 and M. canis B68128 had control OD values <0.1 in the majority of tests. For this reason, results for these two isolates were not analysed further.

Table IIGo summarizes the control OD data for the 14 isolates fully analysed in tests with 32 combinations of process variables. For each of the yeast isolates, control OD readings usually ranged from 0.1 to just over 1.0. The higher control OD values were uniformly and predictably associated with the higher glucose concentrations in each medium and with longer incubation times. These two process variables emerged as having a statistically significant influence on control OD by ANOVA for every yeast isolated tested in RPMI 1640 and for every yeast isolate, except C. krusei ATCC 6258, tested in CYG. For other process variables, the ones that emerged as significant influences on control OD values differed for each yeast isolate and species. Incubation under CO2 was significantly associated with a trend to lower control OD values for the isolates of C. albicans, C. glabrata and C. tropicalis grown in CYG, regardless of other variables, while in RPMI 1640 the effects of incubation under CO2 were not significant for the three C. glabrata isolates. Incubation temperature and inoculum size had a significant influence on control OD values for approximately half of the isolates. The shape of the microplate wells was a significant variable only for C. tropicalis CDC44.


View this table:
[in this window]
[in a new window]
 
Table II. Control values for OD405, corrected for background OD and measured from cultures set up under the 210–5 fractional factorial design with 32 runs detailed in Table IGo (results of ANOVA for the process variables are indicated)
 
For the isolates of filamentous fungi, OD values for growth in CYG were generally much higher than for the same isolates grown in RPMI 1640 (Table IIGo). Incubation time affected the control OD value in almost all cases: the two A. fumigatus isolates grown in RPMI 1640 medium were the notable exceptions. For the moulds, sealing plates with a sticker led to reduced control OD values for almost all of the isolates, with P. lilacinus being the only exception. For this species, however, growth was significantly less in a CO2-rich atmosphere than in air. It should be noted that, in many instances, data concerning mould growth that were excluded from analysis on the basis of control OD values of <0.1 came predominantly from plates that were sealed with stickers or incubated in CO2.

Glucose concentration significantly affected control growth for one A. fumigatus isolate and the isolates of F. oxysporum and P. lilacinus. Growth of the latter two species was also significantly influenced by incubation temperature for tests in RPMI 1640 medium. Control OD values were higher for cultures incubated at 30°C than at 35°C; indeed the higher temperature was more often associated with control OD values below the threshold of acceptability than the lower temperature.

In all the tests, the supplier of the casein hydrolysate or the RPMI 1640 media, the medium used to grow the inoculum and the solvent used to prepare itraconazole stock solutions (in terms of control OD values this means the addition of PEG 200 or DMSO to a final concentration of 1% v/v) were process variables with no significant effect on control OD.

Association between control OD and IC50

It is possible that IC50 end-points derived from dose– response curves might be affected by the overall level of fungal growth as reflected in the control OD value: with lower inoculum concentrations and growth environments that retard fungal growth, addition of a growth inhibitor such as itraconazole may show effects at concentrations lower than would be possible in rapidly growing cultures with heavy starting cell concentrations. Figure 2aGo illustrates this point. Itraconazole IC50 values against A. fumigatus NCPF7099 grown in RPMI 1640 showed a clear trend towards higher IC50 outcomes with increasing control OD value, even though the range of control OD values for this fungus in RPMI was limited. The correlation coefficient, r, of 0.554 for the IC50–control OD association in this case was highly significant (P = 0.001). However, for the same fungus grown in CYG, no correlation was apparent between itraconazole IC50 and control OD (Figure 2bGo).



View larger version (12K):
[in this window]
[in a new window]
 
Figure 2. Association between control OD and IC50. Each data point is the result for one of the 32 process variable combinations in tests with A. fumigatus J960660. (a) tests in RPMI 1640: high correlation between control OD and IC50 (r = 0.554; P = 0.001). (b) tests in CYG medium: no correlation between control OD and IC50 (r = 0.068; P = 0.733).

 
Table IIIGo summarizes the IC50–control OD correlations as illustrated in Figure 2Go for each of the 14 fungal isolates tested with 32 process variable combinations. The correlation was significant at the level of P < 0.05 for only two isolates in RPMI 1640 tests, and for seven of the isolates in CYG tests. The measured IC50 was therefore more likely to be influenced by control OD values in CYG than in RPMI 1640.


View this table:
[in this window]
[in a new window]
 
Table III. Correlations between control OD and itraconazole IC50 for 14 fungal isolates in a 210–5 fractional factorial design with 32 runs
 
Effects of process control variables on itraconazole IC50

Figure 3Go illustrates some of the variability seen in dose– response curves obtained from 32 runs of process variable combinations in a 210–5 fractional factorial model. For C. glabrata J940839 in RPMI 1640 medium, itraconazole showed no growth inhibitory effects at concentrations of <=0.25 mg/L (Figure 3bGo). Above this concentration the inhibitory effects ranged from substantial to minimal, depending on the process variable combination. The effects of itraconazole on this isolate in CYG medium (Figure 3aGo) were even more variable, although, as in RPMI 1640, the usual shape of the dose–response curves was approximately sigmoid. Determination of the IC50 value with such curves was generally straightforward. The same was not true for dose–response curves with C. albicans B2630, an isolate known to show the ‘trailing end-point’ effect.28,29 These were either flat or sloped gently to the right across the whole range of itraconazole concentrations tested; however, their vertical position on the graph varied considerably according to the process variable combination (Figure 3c and dGo). As with C. glabrata J940839, the spread of variation in the curves was greater for runs in CYG than in RPMI 1640: this greater variability in CYG than RPMI 1640 was noted for all 14 fungi tested. The patterns seen with C. albicans B2630 (Figure 3c and dGo) were similar to those with C. tropicalis CDC44, another isolate that shows trailing end-points. Points with percentage control growth values of <0 in Figure 3Go arose from subtraction of mean background OD values from experimental and control values before calculation of growth as a percentage of control OD. The tendency for values outside the 0–100% range was seen more often with low control OD values than with high ones; however, the phenomenon was reproducible and its extent depended on the test isolate and the culture medium (see Figures 3 and 4GoGo).



View larger version (64K):
[in this window]
[in a new window]
 
Figure 3. Itraconazole dose–response curves generated by 32 test runs in a 210–5 fractional factorial design. (a) C. glabrata J940839 in CYG medium; (b) C. glabrata J940839 in RPMI 1640 medium; (c) C. albicans B2630 in CYG medium; (d) C. albicans B2630 in RPMI 1640 medium.

 


View larger version (65K):
[in this window]
[in a new window]
 
Figure 4. Itraconazole dose–response curves generated by 32 test runs in a 210–5 factorial design. All curves shown were from tests in RPMI 1640 medium. (a) C. krusei ATCC 6258; (b) C. parapsilosis ATCC 22019; (c) A. fumigatus NCPF7099; (d) F. oxysporum J990081.

 
Figure 4Go provides further illustration of the process variable effects seen in dose–response curves for itraconazole tested against four fungi in RPMI 1640 medium. For the two yeast isolates recommended by the NCCLS5 for quality control use in antifungal testing, the degree of variation was the least for any of the 14 fungi tested and analysed, indicating that these two isolates are generally more robust to process variation than the other isolates tested (Figures 4a and bGo). These two isolates also gave background-corrected percentage growth control results <0 more often than any of the other Candida spp. isolates tested. In duplicate tests, the itraconazole IC50 values determined for the two QC strains in plate 5, which most closely correlated with the specifications of NCCLS method M27-A,5 were as follows: C. krusei ATCC 6258, 0.13 and 0.25 mg/L (expected values were 0.12–0.5 mg/L); and C. parapsilosis ATCC 22019, 0.13 and 0.13 mg/L (expected values 0.06–0.25 mg/L). These data indicate that our test system conformed with the expectations of the reference method.

The general level of IC50 variability in tests with filamentous fungi was greater than with the yeast species, as exemplified by the dose–response curves for itraconazole with A. fumigatus NCPF7099 (Figure 4cGo) and F. oxysporum J990081 (Figure 4dGo).

Statistical analysis of the process variable effects on IC50 permitted characterization of those variables with the greatest influence on the test result. Table IVGo summarizes the extensive statistical analyses done with the IC50 data by enumerating the number of times each process variable was found to have a significant effect on IC50 for a particular fungus type (yeast or mould) and in CYG or RPMI 1640. Of the three statistical analyses, ANOVA, applied to the data minus outliers or to the (full) data ranks after transformation, was more sensitive to process variations than the Mann–Whitney non-parametric analyses.


View this table:
[in this window]
[in a new window]
 
Table IV. Number of times each process variable demonstrated a statistically significant effect on itraconazole IC50 in a 210–5 fractional factorial design with 32 runs
 
For the yeasts, the atmosphere of incubation (air versus 10% CO2 in air) only once showed a significant effect on IC50 in either of the growth media (Table IVGo), indicating that this parameter was generally not an important variable in susceptibility testing with Candida spp. Similarly, sealing the microplates with adhesive stickers had significant effects on yeast IC50 results for only one of eight isolates, and only when tested by ANOVA (the isolate concerned differed between the analyses). However, both atmosphere of incubation and the use of adhesive stickers led to a higher proportion of significant effects in the tests with moulds. Of the total of 16 significant differences for stickers and incubation atmosphere tabulated in Table IVGo, 10 were recorded from tests with one or both A. fumigatus isolates.

The medium used to prepare the inoculum (‘inoculum source’ in Table IVGo) had essentially no influence on the IC50 results, and the solvent (PEG 200 or DMSO) used to dissolve the stock itraconazole solution had only minimal effects on IC50 in tests with Candida spp. and with moulds tested in RPMI 1640. It was more often a significant process variable in mould tests done in CYG. Whether the microdilution plate wells had U-shaped or flat bottoms influenced IC50 significantly only in a minority of tests with all fungus types.

The suppliers of casein hydrolysate and RPMI 1640 concentrate made no difference to the outcome of tests with filamentous fungi. However, in tests with Candida spp., the IC50 result was frequently affected by the supplier of RPMI 1640. The 10 significant differences for this parameter in Table IVGo were accounted for in nine instances by the three isolates of C. glabrata. Glucose concentration in the medium was a significant factor influencing IC50 only in tests with Candida spp. and only in CYG. It had minimal effect on the outcome of tests with the mould isolates (Table IVGo).

The three process variables that had the maximal impact on IC50, in terms of numbers of times they were found to effect significant changes in the statistical analyses, were glucose concentration, incubation time and incubation temperature (Table IVGo). The effects of glucose concentration on IC50 were seen almost exclusively in tests with Candida spp., not with the moulds, and were more pronounced in CYG than in RPMI 1640. Incubation time effects also led to significant changes in IC50 more often in CYG than in RPMI 1640, and this was true for both fungus types. In contrast, incubation temperature showed significant process effects in tests done in both media.

The relative global importance of the process variables on IC50 was further investigated by a principal component analysis of the rank-transformed data matrix projected on three variables, principal components (PC) 1–3. PC1 is the component that describes the most variation in the system, PC2 the next most variation, and so on. The analysis was performed to determine PC1 to PC3 for the process variables, and again for variation between isolates tested.

In tests with RPMI 1640, incubation temperature, supplier of the medium, inoculum concentration, glucose concentration and incubation time were the more important determinants in PC1, well shape was the most important in PC2 and adhesive sticker in PC3. For tests in CYG, the incubation temperature, inoculum size, glucose concentration and adhesive sticker were the most important process variables in PC1, incubation time, glucose concentration, solvent and sticker in PC2, and use of an adhesive sticker in PC3.

When data ranks for each isolate were projected on PC1–3, scatterplots, usually known as scoreplots, reinforced the differential nature of the effects of process variables on the two fungus types. Figure 5Go shows the scoreplot for PC1 versus PC2 for all 14 fungi tested in RPMI 1640. The two isolates of A. fumigatus can be seen clustered closely together. All eight yeast isolates appear in this scoreplot grouped along with the A. fumigatus isolates, but clearly separated from the other four mould isolates. Even within the cluster of points containing the yeast isolates, the three strains of C. glabrata were well separated from the other species: these were also the isolates that showed the greatest test variability in PC1. The two C. albicans isolates did not appear together in the PC1 versus PC2 scoreplot (Figure 5Go).



View larger version (9K):
[in this window]
[in a new window]
 
Figure 5. Scoreplot (scatterplot) of principal components PC1 versus PC2 for the 14 fungal test isolates. {square}, positions of filamentous fungal isolates; {blacksquare}, the two isolates of A. fumigatus; {circ}, positions of Candida spp.; •, the three isolates of C. glabrata; •, the two isolates of C. albicans.

 

    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
The most obvious conclusion to be drawn from these experiments is that the effects of itraconazole in vitro in microdilution susceptibility tests show a response to changes in process variables that differs between each fungal isolate tested, with some differences significant at the species level, for those species where more than one isolate was tested. This shows that the individual physiological characteristics of each fungus tested invariably confound the efforts of scientists to devise a susceptibility test protocol universally applicable to all fungal types. The protocol currently under consideration for tests with filamentous fungi6 does not differ greatly from the one approved for tests with yeasts, except in terms of the method of preparation of inoculum.5 However, our results indicate that moulds and yeasts differ considerably in the way they are influenced by process variables, in terms of both levels of control growth and IC50 determined from dose–response curves. For example, the growth of Candida spp. was considerably boosted by raising the glucose concentration in the medium, whereas the growth of the moulds was seldom affected by glucose concentration. For the moulds, the access of cultures to atmospheric oxygen had a significant effect on their ability to grow, whereas yeasts, presumably thanks to their capability of fermentative metabolism, were seldom affected by conditions (plates sealed with stickers, incubation in CO2) that reduced the availability of oxygen (Table IIGo).

Many of our findings support detailed recommendations already made in the NCCLS methodology. We confirm that a synthetic growth medium is preferable to a complex medium for antifungal testing. Throughout our experiments, results from CYG broth, which contains a pancreatic casein digest and yeast extract, were more likely to show significant process variable-related changes than those obtained from experiments with RPMI 1640 (Table IVGo). This effect is well illustrated by comparison of the spread of dose–response curves in the two media shown in Figure 3Go. It was notable that control growth yields in RPMI 1640 and CYG were similar for the Candida spp., but were consistently lower for the moulds in RPMI 1640 than in CYG (Table IIGo). We confirm that the starting inoculum size needs to be carefully controlled to obtain consistent IC50 results, although this parameter was of more importance with the Candida spp. than with the filamentous fungi (Table IVGo). The source of the inoculum, however, had no significant influence on susceptibility test outcome for any of the RPMI 1640 data, so that the NCCLS specification of peptone–glucose agar for preparation of yeast inocula5 and of PGA for mould inocula6 is probably not important. Similarly, the shape of the microdilution plate wells appeared to have little significant impact on the outcome of the tests: a U-form is specified in the NCCLS procedures,5,6 but flat-bottomed wells, in our hands, resulted in only occasional significant effects on measured IC50 values and are better for spectrophotometric readings (Table IVGo).

Our results confirm that duration of incubation frequently has a significant impact on the outcome of antifungal susceptibility tests; particularly with Candida spp. (Table IVGo). The 48 h yeast incubation time specified in M27-A5 has been a source of controversy ever since it was first chosen.10,11 Rex et al.10 in particular have shown how some yeast isolates can change from apparent susceptibility to azole antifungals in vitro after 24 h to apparent resistance at 48 h, and tests with animal models of Candida infection showed that the 24 h result was consistent with the response to treatment in vivo. Studies on antifungal susceptibility testing, before the NCCLS undertook preparation of a reference method, had already indicated that results read during the exponential phase of yeast growth were consistent and independent of inoculum size,34 but the principle of exponential-phase end-point readings has generally been ignored in most subsequent research.

Incubation temperature significantly influenced our IC50 determinations for many of the isolates tested (Table IVGo), yet the difference involved was only 5°C. In the case of the mould isolates, it was clear that an incubation temperature of 35°C was generally less suitable for growth than 30°C, significantly so for the F. oxysporum and P. lilacinus isolates tested (Table IIGo). NCCLS method M38-P, for the testing of filamentous fungi,6 specifies an incubation temperature of 35°C. This temperature more closely reflects the temperature encountered by the fungi in the tissues of an infected patient, yet our data indicate that a routine incubation temperature of 30°C would result in more control growth for susceptibility determinations. Comments published formally with method M27-A for yeasts5 also make the point that a temperature lower than 35°C may be more appropriate for some species. The ultimate criterion for a susceptibility test result is its correlation with clinical therapeutic outcome. A physiological temperature would be expected to give the optimum correlation, but a lower temperature may facilitate test performance and reproducibility.

When an antifungal susceptibility test is conducted in accordance with the NCCLS reference methods,5,6 microdilution plates are read by eye by a subjective, four-point scoring system. This involves distinguishing growth turbidities relative to the turbidity of the control, antifungal-free well. Our tests have shown that control turbidities can sometimes be very slight, depending particularly on variables such as glucose concentration and incubation time. We consider that objective growth measurement with a spectrophotometer in these tests is more dependable than scrutiny of plates by eye, since it provides for an objective index of suspicion around readings made on the basis of minimal control growth and which might be better repeated. We consider our cut-off criterion for control growth, i.e. an OD value >=0.1, is a reasonable means for deciding when a test requires repetition. For some slow-growing filamentous fungi, achieving this OD may require incubation periods of a week or more.

The dependence of measured IC50 (or MIC) on the level of control growth was reflected in a marked correlation between the two parameters for some of the isolates tested (Table IIIGo). Growth rates of fungal cells inside infected human tissues are not known; however, it seems plausible that an antifungal agent such as itraconazole, which can only affect actively growing fungi because it inhibits an enzyme involved in the synthesis of new membrane sterols,35 might exhibit differences in its gross antifungal potency for fast-growing versus slow-growing fungal cells. The dependent relationship between control OD and IC50 is, however, not specifically a physiological one, since the latter is always read relative to the former. This effect is perhaps most obvious in our results by comparison of Tables II and IVGoGo, where the process variables most commonly found to influence control growth for an isolate were often the same ones found to influence IC50 values.

For antifungal testing by NCCLS reference methodology, microdilution plates must be incubated for a minimum of 24 h. Unless a procedure is positively adopted to maintain the humidity of the atmosphere in the incubator, prolonged incubation is likely to result in evaporation of water from the wells, with the effects of evaporation clearly graded concentrically from wells around the plate periphery to the centre. Documents M27-A5 and M38-P6 make no recommendations on how to deal with this phenomenon. The use of adhesive stickers to seal microplate wells is a reasonable approach to prevent evaporation and maintain culture sterility over long incubation periods; however, as our results show (Table IIGo), a sticker tends to retard control growth in several filamentous fungi. We therefore recommend that microdilution plates for antifungal testing with long-term incubation should ideally be placed in a hermetically sealed container within an incubator that contains a vessel of water to maintain humidity in the atmosphere above the plate, independent of any effects of forced air circulation in the chosen incubator and of any disturbances caused by repeated opening and closing of incubator doors during the incubation period.

A related consideration is the atmosphere of incubation for antifungal susceptibility tests. This should be ‘ambient air’, according to the NCCLS protocols.5,6 We found that the atmosphere of incubation (air versus 10% CO2 in air) seldom appeared as a significant process variable affecting IC50 values for yeasts, though its impact was evident for tests with some of the moulds (Table IVGo), and it was a frequent factor influencing control growth OD values of all fungal types in RPMI 1640 broth (Table IIGo).

We were not able to confirm the finding of Galgiani & Lewis28 who reported that measured MIC values in microbroth dilution tests were related to the solvent used to prepare stock solutions of antifungal agents. In our hands, the use of PEG 200 versus DMSO as primary solvent did not emerge as a process variable that often led to significantly different IC50 outcomes (Table IVGo), nor were there significant differences in control OD values ascribable to the presence of these solvents at a concentration of 1% v/v in the control wells (Table IIGo). We would strongly recommend against the use of PEG 200 as a routine solvent for water-insoluble antifungal drugs, as its high viscosity makes liquid handling with this substance extremely difficult. The NCCLS procedure for preparing antifungal dilutions with insoluble agents should be followed carefully: this was essentially the procedure we used in the present study. Preparation of an antifungal dilution series at high concentration in an organic solvent such as DMSO, with subsequent dilution of each sequential dilution separately to final concentration in an aqueous medium, overcomes all of the hazards of compound precipitation and unequal concentrations of solvent in broth media that arise due to admixture of an organic solution with an aqueous medium and ‘diluting down’ from the highest concentration.

One obvious process variable we did not investigate is the pH of the medium. Since this is one parameter that has been defined unequivocally in the NCCLS protocol, and both our test media were buffered within the range 7.0–7.2, we decided to exclude pH variations from our design. However, a study by Marr and colleagues,27 which appeared after our experimental work had started, showed that a pH below neutrality may help minimize the trailing end-point phenomenon (see Figures 3d and 4dGoGo in the present study). Future collaborations to evaluate process variables in antifungal susceptibility tests might usefully make use of our process analysis experimental design to maximize the number of variables examined with minimal experimental effort.

Indeed, our study is the first, to our knowledge, to apply the approach of process analysis statistics to susceptibility testing methodology. By selection of combinations of process variables, as in our design, statistical analysis provides insights into the influences of individual variables on the process outcome. This type of experimental design is most often applied to manufacturing processes, where the outcome influenced by the variables is a direct measurement of a ‘real’ property such as mass, length, volume, etc. The design allows for estimation of the contributions of variables, even when not all possible combinations have been tested. An MIC (or its IC50 equivalent in our analyses) is by no means a direct, or even an absolute measurement. As eloquently expressed by Rex and colleagues,16 ‘an MIC is not a physical or chemical measurement . . . any given MIC method measures an MIC—not the MIC’.

This limitation presented handicaps for systematic analysis of the test results by ANOVA. For some isolates, exceptionally high IC50 values against a background of mainly low results inevitably appeared as statistical outliers, even after logarithmic transformation of the data. To accommodate this problem, we resorted to three different statistical approaches to determine significant effects of process variables, which allowed for analysis of the results by parametric and non-parametric methods, and by rank transformation ANOVA, a technique that combines elements of parametric and non-parametric approaches. The relative significance of each process variable differed according to the method of analysis, but the general impression from Table IVGo indicated that glucose concentration, inoculum concentration, incubation temperature and incubation time were the variables that impacted most often on the measured IC50. The additional principal component analysis confirmed this supposition, with PC1 for process variables in tests with RPMI 1640 broth showing exactly these variables as the most significant tested. It was notable that the principal component analysis projection of the test isolates led to a clustering of fungi of similar types (Figure 5Go), with some isolates, notably those of C. glabrata and A. fumigatus, clustering very closely together.

The NCCLS antifungal subcommittee has consistently stressed that its methodology is a reference procedure.5,6 This means that any other test protocol that produces results conforming with those achieved by M27 or M38 may be considered as in conformity with the reference methods, and it paves the way for the development of novel but conforming methods, including commercial antifungal susceptibility test systems. Whether the reference protocol is used to prove conformity with a new procedure or to investigate susceptibilities of fungi isolated from clinical material, it is the responsibility of investigators to follow every detail of the reference method, or to specify any changes made. The everyday realities of laboratory life are such that details of test procedures perceived as being of minor relevance, e.g. well shape, incubation atmosphere, plate sealing method, etc., may be varied without comment or investigation. Our study has shown that the individuality of the behaviour of yeasts and filamentous fungi in microdilution test systems means that even minor variations in process variables can influence the test result, at least occasionally. The NCCLS procedures have highlighted and defined at least the parameters our study found to be the greatest significant influences on itraconazole susceptibility test results: with the careful attention of investigators to the less common technical variables, the interlaboratory reproducibility of the reference procedures could be raised to an even higher level. It is concluded that any unified antifungal susceptibility test system represents a compromise that accounts for the individuality of response of each strain to test conditions, not an idealized method.


    Notes
 
* Corresponding author. Tel: +44-1224-273128; Fax: +44-1224-273144; E-mail: f.odds{at}abdn.ac.uk Back


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
1 . Amsterdam, D. (1996). Susceptibility testing of antimicrobials in liquid media. In Antibiotics in Laboratory Medicine, 4th edn, (Lorian, V., Ed.), pp. 52–111. Williams & Wilkins, Baltimore, MD.

2 . Greenwood, D. (1981). In vitro veritas. Antimicrobial susceptibility tests and their clinical relevance. Journal of Infectious Diseases 144, 380–5.[ISI][Medline]

3 . Johnson, C. C. (1996). In vitro testing: correlations of bacterial susceptibility, body fluid levels, and effectiveness of antibacterial therapy. In Antibiotics in Laboratory Medicine, 4th edn, (Lorian, V., Ed.), pp. 813–34. Williams & Wilkins, Baltimore, MD.

4 . Odds, F. C. (1998). Should resistance to azole antifungals in vitro be interpreted as predicting clinical non-response? Drug Resistance Updates 1, 11–5.[ISI]

5 . National Committee for Clinical Laboratory Standards. (1995). Reference Method for Broth Dilution Antifungal Susceptibility Testing of Yeasts; Approved Standard M27-A. NCCLS, Wayne, PA.

6 . National Committee for Clinical Laboratory Standards. (1999). Reference Method for Broth Dilution Antifungal Susceptibility Testing of Conidium-Forming Filamentous Fungi: Proposed Standard M38-P. NCCLS, Wayne, PA.

7 . Cormican, M. G. & Pfaller, M. A. (1996). Standardization of antifungal susceptibility testing. Journal of Antimicrobial Chemotherapy 38, 561–78.[Abstract]

8 . Espinel-Ingroff, A., Barchiesi, F., Hazen, K. C., Martinez-Suarez, J. V. & Scalise, G. (1998). Standardization of antifungal susceptibility testing and clinical relevance. Medical Mycology 36, 68–78.[ISI][Medline]

9 . Pfaller, M. A., Rex, J. H. & Rinaldi, M. G. (1997). Antifungal susceptibility testing: technical advances and potential clinical applications. Clinical Infectious Diseases 24, 776–84.[ISI][Medline]

10 . Rex, J. H., Nelson, P. W., Paetznick, V. L., Lozano-Chiu, M., Espinel-Ingroff, A. & Anaissie, E. J. (1998). Optimizing the correlation between results of testing in vitro and therapeutic outcome in vivo for fluconazole by testing critical isolates in a murine model of invasive candidiasis. Antimicrobial Agents and Chemotherapy 42, 129–34.[Abstract/Free Full Text]

11 . Lozano-Chiu, M., Arikan, S., Paetznick, V. L., Anaissie, E. J. & Rex, J. H. (1999). Optimizing voriconazole susceptibility testing of Candida: effects of incubation time, endpoint rule, species of Candida, and level of fluconazole susceptibility. Journal of Clinical Microbiology 37, 2755–9.[Abstract/Free Full Text]

12 . Rodríguez-Tudela, J. L. & Martínez-Suárez, J. V. (1994). Improved medium for fluconazole susceptibility testing of Candida albicans. Antimicrobial Agents and Chemotherapy 38, 45–8.[Abstract]

13 . Rodríguez-Tudela, J. L., Berenguer, J., Martínez-Suárez, J. V. & Sanchez, R. (1996). Comparison of a spectrophotometric microdilution method with RPMI-2% glucose with the National Committee for Clinical Laboratory Standards reference macrodilution method M27-P for in vitro susceptibility testing of amphotericin B, flucytosine, and fluconazole against Candida albicans. Antimicrobial Agents and Chemotherapy 40, 1998–2003.[Abstract]

14 . Odds, F. C., Vranckx, L. & Woestenborghs, F. (1995). Antifungal susceptibility testing of yeasts: evaluation of technical variables for test automation. Antimicrobial Agents and Chemotherapy 39, 2051–60.[Abstract]

15 . Pfaller, M. A., Messer, S. A. & Coffmann, S. (1995). Comparison of visual and spectrophotometric methods of MIC determinations by using broth microdilution methods to test five antifungal agents, including the new triazole D0870. Journal of Clinical Microbiology 33, 1094–7.[Abstract]

16 . Rex, J. H., Pfaller, M. A., Galgiani, J. N., Bartlett, M. S., Espinel-Ingroff, A., Ghannoum, M. A. et al. (1997). Development of interpretive breakpoints for antifungal susceptibility testing: conceptual framework and analysis of in vitro–in vivo correlation data for fluconazole, itraconazole, and Candida infections. Clinical Infectious Diseases 24, 235–47.[ISI][Medline]

17 . Davey, K. G., Holmes, A. D., Johnson, E. M., Szekely, A. & Warnock, D. W. (1998). Comparative evaluation of FUNGITEST and broth microdilution methods for antifungal drug susceptibility testing of Candida species and Cryptococcus neoformans. Journal of Clinical Microbiology 36, 926–30.[Abstract/Free Full Text]

18 . Gadea, I., Cuenca, M., Gegúndez, M. I., Zapardiel, J., Valero, M. L. & Soriano, F. (1997). Effect of pH and buffer system on the in-vitro activity of five antifungals against yeasts. Journal of Antimicrobial Chemotherapy 39, 453–9.[Abstract]

19 . Hawser, S. P., Norris, H., Jessup, C. J. & Ghannoum, M. A. (1998). Comparison of a 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-5-[(phenyl-amino)carobonyl]-2H-tetrazolium hydroxide (XTT) colorimetric method with the standardized National Committee for Clinical Laboratory Standards method of testing clinical yeast isolates for susceptibility to antifungal agents. Journal of Clinical Microbiology 36, 1450–2.[Abstract/Free Full Text]

20 . Lynch M. E., Sobel, J. D. & Fidel, P. L., Jr (1996). Role of antifungal drug resistance in the pathogenesis of recurrent vulvovaginal candidiasis. Journal of Medical and Veterinary Mycology 34, 337–9.[ISI][Medline]

21 . Milan, E. P., Burattini, M. N., Kallás, E. G., Fischmann, O., Costa, P. R. & Colombo, A. L. (1998). Azole resistance among oral Candida species isolates from AIDS patients under ketoconazole exposure. Diagnostic Microbiology and Infectious Disease 32, 211–6.[ISI][Medline]

22 . Ruhnke, M., Schmidt-Westhausen, A., Engelmann, E. & Trautmann, M. (1996). Comparative evaluation of three antifungal susceptibility test methods for Candida albicans isolates and correlation with response to fluconazole therapy. Journal of Clinical Microbiology 34, 3208–11.[Abstract]

23 . Simor, A. E., Goswell, G., Louie, L., Lee, M. & Louie, M. (1997). Antifungal susceptibility testing of yeast isolates from blood cultures by microbroth dilution and the E test. European Journal of Clinical Microbiology and Infectious Diseases 16, 693–7.[ISI][Medline]

24 . Still, J. M., Jr, Law, E. J., Belcher, K. E. & Spencer, S. A. (1995). A comparison of susceptibility to five antifungal agents of yeast cultures from burn patients. Burns 21, 167–70.[ISI][Medline]

25 . Torres-Rodríguez, J. M., Mendez, R., López-Jodra, O., Morera, Y., Espasa, M., Jimenez, T. & Lagunaz, C. (1999). In vitro susceptibilities of clinical yeast isolates to the new antifungal eberconazole compared with their susceptibilities to clotrimazole and ketoconazole. Antimicrobial Agents and Chemotherapy 43, 1258–9.[Abstract/Free Full Text]

26 . Lozano-Chiu, M., Nelson, P. W., Lancaster, M., Pfaller, M. A. & Rex, J. H. (1997). Lot-to-lot variability of antibiotic medium 3 used for testing susceptibility of Candida isolates to amphotericin B. Journal of Clinical Microbiology 35, 270–2.[Abstract]

27 . Marr, K. A., Rustad, T. R., Rex, J. H. & White, T. C. (1999). The trailing end point phenotype in antifungal susceptibility testing is pH dependent. Antimicrobial Agents and Chemotherapy 43, 1383–6.[Abstract/Free Full Text]

28 . Galgiani, J. N. & Lewis, M. L. (1997). In vitro studies of activities of the antifungal triazoles SCH56592 and itraconazole against Candida albicans, Cryptococcus neoformans, and other pathogenic yeasts. Antimicrobial Agents and Chemotherapy 41, 180–3.[Abstract]

29 . Revankar, S. G., Kirkpatrick, W. R., McAtee, R. K., Fothergill, A. W., Redding, S. W., Rinaldi, M. G. et al. (1998). Interpretation of trailing endpoints in antifungal susceptibility testing by the National Committee for Clinical Laboratory Standards method. Journal of Clinical Microbiology 36, 153–6.[Abstract/Free Full Text]

30 . Odds, F. C. (1992). Antifungal susceptibility testing of Candida species by relative growth measurement at single concentrations of antifungal agents. Antimicrobial Agents and Chemotherapy 36, 1727–37.[Abstract]

31 . Denning, D. W., Ventekateswarlu, K., Oakley, K. L., Anderson, M. J., Manning, N. J., Stevens, D. A. et al. (1997). Itraconazole resistance in Aspergillus fumigatus. Antimicrobial Agents and Chemotherapy 41, 1364–8.[Abstract]

32 . Odds, F. C. (1993). Effects of temperature on anti-Candida activity of antifungal antibiotics. Antimicrobial Agents and Chemotherapy 37, 685–91.[Abstract]

33 . Fleming, W. W., Westfall, D. P., de la Lande, I. S. & Jellett, L. B. (1972). Log-normal distribution of equieffective doses of norepinephrine and acetylcholine in several tissues. Journal of Pharmacy and Experimental Therapeutics 181, 339–45.

34 . Galgiani, J. N. & Stevens, D. A. (1976). Antimicrobial susceptibility testing of yeasts: a turbidimetric technique independent of inoculum size. Antimicrobial Agents and Chemotherapy 10, 721–6.[ISI][Medline]

35 . Vanden Bossche, H. & Marichal, P. (1992). Azole antifungals: mode of action. In Recent Progress in Antifungal Chemotherapy, (Yamaguchi, H., Kobayashi, G. S. & Takahashi, H., Eds), pp. 25–40. Marcel Dekker, Inc., New York.

Received 3 October 2000; returned 19 March 2001; revised 20 April 2001; accepted 1 May 2001