Modelling the inactivation of Bacillus subtilis spores by ethylene oxide processing

Gisela Cristina Mendes

Teresa Ribeiro da Silva Brandão

Cristina Luisa Miranda Silva

CBQF - Centro de Biotecnologia e Química FinaEscola Superior de BiotecnologiaUniversidade Católica Portuguesa,Rua Dr. António Bernardino de Almeida4200-072 Porto, Portugal

This study was supported by Bastos Viegas, S.A.

ETHYLENE OXIDE IS CURRENTLY A DOMINANT STERILIZATION AGENT USED IN MEDICAL DEVICES INDUSTRY

EO sterilization consumption for

medical devices

0

20406080

100

70's80's

90's

Nowadays

Decade

%

Advantages / Disadvantages

Advantages

Effectiveness DiffusivityBactericidal, fungicidal and virucidal properties

Compatibility with most materials

Process flexibility Low temperature sterilization

Disadvantages

Toxicity of the sterilizing agent

Process complexity

Process cost

Processing time

Advantages / Disadvantages

Objectives

Understanding the full dynamics of the sterilization allows design optimization / efficient control of the process -

Parametric release

Screen the most significantvariables on B. subtilis

inactivation by EO sterilizationModel the inactivation kinetics

of B. subtilis, including the variables’ effects

Provide a method of integrating lethality

Modelling microorganisms inactivation

Experimental design

Bacillus subtilis, var. niger or Bacillus atrophaeus spores (ATCC 9372) inoculated in strips (biological indicators, BIs)

Matrix: Drapes

Temperature and humidity sensorsEO sensor (Infrared analyser in the sterilizer chamber headspace) Sterilization cycles

Modelling microorganisms inactivation - Conditions defined according to the 23 factorial design -

Sterilization cycles

- Target exposure conditions –

40 and 60 ºC 50 and 90 %RH

250 and 1000 mg(EO)/L

Modelling microorganisms inactivation

Survival curves construction

1st order kinetics Gompertz model

Gompertz function has the ability of modelling both linear and asymmetrical sigmoidal data

( )

+−λ

−−⋅=

1t

Aek

expexpANNlog max

00Nlogt.kNlog +−=

Inactivation of B. subtilis spores by EO sterilization - Conditions defined according to the 23 factorial design -

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Legend

⁰ Experimental data

___ Fitted Gompertz model

___ Predicted data

- - - Upper and lower limits of predicted data (considering the maximum fluctuations of temperature and EO concentration)

T=60 °C; EO=233 mg/L;

RH=63 %

T=44 °C; EO=257 mg/L; RH=86 %

T=34 °C; EO=222 mg/L; RH=60 %

T=40 °C; EO=980 mg/L;

RH=90 % T=59 °C; EO=266 mg/L; RH=85 %

T=33 °C; EO=940 mg/L; RH=61 %

T=59 °C; EO=1004 mg/L; RH=98 %

Data analysis

The non-linear regression analysis was carried in

Statistica© 6.0 software (StatSoft, USA), using the Levenberg-

Marquardt algorithm to minimize the sum of the squares of the

differences between the predicted and experimental values.

Data analysis

The experimental inactivation data were successfully fitted with

the Gompertz model:

- High precision of kmax and λ estimates, since low errors were

attained (SHW95%);

- Residuals randomness and normality;

- Coefficient of determination (R2>0.98);

Data analysis and planning future work

The analysis of variance (ANOVA) allowed to identify the most

significant parameters affecting B. subtilis inactivation -

temperature and EO concentration

Additional experiments considering intermediate conditions of

these parameters were defined in order to model their effects

and combined effects on the lethality (runs 9 to 15)

Inactivation of B. subtilis spores by EO sterilization at the additional experimental conditions

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U (s)

log (

N/N

0)

Legend

⁰ Experimental data ___ Fitted Gompertz model

Run 9 to Run 15

Estimated kmax and l parameters of B. subtilis inactivation at the temperature, EO concentration and relative humidity conditions tested

EO concentration influence on kmax and λ

kmax = 3.22x10-6[EO] + 2.74x10-4

kmax = 4.25x10-6[EO] + 2.18x10-3

kmax = 4.46x10-6[EO] + 3.53x10-3

0

1

2

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7

8

0 500 1000 1500

[EO] (mg/L)

k max

x 1

03 (s

-1)

37.0 ºC

50.5 ºC

60.0 ºC

λ = -88.076ln[EO] + 853.05

λ = -229.66ln[EO] + 1733.7

λ = -446.4ln[EO] + 3570.8

0

200

400

600

800

1000

1200

0 200 400 600 800 1000 1200

[EO] (mg/L)λ

(s)

38.0 ºC

50.5 ºC

60.0 ºC

Influence of EO concentration on kmax at 37.0, 50.5 e 60.0 °C

Influence of EO concentration on λ at 38.0, 50.5 and 60.0 °C

T influence on parameters ak/bk and aλ/bλ

ak = 1.42x10 -4T - 4.96x10 -3

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 10 20 30 40 50 60 70

T (ºC)

a k x 1

03 (s

-1)

bk = 5.54x10 -8T + 1.25x10 -6

3.0

3.5

4.0

4.5

5.0

0 10 20 30 40 50 60 70

T (ºC)

b k x 1

06 (L

mg-1

s-1)

aλ = 16.34T - 1063.61

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T (ºC)

a λ (s

)

bλ = -124.74T + 8227.00

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3500

4000

0 20 40 60 80

T (ºC)

b λ (s

)

Influence of T on ak e bk parameters Influence of T on ak e bk parameters

Data analysis

T and EO concentration have a negative effect on λ and a

positive effect on kmax:

- Higher temperatures and EO concentration imply narrow

shoulder times and higher inactivation rates;

- Lower inactivation rates and more evident shoulder phases

were observed at the lowest temperature and EO

concentration;

Mathematical model resulting from the integration of the T and EO concentration parameters for lethality calculation of the EO sterilization process

[ ]

×

−×+−×+

−×−−×−

−−=

7.5-

eEO6101.25T8105.543104.96T4101.42exp7.5)exp(

0NN

log

[ ]

+

−

×+×−+

×−×× 1U3108.23T2101.25)EOln(3101.06T1101.63

Inactivation of B. subtilis spores by EO sterilization - Conditions defined according to the 23 factorial design -

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U (s)

log

(N/N

0)

Legend

⁰ Experimental data

___ Fitted Gompertz model

___ Predicted data

- - - Upper and lower limits of predicted data (considering the maximum fluctuations of temperature and EO concentration)

T=60 °C; EO=233 mg/L;

RH=63 %

T=44 °C; EO=257 mg/L; RH=86 %

T=34 °C; EO=222 mg/L; RH=60 %

T=40 °C; EO=980 mg/L;

RH=90 % T=59 °C; EO=266 mg/L; RH=85 %

T=33 °C; EO=940 mg/L; RH=61 %

T=59 °C; EO=1004 mg/L; RH=98 %

In conclusion

[ ]

×

−×+−×+

−×−−×−

−−=

7.5-

eEO6101.25T8105.543104.96T4101.42exp7.5)exp(

0N

Nlog

A mathematical inactivation model expressed only in terms of the

relevant process variables (T and EO concentration) was

achieved.

The conventional design of EO sterilization cycles usually involves

a significant amount of experimental work, which is time

consuming and also expensive. The results of this work are

certainly a contribution for an efficient control, design and

optimization of the EO sterilization process.

[ ]

+

−

×+×−+

×−×× 1U3108.23T2101.25)EOln(3101.06T1101.63

Thanks’