Patent ID: 12209257

Throughout this specification and figures like reference numbers identify like elements.

DETAILED DESCRIPTION OF THE INVENTION

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only, and are intended to provide an explanation of various embodiments of the present teachings.

In the description below, the phrase “experimental reactor” refers to a reactor that is used for non-commercial purposes, such as low volume runs, mock runs, initial data gathering, etc. Additionally, the phrase “actual reactor” refers to a reactor that is used for any other purposes that the experimental reactor is not used. Such purposes include, for example, commercial purposes. Furthermore, the phrase “hypothetical reactor” refers to an experimental reactor, wherein its data was previously collected, thus, there is no reason to conduct the experiment again. The phrases “first detector” and “second detector” refer to one or more detectors capable of detecting different characteristics of the process stream and its content.

Overall View

In an example, the process for manufacturing a biological product can include continuous viral inactivation, using a PFR, by continuously adding and homogenizing a viral inactivating agent to a product containing feed stream (process stream). From there, the process stream can be pumped through and/introduced to the PFR and maintained at this viral inactivating condition for a predetermined amount of time. Defining the residence time of the viral particles in a PFR is difficult to quantify due to the flow of the process stream in the center of the pipe being twice as fast as the average flow of the process stream and almost stagnant near the wall of the pipe. To determine an optimum residence time of the viral particles, a new design or manufactured PFR can include a set of alternating turns that form a serpentine pattern between an inlet and an outlet, thus creating a serpentine-like flow path, interwoven path, and/or a Jig-in-a-Box (JIB) design that generates Dean Vortices to promote radial mixing can be utilized, as described in commonly owned and co-pending U.S. patent applications entitled “A Novel Continuous Flow Reactor For Low pH Viral Inactivation” and “Continuous Flow Reactor for Viral Inactivation” the specifications of which are incorporated herein by reference. In this new design or manufactured PFR, the ability to predict when the first viral particle exits the reactor is essential. Generally, three alternative approaches may be used to determine or estimate a minimum residence time experienced by a discrete detectable particle or detectable tracer equivalent to an incubation time for batch viral inactivation (Tmin). One approach assumes ideal uniformity in the flow path (i.e. plug flow) and simply divides the reactor volume by the flow rate. However, this idealistic approach can result in underestimating the required residence time, even with the increasing efficiency of the reactor having a serpentine-like flow path or an interwoven flow path. Another approach is to assume that the center of the flow path remains twice as fast as the average velocity of the process stream. This approach, however, can result in overestimating the required residence time of the process stream resulting from the increased efficiency of the serpentine-like or interwoven reactor. Another approach uses process development to determine an efficiency coefficient by which to modify the idealistic approach to account for non-idealities. However, this approach requires testing of an at-scale JIB and fails to account for potential anomalies in viscosity and flow rates. Thus, the system and method of the present invention use an inventive technological solution to accurately extrapolate JIB performance across path lengths, flowrates, reactor designs, internal diameters, and viscosities.

Being able to predict Tminallows for the estimation and/or configuration of the desired flow rates and reactor size required to fit a process.

This is especially useful when submitting data to regulatory agencies, such as the Food and Drug Administration (FDA), where compliance with applicable regulations requires data verification. The systems below will allow a user to run a small scale process stream for data verification purposes and then scale the reactor for mass production, without compromise to the process stream or final results of the process stream during scale-up. Given that the viral inactivating conditions can also degrade the target product as a function of residence time, maximum residence time (Tmax) spent in the reactor (i.e., maximum residence time experienced by the last significant amount of target product leaving the reactor) should also be determined. The present invention is the first known approach to account for the target product stability. The first application of the system and method allows the use of Tminand Tmaxto solve for the operational flow rate and the path length of the JIB. The second application takes the opposite approach, in that the operational flow rate and path length are used to predict the Tminand Tmax. The third application is a way to approximate the internal diameter and path length required for a certain Tminwhen scaling the size of the reactor. In each of these applications, the impact of viscosity, Dean Number, and reactor volume on the residence time distribution are to be determined and quantified. In each of these applications, a user can select if the user would like to use a reactor having a substantially same diameter or a reactor with a different diameter (i.e., scaling the reactor). Alternatively or additionally, the system can recommend whether a same size reaction tube reactor should be used in the actual process as in the experimental reactor or if the experimental reactor should be scaled up or down. In this alternative or additional example, when the reactor is being scaled up or down, the system or the user can select that the experimental reactor and the actual reactor have the same aspect ratio, as shown inFIG.2A, or that the experimental reactor and the actual reactor have different aspect ratios, as shown inFIG.3.

Continuous Flow Reactor Having a Tubular Flow Path with a Set of Alternating Turns that Form a Serpentine Pattern

As stated above, the system can design, select, make, and/or manufacture an actual reactor having a reactor tube with a serpentine pattern. The details of the serpentine pattern are described inFIGS.1A-1D. Referring toFIG.1A, each curve10in the tubular flow path12can include a vertical (L1) center to center distance between the turns of approximately 1.5 cm, such as about 1.479 cm. Additionally, each curve10can include a horizontal (L2) center to center distance between the turns of about 1.375 cm. Furthermore, the radius of each curve10in the tubular flow path12can be substantially constant. In an example, when the ROC is from about 0.85 cm to about 0.99 cm, then the angle of the curvature in each curve10can be about 270°. In another example, the ROC is greater than or equal to about 0.99 cm, then the angle of curvature of each curve can be the same or the angle of the curvature of the first curve10can be about 270° and the angle of the curvature of the second curve10adjacent to the first curve can be equal or greater than 270°. In each exemplary scenario described, above, R1 and R2 can be within 0.05 cm of each other to prevent substantial differences in the Dean Number between alternating turns.

In an example, each curve10in the tubular flow path12can include the same radius, such as a radius of 1 cm. In another example, each curve10in the tubular flow path12can include a different radius. For example, the first curve10can include a radius R1, which can be 1 cm and the second curve10can include a radius R2, which can be 1.02 cm. In this example, the angle of the curvature corresponding to the radius R1 can be about 270° and the angle of the curvature corresponding to the radius R2 can be about 278.27°. In another example, the first half of a curve10in each tubular flow path12can include a first radius R1, which can be 1 cm and the second half of the curve10in each tubular flow path12can include a second radius R2, which can be 1.02 cm.

Referring toFIGS.1B and1C, to accommodate approximately 325 alternating 270° turns into a compact design, the tubular flow paths12in the in-line tubular CVI reactor10can be segmented vertically into a plurality of stacked layers14, such as from 2 layers14to 26 layers14or more, for example, 26 layers14, as shown inFIG.1C. Each layer in the plurality of stacked layers14may include a thickness of from about 0.5 cm or less to about 2 cm or more, such as from about 0.7 cm to about 1.2 cm. In an example, each layer14(a)-14(z) in the stacked layers14can include from about 10.5 turns or less to about 15.5 turns or more in a single plane. For example, each layer14(a)-14(z) in the stacked layers14can include 12.5 turns. In an example, each layer14can be connected to its adjacent lower layer14by a 180° vertical turn16. Alternatively, a second half of the last turn10of the flow path12in each layer14can be turned vertically by 180° to connect the tubular flow path12in the first layer (e.g., layer14(a) to the tubular flow path12in the second layer14(b)). In an example, where the in-line tubular CVI reactor10includes 26 layers14, the 26 layers14can be connected to one another by 25 180° vertical turns16.

Referring toFIG.1B, in an example, each layer14in the in-line tubular CVI reactor100can include a depth L3. The depth L3 can be a distance from a center of the tubular flow path12in a first layer14to the center of the tubular flow path12in a second layer14directly below the first layer14. The depth L3 can be from about 0.7 cm or less to about 1.2 cm or more, for example from about 0.8 cm to about 0.9 cm, such as a depth of about 0.835 cm. In an example, the distance from the bottom portion of the tubular flow path12in a first layer to the top portion of the tubular flow path12in a second layer directly below the first layer can be from about 0.15 cm (1.5 mm) to about 0.4 cm (4 mm), for example, it can be from about 0.17 cm (1.7 mm) to about 0.255 cm (2.55 mm), such as about 0.2 cm (2 mm).

In an example, as shown inFIG.1D, to allow for alterations to the path length and incubation time, in addition to the in-line tubular CVI reactor10having a plurality of layers14, a plurality of in-line tubular CVI reactor10can be connected to one another in series. This can be accomplished by one or more flanged connectors18. In an example, at least 2 in-line tubular CVI reactor10can be connected to one another, such as at least 6 in-line tubular CVI reactor10or more. In this particular example, the tubular flow path12at each end of the in-line tubular CVI reactor10can partially extend out (extended section15) from the in-line tubular CVI reactor100. The extended section15can also include a flange20, as shown inFIG.1B. A connector18can include a horizontal 180° turn and/or be in a shape of a “U.” One end of the connector18can be connected to the tubular flow path12or the flange20of a first-in-line tubular CVI reactor10and the second end of the connector18can be connected to the tubular flow path12or the flange20of an adjacent in-line tubular CVI reactor10.

The connector18can be connected to each tubular flow path12or flange20by a clamp22, as shown inFIG.1D, or by other fastener devices, such as a screw, an adhesive, etc. In an example, a gasket can be placed between the end of the tubular flow path12or flange20and each end of the connector18.

In an example, the in-line tubular CVI reactor10can include a body or footprint of 20×4.9×23 cm and can contain a flow path12length of approximately 16.43 m resulting in approximately a 520 ml flow volume. The body of the in-line tubular CVI reactor10can include a first side24and a second side26, as shown inFIG.1C. In an example, the first side24can include at least one groove or indentation24A and the second side26can include at least one protrusion26A. The at least one indentation24A and the at least one protrusion26A can be arranged such that when two in-line tubular CVI reactors10are facing one another they are aligned and can secure one in-line tubular CVI reactors10to an adjacent in-line tubular CVI reactor10.

Continuous Flow Reactor Having an Interwoven Tubular Flow Path

As stated above, the system can design, select, make, and/or manufacture an actual reactor having an interwoven reactor tube. The details of the interwoven reactor tube are described inFIGS.1E-1J.FIGS.1E-1Jillustrate an exemplary continuous flow reactor tube10Z that can operate at low Re. The continuous flow reactor tube10Z can include the tubular flow path12Z that includes turns or curves14Z and bends16Z. At least two of the turns or curves14Z are disposed on a single longitudinal axis LX, but in different, non-parallel planes, for example, plane A and plane B, such that the turns or curves can form an angle of from about 25° to about 60° around the longitudinal axis LX. Depending on the number of paths used to create the continuous flow reactor tube10Z, the turns can be in two or more different, non-parallel planes, such as from about 6 to about 13 different planes, for example, 8 different planes. Furthermore, at least two of the turns are arranged such that the planes corresponding to the at least two turns can intersect one another, as shown, for example, inFIG.1F. The turns can also create a pattern that can be repeated or not repeated after a predetermined number of turns. For Example, as shown inFIG.1G, which illustrates a cross-section of a single path along its longitudinal axis, the single path can include a pattern50Z that is repeated at least two times (see alsoFIG.1E). Each flow path can include from about 4 turns to about 128 turns or more of alternating turns, such as about 16 to about 32 turns, for example, 12.5 alternating turns. Each turn14Z can include an angle of from about 110° to about 280°, such as about 135° to about 140°. In an example, the first turn can include an angle (such as an angle of about 135°) that is smaller than the angle of the second turn (such as an angle of about 140°). Additionally, as shown inFIGS.1E,1F, and1J, each flow path can also include from about 8 to about 64 or more bends16Z, such as from about 8 to about 16 bends16Z. Each bend16Z can include an angle of from about 15° to less than about 135°, for example, an angle of from about 30° to about 90°, such as an angle of about 45°. In an example, each pattern50Z can be repeated after about 4 bends or more, such as after about 8 bends.

Additionally or alternatively, when the continuous flow reactor includes an interwoven tubular flow path, the interwoven tubular flow path can include from about 6 to about 100, such as from about 6.5 to about 93.2 turns per 1 m3.

In another example, not shown in the figures, a path of the continuous flow reactor tube10Z can include two or more different patterns, which may or may not be repeated. When the continuous flow reactor tube10Z includes a plurality of interwoven flow paths, each path of the continuous flow reactor tube10Z can include a substantially similar pattern. Alternatively, or additionally, each path of the continuous flow reactor tube10Z can include a different pattern. Moreover, each path of the continuous flow reactor tube10Z can include a similar number of repeated patterns (for example two similarly repeated patterns) or can include more or less than two repeated patterns. For example, the second path can include two similarly repeated patterns or can include three similarly repeated patterns.

Referring toFIGS.1H and1I, each of the turns14Z in the continuous flow reactor tube10Z can include a vertical L1 center to center distance between the turns of from about 1 cm to about 2 cm, such as about 1.5 cm. Additionally, each of the turns14Z can include a horizontal L2 center to center distance between the turns of from about 1 cm to about 2 cm, such as about 1.63 cm. Furthermore, each of the turns14Z can include an end to end distance L3 of from about 3 cm to about 4 cm, such as about 3.85 cm. The radius of each turn14Z in the continuous flow reactor tube10Z can be substantially constant. For example, referring toFIG.1I, the radius R1 and R2 can be within 0.05 cm or less of each other, such as about 0.02 cm of each other to prevent substantial differences in the Dean Number between alternating turns. For example, R1 can be about 1.10 cm and R2 can be about 1.12 cm. In an example, the turns14Z can be arranged to generate a vortex to induce mixing the process stream having a laminar flow with a Reynolds number of from about 187.7 to about 375.5.

In an example, the plurality of turns14Z can follow a three-dimensional path that may include flow direction change at approximately 45° at a turn center. Additionally, each of the plurality of turns14Z can include an angle of from about 125° to about 180°.

System Using Serpentine and Interwoven Reactor Tube

Referring toFIG.1K, in order for a system1000to design, select, make, and/or manufacture an actual reactor400that can manage an actual process stream for commercial purposes with the same results as an experimental reactor100, the system1000can include a processor1001and a non-transitory machine-readable storage medium1002storing machine-readable instructions that are executable by the processor1001to estimate and/or determine the required parameters of an actual reactor400. In an example, the processor1001can receive known and empirical values of reactor parameters of an experimental reactor100and/or fluid parameters of a process stream entering the experimental reactor100. The instructions saved on the non-transitory machine-readable storage medium1002storing machine-readable instructions that are executable by the processor1001to use the received known and empirical values of the reactor parameters and/or fluid parameters to determine the non-empirical values of the reactor parameters of the experimental reactor100and/or fluid parameters of the process stream entering or being introduced to the experimental reactor100. The instructions saved on the non-transitory machine-readable storage medium1002storing machine-readable instructions that are executable by the processor1001to forward the empirical values and the non-empirical values and ask the processor1001to determine, design, select, make, manufacture, and/or recommend an actual reactor400for an actual process stream.

In an example, the experimental reactor100can be a hypothetical reactor having certain known parameters. Generally, the experimental reactor100can be a fixed reactor that includes a constant internal diameter i.d. and radius of curvature Rc (cm) of a reactor tube. The known empirical values and the non-empirical values are task-dependent. That is, depending on what the user would like to accomplish, the values of at least some of the reactor parameters and/or the fluid parameters may be empirical or non-empirical. In an example, the primary variables associated with the empirical and non-empirical values relating to the reactor parameters and/or the fluid parameters can be the Tmin, the Tmax, the internal diameter i.d., the volumetric flow rate Q (mL/min) of the process stream, the path length L (cm) of the flow path, the radius of curvature Rc (cm) of the reactor tube, the density (ρ) of the fluid in the process stream, and the dynamic viscosity μ (mPa·s) of the fluid in the process stream, and the variance σ2time(min2).

Determining the Empirical Values Using an Experimental Reactor

In an example, the empirical values can be values that correspond to experimental reactor parameters and/or fluid-phase parameters that are linear to an experimental set of data. In an example, as shown inFIGS.2A and2B, by introducing a process stream at10, using a pulse injection, into the experimental reactor100, the first detector30, which can be in communication with the experimental reactor100can determine and/or be provided with the process stream fluid-phase parameters, such as density (ρ), dynamic viscosity (μ), and the Dean Number (De). Moreover, as shown inFIG.2C, the first detector30can detect the volume/amount of detectable particles or detectable tracer that is injected into the process stream and the time it takes to inject the detectable particles or detectable tracer into the process stream/or the time it takes to introduce the detectable particles or detectable tracer into the experimental reactor100. Additionally, the first detector30can determine or be provided with the type of experimental reactor (e.g., JIB), flow rate Q of the process stream into the experimental reactor100, the path length L of the experimental reactor100, and the volume of the experimental reactor100. In an example, some of the values above may be known to the user and, thus, there is no need for the detector to detect those values. In an example, the detectable particles or detectable tracer can be but not limited to viral/bacteriophage particles, Riboflavin, salts, dyes, protein, and/or sugars. For example, as shown inFIG.2B, the first detector30can determine that the process fluid is water, the experimental reactor type is a JIB, the flow rate of the process stream Q is 50 mL/min, the path length L of the experimental reactor is 1644 cm, the reactor volume is 520 mL, and the Dean Number is 118.94.

Referring toFIG.2B, the second detector40can also be in communication with the experimental reactor100and can detect and measure the time in which the first detectable particle or detectable tracer exits or leaves the experimental reactor100and the time in which the last significant amount of the detectable particles or detectable tracer exit or leave the experimental reactor100. In an example, the second detector40can also detect the parameters that can be detected or measured by the first detector30. Based on the experimental raw data shown inFIG.2B, the second detector40can generate a fitted curve, as shown inFIG.2D. Based on this fitted curve, the variance σ2timeand the standard deviation σtimeis determined. For example, for a flow rate of 50 mL/min in an experimental reactor100having a path length of 1644 cm, based on the fitted curve, the variance σ2timeis 0.5115 min2and the standard deviation σtimeis 0.7152 min.

As shown inFIG.2E, given the variance σ2timeof 0.5115 min2, the experimental reactor path length L of 1644 cm, the Dean Number De of 118.94, and the average residence time TAveof 10.4 min, the comparator50can derive at the height equivalent to a theoretical plate HETP value of 7.87 cm. The comparator50can then create a graph corresponding to the experimental data HETP and Dean Number across a broad array of flow rates and process stream viscosities (e.g., 0 g/L of Dextrose, 50 g/L of Dextrose, 100 g/L of Dextrose, and 200 g/L of Dextrose). In an example, based on the experimental data graph, the comparator50can also create an experimental data fit graph, as shown inFIG.2E. As shown inFIG.2A, at200, the empirical values/variables can be forwarded to or received by the system1000to determine and/or create an actual reactor400of interest. In another example, the empirical values may have been previously derived or determined, thus, there may not be a need for the first detector30or the comparator50, as shown inFIG.3, or for that matter a need for an experimental reactor100, first detector30or the second detector40(not shown in the Figures).

The limiting factors for designing, selecting, making, manufacturing, and/or determining the actual reactor400can be product stability, required viral incubation, process parameters, and/or no operational or kinetics consideration.

When the limiting factor is the product stability, a target protein that is highly sensitive to viral inactivation chemical is provided. In this example, the system1000can be made to determine an acceptable reactor length and flow rate based on the minimum residence time required for viral inactivation kinetics and maximum residence time limitation for product stability. When the limiting factor is the process parameters, the downstream process is limited by the volumetric flow rate Q and the path length L. In this example, the system1000can be made to determine process stream minimal residence time required for viral inactivation and maximum residence time for product stability. When there are no operational or kinetics considerations, the target protein may not include stability considerations. In this example, the system can be made to determine the proper Q and L for a resulting Tmin.

Limiting Factor Product Stability—System and Method to Use Tminand Tmaxto Determine the Operational Flow Rate and the Path Length of an Actual Reactor

In an example, a user, using the system1000, can develop or create an actual reactor based on a desired pre-determined Tminand Tmax. For example, the user requires that the Tminbe 60 minutes and that the Tmaxbe 75 minutes. Additionally, the user can input the desired internal diameter of the reaction tube of the experimental reactor100and the radius of curvature of the experimental reactor100. In this particular example, the experimental reactor100and the actual reactor400can include identical internal diameter i.d. and radius of curvature Rc. Moreover, referring toFIG.2A, the density ρ and the dynamic viscosity μ of the fluid in the process stream is either known or is detected by the first detector30and can be provided to the system at200. Accordingly, the system1000can provide a design and manufacturing specification of an actual reactor400based on the above-inputted parameters. The new design and manufacturing specification of an actual reactor400(i.e., the path length of the reaction tube of the actual reactor400) can vary based on the operational volumetric flow rate Q. This is especially true for a system that is operated under σ2time<σ2maxconditions (i.e., reactor path length L and flow rate Q have multiple combinations that satisfy the Tminand Tmaxrequirement). However, when the system is operated under σ2time=σ2maxconditions, only one reactor path length L and flow rate Q combination satisfies Tminand Tmaxrequirement.

Referring toFIGS.4,5A, and5B, at50, the system1000can determine or the user can input the known values210/210A,250/250A, and empirical values and/or variables260/260A. These empirical values and/or variables260/260A can be divided into reactor parameters of the experimental reactor100and/or the fluid-phase parameters. For example, as shown inFIGS.5A and5B, at212, the desired predetermine Tminof the experimental reactor100can be entered into the comparator50. At214, the desired predetermine Tmaxof the experimental reactor100can be entered into the comparator50. Furthermore, at216, the internal diameter i.d. of the experimental reactor100can be entered into the comparator50. At218, the radius of curvature Rc of the experimental reactor100can be entered into the comparator50. Thus, in this particular example, the inputted known values corresponding to the reactor parameters can be Tmin, Tmax, internal diameter i.d., and the radius of curvature Rc, shown as210A inFIG.5B.

Additionally, referring toFIG.5B, at252, the density (ρ) of the fluid in the process stream from the first detector30can be entered into the comparator50. At254, the dynamic viscosity (μ) can be entered into the comparator50. Thus, in this particular example, the inputted known values corresponding to the fluid-phase parameters can be density ρ and the dynamic viscosity μ, shown as250/250A.

The empirical values can then be determined using pulse injection of the process stream into the experimental reactor100, as described above. For example, referring toFIGS.5Aand5B, at260A, the comparator50can use the desired pre-determined value for Tmin(e.g., 60 min) and the Tmax(e.g., 75 min) to predict and/or determine the empirical values. In this particular example, given the Tminand the Tmax, the comparator50can determine the maximum variance σ2time(i.e. σ2max) and the corresponding average residence time TAve, as shown below:
TAve=Tmin+(n*σmax), whereinncan be 5;  (1)
TAve=Tmax−(m*σmax), whereinmcan be 3  (2)
ΔT=8σmax(3)
15=8σmax(4)
σtime=1.875 min, σ2time=3.52 min2, TAve−69.375 min

Given the determined variance σ2time, TAve, and standard deviation σtime, the comparator50, utilizing the data from the first and second detectors30and40, respectively, can derive at the empirical values to determine the theoretical plate (HETP) in cm2and/or Dean Number De, as discussed above. For example, HETP can be defined as follows:
HETP=(aDe3+bDe2+cDe+d),  (5)

where De is the Dean Number, a, b, c, and d are based on empirical data fits only valid for Dean Number≥100.

H⁢E⁢T⁢P=σtime2*LTAve2
By applying a panel of Dean Number (De)≥100, each De fixes a flow rate Q and returns an HETP, as shown below.

H⁢E⁢TP*TAve2LTA⁢v⁢e=σtime2(6)(a⁢D⁢e3+b⁢D⁢e2+c⁢D⁢e+d)*6⁢9.3⁢7⁢52LTA⁢v⁢e=σtime2=σmax2(7)De≡ρμ⁢2⁢r⁢v⁢rRc=ρμ⁢2⁢Qr⁢⁢π⁢rRc(8)

In an example, as shown inFIG.4, the non-empirical values associated with the reactor parameters can be derived at302or the non-empirical values associated with the fluid-phase parameter can be derived at304. For example, referring toFIGS.5A and5B, the empirical value equations above and the determined σ2time, σtime, and Tave, can be forwarded to the system at200so that the system, at302A, can simultaneously solve the empirical value related equations as shown below, to determine the path length (L) and the flow rate (Q) for an actual reactor400.

(a⁢D⁢e3+b⁢D⁢e2+c⁢D⁢e+d)*TAve2LTA⁢v⁢e≤σmax2(9)
TAve=Tmin+(n*σtime), whereinncan be 5  (10)
TAve=Tmax−(m*σtime), whereinmcan be 3  (11)

For a constant TAve, fixing a Q value fixes a corresponding LTAvevalue. Each Q value and LTAvevalue combination returns a resulting σ2timevalue as shown below.

(a⁢D⁢e3+b⁢D⁢e2+c⁢D⁢e+d)*TAve2LTA⁢v⁢e=σtime2(12)

Solving σ2timeresults in a minimum reactor volume that is constrained by TAve=Tmin+(5*σmax) and TAve=Tmax−(3*σmax).FIG.5Cillustrates a graphical representation of different flow rates and their respective corresponding reactor tube length path L to have the desired predetermined Tminand Tmax. Additionally or alternatively, as shown in steps312and314ofFIG.5B, the output can include the reactor tube path length L and the flow rate Q, respectively.

Given that in this example, the experimental reactor and the actual reactor are the same, based on the selected flow rate Q and the corresponding reactor tube path length, a series of reactors can be connected to one another to achieve the corresponding reactor tube path length.

Limiting Factor is Process Parameters—System and Method to Use Reactor Volume RV and Flow Rate Q to Dictate the Tminand Tmax

In an example, a user, using the system1000, can determine the Tminand the Tmaxof a reactor having known reactor parameters and fluid-phase parameters. For example, as shown inFIG.6A, at210B, the known reactor parameters can include reactor volume RV (e.g. 3120 mL), flow rate Q (e.g., 50 mL), internal diameter i.d., and the radius of curvature Rc. Additionally, as also shown inFIG.6A, at250B, the known fluid-phase parameters can include density (ρ) and the dynamic viscosity (μ) of the fluid in the process stream. In this example, reactor volume RV, flow rate Q, internal diameter i.d., and the radius of curvature Rc can be entered into the comparator50. The comparator50can then determine the empirical values such as variance σ2time, average residence time TAve, and the HETP.

Referring toFIG.6B, in this example, the reactor volume RV of the reactor can be entered into the system at222, the flow rate Q of the process stream can be entered into the system at224, the internal diameter i.d. of the reaction tube of the reactor can be entered into the system at216, and the radius of curvature Rc of the reaction tube of the reactor can be entered into the system at218. Thus, in this particular example, the inputted values corresponding to the reactor parameters at210B can be the reactor volume RV, process stream flow rate Q, internal diameter i.d. of the reaction tube of the actual reactor, and the radius of curvature of the reaction tube of the actual reactor Rc.

Additionally, at252, the density (ρ) of the fluid in the process stream can be entered into the system1000. At254, the dynamic viscosity (μ) can be entered into the system1000. Thus, in this particular example, the inputted known values corresponding to the fluid-phase parameters at250B can be density ρ and the dynamic viscosity μ.

Based on the known process stream flow rate Q (e.g., 50 mL/min), the known reactor volume RV (e.g., 3120 mL), the known internal diameter of a reaction tube i.d., and the known radius of curvature Rc, the processor1001of the system1000can predict and determine Tminand Tmax.

To predict and/or determine the Tminand Tmax, the known values can be entered into the comparator50. The comparator50, at260B, using the experimental reactor as described above, can utilize the process stream flow rate Q and the reactor volume RV to predict and/or determine the average residence time (TAve) as shown in the equations below.

TA⁢v⁢e=R⁢VQ(13)TA⁢v⁢e=3120⁢⁢mL50⁢mLmin(14)TA⁢v⁢e=62.4⁢⁢min(15)

Once the comparator50derives the TAvevalue, the comparator50can then utilize the TAve, Q, De, Rv, and L to predict and/or determine the variance σ2timeusing the experimental reactor100and the equations below.
HETP=(aDe3+bDe2+cDe+d), whereDeis the Dean Number,a, b, c, anddare based on empirical data fits only valid for Dean Number≥100.

For a Q of 50 mL/min and a De of 118.94, the HETP can equal to 7.464 cm. Based on this derived HETP value, the variance σ2timecan be predicted or determined by the comparator50using the equations below.

HETP=σtime2*LTAve2(16)H⁢E⁢TP*TAve2L=σtime2(17)7.464*62.429⁢8⁢6⁢4=σtime2(18)σtime2=2.9⁢5⁢⁢min2(19)

The above empirical values and known values can then be forwarded to the system at200. The processor1001having derived at variance σ2time, can utilize this variance σ2time, the standard deviation σtime, reactor tube length L, radius of curvature Rc, Dean Number De, flow rate Q, and TAve, to estimate and/or determine the Tminand Tmaxfor the actual reactor as shown in the equations below.
TAve=Tmin+(n*σmax), whereinncan be 5  (20)
TAve=Tmax−(m*σmax), whereinmcan be 3  (21)
Tmin=53.81 min  (22)
max=67.55 min  (23)

In an example, as shown inFIG.6B, at316, a display can show the Tminvalue and at318, the display can show the Tmaxvalue. In addition or alternatively, the display can show a graphical representation of the Tminand Tmax, as shown inFIG.6C.

No Operational or Kinetics Considerations—System and Method to Use Tmin and Operational Flow Rate (Q) to Determine Path Length of an Actual Reactor

In an example, a user, using the system1000, can develop or create an actual reactor based on a desired pre-determined Tmin(60 min), process stream flow rate Q (50 mL/min), internal diameter i.d. of the reaction tube (0.635 cm), the radius of curvature Rc, density ρ, and the dynamic viscosity μ. For example, as shown inFIG.7A, at210C, the known reactor parameters can include Tmin, process stream flow rate Q, internal diameter i.d. of the reaction tube, the radius of curvature Rc. Additionally, as also shown inFIG.7A, at250C, the known fluid-phase parameters can include density ρ and the dynamic viscosity μ of the fluid in the process stream. In this example, these known values can be entered into the comparator50, so that it can determine the empirical values, such as variance σ2timeand the average residence time TAve.

Referring toFIG.7B, in this example, the Tmincan be entered into the system at212, the flow rate Q of the process stream can be entered into the system at224, the internal diameter i.d. of the reaction tube of the reactor can be entered into the system at216, and the radius of curvature Rc of the reaction tube of the reactor can be entered into the system at218. Thus, in this particular example, the inputted values corresponding to the reactor parameters at210C can be the Tmin, process stream flow rate Q, internal diameter i.d. of the reaction tube of the actual reactor, and the radius of curvature of the reaction tube of the actual reactor Rc.

Additionally, at252, the density (ρ) of the fluid in the process stream can be entered into the system1000. At254, the dynamic viscosity (μ) can be entered into the system1000. Thus, in this particular example, the inputted known values corresponding to the fluid-phase parameters at250C can be density (ρ) and the dynamic viscosity (μ).

Based on the known Tmin, process stream flow rate Q (e.g., 50 mL/min), the known internal diameter of a reaction tube i.d., and the known radius of curvature Rc, the processor1001of the system1000can predict and determine the reaction tube flow path length L of an actual reactor400.

To predict and/or determine the reaction tube flow path length L of an actual reactor400, the known values can be entered into the comparator50. The comparator50, at260C, using the experimental reactor as described above, can utilize the Tmin, the process stream flow rate Q and the Dean Number De, to predict and/or determine the TAveand variance σ2time, as shown in the equations below.

HETP=σtime2*LTA⁢v⁢eTAve2(24)H⁢E⁢T⁢P=f⁡(D⁢e)=σtime*LTA⁢v⁢eTA⁢v⁢e=(aD⁢e3+b⁢D⁢e2+c⁢D⁢e+d)(25)
Wherein a, b, c, and d are based on empirical data fits only valid for Dean numbers≥100

TA⁢v⁢e=T⁢⁢min+(n*σt⁢i⁢m⁢e),(26)
wherein n can be 5

(27)TA⁢v⁢e=R⁢VQ=C⁢A*LTA⁢v⁢eQ
By re-arranging the equation

σtime=15*(C⁢A*LTA⁢v⁢eQ-T⁢⁢min)(28)σtime=f⁡(D⁢e)*C⁢A*LTA⁢v⁢eQLTA⁢v⁢e(29)

Referring toFIGS.7A and7B, the empirical value equations above and the known values can be forwarded to the system at200so that the system, at302C, can determine the path length (L) given the Tminof 60 min, flow rate Q of 50 mL/min, and an i.d. of 0.635 m by the equation below.

LTA⁢v⁢e=25*C⁢A2*f⁢(D⁢e)2±5*⁢25*C⁢A4*f⁡(D⁢e)4+4*C⁢A3*f⁡(D⁢e)2*Q*Tmin+2*CA*Q*Tmin2⁢C⁢A2(30)

Solving the equation above, L will equal to 108.99 m or a reactor volume of 3.46 L. Referring toFIG.7C, a graphical representation can also be shown at300, that illustrates all data points corresponding to flow rate Q and reactor volume RV combinations that result in a Tminof 60 min.

In all of the above examples, the internal diameter i.d. and the radius of curvature of the experimental reactor100and the actual reactor400remain the same. However, in an example, as described below, the system can also design, select, make, manufacture, and recommend a reactor that includes an internal diameter i.d. of the reaction tube that differs from the internal diameter i.d. of the experimental reactor. This is especially useful when submitting data to regulatory agencies, such as the FDA, where applicable regulations require data to demonstrate compliance. The system below will allow a user to run a small scale process stream for data purposes and then scale it up for mass production, without having any significant changes to the process stream or final results of the process stream during scaled-up production.

Scaling Up or Down Using Aspect Ratio

In an example, a user, using the system1000, can develop or create an actual reactor400based on a desired pre-determined Tmin(60 min), process stream flow rate QExit(100 mL/min), density ρ, and the dynamic viscosity μ, and a known aspect ratio. For example, as shown inFIG.8A, at210D, the known reactor parameters can include Tmin, process stream flow rate Q, and the aspect ratio. The aspect ratio can be defined as the radius of the reaction tube of the experimental reactor over the radius of curvature Rc of the experimental reactor. Moreover, the aspect ratio can be from about 0.01 to about 10, such as from about 0.05 to about 5, for example, from about 0.1 to about 0.5. Additionally, as also shown inFIG.8A, at250D, the known fluid-phase parameters can include density ρ and the dynamic viscosity μ of the fluid in the process stream. In this example, these known values can be entered into the comparator50, so that it can determine the empirical values, such as variance σ2timeand the average residence time TAve.

Referring toFIG.8B, in this example, the Tmin(e.g., 60 min) can be entered into the system at212and the flow rate Q (e.g., 100 mL/min) of the process stream can be entered into the system at224. Thus, in this particular example, the inputted values corresponding to the reactor parameters at210D can be the Tmin, and process stream flow rate Q.

Additionally, at252, the density (ρ) of the fluid in the process stream can be entered into the system1000. At254, the dynamic viscosity μ can be entered into the system1000. Thus, in this particular example, the inputted known values corresponding to the fluid-phase parameters at250D can be density ρ and the dynamic viscosity μ.

To predict and/or determine the reaction tube flow path length L and the internal diameter i.d. of an actual reactor400, the known values can be entered into the comparator50. The comparator50, at260D, using the experimental reactor100as described above, can utilize the Tminand the process stream flow rate Q to predict and/or determine the TAveand variance σ2time, as shown in the equations below.

HETP=σtime2*LTAve2(31)
HETP=f(v)=(av3+bv2+cv+d), whereina, b, c, anddare based on empirical data fits for all Dean numbers  (32)
TAve=Tmin+(n*σtime), whereinncan be 5  (33)
TAve=Tmax−(m*94time), whereinmcan be 3  (34)

TA⁢v⁢e=Lv(35)
Q=v*CA(36)

CA=∏*(i.d.2)2(37)

σtime=15*(Lv-T⁢⁢min)(38)
By re-arranging the equations

σtime=f⁡(v)*LvL(39)

Referring toFIGS.8A and8B, the empirical value equations above and the known values can be forwarded to the system at200. In this particular example, the system at500can ask the user if the user would like to use the actual reactor has a substantially similar diameter as the experimental reactor. If the user replies yes, then the system can determine the length based on the example above andFIGS.7A and7B. However, if the user replies no or selects scaling of the reactor, then the system can acquire the aspect ratio and the processor1001of the system1000can solve for reactor length L in the equation below.
L=1/2*(25f(v)2±5*√{square root over (25f(v)4f(v)2Tmin*v)}+2*Tmin*v)  (40)

In an example, scaling of the reactor can include at least one of (i) scaling dimensions of the experimental reactor to the actual reactor having the same aspect ratio as the experimental reactor, but a different internal diameter; (ii) scaling the dimensions of the experimental reactor to the actual reactor having the same aspect ratio and a same internal diameter as the experimental reactor; (iii) scaling the dimensions of the experimental reactor to the actual reactor having a different aspect ratio than the experimental reactor and a different diameter than the experimental reactor; (iv) scaling the dimensions of the experimental reactor to the actual reactor having a different aspect ratio as the experimental reactor, but a same diameter as the experimental reactor.

Once L has been determined, the system1000, based on the derived values of reactor length L, standard deviation σtime, and the average linear flow velocity (cm/min), can determine the internal using the equations below:

A=∏r2=∏(i.d.2)2⁢TA⁢v⁢eDefined=Lv=C⁢A*LQ(41)TA⁢v⁢eDefined=C⁢A*LQ(42)Q*TA⁢v⁢eDefinedL*∏=r2(43)i.d.=2⁢Q*TA⁢v⁢eDefinedL*∏(44)

For this particular example, in order to design, select, make, and/or manufacture the actual reactor having a fixed aspect ratio a plot can be derived between the HETP and the linear flow velocity. For example,FIG.8Ccan be derived from an experimental small size reactor having a fixed aspect ratio with an internal diameter of from about 0.1 cm to about 0.2 cm, such as 0.156 cm tested with water and an experimental medium-size reactor having the same fixed aspect ratio with an internal diameter of from about 0.6 cm to about 0.7 cm, such as 0.635 cm tested with water and dextrose.

FIG.8Dis a plot between experimental HETP and the predicted HETP that illustrates the parity between the predicted HETP based onFIG.8Cand the experimental HETP.

As shown at312and320ofFIG.8B, the reactor path length and the internal diameter of an actual reactor can be derived and/or determined. As shown inFIG.8E, based on the derived reactor path length, the derived internal diameter, and the predicted HETP values, a plot can be created that illustrate all solutions for reactor volume and internal diameter that satisfies Q=100 mL/min and Tmin=60.

FIG.9illustrates an exemplary embodiment, where the system1000can determine the ideal reactor given the fluid and the reactor volume. In this example, as prior examples above, the user can enter the known parameters into the comparator50. The comparator50, based on the known values, can determine the empirical values, which can be entered into the system1000at200. The non-empirical values, such as the reactor tube length L can then be determined at300as described above. Given the reactor length, the system can determine the reactor volume at700. The system, at750, based on the known fluid characteristics, detectable particles/tracer, and the determined volume of the reactor, can communicate with a database770. The database770can include previously designed or manufactured reactors based on volume of the reactor and known fluid and detectable particle/tracer parameters. The database770can then provide the system1000with a list of different actual reactors each having substantially similar volume that was used with similar fluid and detectable particles/tracer that accomplish the same desired end result. The processor1001of the system1000, can then review the list of the provided actual reactors to select a best actual reactor400for the intended purpose.

Referring toFIG.10A, in another example, the Tmin(e.g., 60 min) can be entered into the system at212, the Tmax(e.g. 75 min) can be entered into the system at213, and the flow rate Q (e.g., 500 mL/min) of the process stream can be entered into the system at224. Thus, in this particular example, the inputted values corresponding to the reactor parameters at210D can be two Tminand a process stream flow rate Q. Additionally, the aspect ratio at600can be entered at this time or at a later time, as discussed below.

Additionally, at252, the density (ρ) of the fluid in the process stream can be entered into the system1000. At254, the dynamic viscosity μ can be entered into the system1000. Thus, in this particular example, the inputted known values corresponding to the fluid-phase parameters at250D can be density ρ and the dynamic viscosity μ.

To predict and/or determine the reaction tube flow path length L and the internal diameter i.d. of an actual reactor400, the known values can be entered into the comparator50. The comparator50, at260D, using the experimental reactor100as described above, can utilize the Tmin, Tmax, and the process stream flow rate Q to predict and/or determine the TAveand variance σ2time, as shown in the equations below.
TAve=Tmin+(n*σmax), whereinncan be 5  (45)
TAve=Tmax−(m(*σmax)), whereinmcan be 3  (46)
ΔT=8σmax(47)
15=8σmax
σtime=1.875 min; σ2time=3.52 min2; TAve=69.375 min

Referring toFIG.10A, the empirical value equations above and/or their corresponding values and the known values can be forwarded to the system at200. In this particular example, the system at500can ask the user if the user would like to use the actual reactor has a substantially similar diameter as the experimental reactor. If the user replies yes, then the system can determine the length based on the example above andFIGS.7A and7B. However, if the user replies no or selects scaling of the reactor, then the system can acquire the aspect ratio and the processor1001of the system1000can divide the experimental HETP experimental data points by JIB internal diameter of a small, a medium, and a large reactor, as shown inFIG.10B. The system can then fit a curve to the dataset, as shown inFIG.10C. Using the equations below, the system can then graph the path length versus internal diameter for a flow rate of 500 mL/min at a Tminof 60 and a Tmaxof 75.

HETP=σtime2*LTAve2(48)HETPi.d.=h=(a⁢D⁢e3+b⁢D⁢e2+c⁢D⁢e+d)=f⁡(De)(49)

a, b, c, and d are based on empirical data fits for all Dean numbers

HETP=(h*i.d.)=σt⁢i⁢m⁢e2*LTAve2*i.d.(50)

Fixing an i.d. returns a path length term as shown inFIG.10D.

Example 1

Residence Time Distribution Generation.

The JIB was designed from previous development projects at Boehringer Ingelheim and was 3D printed utilizing SLA Technology by 3D Systems (Rock Hill, SC). The riboflavin and dextrose used in creating the mobile phases and pulse tracer were purchased through Thermo Fisher Scientific (Suwanee, GA). The viscosities of the solutions were determined by a microVISC S Viscometer utilizing an A05 Chip (San Ramon, CA). The densities of the solutions were determined by a Mettler-Toledo Densito Densometer (Columbus, OH).

The mid-scale 3D printed JIB was tested using an Akta Avant 150, while the large-scale JIB was tested using an Akta Pilot 600 by GE Healthcare (Uppsala, Sweden). The JIB was first flushed with 1 reactor volume of the mobile phase. Next, a fixed volume of riboflavin dissolved in the mobile phase was pulse injected and chased out with the mobile phase. This produced the Residence Time Distribution (RTD) profiles upon exiting the reactor detected and quantified by UV-Vis absorbance at riboflavin's absorbance maximums (i.e. 267, 372, and 445 nm). The RTD peaks were then analyzed by fitting a Gaussian distribution. From this fit, the variance of the peak, σ2timewhich is a measurement of the spread of the RTD, was determined. This method was tested over a series of flow rates and viscosities which were altered by varying concentrations of dextrose. The σ2timevalues were converted into HETP. An HETP vs. Dean number graph was created and a 3rdorder polynomial was fit. Then the following series of equations were utilized:

1. Start governing Equations

HETP=σt⁢i⁢m⁢e2*LTAveTAve2a)H⁢E⁢T⁢P=f⁢(D⁢e)=σtime*LTA⁢v⁢eTA⁢v⁢e=(a⁢⁢De3+b⁢D⁢e2+c⁢D⁢e+d)b)i. a, b, c, and d are based on empirical data fits only valid for Dean numbers ≥100

c) TAve=Tmin+(5*σtime)

TA⁢v⁢e=R⁢VQ=C⁢A*LTA⁢v⁢eQd)

2. Re-arrange Equations

σtime=15*(C⁢A*LTA⁢v⁢eQ-T⁢⁢min)e)σtime=f⁡(D⁢e)*CA*LTA⁢v⁢eQLTA⁢v⁢ef)

3. Solve for L:

LTA⁢v⁢e=25*CA2*f⁡(D⁢e)2±5*25*C⁢A4*f⁡(D⁢e)4+4*C⁢A3*f⁢(D⁢e)2*Q*Tmin+2*CA*Q*Tmin2⁢C⁢A2g)

4. Fill Variables

mAbApproximate KinematicConcentration (g/L)viscosity (m2/s)101.2 × 10−6201.4 × 10−6502.0 × 10−6

The tables below indicate the Tmin at 15 min, 30 min, and 60 min for mid-scale and large-scale reactors.

Mid-scale (0.635 cm i.d.) at 50 mL/minMax ProteinConcentration (g/L)102050Reactor Volume (L)Tmin = 15 min1.131.221.43Tmin = 30 min2.002.122.38Tmin = 60 min3.683.834.16

Large-scale (2 cm i.d.) at 500 mL/minMax ProteinConcentration (g/L)102050Reactor Volume (L)Tmin = 15 min10.4710.4710.47Tmin = 30 min19.0019.0019.00Tmin = 60 min35.4635.4635.46

Evaluating the Effect of Flow Mechanics on Critical Process Parameters in a Continuous Viral Inactivation Reactor

The JIB was designed from previous development projects and was 3D-printed utilizing SLA Technology by 3D Systems (Rock Hill, SC). The riboflavin and dextrose used in creating the mobile phases and pulse tracer were purchased through Thermo Fisher Scientific (Suwanee, GA). The viscosities of the solutions were determined by a microVlSC S Viscometer utilizing an A05 Chip (San Ramon, CA). The densities of the solutions were determined by a Mettler-Toledo Densito Densometer (Columbus, OH).

The small scale and mid-scale 3D-printed JIB were tested using an Akta Avant 150, while the large-scale JIB was tested using an Akta Pilot 600 by GE Healthcare (Uppsala, Sweden). The JIB was first flushed with 1 reactor volume of the mobile phase. Next, a fixed volume of riboflavin dissolved in the mobile phase was pulse injected and evacuated with the mobile phase. This produced the RTD profiles upon exiting the reactor detected and quantified by UV-Vis absorbance at riboflavin's absorbance maximums (i.e. 267, 372, and 445 nm). The internal diameters, flow rates, mobile phases, and a number of JIB s connected in series tested in this study are outlined in Table 1 below.

TABLE 1An outline of all of the mobile phases, internal diameters,path lengths, and flowrate combinations tested viapulse injection experiments using the JIBInternalNumberDiam-of JIB'seterconnected inFlow RatesScale(cm)SeriesMobile PhaseTestedSmall0.1561 (L = 11.09 m)DI Water1-16 mL/minScaleMid0.6351 (L = 16.44 m)DI Water5-100 mL/minScale2DI Water6DI Water150 g/L Dextrose1100 g/L Dextrose1200 g/L Dextrose10-125 mL/minLarge-2.01 (L = 16.60 m)DI Water25-700 mL/minscale

The peaks were then analyzed by fitting a Gaussian distribution. From this fit, the variance of the peak, (σtime2), which is a measurement of the spread of the RTD, was determined. To better understand the influence of the quantitative value of the variance, Equation 1 was calculated, where Q is the volumetric flow rate. Additionally, the dataset was converted using Equations 2 and 3 where HETP is height equivalent to a theoretical plate, TAveis the mean residence time, RV is reactor volume, and L is the length of the flow path of the JIB.

σVolume=σtime2*Q2(1)HETP=σtime2*LTAve2(2)TA⁢v⁢e=R⁢VQ(3)
Flow Rate and Path Length

As seen inFIG.11A, the slowest flow rate produces the widest peak for all three reactor sizes. This is shown in the graph as having a relatively high standard deviation (ex. ˜82 mL for the JIB(1) dataset at 5 mL/min) which describes the volume distance from the center of the peak (i.e. TAve) to 34% of total mass to the left or right. As the flow rate increases the peak narrows, with the standard deviation decreasing at an exponential rate. However, an inflection point is attained when the flow rate increases above 20 mL/min. The peak becomes wider until another inflection point is reached at the 30 mL/min set point. With the flow rate increasing higher, the peaks substantially narrow until the 55 mL/min set point. In total, the progression from slower to faster flow rate displays an initial asymptote, two inflection points, and finally a second asymptote. Looking at the JIB(1), JIB(2) and JIB(6) data inFIG.11A, the phenomenon of 2 asymptotes and 2 inflection points is maintained at identical flow rates across all path lengths.

Comparing the path length, the peaks widen with the longer path length. This is a well-characterized observation that is a reproducible phenomenon for PFR's. When the data points fromFIG.11Aare translated using Equation 3 and converted into height equivalent to an HETP,FIG.11Bis created making the three path length's datasets overlay.

To understand the driving force for this shift (i.e. the two inflection points), further exploration into previously published work on Dean Vortices was undertaken. Flow patterns were visualized using suspensions in water when between two rotating drums (i.e. Taylor-Couette flow). As the flow increased in velocity in the flow cell, Aider made observations at specific Dean number's at which the flow patterns shifted from laminar to chaotic. A similar experiment was conducted in the JIB using suspended mica in water and found the same laminar to a chaotic flow transition. These specific Dean numbers and corresponding observations outlined in Aider et al. are co-plotted onFIG.12. Dean number is defined by Equation 4, where ρ is the fluid density, u is average linear flow velocity, D is a flow path internal diameter, μ is dynamic viscosity, and Rcis the radius of curvature of the serpentine pattern.

D⁢e=(ρ⁢u⁢Dμ)*D2⁢Rc(4)

The two inflection points and the faster flow rate asymptote correspond to the visually observable manifestations of the flow transitioning from the onset of unstable flow, undulated waves, and full turbulence respectively. Given that for flow in a circular straight pipe, the onset of turbulence is typically observed at a Reynolds number of ˜2000. The JIB was able to simulate turbulent flow behavior at a Reynolds number of ˜174. Due to this large discrepancy, the term “weak turbulence” was used.

In order to prove the validity of the shift of 2 asymptotes and 2 inflection points behavior found in the section above was controlled by the Deans number, the mobile phase's viscosity was increased with three concentrations of dextrose.FIG.13Adisplays the reaction of the addition of the dextrose. For all four mobile phases, the standard deviation began at its highest value at the slowest flow rate and narrowed with the increase in flow rate. However, with the increase in dextrose concentration and higher viscosity, the flow rate required to reach inflection point 1 increased. The same is true for inflection point 2 and flow rate required to reach the 2nd asymptote.

To account for this apparent shift in the inflection points and asymptote, the x-axis was normalized by converting the flow rate to Dean Number described in Equation 4 and shown inFIG.13B. Once normalized to the Dean Number, the asymptotes and inflection points align. While the shift in the dataset returns to normal with the correction, the magnitude of the spreading appears only to be corrected for the higher Dean Number (i.e. De>100) operation. This is due to Dean Vortices taking over the radial mass transfer above De=70. For the lower Dean numbers (De<70), where other dispersion mechanisms dominate, the mobile phase's rank from most wide to most narrow is 200, 0, 50, and 100 g/L Dextrose, not following a concentration based trend.

To inform the operation of the JIB, two prediction model approaches can be generated. The first uses a lumped data pool approach utilizing all of the experimental data from the various dextrose mobile phase experiments and normalizing the data to HETP and Dean Number (FIG.13B). From this dataset, a 3rdorder polynomial can be fit to this data pool resulting inFIG.13Cand Equation 5 where a, b, c, and d are based on the polynomial fit. Equation 5 allows for the prediction of how wide a peak will become as a function of Dean Number. A parity plot can be generated for this model (FIG.13D) which describes how well the model predicts the volumetric spreading of the pulse injection in the JIB. The fit has a relatively low R2value of 0.8405, which was expected due to a large amount of variability in the De<100 dataset. Using the Equations 2 and 6, an approximation can be made of when Tmin(5σ) (i.e. the time estimate in which the first detectable particle, such as a viral particle or a surrogate tracer exits the reactor) is expected to occur shown inFIG.13E. The significance of the Tmin(5σ) will be explained later. The fit improves with an R2value of 0.9175, however, all data points below the y=x line represent scenarios where the model predicted a longer incubation than what actually occurred. Most of the error occurs at the larger Tmintime points which correspond to the lower Deans number experiments. This is a significant issue since the key unit operation specification is residence time.
HETP=aDe3+bDe2+cDe+d(5)

The second approach is applicable if a criterion of the JIB unit operation is to maintain a Dean Number of >100. When this condition is true, an exclusion criterion of only allowing data points collected at the De>100 set point are implemented into the model (FIG.14A). This approach aims to reduce the noise in the model by removing the highly variable lower Dean number dataset. When this change is applied, the variability decreases which allows the spreading of the peak in the JIB to be predictable for variable viscosity and density fluids operated at different flow rates (FIG.14B).FIG.14Cdisplays the parity plot of volumetric spreading of the pulse, and this second model approach has an increased R2value of 0.9201 compared to the lumped dataset approach. When the Tmin(5σ) is calculated and plotted (FIG.14D) a better prediction model is generated with an R2value of 0.9812.

This model has two main applications to be used when determining the design and conditions of the JIB based CVI unit operation:1. Given product stream requirements (i.e. Tminand Tmax), determine a length of a reactor and operational flow rates2. Given the size of the reactor and operational flow rate, predict product stream outputs

Starting with an understanding of the minimum residence time required for required viral inactivation and the maximum time the target molecule can be in the acidic condition before impacting product quality, Equations 6 and 7 can be applied to help determine the flow rate and path length required to meet those specifications.

TA⁢v⁢e=Tmin+(n*⁢σtime)(6)TA⁢v⁢e=Tmax-(m*⁢σtime)(7)T⁢⁢min⁡(2⁢v)=TA⁢v⁢e2(8)

Table 2A illustrated below outlines the quantitative aspect of the choice for the “n” and “m” value for σtime.

TABLE 2ATangible Quantifications of the Standard DeviationParameter Relating to Viral Clearance Risk AssessmentViral Clearance Risk AssessmentTimeQuantity of pulse that hasTime point(min)exited the reactor (%)TAve78.850%Tmin(2σ)71.82.275%Tmin(3.5σ)66.50.023%Tmin(5σ)61.20.00003%Tmin(2v)390%

FIG.15Aillustrates the resulting decisions on an RTD profile. The theoretical onset of the breakthrough of virus and target product starts with Tmin(2v) defined by Equation 8. This value is derived from Hagen and Poiseuille which states that for flow in a circular pipe, the fastest portion of the flow occurs in the geometric center of the cross-sectional area of the flow path and operates at two times the average velocity of flow. This is thought of as our “speed of light.” A condition that it is close to impossible to reach in real-world practice due influencing mass transfer phenomenon (i.e. convection, diffusion, and Dean Vortices) and would require a highly specific and extreme set of conditions to attain operating in the JIB.

The exiting pulse injection is thought of as a Gaussian peak, and therefore its spread is thought of in terms of σ. For example, n=5 (i.e. Tmin(5σ)) is understood to represent that 0.00003% of the product exited the reactor in-between Tmin(2v) and Tmin(5σ). This difference between Tmin(2v) and Tmin(5σ) can be visualized inFIG.15A. This is considered to be a conservative ideology that overestimates the under incubated population. The intersection of Tmin(2v) and Tmin(5σ) are both derived estimation ideologies that ignore real-world possibilities. Where Tmin(2v) is the “speed of light” discussed above and there is no limit to the “n” value for σtime. A large enough selected “n” value can calculate a Tminto occur in negative time.

In a similar fashion, Table 2B andFIG.15Bcorrespond to a Tmaxdecision where m=3 (i.e. Tmax(3σ)) would predict that ˜99.865% of the product pool would exit the reactor less than or equal to Tmax.

TABLE 2BTangible Quantifications of the Standard DeviationParameter Relating to Product Quality Risk AssessmentProduct Quality Risk AssessmentTimeQuantity of pulse that hasTime point(min)exited the reactor (%)TAve78.850%Tmax(1σ)81.984.134%Tmax(2σ)85.397.725%Tmax(3σ)88.799.865%

This decision would be reliant on the product stability data or accepted yield loss in the presence of the acidic or any other viral inactivating condition. If we combine Equations 6 and 7 we get Equation 9.
Tmax−Tmin=ΔT=(n+m)*σtime(9)

With a defined Tminand Tmax, a TAveand σtimecan be calculated. Using Equations 2, 3, and 5, multiple path lengths and flow rate combinations (i.e. starting with flow rates that yield a De>100) can be found to meet the specifications resulting inFIG.5C. With calculated TAveand σtimeconstrains, the minimum flow rate and minimum reactor volume can be calculated. This is an unadvisable location to operate as any increase or decrease in flow rate will result in residence times that are unbounded by Tminor Tmax. As the reactor volume increases, the volumetric flow rate increases as well to maintain the target Tmax. When the flow rate increases, the efficiency of the JIB also increases. This allows more freedom in JIB operation displayed as error bars inFIG.5C. In addition to the increasing efficacy, alteration of the operation window can be modulated by altering the n, m, Tmin, or Tmaxvalues of Equation 6 and 7.

To visualize this ideology and phenomenon, and given a process where the Tmin(5σ) and Tmax(3σ) are strictly defined at >60 min and <79 min respectively,FIG.16was generated. For the smaller reactor size operated at a slower flow rate, the σtimevalue is larger compared to the higher flow rate with a larger reactor size, visualized by the difference in width of the two peaks. Operating at the target flow rates, both designs obey the Tminand Tmaxconstrains. With the increased sized reactor and corresponding faster flow rate, the σtimedecreases allowing flexibility for deviations during operation.

When the CVI is implemented into real-world operation in a GMP setting, variable flow rates and viscosity are inevitable. With the work conducted in this experimental series, this variability can be addressed by understanding process operational extremes and corresponding worst-case conditions and predict how they propagate into the process outputs. Based on these isocratic viscosity experiments, it appears that the worst-case for viral incubation time is a high viscosity solution. Given that a chromatography elution peak's viscosity experiences one peak maximum, this should, therefore, be considered the worst-case. All other portions of the peak (i.e., the front and tail) will have lower viscosities relative to peak max, a larger Dean number, and therefore better mixing.

To validate this claim, a mock protein peak was generated using dextrose to increase viscosity and NaCl to generate a conductivity trace. In the theoretical case of a protein A column, where the mAb will elute from the column in a Gaussian-like shape with some tailing. This general peak shape was generated used the gradient function of the Akta Avant 150, where the A1 line contained DI Water, A2 contained Riboflavin dissolved in water, and B1 Contained 200 g/L dextrose with approximately 150 mM NaCl. To evaluate the different viscosity gradient locations, four pulse injection locations were chosen with the first occurring before the addition of dextrose (0 g/L dextrose), the 2 peak mid-heights (50 g/L dextrose) and the peak max (100 g/L dextrose).FIG.17Adisplays all four injections. To insert the peak without disturbing the density gradient, the A pump was switched from A1 to A2 while maintaining the gradient slope. This explains why the pulse injections are at different heights underneath the dextrose curve. Since the injections overlapped, the pulse injection locations were evaluated in their own experiment.

The phenomena of peak spreading as a function of viscosity occurred in the dynamic composition setting.FIG.17Bshows how well the prediction model was at predicting Tmin(5σ). The difference between isocratic prediction and the dynamic experimental result was less than 1 minute, while the difference between the first water pulse injection and the pulse at peak max was approximately 4 minutes making it the worst-case for viral incubation.

Example 2

Mobile Phases and Flow Chamber:

The JIB was designed from previous development projects, which is described in the U.S. Pat. Ser. No. 62/742,534 (incorporated in its entirety by reference herein), and was 3D printed utilizing SLA Technology by 3D Systems (Rock Hill, SC). The riboflavin, Tris Buffer Saline (TSB), and dextrose used in creating the mobile phases were purchased through Thermo Fisher Scientific (Suwanee, GA). The viscosities of the solutions were determined by a microVlSC S Viscometer utilizing an A05 Chip (San Ramon, CA). The densities of the solutions were determined by a calibrated pipette and a scale.

Bacteriophage Selection:

ΦX174 and the corresponding host bacteriaE. ColiC were purchased from ATCC (ATCC Catalog #: 13706-B1 and 13706 respectively). The concentration of ΦX174 was quantified by utilizing standard plaque-forming assays, which entailed co-plating the fluid in question and the host bacteriaE. ColiC with plating agar (i.e., Tryptic Soy Broth with 0.7% agarose) onto Tryptic Soy Agar plates. The bacteriophage ΦX174 was chosen as the appropriate tracer for this experiment due to some of its innate characteristics. ΦX174 is a relatively resilient bacteriophage where chances of loss in infectivity while suspended in an appropriate mobile phase conditions are low, but can also be easily sanitized with 0.1 M NaOH and a reasonable contact time. The surface characteristics of this bacteriophage are relatively inert compared to other viruses. Previous experience with this bacteriophage had shown significantly less surface adsorption relative to other viral models to both positively charged, hydrophobic, and multi-modal chromatography resins. Therefore, the probability of the virus non-specifically adsorbing in a slightly basic solution with a low ionic strength (i.e., pH 7.5 with 150 mM NaCl) to the 3D printed plastics was low. The plaque morphology of ΦX174 was also advantageous. ΦX174's plaque-forming units (pfu) create very large, bullseye type plaques that are easy to identify.

Preliminary Work

To determine the efficacy of the experimental protocol, a few preliminary experiments were conducted. First, pulse injections of ΦX174 were introduced into the JIB at the highest Dean number (i.e., high flow rate and low viscosity), which corresponds to the most chaotic flow stream due to Dean vortices. The discharge of the JIB was then collected and titered which was able to determine the mass balance of the injection. The result showed that recovered bacteriophage titer was within the typical error associated with a titering assay (i.e., (+/−) 0.5 logs). Sampling the dead volume remaining in the outlet valve, ˜300 pfu/mL persisted. A sanitization program was then created to thoroughly sanitize the injection valve, JIB, and outlet valve with 0.1 M NaOH with a contact time of ≥15 min. After the sanitization cycle, no infectious particle remained in the outlet lines.

Determining Minimum Residence Time (ΦX174):

A 0.32 and 0.64 cm i.d. 3D printed JIB's were tested using an Akta Explorer 100 by GE Healthcare (Uppsala, Sweden). To prepare for the experiment, a 30 mL aliquot of the mobile phase was taken and set aside for spiking. The aliquot was then spiked at 0.06% (v/v) targeting a mobile phase concentration of 106.5pfu/mL. The spike was purposely spiked at a considerably low level to ensure the fluid properties of the experimental injection were not changed by the ΦX174 spike. Using a syringe, 25 mL of spiked sample was then loaded into a 50 mL capacity Superloop by GE Healthcare (Uppsala, Sweden) while the remaining sample was held on the bench as a holding sample to determine if significant ΦX174 death occurred as a function of mobile phase condition independent of flowing through the JIB. ΦX174 never experienced an off-target mobile phase concentration within the typical error of a titering assay (i.e., (+/−) 0.5 logs). Finally, the empty fraction collection containers were then weighted to determine the tare weight.

To start the experiment, the Akta began pushing mobile phase through the injection valve, in the “Inject” position, into the Superloop to introduce the ΦX174 spiked buffer into the JIB for 3% of the total reactor volume, with the discharge of the flow directed into a large volume container. The injection valve then switched to “Load” position to stop flow of mobile phase through the Superloop and redirected to go directly to the JIB to flush out the injection with the discharge remaining in the same large volume container. After a predetermined amount of time, the outlet valve switched to direct flow from the large volume container to a small volume container. The outlet valve subsequently switched two more times creating two more fractions. The time and volume of the three small and one large fractions were determined by Tmin 3, 4, and 5σ using the modified peak analysis. The outlet flow path for the three small volume fractions were 1 mm capillary PEEK tubing by GE Healthcare (Uppsala, Sweden). When the experiment was completed, the three small and one large container were then weighed. To sample for virus, the dead volume of the capillary tube was then drained in sterile tubes for a sample volume of ˜100 uL. The sample left behind in the capillary tube would have been the last drop of that fraction and can be thought of as an instantaneous grab sample. The entire volume of this grab sample was then titered without dilution; therefore, the issue of “probability of detection of viruses at low concentrations” outlined in ICH Q5A does not apply.

After all post Akta experiment activities were completed, the remaining spiked mobile phase was expelled from Superloop, and the Superloop was taken offline. The injection loop position was then replaced with PEEK capillary tubing and the sanitization step outlined above was completed, and then subsequently quenched with TSB.

Results

Through preliminary results with the bacteriophage, it was found that the calculation model discussed above provided a very conservative estimate of the minimal residence time (Tmin). To account for this, the raw peaks were modified.FIG.18displays a comparison of the peak generated from the detector of the Akta as the dye pulse injection (i.e., Raw Trace, dashed line) exits the JIB and a modified peak (i.e., Modified Trace, solid line). The Modified Trace was created by determining the peak's maximum absorbance and mirroring the left side trace to the right side of the peak maximum.FIG.19displays the calculated Tmin(5σ) as a function of the original Raw Trace versus the Modified Version.

FIG.20displays the results of the low viscosity bacteriophage experiments that utilized TSB as the mobile phase. The sampling strategy for these experiments sought to collect an instantaneous grab sample at the discharge of the JIB at Tmin 3, 4, and 5σ. For the three flow rates tested 4σ and 5σ tested negative for virus, while 3σ tested positive for virus. It is known from the equations that increasing the viscosity decreases the JIB efficiency. Since 4σ and 5σ were negative for virus for the low viscosity, the same volumetric sampling strategy was utilized assuming this would be enough volumetric space to capture the reduced efficacy. When the viscosity is increased by the addition of dextrose, the breakthrough of virus occurred sooner for the higher viscosity. However, as shown inFIG.21, the 75 mL/min data point lacks a (−) virus result. This is due to the sampling strategy, not the efficacy of the reactor or the calculations.

Example 3

Determining Parameters to Scale a Reactor 5×

In an example, a user, using the reactor inFIG.1C, can calculate the number of devices connected in series (e.g. as shown inFIG.1D) to provide the target residence time distribution for a process. In addition, a user can leverage the work completed to calculate the dimensions of a larger scale reactor. In this experiment the inner diameter (i.d.) of the flow path and the radius of curvature of the reactor shown inFIG.1Cin the system shown inFIG.2Awere determined. The i.d. of the flow path in the reactor inFIG.1Cwas determined to be 0.635 cm and radius of curvature was 0.6825 cm in the system shown inFIG.2A. To obtain these parameters, a tracer was pulse injected into the reactor shown inFIG.1Cand was flushed out at various flow rates. The results of each of these experiments are shown inFIGS.2B-C. Once the pulse injection experiments were completed, the resulting peaks were analyzed by fitting a Gaussian curve to the discharging peaks as shown inFIG.2Dand a subsequently ρ2Timewas calculated by the fit. The σ2Timevalue was then converted to HETP (using Equation 1, below) as shown inFIG.2E, and plotted against the Dean number (using Equation 2, below)

HETP=σtime2*LTAve2(1)De=(ρ⁢u⁢Dμ)*D2⁢Rc(2)

A best-fit line was applied to the dataset shown inFIG.2Eand expressed in Equation 3, below.
HETP=f(De)=(aDe3+bDe2+cDe+d),  (3)

wherein a, b, c, and d are based on empirical data fits for all Dean numbers. Equation 1 and 3 were then combined and rearranged to create Equation 4, below.

σtime=(a⁢D⁢e3+b⁢D⁢e2+c⁢D⁢e+d)*TAve2L(4)

It is required that 99.99997% of the product remain within the reactor for ≥60 minutes (i.e., Tmin), which makes n=5 (in Equation 5, below). Additionally, it is required that 99.865% of the product exit the reactor for ≤90 minutes (i.e. Tmax), which makes m=3 (in Equation 6, below). The maximum kinematic viscosity allowable within the reactor was 1.5*10−6m2/s. Based off this viscosity and the reactor dimensions mentioned previously, to satisfy the requirement of Dean number≥100, the flow rate within the reactor must be ≥65 mL/min and due to arbitrary process constraints, the flow rate must be ≤95 mL/min
TAve=Tmin+(n*σmax), whereinncan be 5 andTmincan be 60 min  (5)
TAve=Tmax−(m*(σmax)), whereinmcan be 3 andTmaxcan be 90 min  (6)

From the above constraints, a locus of solutions can be generated to satisfy the constraints. As seen in the table below, four flow rates were solved. The target flow rate, reactor design (i.e., i.d. of the flow path and the radius of curvature), and maximum kinematic viscosity calculate worst-case Dean number. The outputted Dean number was then entered into Equation 3 and that value was inputted into Equation 4. Through a guess and check method, different reactor volumes were cast through a stepwise cascade of equations. A proposed reactor volume was then divided by flow rate to calculate average residence time (see Equation 7, below) and divided by the cross-sectional area of the internal diameter of the flow path to obtain the path length. The path length and average residence time were also inputted into Equation 4 to get a σtimevalue. Equations 5 and 6 were then used to find the Tminand Tmax.

TA⁢v⁢e=Reactor⁢⁢VolumeFlow⁢⁢Rate=Linear⁢⁢flow⁢⁢velocityPath⁢⁢Length(7)

Table 1 displays the maximum and minimum reactor volume solutions for each flow rate as a function of a Tminof 60 min or a Tmaxof 90 min. These reactor specifications are displayed inFIG.22and are similar toFIG.5C. Any flow rate and reactor volume chosen between those two lines will satisfy the target residence time distribution selected (i.e. Tminand Tmax). The difference betweenFIG.22andFIG.5Cis the viscosity term used in the two figures. The higher viscosity ofFIG.22reduces the efficacy of the reactor and lends itself to faster flow rates and higher volume reactor. Table 2 displays the decision of reactor operation specification. The flow rate chosen was selected relatively arbitrary with the desire to keep the flow rate low. With the selection of a flow rate, the reactor volume was selected for being the mid-point between the maximum and minimum reactor volume.

TABLE 1Maximum and Minimum Reactor Volume Solutions for each FlowRate as a Function of a Tminof 60 min or a Tmaxof 90 minFlowRateDeanHETPVolumeTAveσtimeTmin(5σ)Tmax(3σ)(mL/min)number(cm)(L)(min)(min)(min)(min)6598.7714.314.6271.132.2360.0077.8298.7714.315.3882.792.4070.7890.0075113.979.335.1168.191.6460.0073.11113.979.336.3484.521.8275.4090.0085129.166.255.6366.211.2460.0069.93129.166.257.2985.761.4178.6990.0095144.364.656.1865.021.0060.0068.04144.364.658.2286.521.1680.7390.00

TABLE 2decision of reactor operation specificationFlow RateDeanHETPVolumeTAveσtimeTmin(5σ)Tmax(3σ)(mL/min)number(cm)(L)(min)(min)(min)(min)70106.3711.565.3376.13266.1582.12

Additionally, the reactor design specifications in terms of internal diameter, a radius of curvature, flow rate, and path length were determined to satisfy a large scale operation. In this example, the user required the dimensions to operate at 5× the process volumetric flow rate (i.e. 350 mL/min) and also desired to keep the ratio between the internal diameter and radius of curvature to be constant. The dataset shown inFIG.2Ewas divided by the internal diameter of the reactor used to generate the dataset (i.e., 0.635 cm) resulting in a figure similar toFIG.10C. A best-fit line was then applied to the dataset. Table 3 displays the known and unknowns for the new reactor's specifics. With a target flow rate and a TAvefrom Table 2, reactor volume can be calculated from Equation 7. From the above constraints, a locus of solutions was generated to satisfy constraints. As seen in Table 4 below and plotted inFIG.23, three internal diameters are solved for. Any internal diameter selected along this plotted line will be able to supply the proper residence time distribution. Final selection of an internal diameter will be decided on a basis of a compromise of residence time distribution and pressure drop in the reactor where the smaller the internal diameter and longer path length will increase the pressure. The fixed flow rate and average residence time fixed the reactor volume (Equation 7). The variable internal diameter divided by the reactor volume returned the path length of the reactor. The outputted Dean number was then entered into Equation 8, below, and multiplied by the internal diameter to return the HETP. The path length and average residence time were also inputted into Equation 4 to get a σtimevalue. Equations 5 and 6 were then used to find the Tminand Tmax. As the internal diameter increases the Dean number decreases which subsequently decreases reactor efficiency. Any internal diameter chosen between 1.5-1.7 cm will provide the appropriate residence time distribution.

H⁢E⁢T⁢PD=h=(a⁢D⁢e3+b⁢D⁢e2+c⁢D⁢e+d)(8)

TABLE 3Known and Unknowns for the New Reactor's SpecificsFlow RateDeanHETPVolumeTAveσtimeTmin(5σ)Tmax(3σ)(mL/min)number(cm)(L)(min)(min)(min)(min)350??~26.6476.13???

TABLE 4Derived Internal DiametersInternalEstimatedEstimatedEstimatedFlow RateDiameterDeanHETPVolumeTAveEstimatedTmin(5σ)Tmax(3σ)(mL/min)(cm)number(cm)(L)(min)σtime(min)(min)(min)3501.522510.326.6476.132.066.282.11.621112.426.6476.132.364.583.11.719813.026.6476.132.563.483.7

From the foregoing description, those skilled in the art can appreciate that the present teachings can be implemented in a variety of forms. Therefore, while these teachings have been described in connection with particular embodiments and examples thereof, the true scope of the present teachings should not be so limited. Various changes and modifications may be made without departing from the scope of the teachings herein.

The scope of this disclosure is to be broadly construed. It is intended that this disclosure disclose equivalents, means, systems, and methods to achieve the devices, activities and mechanical actions disclosed herein. For each device, article, method, mean, mechanical element or mechanism disclosed, it is intended that this disclosure also encompass in its disclosure and teaches equivalents, means, systems, and methods for practicing the many aspects, mechanisms and devices disclosed herein. Additionally, this disclosure regards a coating and its many aspects, features, and elements. Such a device can be dynamic in its use and operation, this disclosure is intended to encompass the equivalents, means, systems, and methods of the use of the device and/or article of manufacture and its many aspects consistent with the description and spirit of the operations and functions disclosed herein. The claims of this application are likewise to be broadly construed.

The description of the inventions herein in their many embodiments is merely exemplary in nature and, thus, variations that do not depart from the gist of the invention are intended to be within the scope of the invention. Such variations are not to be regarded as a departure from the spirit and scope of the invention.