Biocatalytic continuous manufacturing of diabetes drug: plantwide

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Biocatalytic continuous manufacturing of diabetes drug: plantwide process modeling, optimization, environmental and economic analysis Chi-Hung Ho, Jieran Yi, and Xiaonan Wang ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.8b04673 • Publication Date (Web): 05 Dec 2018 Downloaded from http://pubs.acs.org on December 8, 2018

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ACS Sustainable Chemistry & Engineering

Biocatalytic continuous manufacturing of diabetes drug: plantwide process modeling, optimization, environmental and economic analysis Chi-Hung Ho, Jieran Yi and Xiaonan Wang* Department of Chemical and Biomolecular Engineering, Faculty of Engineering, National University of Singapore, Block E5, Engineering Drive 4, 117585 Singapore * Corresponding author Email: [email protected]

Tel: +65 6601 6221

ABSTRACT

This paper proposes a comprehensive framework of biocatalytic continuous manufacturing of sitagliptin, the active pharmaceutical ingredient of the leading dipeptidyl peptidase-4 inhibitor antidiabetic drug. Continuous manufacturing has the advantages of quality consistency, reduced waste generation and cost-effectiveness in comparison to batch processes. Furthermore, compared to traditional catalysts, biocatalysts have lighter environmental footprints. An end-to-end continuous manufacturing process is designed and the reaction kinetics of the bio-catalytic reaction is determined according to the published data. Based on the steady-state model of a plug-flow microreactor, the optimal productivity is determined to be 2.6 x 10−2 mol hr-1 using surrogatebased optimization. In addition, an assessment of the process’ environmental impacts demonstrates its sustainability with a lower E-factor of 53 compared to 200 of traditional processes. A comprehensive techno-economic analysis has also been performed, validating the economic feasibility of this process with a Net Present Value of $150 million over a 20-year time period.

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Therefore, this present paper demonstrates the feasibility, sustainability and economic competitiveness of the proposed process.

KEYWORDS: Continuous pharmaceutical manufacturing; Biocatalysis; Techno-economic analysis; Environment; Surrogate-based optimization

INTRODUCTION The rapid economic growth across the world has significantly improved people’s overall quality of life, yet has also raised the prevalence of diabetes to over 8% in recent years.1 Majority of diabetes patients suffer from Type II diabetes to which the recombinant insulin is the most common treatment.2 Dipeptidyl peptidase IV (DPP-4) inhibitors, as a supplementary treatment to insulin injection, have been widely studied because of their advantages. They are orally active without the need of intravenous injection and patients do not suffer from side effects such as hypoglycemia commonly seen in those receiving insulin treatment.3 The mechanism of action of DPP-4 inhibitors is by suppressing the DPP-4 enzyme to decrease the degradation of glucagon-like peptide-1, a hormone which accelerates insulin synthesis and blocks glucagon secretion.4 Among DPP-4 inhibitor drugs, Sitagliptin is the most commonly used one with a market share of 14.8% of all diabetes drugs in 2016.5 To date there has not been any literature discussing the manufacturing process of sitagliptin. Therefore, one of the objectives of this paper is to propose a sitagliptin manufacturing process in a continuous manner with promising technologies and green engineering principles that can shed light on the manufacturing of other Active Pharmaceutical Ingredients (APIs) as well.


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The chemical synthetic route for the final product – sitagliptin monophosphate 13 starting with chloropyrazine 1 is shown in Scheme 1.6 Among Species 1 to 13, Species 1, 3, 8 and 9 are the raw input materials added while the others are intermediates formed during the process. The R1-R8 and M6 below the intermediate species correspond to the locations at which they are formed in the manufacturing process. More elaborations on the manufacturing process can be found in the next section. In the rest of the paper, Species 1 to 13 will be referred to by their numbering instead of species names. Scheme 1. Synthetic route of sitagliptin monophosphate from chloropyrazine

The step converting prositagliptin ketone 11 to sitagliptin 12 by transferring an amino group to the ketone group traditionally involves rhodium-based chiral catalyst which is harmful to the environment. Along with the advancement in metabolic and protein engineering, the effective design of microbial metabolism has brought about more eco-friendly biocatalysts for application.7,8 Biocatalysts have been applied in various industries such as papain in the food industry and enzymes from Mycobacterium sp. in the pharmaceutical industry.9,10 Derived from


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renewable materials, biocatalysts are generally environmentally benign and can be used in moderate reaction conditions such as ambient temperature and neural pH.11,12 Their high selectivity also simplifies the manufacturing process and reduces the use of solvents.13 According to Henderson et al., compared to chemical processes, a biocatalytic process could generate less environmental impact, with 60% less consumption of energy, 70% less consumption of oil equivalents, 86% less consumption of net mass, 50% less greenhouse gas emissions and 77% less acidification potential.14 In the case of sitagliptin, a biocatalytic manufacturing with novel transaminase was developed by Savile et al in 2009. It provides a 10-13% higher yield, a 19% reduction in total waste and a high enantiomeric excess of over 99.5% compared to the traditional heavy metal catalyst.15 On the other hand, despite that batch manufacturing is currently dominant in the pharmaceutical industry because of the stringent regulations of the Federal Drug Administration (FDA) regarding changing manufacturing manner, continuous manufacturing has attracted the attention from both academy and industry due to its lower environmental footprint, and has been voted as the top research domain in green engineering.16–18 Application of a wide range of unit operations in continuous manufacturing has been studied, e.g., continuous crystallization in pilot plant, nanofiltration and recycle of catalyst, screw element performance of twin screw granulator, and simulation of a tablet coating and powder mixing process.19–23 The growing attention can also be attributed to the advancement in online process analytical technologies that are commonly used in continuous manufacturing, which offers improved controllability and consistent quality to ensure meeting the principles of Quality by Design (QbD).24,25 On top of that, continuous processes feature lower supply chain inventory, higher overall equipment effectiveness and reduced resource consumption than batch processes, saving up to 30% of the total cost.26–28 Therefore, a process


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flowsheet incorporating these two technologies is proposed in this work, with kinetic modeling of the biocatalytic reaction and productivity optimization through surrogate-based optimization. This paper also aims to assess the environmental sustainability and economic potential of the investigated manufacturing process. For the pharmaceutical industry, the complex synthesis and separation steps often generate high amount of wastes.29 Companies such as GSK, Pfizer and AstraZeneca have developed strategies for environmental issues through proper solvent selection and life cycle assessment (LCA) of the processes. The quantitative and comparative environmental evaluations play a critical role for enterprises to meet green chemistry principle.30–32 Therefore, the Environmental factor (E-factor) is adopted to assess the material efficiency of this process. Moreover, from an economic perspective, reducing the cost of manufacturing which was used to be regarded as negligible in the pharmaceutical industry has been attracting increasing attention today.33 From the initialization of drug discovery to the final clinical approval, it typically costs $0.8 to $2 billion and takes 10 to 15 years.34 Hence, there is only 25 to 50% of the term of patent left when commercialization begins. With such inherent difficulty as well as the increasing R&D cost and competition from growing generic manufacturers, pharmaceutical companies have found it more and more difficult to meet the profit expectations.33 As a necessary step, a framework of techno-economic analysis is developed and conducted to determine the economic benefit of the proposed process. The systematic method adopted is able to determine the capital expenditure (Capex), operating expenditure (Opex) and Net Present Value (NPV) over the lifetime of a common pharmaceutical plant. This paper is organized as follows. Firstly, after the background introduction there follows the process design illustrated by the flowsheet along with the process description. Subsequently, the methodologies of kinetic modeling, process optimization, environmental impact and techno-


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economic analysis are introduced. Finally, the results showing the prospect of the process and applicability of the proposed methodologies conclude the paper.

PROCESS DESIGN Based on the reaction route shown in Scheme 1 as well as the downstream separation and purification procedures in literatures, a flowsheet of the biocatalytic continuous manufacturing of sitagliptin is designed as shown in Scheme 2. This design consists of multiple different operation units such as reactors, liquid-liquid extractors, mixers and crystallizers to achieve the synthesis and purification outcome in a continuous manner. In order to reduce the amount of waste solvent generated from the process, several evaporators are designed to evaporate the organic solvents for recycling.


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Scheme 2. Flowsheet of biocatalytic continuous sitagliptin manufacturing


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Microreactor One of the challenges in enzyme-catalysed reactions is the issue of mass transfer: the accumulation of products on the surface of transaminase enzyme may decrease the reaction efficiency. One solution is to increase the flow rate to a sufficiently high level to reach turbulence; yet the intensive shear generated may destroy the transaminase, and a high flow rate may lead to insufficient residence time and consequently low conversion. Another solution is to use plug-flow microreactors, whose small dimension provides an outstanding performance in reaching a more homogeneous mixture.35 They can also provide a large interfacial area for heterogeneous catalysis through its high surface area-to-volume ratio of typically 10,000 to 50,000 m-1.36 In 2005, Roberge et al. suggested that applying microreactors to reaction involving solid phase was challenging; however, with the advancements in microreactor technology in the recent decades, multiple successful cases of microreactors with solid phase have been reported.37,38 Also, microreactors often feature better heat transfer performance and avoid hot spots formation, resulting in a narrow residence time distribution and a higher reaction selectivity.35,39 On the industrial scale, the integration of microreactors and microseparators can meet the demand by parallelized number-up rather than scale-up, which on the other hand is often a challenge in the traditional batch manufacturing.34 Small volumes make the microreactors safer than traditional reactors and their low flow rates match the relatively small scale of production in the pharmaceutical industry.35,38 Most importantly, they can reduce the waste generation significantly. Based on these considerations, the process in this work is designed to comprise microreactors and microseparators. Such combination of biocatalyst and micro-scale operation units is expected to bring about significant benefits such as environmental friendliness.40


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In the meanwhile, immobilized biocatalyst technologies, such as powdered nanomaterials and monolithic carriers incorporated with microreactor in the form of packed-bed or wall-coated, have drawn increasing attentions. However, because leaching of biocatalyst may occur and the resulting repeated shutting down, replenishing transaminase and starting up again could be harmful to the economic interest of companies, immobilization is not an ideal option for a year-long manufacturing process.41–43 Therefore, free enzyme with recycling is chosen for design in this paper.

Process Description Scheme 2 shows the flowsheet of the proposed processes. Choloropyrazine 1 is first mixed with 35% aqueous solution of hydrazine in M1 and then fed to R1 which should be maintained at 60 to 65 °C. The outlet from R1 containing the first intermediate – hydrazine adduct 2 is then thoroughly mixed with 10% 2-propanol/dichloromethane in M2 so that the majority of 2 will dissolve in the organic phase. E1 then separates the aqueous phase and discards it. The organic phase is further passed to D1 in which majority of the 2-propanol/dichloromethane are evaporated and recycled back into M2. The remaining is mixed with isopropyl acetate (IPAc) in DT1 and then with trifluoroacetic anhydride 3 in M3. After the trifluoroacetylation reaction in R2, the outlet stream from R2 is mixed with antisolvent heptane in C1 to crystalize the product 4 which is separated out by filtration in F1. The filtrate is passed to D2 where a fraction of heptane is recovered. Due to their similar boiling points, IPAc and the small amount of 2-propanol are likely to be recovered together. To prevent the accumulation of 2-propanol, a fraction of this stream is purged before recycling back into DT1. The solids coming out from F1 are subsequently mixed and reacted with superphosphoric acid at 75 °C in R3 to form 5. Hydrogen gas is then used to hydrogenate 5 to 6,


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catalyzed by Pd supported on carbon in R4. After filtering the reaction mixture to remove the catalyst, excess HCl is added to convert 6 to its HCl salt 7 in M6 which is then precipitated out in C2 by adding isopropyl alcohol (IPA) as antisolvent. The HCl salt is then separated from the solution by filtration in F3. The filtrate is passed into D3 where a portion of IPA solvent is evaporated for recycling. In M7, 2,4,5-trifluorophenylacetic acid 8, Meldrum’s acid 9, pivaloyl chloride and IPEA are mixed at 1:1:1:2.1 mole ratio. The mixture is then fed to R5 maintained at 50 °C to produce tricarbonylic intermediate 10. This single reaction can achieve 95% yield with 97% conversion according to literature.15 10 is then mixed with 7 and TFA at 1:1:2 mole ratio in M8 then fed into R6 at 70 °C to form prositagliptin ketone 11. Water is added to precipitate out 11 from the solution in C3. Subsequently the solvent for the biocatalytic reaction – 50% DMSO/H2O solution is added to dissolve the solids followed by excess i-PrNH2 before feeding the mixture to R7. In Scheme 2 from R7 onwards, it illustrates the design for the downstream processes to separate, purify and phosphorylate the sitagliptin 12. The reaction mixture leaving R7 contains transaminase/PLP, DMSO/H2O solvent, unreacted 11, 12, excess i-PrNH2 and the by-product acetone. Ultrafiltration is implemented after R7 to recycle the transaminase/PLP. Transaminase is large in molecular size so it cannot pass through the membrane while other substances partially pass through the membrane. All the enzyme and other substances that do not pass through the membrane are recycled back into R7. For the portion that passes through the membrane, concentrated hydrochloric acid is added to it in M10 to adjust the pH to 1 – 2 in order to convert 12 to the more water-soluble salt form, and then it is mixed with IPAc in E2.44 The organic phase leaving E2 will contain mainly the unreacted 11 and is passed into D4 in which majority of IPAc solvent is evaporated for recycling back into M11.


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The aqueous phase leaving E2 containing 12 will be adjusted to pH 11 – 12 by mixing with sodium hydroxide in M12 so that 12 returns to the uncharged form and is more soluble in organic IPAc. IPAc is then added to extract 12 in E3, and the aqueous phase is extracted by IPAc again in E4 to reduce the loss of 12. The organic IPAc phase from E3 and E4 are combined in M15 and then washed by brine to remove the impurities such as DMSO. After removing the residual water using anhydrous Na2SO4, majority of the IPAc solvent is distilled away and ethanol is added to the remaining sticky phase. Excess phosphoric acid is then added to phosphorylate 12 to sitagliptin monophosphate 13 in R8. The reaction mixture is cooled to crystalize 13 in C4 and the solution is then removed by filtration. The filtrate out of F6 is passed into Fractional D6, in which IPAc and ethanol are recovered from different levels. The IPAc is combined with that evaporated in D5 and recycled back into M13 and M14 while the ethanol is recycled back into DT3. After washing the solids coming out of F6 in WT1 with ethanol and drying in DR1, the final product 13 is obtained. In the continuous manufacturing process described above, in order to minimize solvent waste generation, the organic solvents are evaporated and recycled at the possible locations. However, the stream passed into a distillation unit for evaporation not only contains the solvent but also some other species. In order to obtain a more conservative estimation of E-factor and costs, it is assumed that not all the solvent can be recycled via evaporation and a small portion is removed from the bottom of the distillation unit. If the solvent and some other species that are expected to be present in the stream entering the distillation unit have similar volatility, a small portion of the evaporated stream is purged before recycling back to the process to prevent the accumulation of impurities. This is again an estimation aiming at more conservative E-factor and cost calculations – in some pharmaceutical companies, solvents can be recycled completely without purging enabled by other advanced separation techniques.


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METHODOLOGY Determination of Kinetic Constant It is important to increase the process efficiency and profitability based on the understanding of reaction kinetics. The kinetic constant of the biocatalytic reaction which converts 11 to 12 in R7 is estimated based on published data as shown in Table S1.15 Provided that the transaminase behaves like a chemical catalyst without saturation occurring, when the reactant isopropylamine (i-PrNH2) is in excess compared to prositagliptin ketone (11) as mentioned in data source, the reaction in R7 can be approximated as a first-order reaction.15 Jolliffe et al. also suggested that the reaction of large organic compounds and excess of small compounds could be regarded as a firstorder reaction.45 The molecular weight of 11 is 406 Dalton, while that of isopropylamine is 59 Dalton, justifying the assumption of a first-order reaction. With the data of reaction time and conversion, according to Equation 1, natural logarithm of prositagliptin ketone concentration can be plotted against reaction time, and the kinetic constant could be obtained. 𝐶𝐴 and 𝐾 represent the concentration of 11 in the inflow and the reaction rate constant, respectively. 𝑑𝐶𝐴 𝑑𝑡

(1)

= −𝑘𝐶𝐴

Productivity Optimization The main reaction in R7 is optimized for productivity (i.e., the hourly production) maximization. To simplify the problem, some assumptions are made, including no radial concentration gradient and reactions taking place only inside reactors but not in the pipeline. The steady-state second order ordinary differential equation model is described in Equations (2)-(6) and the optimization problem is formulated as Equations (7)-(11): 𝑆𝐷

𝑑2 𝐶𝐴 𝑑𝑥 2

−𝐹

𝑑𝐶𝐴 𝑑𝑥

(2)

− 𝐾𝑆𝐶𝐴 = 0

𝐵. 𝐶. 1: 𝐹𝐶𝐴0 = 𝐹𝐶𝐴 − 𝑆𝐷


at 𝑥 = 0

(3)


13


𝐵. 𝐶. 2:


(4)

= 0 at 𝑥 = 𝐿

𝐶𝐴 = 𝐹𝐶𝐴0 ( 𝑎𝑒 𝑏𝑥 − 𝑏𝑒 𝑏𝑥 ) / (𝑆𝐷𝑎𝑏(𝑒 (𝑏−𝑎)𝑥 − 1) + 𝐹(𝑎 − 𝑏𝑒 (𝑏−𝑎)𝑥 )) where 𝑎 =

𝐹+√𝐹2 +4𝑘𝐷𝑆 2 2𝑆𝐷

max

𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦 (𝐶𝐴 𝐹)

s.t.

𝑖𝑚𝑝𝑢𝑟𝑖𝑡𝑦 = 0.18 ×

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,𝑏 =

𝐹−√𝐹2 +4𝑘𝐷𝑆 2 2𝑆𝐷

(5) (6)

(7)

𝐹𝑆 𝑥

− 0.01

(8)

𝐶𝐵 = (𝐶𝐴0 − 𝐶𝐴 ) × (1 − 𝑖𝑚𝑝𝑢𝑟𝑖𝑡𝑦)

(9)

𝐶𝐴 ≥ 0

(10)

𝐶𝐵 ≥ 0

(11)

The decision variables 𝐶𝐴0 , 𝐶𝐴 , 𝑥, and 𝐹 represent the concentration of 11 in the inflow, the concentration of 11 in the outflow, the length of the microreactor and the volumetric flow rate respectively. The parameters 𝐾, 𝑆, and 𝐷 represent the reaction rate constant, the cross-sectional area (7.85 × 10−5 m2) and the mass diffusivity (3.6 × 10−7 m2 hr-1), respectively.46 The inner diameter of microreactor is designed as 5 × 10−3 m for better mass and heat transfer.47 One of the constraints is the formation of impurity in this reaction. According to Savile et al., the addition of feed over 2-3 hr would transform 2-5% of the product into impurity due to the imine dimer formation between the substrate and the product.15 To model the formation of impurities, a linear relationship between feed addition time and impurity formation, i.e., between flow rate and impurity formation, is assumed. Surrogate-based optimization by pySOT package programed in Python is used to identify the global optimum of the abovementioned problem.48 The implementation steps of surrogate optimization and parameters option adopted in this present paper are summarized in the Table S2, and a flowchart is shown in Figure S1 for a clear visualization of this method. One significant


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advantage of this method is its capability to approximate the global optimum of a high-dimension problem within a short time.49 In recent decades, it has become an emerging research domain in various areas, such as separation of a mixture of hydrocarbons, Fischer-Tropsch synthesisand aerodynamic turbomachinery designs.50–52 Despite that Boukouvala et al. has utilized surrogatebased optimization to optimize the quality consistency of pharmaceutical in a compaction tablet manufacturing process, to date there are limited literatures discussing the application of this method in the pharmaceutical industry.53 Because of the complexity of the steady-state model in Equation (5) and (6), the case discussed here represents a suitable example to demonstrate the feasibility and competency of this optimization method in the pharmaceutical industry.

Mass Balance In order to complete an overall mass balance for the entire continuous manufacturing process, some experimental data can be found in literatures, such as the yield of 4 from 2 is 49%, but there are still many unknown variables to be assumed (e.g., the conversions and yields of some reaction steps, the mass fraction of product crystalized in the crystallizers, etc.). A “trial-and-error” approach is adopted to estimate those unknown variables so that the results of mass balance agree with the experimental data from literature and are also able to justify the steps adopted in the manufacturing process. For example, if the fraction of 2 dissolved in organic phase in E1 is very small, it does not justify the choice of 10% 2-propanol/dichloromethane as the solvent for extraction and thus the assumption is rejected to seek further solutions.

Analysis of Environmental Impacts


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Although multiple green chemistry metrics have been proposed for the quantitative comparison of different processes, the E-factor proposed by Sheldon is one of the most widely used and handy approaches.54 E-factor is defined as the amount of wastes generated per unit mass of product or API produced, as shown in Equation 12. E-factor =

𝑚𝑤𝑎𝑠𝑡𝑒𝑠

(12)

𝑚𝐴𝑃𝐼

E-factor is potentially affected by the mass fraction of species purged, and the mass fraction of solvent evaporated for recycling. Unlike the other unknowns such as conversion and solubility of the substances that are fixed by nature, these values can be altered by the actual design of the process. Hence, the sensitivity analysis is carried out to observe how much change in these values can affect the E-factor of the entire end-to-end process.

Techno-economic Analysis A systematic techno-economic analysis method adopted by Jolliffe et al. has been adopted here for economic analysis, which is summarized in Table 1.55

Table 1. Summary of Capex and Opex Capex

Sum of items (5), (8)

Installed cost

Free-On-Board (FOB) cost × 1.43

(1)

Instrument

Installed cost × 0.12

(2)

Piping

Installed cost × 0.3

(3)

Construction cost

(Installed cost + Instrument + Piping) × 0.3

(4)

BLIC

Sum of items (1) – (4)

(5)

Working capital

Annual material cost × 0.035

(6)

Contingency

BLIC × 0.2

(7)

WCC

Sum of items (6), (7)

(8)

Opex

Sum of items (9) – (11)


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Waste disposal

Solvent volume × $0.51/L

(9)

Utility

Material weight × $1.4/kg

(10)

Annual material cost

Material weight x unit price

(11)

Amount of sitagliptin monophosphate contained in 100 mg Januvia® tablet

100 mg56

(12)

Unit price of 100 mg Januvia® tablet

$15 (price in CVS Pharmacy on July 25th, 2018)57

(13)

Revenue

The process is assumed to be implemented at an existing manufacturing plant equipped with fundamental infrastructure and auxiliary equipment. A 335-day working year is considered with the other 30 days for other functions such as maintenance. To quantify the economic feasibility of this process over a typical pharmaceutical plant lifetime, the NPV is determined by Equation (13), with 𝜏 and 𝑟𝑑 representing a plant lifetime of 20 years and a discount rate of 5%, respectively.33 𝑁𝑃𝑉 = ∑𝜏𝑖=1 {

𝑅𝑒𝑣𝑒𝑛𝑢𝑒 (1+𝑟𝑑 )𝑖

𝑂𝑝𝑒𝑥 𝑖 𝑑)

− (1+𝑟

} − 𝐶𝑎𝑝𝑒𝑥

(13)

Capex The Capex is computed as the sum of Battery Limits Installed Cost (BLIC) and Working Capital and Contingency (WCC) as shown in Table 1. Firstly, the Free-on-board (FOB) costs of process vessels in this present paper are determined by the given power law Equation (14): 𝑛

𝑆

𝐶𝑜𝑠𝑡𝐵 = 𝐶𝑜𝑠𝑡𝐴 (𝑆𝐵 ) 𝑓

(14)

𝐴

𝐶𝑜𝑠𝑡𝐵 and 𝐶𝑜𝑠𝑡𝐴 represent costs of the same equipment with different dimensions 𝑆𝐵 and 𝑆𝐴 . 𝑛 varies among different types of equipment, generally ranging from 0.2 to 1.0. Other factors such as utilized materials and inflation effect are summarized as 𝑓. The required vessel dimensions can be derived from the volumetric flow rates of the incoming streams since the annual production is typically proportional to the flow rates and proportional to the dimensions of vessels. Based on the benchmark vessel dimensions (𝑆𝐴 ) and the corresponding incoming flow rate, the required vessel


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dimensions in this present paper (𝑆𝐵 ) are calculated from the respective incoming flow rates as summarized in Table S3. With the FOB costs in a recent literature (𝐶𝑜𝑠𝑡𝐴 ) are used as the reference costs, and the detailed FOB costs calculated (𝐶𝑜𝑠𝑡𝐵 ) are given in Table S4.55 Secondly, the installed equipment cost is calculated as 1.43 times of the FOB cost. Process piping and instrumentation costs are computed as 30% and 12% of the installed equipment cost, respectively. The summation of installed, piping and instrumentation costs accounts for the total physical plant cost. Lastly, the sum of the physical plant cost and the construction cost which is set at 30% of the physical plant cost gives the BLIC. The other part of Capex is the WCC, which represents the in-process inventory and consists of working capital and contingency. The working capital for continuous manufacturing is taken to be 3.5% of the overall annual material costs and the contingency is computed as 20% of the BLIC. Opex The Opex consists of annual material costs, utility and waste disposal. The annual material costs are computed as the flow rates times 8,040 hours and the unit prices of solvents or reagents. The unit prices of all ingredients are from the vendors.58,59 The utility costs are calculated based on the approximation of $1.40 for per kilogram of materials, and the waste disposal costs are computed as $0.51 for per liter of solvents to be disposed (shown in Table S5). Since the process here utilizes only microreactors and microseparators, the production of only one process line is not at industrial scale. Thus, the labor cost of such a small-scale operation is neglected. Typically, continuous manufacturing saves much more in labor cost than its batch counterpart. The labor cost in batch manufacturing would be two times higher than that in continuous manufacturing because handling the intermediates between batches is labor-intensive.16,55 Also, the quality control system in batch


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manufacturing depends on the time-consuming off-line analysis, which generates extra labor cost.60 Revenue The amount of sitagliptin monophosphate (13) in each Januvia® oral tablet is specified as 100 mg in its drug label.56 The unit price of 100 mg Januvia® oral tablet is determined as $15 from CVS Pharmacy on the date of July 25th 2018, as indicated in Table 1.57

RESULTS AND DISCUSSION Determination of Kinetic Constant As shown in Figure 1, the reaction rate constant of the biocatalytic reaction (R7) is determined to be 0.0788 hr-1 at 45°C, assuming isothermal conditions. The coefficient of determination (R2) is calculated to be 0.89, which is satisfactory according to the standard of R2 estimated for ibuprofen manufacturing process in recent literature and further supports the assumption of a firstorder reaction.61

Figure 1. Determination of kinetic constant and reaction order of biocatalytic reaction in R7.


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Productivity Optimization Surrogate-based optimization is conducted to find the optimized productivity in manufacturing sitagliptin 12. The results of optimization are summarized in Table 2. The optimized productivity of 12 in R7 is 2.6 × 10−2 mol hr-1. The optimized concentration of incoming prositagliptin ketone 11, the volume of R7 and volumetric flow rate are 399.95 mol m-3, 4.0 × 10−5 m3 and 1.3 × 10−4 m3 hr-1 respectively. The residence time is 0.34 hr. Average conversion and yield are 99.86% and 51.98% respectively. The residence time of 0.35 hr is sufficient for an almost complete reaction, with concentration of 11 decreasing from 0.40 M at the inlet to 5.4 × 10−4 M, nearly 0 at the outlet. However, the concentration of 12 generated is only 0.21 M. It is noteworthy that to maximize productivity, all decision variables except the feeding flow rate have almost reached their respective upper bounds. Figure 2 shows that flow rates lower than 1.3 × 10−4 m3 hr-1 lead to low throughput, while the productivity decreases from the optimal to 0 when the flow rate exceeds 1.5 × 10−4 m3 hr-1 regardless of the length of reactor, due to the formation of impurities according to the constraint in Equation 8. Therefore, the productivity can be optimized to 2.6 × 10−2 mol hr-1 when a balance is reached between a high throughput and a sufficiently long residence time. Table 2. Results of surrogate-based optimization for maximizing productivity in Reactor 7 Productivity

Concentration of incoming prositagliptin ketone

Reactor volume

Flow rate

Concentration of unreacted prositagliptin ketone

2.6 x 10−2 mol hr-1

399.95 mol m-3

4 x 10−5 m-3

1.3 x 10−4 m3 hr-1

0.54 mol m-3

(0.4 M)

(40 mL)

(130 mL hr-1)

(5.4 x 10−4 M)

Concentration of sitagliptin

Residence time

Conversion

Yield

Program execution time

207.93 mol m-3 (0.21 M)

0.35 hr

99.87%

51.98%

2.81 s


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Figure 2. Optimization of productivity of sitagliptin in reactor 7.

Mass Balance Figure 3 shows the mass flow rate of the major species at the outlet of selected operation units in the main streams (orange line in Scheme 2). How the initial material choloropyrazine transforms into the final product sitagliptin monophosphate throughout the process is demonstrated. It can be observed that the section from M1 to R3 (a to f in Figure 3) and the one from F4 to E2 (m to p in Figure 3) are the two sections that most losses happen. Therefore, major priority of the process optimization should be allocated to these two sections in the future. From F6, the product sitagliptin monohydrate can be produced at a rate of 88.6 kg in a 335-day working year. The details of the mass balance for the whole process is given in Table S6.


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Figure 3. Mass flow rate of key species at outlets of the selected operation units.

Analysis of Environmental Impacts The mass flow rates of all the waste leaving the operation units in various streams are summarized in Table 3. The waste includes by-products, unreacted reactants, unrecovered APIs and unrecovered solvent. The by-products include the small molecules formed at the same time as the major species as well as the side products when the reactants in a step are converted to


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undesirable products instead of the desired major species. More details of the identities of the byproducts can be found in Table S6 in Supporting Information. Table 3. Mass flow rate of wastes generated throughout the process (kg hr-1) Byproducts

Unreacted reactants

Unrecovered API

Unrecovered solvent (excluding water)

Aqueous phase from E1

0.0098

0.0100

0.0000

0.0000

Filtrate from F1

0.0375

0.0550

0.0000

0.0396

Purging after F1

0.0000

0.0000

0.0000

0.0010

Filtrate from F3

0.1088

0.0129

0.0000

0.0600

Filtrate from F4

0.0072

0.0057

0.0000

0.1243

Organic phase from E2

0.0075

0.0086

0.0010

0.0470

Purging after D4

0.0000

0.0016

0.0000

0.0019

Aqueous phase from E4

0.0000

0.0002

0.0001

0.0000

Purging after E5

0.0002

0.0193

0.0000

0.0050

Filtrate from F6

0.0000

0.0048

0.0012

0.0140

Ethanol from washing tank 1

0.0000

0.0000

0.0000

0.0001

Total

0.1710

0.1181

0.0024

0.2931

Table 3 reveals that when 13 is produced at 0.01102 kg hr-1, since water has negligible environmental impact, it may be excluded from the total wastes. The process leads to a total waste generation rate of 0.5846 kg hr-1 considering solvent recovery in a range of 90-95%, and an Efactor of 53. For every kg of sitagliptin monohydrate produced, 53 kg of waste is generated, with unrecovered solvent accounting for 50% of it (26.6 kg). In the pharmaceutical industry which mainly relies on batch manufacturing, the generation of massive amounts of waste with E-factors as high as 200 is common.62 Compared to this standard, an E-factor of 53 shows that the proposed


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process leaves a relatively smaller environmental footprint. Table 4 shows that higher waste disposal costs are required in sitagliptin production due to the complexity of this process. Hence, although in Table 4 lower E-factors of 44.7 and 12.2 in continuous ibuprofen and artemisinin manufacturing respectively have been reported, Table 5 reveals that considering the differences in the complexity of the manufacturing process such as the number of operation units involved, the proposed process shows a remarkable performance in terms of green engineering. Taking Artemisinin as a baseline, the total number of unit operations of sitagliptin process is 900% more with a 330% increase in E-factor. The material efficiency of sitagliptin manufacturing is more evident when compared to ibuprofen, with an increase in the number of unit operations by 1,100% coupled with a slight rise of the E-factor by 19%.45,61 In addition to comparing the E-factor of sitagliptin to those of ibuprofen and artemisinin, the comparisons of different processes for sitagliptin manufacturing, such as between heavy metal-catalyzed and enzyme-catalyzed or between before and after process optimization, are expected to bring about more valuable information for decision-makers. With solvents constituting a large portion of the total wastes as shown in Table 3, the mass fraction purged before recycling at each recycling stream, as well as the mass fraction evaporated for recycling may significantly affect the E-factor. Figure 4 shows the variation in E-factors from 54.9 to 98.7 as the mass fraction of the vapors purged varies from 0.01 to 0.95. Purging a greater portion of substances requires a higher flow rate of fresh solvent to make up the loss, and this naturally leads to more waste generated and higher costs as well as a higher E-factor. Figure 4 also shows the change in E-factor from 189.1 to 54.2 as the mass fraction of solvent evaporated at all such recycling streams varies from 0.01 to 0.95, demonstrating that solvent recycling can


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significantly increase material efficiency. However, for the proposed process, it seems that the effect of recycling is more significant than purge with regard to environmental footprint reduction. Table 4. Details of manufacturing processes of sitagliptin, ibuprofen and artemisinin Ibuprofen61

Sitagliptin

Artemisinin45

Continuous

Full batch

Lean batch

Continuous

Full batch

Lean batch

Continuous

8

3

3

3

2

2

2

Number of mixers and separation units

52

12

4

2

17

12

4

Total number of operation units

60

15

7

5

19

14

6

Number of heat exchangers and pumps

64

8

8

10

2

2

6

Total number of equipment (unit operation, heat exchanger and pump)

124

23

15

15

21

16

12

Annual production (kg)

88.6

100

100

100

100

100

100

53

131.6

98.5

44.7

15.6

15.4

12.2

BLIC ($)

5,017,431

733,989

385,950

325,773

807,162

601,823

649,326

WCC ($)

1,019,349

163,093

89,412

66,055

162,218

121,141

129,917

Capex ($)

6,036,780

897,082

475,362

391,828

969,380

722,965

779,243

453,215

46,558

34,919

25,716

2,244

2,219

1,476

17,308

12,732

9,549

4,383

2,096

2,073

1,594

470,523

62,762

47,072

30,657

4,340

4,293

3,070

11,900,535

1,679,238

1,061,979

773,877

1,023,465

776,460

817,505

Number of reactors

E-factor

Material ($) Utility and waste disposal ($) Opex ($) Total cost for a 20-year time ($)


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Table 5. Summary of manufacturing processes of sitagliptin, ibuprofen and artemisinin Ibuprofen61 (as baseline)

Sitagliptin

Artemisinin45 (as baseline)

Sitagliptin

5

60 (+1,100%)

6

60 (+900%)

44.7

53 (+19%)

12.2

53 (+330%)

15

124 (+720%)

12

124 (+930%)

Opex

30,657

470,523 (+1,430%)

3,070

470,523 (+15,220%)

Capex

391,828

6,036,780 (+1,440%)

779,243

6,036,780 (+670%)

Total cost for a 20-year time

773,877

11,900,535 (+1,430%)

817,505

11,900,535 (+1,350%)

Total number of operation units E-factor Total number of equipment


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Figure 4. Sensitivity analysis of E-factor to mass fraction recycled (green dots) and mass fraction purged (blue dots).

Techno-Economic Analysis A comprehensive techno-economic analysis is conducted and the results are summarized in Table 6. In the first year of plant operation, a total profit of $6.9 million is expected without considering the costs of new drug discovery. The installed cost accounts for 45% of Capex, followed by construction (19%) cost and contingency (16%), with the working capital making up the smallest fraction of the Capex. For the Opex, the annual material cost accounts for almost 96% of it. Figure 5 clearly demonstrates that from a long term perspective, this process is economically feasible with a NPV of $150 million in a 20-year plant lifetime. Table 4 reveals that, due to the complexity of the sitagliptin process, the Capex, Opex and total cost outnumber those of the other two drugs to a great extent. Table 5 clearly shows that using the manufacturing process of ibuprofen as the baseline, the Capex, Opex and total cost of the sitagliptin manufacturing process increase roughly proportionally to the number of operation units, showing a consistent trend. On the other hand, if the process of artemisinin manufacturing is used as the baseline, the majority of the increase in total cost is attributed to the tremendous upsurge in Opex. Also, it can be observed that although the number of unit operations involved in the manufacturing process of sitagliptin is nine times larger than that for artemisinin manufacturing, the Capex only increases by 670%. This could be due to the differences in prices and number of different operation units. Furthermore, an economic comparison between batch and continuous sitagliptin manufacturing is to be conducted in the near future. The first and foremost priority of future work would be given to process


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optimization. Once implemented, the economic comparison is expected to show even greater attractiveness of continuous process and thus create considerable incentives to the enterprises. Table 6. Capex, annual Opex and revenue of biocatalytic continuous sitagliptin Capex

$6,036,780

Installed cost

$2,718,002

Instrument

$326,160

Piping

$815,401

Construction cost

$1,157,869

BLIC

$5,017,431

Working capital

$15,863

Contingency

$1,003,486

WCC

$1,019,349

Opex

$470,523

Waste disposal

$3,814

Utility

$13,494

Annual material cost

$453,215

Revenue

$13,493,438

Amount of sitagliptin monophosphate produced in 335 working days

88,626,848 mg/year

Amount of sitagliptin monophosphate contained in Januvia® tablet

100 mg56

Unit price of Januvia® tablet (100 mg)

$15 (price in CVS Pharmacy on July 25th, 2018)57

manufacturing


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Figure 5. Business potential of biocatalytic continuous sitagliptin manufacturing in a 20-year plant lifetime.

In this paper, the biocatalytic continuous manufacturing process of sitagliptin is designed, and its environmental impacts and economic potentials are analyzed and compared with the processes of ibuprofen and artemisinin manufacturing. The surrogate-based optimization has shown its ability to obtain the global optimum within a short computational time. As such data-driven optimization methods have not been fully explored in the pharmaceutical industry, it also highlights the potential of surrogate-based global optimization in solving other problems in this industry. However, it is noted that the optimized decision variables for any single step may not necessarily be optimal for an integrated continuous process, which links all reaction and separation steps together and would eventually result in a high-dimension complex objective function to be


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optimized. This complex function is expected to be solved efficiently using the proposed methodologies. In addition to offline optimization, surrogate-based optimization is highly suitable and promising for online optimization as well due to its strength of high efficiency. A comparison of this method with other optimization methods such as genetic algorithm and gradient descent can be conducted to demonstrate how efficient surrogate-based optimization can be and how much loss it may suffer. Moreover, this paper assumes an isothermal reaction in R7 at 45 °C, similar to the experiments reported in Savile et al. Temperature is another critical factor in this reaction as Savile et al. reported that 11 is more susceptible to temperature than the transaminase, which degrades at temperatures beyond 50 °C.15 The effect of temperature on reactions is yet to be elucidated. An optimization problem considering more factors such as the integration of the entire process and including the effects of temperature can further raise the process productivity as well as further demonstrate the potential of the proposed methodology in the pharmaceutical industry. On the other hand, even though impurity and productivity are common Critical Quality Attributes (CQAs) in this industry, the profit margin may remain the most crucial concern for the enterprises. The optimized decision variables may not be consistent in the cases of productivity optimization and profit optimization. For example, when productivity is optimized as in the present work, nearly half of the chemical 11 is transformed into impurities instead of the desired product, increasing the raw material costs required to ensure the desired throughput. In addition, the high concentration of impurities and low concentration of 12 make the separation steps more difficult, also generating higher costs. In this perspective, economic optimization can be researched in a future study. Regarding the environmental impact, the proposed process has the potential to become even more environmentally benign. According to the right part of Figure 6, in this process the three solvents used in largest quantities are water, 12M HCl and DMSO. Despite its high proportion in


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the total amount of solvent, water is non-toxic and hence has limited impact on the environment. Thus, an even lower environmental impact can be achieved by decreasing the usage of DMSO and 12M HCl, or by replacing them with other solvents. It is also noteworthy that biocatalysis might not be as green as in the general conception, despite its popularity in recent years.63 Producing transaminase in order to produce 12 is, in fact, similar to producing 2 APIs simultaneously. Producing transaminase requires multiple separation processes, such as filtration, which is similar to producing biopharmaceuticals.64 The energy and waste analysis of transaminase manufacturing has not been elaborated in this present study. GSK and Roche have leveraged on LCA successfully to identify the main sources of malignant environmental impacts and design strategies.65 Therefore, in the future, a comprehensive LCA that compares first-generation and biocatalytic manufacturing processes can be performed for a greener process design.

Figure 6. Composition of solvent Cost (left) and volume (right).


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Considering the economic aspect of the process, to compensate for the drug discovery costs and to avoid the trickling-down of these costs to consumers through steeper drug prices, several approaches that improve the profitability of this process can be considered. Firstly, according to Figure 5, the annual material cost is quite close to Capex, and it constitutes a large fraction of the total cost in a 20-year plant lifetime. From the left part of Figure 6, the two solvents with the highest proportion in total solvent cost are Trifluoroacetic anhydride and DMSO, accounting for over 43% of solvent cost combined. Greater savings could be achieved if more economical solvent substitutions or processes with lower solvent requirements are developed. Secondly, there are 60 unit operations in this process, a number far larger than those in the ibuprofen and artemisinin manufacturing processes as shown in Table 5. This may imply that there is a huge room for improvement and significant savings on the installed costs can be obtained through the optimization of process design. This prospect provides companies with an opportunity to reduce drug prices to levels that are affordable for patients in the developing regions such as Africa. In the current cost estimation, some items are not included such as labor, recycling equipment, transaminase (R7), Pd catalyst (R4), the excipient ingredient and the respective equipment like the tablet press. The costs of the control systems and process analytic equipment such as pH meter, which play an indispensable role in continuous processes, have also not been considered here. Figure 5 shows that the BLIC accounts for approximately 42% of the total cost in a 20-year plant lifetime. Since excipient-related equipment would only require roughly three to four additional unit operations as compared to the existing 60 unit operations, it can be inferred that the addition of excipient-related cost would not bring about a too great increase in the total cost determined in this study. As for the labor cost, according to Schaber et al., although transitioning from batch to continuous manufacturing can save labor cost up to 45%, the percentage of labor cost in Opex of


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a continuous process could still be up to 45%.33 In other words, in a 20-year plant lifetime, the labor cost could account for approximately 22% of the total cost in Figure 5. On top of that, for the catalyst especially the biocatalysts, it is highly possible that the cost of catalyst production would raise the total cost significantly. As mentioned in last paragraph, producing the transaminase is similar to producing therapeutic proteins such as recombinant human insulins, which involves cell culturing and various purification steps. Although the purity of transaminase required in R7 does not have to be as high as that of insulin product, the cost of this biocatalyst production would still not be negligible. Thus, the total cost could experience a nontrivial rise after taking into account the labor and enzyme production costs. Even the other uncovered costs like those arising from the recycling and process analysis equipment would also have a nonnegligible influence when combined together. To evaluate the potential impact of these uncovered costs, a cost sensitivity analysis is conducted, and the results are shown in Figure S2. From the figure, it can be observed that if the uncovered costs cause the total cost to double, which is actually an extreme case that is not likely to happen in reality, the result of NPV would not be affected to a great extent. It is only when the total cost is roughly 14 times of that computed originally will the NPV approach 0. As a result, the continuous process is expected to remain economically feasible even after including the uncovered costs. With an annual production of 88.6 kg per process line, a parallel combination of these process lines and process optimization can make it more promising in meeting the global demand for sitagliptin. Overall, the proposed continuous manufacturing process could benefit diabetic patients with a more stable supply and a more reasonable price of the drug.


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CONCLUSION In this paper, a continuous sitagliptin manufacturing process using microreactor systems and involving bio-catalysis is proposed. Based on the results of kinetic constant determination, the steady-state model in the biocatalytic reaction step in R7 is developed, and the productivity of sitagliptin is optimized by surrogate-based optimization which has a high potential in the pharmaceutical industry. The mass balance in every stream across the entire end-to-end process has then been carried out, and the computed E-factor is as low as 53 which can be attributed to the greener and more economical nature of microreactors and continuous manufacturing. Compared to a typical E-factor of 200 in the pharmaceutical industry when conventional batch processes are employed, the proposed process shows significant improvement in performance with regard to green engineering. A comprehensive techno-economic analysis has also been performed to validate the economic feasibility of this process. With a revenue of $162 million and a total cost of $12 million over a 20-year plant lifetime, the profitability of this process can benefit companies with greater competitiveness and more importantly, benefit the diabetic patients with a more affordable drug price. In addition, the analysis provides insights into how to further boost the sustainability and the overall profit such as through solvent substitution and development of simplified processes. Future studies could possibly look at developing a framework for the integration and optimization of the whole process, profit optimization, and the comparison between chemocatalytic and biocatalytic processes based on LCA. Lastly, an optimal solution must be determined based on the trade-off between the economic and environmental performance to reach a balance among the three domains of concern – wellness of patients, competitiveness of enterprises and the global environment.


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ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: Data for the determination of kinetic constant, details of surrogate-based optimization, summary of incoming volumetric flow rate and dimensions of all equipment, summary of Free-On-Board cost of all equipment, waste disposal and utility costs, mass balance of whole process, sensitivity analysis on uncovered cost.

NOMENCLATURE 𝐶𝐴 = concentration of prositagliptin ketone in the outflow of reactor 7 𝐶𝐴0 = concentration of prositagliptin ketone in the inflow of reactor 7 𝐶𝑎𝑝𝑒𝑥 = capital expenditure 𝐶𝐵 = concentration of sitagliptin 𝐶𝑜𝑠𝑡𝐴 = costs of equipment with benchmark dimensions 𝐶𝑜𝑠𝑡𝐵 = costs of equipment used in the proposed process 𝐷 = diffusivity 𝐹 = volumetric flow rate 𝑓 = summarized factor which includes utilized materials and inflation rate, etc. 𝑖 = index of years 𝐾 = kinetic constant 𝑁𝑃𝑉 = net present value 𝑚𝐴𝑃𝐼 = mass of product or APIs produced 𝑚𝑤𝑎𝑠𝑡𝑒𝑠 = mass of wastes generated 𝑂𝑝𝑒𝑥 = operating expenditure


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𝑟𝑑 = discount rate 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 = revenue from Januvia® tablet 𝑆 = cross sectional area 𝑆𝐴 = benchmark dimensions of equipment 𝑆𝐵 = dimensions of equipment used in the proposed process 𝑥 = length of microreactor 𝜏 = plant lifetime

ACKNOWLEDGMENT The authors thank the MOE AcRF Grant in Singapore for financial support to the project (R-279000-513-133 and R-279-000-541-114). The authors are grateful to Prof C. A. Shoemaker, Prof. W. Sun and X. Gu for the helpful discussions of the pharmaceutical manufacturing.

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TOC/Abstract Graphic:


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SYNOPSIS The biocatalytic continuous pharmaceutical manufacturing with microreactors has been optimized for reduced environmental burden while maintaining economic competitiveness.


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Biocatalytic continuous manufacturing of diabetes drug: plantwide

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