Advances in the Process Development of Biocatalytic Processes

Concept Article pubs.acs.org/OPRD

Advances in the Process Development of Biocatalytic Processes Par̈ Tufvesson,* Joana Lima-Ramos, Naweed Al Haque, Krist V. Gernaey, and John M. Woodley Center for Process Engineering and Technology, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Anker Engelunds Vej 1, Building 101A, DK-2800 Kongens Lyngby, Denmark ABSTRACT: Biocatalysis is already established in chemical synthesis on an industrial scale, in particular in the pharmaceutical sector. However, the wider implementation of biocatalysis is currently hindered by the extensive effort required to develop a competitive process. In order that resources spent on development are used in the most efficient manner for these challenging systems, a holistic view on process development and a more in-depth understanding of the underlying constraints (process related as well as biocatalyst related) are required. In this concept article a systematic approach to solve this problem is proposed, involving the use of process tools and methods to assist in development.

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advances in protein engineering now make it possible to fit the biocatalyst to the process. Once initial activity for the desired reaction has been detected, the enzyme performance can indeed be enhanced by protein engineering to improve the desired properties such as activity, stability, and selectivity.10 There are several examples where new biocatalytic routes have been established through significant improvement of an existing enzyme via iterative rounds of mutagenesis and screening.11,12 However, despite the remarkable advances in state-of-the-art protein engineering, we are convinced that it is still not possible to completely fit the biocatalyst to any process. For example, the operating space for a biocatalyst can be expanded significantly from pH 7 and ambient temperature, but enzymes still have limitations when compared to chemical catalysts (which in general can withstand high concentrations and temperatures). Furthermore, the thermodynamic constraints of the process cannot be addressed by biocatalyst modifications directly. Thus, the likely operating space for the biocatalyst with regard for the process feasibility and cost of changing key parameters needs to be considered in the design of the process.13 A biocatalytic process involves developing several different subprocesses or systems: (1) production of the biocatalyst (fermentation and expression), (2) biocatalyst development (protein engineering and formulation, e.g., immobilisation), and (3) process and downstream process (DSP) design. Changes to one part can influence the otherseither positively or negativelyand can also affect the performance requirements. For example, if the expression of the catalytic enzyme is very efficient, this lowers the demand for specific activity of the enzyme itself. Likewise, an enzyme that can produce product to very high concentrations, thereby relaxes the demands on the downstream process (DSP). On the other hand, changes to the protein which may bring reaction benefits can also negatively influence the ability to produce the biocatalyst (e.g., expression or secretion). Invariably, opposing targets will require compromises in key parameters, e.g. a higher temperature will lead to a more active but less stable biocatalyst.

INTRODUCTION Biocatalysis offers the potential for very selective and resourceefficient synthetic processes1,2 and is already established as a complement to traditional chemical catalysis on an industrial scale, especially in the pharmaceutical sector. In fact, today there are many examples of biocatalytic processes that outperform conventional processes with regard to both quality and economic profitability.3−5 In nature, enzymes have evolved for the conditions under which the host organism lives. Such conditions are usually quite different from typical industrial chemical process conditions in terms of substrate concentration, temperature, solvent, and pH. Indeed, for an economic and environmentally sustainable process, the requirements for high catalyst productivity at elevated concentrations of substrates are very demanding.6,7 Nevertheless, for certain types of biocatalytic processes, such as lipase-catalysed resolutions for production of optically pure APIs, these requirements can regularly be met. For more complex enzyme reactions (e.g., those which are thermodynamically unfavorable), or processes for which the relatively low added value puts even more stringent demands on process intensity and productivity, extensive development effort is required. Although the results achieved through the use of different technologies can be impressive, the outcome is uncertain, and the potential benefits have to be weighed against the development cost and time. Naturally, this still presents a significant obstacle to the implementation of these types of processes. There are two main parts in the development of a biocatalytic process, a biocatalyst part and a process part. The biocatalyst part uses biological methods, such as protein engineering and fermentation technology to improve the biocatalyst and also to develop a cost-effective catalyst. The process part is focused on introducing engineering solutions such as substrate feeding, in situ product removal, enzyme immobilisation, and two-liquid phase operation to enable the process to run effectively. Additionally, it is important to recognise the interaction between the two parts. A key issue, raised 10 years ago by Woodley and Burton8 when developing a biocatalytic process, is whether the process should be fitted to the enzyme or if the enzyme should be modified to fit the process. Bornscheuer and co-workers argued recently9 that the © XXXX American Chemical Society

Received: June 24, 2013

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dx.doi.org/10.1021/op4001675 | Org. Process Res. Dev. XXXX, XXX, XXX−XXX

Organic Process Research & Development

Concept Article

Figure 1. Systematic and iterative approach for developing biocatalytic processes. By breaking down the overarching process targets into targets for each section, each subsection can be developed independently. However, at different stages of development the process is re-evaluated to make it possible to adjust development targets, depending on the outcome of other subparts.

reduce the time for development. This article therefore proposes a framework for process design and development for biocatalytic processes and discusses the available tools to achieve this. The use of tools such as conceptual process design and assessment, high-throughput experimentation and data collection, physical property estimation, as well as catalyst and process models, will be discussed with examples.

This raises the question in which order the different parts (i.e., biocatalyst or process development) should be addressed. Similar to the development of other catalytic processes, the number of process parameters that can be altered are many (e.g., temperature, pH, solvent, biocatalyst formulation); thus, the number of conceivable options for a biocatalytic process is very large. As nicely illustrated by Murray and co-workers for chemically catalysed processes14 the number of possible alternatives can even be in the range of millions. Furthermore, with current attrition rates (only 1 out of 10 drugs that enter phase I reach the market15) in the pharmaceutical industry, there is a growing trend toward short lead times for process development.16 This presents a huge development challenge, and it is quite clear that a high-throughput experimentation platform alone will not solve the problem of finding the best solution. Consequently, it is important that the development of any new process is done in the most effective way. A systematic approach to solve the process development problem would help to make sure that time and other resources spent are used in the most efficient manner. In addition, we believe that a more thorough understanding of the underlying constraints (biocatalyst as well as chemistry and process related) would help to decrease the uncertainty of the outcome of the development effort and help to discard nonviable options early. Finally, the use of process tools and methods to assist in achieving an efficient workflow for development is also likely to

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TOWARDS A DEVELOPMENT FRAMEWORK FOR BIOCATALYTIC PROCESSES Traditionally, process development is done in an evolutionary manner through incremental improvements to the different parts. This approach, although appealing in its simplicity, has the drawback that it is time-consuming, very difficult to find the true optimum, and very difficult to anticipate the final performance of the future process. Furthermore, the gain in knowledge and understanding of the process is limited. In fact, systematic approaches to process development were adopted many years ago in the design and operation of large-scale chemical processes. Likewise, in other parts of the field of biotechnology, significant progress has been made towards a more rational and systematic process development methodology.17−21 However, such approaches are not yet well adopted or widely implemented in the development of biocatalytic processes. For conventional chemical processes, developing a new process is usually seen to involve the following steps:22 B



Concept Article

Table 1. Approximate threshold values (targets) for the process metrics for scale-up of a biocatalytic process in different sectors industrial chemical sector

a

biocatalyst yield (kg product/kg biocatalyst)a

pharma/fine

10s to 100s

speciality bulk

100s to 1000s 1000s to 10,000s

reaction yield (%) high (single reaction step >90−95; overall reaction yield >80) high (>80) very high (>99)

product concentration (g/L)

typical scale (m3)

50−100

0.1−10

100−200 200−400

10−25 >25

Crude or immobilised enzyme. For whole-cell catalysts, requirements are lower due to lower costs.

• chemical route synthesis (selecting the chemical synthesis steps) • conceptual process design (selecting the unit operations) • process development • implementation and operation In the following section we will discuss how this structure can be adapted to the development of biocatalytic processes. Chemical Route Synthesis and Conceptual Process Design. The purpose of chemical route synthesis and conceptual process design is to identify one or a limited number of process options that are likely to be successful. At this stage, many different chemical routes and processes are considered and compared side-by-side. The challenge is how to consider all relevant options and how to evaluate them, while at the same time minimizing the work load and time required for so doing.23 Different strategies to address this issue have been suggested in the scientific literature.24 For example, one approach is to generate all possible process options and then select the best option on the basis of a computer-based simulation and analysis.25,26 However, this requires a lot of information (in the form of mathematical models) about the alternativesinformation that is generally not available or not readily accessible for biocatalytic systems. In addition, as the number of options is virtually infinite (e.g., in terms of biocatalysts generated by protein engineering), this approach is not always possible for biocatalytic processes. A more realistic approach would be a knowledge-based and iterative approach where the search space is gradually reduced by decomposing the problem and eliminating the least attractive options in stages. During the design process, as more information becomes available (e.g., from experiments), the conceptual design becomes more defined. Naturally, combinations of the two, so-called hybrid approaches, have also been suggested. Figure 1shows a schematic representation of the development process for a biocatalytic process. Different chemistry and process options are generated and evaluated against the quality and economic targets for the desired product. The most promising (feasible) option(s) is(are) then selected for further development. It is clear that it is desirable to discard as quickly as possible nonviable options to focus man-power on the most promising possibilities. This can be done by analysing the constraints on the basis of the information related to the process (economic as well as thermodynamic). Clearly, this means that there is still a considerable need for information about the different systems. These data include the following: - cost and availability of reactants - physicochemical properties of the reaction system (e.g., reaction thermodynamics, solubility, and volatility of reactants) - crude information about the biocatalyst (kinetic and stability data for the biocatalyst at process relevant conditions)

More than eliminating nonfeasible options, the chemical route synthesis and conceptual design steps identify the process targets, which will be translated into subtargets for the development of the fermentation, biocatalyst development, and the process and DSP development. Importantly the process targets will also be used to define the conditions in the biocatalyst screen. For an efficient development process, as much information as possible should be acquired with the least amount of effort. This also highlights the need for tools that can be used with minimal information. Methods and tools are needed that, on the basis of the limited information at hand, are able to rank the generated alternatives and to discard nonviable options as well as to set the targets for the subsequent processes. Tools for assessing the process economy and estimating physical properties, as well as tools for biocatalyst and process simulation, will be discussed in the following sections. Process Evaluation: Setting Economic and Environmental Targets. It is critical to have an understanding of the factors that determine process and development costs so that the right challenges are addressed, under relevant process conditions. In the early stages of development only conceptual economics are required, whereas in later stages more detailed and accurate estimates are required to make feasibility assessments and finally a fully detailed estimate.27−29 In the absence of detailed information, the calculations can be based on material and energy balances and rules-of-thumb for catalyst costs.6 It is important that prices for chemicals are selected such as that the sales volume is considered since prices for sales in gram quantities differ from bulk prices by orders of magnitude. One important economic measure is the cost of the biocatalyst per unit of product produced. The production cost of the catalyst itself is very dependent on the efficiency of the production. For example an enzyme yield in the gram per litre range is necessary to avoid excessive costs. Methods for the efficient expression and production of advanced biocatalysts are available for industrial high cell density fermentation processes, thus increasing production efficiency.30,31 The possibility of obtaining the desired biocatalyst at a reasonable cost has in recent years increased markedly. Likewise, the preparation and form of the biocatalyst is another important consideration. For cost reasons, the biocatalyst should be used in the crudest form possible since every preparation step adds significantly to the total cost, and normally decreases retained activity.6 Often it is desirable to immobilise the biocatalyst on a solid support or in an insoluble porous matrix in order to be able to increase the productivity of the catalyst by multiple reuses32 which adds to the development time and cost of the biocatalyst preparation but could also potentially lower the cost contribution of the biocatalyst to the overall process. There are a number of other metrics particularly important for an economically feasible biocatalytic process such as the concentration of the product achievable (mass product/reactor volume), the yield of the process such that sufficient use is C



Concept Article

Figure 2. Conceptual representation of the use of constraint analysis to identify a potential operating space or the need for development of the biocatalyst. In the example to the right, the process window for the biocatalyst overlaps the process window for the process, and thus a feasible solution can be found. In the example to the left there is no such operating window, and therefore the biocatalyst and/or process need to be developed. The analysis can help identify suitable process conditions (e.g., for biocatalyst screening).

optimisation may require a compromise in the reaction conditions such that pH, temperature, and solvent changes in the integrated process are minimised. Likewise, residual reagents and impurities from preceding steps may need to be accommodated in the biocatalyst screening.

made of the substrate (mole product/mol substrate), and the space-time yield (mass product/time/reactor volume). The balance between these three metrics, together with biocatalyst yield (mass of product/mass of biocatalyst), is dependent upon the economic drivers of a given process and will clearly be dependent upon the industrial sector (Table 1). In addition to metrics that fundamentally determine the process costs, technical feasibility metrics such as biocatalyst loading and separation time by extraction can also be critical and should be used when applicable.33 In the same way it is important to consider the environmental profile of the process already in the design phase, as this is the stage at which most decisions are made that will influence the environmental impact of a process. In a way analogous to that of the economic impact, the environmental impact varies greatly between different types of processes. For instance for pharmaceutical processes, the use of solvents, toxic materials, and waste generation are the primary concerns, while for bulk processes, emissions contributing to global warming and use of nonrenewable resources are added to the list of concerns.7 Evaluation of Development Needs. When evaluating the process alternatives it is also necessary to identify and assess the development effort needed to fulfill the economic targets (metrics). As can be understood from Figure 2, in some cases the analysis will identify a potential operating window wherein the process may be feasible (Figure 2, right), or when this cannot be found (Figure 2, left), identify the need for development of the biocatalyst and the conditions under which it needs to operate (e.g., concentration and temperature) and the development needed (e.g., through protein engineering), as well as the probability that these targets can be met. This analysis will provide vital information for the screening conditions employed in the protein evolution. A further implication of the constraints analysis is that collecting information about the process and the biocatalyst sometimes needs to be decoupled. That is, in those cases where the current form of the biocatalyst is not fit to operate under process relevant conditions, information about the process still needs to be acquired, but independently of the biocatalyst. This can sometimes be done through simulations but will in most cases require experimentation (without biocatalyst). A particularly interesting case is the fitting of one (or two) biocatalytic steps into a complete multistep synthetic route, such as frequently occurs in pharmaceutical processes. Here,

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PHYSICOCHEMICAL DATA Basic physicochemical data related to the process components are also required for the conceptual design and analysis of the process at an early stage.34,35 This information will set the foundation for the type of process that will be developed. For instance solubilities, melting points, and volatilities of the reaction components determine if the process will be operated with multiple phases and give fundamental information on DSP design and/or opportunities for in situ product removal (ISPR). Information can sometimes be found in the scientific literature (available through online databases such as the NIST Chemistry WebBook36 or Reaxys37) or through the accumulated experience within an organisation from similar processes. If this is not the case, the use of software or algorithms to predict, for instance, the physical properties of the components can provide a first estimate for the assessment, although care should be taken with respect to the accuracy of the information. Software for estimating properties such as Log P and volatility is widely available through Internet portals such as Chemspider38 (including software from ACD Laboratories, EPI, and ChemAxon). A less developed area is the computer-aided methods to predict the reaction’s Gibb’s free energy changes, ΔG, and from these data (by calculation) the thermodynamic yield of a given reaction.39 Different in silico methods have mostly been developed for nonaqueous environments, and ongoing research is addressing the need for better methods to suit the needs of biocatalysis.40

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PROCESS MODELS A combination of modelling and process simulation can drastically reduce time and cost for process development and also help to choose the most cost-effective process design.41 In principle there are two alternative approaches for process models: The first is an empirical approach, as suggested by Murray,14 which is based on design of experiments (DoE) and the statistical relationship between the parameters (or properties) and the process outcome (for instance by principal component analysis (PCA)). The second approach is based on D



Concept Article

a mechanistic relationship between the process parameters, the properties they give to the system, and the process outcome. The latter approach requires a deeper understanding of the system but can on the other hand facilitate process development of other similar processes once implemented (for subsequent development projects), and can be extrapolated outside of the investigated range.42 However, for certain phenomena or processes, mechanistic models may not be available. The availability of a process model can be instrumental in the effective implementation and operation of an enzymatic process. Often, the ratio of Kcat/Km is used to describe the biocatalyst performance.43 However, for more complex biocatalytic reactions it is important to include also the inhibition parameters of the substrate(s) and product(s) to accurately quantify the catalyst effectiveness and to provide guidance for biocatalyst improvement.44−46 Although a detailed kinetic model is not required in the suggested methodology, the kinetic parameters provide an insight into the biocatalytic reaction mechanisms (such as inhibition). Nonetheless, whenever models are used, it is important that the model complexity is adapted to the ultimate objective of the model.42 Models for enzyme deactivation and facilitated methods for estimating the biocatalyst productivity or total turnover number (TTN) are also helpful for a thorough understanding of the system and to assess the process performance.47,48 However, frequently it is difficult to deduce mechanistic relationships between the parameters and outcome. In these cases a statistical approach can be more useful. For example Adlercreutz and coworkers used principal component analysis to correlate the molecular properties of solvents to their effects on biocatalyst (lipase) stability.49 The development of kinetic models and determination of catalyst parameters is relatively widely established in the field of biocatalysis. However, the information is often only presented as a result in itself and seldom coupled to process models or demonstrations of how the parameter(s) affect(s) the process. There is a need to further integrate them into the decisionmaking process; selecting the best process or operating conditions or assessing the feasibility of different process options. The kinetic and stability models can be combined with process (mass balance) models to describe the dynamic behaviour of the process.42,50 Process models can also be used to simulate different process scenarios for different reactor configurations, via “what if” analyses.51 For instance, whether it would be beneficial to run a reaction in a batch reactor, a packed bed reactor, or a continuous stirred tank reactor52 or likewise evaluating opportunities for in situ product removal.53,54 It can also be used for optimisation of reaction conditions, for instance finding the optimal ratio of enzyme and/or cosubstrate concentrations to provide the highest product yield.55,56 For example, Ž nidaršič-Plazl and Plazl57 modelled the continuous extraction of steroids using ethyl acetate in a microsystem and achieved good agreement between model calculations and experimental data. A further development by the Jensen group at MIT is the use of automated continuous flow systems coupled with online analysis to model and optimise chemical syntheses.58,59

mechanistic) of the biocatalyst or process. It is often the case, when developing a biocatalytic process, that the amount of available catalyst is very small (or at least very expensive) in the early stages of development. Likewise, the number of alternative catalysts can be very large. This is particularly true for enzyme variants, as discussed above. This highlights the need for high-throughput miniaturised equipment, either multiwell plates or microchannel flow reactors.60−62 Although (as discussed above) a high-throughput platform in itself cannot solve the problem, this is an increasingly important research area that is presented with its own set of challenges (e.g., how low of a reactor volume is feasible in the light of the material needed for subsequent analytics and how can analytics be integrated and automated). Already some efforts are being made in order to facilitate and increase the ability to collect process data in an automated and parallelised manner.63−65

HIGH-THROUGHPUT EXPERIMENTATION: DATA COLLECTION AT MINIATURE SCALE In most cases many experiments need to be carried out to characterise a process or build a model (empirical or

ACKNOWLEDGMENTS We acknowledge the support of the BIOINTENSE project (Grant Agreement No.: 312148), financed through the European Union 7th Framework Programme. N.A.-H. acknow-

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CONCLUSIONS AND OUTLOOK Biocatalysis holds great potential for clean and resourceefficient processes, but an expansion of the types of reactions that reach industrial implementation is currently held back due to the complex, costly, and time-consuming task of developing a competitive process. We believe that by employing a more structured systematic approach to process development and effective use of process development tools, the efforts that are put into development can have a greater effect and can be directed at the most relevant targets. We also believe that this will become even more important for future processes such as multienzymatic,66 cell-free biocatalysis,67,68 or challenging chemistries (e.g., thermodynamically unfavorable reactions). Developing such a framework requires a holistic approach; high-throughput experimentation, analytics (online, integrated), kinetic and process models, and evaluation tools all need to be arranged and employed in a structured way, i.e. within a process development framework (possibly through integration into a software tool). Conceptual process analysis at the outset of any development effort, also of academic studies, should become mainstream in order to address the relevant bottlenecks and to set targets for further development. Accessible in silico experimentation tools such as those that predict reaction thermodynamics and other physicochemical properties in dilute water solutions would be very desirable as would easy-to-use miniaturised equipment and integrated analytics for collecting data that can be fed into catalyst kinetic and process models that should be used not only for optimisation but also for feasibility analyses. This would provide better process understanding that would greatly reduce development time and effort and would also be in line with regulatory trends for Quality-by-Design (QbD).69 It is also important that the tools or methods reported are placed in the broader development and business context.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] Notes

The authors declare no competing financial interest.

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ledges support from AMBIOCAS, financed by the European Union through the 7th Framework People Programme (Grant Agreement No. 245144). J.L.-R. acknowledges support from BIOTRAINS Marie Curie ITN, financed by the European Union through the 7th Framework People Programme (Grant Agreement No. 238531).

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