Oxytocin injection capable of being stored at room temperature and preparation method thereof

Abstract

The invention belongs to the field of pharmaceutical preparations, and specifically relates to an oxytocin injection that can be stored at room temperature and a preparation method thereof. The oxytocin injection of the invention comprises oxytocin, a pH regulator, ammonia water, mannitol and water for injection, can be stably stored at room temperature, has a low content of impurities, and has low drug toxicity, thereby significantly reducing storage costs.

Description

Technical Field

[0001] The present invention belongs to the field of pharmaceutical preparations, and more specifically, relates to an oxytocin injection capable of being stored at room temperature and a preparation method thereof. Background Art

[0002] Oxytocin is a peptide chain composed of 9 amino acids containing disulfide bonds. Its pharmacological action is to stimulate the contraction of uterine smooth muscle. It is used for induction of labor, acceleration of labor, and uterine bleeding caused by weak or poor uterine contractions after delivery and abortion.

[0003] At present, the domestic formula of oxytocin injection is composed of injection water, acetic acid and oxytocin. This formula requires oxytocin injection to be stored in a refrigerator (2-8°C), which greatly increases the storage and transportation costs. In addition, the generation pathway and type of degradation impurities in the storage process of oxytocin injection are inconsistent with those of foreign original drugs, which may cause safety issues.

[0004] The prescription of the original foreign drug is composed of water for injection, acetic acid, chlorobutanol and oxytocin. Chlorobutanol is a preservative and is the main cause of adverse reactions. 100mL of oxytocin injection contains 0.2g of chlorobutanol, and the impurity problem is quite serious. Summary of the invention

[0005] In order to overcome the deficiencies of the prior art, the present invention provides an oxytocin injection and a preparation method thereof, which can be stored at room temperature and has reduced impurities.

[0006] In a first aspect, the present invention provides an oxytocin injection, which comprises oxytocin, a pH regulator, ammonia water, mannitol and water for injection; the content is 1-10 units / mL; the content of mannitol is 0.1-3 mg / mL; the content of ammonia water is 0.01-0.3 mg / mL; and the pH regulator is glacial acetic acid or acetic acid.

[0007] In some embodiments, the content of oxytocin is 5 units / mL; the content of mannitol is 1.72 mg / mL; and the content of ammonia water is 0.14 mg / mL.

[0008] In another aspect, the present invention provides a method for preparing an oxytocin injection, the method comprising the following steps:

[0009] Step 1: Prepare 200 units / ml oxytocin solution;

[0010] Step 2: prepare 1 mol / L acetic acid solution for later use;

[0011] Step 3: prepare 1% ammonia solution for later use;

[0012] Step 4: Weigh the water for injection, add mannitol, 1% ammonia solution, glacial acetic acid and oxytocin solution, stir evenly, add acetic acid to adjust the pH value to 3.9±0.2, add water to the full amount of the prescription, and filter through a filter membrane to obtain.

[0013] In some embodiments, step 1 comprises weighing 17.96 mg of oxytocin raw material, adding 50 ml of water for injection, and shaking to dissolve to obtain a 200 unit / ml oxytocin solution.

[0014] In some embodiments, in step 4, the amount of water for injection is 450 g, the amount of mannitol added is 0.86 g, the amount of 1% ammonia solution is 3.65 g, the amount of glacial acetic acid is 0.89 g, and the amount of oxytocin solution is 12.5 mL.

[0015] In some embodiments, the filter membrane is a 0.22 μm PES filter membrane.

[0016] In some embodiments, the following steps are also included:

[0017] Introduce a combination of static mixer and dynamic mixer for mixing;

[0018] A constant pH control strategy was introduced to monitor the pH value of the reaction system in real time through an online pH meter, and the dripping speed of ammonia water was dynamically adjusted in combination with a PID control algorithm;

[0019] Establishing the mixing function between the mixing degree M and the Reynolds number Re, the mixing time t and the mixer structure parameter λ;

[0020] Based on the mixing degree function, a mixing degree deviation function between the mixing degree and the pH deviation value PHZ is determined by data fitting;

[0021] Based on the pH control strategy, the mixing degree deviation function and the mixing degree deviation function, a coordinated pH control strategy is determined.

[0022] Compared with the prior art, the advantages and positive effects of the present invention are:

[0023] 1. The effect of mannitol on oxytocin is mainly coulombic action, which helps the oxytocin molecules to maintain a uniform dispersion state in the solution. Mannitol has a good dispersing effect on oxytocin, which helps to reduce the aggregation of oxytocin molecules in the solution, thereby maintaining the stability of oxytocin molecules. The oxytocin injection of the present invention can be stably stored at room temperature, significantly reducing the storage cost;

[0024] 2. The oxytocin injection of the present invention significantly reduces the content of impurities and reduces drug toxicity. Mannitol has a good dispersing effect on oxytocin, can maintain the stability of oxytocin molecules, and reduce impurities generated by aggregation. At the same time, mannitol is used to replace chlorobutanol. The stabilizing effect of mannitol on oxytocin molecules helps to reduce the degradation and metabolism of the drug in the body, thereby maintaining the effectiveness of the drug and reducing toxicity. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below.

[0026] Figure 1 It is the chromatogram of related substances of the oxytocin injection of the present invention on day 0;

[0027] Figure 2 It is the chromatogram of related substances of the oxytocin injection of the present invention after 3 months;

[0028] Figure 3 Chromatogram of related substances of the reference preparation on day 0;

[0029] Figure 4 This is the chromatogram of related substances of the reference preparation after 3 months. DETAILED DESCRIPTION

[0030] Example 1

[0031] The present invention provides an oxytocin injection, which uses mannitol with lower toxicity to replace chlorobutanol with greater side effects, and uses ammonia water combined with acetic acid to generate ammonium acetate, which stabilizes the oxytocin molecules and prevents them from undergoing secondary degradation. The composition of the injection is shown in Table 1:

[0032] Table 1 Injection composition

[0033]

[0034] Example 2

[0035] The present invention also provides a method for preparing oxytocin injection, which specifically comprises:

[0036] ① Weigh 17.96 mg of oxytocin raw material, add 50 ml of water for injection, shake to dissolve, and obtain 200 units / ml oxytocin solution;

[0037] ② Take 6g of glacial acetic acid, add water for injection, mix well and make up to 100ml to obtain 1mol / L acetic acid solution;

[0038] ③ Take 3.86g of 25.9% concentrated ammonia solution, add water to 100g, shake well to obtain 1% ammonia solution, and set aside;

[0039] ④ Weigh 450g of water for injection, add 0.86g of mannitol, 3.65g of 1% ammonia solution, 0.89g of glacial acetic acid and 12.5ml of oxytocin solution, stir evenly, add 1mol / L acetic acid to adjust the pH to 3.9±0.2, add water to the full amount of the prescription, and filter through a 0.22μm PES filter membrane to obtain.

[0040] Example 3

[0041] In this embodiment, the production process of oxytocin injection in embodiment 2 is further optimized. In particular, in the step of generating ammonium acetate, the process of generating ammonium acetate involves an acid-base neutralization reaction of glacial acetic acid and an ammonia solution, and the reaction rate is greatly affected by the pH value.

[0042] In the optimal pH range, the reaction rate is the fastest, which is not only conducive to improving production efficiency, but also shortening the reaction time. However, if the pH value deviates from this range, the reaction rate will be significantly reduced, which may lead to a longer reaction time. In addition, pH fluctuations may also cause side reactions, produce impurities, and affect the purity, crystal morphology and particle size distribution of ammonium acetate, thereby affecting the stability and solubility of the product.

[0043] In order to ensure that the reaction can be carried out in the optimal pH environment, a constant pH control strategy is introduced to improve reaction efficiency and product quality, and an online pH meter is used to monitor the pH value of the reaction system in real time. By combining the PID control algorithm, the dripping speed of ammonia water is dynamically adjusted to ensure that the pH value always remains in an ideal state. This constant pH control can not only minimize side reactions and ensure product quality, but also prevent the reaction exothermic rate from being too fast due to pH loss of control, thereby avoiding a sharp increase in equipment temperature and pressure and ensuring the safety of the production process.

[0044] Through this method, the generation process of ammonium acetate can be effectively controlled, production efficiency can be improved, and high purity and safety of the product can be guaranteed. The specific control functions are as follows:

[0045]

[0046] In the formula, is the ammonia droplet acceleration at time t, pH(t) is the pH value at time t, pH set is the target pH value, K p , K i , K d They are proportional, integral and differential control coefficients respectively. By properly adjusting the control parameters, the automatic optimization control of the reaction process can be achieved to ensure the yield and purity of ammonium acetate.

[0047] In addition, the specific steps of preparing oxytocin injection involve the preparation of oxytocin solution, the preparation of acetic acid solution, the preparation of ammonia solution, the mixing and pH adjustment of each component, and aseptic filtration. For this purpose, a combination of a static mixer and a dynamic mixer is introduced to accurately control the pH value, and the dripping speed of ammonia water is dynamically adjusted in combination with a PID control algorithm. Because, in the preparation step of the oxytocin solution, the oxytocin raw material needs to be dissolved in the water for injection, and it is fully dissolved by shaking to form a uniform solution. This mainly involves the mixing of solid-liquid phases. The static mixer can achieve repeated segmentation and reorganization during the flow of the solution through internal structural design, such as spiral blades or obstacles, to accelerate the dissolution process of solid particles. However, it may be difficult to completely break up the agglomeration between oxytocin particles by relying solely on a static mixer, resulting in incomplete local dissolution. The introduction of a dynamic mixer for shaking can introduce turbulence and shear through mechanical action, accelerate the dispersion and dissolution of oxytocin particles, and ensure that a uniform solution is finally obtained. In the preparation process of acetic acid solution, glacial acetic acid needs to be quickly mixed with water for injection and fixed to a specific concentration. Glacial acetic acid has certain volatility and corrosiveness. If the mixing is not sufficient, it may cause local high concentration of acetic acid to damage the equipment and operators. Static mixers can achieve multiple divisions and flattening of acetic acid and water in a short distance, significantly improve radial and axial mixing, and reduce the risk of local high concentration of acetic acid. However, the mixing effect of static mixers may be limited when dealing with high viscosity difference systems. The introduction of dynamic mixers can destroy the agglomeration of acetic acid in water through strong shearing action, accelerate the dissolution and uniform dispersion of acetic acid, and ensure that an acetic acid solution with accurate concentration is finally obtained; in the preparation process of ammonia solution, similar to the preparation of acetic acid solution, this step requires the concentrated ammonia solution to be quickly mixed with water and diluted to a specific concentration. Ammonia water is highly volatile, and ammonia gas is easily generated during the mixing process, causing environmental pollution and ammonia water loss. Static mixers can achieve rapid mixing with water at the inlet of ammonia water, reduce the local concentration of ammonia water, and reduce ammonia volatilization. However, it may be difficult to completely suppress the generation of ammonia by relying solely on a static mixer. The introduction of a dynamic mixer for stirring can speed up the dissolution rate of ammonia water, promote the full contact between ammonia water and water through surface renewal, and minimize the loss of ammonia volatilization; in the process of mixing and pH adjustment of each component, it involves the mixing of multiple components, including water for injection, mannitol, ammonia solution, glacial acetic acid and oxytocin solution, and the pH value of the system needs to be precisely controlled. The properties of each component are quite different, and a single mixing method is difficult to achieve rapid and uniform mixing. Using a static mixer, multiple cutting and reorganization of each component can be achieved in the mixing channel through the diversion-confluence method to obtain a uniform mixed liquid. However, the mixing effect of the static mixer may be limited when dealing with systems with large viscosity differences and complex rheology.The introduction of a dynamic mixer for stirring can further strengthen turbulent mixing, destroy the interface between components, accelerate the mutual dissolution and dispersion of components, and ensure that a uniform and stable mixed solution is finally obtained. During the pH adjustment process, the acetic acid solution needs to be slowly added and the pH value changes need to be monitored in real time. The static mixer can extend the contact time between the acetic acid solution and the mixed solution through a special internal structure to achieve uniform pH adjustment. However, it may be difficult to achieve rapid pH response and precise control by relying solely on a static mixer. The strong stirring effect of the dynamic mixer can accelerate the neutralization reaction of acetic acid and ammonia water, shorten the pH adjustment time, and improve the sensitivity and accuracy of pH control; in the sterile filtration step, in the final sterile filtration step, the mixed solution needs to pass through a 0.22μμm PES filter membrane to remove possible microorganisms and particulate impurities. The static mixer can form a uniform flow velocity distribution on the surface of the filter membrane through the flow channel design of the internal structure, reduce local blockage of the filter membrane, and extend the service life of the filter membrane. However, it may be difficult to completely avoid the deposition of particulate matter and the formation of filter cakes in the mixed solution by relying solely on a static mixer. Introducing a dynamic mixer for mild stirring can maintain the uniformity of the mixed liquid, prevent the deposition of particles, and further improve the clarity and recovery rate of the filtrate.

[0048] To this end, in each step of the preparation of oxytocin injection, the introduction of a combination of static mixers and dynamic mixers can give full play to the respective advantages of the two mixing methods and overcome the limitations of a single mixing method. The static mixer achieves efficient segmentation-recombination in the mixing process through a carefully designed internal structure, while the dynamic mixer introduces strong turbulence and shearing through mechanical stirring, which accelerates the dissolution and uniform dispersion of the components. The organic combination of the two mixing methods can significantly improve the mixing efficiency, shorten the process time, ensure the product quality of each step, and finally obtain a stable and uniform oxytocin injection. At the same time, the reasonable selection and optimization of the type and operating parameters of the mixer can also take into account the equipment investment and operating costs, and achieve economic and efficient processes. This is why in the preparation process of oxytocin injection, a combination of static mixers and dynamic mixers is introduced. In addition, in the pH adjustment process, the static mixer helps to achieve uniform pH adjustment by extending the contact time, and the dynamic mixer accelerates the neutralization reaction rate through strong stirring. The introduction of a combination of static mixers and dynamic mixers can improve the sensitivity and accuracy of pH control.

[0049] In the mixing and pH adjustment stage, in order to obtain a stable and uniform mixed liquid, a combination of static mixer and dynamic mixer is introduced. The static mixer uses a special internal structure to achieve efficient mixing during the flow of the mixed liquid; the dynamic mixer further enhances the mixing effect through mechanical actions such as stirring or oscillation.

[0050] A mixing function is established between the mixing degree M and the Reynolds number Re, the mixing time t and the mixer structure parameter λ. The specific relationship between the mixing degree M and the Reynolds number Re, the mixing time t and the mixer structure parameter λ is:

[0051] M=1-exp[-α×(Re×t) β ×λ γ ];

[0052] M is the degree of mixing, Re is the Reynolds number, t is the mixing time, λ is the mixer structure parameter, and α, β, and γ are empirical coefficients.

[0053] It should be noted that the Reynolds number Re is a dimensionless number that characterizes the flow state of a fluid, and its definition formula is: Re = ρvd / μ, where ρ is the fluid density, v is the flow velocity, d is the characteristic dimension (such as the pipe diameter), and μ is the fluid viscosity.

[0054] To determine Re, ρ and μ can be obtained by consulting literature or experimental measurement to obtain the physical properties of the fluid. According to the structural dimensions and operating conditions (such as feed flow rate) of the mixer, the characteristic flow velocity v and characteristic dimension d are calculated and substituted into the above formula to obtain Re.

[0055] The mixing time t refers to the residence time of the mixture in the mixer. For a continuous mixing process, t can be determined by a tracer experiment. The specific method is to add a tracer (such as a dye) to the mixture, continuously monitor the tracer concentration change curve over time at the mixer outlet, and estimate the mixing time t based on the average residence time of the tracer.

[0056] For intermittent mixing processes, the mixing time t is the duration of stirring or shaking and can be set and measured directly by a control device (such as a timer).

[0057] The mixer structural parameter λ reflects the influence of the geometric characteristics of the mixer on the mixing process. The specific form of λ depends on the type and internal structure of the mixer.

[0058] For a static mixer, λ can be the number of turbulent elements, the torsion angle, the aspect ratio, etc.; for a dynamic mixer, λ can be the number of stirring blades, the tilt angle, the power consumption, etc.

[0059] Determining λ requires analysis and calculation based on the design parameters and internal structure of the mixer. Visual experiments (such as dyeing experiments) or numerical simulations (such as computational fluid dynamics CFD) can also be used to assist in analyzing the flow field characteristics inside the mixer to obtain a more accurate λ value.

[0060] α, β, and γ are empirical coefficients in the mixing function, reflecting the relative importance of each influencing factor (Re, t, λ) to the mixing degree M.

[0061] The determination of α, β, and γ is mainly done through the method of fitting experimental data. The specific steps are as follows:

[0062] (1) Design a series of mixing experiments, change the levels of Re, t, and λ, and measure the degree of mixing M under each experimental condition;

[0063] (2) Substitute the test data (Re, t, λ, M) into the mixing function to form a nonlinear system of equations;

[0064] (3) Solve the system of equations using numerical algorithms such as the least squares method to minimize the sum of squared errors between the predicted values ​​and the measured values, thereby obtaining estimated values ​​of α, β, and γ;

[0065] (4) Use new test data to test the fitting results and adjust the values ​​of α, β, and γ if necessary.

[0066] In practical applications, it is also necessary to select appropriate test instruments and numerical calculation tools according to the characteristics of the specific problem, and combine experience judgment and repeated experiments to obtain reliable parameter values. At the same time, attention should also be paid to the applicable range and error analysis of the parameters to ensure the predictive performance of the hybrid function.

[0067] Establishing the relationship between mixing degree and Reynolds number, mixing time and mixer structural parameters can provide important theoretical guidance for the optimization control of the mixing process, mixer selection, process scale-up and transfer, online monitoring and fault diagnosis, and energy consumption optimization. By rationally using the relationship, the mixing efficiency can be significantly improved, the product quality can be improved, the production cost can be reduced, and ultimately the intelligent and lean management of the mixing process can be achieved.

[0068] Furthermore, since the mixing effect affects the generation rate of ammonium acetate and the quality of the product, the change in the degree of mixing will also cause the pH value to fluctuate. Based on the degree of mixing function, the degree of mixing deviation function between the degree of mixing and the pH deviation value PHZ is determined by data fitting, and the empirical relationship between the degree of mixing M and the pH deviation ΔpH is obtained by fitting the experimental data:

[0069] ΔpH=f(M)=a×M^b+c

[0070] Where a, b, and c are fitting coefficients.

[0071] Based on the pH control strategy, the mixing degree deviation function and the mixing degree deviation function determine the coordinated pH control strategy. Specifically, on the basis of constant pH control, a feedback correction of the mixing degree can be introduced. That is, according to the real-time measured pH deviation ΔpH, the current mixing degree M is calculated by the relationship between the mixing degree and the pH deviation. Then, the mixing degree M is substituted into the relationship between the mixing degree and the operating parameters, and the operating conditions of the mixer are dynamically adjusted (such as adjusting the feed flow rate, changing the speed of the mixer, etc.), so as to realize automatic optimization control of the mixing process.

[0072] The corrected control equation is as follows:

[0073]

[0074] M(t) is the real-time mixing degree, M set is the target mixing degree, K m is the mixing degree control coefficient.

[0075] The objective function comprehensively considers the deviation of each process parameter from the optimal value:

[0076] minJ=w 1 ×(pH-pH opt ) 2 +w 2 ×(MM opt ) 2 +w 3 ×(TT opt ) 2 +w 4 ×(PP opt ) 2 +w 5 ×(RR opt ) 2 +w 6 ×(QQ 。pt ) 2

[0077] J: Objective function, which represents the weighted sum of squares of deviations of each process parameter from the optimal value.

[0078] w1, w2, w3, w4, w5, w6: weight coefficients of each item, which are set according to the importance and sensitivity of different parameters.

[0079] pH, M, T, P, R, Q: respectively represent process parameters such as pH value, mixing degree, temperature, pressure, feed ratio and product quality.

[0080] pH_opt represents the optimal pH value, M_opt represents the optimal mixing degree, T_opt represents the optimal temperature, P_opt represents the optimal pressure, R_opt represents the optimal feed ratio, and Q_opt represents the optimal product quality.

[0081] Constraints

[0082] Set the constraints for each process parameter:

[0083] pH min ≤pH≤pH max

[0084] M min ≤M≤M max

[0085] T min ≤T≤T max

[0086] P min ≤P≤P max

[0087] R min ≤R≤R max

[0088] Q min ≤Q≤Q max

[0089] V(NH 3 ·H 2 O) min ≤V(NH 3 ·H 2 O)≤V(NH 3 ·H 2 O) max

[0090] Re min ≤Re≤Re max

[0091] t min ≤t≤t max

[0092] λ min ≤λ≤λ max

[0093] pH_min and pH_max represent the constraint range of pH value parameters; M_min and M_max represent the constraint range of mixing degree parameters, T_min and T_max represent the constraint range of temperature parameters, P_min and P_max represent the constraint range of pressure parameters; R_min and R_max represent the constraint range of feed ratio parameters; Q_min and Q_max represent the constraint range of product quality parameters.

[0094] Re_min and Re_max represent the constraint range of Reynolds number parameters, t_min and t_max represent the constraint range of mixing time parameters, λ_min and λ_max represent the constraint range of mixer structure parameters; V(NH 3 ·H 2 O) min and V(NH 3 ·H 2 O) max Represents the constraint range of ammonia volume.

[0095] It should be noted that obtaining the optimal value and constraint range of each process parameter usually requires comprehensive consideration of multiple factors such as theoretical analysis, experimental research, and production experience. Usually, the following methods can be used to obtain it:

[0096] Theoretical analysis method: Based on the principles of chemical reaction kinetics and thermodynamics, the influence of various process parameters on reaction rate, conversion rate, selectivity, etc. is derived. Fluid mechanics and transfer phenomenon theory are used to analyze the flow characteristics, heat and mass transfer laws in the mixing process, and determine the optimal mixing time, Reynolds number and mixer structure parameters. Mass balance and energy balance are used to calculate the feed ratio of each material component and product quality indicators, and optimize the material dosing plan.

[0097] Experimental research: Through single factor test and orthogonal test and other methods, the influence of each process parameter on product quality and yield is investigated to determine the optimal level and constraint range of each parameter. Experimental design methods such as response surface method and central composite design are used to construct a mathematical model between process parameters and product quality and optimize the combination of various parameters. Online analysis technologies such as near infrared spectroscopy and Raman spectroscopy are used to monitor key process parameters and product quality indicators in real time to provide a basis for optimization control.

[0098] Production experience: Summarize historical production data, analyze the correlation between each process parameter and product quality, and determine the optimal operating range of each parameter. Learn from the production experience of similar products, refer to industry standards and regulatory requirements, and set the control indicators and limits of each process parameter. Fully consider the performance characteristics and operational flexibility of the equipment, and reasonably set the adjustment range and response speed of each process parameter.

[0099] Data-driven modeling and optimization: Collect a large amount of production data, including real-time monitoring values ​​of various process parameters, product quality test results, etc., and build a data warehouse. Use machine learning algorithms, such as support vector machines and artificial neural networks, to establish a nonlinear relationship model between process parameters and product quality. Use intelligent optimization algorithms, such as genetic algorithms and particle swarm optimization, to search for the optimal combination of process parameters under constraints.

[0100] Expert experience and knowledge base: Invite process experts, equipment experts, quality experts, etc. to provide guidance on the setting of various process parameters. Establish a knowledge base and expert system to summarize the successful experience and lessons of optimization control and provide intelligent support for subsequent optimization decisions.

[0101] In practical applications, it is usually necessary to combine the above methods and continuously iterate and optimize to obtain reliable and robust optimal values ​​and constraint ranges of process parameters. At the same time, it is also necessary to dynamically adjust the optimization goals and control strategies according to different production stages and product requirements to ensure continuous improvement of the production process and steady improvement of product quality.

[0102] In addition, attention should also be paid to the verification and evaluation of optimization results. On the one hand, it is necessary to evaluate the feasibility and effectiveness of the optimization control strategy through verification in multiple stages such as small-scale tests, pilot tests and industrial production; on the other hand, it is also necessary to establish a sound quality management system to comprehensively monitor and evaluate product quality, timely discover and solve potential quality problems, and continuously improve and update the optimization control model.

[0103] In short, obtaining the optimal value and constraint range of each process parameter is a systematic, dynamic and continuous process, which requires the organic combination of theoretical guidance, experimental research, production practice and intelligent optimization. Only on the basis of in-depth understanding of the process mechanism, continuous accumulation of production data and continuous optimization of control strategies can the intelligent and optimal control of the preparation process of oxytocin injection be realized, and patients can be provided with safer, more effective and high-quality drugs.

[0104] By solving this optimization problem, a set of optimal process parameter combinations can be obtained to achieve overall optimization control of the preparation process. It should be noted that in practical applications, the specific forms of the objective function and constraints may be adjusted according to different production requirements and conditions. In addition, it is also necessary to select appropriate optimization algorithms, such as sequential quadratic programming (SQP), genetic algorithm (GA), particle swarm optimization (PSO), etc., to efficiently solve such nonlinear, multi-constrained optimization problems.

[0105] In general, by constructing a complete objective function and constraints, and combining advanced optimization algorithms and process control technologies, intelligent and optimal control of the oxytocin injection preparation process can be achieved, providing strong support for improving drug quality and production efficiency.

[0106] Preferably, the optimization algorithm in this embodiment is a genetic algorithm (GA), and the specific steps are as follows:

[0107] Step 1: Coding and initialization

[0108] The process parameters such as pH, mixing degree, temperature, pressure, feed ratio and product quality are converted into genes in the genetic algorithm. For example, the pH value range is [pH_min, pH_max], which can be linearly mapped to a real number code in the interval [0, 1]. Similarly, other process parameters are encoded in a similar way.

[0109] A certain number (e.g., 100) of initial populations are randomly generated, and each individual represents a set of possible process parameter combinations. For example, an individual can be encoded as [0.2, 0.5, 0.8, 0.3, 0.6, 0.9], which represent the values ​​of pH, mixing degree, temperature, pressure, feed ratio, and product quality, respectively.

[0110] Step 2: Fitness Evaluation

[0111] Decode each individual into the actual process parameter value and substitute it into the objective function to calculate the fitness value. For example, decode [0.2, 0.5, 0.8, 0.3, 0.6, 0.9] into [pH = 6.5, M = 85%, T = 90 ° C, P = 0.3 MPa, R = 3: 1, Q = 95%], and then calculate the objective function value J = w1 × (6.5-7.0) ^ 2 + w2 × (85%-90%) ^ 2 + ... + w6 × (95%-98%) ^ 2.

[0112] For individuals that violate the constraints, such as pH <6.0 or pH>8.0, a certain penalty is given, such as multiplying their fitness value by a very small penalty factor (such as 0.001) to reduce their probability of being selected.

[0113] Step 3: Select an operation

[0114] According to the fitness value of the individual, a roulette wheel selection strategy is used to select a part of excellent individuals as parents. For example, the larger the fitness value (that is, the smaller the objective function value), the greater the probability of being selected.

[0115] During the selection process, the best individual and average fitness value of the current population are recorded to monitor the convergence of the algorithm. If the fitness value of the best individual has not increased significantly for many consecutive generations (such as 50 generations), it may be close to the global optimum.

[0116] Step 4: Crossover Operation

[0117] The selected parent individuals are randomly paired and arithmetic crossover is performed according to a certain crossover probability (such as 0.8). For example, for two parent individuals [0.2, 0.5, 0.8, 0.3, 0.6, 0.9] and [0.4, 0.7, 0.6, 0.5, 0.2, 0.8], two offspring individuals [0.3, 0.6, 0.7, 0.4, 0.4, 0.85] and [0.1, 0.4, 0.9, 0.2, 0.8, 0.95] are generated.

[0118] The new individuals generated by the crossover operation represent new combinations of process parameters and help explore better solutions.

[0119] Step 5: Mutation Operation

[0120] For the offspring individuals generated by crossover, some genes are randomly selected for uniform mutation according to a certain mutation probability (such as 0.1). For example, the second gene 0.6 in [0.3, 0.6, 0.7, 0.4, 0.4, 0.85] is mutated to a random value 0.9 to obtain the new individual [0.3, 0.9, 0.7, 0.4, 0.4, 0.85].

[0121] The mutation operation introduces new parameter values, which helps to escape from the local optimum and maintain the diversity of the population.

[0122] Step 6: Constraint Processing

[0123] For the new individuals generated by crossover and mutation, check whether their decoded process parameters meet the constraints. For example, check whether the pH is in the range of [6.0, 8.0], whether the mixing degree is in the range of [60%, 100%], etc.

[0124] For individuals that do not meet the constraints, the genes that violate the constraints can be randomly modified until the constraints are met. For example, if pH = 5.8, a new value within [6.0, 8.0] can be randomly generated to replace the original gene.

[0125] Step 7: Termination condition judgment

[0126] Repeat steps 2 to 6 until the preset termination condition is met. For example, the algorithm is terminated when the number of evolutionary generations reaches 1000, or when the fitness value of the best individual changes by less than 0.0001 for 100 consecutive generations.

[0127] The optimal individual at the time of algorithm termination is recorded as the final optimization result. For example, the optimal individual at the time of termination is [0.32, 0.78, 0.64, 0.55, 0.48, 0.92], and the corresponding process parameter combination is [pH = 6.8, M = 89%, T = 87 ° C, P = 0.55 MPa, R = 2.4: 1, Q = 96%].

[0128] Step 8: Result analysis and application

[0129] The objective function values ​​and process parameters before and after optimization were compared to evaluate the optimization effect. For example, the objective function value before optimization was 0.25, and the process parameters were [pH = 7.2, M = 80%, T = 95 ° C, P = 0.2 MPa, R = 4: 1, Q = 93%]; the objective function value after optimization was reduced to 0.08, and the process parameters were adjusted to [pH = 6.8, M = 89%, T = 87 ° C, P = 0.55 MPa, R = 2.4: 1, Q = 96%], and the product quality and yield were significantly improved.

[0130] Apply the optimized process parameter combination to actual production and conduct necessary test verification. For example, in the pilot and pilot stages, conduct multiple batches of trial production according to the optimized parameter combination to evaluate the stability and consistency of product quality. If the test results meet the requirements, they can be promoted and applied in large-scale production; otherwise, further optimization and adjustment of parameters are required.

[0131] Example 4

[0132] After optimizing the composition and preparation method of oxytocin injection, the oxytocin injection of the present invention and the reference preparation ( Specification: 10 USP Units / ml, active ingredient: oxytocin 10 units, containing chlorobutanol 5 mg / ml, pH 3.5) to investigate the content of related substances.

[0133] The relevant substances were determined by reference to high performance liquid chromatography (General Chapter 0512 of the Chinese Pharmacopoeia 2020 Edition).

[0134] Take the oxytocin injection prepared in Example 2 of the present invention.

[0135] Control solution: Accurately measure 1 ml of the reference preparation solution, place it in a 100 ml volumetric flask, dilute to the scale with 0.1% acetic acid solution, and shake well.

[0136] System suitability solution: Take appropriate amounts of oxytocin and impurity II, III and V reference substances and dilute them with 0.1% acetic acid solution to prepare a solution containing approximately 20ug of oxytocin and 0.4ug of each impurity per 1ml.

[0137] Chromatographic conditions: octadecylsilane bonded silica gel as filler (ZORBAX SB C18 4.6*250mm 3.5μm or chromatographic column with equivalent performance); 0.05mol / L sodium dihydrogen phosphate solution (adjusted to pH 6.0 with sodium hydroxide) as mobile phase A, acetonitrile as mobile phase B, gradient elution according to Table 2; flow rate 1.0ml per minute; detection wavelength 220nm; column temperature 35℃; injection volume 100μl.

[0138] Table 2 Gradient elution conditions

[0139] Time (minutes) Mobile phase A (%) Mobile phase B (%) 0 85 15 30 70 30 40 70 30 40.1 85 15 45 85 15

[0140] System suitability requirements: In the system suitability solution chromatogram, the separation between the oxytocin peak and each impurity peak should be no less than 1.5.

[0141] Determination method: Accurately measure the control solution and the injection of the present invention, inject them into the liquid chromatograph respectively, and record the chromatogram.

[0142] Limit: If there are chromatographic peaks with the same retention time as impurities II, III and V in the injection chromatogram of the present invention, the impurities II and III shall not exceed 2 times (2.00%) the main peak area of ​​the control solution according to the corrected peak area (multiplied by the correction factor 1.4), the impurity V shall not exceed 2 times (2.00%) the main peak area of ​​the control solution according to the corrected peak area (multiplied by the correction factor 1.6), the peak area of ​​other single impurities shall not exceed 2 times (2.00%) the main peak area of ​​the control solution, and the sum of the peak areas of each impurity shall not exceed 5 times (5.00%) the main peak area of ​​the control solution according to the corrected peak area. Peaks in the injection chromatogram of the present invention that are less than 0.05 times the main peak area of ​​the control solution are ignored.

[0143] The results are shown in Tables 3, 4 and Figure 1-Figure 4 The oxytocin injection of the present invention is significantly superior to the foreign reference preparation in terms of related substances at 25°C.

[0144] Table 3 Oxytocin injection test results

[0145]

[0146] Table 4 Test results of reference preparations

[0147]

[0148] In summary, the oxytocin injection of the present invention can be stably stored at room temperature, has a low content of impurities, and has low drug toxicity, thereby significantly reducing storage costs.

[0149] Although some specific embodiments of the present invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It will also be appreciated by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A method for preparing an oxytocin injection that can be stored at room temperature, characterized in that: The steps include: Step 1: Prepare 200 units / ml oxytocin solution; Step 2: prepare 1 mol / L acetic acid solution for later use; Step 3: prepare 1% ammonia solution for later use; Step 4: Weigh water for injection, add mannitol, 1% ammonia solution, glacial acetic acid and oxytocin solution, stir and mix evenly, add acetic acid to adjust the pH value to 3.9±0.2, add water to the full amount of oxytocin injection solution, and filter through a filter membrane to obtain; Among them, a combination of static mixer and dynamic mixer is introduced for mixing; A constant pH control strategy is introduced to monitor the pH value of the reaction system in real time through an online pH meter, and the dripping speed of ammonia water is dynamically adjusted in combination with a PID control algorithm. The specific control function is as follows: In the formula, is the ammonia droplet acceleration at time t, pH(t) is the pH value at time t, pH set is the target pH value, K p , K i , K d are proportional, integral and differential control coefficients respectively; A mixing function is established between the mixing degree M and the Reynolds number Re, the mixing time t and the mixer structure parameter λ. The specific relationship between the mixing degree M and the Reynolds number Re, the mixing time t and the mixer structure parameter λ is: M=1-exp [ -α× ( Re×t ) β ×λ γ] ; M is the degree of mixing, Re is the Reynolds number, t is the mixing time, λ is the mixer structural parameter, α, β, γ are empirical coefficients; Based on the mixing degree function, the mixing degree deviation function between the mixing degree and the pH deviation value PHZ is determined by data fitting, and the empirical relationship between the mixing degree M and the pH deviation ΔpH is obtained by fitting the experimental data: ΔpH=f(M)=a×M^b+c In the formula, a, b, and c are fitting coefficients; Based on the pH control strategy, the mixing degree deviation function and the mixing degree deviation function determine the collaborative pH control strategy, which specifically includes: on the basis of constant pH control, introducing feedback correction of the mixing degree: according to the real-time measured pH deviation ΔpH, the current mixing degree M is calculated through the relationship between the mixing degree and the pH deviation; then, the mixing degree M is substituted into the relationship between the mixing degree and the operating parameters, and the operating conditions of the mixer are dynamically adjusted to realize automatic optimization control of the mixing process.

2. The method for preparing the oxytocin injection that can be stored at room temperature as claimed in claim 1, characterized in that: The step 1 comprises weighing 17.96 mg of oxytocin raw material, adding 50 ml of injection water, and shaking to dissolve to obtain a 200 unit / ml oxytocin solution.

3. The method for preparing the oxytocin injection that can be stored at room temperature as claimed in claim 1, characterized in that: In step 4, the amount of water for injection is 450 g, the amount of mannitol added is 0.86 g, the amount of 1% ammonia solution is 3.65 g, the amount of glacial acetic acid is 0.89 g and the amount of oxytocin solution is 12.5 mL.

4. The method for preparing the oxytocin injection that can be stored at room temperature as claimed in claim 3, characterized in that: The filter membrane is a 0.22 μm PES filter membrane.

5. An oxytocin injection solution that can be stored at room temperature and is prepared by the preparation method according to any one of claims 1 to 4, characterized in that: The oxytocin injection comprises oxytocin, a pH regulator, ammonia water, mannitol and water for injection; the content of the oxytocin is 1-10 units / mL; the content of the mannitol is 0.1-3 mg / mL; the content of the ammonia water is 0.01-0.3 mg / mL; and the pH regulator is glacial acetic acid or acetic acid.

6. The oxytocin injection solution that can be stored at room temperature as claimed in claim 5, characterized in that: The content of oxytocin is 5 units / mL; the content of mannitol is 1.72 mg / mL; and the content of ammonia water is 0.14 mg / mL.

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