Systematic and Model-Assisted Process Design for the Extraction and Purification of Artemisinin from Artemisia Annua L.—Part IV: Crystallization

processes

Article Systematic and Model-Assisted Process Design for the Extraction and Puriﬁcation of Artemisinin from Artemisia annua L.—Part IV: Crystallization

Maximilian Johannes Huter, Axel Schmidt , Fabian Mestmäcker, Maximilian Sixt and Jochen Strube * Institute for Separation and Process Technology, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germany; [email protected] (M.J.H.); [email protected] (A.S.); [email protected] (F.M.); [email protected] (M.S.) * Correspondence: [email protected]; Tel.: +49-5323-72-2355

Received: 30 August 2018; Accepted: 26 September 2018; Published: 2 October 2018

Abstract: In this study, process integration for crystallization of a priori purified Artemisia annua L. is investigated. For this total process, the integration operation boundaries and behavior of the crystals are studied. This is performed focusing on a conceptual process design study for artemisinin, aiming towards the development of a crystallization step under given parameters by process integration. At first, different crystallization systems consisting of ethanol-water or acetone-water mixtures are compared. In subsequent steps, the metastable zone width and the behavior of the crystals regarding agglomeration and breakage are checked. Furthermore, the sensitivities of process variables based on several process parameters are investigated. Additionally, the final process integration of crystallization as a combined purification and isolation step is studied.

Keywords: artemisinin; conceptual process design; crystallization

1. Introduction Highly purified substances from natural sources are used to treat various diseases. In addition, the social demand continues to rise [1,2]. To obtain pure substances from plant material, several process steps are necessary. A typical process may include extraction as a capture step, followed by purification steps such as liquid-liquid extraction, chromatography and crystallization [3–6]. The whole study includes a detailed investigation of each process step, a conceptual process design and cost estimation. These aspects are divided into the following five articles:

Part 0: Sixt, M.; Strube, J. Systematic and model-assisted evaluation of solvent based- for pressurized hot water extraction for the extraction of Artemisinin from Artemisia annua L. Processes 2017, 5, 86, doi:10.3390/pr5040086 [7]. Part I: Sixt, Schmidt et al. Systematic and model-assisted process design for the extraction and purification of Artemisinin from Artemisia annua L.—Part I: Conceptual process design and cost estimation. Processes 2018, 6, 161, doi:10.3390/pr6090161 [8]. Part II: Schmidt, Sixt et al. Systematic and model-assisted process design for the extraction and purification of Artemisinin from Artemisia annua L.—Part II: Model-based design of agitated and packed columns for multistage extraction and scrubbing. Processes 2018, 6, 179, doi:10.3390/pr6100179 [9]. Part III: Mestmäcker, Schmidt et al. Systematic and model-assisted process design for the extraction and purification of Artemisinin from Artemisia annua L.—Part III: Chromatographic Purification. Processes 2018, 6, 180, doi:10.3390/pr6100180 [10].

Processes 2018, 6, 181; doi:10.3390/pr6100181 www.mdpi.com/journal/processes ProcessesProcesses 2018 2018, 6, 181;, 6, 181;doi: 10.3390/pr6100181doi:10.3390/pr6100181 3 of 173 of 17 becausebecause of the of theinfluences influences of side of side components components on solubilityon solubility and and kinetics kinetics [15 –[17,26,32,42,4315–17,26,32,42,43]. With]. With the the shiftshift of the of thecrystallization crystallization step step to earlier to earlier stages stages, it becomes, it becomes more more affectable affectable by theby thevariability variability of the of the sideside component component spectrum spectrum, which, which is a issignificant a significant factor factor for forplant plant-based-based material material. For. Forthis this reason, reason, the the criticalcritical impurities impurities have have to be to determined be determined and and methods methods for batchfor batch variability variability of the of theplant plant material material have ha ve to beto usedbe used [44 ][.44 To]. crystallizeTo crystallize artemisinin artemisinin, those, those critical critical side side components components that that increase increase the thesolubility solubility havehave to be to removbe removed byed precedingby preceding unit unit operations. operations. In thisIn this study, study, the thecrystallization crystallization of a ofrtemisinin artemisinin is investigated is investigated with with a focus a focus on processon process integration integration intointo a conceptual a conceptual process process design design. Therefore,. Therefore, critical critical variables variables, boundaries, boundaries and and sensitivities sensitivities of theof the crystallizationcrystallization process process are areresearched researched, creating, creating a process a process design design space space for anfor organican organic molecule molecule out outof of a multicomponenta multicomponent mixture mixture. This. This includes includes separation separation, and, and tests tests mechanic mechanical andal and thermodynamic thermodynamic influencesinfluences with with a reduced a reduced number number of experiments of experiments. Furthermore. Furthermore, this, this study study is executed is executed focusing focusing on on the themodeling modeling of a ofrtemisinin artemisinin crystallization crystallization. For. Forthis this modeling modeling and and the thecrystallization crystallization process process itself, itself, severalseveral effects effects are are separated separated in orderin order to create to create a predicti a predictive model.ve model. The The modeling modeling itself itself will will be be publishedProcessespublished 2018in a in,nother6, a 181nother manuscript manuscript, as ,it as would it would exceed exceed the thelimits limits of this of this paper. paper. 2 of 16 In addition,In addition, other other process process positions positions with within thein thedescribed described process process design design (see (see Figure Figure 1) are 1) aretested tested as aasPart combined a combined IV: Huter, purification purification Schmidt and et and al.isolation Systematic isolation step step in and order in model-assisted order to reduce to reduce the process thenumber number design of process of process for the steps extractionsteps and and save save and costscosts. This. This ispuriﬁcation performed is performed of with Artemisinin with a focus a focus ofromn creating onArtemisia creating a process annua a processL.—Part design design fieldIV: fieldCrystallization. that that enables enables a (this controlled a controlled article). crystallizationcrystallization with with the theaid aidof modelling of modelling. It .should It should contain contain predictive predictive particle particle size size distribution distribution and and ensureensure theA model-basedtheproduct product quality quality optimization attributes attributes forof the theof theproduct extraction product that step that are was adesiredre performeddesired in a in crystallized a in crystallized a previous product product study [41 by ][.41 the]. ThisinstituteThis should should, [ 7on]., The theon the generalone one hand concepthand, increase, increase is based the theyield on yield the of examplethe of thepharmaceutical pharmaceutical of 10-deacetylbaccatin substance substance, III,and, whichand on theon is the obtainedother other handfromhand, the,yew. thecrystals crystals The should process should become ﬂow become sheetmore more andadjustable adjustable the target for forsubsequent component subsequent steps are steps depictedsuch such as filtering, as in filtering, Figures drying1 dryingand and2[ and 6]. formulationAformulation detailed in process general, in general, description as these as these and subsequent a subsequent summary steps of steps properties are are affected affected are given by bythe in the Partparticle particle 1 This size article size distribution distribution focusses on [25,32,41the[25,32,41 crystallization]. ]. step of this conceptual process design.

FigureFigure 1. Basic 1.1. Basic process process design design for plant for plant-basedplant-based-based substances substances (LLE, (LLE,(LLE, liquid liquid-liquidliquid-liquid-liquid extraction) extraction)extraction) [8]. [[88].].

FigureFigure 2. Structure 2.2. Structure of artemisinin of artemisinin [8]. [[8].].

2. Material2. MaterialCrystallization and and Methods Methods is a typical final purification and isolation step preparing for formulation steps of plant-based or semi-synthetical processes, e.g., artemisinin [11–23]. The crystallization step itself is 2.1.affected 2.1Raw. Raw and inandPurified various Purified Material ways, Material based on mechanical and thermodynamic influences, e.g., [24]. The main driving force for crystallization is the solid-liquid equilibrium and, therefore, the solubility of the target Raw Artemisia annua L. and purified artemisinin (CfM Oskar Tropitzsch, Markredwitz, componentRaw Artemisia in a system. annua L. This and solubility purified isartemisinin measurable (CfM viadifferent Oskar Tropitzsch, methods like Markredwitz, the isothermal Germany) are used for this study. The raw plant material is ground to approximately 1 mm with a Germany)excess methodare used orfor the this polythermal study. The raw method, plant material e.g., [25]. is ground Several to solvents approximately have been 1 mm screened with a for artemisinin [26–29] and there are already described crystallization steps using ethanol, which are used commercially as re-crystallization steps [20]. Due to this, ethanol-mixtures are investigated focusing on target yield and process range. In addition, acetone-water mixtures are also screened based on the good solving properties of the pure solvent for artemisinin [27,28], its high availability, and its usage as the extraction solvent in this process Part 1. This solubility of the target component can be influenced by different approaches like cooling, heating or evaporation. The best controllable and therefore most common crystallization is cooling crystallization [25,30], which is used in this study. Next to solubility, the supersolubility is a vital property of the target component. Primary nucleation leads to a wide and instable particle distribution. To overcome this issue the usage of seed particles is favorable. These seed particles grow in the metastable zone, the area between the solubility and supersolubility curve without primary nucleation [30–33]. For the design space of the crystallization step, information on the metastable zone width (MSZW) is necessary. This information on supersolubility can be obtained via isothermal or polythermal methods and the measured values are affected by several parameters like agitator type, stirring speed or temperature gradient [34–37]. Processes 2018, 6, 181 3 of 16

Even though seeding strategies prevent primary nucleation, secondary nucleation like breakage or attrition and agglomeration as counterpart affect the particle distribution. These effects are component-specific, and therefore have to be determined for the chosen artemisinin system [24,31]. Artemisinin forms two polymorphs, which means it grows in different crystal shapes. On the one hand, there is a triclinic, and on the other hand, an orthorhombic crystal shape. Both crystal structures lead to rod-like particles which increase sensibility with regard to breakage, but at temperatures below 130 ◦C, the orthorhombic configuration is present [38]. The final step for the process design of the crystallization step is the determination of the particle growth and sensitivities based on the operating parameters. The growth of the particles is measurable using several methods and can be separated into methods for a single crystal and for crystal suspensions, e.g., [32]. The simplest and most common approach is the measurement of the desupersaturation curve in a seeded isothermal batch experiment. With information on the concentration and solubility, supersaturation versus time is known, and kinetics can be calculated [32,39]. Alternatives include, for example, the usage of differential scanning calorimetry (DSC) approaches for reduced substances, or direct measurements of kinetics [32,40]. Due to the slow growth of particles, the process time has to be adequately chosen to meet process quality attributes like yield or x50 [25]. Based on the controllable parameter influences and sensitivities for the process, quality attributes have to be investigated in order to create a suitable process [41]. Alongside the possibility of creating solids, the crystallization step makes it possible to purify crystals out of a multicomponent solution [21,24]. An integration of the crystallization step provides financial advantages, due to its low operation costs. By integrating the crystallization step into the downstream of artemisinin as a purification step, the composition of the feed solution is essential, because of the influences of side components on solubility and kinetics [15–17,26,32,42,43]. With the shift of the crystallization step to earlier stages, it becomes more affectable by the variability of the side component spectrum, which is a significant factor for plant-based material. For this reason, the critical impurities have to be determined and methods for batch variability of the plant material have to be used [44]. To crystallize artemisinin, those critical side components that increase the solubility have to be removed by preceding unit operations. In this study, the crystallization of artemisinin is investigated with a focus on process integration into a conceptual process design. Therefore, critical variables, boundaries and sensitivities of the crystallization process are researched, creating a process design space for an organic molecule out of a multicomponent mixture. This includes separation, and tests mechanical and thermodynamic influences with a reduced number of experiments. Furthermore, this study is executed focusing on the modeling of artemisinin crystallization. For this modeling and the crystallization process itself, several effects are separated in order to create a predictive model. The modeling itself will be published in another manuscript, as it would exceed the limits of this paper. In addition, other process positions within the described process design (see Figure1) are tested as a combined purification and isolation step in order to reduce the number of process steps and save costs. This is performed with a focus on creating a process design field that enables a controlled crystallization with the aid of modelling. It should contain predictive particle size distribution and ensure the product quality attributes of the product that are desired in a crystallized product [41]. This should, on the one hand, increase the yield of the pharmaceutical substance, and on the other hand, the crystals should become more adjustable for subsequent steps such as filtering, drying and formulation in general, as these subsequent steps are affected by the particle size distribution [25,32,41].

2. Material and Methods

2.1. Raw and Puriﬁed Material Raw Artemisia annua L. and puriﬁed artemisinin (CfM Oskar Tropitzsch, Markredwitz, Germany) are used for this study. The raw plant material is ground to approximately 1 mm with a Grindomix® Processes 2018, 6, 181 4 of 16

200 knife mill (Retsch GmbH, Haan, Germany). For storage, the plant material and the puriﬁed artemisinin are stored at −20 ◦C to minimize degradation.

2.2. Chemicals Acetone, ethanol in analytical grade (VWR®, Darmstadt, Germany) and water (Sartorius® arium® pro, Göttingen, Germany) are used as solvent mixtures.

2.3. Crystallization System Screening Solvent screening is performed with a MKR 13 Thermo Shaker (Hettich Benelux, Geldermalsen, The Netherlands) using 50 mL Falcons® (VWR®, Darmstadt, Germany) and puriﬁed artemisinin given in surplus. The mixtures are kept for 3 h at constant temperature before a sample was taken and analyzed.

2.4. Seed Crystals Seed crystals for the crystallization experiments were obtained from puriﬁed artemisinin, which is sieved into the class 100 < x (ferret max.) < 150 µm with. The particle fractions x < 100 µm and x > 150 µm are used in agglomeration and breakage experiments.

2.5. Metastable Zone Width and Crystallization Experiments Solubility and supersolubility curves are measured with an EXcell 230 (Exner, Recklinghausen, Germany) turbidity sensor and a Conducell 4UxF (Hamilton Company, Reno, NV, USA) conductivity and temperature sensor in a 1 L stirred double jacked vessel. For the experiments, the vessel is filled with 800 mL solvent, resulting in a height to diameter ratio of 1.15 by a ViskoPakt®-rheo 110 (HiTec Zang, Herzogenrath, Germany) with a four-blade axial flow Polytetrafluoroethylene (PTFE) agitator with a blade angle of 45◦ and 5 cm diameter, positioned 3 cm from the bottom. The temperature of the cooling agent is controlled with a FP50-MA (Julabo, Seelbach, Germany) thermostat. For the metastable zone width (MSZW) experiment, the temperature is lowered until nucleation occurs; afterwards, the solution is heated until the turbidity reaches a constant value. Variables for this are temperature gradient and stirrer speed. For agglomeration and breakage experiment crystals, 0.2 g of three different sieving fractions are added in a saturated artemisinin solution at 20 ◦C. Afterwards, the particles are stirred for 2 h at 100 RPM or 400 RPM and the resulting particle size distributions are measured. The influences on process variables (x50, yield and Space-Time-Yield) are investigated by a full factorial design of experiments comprising start temperature, final temperature, duration of crystallization step and initial supersaturation of the system. For process design experiments the same setup is used as for the measurement of MSZW. The stirring speed is kept constant at 100 RPM and 0.2 g purified artemisinin is used as seed crystals. After each crystallization experiment the resulting particles are filtered and dried at 60 ◦C before they are analyzed. The remaining mother liquid is completely evaporated to dryness (IKA®-Werke GmbH & Co. KG, Staufen, Germany) and the resulting artemisinin is dried and sieved for additional experiments.

2.6. Analytics The analysis of artemisinin is performed by High performance liquid chromatography (HPLC) using an Elite LaChrom® (Hitachi High Technologies America, Schaumburg, IL, USA) device equipped with an Evaporation Light Scatting Detector (ELSD) Alltech® 3300 (Grace®, Columbia, SC, USA). The analytical column is a PharmPrep® RP18 250 mm × 4 mm i.d. by Merck® (Merck KGaA, Darmstadt, Germany) operated at 25 ◦C. The eluents are acetonitrile (VWR®, Darmstadt, Germany) and water (Sartorius® arium® pro, Göttingen, Germany) 60/40 in isocratic mode at 1 mL/min flow. The injection Processes 2018, 6, 181 5 of 16 volume is 10 µL and all samples are passed through a 0.2 µm syringe filter. The evaporator temperature of the ELSD is set to 36 ◦C and the air flow is 1.6 mL/min. Calibration is performed with an external standard ordered from CfM Oskar Tropitzsch (Marktredwitz, Germany) over a range from 1 g/L to 0.001 g/L. The basis of the HPLC analysis protocol can be found in Lapkin et al. [45]. It was slightly modified to fit the setup used in this study. For the H-NMR (Hydrogen Nuclear magnetic resonance) analytics, a Bruker AVANCE III 600 MHz spectrometer (Bruker, Rheinstetten, Germany) is used. It is equipped with a broad-band observe probe head with z-gradient. The samples are completely evaporated to dryness using a rotary evaporator (IKA®-Werke GmbH & Co. KG, Staufen, Germany). The final analyte is then dissolved in deuterated benzene (C6D6) for measurement. The H1 spectroscopy of the purified artemisinin is shown with the other spectrums. 1H NMR (600 MHz, C6D6, 7.16 ppm): 5.51 (s, 1H), 3.35 (dq, J = 7.3, 5.0 Hz, 1H), 2.23 (ddd, J = 14.5, 13.3, 4.0 Hz, 1H), 1.60 (ddd, J = 14.6, 4.8, 2.9 Hz, 1H), 1.48 (dddd, J = 14.0, 7.0, 4.1, 3.0 Hz, 1H), 1.26 (s, 3H), 1.25–1.20 (m, 2H), 1.12–1.02 (m, 2H), 1.00 (d, J = 7.25 Hz, 3H), 0.92 (ddd, J = 11.1, 11.1, 6.8 Hz, 1H), 0.71–0.62 (m, 1H), 0.60 (d, J = 6.3 Hz, 3H), 0.47–0.37 (m, 2H). Crystal sizes for agglomeration and breakage experiments are measured using Axiolab A (Zeiss, Oberkochen, Germany) light microscope. The characteristic diameter for the measurements is the ferret max. For the growth experiments the Qicpic (Sympatec GMBH, Clausthal-Zellerfeld, Germany) is used to measure Q3 distributions of the initial and final crystals. As characteristic length, the maximal ferret diameter is chosen.

3. Results and Discussion Crystallization of artemisinin is the last unit operation step of downstream and the ﬁrst step of formulation in the puriﬁcation process. Therefore, yield has to be optimized and the solvents used should be less toxic and easy to remove from the resulting particles. In addition, the particle distribution should be narrow.

3.1. Crystallization of Artemisinin

3.1.1. System Selection Appropriate choice of solvent system is a vital point for any crystallization process. It defines the solubility of the target component and therefore the possible yield for each crystallization step. A system should comprise the properties of significant concentration gradient of the target component over the temperature and a minor final solubility at low temperatures to obtain a high yield in each crystallization step. Figure3 shows the results for mixtures of acetone and ethanol mixed with different amounts of water to generate different solubility curves. Acetone mixtures have, in general, a higher solubility of artemisinin compared to ethanol mixtures, which agrees with previous studies focusing on the pure solvents [23]. Acetone-water 50/50 wt.% and ethanol-water 60/40 wt.% both show a good combination of low concentrations at 4 ◦C and solubility gradient with change of temperature. Comparing those two systems, acetone-water 50/50 wt.% has a slightly stronger gradient, and ethanol-water 60/40 wt.% has a lower concentration at 4 ◦C with about 4 g/kg. For the following experiments, ethanol-water 60/40 wt.% is chosen due to the lower final concentration, its known usage for commercial crystallization of artemisinin, and its low toxicity, which is important due to its position as the final Downstream Processing (DSP)step. These reasons are not outweighed by the slightly increased gradient. The ethanol-water 60/40 wt.% system is measured in greater detail, as depicted in Figure4. The measurements for ethanol-water 60/40 wt.% demonstrate an exponential behavior, resulting in strongly increasing solubility at higher temperatures. 35 ◦C was taken as the maximum temperature, as it gets unstable at temperatures greater than 40 ◦C. Processes 2018, 6, 181 6 of 16 Processes 2018, 6, 181; doi:10.3390/pr6100181 6 of 17

Processes 2018, 6, 181; doi:10.3390/pr6100181 7 of 17

Acetone mixtures have, in general, a higher solubility of artemisinin compared to ethanol mixtures, which agrees with previous studies focusing on the pure solvents [23]. Acetone-water 50/50 wt.% and ethanol-water 60/40 wt.% both show a good combination of low concentrations at 4 °C and solubility gradient with change of temperature. Comparing those two systems, acetone-water 50/50 wt.% has a slightly stronger gradient, and ethanol-water 60/40 wt.% has a lower concentration at 4 °C with about 4 g/kg. For the following experiments, ethanol-water 60/40 wt.% is chosen due to the lower final concentration, its known usage for commercial crystallization of artemisinin, and its low toxicity, which is important due to its position as the final Downstream Processing (DSP)step. These reasons are not outweighed by the slightly increased gradient. The ethanol-water 60/40 wt.% system is measured in greater detail, as depicted in Figure 4. The measurements for ethanol-water 60/40 wt.% demonstrate an exponential behavior, resulting in strongly increasing solubility at higher temperatures. 35 °C was taken as the maximum temperature, as it gets unstable at temperatures greater than 40 °C.

3.1.2. Metastable Zone Width Based on the solubility curve, measured with the isothermal excess method, the MSZW is measured to set the boundaries for the crystallization step. In the literature, the influences of temperature gradient and energy input on the measured nucleation temperature are mentioned [35,36]. To measureFigureFigure the 4 4.correct. MetastableMetastable values zone zone, stirring width width of ofspeed a artemisininrtemisinin and temperature in in ethanol ethanol-water-water gradient 60 60 wt.% wt.%. are. investigated for the polythermal method. The results are summarized in Table 1. 3.1.2.Obviously, Metastable the Zone resulting Width solubility curve of the polythermal measurement differs from prior Table 1. Influences of stirring speed and temperature gradient on measured metastable zone width. measurementsBased on the via solubilitythe isothermal curve, measured excess method. with the This isothermal difference excess grows method, with the increasing MSZW is temperatures.measured to For set the process boundariesVariation design for, the the polythermal crystallization valuesNucleation step. are chosen In Temperature the, literature,because (they°C the) are inﬂuences performed of intemperature the crystallization gradient Stirringvessel and energy itself speed and input detected on100 the measuredRPMwith inline nucleation measurement14.4 temperatures. This±0.4 leads are mentioned to less handling [35,36]. influenceTo measure, and the therefore correct(at 1less values, °C/min) possibility stirring of speed200 handling RPM and issues temperature.15.8 The results gradient for ±the are0.7 metastable investigated zone for are the listedpolythermal in Table method. 2. The results are summarized400 RPM in Table116.4. ±0.2 Temperature gradient 0.1 °C/min 20.8 ±1.1 Table 2. Metastable zone width of artemisinin in 60 wt.% ethanol (MSZW: metastable zone width). (at 400 RPM) 0.5 °C/min 20.6 ±0.0 Concentration (g1.0Artemisinin °C/min/kg Solvent)16.4 MSZW (°C ) ±0.2 6.4 8.4 ±0.7 8.5 7.6 ±0.4 10.4 6.8 ±0.2 12.9 6.1 ±0.7 15.3 5.5 ±0.3

At minor concentrations, the MSZW starts with 8.4 °C. In contradiction to this, the difference of nucleation and solubility temperature only covers a range of 5.5 °C at the highest measured concentration. This provides a descending zone, where nucleation does not occur. This leads to a shrinking design space of the crystallization step at higher temperatures.

Processes 2018, 6, 181 7 of 16

Table 1. Inﬂuences of stirring speed and temperature gradient on measured metastable zone width.

Variation Nucleation Temperature (◦C) Stirring speed 100 RPM 14.4 ±0.4 (at 1 ◦C/min) 200 RPM 15.8 ±0.7 400 RPM 16.4 ±0.2 Temperature gradient 0.1 ◦C/min 20.8 ±1.1 (at 400 RPM) 0.5 ◦C/min 20.6 ±0.0 1.0 ◦C/min 16.4 ±0.2

Based on these experiments, the MSZW should be measured at the highest stirring rate and a low temperature gradient in order to set robust boundaries for the crystallization process. The resulting graphs for solubility (polythermal and isothermal method) and supersolubility are displayed in Figure4. Obviously, the resulting solubility curve of the polythermal measurement differs from prior measurements via the isothermal excess method. This difference grows with increasing temperatures. For the process design, the polythermal values are chosen, because they are performed in the crystallization vessel itself and detected with inline measurements. This leads to less handling inﬂuence, and therefore less possibility of handling issues. The results for the metastable zone are listed in Table2.

Table 2. Metastable zone width of artemisinin in 60 wt.% ethanol (MSZW: metastable zone width).

◦ Concentration (gArtemisinin/kgSolvent) MSZW ( C) 6.4 8.4 ±0.7 8.5 7.6 ±0.4 10.4 6.8 ±0.2 12.9 6.1 ±0.7 15.3 5.5 ±0.3

At minor concentrations, the MSZW starts with 8.4 ◦C. In contradiction to this, the difference of nucleation and solubility temperature only covers a range of 5.5 ◦C at the highest measured concentration. This provides a descending zone, where nucleation does not occur. This leads to a shrinking design space of the crystallization step at higher temperatures.

3.1.3. Agglomeration and Breakage The particle size distribution of a crystallization process is, on the one hand, affected by the growth of the particles, and on the other hand, by agglomeration, attrition and breakage of the particles. These effects depend on the shear rate, which leads to the assumption that those effects can be separated with different stirrer speeds [24]. At low stirring rates, agglomeration could be assumed to be the predominating effect, and at high shear rates, breakage or attrition should predominantly be present. The following particle size distributions result from high and low stirring rates for different particle sieving classes, which are summarized in Table3. The effects are measured in a saturated solution at 20 ◦C. As the “low” stirring rate, 100 RPM is chosen, and 400 RPM is chosen as the “high” stirring rate; each experiment is performed twice, to indicate reproducibility. The initial and ﬁnal particle size distributions are documented in Figures5 and6.

Table 3. Sieving fractions for particles.

Particle Class Sieving Fraction Small x < 100 µm Seed 100 µm < x < 150 µm Large 150 µm < x Processes 2018, 6, 181; doi:10.3390/pr6100181 8 of 17 be present. The following particle size distributions result from high and low stirring rates for different particle sieving classes, which are summarized in Table 3. The effects are measured in a saturated solution at 20 °C . As the “low” stirring rate, 100 RPM is chosen, and 400 RPM is chosen as the “high” stirring rate; each experiment is performed twice, to indicate reproducibility. The initial and final particle size distributions are documented in Figure 5 and Figure 6.

Table 3. Sieving fractions for particles.

Particle Class Sieving Fraction Small x < 100 µm Seed 100 µm < x < 150 µm Large 150 µm < x

The crystals show no significant changes between start particle size distribution (PSD) and final PSD after 2 h based on agglomeration. There is no detectable shift to larger particle sizes for any of the fractions (see Figure 5a–c). Furthermore, at low stirring speed (100 RPM), attrition and breakage are detectable for large particles (see Figure 5c). As can be seen, the PSD shifts to smaller particle sizes.Processes These2018, 6, 181 experiments demonstrate that agglomeration has no detectable effect on8 of the 16 crystallization of artemisinin. The results for high stirring speed are visualized in Figure 6.

(a) (b) (c)

Figure 5.5. AgglomerationAgglomeration experiments experiments of of different different particle fractionsfractions:: ( (aa)) small small particles particles,, ( (b)) seed particlesparticles,, (c) large particles.

(a) (b) (c)

Figure 6 6.. BreakageBreakage experiments experiments of of different different particle particle fractions fractions:: (a) ( asmall) small particles particles,, (b) (seedb) seed particles particles,, (c) large(c) large particles particles..

The particle crystals distributions show no significant prove, changeslike the previous between startexperiments, particlesize that distribution there are no (PSD) or only and minor final changesPSD after for 2 hsma basedll and on seed agglomeration. particles. For There large is noparticles detectable, the effect shift to of larger breakage particle is significantly sizes for any higher of the thanfractions at low (see stirring Figure rates5a–c).. Since Furthermore, the probability at low stirringof breakage speed increases (100 RPM), with attrition greater and particle breakage size and are agglomerationdetectable for largecan be particles neglected, (see the Figure process5c). Asshould can bebe seen,operated thePSD at low shifts stirring to smaller speeds particle. This would sizes. reduceThese experiments the breakage demonstrate effects and generate that agglomeration the narrowest has particle no detectable size distribution effect on the for crystallizationthe process. of artemisinin. The results for high stirring speed are visualized in Figure6. The particle distributions prove, like the previous experiments, that there are no or only minor changes for small and seed particles. For large particles, the effect of breakage is significantly higher than at low stirring rates. Since the probability of breakage increases with greater particle size and agglomeration can be neglected, the process should be operated at low stirring speeds. This would reduce the breakage effects and generate the narrowest particle size distribution for the process.

3.1.4. Process Design The crystallization process is affected by several thermodynamic influences. While MSZW and mechanical effects of stirring speed have been investigated in the prior sections, influences of start temperature, final temperature, supersaturation and duration have to be investigated, as they are important operating parameters. The results for the examined study are given in the following Table4. The influences are analyzed regarding their effects on yield, Space-Time-Yield (STY) and x 50. The considered yield is based on the thermodynamic limit and follows Equation (1).

CStart,measured − CFinal,measured YTDL = (1) CStart,measured − CFinal. theoretical Processes 2018, 6, 181; doi:10.3390/pr6100181 9 of 17

3.1.4. Process Design The crystallization process is affected by several thermodynamic influences. While MSZW and mechanical effects of stirring speed have been investigated in the prior sections, influences of start temperature, final temperature, supersaturation and duration have to be investigated, as they are important operating parameters. The results for the examined study are given in the following Table 4. The influences are analyzed regarding their effects on yield, Space-Time-Yield (STY) and x50. The considered yield is based on the thermodynamic limit and follows Equation (1). 퐶푆푡푎푟푡,푚푒푎푠푢푟푒푑 − 퐶퐹𝑖푛푎푙,푚푒푎푠푢푟푒푑 푌푇퐷퐿 = (1) 퐶푆푡푎푟푡,푚푒푎푠푢푟푒푑 − 퐶퐹𝑖푛푎푙.푡ℎ푒표푟푒푡𝑖푐푎푙

Table 4. Design of experiments for growth experiments (STY: Space-Time-Yield; TDL:

ProcessesThermodynamic2018, 6, 181 limit). 9 of 16

Pattern SInitial (-) TStart (°C ) TFinal (°C ) Duration (min) x50 (µm) YieldTDL (-) STY (g/(l·h)) Table1 0000 4. Design 0.175 of experiments 25 for growth5 experiments (STY:180 Space-Time-Yield;221.19 ±33.23 TDL: Thermodynamic0.89 1.17 limit). 2 −−−+ 0.1 30 10 120 233.14 ±4.16 0.80 2.66 ◦ ◦ 3 −+−−Pattern S0.1Initial (−)T20Start ( C) TFinal10 ( C) Duration240 (min)558.85 x50 ( µm)±9.46 Yield0.65TDL (−) STY (g/(L1.26· h)) 4 10000 0000 0.175 0.175 25 255 5 180 221.19313.04 ±±33.2327.12 0.890.83 1.171.30 5 2 0000−−− +0.175 0.1 25 305 10 120180 233.14258.15 ±±21.904.16 0.800.87 2.661.22 − −− ± 6 3 +−+++ 0.250.1 30 200 10 240120 558.85226.17 ±15.179.46 0.650.90 1.261.77 4 0000 0.175 25 5 180 313.04 ±27.12 0.83 1.30 7 5−+−+ 0000 0.1 0.175 20 25105 180120 258.15279.55 ±±21.9010.63 0.870.61 1.222.62 8 6+−+− +− ++0.25 0.25 30 300 0 120240 226.17201.55 ±±15.175.36 0.900.93 1.770.78 9 7 +−−−−+ −+0.25 0.1 30 2010 10 120240 279.55207.27 ±±10.6327.87 0.610.86 2.621.26 − − ± 10 8−++− + + 0.10.25 20 300 0 240 201.55294.79 ±45.225.36 0.930.85 0.780.77 9 +−−− 0.25 30 10 240 207.27 ±27.87 0.86 1.26 11 10 −−+−−++ − 0.1 0.130 200 0 240 294.79261.51 ±±45.2239.31 0.850.92 0.770.79 12 11 −+++−− +− 0.1 0.120 300 0 240120 261.51161.75 ±±2.0039.31 0.920.84 0.791.58 13 12 −−++−+++ 0.1 0.130 200 0 120 161.75211.70 ±±14.062.00 0.840.88 1.581.79 −− ± 14 13 +−−+ ++0.25 0.1 30 30100 120 211.70212.82 ±14.0630.67 0.880.85 1.792.61 14 +−−+ 0.25 30 10 120 212.82 ±30.67 0.85 2.61 15 15++−+ ++ −+0.25 0.25 20 2010 10 120 308.01308.01 ±±36.3336.33 0.680.68 2.562.56 16 16++−− ++ −− 0.250.25 20 2010 10 240 249.53249.53 ±±47.1847.18 0.820.82 1.121.12 17 17+++− +++ − 0.250.25 20 200 0 240 188.54188.54 ±±4.044.04 0.880.88 0.780.78 18 −−−− 0.1 30 10 240 224.40 ±16.11 0.84 1.24 18 −−−− 0.1 30 10 240 224.40 ±16.11 0.84 1.24 19 ++++ 0.25 20 0 120 160.28 ±5.93 0.83 1.69 19 ++++ 0.25 20 0 120 160.28 ±5.93 0.83 1.69

To obtain information about reproducibility, thethe centercenter points are performed 3 times. The results for volume volume-based-based particle size sum distribution and reduction of concentration for the center points (CP) areare plotted in the following Figure7 7a,b.a,b.

(a) (b)

FigureFigure 7. Process7. Process reproducibility: reproducibility (a): particle(a) particle size size distribution, distribution (b), concentration(b) concentration profile. profile (CP:. (CP: Center Center point). point) The figure points out a slight variance in particle size distributions and the concentration curves. WhileThe the figure particle points size out distribution a slight variance of CP2 in differs particle from size CP1 distributions and CP3, and it had the theconcentration slowest reduction curves. Whileof concentration the particle during size distribution the beginning of CP of2 thediffers crystallization. from CP1 and The CP maximal3, it had measuredthe slowest concentration reduction of difference of the center points is detectable at the beginning of the processes at 25 min, with a difference of about 1.6 g/kg, which decreases to 0.45 g/kg until the end of the process. Based on the results of the experiments, the center points show a good reproducibility regarding concentration measurement, and the particle size differs slightly. Significances and main effects on the different process variants are summarized in Figures8–10, comprising a tornado sensitivity diagram (named pareto chart, using the software Minitab 17™ (Minitab Inc., State Collage, PA, USA)) and a main effect plot. Based on this, only significant parameters are investigated regarding their effect on the process variables. Processes 2018, 6, 181; doi:10.3390/pr6100181 10 of 17

concentration during the beginning of the crystallization. The maximal measured concentration difference of the center points is detectable at the beginning of the processes at 25 min, with a difference of about 1.6 g/kg, which decreases to 0.45 g/kg until the end of the process. Based on the results of the experiments, the center points show a good reproducibility regarding concentration measurement, and the particle size differs slightly. Significances and main effects on the different process variants are summarized in Figure 8–10, comprisingProcesses 2018, 6a, 181; tornado doi:10.3390/pr6100181 sensitivity diagram (named pareto chart, using the software Minitab10 17 of™ 17 (Minitab Inc., State Collage, PA, USA)) and a main effect plot. Based on this, only significant parametersconcentration are duringinvestigated the begin regardingning of their the effect crystallization on the process. The variables. maximal measured concentration difference of the center points is detectable at the beginning of the processes at 25 min, with a difference of about 1.6 g/kg, which decreases to 0.45 g/kg until the end of the process. Based on the results of the experiments, the center points show a good reproducibility regarding concentration measurement, and the particle size differs slightly. Significances and main effects on the different process variants are summarized in Figure 8–10, comprising a tornado sensitivity diagram (named pareto chart, using the software Minitab 17™ Processes(Minitab2018 Inc.,, 6, 181 State Collage, PA, USA)) and a main effect plot. Based on this, only significant10 of 16 parameters are investigated regarding their effect on the process variables.

(a) (b)

Figure 8. Influences on YieldTDL: (a) significances and (b) main effects (TDL: Thermodynamic limit).

The yield is strongly affected by the starting and the final temperature. Figure 8a also proves a slight significance of the duration of the crystallization step. The main effect plot (Figure 8b) depicts that for high differences of starting and final temperature the yield maximizes, but with the solubility curve in mind, further cooling is not rational. In addition, higher starting temperatures and therefore higher starting concentrations are also limited due to the instability of the system. The duration main (a) (b) effect plot indicates that the crystallization should exceed 120 min for higher yields, but as Figure 8b

showsFigureFigure, the highest 8.8.Inﬂuences Influences duration onon YieldYield itselfTDLTDL does::( (a)) significances signiﬁcances not necessarily and ( blead) main to theeffects highest (TDL: yield. ThermodynamicThermodynamic limit).limit).

The yield is strongly affected by the starting and the final temperature. Figure 8a also proves a slight significance of the duration of the crystallization step. The main effect plot (Figure 8b) depicts that for high differences of starting and final temperature the yield maximizes, but with the solubility curve in mind, further cooling is not rational. In addition, higher starting temperatures and therefore higher starting concentrations are also limited due to the instability of the system. The duration main effect plot indicates that the crystallization should exceed 120 min for higher yields, but as Figure 8b shows, the highest duration itself does not necessarily lead to the highest yield.

Processes 2018, 6, 181; doi:10.3390/pr6100181 11 of 17

(a) (b) medium particle length, it is favorable to have a high initial supersaturation and an increased process

duration. In contrast,Figure Figurethe temperature 9.9. InﬂuencesInfluences difference onon xx5050::( (a) significances signiﬁcancesshould be small and ( .b ) main effects.effects.

Figure 9. Influences on x50: (a) significances and (b) main effects.

The x50-value is mainly affected by the interaction of the start and final temperature and, in addition, by the interaction of initial supersaturation and the duration of the crystallization experiment. Furthermore, Figure 9a shows that the final temperature itself has an influence on the (a) (b) medium particle size. The main effect plot for x50 proves that low temperature differences create greater particlesFigure in 10.10 compa. InfluencesInfluencesrison onto Space Space-Time-Yield: large- T differences.ime-Yield: ((a ) In) significancessignificances addition, andhighlyand ((bb)) mainmain supersaturated effects.effects. and long experiments create larger particles than low supersaturated and short experiments. To increase the TheSpace yield-Time is- stronglyYield of affected the process by the is starting mainly and affected the final by temperature. the duration Figure of the8a alsoprocess, proves final a slighttemperature significance and ofinteraction the duration of those of the processcrystallization parameters. step. TheFigure main 10 effectb also plot concludes (Figure8 b) that depicts long thatprocesses for high generate differences a significant of startingly worse and final STY temperature and that a theminimum yield maximizes, temperature but of with 10 °C the should solubility be curveused. in mind, further cooling is not rational. In addition, higher starting temperatures and therefore higherThis starting study concentrations applied on these are characteristic also limited due numbers to the (yield, instability STY, of x50 the) prove system.s that The even duration though main the effectaverage plot yield indicates is reduced that thewith crystallization higher final temperatures, should exceed enhanced 120 min for cooling higher is yields,not necessary but as Figure to create8b shows,a suitable the process. highest durationDue to the itself lower does solubility not necessarily change leadafter to 10 the °C highest, further yield. cooling does not increase the STYThe of x 50the-value process. is mainly From a affectedprocessing by point the interaction of view, the of duration the start should and finalbe as temperature short as possible, and, inas the addition, STY already by the showed. interaction In contradiction of initial supersaturation to STY, the short andened the process duration time ofnegatively the crystallization affects the growth and the yield of the process. Focusing on high yield, the starting temperature should be as high as possible, due to the exponential increasing solubility at high concentrations, but also affects the medium crystal size negatively. This study also emphasizes that crystallization becomes a time- consuming unit operation, if high yields and large crystals should be achieved. Initial supersaturation variation also indicates that high initial supersaturations do not enhance yield or STY, but decrease the medium crystal size, which affects solid-liquid separation and drying.

3.2. Process Integration of Crystallization Step The conceptual process design for artemisinin, mentioned in Figure 1, uses crystallization as an isolation step of the target component in preparation for the formulation steps, while it neglects the capability of purifying the target component. However, any reduction in the number of separation units provides a major possibility to save costs. Because of this, a process integration study for the crystallization unit as a purifying and formulation step is examined. The different possible process points are summarized in Figure 11.

Processes 2018, 6, 181 11 of 16 experiment. Furthermore, Figure9a shows that the final temperature itself has an influence on the medium particle size. The main effect plot for x50 proves that low temperature differences create greater particles in comparison to large differences. In addition, highly supersaturated and long experiments create larger particles than low supersaturated and short experiments. To increase the medium particle length, it is favorable to have a high initial supersaturation and an increased process duration. In contrast, the temperature difference should be small. Space-Time-Yield of the process is mainly affected by the duration of the process, final temperature and interaction of those process parameters. Figure 10b also concludes that long processes generate a significantly worse STY and that a minimum temperature of 10 ◦C should be used. This study applied on these characteristic numbers (yield, STY, x50) proves that even though the average yield is reduced with higher final temperatures, enhanced cooling is not necessary to create a suitable process. Due to the lower solubility change after 10 ◦C, further cooling does not increase the STY of the process. From a processing point of view, the duration should be as short as possible, as the STY already showed. In contradiction to STY, the shortened process time negatively affects the growth and the yield of the process. Focusing on high yield, the starting temperature should be as high as possible, due to the exponential increasing solubility at high concentrations, but also affects the medium crystal size negatively. This study also emphasizes that crystallization becomes a time-consuming unit operation, if high yields and large crystals should be achieved. Initial supersaturation variation also indicates that high initial supersaturations do not enhance yield or STY, but decrease the medium crystal size, which affects solid-liquid separation and drying.

3.2. Process Integration of Crystallization Step The conceptual process design for artemisinin, mentioned in Figure1, uses crystallization as an isolation step of the target component in preparation for the formulation steps, while it neglects the capability of purifying the target component. However, any reduction in the number of separation units provides a major possibility to save costs. Because of this, a process integration study for the crystallization unit as a purifying and formulation step is examined. The different possible process pointsProcesses are2018 summarized, 6, 181; doi:10.3390/pr6100181 in Figure 11 . 12 of 17

Figure 11.11. Possible process points for crystallization step.step. (SLE: Solid-liquidSolid-liquid extraction).extraction)

Cooling to −20 ◦C of the SLE (Solid-liquid extraction)-extract and LLE#1-rafﬁnate in ethanol-water Cooling to −20 °C of the SLE (Solid-liquid extraction)-extract and LLE#1-raffinate in ethanol- 60/40 wt.% does not lead to crystals. In contradiction to these results, it is possible to create crystals water 60/40 wt.% does not lead to crystals. In contradiction to these results, it is possible to create out of the LLE#2-rafﬁnate and the chromatographic solution. This leads to the assumption that the crystals out of the LLE#2-raffinate and the chromatographic solution. This leads to the assumption critical side components, which drastically increase the solubility of the target component, are removed that the critical side components, which drastically increase the solubility of the target component, are removed by the LLE#2 step. The results for the chromatography fraction and the LLE#2-raffinate are shown in Figure 12.

(a) (b)

Figure 12. HPLC analysis of crystallization experiments of (a) LLE#2-raffinate and (b) chromatography fraction. (HPLC: High performance liquid chromatography)

Both experiments prove a selective reduction of artemisinin with a high purity of the dissolved crystals. This leads to the conclusion that the chromatographic step could be substituted by the crystallization step. An additional H-NMR analysis (see Figure 13) is performed to control these results from HPLC analysis, focusing on purity. As a reference for the measured solutions, a purified commercial artemisinin substance is used.

Processes 2018, 6, 181; doi:10.3390/pr6100181 12 of 17

Figure 11. Possible process points for crystallization step. (SLE: Solid-liquid extraction)

Cooling to −20 °C of the SLE (Solid-liquid extraction)-extract and LLE#1-raffinate in ethanol- Processeswater 60/402018, 6 ,wt.% 181 does not lead to crystals. In contradiction to these results, it is possible to 12create of 16 crystals out of the LLE#2-raffinate and the chromatographic solution. This leads to the assumption that the critical side components, which drastically increase the solubility of the target component, byare the removed LLE#2 by step. the The LLE#2 results step. for The the results chromatography for the chromatography fraction and the fraction LLE#2-rafﬁnate and the LLE#2 are shown-raffinate in Figureare shown 12. in Figure 12.

(a) (b)

FigureFigure 12.12HPLC. HPLC analysis analysis of crystallization of crystallization experiments experiments of (a) LLE#2-rafﬁnate of (a) LLE#2 and (-braffinate) chromatography and (b) fraction.chromatography (HPLC: Highfraction. performance (HPLC: High liquid performance chromatography). liquid chromatography)

BothBoth experimentsexperiments proveprove aa selectiveselective reductionreduction ofof artemisininartemisinin withwith aa highhigh puritypurity ofof thethe dissolveddissolved crystals.crystals. This This leads leads to the conclusion that the chromatographic step could be substituted substituted by by the the crystallizationcrystallization step. step. An An additional HH-NMR-NMR analysis (see F Figureigure 13) is performed performed to to control control these these resultsresults fromfrom HPLCHPLC analysis,analysis, focusingfocusing onon purity.purity. AsAs aa referencereference forfor thethe measuredmeasured solutions,solutions, aa puriﬁedpurified commercialProcessescommercial 2018, 6artemisininart, 181;emisinin doi:10.3390/pr6100181 substancesubstance isis used.used. 13 of 17

FigureFigure 13.13. H-NMRH-NMR measurement of process points. (HNMR: Hydrogen Nuclear magnetic resonance).resonance).

TheThe H-NMRH-NMR analysisanalysis proves thatthat thethe purities ofof bothboth dissolved crystal solutions are higher than thethe puritypurity ofof thethe chromatographychromatography fraction.fraction. The LLE#2-rafﬁnateLLE#2-raffinate has the lowest purity, containingcontaining thethe mostmost signalssignals differingdiffering fromfrom thethe referencereference material.material. TheseThese measurementsmeasurements supportsupport thethe HPLCHPLC analysisanalysis results.results. Hence,Hence, thethe quality of the product is conﬁrmed,confirmed, the chromatographic stepstep isis not essential withwith regardregard toto puritypurity (as(as provenproven experimentally,experimentally, seesee FigureFigure 1313)) andand cancan bebe substitutedsubstituted withinwithin thethe totaltotal processprocess byby aa coolingcooling crystallizationcrystallization step,step, asas longlong asas criticalcritical sideside componentscomponents thatthat stronglystrongly affectaffect thethe crystallization are removed by an optimized LLE. This would reduce the costs (see Part I) by about 40%. The resulting chromatography reduced process is visualized in Figure 14.

Figure 14. Reduced purification process of artemisinin.

One solvent change step and the chromatographic purification step can be excluded from the total process. This leads to only three orthogonal separation units being necessary to purify artemisinin. The influences of the side components on growth kinetics, on the other hand, must be investigated more closely in future, as well. This will result in the final statement on cost reduction, due to possible shifts in solubility and growth kinetics caused by side components. In addition, appropriate reduction of critical side components for early crystallization without losing yield have to be solved by optimization of the LLE-steps (see Part II).

4. Conclusions This study comprises a systematic integration of the crystallization step in a purification process from Artemisia annua L. Several aspects containing metastable zone width, agglomeration, and breakage are researched. In addition, sensitivities of different product quality attributes and their influences are investigated at pilot scale. It is demonstrated that the detected metastable zone width

Processes 2018, 6, 181; doi:10.3390/pr6100181 13 of 17

Figure 13. H-NMR measurement of process points. (HNMR: Hydrogen Nuclear magnetic resonance).

The H-NMR analysis proves that the purities of both dissolved crystal solutions are higher than the purity of the chromatography fraction. The LLE#2-raffinate has the lowest purity, containing the most signals differing from the reference material. These measurements support the HPLC analysis Processesresults.2018 Hence,, 6, 181 the quality of the product is confirmed, the chromatographic step is not essential13 with of 16 regard to purity (as proven experimentally, see Figure 13) and can be substituted within the total process by a cooling crystallization step, as long as critical side components that strongly affect the crystallizationcrystallization areare removedremoved byby anan optimizedoptimized LLE.LLE. ThisThis wouldwould reducereduce thethe costscosts (see(see PartPart I)I) byby aboutabout 40%.40%. TheThe resultingresulting chromatographychromatography reducedreduced processprocess isis visualizedvisualized inin Figure Figure 14 14. .

FigureFigure 14.14. ReducedReduced puriﬁcationpurification processprocess ofof artemisinin.artemisinin.

OneOne solventsolvent change change step step and and the the chromatographic chromatographic purification purification step step can becan excluded be excluded from from the total the process.total process This leads. This to leads only three to only orthogonal three orthogonal separation separation units being units necessary being to necessary purify artemisinin. to purify Theartemisinin. influences The of theinfluence side componentss of the side on components growth kinetics, on growth on the other kinetics, hand, on must the beother investigated hand, must more be closelyinvestigated in future, more as closely well. This in future will result, as well. in the This final will statement result in on the cost final reduction, statement due on to cost possible reduction shifts, indue solubility to possible and growthshifts in kinetics solubility caused and by growth side components. kinetics caused In addition, by side components appropriate. reductionIn addition, of criticalappropriate side components reduction of for critical early crystallizationside components without for early losing crystallization yield have to without be solved losing by optimization yield have ofto thebe solved LLE-steps by optimization (see Part II). of the LLE-steps (see Part II).

4.4. ConclusionsConclusions ThisThis studystudy comprisescomprises aa systematicsystematic integrationintegration ofof thethe crystallizationcrystallization stepstep inin aa purificationpurification processprocess fromfromArtemisia Artemisia annua annuaL. SeveralL. Several aspects aspects containing containing metastable metastable zone width, zone agglomeration, width, agglomeration, and breakage and arebreakage researched. are researched. In addition, In sensitivities addition, sensitivities of different productof different quality product attributes quality and attributes their influences and their are investigatedinfluences are at pilotinvestigat scale.ed It at is demonstratedpilot scale. It is that demonstrated the detected that metastable the detected zone metastable width is influenced zone width by temperature gradient and stirring speed. To set robust boundaries, the smallest detected MSZW is chosen for the process. On the one hand agglomeration does not affect the crystallization of artemisinin, on the other hand breakage and attrition are vital effects even for the seed crystals at high stirring rates. Therefore, a low stirring rate is useful to reduce the mentioned effects and create narrow particle distributions. The process design experiments show no single solution to optimize the crystallization process. Based on the different product quality attributes and characteristic performance numbers, the process parameters have to be adjusted appropriately as proposed. To create the optimized process, the quality attributes and/or characteristic process performance numbers have to be weighted and the resulting multi parameter problem can be solved adequately. Hence, no processes reach a yield greater than 95%, an industrial process needs recycling of the remaining mother liquid, a multi-step crystallization which can be designed and optimized with the aid of the modelling approach [24]. In addition, a process integration study has been performed for the crystallization step. The results display the possibility of substituting the chromatographic step. This is accomplished without loss of product purity with an optimized LLE in combination with crystallization. This substitution has a major impact on production costs and would reduce them by around 40% by using crystallization as a combined purification and isolation step preparing the final formulation. However, effects on kinetics and solubility caused by remaining side components need to be further investigated, as these may reduce the potential for cost reduction through yield losses. Moreover, the natural variability of plant material, which leads to different side component spectra, can affect the downstream process and is considered in conceptual process design [44]. If the critical side components are removed early, as in this study, process integration of the crystallization step provides an enormous potential for cost savings. Processes 2018, 6, 181 14 of 16

Author Contributions: M.J.H. conceived and designed the crystallization experiments as well as wrote the paper. M.S. designed the solid-liquid extraction experiments. A.S. designed the liquid-liquid extraction experiments and F.M. designed the chromatographic experiments. J.S. substantively revised the work and contributed the materials. J.S. is responsible for conception and supervision. Funding: The authors want to thank the Bundesministerium für Wirtschaft und Energie (BMWi), especially M. Gahr (Projektträger FZ Jülich), for funding this scientific work. We also acknowledge the financial support obtained from the Deutsche Forschungsgemeinschaft (DFG) in Bonn, Germany (project Str 586/4-2). Acknowledgments: We gratefully acknowledge the support of the ITVP lab team. In addition, we want to thank J. Namyslo from the IOC for performing the H-NMR analysis and the PuK for performing the QicPic measurements. Special thanks are also addressed to Sebastian Weismann and Thomas Weber for excellent laboratory work and fruitful discussions. Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations c Concentration in the liquid phase, g/kg q0 Number-based particle size density distribution, 1/mm Q0 Number-based particle size sum distribution, - q3 Volume-based particle size density distribution, 1/mm Q3 Volume-based particle size sum distribution, - S Supersaturation, - T Temperature, ◦C x Particle length, µm x50 Median value of particle size distribution Y Yield, - ELSD Evaporation Light Scattering Detector HPLC High performance liquid chromatography H-NMR Hydrogen Nuclear magnetic resonance MSZW Metastable zone width RPM Rounds per minute STY Space-Time-Yield

References

1. Kassing, M.; Jenelten, U.; Schenk, J.; Hänsch, R.; Strube, J. Combination of rigorous and statistical modeling for process development of plant-based extractions based on mass balances and biological aspects. Chem. Eng. Technol. 2012, 35, 109–132. [CrossRef] 2. Ditz, R.; Gerard, D.; Hagels, H.; Igl, N.; Schäffler, M.; Schulz, H.; Stürtz, M.; Tegtmeier, M.; Treutwein, J.; Strube, J.; et al. Phytoextracts. Proposal Towards a New Comprehensive Research Focus; ProcessNet-Subject Division Plant Based Extracts—Products and Processes: Frankfurt, Germany, 2017. 3. Both, S. Systematische Verfahrensentwicklung Für Pflanzlich Basierte Produkte im Regulatorischen Umfeld; Shaker: Aachen, Germany, 2015; ISBN 978-3-8440-3727-2. 4. Koudous, I. Stoffdatenbasierte Verfahrensentwicklung zur Isolierung von Wertstoffen aus Pflanzenextrakten, 1st ed.; Shaker: Herzogenrath, Germany, 2017; ISBN 978-3-8440-5271-8. 5. Koudous, I.; Both, S.; Gudi, G.; Schulz, H.; Strube, J. Process design based on physicochemical properties for the example of obtaining valuable products from plant-based extracts. C. R. Chim. 2014, 17, 218–231. [CrossRef] 6. Sixt, M.; Koudous, I.; Strube, J. Process design for integration of extraction, purification and formulation with alternative solvent concepts. C. R. Chim. 2016, 19, 733–748. [CrossRef] 7. Sixt, M.; Strube, J. Systematic and model-assisted evaluation of solvent based- or pressurized hot water extraction for the extraction of artemisinin from Artemisia annua L. Processes 2017, 5, 86. [CrossRef] 8. Sixt, M.; Schmidt, A.; Mestmäcker, F.; Huter, M.J.; Uhlenbrock, L.; Strube, J. Systematic and model-assisted process design for the extraction and purification of Artemisinin from Artemisia annua L.—Part I: Conceptual process design and cost estimation. Processes 2018, 6, 161. [CrossRef] Processes 2018, 6, 181 15 of 16

9. Schmidt, A.; Sixt, M.; Huter, M.; Mestmäcker, F.; Strube, J. Systematic and model-assisted process design for the extraction and purification of Artemisinin from Artemisia annua L.—Part II: Model-based design of agitated and packed columns for multistage extraction and scrubbing. Processes 2018, 6, 179. [CrossRef] 10. Mestmäcker, F.; Schmidt, A.; Huter, M.; Sixt, M.; Strube, J. Systematic and model-assisted process design for the extraction and purification of Artemisinin from Artemisia annua L.—Part III: Chromatographic Purification. Processes 2018, 6, 180. [CrossRef] 11. Kumar, S.; Gupta, S.K.; Singh, D.; Gupta, M.M.; Jain, D.C.; Kahol, A.P.; Kahuja, S.P.S.; Ram, G. Process for Isolating Artemisinin from Artemisia annua. U.S. Patent 6,685,972, 3 March 2004. 12. Liu, N.Q.; Schuehly, W.; Von Freyhold, M.; Van der Kooy, F. A novel purification method of artemisinin from Artemisia annua. Ind. Crops Prod. 2011, 34, 1084–1088. [CrossRef] 13. Harjo, B.; Wibowo, C.; Ng, K.M. Development of natural product manufacturing processes. Chem. Eng. Res. Des. 2004, 82, 1010–1028. [CrossRef] 14. ElFeraly, F.; Elsohly, H.N. Method for the Isolation of Artemisinin from Artemisa annua. U.S. Patent 4,952,603, 28 August 1990. 15. Malwade, C.R.; Qu, H.; Rong, B.-G.; Christensen, L.P. Conceptual process synthesis for recovery of natural products from plants: A case study of artemisinin from Artemisia annua. Ind. Eng. Chem. Res. 2013, 52, 7157–7169. [CrossRef] 16. Malwade, C.R.; Qu, H.; Rong, B.-G.; Christensen, L.P. Purification of artemisinin from quercetin by anti-solvent crystallization. Front. Chem. Sci. Eng. 2013, 7, 72–78. [CrossRef] 17. Malwade, C.R.; Buchholz, H.; Rong, B.-G.; Qu, H.; Christensen, L.P.; Lorenz, H.; Seidel-Morgenstern, A. Crystallization of artemisinin from chromatography fractions of Artemisia annua extract. Org. Process Res. Dev. 2016, 20, 646–652. [CrossRef] 18. Lévesque, F.; Seeberger, P.H. Continuous-flow synthesis of the anti-malaria drug artemisinin. Angew. Chem. Int. Ed. 2012, 51, 1706–1709. [CrossRef][PubMed] 19. Turconi, J.; Griolet, F.; Guevel, R.; Oddon, G.; Villa, R.; Geatti, A.; Hvala, M.; Rossen, K.; Göller, R.; Burgard, A. Semisynthetic artemisinin, the chemical path to industrial production. Org. Process Res. Dev. 2014, 18, 417–422. [CrossRef] 20. Tu, Y. From Artemisia annua L. to Artemisinins: The Discovery and Development of Artemisinins and Antimalarial Agents; Elsevier Science: San Diego, CA, USA, 2017; ISBN 978-0-12-811655-5. 21. Lorenz, H. Purification by crystallization. In Crystallization: Basic Concepts and Industrial Applications; Beckmann, W., Ed.; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2013; pp. 127–148, ISBN 978-3-527-32762-1. 22. Chemat, F.; Strube, J. Green Extraction of Natural Products: Theory and Practice, 1st ed.; Wiley-VCH: Weinheim, Germany, 2015. 23. Strube, J.; Grote, F.; Josch, J.P.; Ditz, R. Process development and design of downstream processes. Chem. Ing. Tech. 2011, 83, 1044–1065. [CrossRef] 24. Lucke, M.; Koudous, I.; Sixt, M.; Huter, M.J.; Strube, J. Integrating crystallization with experimental model parameter determination and modeling into conceptual process design for the purification of complex feed mixtures. Chem. Eng. Res. Des. 2018, 133, 264–280. [CrossRef] 25. Beckmann, W. Crystallization. Basic Concepts and Industrial Applications; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2013; ISBN 978-3-527-32762-1. 26. Qu, H. Chromatography-crystallization hybrid process for artemisinin purification from Artemisia annua. Chem. Eng. Technol. 2010, 33, 791–796. [CrossRef] 27. Lapkin, A.A.; Peters, M.; Greiner, L.; Chemat, S.; Leonhard, K.; Liauw, M.A.; Leitner, W. Screening of new solvents for artemisininextraction process using ab initio methodology. Green Chem. 2010, 12, 241–251. [CrossRef] 28. Liu, Y.; Lü, H.; Pang, F. Solubility of artemisinin in seven different pure solvents from (283.15 to 323.15) K. J. Chem. Eng. Data 2009, 54, 762–764. [CrossRef] 29. Wang, L.-H.; Song, Y.-T.; Chen, Y.; Cheng, Y.-Y. Solubility of artemisinin in ethanol + water from (278.2 to 343.2) K. J. Chem. Eng. Data 2007, 52, 757–758. [CrossRef] 30. Hofmann, G.; Beckmann, W. Kristallisation in der Industriellen Praxis; Wiley-VCH: Weinheim, Germany, 2004; ISBN 978-352-760-273-5. Processes 2018, 6, 181 16 of 16

31. Wieckhusen, D. Development of batch crystallizations. In Crystallization: Basic Concepts and Industrial Applications; Beckmann, W., Ed.; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2013; pp. 187–202, ISBN 978-3-527-32762-1. 32. Myerson, A.S. Handbook of Industrial Crystallization, 2nd ed.; Butterworth-Heinemann: Boston, MA, USA, 2002; ISBN 978-008-053-351-3. 33. Mullin, J.W. Crystallization, 4th ed.; Butterworth-Heinemann: Oxford, UK, 2001; ISBN 978-075-064-833-2. 34. Kim, K.-J.; Mersmann, A. Estimation of metastable zone width in different nucleation processes. Chem. Eng. Sci. 2001, 56, 2315–2324. [CrossRef] 35. Liu, J.; Svärd, M.; Rasmuson, Å.C. Influence of agitation on primary nucleation in stirred tank crystallizers. Cryst. Growth Des. 2015, 15, 4177–4184. [CrossRef] 36. Bogacz, W.; Wójcik, J. The metastable zone of aqueous solutions. Chemik 2014, 68, 198–201. 37. Mohan, R.; Lorenz, H.; Myerson, A.S. Solubility measurement using differential scanning calorimetry. Ind. Eng. Chem. Res. 2002, 41, 4854–4862. [CrossRef] 38. Chan, K.-L.; Yuen, K.-H.; Takayanagi, H. Polymorphism of artemisinin from Artemisia annua. Phytochemistry 1997, 46, 1209–1214. [CrossRef] 39. Glade, H.; Ilyaskarov, A.M.; Ulrich, J. Determination of crystal growth kinetics using ultrasonic technique. Chem. Eng. Technol. 2004, 27, 736–740. [CrossRef] 40. Mohan, R.; Boateng, K.A.; Myerson, A.S. Estimation of crystal growth kinetics using differential scanning calorimetry. J. Cryst. Growth 2000, 212, 489–499. [CrossRef] 41. Variankaval, N.; Cote, A.S.; Doherty, M.F. From form to function: Crystallization of active pharmaceutical ingredients. AIChE J. 2008, 54, 1682–1688. [CrossRef] 42. Josch, J.P.; Both, S.; Strube, J. Characterization of feed properties for conceptual process design involving complex mixtures, such as natural extracts. FNS 2012, 3, 836–850. [CrossRef] 43. Suberu, J.O.; Yamin, P.; Leonhard, K.; Song, L.; Chemat, S.; Sullivan, N.; Barker, G.; Lapkin, A.A. The effect of O-methylated flavonoids and other co-metabolites on the crystallization and purification of artemisinin. J. Biotechnol. 2014, 171, 25–33. [CrossRef][PubMed] 44. Sixt, M.; Strube, J. Systematic design and evaluation of an extraction process for traditionally used herbal medicine on the example of hawthorn (Crataegus monogyna Jacq.). Processes 2018, 6, 73. [CrossRef] 45. Lapkin, A.A.; Walker, A.; Sullivan, N.; Khambay, B.; Mlambo, B.; Chemat, S. Development of HPLC analytical protocols for quantification of artemisinin in biomass and extracts. J. Pharm. Biomed. Anal. 2009, 49, 908–915. [CrossRef][PubMed]

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).