Prediction method of cell culture cooling point and application thereof

Abstract

The invention provides a prediction method of a cell culture cooling point and application thereof, and relates to the technical field of fermentation culture.The method comprises the steps that cell density data are obtained, the current specific growth rate mu current of cells is calculated after smoothing algorithm processing, and when mu current is smaller than or equal to a mu threshold value, it is judged that the cooling point is reached; the mu threshold value is equal to k * mu max; wherein the mu max is the maximum specific growth rate of the cells, the k is 0.4-0.6, and the mu max and the mu current are from the same batch of cells. According to the method, the specific growth rate mu of the cells is used as a credible process control decision variable for the first time, the hysteresis of a traditional living cell density method is overcome through robust data processing, prospective capture of the physiological state of the cells is achieved, and the product yield and the process robustness are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of fermentation culture technology, and in particular to a method for predicting the cooling point of cell culture and its application. Background Technology

[0002] In the process of using cells to produce recombinant protein drugs on a large scale, cooling is usually required to shift the physiological state of cells from the high-proliferation phase to the high-productivity stable phase.

[0003] Currently, methods for determining the cooling point in industrial production are mostly based on empirical judgment or indirect indicators, such as the fixed-time-point method: cooling is performed at a predetermined time point after the start of culture. This method is simple but cannot adapt to differences in cell growth status between different batches and is easily affected by factors such as inoculation density, culture medium composition, and seed culture state. Its drawback lies in its lag, leading to premature or delayed cooling, affecting the final yield. Another method is based on live cell density: cooling is performed when the live cell density reaches a predetermined threshold. This method is an improvement over the fixed-time-point method, but it is still a lag control. By the time the density reaches the threshold, the inflection point of cell growth deceleration has often passed, metabolic stress has increased, and the optimal intervention window has been missed.

[0004] Existing technologies lack a means to directly and proactively reflect the physiological state of cell populations and to conduct precise process control accordingly.

[0005] In view of this, the present invention is hereby proposed. Summary of the Invention

[0006] One of the objectives of this invention is to provide a method for predicting the cooling point of cell culture, in order to solve the technical problem that the existing methods for determining the cooling point have a lag, resulting in premature or late cooling and affecting the final yield.

[0007] A second objective of this invention is to provide the application of the above-mentioned prediction method in cell culture.

[0008] The third objective of this invention is to provide a cell culture method based on controlling the cooling point.

[0009] The fourth objective of this invention is to provide a cell culture process control system.

[0010] In order to achieve the above-mentioned objectives of the present invention, the following technical solution is adopted: In a first aspect, the present invention provides a method for predicting the cooling point of cell culture, comprising the following steps: A. Obtain cell density data, and smooth the cell density data to obtain smoothed cell density data X. 平滑 Smoothing formula: X平滑,t =(X t + X t-1 + ... + X t-n ) / n; Among them, X 平滑,t X represents the smoothed cell density value at time t. t This represents the raw cell density measurement collected at time t, where n is the size of the sliding window; B. According to X 平滑 Calculate the current specific growth rate μ of the cell current Calculation formula: μ current = (ln(X 平滑,t ) - ln(X 平滑,t-1 )) / (T t - T t-1 ); Among them, X 平滑,t and X 平滑,t-1 Smooth cell density at time t and time t-1, respectively. t and T t-1 For the corresponding time; When μ current When the value is ≤ μ threshold, the cooling point is determined to have been reached; The μ 阈值 =k*μ max μ max The maximum specific growth rate of cells is given by k, which ranges from 0.4 to 0.6, and μ. max With μ current Cells from the same batch.

[0011] Furthermore, the smoothing algorithm includes a moving average method or an exponential smoothing method, preferably a moving average method.

[0012] Furthermore, the sliding window size of the moving average method is 2 to 5 data points; Preferably, the smoothing coefficient of the exponential smoothing method is 0.1 to 0.3.

[0013] Furthermore, the μ max Determined in the following ways: Cell density data were collected at least at two time points during the exponential growth phase of the cell cycle. A linear regression was performed on ln(X) versus time, and the resulting slope was μ. max ;or, μ is obtained based on historical successful batch data statistics. max Fixed value.

[0014] Furthermore, k is 0.5.

[0015] Furthermore, the cell density data is derived from offline sampling or online sensor detection; Preferably, the offline sampling is the trypan blue staining counting method; Preferably, the online sensor detection is performed using capacitance or online microscopy.

[0016] Secondly, the present invention provides the application of the above-mentioned prediction method in cell culture; Preferably, the cell culture includes product-directed cell culture; Preferably, the product comprises at least one of recombinant protein, viral vector, exosome, nucleic acid, or glycopeptide.

[0017] Thirdly, the present invention provides a cell culture method based on controlling the cooling point, including using the above-mentioned prediction method to determine whether the cooling point has been reached, and prompting a cooling operation when the cooling point is reached.

[0018] Furthermore, the cooling operation includes reducing the culture temperature from the proliferation phase temperature of 35-37°C to the expression phase temperature of 30-34°C.

[0019] Fourthly, the present invention provides a cell culture process control system, comprising: The data acquisition module is used to acquire cell density data; The data processing module is used to execute the prediction methods described above; The control execution module is used to prompt for cooling operation when the cooling point is reached.

[0020] This invention provides a method for predicting the cooling point in cell culture. Through integrated data smoothing processing, it eliminates measurement noise and random fluctuations to ensure robustness, overcoming the technical obstacle of high μ signal noise. For the first time, it uses the cell specific growth rate μ as a reliable process control decision variable, achieving forward-looking capture of cell physiological states and avoiding the lag of traditional VCD methods. Cell density data can be obtained from offline sampling or online acquisition. The method makes a judgment based on the ratio k of the current specific growth rate to the maximum specific growth rate, reducing the sensitivity to differences in seeding density, culture medium titer, and other conditions between different batches, thus enhancing process robustness. This solves the technical problem of existing methods for determining the cooling point having a lag, leading to premature or delayed cooling and affecting the final yield. Attached Figure Description To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 A process flow diagram of a cell culture method based on controlling the cooling point provided by the present invention; Figure 2 VCD statistics for different culture times treated by the cell culture method based on controlled cooling point provided by this invention; Figure 3 VIA statistics for different culture times treated by the cell culture method based on controlled cooling point provided by this invention; Figure 4 Lac statistics for different culture times treated by the cell culture method based on controlled cooling point provided by this invention; Figure 5 This is a comparison chart of recombinant protein yield and quality under different culture times treated by the cell culture method based on controlled cooling point provided by the present invention. Detailed Implementation

[0022] Unless otherwise defined herein, the scientific and technical terms used in conjunction with this invention shall have the meanings commonly understood by one of ordinary skill in the art. The meaning and scope of terms shall be clear; however, in any case of potential ambiguity, the definitions provided herein shall prevail over any dictionary or foreign definitions. In this application, unless otherwise stated, the use of "or" means "and / or". Furthermore, the use of the term "comprising" and other forms is non-limiting.

[0023] Unless otherwise stated, the methods and techniques of the present invention are generally carried out according to conventional methods well known in the art and as described in various general and more specific references, which are cited and discussed throughout this specification.

[0024] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] This invention provides a method for predicting the cooling point of cell culture, comprising the following steps: A. Obtain cell density data, and smooth the cell density data to obtain smoothed cell density data X. 平滑 Smoothing formula: X 平滑,t =(X t +X t-1 +...+X t-n ) / n; where X 平滑,tX represents the smoothed cell density value at time t. t This represents the raw cell density measurement collected at time t, where n is the size of the sliding window; B. According to X 平滑 Calculate the current specific growth rate μ of the cell current Calculation formula: μ current = (ln(X 平滑,t ) - ln(X 平滑,t-1 )) / (T t -T t-1 ); where X 平滑,t and X 平滑,t-1 Smooth cell density at time t and time t-1, respectively. t and T t-1 For the corresponding time; When μ current When μ is ≤ μ threshold, the cooling point is determined to have been reached; the μ 阈值 =k*μ max μ max The maximum specific growth rate of cells is given by k, which ranges from 0.4 to 0.6, and μ. max With μ current Cells from the same batch.

[0026] By integrating data smoothing processing to eliminate measurement noise and random fluctuations to ensure robustness, and overcoming the technical obstacle of high μ signal noise, this method, for the first time, uses the cell specific growth rate μ as a reliable process control decision variable. This enables proactive capture of cell physiological states and avoids the lag inherent in traditional VCD methods. Cell density data can be obtained from offline sampling or online acquisition. Judgment is made based on the ratio k of the current specific growth rate to the maximum specific growth rate, reducing the sensitivity to differences in seeding density, culture medium titer, and other conditions between different batches, thus enhancing process robustness. This method also solves the technical problem of lag in existing methods for determining the cooling point, which leads to premature or delayed cooling and affects the final yield.

[0027] Wherein, k can be, but is not limited to, 0.4, 0.42, 0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56, 0.58 or 0.6, or any value between 0.4 and 0.6, preferably 0.5.

[0028] The acquired raw cell density measurements (X) are processed using a smoothing algorithm to eliminate measurement noise and random fluctuations. This involves replacing the most recent data point with the average of data from the last few time points to ensure more stable data. In some specific embodiments, the smoothing algorithm includes a moving average or exponential smoothing, preferably a moving average. In some specific embodiments, the window size for the moving average is 2 to 5 data points. In some specific embodiments, the smoothing coefficient α for the exponential smoothing is 0.1 to 0.3.

[0029] In some specific implementations, the μ max Determined in the following ways: Cell density data were collected at least at two time points during the exponential growth phase of the cell cycle. A linear regression was performed on ln(X) versus time, and the resulting slope was μ. max ;or, μ is obtained based on historical successful batch data statistics. max Fixed value.

[0030] Specifically, μ can be increased by increasing the number of cell density data collected. max The accuracy of the values. In some specific implementations, cell density data are collected every 12–24 hours during the exponential growth phase of the cells. High-frequency collection can further improve accuracy. max The accuracy of the value.

[0031] In practical applications, for mature and stable cell culture processes, μ max This can be determined by combining statistical data from previous batches of cell culture to accurately grasp μ. max This improves stability.

[0032] In some specific implementations, the cell density data is derived from offline sampling or online sensor detection; wherein, the offline sampling is trypan blue staining and counting; and the online sensor detection is capacitance method or online microscopy.

[0033] The above-described prediction method can proactively determine the inflection point of cell growth deceleration, thereby triggering a cooling operation to shift the metabolic flow from growth to product synthesis, so as to maximize product yield. According to another aspect of the present invention, the application of the above-described prediction method in cell culture is also provided.

[0034] In some specific embodiments, the cell culture includes product-directed cell culture; in some specific embodiments, the product includes at least one of recombinant protein, viral vector, exosome, nucleic acid, or glycopeptide.

[0035] In some specific embodiments, the cells include mammalian cells; in some specific embodiments, the mammalian cells include at least one of CHO cells, HEK293 cells, or insect cells.

[0036] According to another aspect of the present invention, a cell culture method based on controlling the cooling point is also provided, comprising using the above-described prediction method to determine whether the cooling point has been reached, and prompting a cooling operation when the cooling point is reached.

[0037] In some specific embodiments, the cooling operation includes reducing the culture temperature from the proliferation phase temperature of 35-37°C to the expression phase temperature of 30-34°C.

[0038] According to another aspect of the present invention, a cell culture process control system is also provided, comprising a data acquisition module for acquiring cell density data, a data processing module for performing the above-described prediction method, and a control execution module for prompting a cooling operation when a cooling point is reached.

[0039] The present invention will be further illustrated by the following examples. Unless otherwise specified, the materials in the examples are prepared according to existing methods or purchased directly from the market.

[0040] Example 1 A method for predicting the cooling point of cell culture, specifically following these steps: 1. Determine μ 阈值 1) Obtain μ max An exponential model linear fitting method was used. Cell density data were collected every 12–24 hours during the exponential growth phase after cell inoculation. Linear regression was performed on the ln(X) data, and the resulting slope was used as the μ value for that batch. max .

[0041] 2) Calculate μ 阈值 =k*μ max k is selected from 0.4 to 0.6.

[0042] 2. Calculate the current specific growth rate μ of the cell. current 1) Obtain cell density (X) data, and smooth the VCD data using the moving average method to obtain X. 平滑 The specific calculation is based on the following formula: X 平滑,t =(X t +X t-1 +...+X t-n ) / n Among them, X 平滑,t X represents the smoothed cell density value at time t.t This represents the raw cell density measurement value collected at time t, where n is the size of the sliding window, preferably 2 to 5 data points.

[0043] 2) Using X 平滑 Specifically, the current specific growth rate μ of the cell is calculated according to the following formula. current : μ current = (ln(X 平滑,t )-ln(X 平滑,t-1 )) / (T t -T t-1 ) Among them, X 平滑,t and X 平滑,t-1 Smooth cell densities at time t and t-1, respectively; T t and T t-1 For the corresponding time.

[0044] When μ current ≤μ 阈值 At that time, the cooling point is reached.

[0045] Experiment 1 verifies the cooling control effect based on the specific growth rate (μ) threshold. This embodiment aims to verify the application effect of the prediction method in experimental groups 1-5 in cell culture.

[0046] 1. Materials and Methods a) Cell line: CHO-K1 cells (ATCC source).

[0047] b) Basal medium: CHO MaxD; Supplemental medium: MaxFA / MaxFB.

[0048] c) Culture conditions: Fed-batch culture was conducted in 125 mL shake flasks at a working volume of 25 mL, at 36.5°C, 5% CO2, and 140 rpm. The inoculum density was 1 × 10⁻⁶. 6 cells / mL.

[0049] d) Experimental Design: Following the prediction method in Example 1, six experimental groups were set up with k values ​​of 0.3, 0.4, 0.5, 0.6, 0.7, and a fixed live cell density cooling method, numbered Group 1 to Group 6. The cooling condition for the fixed live cell density method was that the live cell density (VCD) reached 15~18×10⁻⁶. 6 When the cells / mL is reached, the temperature is lowered to 33°C.

[0050] 2. Implementation Process 1) Determine μ 阈值 Data on the early exponential growth phase of the culture were collected every 24 hours, as shown in Table 1.

[0051] Table 1

[0052] The collected data were smoothed to exclude transient high values ​​in the early stages of culture, and the fastest sustainable growth rate during the steady exponential growth phase (D2-D4) was selected as the maximum specific growth rate (μ). max =0.7d -1 ), calculate μ 阈值 =k*μ max The results are shown in Table 2.

[0053] Table 2

[0054] 2) Continuously monitor cell density and calculate the current specific growth rate μ of cells according to the method in Example 1. current , compared to μ current and μ 阈值 The cooling point was determined, and the results are shown in Table 2. Cooling operations were performed at the cooling point to reduce the temperature to 33°C.

[0055] 3) Continue culturing until D16. Upon completion of culturing, test the six key process indicators and product quality indicators. The results are as follows: Figures 2-5 As shown in Table 3.

[0056] Table 3

[0057] Data shows that compared to group 6, group 1 had the smallest k-value, resulting in a severely delayed cooling point, significantly increased lactate concentration, and lower cell viability, final protein titer, and product quality compared to group 6. Group 5 had the largest k-value, leading to an earlier cooling point. Although cell viability and product quality were similar to group 6, its final protein titer showed the lowest trend. Groups 2-4 had k-values ​​ranging from 0.4 to 0.6, keeping the cooling point within the optimal range. Cell viability and final protein titer were higher than group 6, with significantly reduced lactate content. Groups 2-4 maintained higher cell viability, significantly reduced accumulation of metabolic byproducts, and exhibited healthier metabolic status. The consistent proportion of the main peak demonstrates a fundamental improvement in cell metabolic state and product quality. Using the robustly treated μ-value as the core decision variable can accurately capture the optimal inflection point of cell physiological state transition, thus achieving excellent industrial application value.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the cooling point of cell culture, characterized in that, Includes the following steps: A. Obtain cell density data, and smooth the cell density data to obtain smoothed cell density data X. 平滑 Smoothing formula: X 平滑,t =(X t +X t-1 +...+X t-n ) / n; Among them, X 平滑,t X represents the smoothed cell density value at time t. t This represents the raw cell density measurement collected at time t, where n is the size of the sliding window; B. According to X 平滑 Calculate the current specific growth rate μ of the cell current Calculation formula: μ current = (ln(X 平滑,t ) - ln(X 平滑,t-1 )) / (T t -T t-1 ); Among them, X 平滑,t and X 平滑,t-1 Smooth cell density at time t and time t-1, respectively. t and T t-1 For the corresponding time; When μ current When the temperature is ≤ μ threshold, the cooling point is determined to have been reached; The μ 阈值 =k*μ max μ max The maximum specific growth rate of cells is given by k, which ranges from 0.4 to 0.6, and μ. max With μ current Cells from the same batch.

2. The prediction method according to claim 1, characterized in that, The smoothing process employs either the moving average method or the exponential smoothing method, with the moving average method being preferred.

3. The prediction method according to claim 2, characterized in that: Preferably, the sliding window size of the moving average method is 2 to 5 data points; Preferably, the smoothing coefficient of the exponential smoothing method is 0.1 to 0.

3.

4. The prediction method according to claim 1, characterized in that, The μ max Determined in the following ways: Cell density data were collected at least at two time points during the exponential growth phase of the cell cycle. A linear regression was performed on ln(X) versus time, and the resulting slope was μ. max ;or, μ is obtained based on historical successful batch data statistics. max Fixed value.

5. The prediction method according to claim 1, characterized in that, The value of k is 0.

5.

6. The prediction method according to claim 1, characterized in that, The cell density data is derived from offline sampling or online sensor detection. Preferably, the offline sampling is the trypan blue staining counting method; Preferably, the online sensor detection is performed using capacitance or online microscopy.

7. The application of the prediction method according to any one of claims 1-6 in cell culture; Preferably, the cell culture includes product-directed cell culture; Preferably, the product comprises at least one of recombinant protein, viral vector, exosome, nucleic acid, or glycopeptide.

8. A cell culture method based on controlling the cooling point, characterized in that, This includes using the prediction method described in any one of claims 1 to 6 to determine whether the cooling point has been reached, and prompting a cooling operation when the cooling point is reached.

9. The cell culture method according to claim 8, characterized in that, The cooling operation includes reducing the culture temperature from the proliferation phase temperature of 35-37℃ to the expression phase temperature of 30-34℃.

10. A cell culture process control system, characterized in that, include: The data acquisition module is used to acquire cell density data; The data processing module is used to execute the prediction method according to any one of claims 1 to 6; The control execution module is used to prompt for cooling operation when the cooling point is reached.

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