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During the last few years, a new indicator appeared on the market: Enzyme indicators (EI). Compared to the current biological indicators (BIs), these new types of indicators could offer advantages in the cycle development of bio-decontamination processes. A study by Optima and Metall+Plastic in cooperation with Protak Scientific provides exciting new findings and application processes.
BIs are tried and tested for isolator cycle developments intended for bio-decontamination processes. Nevertheless, BIs have certain restrictions, they are time and labor extensive during cycle development and have to be incubated for seven days before a final result can be seen. The result is either “growth” or “no growth” of live-spores of “Geobacillus Stearothermophilus” (used as a reference organism).
EIs on the other hand provide results within several minutes. After exposure, the indicators are evaluated with a luminometer. The RLU-value, in percentage, is determined from the measured luminescence via standardization (RLU – Relative Light Units). This achieves quantitative analysis more quickly. If the enzyme activity can be correlated with the killing of spores, it provides information on the decontamination process.
To apply EIs correctly and avoid possible downfalls, it is necessary to understand strengths and weaknesses of the indicators.
In a first step it is determined how irregular or stable the EIs react to gassing with H2O2. This information will validate EI results. Significant findings indicate that variability (CV – Coefficient of Variability) is limited and a 30 % worst-case variability scenario is possible, provided no other value can be determined for the respective application. This is likely if only one EI per location is applied
It was also confirmed that an enzyme activity reduction can vary depending on local conditions. These discrepancies are based on different interactions with the H2O2 and the EIs, the characteristics of used decontamination systems or the orientation of the EIs. The evaluation was based on entire groups of indicators and the arithmetic means of the obtained RLU values, as well as standard deviations.
The next step is to examine the qualitative correlation between EIs and BIs: will both indicator types lead to identical results if they are at identical locations and have identical decontamination cycles? D-values were the defined measurement for micro-biological killing per BIs location. EIs and BIs, both, correspondingly showed the best-case-locations. The worst-case locations for BIs and EIs were also identified. However, the latter would show additional potential worst-case locations in a risk-based approach. Therefore, a qualitative correlation was given.
To further examine the significance of EIs, the authors also tested what happens if the gassing time is continuously increased from 6 to 8 minutes and up to 11 minutes. A homogeneous result was achieved by examining 35 locations. The ranking of the measured RLUrem values (RLUrem : standardized relative light intensity as a measure for the remaining enzyme activity, rem = remaining) was the same during different gassing times. For example, locations with a high RLUrem value showed a lower RLUrem after a longer gassing time, but the “sequence“ of decontamination success of the EI-locations was unchanged. The EIs were able to identify the best and worst-case locations independent from the total gassing time.
The results, up to this point, only present information of the inactivity of the enzymes on the indicator. The question, however, is it possible to create a quantitative correlation between the enzyme activity reduction and the killing of spores? Further tests were conducted.
If several groups of BIs are placed in a fixed location in an isolator, the kill kinetics can be examined with the following process: Groups of BIs are placed into individual tubes with a culture medium, at a defined time therefore, interrupting the exposure. If the BIs are now incubated it will show “growth” or “no growth. For the BI groups with a short exposure time, growth (full growth) is expected, followed by an interval in which parts of a group show growth and the rest of the group shows no growth (fractional growth). After a certain point in time, all BIs of a group show no growth (total kill).
If this study is performed with a group of EIs, that are exposed at the same time to the cycle as the group of BIs, the observed growth can be correlated to the quantitative EI results (see picture on page 35). The obtained RLUrem values for this time period, when the last full growth and the first complete kill is achieved, present threshold values that limit the range, where fractional growth of a group can be expected. This correlation will show if full growth, fractional growth or total kill can be expected, depending on each individual case and certain EI results (RLUrem value).
Furthermore, an additional approach was discussed: from the experimental BI group results, SLR-values (Spore Log Reduction) can be calculated via the Halvorson-Ziegler equation, that then can be correlated with the EI results.
The cited correlations were determined during an isolator test, where H2O2 easily reached the indicator locations on the work surface of the manipulation unit. The lessons
learned were then applied to a hands-on decontamination application. The isolator was equipped with a typical load. Several indicator locations, in the isolator, were defined (e.g. different materials and difficult to reach geometrical locations) and investigated with two different decontamination systems.
First, the correlation threshold values were defined. Based on these values and the measured RLUrem per location, a prognosis for BI group results per location was established. These results were collated with experimental defined BI results according to location.
For each decontamination system 35 and 36 locations were evaluated. For the majority of locations the measured, results matched the expected prognosis. Five locations did not match, but were close to the values that would have fulfilled the criteria in accordance to the EI variability of 30%. The worst-case locations are most important for the hands-on cycle development. In this test, full growth of BIs was correctly predicted for all worst-case locations.
However, the authors would like to mention that in this context it is advised to perform additional studies and use a wider range of data to ensure the accuracy of the study. It is also recommended to examine additional data for locations with fractional growth.
Upon the lessons learned, the authors present first recommendations of how to use EIs complementary to BIs during cycle development.
First, threshold values for the used EIs and BIs have to be determined, beginning with the transfer from full to fractional growth and from fractional growth to total kill, according to the described process. To determine the current correlation of both indicator types, a minimum of three EIs and BIs per location, should be used in the same time intervals.
Either single or triple EIs should be used to examine locations in the isolator. Using triple EIs, the correct variability (CV),for each case, can be calculated. Alternatively, an EI variability of 30% can be assumed.
In accordance with local EI results (RLUrem values) a prognosis for theoretical BI-group results can be prepared (full growth, fractional growth, total kill). In addition, this process identifies potential worst-case locations (these locations show the highest remaining enzyme activity, RLUrem). In the next step, a D-value determination can be performed for these positions in a conventional way, using BIs at worst-case locations. It is recommended to perform verification cycles using BIs. Cycles for validation should also be performed with BIs.
The targeted determination of the D-value, of the worst-case location, reduces time and labor. It is significant that the indicators can be evaluated directly after the cycle and therefore, a quantitative result is available in a timely manner This also acts as an additional control function: BI results, based on EIs, should coincide with real defined BI results. If deviations arise, they have to be examined more closely.
EIs also provide another benefit. Since they are not based on living organisms, EIs can be placed in the isolator during decontamination, before production, to examine the decontamination success Significant deviations for each decontamination cycles and trends can so be detected.
