1 Cover page

2 Optimization for Enzymatic Production of R-PAC Supervisor Prof. Peter L. Rogers Co - supervisor Dr. Bettina Rosche Noppol Leksawasdi

3 Scope of Presentation  Introduction and Objectives  Materials and Methods  Results and Discussions  Conclusions  Future work

4 What is R-PAC ?  PAC is for Phenyl-Acetyl-Carbinol  R- is from Rectus (Latin) means “right” S- is from Sinister (Latin) means “left”

5 What is the commercial value of R-PAC ?  It is used as precursor for production of pharmaceutical compounds ephedrine and pseudoephedrine  Ephedrine is used to treat asthma and bronchial congestion as a vasodilator  Pseudoephedrine is for treatment of cold and flu symptoms

6 Ephedrine products

7 How can ephedrine be produced ?  Plant extraction from Ephedra plant (in China), limitation on space, labour cost  Chemical synthesis (in literature)  Combined (biological & chemical) synthesis, Advantage over chemical method for specificity and mild reaction conditions

8 a) R-PAC biotransformation What is the mechanism for combined method of ephedrine synthesis ?

9 b) Ephedrine production from R-PAC What is the mechanism for combined method of ephedrine synthesis ?

10 Reactions involving R-PAC biotransformation

11 What is the role of PDC in yeast ?

12 The use of PDC as biocatalyst in biotransformation process  Stability of the enzyme depends on microbial strains  Deactivation of enzyme occurs more rapidly with the presence of benzaldehyde  Biotransformation with live whole cells may result in formation of byproduct such as benzyl alcohol  Optimal pH is between 6.0 – 7.0 but after biotransformation the pH may rise to 7.8 – 7.9 (another deactivation problem)

13 What is the structure of yeast PDC ?  Tetrameric enzyme with four monomers  Molecular weight of 230 – 250 kD  563 amino acids / single polypeptide chain  Three compact domains   Obligatory cofactors : TPP and Mg 2+

14 PDC Structure

15 PDC Structure

16 One phase biotransformation  Both substrates and product are in aqueous phase  Formation of benzaldehyde emulsion when the concentration above 100 mM which is toxic to PDC enzyme  Shin & Rogers (1996) produced 22.0 g/l  Maximum [R-PAC] predicted by the model with fed batch profile is 33.3 g/l (Chow, 1998)  Provide basic understanding to fed batch, two phase, and membrane reactor

17 Two phase biotransformation  Separate hydrophobic and hydrophilic species into corresponding phase  Allow fine tune feeding of benzaldehyde by mass transfer  Higher [R-PAC] production than one phase system, increase enzyme stability  Production of [R-PAC] can be further enhanced by feeding in pyruvic acid to control the pH and replenish depleted pyruvate substrate

18 Two phase biotransformation Pyruvate, Enzyme, Buffer, Cofactors Octanol, Benzaldehyde, Benzoic acid Hydrophobic Hydrophilic R-PAC

19 Emulsion system under microscope

20 Objectives Improve [R-PAC] and productivity of enzymatic biotransformation process 1. Compare PDC enzyme from yeast and filamentous fungi and select the best PDC for biotransformation Perform batch biotransformation to obtain kinetic data for modelling and simulation of R-PAC production process 3. Bioreactor design for laboratory scale R-PAC production in one- and two-phase system 2. Evaluate kinetic parameters of the selected PDC

21 Materials and Methods

22 Materials and Methods

23 Materials and Methods

24 Materials and Methods

25 Materials and Methods

26 Results and Discussions 1.Compare yeast and filamentous fungi PDC in small scale biotransformation to select the best one for initial rate and laboratory scale experiment 2.Initial rate experiments for effect of enzyme activity, pyruvate and benzaldehyde in 2.5 M MOPS & cofactors 3.Model initial rate profiles and simulate the rate equations to existing data kinetics 4.Laboratory scale biotransformation with enzyme in different purification stages

27 Substrate 1: Pyruvate 1.43 M (aqueous) Substrate 2: Benzaldehyde 1.45 M (octanol) Buffer: MOPS 2.50 M (aqueous) Enzyme: Carboligase 8.50 U/ml (aqueous) pH & Temp: 6.5, 4 ºC Aqueous-octanol two phase in small scale (1.5 ml) Biotransformation of different enzymes 84 hrs pH 7.74 pH 7.77

28 Enzyme activity effect on initial rates Partially purified yeast PDC enzyme

29 Enzyme activity effect on initial rates

30 Enzyme activity effect on initial rates

31 Enzyme activity effect on initial rates

32 Enzyme activity effect on initial rates Thus… …Up to 4 U/ml

33 [Benzaldehyde] effect on initial rates

34 [Benzaldehyde] effect on initial rates

35 [Benzaldehyde] effect on initial rates

36 [Benzaldehyde] effect on initial rates (Palmer, 1991, p.252) Monod-Wyman-Changeux (MWC) Model R 2 = , RSS =

37 [Benzaldehyde] effect on initial rates Monod-Wyman-Changeux (MWC) Model Parameter VmVm KbKb h Value x Unit mM / minmM – K b = Microscopic binding constant h= Hill coefficient (Co-operativity)

38 [Pyruvate] effect on initial rates

39 [Pyruvate] effect on initial rates

40 [Pyruvate] effect on initial rates

41 Michaelis – Menten kinetics Model R 2 = , RSS = [Pyruvate] effect on initial rates

42 Michaelis - Menten Model Value Unit mM / minmM Parameter VmVm KmKm [Pyruvate] effect on initial rates 4  C

43 Models for R-PAC production R-PAC Pyruvate Benzaldehyde

44 Models for R-PAC production Enzyme activity Acetaldehyde Acetoin

45 Simulation of model to experiment Substrate 1: Pyruvate 225 mM Substrate 2: Benzaldehyde 150 mM Buffer: Phosphate 40 mM Benzaldehyde emulsion one-phase (Shin, 1994) Benzaldehyde emulsion one-phase (Shin, 1994)

46 Simulation of model to experiment R-PAC V mO =  mol/min /U K b = 1.00E-04 mM h = 2.34 K m = 10.6 mM Enzyme activity K d = 2.29E-04 mM -1 min -0.5 Acetaldehyde & Acetoin V q = 1.65E-03 min -1 V r = 4.81E-05 min -1

47 Simulation of model to experiment Establishing communication with EXCEL Processing all differential models Constructing numerical integration table Transferring parameter values Simulation request

48 Simulation of model to experiment R 2 = , RSS = 1721 Fitting experimental data plotting Completed

49 Analog pH control system Digital pH control system On/Off PID AAA Laboratory scale biotransformation

50 Substrate 1: Pyruvate mM Substrate 2: Benzaldehyde 1.8 M Buffer: Phosphate 50.0 mM Enzyme: Carboligase 3.0 U/ml pH & T: 7.0 & 25  C (controlled) Feeding : 3.60 M pyruvic acid Stirring : 250 rpm overhead stirrer Aqueous-octanol two-phase in laboratory scale (100 ml) Laboratory scale biotransformation

rpm500 rpm 0:00:000:00:02 Laboratory scale biotransformation

52 Laboratory scale biotransformation

53 Laboratory scale biotransformation pH =  0.010

54 Conclusions  PDC enzyme from Candida utilis is the most suitable for initial rate and large scale biotransformation due to its high stability and R-PAC productivity  Initial rate of R-PAC production is directly proportional to enzyme activity level up to 4 U/ml  Effect of [pyruvate] to initial rate profile follow Michaelis-Menten kinetics  Effect of [benzaldehyde] to initial rate profile can be better described with MWC model than Michaelis-Menten kinetics

55  The mathematical models describing R-PAC biotransformation profile have been simplified with fewer parameters and improved fitting  Digital pH controlling system is able to control pH level with greater accuracy than the on/off analog pH controlling system for 100 ml biotransformation volume  Biotransformation with untreated whole cells provides promising aspects for future R-PAC production with lower cost and simpler way of catalyst preparation Conclusions

56  Model the enzyme deactivation kinetics profile of Candida utilis in MOPS buffer  Initial rate experiments to investigate the effect of benzoic acid and by-products such as acetaldehyde and acetoin on R-PAC production  Develop and experimental verification of optimal feeding profile for benzaldehyde emulsion batch system in laboratory scale  Laboratory scale biotransformation of R-PAC in aqueous-octanol two-phase system with varied conditions such as temperature, enzyme activity level, and initial [substrate] Future work

57 Acknowledgments  Professor Peter L. Rogers  Dr. Bettina Rosche  Dr. Russell Cail & Dr. Malcolm Noble  Wolfgang Nittel, Sue Jackson  Vanessa Sandford  Martin Zarka & Tony Gellert  Dr. Christopher Marquis  Mallika Boonmee, Alan Rushby  Thai government  Lia, Allen, Onn, Ronachai, Apple  Everyone in G17 & Biotechnology colleagues

58 Q & A

59 Development of by-products rate model

60 Development of by-products rate model

61 What are the differences between mathematical modelling and simulation ?  Modelling process estimates parameter values in mathematical model by aiming to minimize Residual Sum of Square (RSS)  Simulation process assesses the fitting of predicted values from model to experiment data Time Independent System

62 What are the differences between mathematical modelling and simulation ? Time Dependent System  Involve set of differential equations, e.g. kinetics time course  Parameter set estimated from modelling process is used only as “initial guess” in simulation process  Simulation process executes both parameter estimation and curve fitting assessment

63 Fermentation culture Wet mycelial mass Preparation of fungal mycelium powder Wash twice w/ RO waterFiltration Freeze dried for 24 hrs Freeze dried mycelial mass Rocklabs ring grinder Mycelium powder

64 Retain crude extract supernatant Preparation of fungal partial purified PDC powder Dissolve in MES buffer Acetone precipitation Freeze dried for 24 hrs Partial purified PDC powder Mycelium powder % (v/v) Perform at 6 degC Retain PDC ppt (orange colour)

65 Fermentation culture Wet cells mass Preparation of frozen yeast whole cells Wash twice w/ RO waterCentrifugation Resuspend in citrate buffer Concentrated cells mixture Keep in –20 degC Frozen yeast whole cells Adjust OD to 180

66 Cell walls weakened whole cells Preparation of yeast partial purified PDC powder Melt & Treat with liquid N 2 Glass beads treatment Follow similar treatment as filamentous fungi Partial purified PDC powder Frozen yeast whole cells 1:1 ratio, V1 / I5, 3 times Perform 3 times Yeast crude extract