Estimation of the Progress of Streptomyces Clavuligerus Fermentations for Improved On-line Control of Antibiotic Production

Copyright

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~lodeIIing

and Control of

Biotechnological Processes. :'\"oordwi.ikerholl( . I ~H'F)

ESTIMATION OF THE PROGRESS OF STREPTOMYCES CLAVULIGERUS FERMENTATIONS FOR IMPROVED ON-LINE CONTROL OF ANTIBIOTIC PRODUCTION L. A. Tarbuck, M. H. Ng, SrilUlIl

J.

R. Leigh and

J.

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Abstract . Progress in the optimisation of the production of an antibiotic in aerobic batch fermentations is described. The application of a variant of the Kalman filter to the data indicate that model fit is improved using a time-varying paramete r to predict biomass development during the fermentations. Production o f the anti biotic is shown to be improved under conditions of limited growth, and the progress in the description of the controlling parameters and factors is discussed. Keywords. Biocontrol;

fermentation process; modelling; non - linear estimation; Kalman

filter; an ti biotics ; Streptomyces.

INTRODUCTION The aims of the project are to provide reliable on line estimates of biomass and secondary product formation in stirred batch reactors that are suitable for integration into control strategies

model 540A) . These data were logged on a LSI 11

f o r industrial fermentation . Previous papers have

minute intervals,

described progress in the investigation of variants of the extended Kalman filter applied to the estimation of biomass and product formation in sorbose and cellulose fermentations (Swiniarski et al., 1982; Leigh and Ng, 1984). The major problem of unmodelled batch-to-batch deviations in nominally identi cal batches was treated in these particular fermentations by identifying by sensitivity analysis those parameters that vary from batch to batch and that most affect model fit.

the VAX computer for storage and off -line data handling and model development. On-line control of

microprocessor together with pH , temperature ,

stirring speed and volume of acid and alkali added to maintain pH control. Data is logged at 30

stirring speed , dissolved oxygen tension and air

flow rate will be established with the installation of the Motorolla 68000 hardware under development (Fig, 2). Software under development will allow the real-time continuous graphical display of the progress of the fermentations, and application of the models developed. FERMENTATION DETAILS

From these , one pa rticular parameter, Yxc, was

then redesigned as a time varying parameter , Yxc(t), to be estimated on -line along with process states (Leigh and Ng, 1984).

ORGANISM, The bacterium Streptomyces clavuligerus was first described by Higgens and Kastner (1971) , and the strain used in all fermentations was obtained from the American Type Culture Collectior (ATCC) No . 27064.

The present paper describes progress in the application of the Kalman filter, including the adapt ive feature described above, to a typical antibiotic-producing system. The product , clavulanic acid , is typical of the secondary metabolite produced by the Streptomyces species of current pharmaceutical interest . Specific bio chemical pathways leading to product formation have not yet been elucidated (Butterworth, 1984) and optimisation of production is limited to manipulation of the fermentation process, using wild-type strains of the bacteria (ie . non genetically improved or engineered strains).

MEDIA. No manufacturing process information has yet been published and most information available is based on laboratory scale production of the type used in this project . Fermentation media suitable for the production of clavulanic acid have been described in various patents issued

(British patent No . 1,563 , 103; Japanese patents 53-104796 and 55 - 162993) . All patents show soyabean flour to be the prefered industr ial substrate. For the early stages of the project this was unsuitable, as it produces an opaque , particulate medium that make biomass estimations difficult. Such estimations would of course be available once the Kalman filter was fully developed , and indicate the potential of the development of this method . A relatively clear medium was selected for the project, containing glycerol , malt extract and bacteriological peptone described in the British patent . (Soyabean flour medium will be used to ensure comparability with industrial processes when modelling has progressed sufficiently). A defined medium described by Aharonowitz and Demain (1977) containing glycerol , asparagine, phosphate and trace salts was also used for a limited number of exgeriments . All fermentations were carried out at 26 C.

The work described is supported by the Science and Engineering Research Council at the School of Biotechnology and the School of Electrical, Electronic and Control Engineering. EQUIPMENT The fermentations were carried out using an upgraded LH Engineering Series 1000 10 litre fermenter, instrumented as shown in Fig. 1. Dissolved oxygen concentrations were measured using a LH Engineering % oxygen meter and autoclavable electrode El-01-18. Carbon dioxide in the exit gases was measured using an infra-red detector (Analytical Development Co. Ltd) , and exit gas oxygen concentrations were determined using a paramagnetic analyser (Sybron,

191 MCB- C·

and is communicated in bursts to

L. A. Tarhuck 1'1 al.

192

RESULTS AND DISCUSSION

INOCULA PREPARATION Standardisation and control of the preparation of the inocula has been shown to be important in reducing the batch-to-batch deviations. To prepare inocula lOml agar slants were grown f or 7 days at 0 26 C. The resulting aerial mycelia and spores were suspended in lOml fermentation broth and transferred to 250 ml liquid medium in a vegetative stage flask. The flasks were incubated at 26 C on a r ota r y shaker (220 r pm) for 72 hours. A total of 1 litre o f inoculum was then used to inoculate 9 litres of medium in the ferm ente r. BIOMASS AND CLAVULAN I C ACID ASSAYS Biomass was measured using optical density of the cultures. The bacterial flocs were disrupted by agitation with 3 . 5mm d iamete r glass beads before

The results presented represent the initial studies using t he fermenter under manual control to provide data on the characteristics of S. clavuligerus ferme ntati ons . These studies allowed the main con tro lling pa ra meters to be deduced and p r ovided the data base for model development and off-line simulation. The approach to the initial cont rol strategy was determined by the decision to use a complex undefined medium, where no single l im iting substrate is disce r nable and substrate utilisation rates are not easily determined. Growth rates and

clavulanic acid production were followed in nominally identical batches with air flow rates of 1.0, 0.5 and 0 . 2 5 v/v min, and stirring speeds between 250 and 500rpm. The conditions and results for seven batches are given in Table 1.

dilution and measurements were made, t ypically

Clavulanic acid production was found to vary

5 g beads to 10ml culture. Optical density

inversely with the stationary phase biomass density, Fig. 3 . There was little correlation between the actual growth rates and the biomass density in the stationary phase. The actual doubl-

measurements were made on a Perkin-Elmer 402

double beam spectrophotometer at 650nm. The wavelength was chosen as that at which the culture medium absorbance is minimal. All OD 650

measurements_ye re converted to dry

cell mass

(DCM) as g L using a standard calibration graph prepared for S. clavuligerus. Clavulanic acid concentrations in the broths were

determined by high performance liquid chromatography (HPLC) using a modification of t he method described by Foulstone and Reading (1982). Broth

ing time during the growth phases did, howeve r, show a trend associated with clavulanic acid

production, indicating an optimum doubling time o f approximately 6 hours (Fig . 4). These results indicate two separate controlling factors that can be used to enhance antib i otic p r oduction. The con di tions that control these factors were

identified as the o x ygen transfer rate, controlled by stirring speed and air flow rate, and s hear

filtrates were derivatised at room temperature

forces in the liquid, also a factor of the stirring

with imidazole, and separated using a Spherisorb ODS2 5u column with 0.05M KH PO b uf f e r + 9% acetonitrile (pH 3.2). standird~ were prepar ed from a laboratory reference p re parati on of potassium clavulanate provided by Beechams

speeds. The results given in Table 1 show that in batches 1 and 2 , where all conditions were the

Pharmaceuticals.

MODELLING AND STATE ESTIMATION The model used has already been described in a previous publication (Leigh and Ng, 1984). The combination of Leudeking and Pirt and Kog a 's models has been used to describe the type of process considered here. The substrate consumption consists

of two parts. The first is used in bacterial growth and the second is consumed in the product formation. The model is as follows: Dynamic equations: dx

11 sx m

dt

K +s s

ds

-

1

--

dt

Y xs

dp

lJ'sx

dt

K'+s s

m

Kd x

11 sx m

-

K +s s

1

lJ 'sx

m

,

Y xs

K'+s

s

+ m x p

1 Y xc

was increased, it can be seen tht the in creased

oxygen transfer resulted in relatively high stationa ry phase biomass densities, but the growth rate was reduced, expressed as a lengthening of

the doubling times. Such a reduction in the growth rate would appear to be a function of the shear f o rces at the higher speeds . In initi al batches pH was not controlled , and during the course of the fermentations it varied between pH 6 and pH 8. Clavulanic acid is relatively stable between pH 6.2 and 7.5. Control of the pH during the experiments at two po ints within this range was introduced to reduce the batch-to-batch deviations. Antibiotic production at pH 7.2 was poor. At pH 6.5 the results support the trend shown in those without p H control of increased antibiotic production with reduced biomass densities. The doubling times were affected by the control of pH, being longer at pH 6 .5 than in any ot her experiments, and indicate the sensitivity of growth r ates to changes over a relatively narrow pH range. The optimum p H for antibiotic p roduction was not determined by these experiments.

Measurement equation

CER

same except a ir flow rate , the hig he r aeration

p roduced a faster doubling time during the growth phase , higher stationary phase biomass densi ty but lower clavulani c acid production . If these batches are compared with batch 5 , where s tirring speed

11 sx m

+ m x c

K +s S

The extended Kalman filter with time-varying parameter developed previously is applied to estimate the biomass and product formation.

Carbon dio xide evolution rates (CER) show a rapid utilisation of nutrients during the growth phase in those batches with the higher growth rates, and also later falling CER indicative of nutrient depletion (figs . 5 and 6). Batches with lower growth rates and higher antibio tic production produced distinctive CER patterns suggsting a more ordered sequential removal of substrates. (Figs. 7 and 8). Relatively stable CER were given in the antibiotic phases of these batches. These results show that the more controlled uptake of nutrients during the growth phase with lower doubling times and restricted stationary phase biomass densities avoids nutrient depletion before the antibiotic production phase. In other bacterial antibiotic

193

Progress of SlrI'PloIIIY(1',I CIOl'ldignlls Fennentations

Table 1. Summary of Batch Conditions and Results for S. clavuligerus

Air Flow

Batch

(v/v min

1 2 3 4 5 6 7

-1

)

Doub lin 9: (h)

Speed (rpm)

time

4.7 7.9 5.5 6.0 9.5 17.8 12.0

250 2 50 2 50 375 500 400 375

0. 5 0.25 0.25 0.25 0.25 0.25 1.00

Biomass -1 max (g 1 )

a

5.5 4.5 3.9 3.4 6.6 2.9 10.1

bspecific CA

CA max -1 mg L

1.01 2.68 4.79 10.70 <0.05 2.31 0.53

5.2 2.0 17.1 36.0 <0.05 6.7 5.5

a - CA = clavulanic acid b* amount of clavulanic acid produced per unit of biomass

pH Set

~

7.2 6.5 6.5

l (mg. g biomass . L- ).

Ta ble 2. Summary of Batch Conditions and Results for S. clavuligerus Gro wn on Defined Medium

Batch

8 9 a lO b ll

Air -1 (.;jv min )

Speed (rpm)

0.40 0.25 0.40 0.2 5

500 500 500 500

Doubling time (h)

7.4 13.6 9.9 6.34

Max Biomass l (g L- )

4.25 2.81 3.67 5.50

CA max

mg L

-1

)

trace trace

1.4 2.7

-1 a - with 0.1 g L_l yeast extract b - with 0. 3 g L yeast extract

producing systems idiolite production has been shown to be highly sensitive to nutrient uptake rates over a very narrow range. The rate of nutrient uptake may be controlled by incorporating a fedbatch system into the optimisation strategy. The use of complex medium precludes the identification of which substrates were used in the sequence of removal, but the RQ values in most of the batches suggest more oxidised substrates are used in preference to glycerol during the growth phase (RQ >0.75), and this may n o t be required to produce adequate growth prior to antibiotic production (Fig. 9). Several batches were run with a defined medium containing glycerol and arginine to determine if the dominant controlling factor in antibiotic production was the growth rate or the variety of carbon sources in the undefined medium. These experiments are summarised in Table 2. The first two batches showed only traces of clavulanic acid produced, but the doubling time and maximum biomass densities were controlled by the aeration rate, as in the undefined medium. The second pair of batches were supplemented with yeast extract (a complex nitrogen source) and clavulanic acid production improved. This indicated the nitrogen nutrition was a determining factor in the antibiotic production. The amounts of clavulanic acid produced, although low, were in the range found for fermentations with similar conditions in the defined medium, indicating the growth rate rather than varieties of carbon source was the main factor. The results also supported the trend noted in the complex medium that clavulanic acid production was improved through a reduction in the doubling times. CER, RQ and Kla wer all tested in the development of the biomass model. Of these CER provided the best fit, as shown in Fig. 10. Application of the Kalman filter has produced good fit of the predicted biomass to the actual values (Fig. 11). Yield coefficient of carbon dioxide Yxc is used as the time varying pararr.eter. The apparent underestimation of product shown in Fig. 11 is through the use of

the model based on proportional biomass/production, rather than the inverse relationship described in the results above. The development of the idiolite production model is at present continuing, using Kla, RQ, biomass and dissolved oxygen tension as the controlling parameters. The main difficulty encountered has been the non-Monod biomass development in the higher yielding batches, so that growth and stationary phases are not distinct. The system being modelled is more complex than those considered during the development of the variants of the Kalman filter, as a multi-step reaction where the biosynthetic pathway is not yet known. SUMMARY The results for the optimisation of clavulanic acid show the factors related to the production are the doubling time during the growth phase and the stationary phase biomass density. Control of these factors may be achieved by a combination of control of oxygen transfer, via air flow rates and stirring speed, and by shear of turbulence control, also a factor of the stirring speed. Model development and application of the Kalman filter to provide reliable estimates of biomass have been proven, although the development of the idiolite model is continuing. REFERENCES Aharonowitz, Y. and Demain, A.L . . (1977). Influence of inorganic phosphate and organic buffers on cephalosporin production by Streptomyces clavuligerus. Arch. Microbiol., 115, 169-173. Butterworth, D. (1984). Clavulanic acid: properties, biosynthesis and fermentation. In E. J • Vandamme (Ed.), Biotechnology of Industrial Antibiotics. Marcel Dekker, N.Y. and Basle. Foulstone, M. and Reading, C. (1982). Assay of arooxicillin and clavulanic acid in biological fluids with high performace liquid chromatography. Antimicrobial Agents and Chemotherapy, 22 (5), 753-762.

L. .\ . T arbuck 1'1 (1 /.

194

Higgins , C.E . and Kastne r, R.E . (1 971) . S trepto myces c lavuligerus Sp . No v ., a S-l a c tarn antibi o tic p r o duce r. Int . J . Sys t. Ba cterio l ., ~, 326 - 332 . Leigh , R . J. and Ng , M. H. (19 8 4 ) . Estimatio n o f bio mass and se c on d ar y p roduct in batch ferm e ntati o ns. P r esented a t the 6 th Intern a tion a l Confe rence o n Analysis and Opt i mi sation o f Systems. S wini a rski, R. et a l. (1 9 82) . Prog ress towa rds estimati on o f bio mass in a b a tc h ferm e ntation p r o cess . Proce e d ings o f I FAC Wo rksho p , Mode lling an d Co ntro l of Bi o t e chnica l Processes, He l s i n ki , Finl a n d .

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195

Prof(ress of SII"I'/II/lI/I.\'u'.\ C/m'lIliglTlI.' Ferlllt' I11 ;l1io ll s

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• • •

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~~:mass

1

( g L- ) -1 - Cl avulani c a c id (mg L )

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1)

25

10

20

15

10

O

~

______

o

~~~

20

____, -______- ,________, -______- ,

40

80

60

Time

100

(hou r s)

Figure 5 . Carbon dioxide evoluti on rate . Bat ch L.

•

•

10

35

16

14

•

C. A.

•

Biomass

-

c.

E.

R.

30

12

25

10 20

,-

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r

,-

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U

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is

.~

~ ~

~

~

0

'"

15

'0

4

c

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~

U

O~=====-~~----~~-----r------~-------' Time (hours )

Fig ure 6. Carbon dioxide evolution rate , batch 2 .

L. .-\. T a rbll ck 1'1 Ill.

196

O ~====~~----~-------r------.------. o

20

40

60

80

100

Time

(hou r s)

Figure 7 . Ca rbon dioxide evol ution rate. Batch J .

35 16

• -

12

.8 u

C•

A.

Biomass

c.

L

R.

30

14

'c.i"

•

25

"

~ :E

l5

. ~ ~

E

.~

m

20

".s'" ..l

;;

15

,

u

.. u

c

. ~

u

00

20

40

60

80 Time

Figure 8 . Carbon dioxide evoluti o n rate. Batch 4.

100 ( hours )

Progress of

S/II '/)/IIIII.'"(('I

1 ~7

C/IIl'lIligt'rIl.\ Fcr m c llt atiolls

2.0

1.5

Batch

Batch Batch Batch 4

1.6

I..

1.2

1.0

;:... KX':\'~::,,:,\\. \r'

0.8

l

0 .6

);.-\

;

....., '

.......

0 .4

I

0 0

10

20

30

40

50

60

70

80 Tlme

figure 9 . HQ values for batches 1 , 2 , J and '1 .

o

10

20

30

40 50 Time

Exp Res • •

Exp.

Model - ---

Model ----

KF E s t - _

KF Est. _

60

70

80

90

o

10

20

30

40 50 60 Time

18 16 14 12 10

,, • Exp. ~ . --- Model '..... -KF Est. .. .....

o

10

20 30

40 50 60 Time

70

80 90

Figure 10. State estimation on Clavulanic acid fermentation (I).

70

Res .•

80 90

90 (hound

198

1.. ..\. TariJud. 1'1 Ill.

I

I

I

"C

~

"

"C 0

.t ,,

/ /

0

10

20

10

20

30

Time

40 50 60 T i me

70

80

70

80

Yxc 16

0.076

14 I I I

0.074

12 10

0.072

0 . 070

,

,

•

EXP

~S

0.068

.-- MODEL " -

o

10

20

30

40

KF EST " ..

50 60 Time

70 80

o

30

40 50 Time

Figur e 11. State estimation on cl avul anic acid fermentdti on

60

( Il ) .