High-Throughput Automated Design of Experiment (DoE) and Kinetic

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High-Throughput Automated Design of Experiment (DoE) and Kinetic Modeling to Aid in Process Development of an API Christopher Nunn, Andrew DiPietro, Neil Hodnett, Pu Sun, and Kenneth M. Wells Org. Process Res. Dev., Just Accepted Manuscript • DOI: 10.1021/acs.oprd.7b00295 • Publication Date (Web): 14 Dec 2017 Downloaded from http://pubs.acs.org on December 14, 2017

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Organic Process Research & Development

High-Throughput Automated Design of Experiment (DoE) and Kinetic Modeling to Aid in Process Development of an API Christopher Nunn1*, Andrew DiPietro1, Neil Hodnett1, Pu Sun2, Kenneth M. Wells1 1

GlaxoSmithKline, Product Development, 709 Swedeland Road, King of Prussia, PA 19406 2

*

709

Unchained Labs, 6870 Koll Center Parkway, Pleasanton, CA 94566

Swedeland

Road,

King

of

Prussia,

PA

1940.

Tel.

610-270-6221;

email:

[email protected]; [email protected]

ACS Paragon Plus Environment

1

Organic Process Research & Development 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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ToC graphic 1

ToC graphic 2


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ABSTRACT

A Design of Experiment (DoE) and kinetic screening study was carried out using an automated reaction screening platform and, as a case study, an early stage in the synthesis of a late phase developmental candidate was investigated. Key impurities were tracked, kinetically modeled, and significant factors impacting impurity formation were identified. In particular, factors that influence the formation of the diastereomer 4, a precursor to an API impurity identified as a Critical Quality Attribute (CQA), were identified and optimized to minimize its formation. Acetic acid, methanesulfonic acid, volumes of solvent, amino alcohol, and reaction B temperature were observed to be the most significant factors along with a factor interaction between methanesulfonic acid and reaction B temperature. From the experimental data, diastereomer levels of 2.5–5.4 mol% were observed and a kinetic model was developed around the diastereomer formation. Good agreement between the model and experimental data gave confidence in understanding the contributing factors of diastereomer generation, and enabled confirmation of process parameter recommendations to support risk assessments and Quality by Design (QbD) activities. In total, automation provided a 4–5 times savings in FTE hours over a manual process when conducting these experiments, and greatly accelerated the generation of supporting information for a drug file.

Keywords: Automation, Design of Experiment, DoE, Automated Reaction Sampling, Kinetic Modeling, Factor Analysis

INTRODUCTION


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Automation has become a well-established tool for drug discovery and catalysis screening,1-5 but the adoption for use in process development has been slower due to the complexity and flexibility required in those workflows. In particular, high-throughput tools for process development can be a resource efficient way to monitor and characterize chemical reactions that often have been complex and cumbersome to execute manually. 24-hour operation of a manufacturing or pilot plant is routine but it is often difficult to follow reactions in a laboratory setting where work is typically constrained to daytime hours. Finding an automated tool that has high adaptability and adequate robustness across a range of chemistry types and process flows has been challenging and will be essential to accurately characterize these reactions.6 A myriad of approaches have been attempted to address the need to automate manual processes.7-10 Herein, we describe the use of an automated reactor platform provided by Unchained Labs (formerly Symyx, Freeslate) to prepare and sample reaction mixtures under temperature and pressure in a DoE-type experiment. The concept of DoE takes a complex system with many variables and establishes an empirical relationship between those variables and specific responses of the system using a minimum number of experiments.13,14 Other methods like one-factor-at-a-time (OFAT) approaches can also provide valuable information, but struggle to characterize systems with interacting variables and may need a larger number of experiments to reach the same conclusion. The use of design of experiment coupled with kinetic modeling can greatly increase the understanding of an API manufacturing process and reduce the number of experiments required to describe each system.15 Although such approaches can give crucial insight into a process, generating this data can be time consuming and logistically difficult, particularly if many time points are desired. Utilizing


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automation for these types of experiments has the potential to save resources as well as provide comprehensive data sets. An early stage in the synthesis of a late phase developmental candidate was selected as a case study to investigate the coupling of DoE and kinetic analysis as outlined above. As this process is currently in development, the establishment of a detailed understanding of the reaction mechanism and corresponding kinetic parameters are pending additional investigation. The process is a two-step telescoped process whereby methanesulfonic acid-promoted deprotection of the dimethylacetal in starting material 1 is followed by the highly diastereoselective formation of oxazolidine product 3 using an amino alcohol (Scheme 1).

Scheme 1. Reaction Scheme to Form Product 3. Although two separate chemical transformations occur in this process, Reaction B is the main focus of this study, as Reaction A has been studied extensively previously. This process produces three main impurities (Figure 1), including diastereomer 4 of product 3 which is linked directly to a drug substance Critical Quality Attribute (CQA).

Figure 1. Main impurities formed during the formation of product 3.


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Levels of diastereomer 4 greater than 0.6% in the final API would fail drug substance specifications, so its origin, fate, and control is crucial. This study aims to build a fundamental understanding of diastereomer 4 generation by building a kinetic model and identifying key process parameters that control its formation. To accomplish this, we utilized DoE to select the experimental conditions and automation to execute the experimental methods. METHODOLOGY Experimental Design Parameters investigated in this study (Table 1) used the current operating conditions as center points and varied outside the expected operating range to increase model robustness and give a more complete understanding of the system. Reaction time and temperature in Reaction A were coupled to avoid confounding variables when designing the experiments and analyzing the resulting data. Table 1. Parameter ranges investigated in DoE. Factor

Low

Center Point

High

1

Acetic Acid (Equivalents)

3.5

4.5

5.5

2

Methanesulfonic Acid (Equivalents)

0.25

0.30

0.35

3

Acetonitrile (Volumes)

9

10

11

Reaction A Temperature (°C)

65

70

75

Reaction A Time (hr)

5

7

9

5

Amino Alcohol (Equivalents)

1.3

1.4

1.5

6

Reaction B Temperature (°C)

55

60

65

4


6

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The parameters were studied using a resolution IV, three level fractional factorial DoE with 24 experiments, including eight center points. The design was divided into three blocks of eight reactions with the conditions being generated using Design ExpertTM.a,11 Reaction Preparation A 1g basis of starting material 1 was used for all reactions to provide an approximate final volume of 15–20 ml in each well. Solid and liquid dispenses were done on deck with the Unchained Labs Freeslate platform using automated solid and liquid handling systems coupled with the Optimization Sampling Reactor (OSR) module. The OSR allows eight parallel reactions to be run simultaneously with automatic sampling from -20 °C to 400 °C and up to 400 psig pressure. A full list of the experimental conditions for each run is located in the Supporting Information. Reaction A: 1g of starting material 1 and acetonitrile (amount varied by experimental run) were charged to a curved-bottom 30 ml glass vial, then inserted into the reactor module. The automated system then added the different reagents (acetic acid and methanesulfonic acid) based on experimental conditions using the on-deck automated syringe pump. The reactor headspace was then purged with nitrogen and heated based on the prescribed time and temperature conditions for that well. Reactors at 65 °C were held for nine hours, 70 °C held for seven hours, and 75 °C held for five hours. All reactions were held under a nitrogen headspace to mimic conditions at larger scale. Samples were scheduled at the start and end of each time period to monitor reaction progress by HPLC. Samples were diluted on deck by the robot immediately after sampling with a 50/50 volume% mixture of Acetonitrile and Water, and held at 10 °C until the following day when analysis could be completed. Since the effect of reaction time was

a

Design ExpertTM, version 10.0.3.1; Stat-Ease Inc.; Minneapolis, MN


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investigated as a variable, some reactions did not achieve the typical reaction endpoint of < 3% area of starting material 1. Reaction B: The resulting solution from Reaction A was kept in the OSR reactor block and cooled to 20 °C for 10 minutes and depressurized. Then, 1.3–1.5 equivalents of a 70/30 v/v mixture of the aqueous amino alcohol and acetonitrile were added to the reactor by the automated liquid handler. The reactors were heated to their respective temperatures between 55 °C and 65 °C and the nitrogen headspace reestablished. Samples were taken according to the library design every half hour for four hours, every hour after that until eight hours, then every two hours until the typical reaction completion time at 14 hours. Samples were diluted on deck by the robot immediately after sampling with a 50/50 volume% mixture of Acetonitrile and Water, and held at 10 °C until the following day when analysis could be completed. Automation Equipment At GlaxoSmithKline, a high-throughput automated reaction screening tool

11,12

has been

purchased from Unchained Labs (formerly Freeslate, Symyx) for use in chemical process development.


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Figure 2. OSR system in a purge (glove) box located at GSK in King of Prussia, PA

Figure 3. OSR vial inserts with stir top The OSR instrument allows for automated sampling of the reactors while at sub-ambient or elevated temperature and under pressure. One major benefit this approach offers is the time savings realized when a scientist is not required to physically take samples over the time period. We have defined FTE hours in this case as the time a scientist is physically involved in performing experiments. In a case where depressurization and cooling of the reaction vessel is required, this process could take 3–5 minutes per reactor or more. Assuming three minutes to sample while taking 150 samples per set of eight reactions would result in 7.5 FTE hours per run that would be needed. In our automated case, one hour was spent initially prepping the robot and 1.5 hours for cleanup with no time added for sampling and dilution. Assuming a similar time frame to prepare and cleanup the manual reactions, we see a four times increase in productivity using this conservative approach. This estimate also does not take into account the additional


9


value gained in eliminating the need for off-hours sampling by a scientist. The unique method of sampling executed by the platform accounts for the majority of the value gained. The sampling mechanism utilizes an antechamber where the sampling needle can equilibrate to the pressure in the reactor before drawing a sample, thereby protecting the reaction vessel and sampling needle from sudden depressurization effects. The sample is then withdrawn behind a ball valve, protecting it as the sampling needle equilibrates back to atmospheric pressure. Below is a depiction of the sampling mechanism (Figure 4). Backing Solvent Gas

upstream

Sample

Backing Solvent

Injection Tip Valve

downstream

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Syringe Pump

P

O-Ring Antechamber Vent

Antechamber Inert Gas

Injection Port Ball Reactor Vent

Reactor FeedGas


10

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Figure 4. Depiction of OSR sampling mechanism to sample under pressure. This tool has the potential to provide value for late phase project DoEs, process optimization and robustness studies, and for generation of detailed kinetic models. The design parameters generated by Design ExpertTM were translated to Library Studio for execution on the Unchained Labs’ Freeslate platform. Unchained Labs’ LEA Automation Studio9 was used to execute the libraries and monitor system diagnostics. Naphthalene was used as an internal standard in each reaction to eliminate errors due to minor variations in the volume sampled by the Freeslate platform. Polyether ether ketone (PEEK) stir paddles attached to the underside of the module’s stir top allowed effective overhead stirringb. At specified time points, 40 µl samples were taken from each of the wells, diluted on deck, and held at room temperature. Samples generated were analyzed using HPLC. Experiment Analysis Kinetic data was generated by converting the observed peak areas from HPLC to molar concentrations. To correct for any volumetric sampling errors from the sampling needle or the HPLC auto samplers, area counts were normalized against an internal standard (naphthalene). To convert from raw area counts to moles, calibration curves for each major component were generated based on 3–4 standards and response factors calculated for use in this analysis. The correlation indicated a linear relationship in the desired concentration range for each major component. Response factors were obtained from each calibration by taking the inverse of the slope of the linear trends. Only significant components were considered. Equations used to calculate molar concentrations are outlined in the Supporting Information.

b

A mixing analysis performed using DynoChem found that the OSR stir paddles produce similar Reynolds number in the vessel when compared to a Mettler Toledo EasyMax 102 at the same stir rate.


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RESULTS & DISCUSSIONS Experimental Results A typical reaction profile from Reaction B is shown below (Figure 5). Product 3 shows about 90% mole conversion over the course of the reaction and the reaction forms about 3.8 mole % diastereomer 4. 100 90 80 70 60 Mol %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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50 40 30 20 10 0 0

60

120

180

240

300

360

420

480

540

600

660

720

780

840

900

Time (min) Intermediate (2)

Product (3)

Diastereomer (4)

Impurity 8

Impurity 9

Figure 5. Reaction B progression of a center point run. Some unexpected behavior was seen in the profile of impurity 8, showing a temporary spike in concentration in the 1.5–2 hour range corresponding to a drop in intermediate 2 and impurity 9 concentrations. It is possible these fluctuations were caused by interconversion of transient intermediates described in the proposed reaction mechanism, but they were determined to not have a significant impact on the final kinetic and DoE analysis. To demonstrate the


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reproducibility of sampling by the OSR across reactors, all center point data relating to the generation of diastereomer 4 were overlaid (Figure 6). 4.5 4 3.5 3 Mol %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


2.5 2 1.5 1 0.5 0 0

100

200

300

400

500

600

700

800

900

1000

Time (minutes) STD 17

STD 19

STD 18

STD 23

STD 20

STD 22

STD 24

STD 21

Figure 6. Diastereomer 4 generation trends in center point runs. The results for generation of diastereomer 4 were very similar across all center points with a % relative standard deviation (RSD) of only 2.6% based on endpoint. This gives confidence in the consistency of the OSR’s robotic sampling arm and the robustness of the analytical method and data analysis approach. By taking the diastereomer 4 responses and plotting them against the formation of product 3, we can gain some insight into the possible mechanism for the formation of diastereomer 4 (Figure 7).


13

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Diastereomer 4 Mol %


Page 14 of 24

4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 0

10

20

30

40

50

60

70

80

90

100

Product 3 Mol % STD 17

STD 19

STD 18

STD 23

STD 20

STD 22

STD 24

STD 21

Figure 7. Diastereomer 4 formation versus product 3 formation for center point runs. The linear correlation suggests that both compounds are formed via the same mechanistic pathway and are competing for intermediate 2. Alternative mechanistic pathways such as epimerisation of product 3 would be expected to result in a non-linear relationship between diastereomer 4 and product 3 concentrations. DoE Analysis The molar amounts of each major reaction component in the final sample taken in reaction B were analyzed in Design ExpertTM in order to identify the statistically significant effects in the formation of diastereomer 4. A summary of the key DoE responses and experimental results examined are shown in Table 2. The full experimental data can be found in the Supporting Information. Table 2. Summary of key DoE responses and effects. Mol % Starting Material 1 Minimize

Goal Range

Mol % Intermediate 2

Mol % Product 3

Minimize

Maximize

Mol % Diastereomer 4 Minimize

Max

15.9

21.6

90.6

5.4

Min

1.2

0

61.7

2.5


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Standard Deviation

3.6 a

b

Factor A. Acetic acid amount (molar equivalents) B. Methanesulfonic acid amount (molar equivalents) C. Solvent amount (volumes) D. Reaction A Temperature (°C) E. Amino alcohol amount (molar equivalents) F. Reaction B Temperature (°C) BF

In Model

+/-

Yes

-

No

Yes

-

Yes

Yes

+

No

Yes

-

No

6.2

7.6

0.7

a

a

a

In Model

+/-

In Model

+/-

In Model

+/-

Yes

+

Yes

+

No

Yes

-

No

No

No +

Yes

-

No

Yes

-

Yes

+

Yes

-

No

Yes

-

Yes

+

Yes

+

No

No

Yes

+

AC

No

Yes

BD

Yes

+

No -

No

AB Yes + No a. This factor is included in the empirical model for this response. b. Indicates a positive or negative effect on the response.

Yes

+

No

No

No

No

No

The variation in the amount of starting material 1, intermediate 2, and product 3 at the end of each reaction was expected as some experimental conditions were designed to result in incomplete conversion in both Reactions A and B. At the minimum formation of 4 (2.5 mol%) we see 84.1 mol% conversion to product, and at the highest mol conversion to product (90.6 mol%) we see 4.3 mol% of diasteroeomer 4. Both of these conditions would be sufficient to pass product specifications. The half normal probability plot for the main factors affecting diastereomer 4 levels can be seen in Figure 8.


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Figure 8. Half Normal Plot.

Figure 9. 3D surface plot for factors A and F on the formation of diasteroeomer 4.


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Factors A, F, B, BF, C, and E were identified as significant by the model (having a p-value of over 0.05). Based on these experimental results, acetic acid amount, reaction B temperature, and methanesulfonic acid amount should be set to their low levels and amino alcohol amount should be set to the high level to minimize the amount of diastereomer 4 formed in the reaction. These results were consistent with those seen in other commensurate manual studies performed.

Kinetic Model Reaction Mechanism The kinetic model development began with proposing an assumed reaction mechanism for Reaction B (Scheme 2).

Scheme 2. Proposed Reaction B Mechanism. A plot of ln(C2) (concentration of intermediate 2) versus time (Figure 9) through 96% conversion of intermediate 2 yields a straight line, indicating pseudo-first order kinetics with respect to intermediate 2. While the plot is only shown for one center point run, a similar fit can


17


be obtained for each reaction profile. Deviations from the expected linearity are likely due to the variations seen in the trend for intermediate 2. -5 0

100

200

300

400

500

600

700

800

900

-5.5 -6 -6.5 -7 ln(C2)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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-7.5 y = -0.0038x - 6.3189 R² = 0.9482

-8 -8.5 -9 -9.5 -10

Time (min)

Figure 10. First order plot for conversion of intermediate 2 from a center point run. Based on the proposed mechanism, the rate law for formation of 3 and 4 contains terms for the amino alcohol and acetic acid. However, the concentration of the amino alcohol and acetic acid was not measured over the course of the reaction so it is not possible to perform a similar graphical analysis. Thus, we assumed the reaction was first order in both acetic acid and the amino alcohol. Methanesulfonic acid is neutralized upon addition of the amino alcohol and is not included in the rate law. In addition, it was assumed that the rates of formation of product 3 and diastereomer 4 are first order with respect to their corresponding ring-closed oxazolidine intermediate (6 and 7, respectively).


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Equation 1 and Equation 2 show the rate law which governs each reaction, assuming this proposed mechanism and first order kinetics with respect to intermediate 2, the amino alcohol, and acetic acid. Equation 1. Assumed rate law for the formation of product 3. =

d = ∙ =

∙ ∙ ℎ ∙

Equation 2. Assumed rate law for the formation of diastereomer 4. =

d = ∙ = ∙ ∙ ℎ ∙

Further mechanistic studies would be required to verify the validity of the assumptions made to determine the order of reactants, especially with respect to the two ring-closed intermediates, as they are unstable and not detected in our HPLC analysis. The proposed mechanistic pathway provided a basis for a preliminary kinetic model in DynoChem. Model Development Using DynoChem Using the rate laws presented above as a starting point, the reaction network was modeled using DynoChem.c,16 Due to our inability to observe intermediates 5, 6, and 7 using HPLC, simplification was needed in order to ensure a better fit for the data. As the equilibrium between intermediates 5, 6, and 7 is assumed to be established rapidly, determination of the individual concentrations of each species was not necessary for the model to have high accuracy. Thus, the model was simplified by treating the three interconverting intermediates (5, 6, 7) as a single component, with the concentrations of components 3 and 4 acting as a surrogate measurement for the relative concentrations of 6 and 7. In addition, impurities 10, 11, and 12 were added to the

c

DynoChem®, version 4.1; Scale-up Systems Ltd.; Dublin, Ireland


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model to account for degradation of 8 and 9. The reactions used to fit the experimental data in the mechanistic model can be seen in Table 3. Table 3. Reactions used to generate Reaction B DynoChem model ID Reaction CH3OOH CH3OO- + H+ I II Intermediate 2 + Amino Alcohol -> Dynamic Mixture + Water III Dynamic Mixture + H+ -> Product 3 + Methanol + H+ IV Dynamic Mixture + H+ -> Diastereomer 4 + Methanol + H+ V Impurity 9 + Amino Alcohol -> Impurity 10 + Methanol VI Impurity 8 + Amino Alcohol -> Impurity 11 VII Impurity 8 + Impurity 8 -> Impurity 12 VIII Amino Alcohol + Methanesulfonic Acid -> Salt A hydrogen ion was entered as a catalyst in reactions III and IV to account for the observed effect of acetic acid on the formation of diastereomer 4. In addition, reaction VIII captures the effect of methanesulfonic acid on Reaction B that is observed in the half-normal plot (Figure 8). Figure 10 shows an example of the model fit against center point conditions.

Figure 11. DynoChem simulation of mechanistic model versus center point conditions.


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For the main reactions (II, III, and IV), the confidence intervals were generally better than + 20% excluding the activation energy for reaction II. The confidence intervals for the degradation of impurity 8 and impurity 9 were generally worse (> + 30%) due to the low levels of the impurities and the interconversion of the impurities which resulted in irregular disappearance trends. Overall, the model fits the experimental data reasonably well, with the predicted trends matching experimental data. It is clear, however, that the model is deficient when predicting the disappearance of intermediate 2, which could be due to the inability to observe some intermediates directly via HPLC. More quantitative data is needed to refine the model and draw a conclusion regarding the mechanism of this reaction. Overall, this methodology shows the ability to generate the data necessary to build a preliminary kinetic model. In conjunction with examination of similar reaction and isolation studies, a range for each parameter can be recommended to be included in the regulatory submission. Ranges were determined by calculating the amount of diastereomer 4 in the final API and ensuring that the final product specification of < 0.6% is met (assuming 90% downstream purging). The following ranges were recommended to be included in the regulatory submission for this manufacturing process (Table 4). Table 4. Recommended ranges for inclusion in the regulatory submission. Process Parameter

Low

Center Point

High

Acetic Acid (Equivalents)

4.0

4.5

5.0

Methanesulfonic (Acid Equivalents)

0.27

0.30

0.33

Acetonitrile (Volumes)

9

10

11

Reaction A Temperature (°C)

65

70

75

Amino Alcohol (Equivalents)

1.4

1.45

1.5


21


Reaction B Temperature (°C)

55

60

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65

Operating within the process parameter ranges given ensures the resulting product meets specifications and adheres to impurity guidelines.

CONCLUSION A multivariate DoE study on a late-phase developmental candidate was completed using an OSR module and the kinetics of the reaction was modeled using DynoChem software. Data generated was fitted to a kinetic model with reasonable agreement when considering the full reaction mechanism. The data suggests that the reaction should be run at the center point conditions (presented in Table 1) used in this DoE in order to maximize product and minimize formation of diastereomer 4. Additionally, the experiment showed a variety of acceptable operating parameters (Table 4) that produced product within specifications. More experimentation is needed to determine the effect of changing reagent amounts on downstream isolation of product 3. Good adherence to the model gave confidence in understanding the risks of diastereomer 4 generation during scale-up and enabled confirmation of current process parameters to support risk assessments and Quality by Design (QbD) activities. Use of an automated reaction screening platform allowed for several thousand data points to be collected over the course of 22 hours without the need for scientist intervention and allowed for a 4–5 times savings of FTE hours compared to a manual process. Additionally, the platform obviates the need to have scientists working during off hours while maintaining the quality and consistency of the reaction mixture. These types of robust and flexible tools will be essential for increasing the usefulness of automation in process development. SUPPORTING INFORMATION


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Full table of responses for experiments with time course information. ANOVA results from DoE analysis. Calibration information for HPLCs used along with equations used for conversion to mole basis. List of assumptions used for model generation in DynoChem.


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