Lipid composition, lipid nanoparticle, preparation method of lipid nanoparticle, pharmaceutical composition, pharmaceutical preparation and application

Abstract

The invention discloses a lipid composition, lipid nanoparticles, a preparation method of the lipid nanoparticles, a pharmaceutical composition, a pharmaceutical preparation and application. The lipid composition is prepared from a cationic lipid SM-102, a cationic lipid NT-O14B, cholesterol, distearoyl phosphatidylcholine (DSPC) and a phospholipid polyethylene glycol (PEG) derivative. The invention provides the lipid composition which can be applied to preparation of lipid nanoparticles for brain delivery, through optimal combination of specific lipid components, the efficiency of penetrating through the blood brain barrier during LNP drug delivery is improved, reliable technical support is provided for application of siRNA drugs in nervous system diseases, and the lipid composition has the application prospect of promoting treatment of the nervous system diseases.

Description

Technical Field

[0001] The present application relates to the field of drug delivery and bio-nanomaterials, and specifically to a lipid composition, lipid nanoparticles and preparation methods thereof, a pharmaceutical composition, a pharmaceutical preparation and applications. Background Art

[0002] Neurological diseases, including Alzheimer's Disease (AD), Parkinson's Disease (PD), and Glioblastoma Multiforme (GBM), are complex and difficult to cure, which imposes a significant burden on patients' quality of life and social medical resources. In recent years, RNA-based therapeutics have shown broad prospects in the treatment of neurological diseases. In particular, new drugs such as small interfering RNA (siRNA) and messenger RNA (mRNA) have demonstrated their ability to inhibit a variety of nervous system targets in basic research. However, this type of RNA drug is easily degraded and difficult to penetrate the blood-brain barrier (BBB), and faces many bottlenecks in practical applications.

[0003] Lipid nanoparticles (LNP) are a drug delivery carrier platform that has attracted much attention in recent years, especially in the delivery of RNA drugs. The design of LNP usually contains a combination of multiple lipid components, such as ionized lipids, co-lipids and polyethylene glycol (PEG) modified lipids (PEG-lipids). By adjusting the ratio and structure of these components, LNP can achieve a longer circulation time, low toxicity and higher targeting in vivo.

[0004] However, although LNPs have shown efficient delivery capabilities in the treatment of infectious diseases and certain cancers, there are still significant challenges in the treatment of neurological diseases, especially in brain drug delivery. The blood-brain barrier is a natural physiological barrier of the brain and is highly selective for the penetration of most drugs (especially macromolecules and nanodrugs). At present, the distribution ratio of LNP-based RNA drugs in the brain after intravenous injection is usually less than 1%, which greatly limits the therapeutic effect of the drug. Therefore, improving the distribution efficiency of LNP in the brain has become a technical bottleneck in this field.

[0005] To this end, relevant research has developed strategies such as cationic lipid development, formulation optimization, and targeted ligand modification to enhance the brain delivery ability of LNP. For example, in recent years, some studies have screened specific ionized lipid combinations to enhance the uptake rate of LNP in glioblastoma cells; in addition, some researchers have also used the addition of brain-targeted peptides to enhance its efficiency in penetrating the BBB. These studies have demonstrated the potential of LNP in brain delivery to a certain extent, but overall, current research results show that the delivery efficiency of conventionally designed LNPs in the brain is still not high, and the problem of low accumulation in the brain has not been effectively solved.

[0006] For example, the brain delivery efficiency of some LNPs in existing technologies is generally less than 1%, which is far from meeting the clinical requirements for efficient treatment of brain diseases.

[0007] Therefore, there is an urgent need to develop LNPs with new formulations in this field. Summary of the invention

[0008] Based on this, it is necessary to provide at least one lipid composition, lipid nanoparticles and preparation methods thereof, pharmaceutical compositions, pharmaceutical preparations and applications.

[0009] In a first aspect of the present application, a lipid composition comprising cationic lipid SM-102, cationic lipid NT-014B, cholesterol, distearoylphosphatidylcholine (DSPC) and a phospholipid polyethylene glycol (PEG) derivative is provided.

[0010] In the second aspect of the present application, a lipid nanoparticle is provided which is prepared using the lipid composition as described in the first aspect.

[0011] In the third aspect of the present application, a method for preparing lipid nanoparticles containing nucleic acids is provided, the preparation method comprising: using microfluidic technology to mix an organic phase containing the lipid composition as described in the second aspect and an aqueous phase containing nucleic acids to obtain lipid nanoparticles; wherein the flow ratio of the aqueous phase to the organic phase is (3 to 10):1, and the NP ratio in the mixture is (6 to 8):1.

[0012] In the fourth aspect of the present application, provided are lipid nanoparticles prepared using the preparation method described in the third aspect.

[0013] In the fifth aspect of the present application, a pharmaceutical composition or pharmaceutical preparation is provided, which comprises the lipid nanoparticles as described above.

[0014] In the sixth aspect of the present application, there is provided use of the pharmaceutical composition or pharmaceutical preparation as described in the fifth aspect in the preparation of a drug for treating a nervous system disease.

[0015] The present application provides a lipid composition that can be used to prepare lipid nanoparticles for brain delivery. The lipid composition improves the efficiency of LNP in penetrating the blood-brain barrier when delivering drugs through an optimized combination of specific lipid components, provides reliable technical support for the application of siRNA drugs in neurological diseases, and has an application prospect of promoting the treatment of neurological diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the implementation methods and examples of the present application and to more completely understand the present application and its beneficial effects, the following is a brief introduction to the drawings required for use in the description of the implementation methods or examples. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. It should also be noted that the drawings are all drawn in a simplified form and are only used to conveniently and clearly assist in explaining the present application.

[0017] Figure 1 In one embodiment of the present application, the average particle size and polydispersity index of the prepared LNP are characterized; wherein, "Z-average Diameter" is the Z average diameter, and "Formulation" is the formula.

[0018] Figure 2 In one embodiment of the present application, the encapsulation efficiency of the prepared LNP is characterized; wherein, "Encapsulation Efficiency" refers to the encapsulation rate, and "Formulation" refers to the formula.

[0019] Figure 3 In one embodiment of the present application, the zeta potential of the prepared LNP is characterized; wherein, "Potential" is the potential, and "Normalized Total Counts" is the standardized total counts.

[0020] Figure 4 In one embodiment of the present application, the apparent pKa value of the prepared LNP is characterized.

[0021] Figure 5 This is a fluorescent image in vivo in one embodiment of the present application; wherein, “Cy5 Fluorescence Intensity at 1 Hour Post-Injection” is the Cy5 fluorescence intensity 1 hour after injection.

[0022] Figure 6This is an in vitro fluorescent image in one embodiment of the present application; wherein, “Brain” refers to the brain, “Spleen” refers to the spleen, “Kidney” refers to the kidney, “Heart” refers to the heart, “Lungs” refers to the lungs, “liver” refers to the liver, “High” refers to high, and “Low” refers to low.

[0023] Figure 7 Quantitative analysis of the total signal value of each organ in one embodiment of the present application; data are expressed as mean ± standard error (sem), and the number of samples is n = 3 (bd,i), n = 4 (k), n = 3 or 5 (i). Data were analyzed using one-way analysis of variance (ANOVA) and post hoc Tukey test, ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05, NS indicates no significant difference; wherein, "Brain" is the brain, "Spleen" is the spleen, "Kidney" is the kidney, "Heart" is the heart, "Lungs" is the lung, "liver" is the liver, and "Fluorescence Intensity" is the fluorescence intensity.

[0024] Figure 8 This is a curve of the change of the Cy5 fluorescence signal in the brain within 10 hours in one embodiment of the present application, quantified using IVIS; wherein, “Brain” refers to the brain, and “Post Injection” refers to after injection.

[0025] Fig. 9 This is a curve of the change of the Cy5 fluorescence signal in the liver within 10 hours in one embodiment of the present application, quantified using IVIS; wherein, "Liver" refers to the liver, and "Post Injection" refers to after injection.

[0026] Fig.10 It is the in vitro knockdown efficiency of the PLK1 gene in one embodiment of the present application; wherein, “Level” refers to the level, and “Formulation” refers to the formulation.

[0027] Fig.11 It is the in vivo knockdown efficiency of the PLK1 gene in one embodiment of the present application; wherein, “Level” refers to the level, and “Formulation” refers to the formulation.

[0028] Fig.12 The in vivo fluorescence image of U87-luc glioma in one embodiment of the present application is shown; wherein, "Day" means day.

[0029] Fig.13 The U87-luc glioma MRI image in one embodiment of the present application is shown; wherein, “Day” means day.

[0030] Fig.14 The size of glioma during treatment in one embodiment of the present application is shown. Data are n=5, mean ± sem of biological independent samples. The differences between treatment groups were analyzed using one-way analysis of variance and Tukey's multiple comparison test, ****P<0.0001, ***P<0.001, **P<0.01, *P<0.05, NS represents no significance; where "Bioluminescence" is bioluminescence, and "Post Implantation" is post-implantation.

[0031] Fig.15 The weight changes of mice during treatment in one embodiment of the present application are shown. Data are n=5, mean ± sem of biological independent samples. The differences between treatment groups were analyzed using one-way ANOVA and Tukey's multiple comparison test, ****P<0.0001, ***P<0.001, **P<0.01, *P<0.05, NS represents no significance; where "Body Weight" is body weight, and "Post Implantation" is post-implantation.

[0032] Fig.16 The expression of PLK1 in mouse tumors after the mice were killed on the twelfth day after treatment in one embodiment of the present application is shown; "Protein Expression" refers to protein expression.

[0033] Fig.17 The amount of drug entering the brain in one embodiment of the present application is shown as a percentage of the total injected dose, and the cumulative amount of Cy5-siNC in major organs is quantitatively analyzed; the data are expressed as mean ± standard error (sem), and the number of biological independent samples is n = 3. One-way analysis of variance (ANOVA) and post hoc Tukey's multiple comparison test were used to analyze the data, ****P<0.0001, ***P<0.001, **P<0.01, *P<0.05, NS indicates no significant difference; where "tumor" is tumor, "Brain" is brain, "Spleen" is spleen, "Kidney" is kidney, "Heart" is heart, "Lungs" is lung, and "Uptake" is intake.

[0034] Fig.18 The MRI and in vivo fluorescence simultaneous verification results of different formulations in the comparative example are shown. DETAILED DESCRIPTION

[0035] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present application are given in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used herein in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0037] In this application, unless otherwise specified, "one or more" means any one of the listed items or any combination of the listed items. Similarly, "one or more" and the like, when used in other ways to mean "one or more", are also understood in the same way unless otherwise specified.

[0038] The terms "combination thereof", "any combination thereof", "any combination thereof" and the like used in this application include all suitable combinations of any two or more of the listed items.

[0039] In this application, the word "suitable" in "suitable combination", "suitable method", "any suitable method", etc. is based on the ability to implement the technical solution of this application, solve the technical problems of this application, and achieve the expected technical effects of this application.

[0040] In this application, "further", "further", "particularly", "for example", "such as", "example", "for example", etc. are used for descriptive purposes, indicating that the previous and subsequent different technical solutions are related in terms of the content covered, but should not be understood as a limitation on the previous technical solution, nor can it be understood as a limitation on the protection scope of this application. In this application, unless otherwise specified, A (such as B) means that B is a non-limiting example of A, and it can be understood that A is not limited to B.

[0041] In this application, "optionally", "optional", and "optional" mean optional, that is, any one of the two parallel solutions of "yes" or "no". If there are multiple "optional" in a technical solution, unless otherwise specified and there is no contradiction or mutual restriction, each "optional" is independent. Unless otherwise specified, the descriptions of "optionally include", "optionally contain", etc. in this application, taking "optionally include" as an example, mean "may include or not include".

[0042] The terms "contain", "include" and "include" used in this application are synonymous, which are inclusive or open-ended and do not exclude additional, uncited members or features. Members or features include materials or components, etc.; non-limiting examples of members or features include actions, conditions for the occurrence of actions, timing, states, etc.

[0043] The term "selected from the group consisting of..." used in the present application means including one or more thereof.

[0044] In this application, the technical features or technical solutions described in an open language include closed technical features or technical solutions composed of the listed contents, and also include open technical features or technical solutions containing the listed contents.

[0045] In the present application, exemplary descriptions such as "in some implementation modes (or examples)" and "in one implementation mode (or example)" may include but are not limited to the following meanings: these solutions may be combined with other solutions in a suitable manner to form new technical solutions.

[0046] In the present application, in "the first aspect", "the second aspect", "the third aspect", "the fourth aspect", etc., the terms "first", "second", "third", "fourth", etc. are used only for descriptive purposes and cannot be understood as indicating or implying relative importance or quantity, nor can they be understood as implicitly indicating the importance or quantity of the indicated technical features. Moreover, "first", "second", "third", "fourth", etc. only serve the purpose of non-exhaustive enumeration and description, and it should be understood that they do not constitute a closed limitation on quantity.

[0047] In the present application, when it comes to a numerical interval (i.e., a numerical range), unless otherwise specified, the distribution of the optional numerical values ​​in the numerical interval is considered to be continuous, and includes the two numerical endpoints (i.e., the minimum and maximum values) of the numerical interval, and each numerical value between the two numerical endpoints. Unless otherwise specified, when the numerical interval only refers to an integer in the numerical interval, it includes the two endpoint integers of the numerical range, and each integer between the two endpoints, which is equivalent to directly listing each integer. When multiple numerical ranges are provided to describe features or characteristics, these numerical ranges can be merged. In other words, unless otherwise specified, the numerical range disclosed herein should be understood to include any and all sub-ranges included therein. The "numerical value" in the numerical interval can be any quantitative value, such as a number, a percentage, a ratio, etc. "Numerical interval" allows broadly including numerical interval types such as percentage intervals, ratio intervals, and ratio intervals.

[0048] In this application, if there are multiple steps involved in the method flow, unless there is a clear different description in this document, there is no strict order restriction for the execution of these steps, and they can be executed in other orders than described. Moreover, any step can include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn, alternating or simultaneously with other steps or parts of sub-steps or stages of other steps.

[0049] Unless otherwise specified, the term "siRNA" in this application stands for small interfering RNA (siRNA), which is a type of small molecule RNA with a double-stranded structure, usually composed of 20 to 25 nucleotides. siRNA can bind to specific mRNA in cells and guide its degradation through the RNA interference (RNAi) mechanism, thereby inhibiting gene expression. siRNA has important potential in gene therapy, cancer therapy and other disease treatments, and can achieve targeted interference with specific genes.

[0050] Unless otherwise specified, the term "in vivo fluorescence imaging (IVIS)" in this application stands for in vivo fluorescence imaging, which is a non-invasive imaging technology that uses the distribution of fluorescent markers in the body to perform real-time imaging and quantitative analysis. This method usually uses fluorescent dyes or fluorescent proteins to mark them on targeting molecules to monitor their distribution, transport and targeting effects in the body. IVIS is an effective tool commonly used in biomedical research such as studying drug distribution, gene delivery and tumor targeting.

[0051] Unless otherwise specified, the term "polydispersity index (PDI)" in this application is the full name of polydispersity index, which is an important parameter for measuring the uniformity of nanoparticle dispersion. The value of PDI ranges from 0 to 1. The lower the value, the more uniform the particle size distribution of the particles is, and the closer it is to monodispersity; the higher the value, the wider the particle size distribution is and the more uneven the particles are. For nano drug carrier systems, PDI values ​​are usually used to evaluate the stability and controllability of the system. Low PDI values ​​usually mean that the system has better stability in the body.

[0052] Unless otherwise specified, the term "nanoprecipitation" in this application refers to a method for preparing lipid nanoparticles (LNPs), based on a precipitation process initiated by mixing a solvent and a non-solvent. In the formation mechanism of LNPs, lipid materials are usually dissolved in organic solvents. When mixed with water, due to solubility differences, the lipid materials are rapidly precipitated and self-assembled into nanoparticles. This process has the advantages of being simple and fast, suitable for large-scale preparation, and can effectively encapsulate drugs or gene vectors.

[0053] The term "glioma" in this application is one of the most common malignant tumors in the central nervous system, originating from glial cells. Depending on the cell type and the degree of malignancy, gliomas can be further divided into types such as astrocytomas, oligodendrogliomas and mixed gliomas. Glioma is highly invasive and recurrent, and is difficult to treat, with a poor prognosis. At present, surgery, radiotherapy and chemotherapy are the main treatments, but for its high recurrence and difficult to control characteristics, it is still urgent to develop new targeted therapy strategies.

[0054] Existing technologies lack systematic optimization methods. Current formulation optimization and ligand modification are mostly based on the adjustment of a single factor. There is a lack of comprehensive and systematic optimization solutions, which fail to effectively address the impact of multiple factors on the brain delivery effect of LNP. In addition, formulation optimization is costly. The formulation optimization of existing LNPs usually requires complex experiments and high-cost raw materials, resulting in high production process costs, which limits large-scale applications, especially in clinical transformation.

[0055] This application successfully optimized a LNP formula with significant brain delivery effect through the experimental design (DoE) method. The provided LNP formula greatly improves the accumulation of LNP in the brain, significantly improves the delivery efficiency of the drug, and overcomes the problems of low brain drug accumulation and insufficient delivery efficiency in the prior art. The accumulation of the provided LNP formula in the brain is significantly higher than that of the traditional formula, showing a stronger ability to cross the blood-brain barrier.

[0056] In the first aspect of the present application, a lipid composition is provided, which comprises a cationic lipid SM-102, a cationic lipid NT-O14B, cholesterol, distearoylphosphatidylcholine (DSPC) and a phospholipid polyethylene glycol (PEG) derivative; wherein the molar ratio of SM-102, NT-O14B, cholesterol, DSPC and the phospholipid PEG derivative in the lipid composition is: (25-33):(5-15):(45-55):(8-15):(1-2); the phospholipid PEG derivative comprises one or more of DMG-PEG, DSG-PEG, DMPE-PEG, DPPE-PEG and DSPE-PEG.

[0057] Compared to known monocationic lipid formulations, the lipid composition in the present application comprises dicationic lipids.

[0058] Without wishing to be bound by any theory, it is believed that as long as the relative amount of lipids meets the above ratio, the lipid composition or LNP formulation can be used to prepare LNPs that show a stronger ability to cross the blood-brain barrier than known LNPs. Exemplarily, the data of the inventors using the following three formulations C1, C2 and C3 show that the three formulations take into account biosafety, high efficiency of gene silencing in vitro and in vivo, significant inhibition of tumor growth (such as glioblastoma, etc.), and a high proportion of drug delivery to the brain (for example, more than 2% of the total dose of siRNA can be delivered, which is much higher than the 0.2% or less of known LNPs) and other effects.

[0059] In some embodiments, the molar ratio of SM-102, NT-014B, cholesterol, DSPC and phospholipid PEG derivative in the lipid composition is: 30.4:7.6:50.42:10.08:1.5.

[0060] In some embodiments, the molar ratio of SM-102, NT-014B, cholesterol, DSPC and phospholipid PEG derivative in the lipid composition is: 27.74:10.26:48.4:12.1:1.5.

[0061] In some embodiments, the molar ratio of SM-102, NT-014B, cholesterol, DSPC and phospholipid PEG derivative in the lipid composition is: 32.3:5.7:51.8:8.64:1.5.

[0062] In a second aspect of the present application, there is provided an LNP prepared using the lipid composition as described in the first aspect.

[0063] In a third aspect of the present application, a method for preparing LNP containing nucleic acid is provided, comprising:

[0064] Using microfluidic technology, an organic phase containing the lipid composition as described in the first aspect and an aqueous phase containing nucleic acids are mixed to obtain LNP; wherein the flow ratio of the aqueous phase to the organic phase is (3-10):1, and the nitrogen-phosphorus (NP) ratio in the mixture is (6-8):1.

[0065] Among them, the NP ratio refers to the ratio of the number of positive charges to negative charges, which has an important influence on the stability, potential and other properties of nanoparticles. In LNP, the positive charge is usually provided by cationic lipids with ionizable ammonium groups (N), while the negative charge is provided by nucleic acid molecules with a large number of phosphate groups (P). The two are combined together by electrostatic adsorption to form stable nanoparticles.

[0066] Without wishing to be bound by any theory, it is believed that when the NP ratio is (6-8):1, the prepared LNPs have a more reasonable particle size range and better stability.

[0067] In some embodiments, the NP ratio is 7:1.

[0068] When LNPs are prepared using microfluidic technology, when the flow times of the aqueous phase and the organic phase are the same, the flow ratio can also be understood as a volume ratio.

[0069] In some embodiments, the flow ratio is 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, or a range or value between any two values. In some embodiments, the flow ratio is (5-7):1.

[0070] The nucleic acid in the present application includes siPLK1.

[0071] In some embodiments, LNPs are prepared using a microfluidic mixer.

[0072] Microfluidic mixers are devices used to achieve fluid mixing in microfluidic systems. They are widely used in biomedical diagnostic research, DNA analysis, chemical synthesis, and genomics analysis. In microfluidic systems, due to the small channel size, the fluid flow state is laminar rather than turbulent. In the laminar state, mixing is mainly achieved by molecular diffusion.

[0073] LNPs can be prepared using a microfluidic mixer according to conventional methods in the art.

[0074] In some embodiments, the preparation method comprises:

[0075] The organic phase and the aqueous phase are mixed in a microfluidic chip to obtain LNP.

[0076] In some embodiments, the preparation method further comprises: using a third channel to introduce a buffer to dilute the LNP.

[0077] The buffer used can be a conventional buffer in the art. In some embodiments, the buffer is selected from the group consisting of citric acid solution, Tris buffer, sodium acetate buffer and PBS solution. Exemplarily, the buffer is citric acid solution, Tris buffer, sodium acetate buffer, or PBS solution.

[0078] In some embodiments, the flow ratio of the buffer to the total flow of the aqueous phase and the organic phase is (0.5-5): 1. Without wishing to be bound by any theory, it is found that when the flow ratio is not within this range, the stability of the LNP may be affected.

[0079] In some embodiments, the flow ratio of the aqueous phase, the organic phase and the buffer is (5-7):1:(5-7).

[0080] In a fourth aspect of the present application, provided is LNP prepared by the preparation method described above.

[0081] In some embodiments, the LNP satisfies one or more of the following conditions 1)-5):

[0082] 1) The average hydrated particle size is 60nm to 120nm;

[0083] 2) The polydispersity coefficient is less than 0.1;

[0084] 3) Zeta potential is around -20mV; for example -30 to -10mV;

[0085] 4) an apparent pKa value of 6-7; and

[0086] 5) The encapsulation efficiency is above 90%.

[0087] In some embodiments, the average hydrated particle size is measured by DLS. DLS is the particle size of the composite particles in solution, i.e., the hydrated particle size (hydrodynamic diameter).

[0088] In some embodiments, the average hydrodynamic diameter of the LNP is equivalent to a particle size of 60 nm, 70 nm, 80 nm, 90 nm, 100 nm, 110 nm, 120 nm, or a range or value between any two values. In some embodiments, the average hydrodynamic diameter of the LNP is equivalent to a particle size of 90 nm-120 nm.

[0089] Unless otherwise specified, the term "apparent pKa value" in the present application refers to the pH value at which ionizable lipids in LNP are protonated by 50%.

[0090] The average hydrodynamic diameter in the present application can be measured, for example, by DLS (Dynamic Light Scattering) technology.

[0091] In some embodiments, the zeta potential of the LNP is -30mV, -29mV, -28mV, -27mV, -26mV, -25mV, -24mV, -23mV, -22mV, -21mV, -20mV, -19mV, -18mV, -17mV, -16mV, -15mV, -14mV, -13mV, -12mV, -11mV, -10mV, or a range or value between any two values. In some embodiments, the zeta potential of the LNP is -16.13mV, -18.98mV, or -24.47mV.

[0092] In the fifth aspect of the present application, a pharmaceutical composition or pharmaceutical preparation is provided, which comprises the LNP as described above.

[0093] In some embodiments, the pharmaceutical preparation is in an intravenous injection form.

[0094] In some embodiments, the delivery dose of the pharmaceutical preparation is 1 mg to 2.5 mg siRNA / kg subject weight. Exemplarily, the delivery dose is 1 mg siRNA / kg subject weight, 1.5 mg siRNA / kg subject weight, 2 mg siRNA / kg subject weight, 2.5 mg siRNA / kg subject weight, or a range or value between any two values.

[0095] In a sixth aspect of the present application, there is provided use of the LNP as described above or the pharmaceutical composition or pharmaceutical preparation as described above in the preparation of a drug for treating a nervous system disease.

[0096] In some embodiments, the pharmaceutical formulation delivers a dose of 1 mg to 2.5 mg siRNA / kg body weight of the subject.

[0097] In some embodiments, the concentration of the lipid nanoparticles in the pharmaceutical preparation is 0.01 mg / mL to 8 mg / L. Exemplarily, the concentration of the lipid nanoparticles in the pharmaceutical preparation is 0.01 mg / mL, 0.02 mg / mL, 0.05 mg / mL, 0.07 mg / mL, 0.09 mg / mL, 1 mg / mL, 1.5 mg / mL, 2 mg / mL, 2.5 mg / mL, 3 mg / mL, 3.5 mg / mL, 4 mg / mL, 4.5 mg / mL, 5 mg / mL, 5.5 mg / mL, 6 mg / mL, 6.5 mg / mL, 7 mg / mL, 7.5 mg / mL, 8 mg / mL, or a range or value between any two values.

[0098] Unless otherwise specified, the term "neurological disease" in this application includes diseases of the brain, spinal cord, peripheral nerves and other nervous systems. Common types include neuritis, neuralgia, neuroma and neurodegenerative diseases.

[0099] In some embodiments, the neurological disease is selected from the group consisting of glioma, Parkinson's disease, Alzheimer's disease and epilepsy.

[0100] Some examples are provided below.

[0101] The embodiments of the present application will be described in detail below in conjunction with examples. It should be understood that these examples are only used to illustrate the present application and are not intended to limit the scope of the present application. The experimental methods for which the conditions are not specified in the following examples are preferably referred to the guidance given in the present application, and can also be based on the experimental manual or normal conditions in the art, or can also be based on the conditions recommended by the manufacturer, or with reference to experimental methods known in the art.

[0102] The lipids used in the following examples, NT-O14B was purchased from MCE, and the other lipids were purchased from Sigma.

[0103] In the following examples, the excitation light for in vivo fluorescence imaging is 630 nm, and the emission light is 699 nm.

[0104] Example 1 Preparation of LNP and Effect Determination

[0105] LNP includes five lipid components: SM-102, NT-O14B, Chol (cholesterol), DSPC and DMG-PEG. The present application obtains LNP formulations with significant brain delivery effects through DoE, exemplarily including formulation C1 (the molar ratio of SM-102, NT-O14B, Chol, DSPC and DMG-PEG is 27.74:10.26:48.4:12.1:1.5), formulation C2 (the molar ratio of SM-102, NT-O14B, Chol, DSPC and DMG-PEG is 30.4:7.6:50.42:10.08:1.5) and formulation C3 (the molar ratio of SM-102, NT-O14B, Chol, DSPC and DMG-PEG is 32.3:5.7:51.8:8.64:1.5).

[0106] According to the composition of the above formula C1, formula C2 and formula C3, SM-102, NT-O14B, Chol, DSPC and DMG-PEG lipid molecules were dissolved in ethanol, and the aqueous phase containing siRNA (such as: siNC-Cy5 or siPLK1) was diluted in citric acid buffer (50mM, pH 4), and the two-phase solution was mixed. The microfluidic mixer was prepared by 3D printing. After the LNP was synthesized in the microfluidic chip, the third PBS channel was introduced to quickly dilute the LNP. All solutions were pumped into the micromixer through a precision syringe pump (Chippump-4, Suzhou Jingxin Technology Co., Ltd.) and ensured to be completely mixed under an inverted microscope (MI52-N, Mingmei, China). Here, the lipid molecules will nanoprecipitate from the solubility to supersaturation to form lipid nanoparticles. The particle size range of the obtained LNP is 60nm-90nm, and the PDI is 0.01-0.10.

[0107] In all preparations, the aqueous phase solution, ethanol solution and PBS solution were mixed at a fixed flow rate ratio of 5:1:5, and the product was collected from the outlet. To purify the product, it was dialyzed against 1X PBS to remove ethanol and then concentrated using a 30K MWCO centrifugal filter. Subsequently, the product was filtered through a 0.22 μm filter. The physical properties of the three purified formulations were characterized, including the average particle size and polydispersity index (PDI) ( Figure 1 ), encapsulation efficiency ( Figure 2 )、zeta potential( Figure 3 ) and the apparent pKa value ( Figure 4 ) etc. The results showed that the particle size of LNP was between 80nm and 120nm, and the PDI was well maintained between 0.01 and 0.10. The prepared LNP was stored at 4°C for future use.

[0108] In addition, several commercial LNPs were prepared according to the above method for comparison with the LNPs in this application. One was a SM-102 LNP formula (SM-102: Chol: DSPC: DMG-PEG = 50: 38.5: 10: 1.5, hereinafter referred to as O2). The LNPs synthesized according to the above method had a particle size of about 78 mm and a PDI of less than 0.1.

[0109] Effect measurement

[0110] Four-week-old female Balb / c nude mice were purchased from Hangzhou Ziyuan Experimental Animal Technology Co., Ltd. To establish an orthotopic U87MG-luc glioblastoma mouse model, 2.5 μL of PBS suspension containing 5×10^4 U87MG-luc cells was stereotaxically injected into the striatum of the mouse brain. Seven days after tumor implantation, 150 mg / kg of D-luciferin potassium salt was injected intraperitoneally into the mice. All mice receiving tumor transplantation were confirmed for tumor implantation and progression using the Tanon ABL X5 in vivo fluorescence imaging system.

[0111] 1) Distribution of LNP

[0112] Biodistribution of LNPs in tumor-free mice

[0113] Different formulations of Cy5-siNC / LNP were injected into Balb / c nude mice via the tail vein, and each mouse received a siRNA dose of 0.8 mg / kg. After injection, fluorescence imaging was performed at specific time points. Organs were then removed and subjected to fluorescence imaging.

[0114] Biodistribution of LNPs in tumor-bearing mice

[0115] Cy5-siNC / LNP of different preparations were injected into tumor mice through the tail vein, and each mouse received a siRNA dose of 0.8 mg / kg. Subsequently, the mice were killed after 1 hour, and the brain tissue of some mice was collected for in vitro bioluminescence and fluorescence imaging (using IVIS). The main organs were removed for quantitative analysis of tissue-specific Cy5-siNC. Specifically, after the tissue was fully homogenized in 0.8 mL of 1% Triton X-100, 0.8 mL of DMSO was added and incubated overnight at room temperature. Then centrifuged at 15,000 rpm for 30 minutes, the supernatant was collected, and the fluorescence intensity of Cy5 was measured using a microplate reader (λex=635 nm, λem=680 nm). The fluorescence intensity of the tissue was standardized to the total injection dose to calculate the percentage of tissue fluorescence intensity to the injection dose, and then the percentage of injection dose per gram of tissue (%ID / g) was calculated.

[0116] The distribution of formula C2 and control group O2 (SM-102 LNP formula, SM-102: Chol: DSPC: DMG-PEG = 50: 38.5: 10: 1.5) in major organs was qualitatively compared through in vivo and in vitro fluorescence images and quantitative analysis of the total signal value of each organ. The results showed that in the case of encapsulating siNC-Cy5 siRNA, the brain fluorescence intensity of the mouse group of the present application LNP was significantly stronger than that of the control group ( Figure 5 , Figure 6); The mice were killed, the brains were taken out for homogenization, and then the clear solution was taken to measure the fluorescence value of Cy5. The fluorescence value of the LNP in the present application was higher ( Figure 7 In addition, the change curve of Cy5 fluorescence signal in brain and liver within 10 hours was quantified using IVIS (see Figure 8 and Fig. 9 ).

[0117] 2) In vitro gene silencing

[0118] U87MG cells were cultured in DMEM containing 10% FBS for 24 h. Subsequently, the culture medium was replaced with new culture medium containing siPLK1 or siPLK1 / LNPs with a final siRNA concentration of 300 nM. After 48 h of incubation, the cells were washed with PBS, and total RNA was extracted using an animal RNA extraction kit (GenStar) according to the instructions. cDNA synthesis was performed using an RT kit (Sparkjade). Quantitative RT-PCR reactions were monitored on a LightCycler 96 Real-Time PCR System (Roche). The relative expression of PLK1 was calculated using the 2-ΔΔCt method and normalized to GAPDH. Primer sequences were provided by Generalbiol and are listed in Table 1. In vitro knockdown efficiency is shown in Fig.10 , the results showed that the optimal formulation could knock down PLK1 mRNA to more than 50%.

[0119] Table 1 Primers used for PCR and their sequences

[0120] Primer Sequence(5′to3′) SEQ ID NO: GAPDH-Forward TTCACCACCATGGAGAAGGC 1 GAPDH-Reverse GGCATGGACTGTGGTCATGA 2 PLK1-Forward CACCAGCACGTCGTAGGATTC 3 PLK1-Reverse CCGTAGGTAGTATCGGGCCTC 4

[0121] 3) In vivo gene silencing

[0122] Bioluminescence imaging was performed on the U87MG-Luc orthotopic glioblastoma mouse model to confirm tumor establishment on day 7 after tumor implantation. The in vivo knockdown efficiency of LNPs with different formulations is shown in Fig.11 ,The results showed that the knockdown efficiency of LNP prepared by C2 was better, reaching about 50%.

[0123] The mice were randomly divided into groups. The control group received PBS injection, while the siPLK1 / C2 group received siRNA (concentrated LNP, concentration 6mg / mL) intravenously every two days at a dose of 2.5mg / kg for a total of 3 times. On day 12, the mice were sacrificed and brain tumor tissues were collected for RT-qPCR ( Fig.16). For Western blot analysis, tumor tissues were lysed using RIPA lysis buffer (Glpbio) according to the instructions. Protein concentration was determined using a BCA protein quantification kit (Pierce). Protein samples were separated on a 12% SDS-PAGE gel and transferred to a polyvinylidene difluoride membrane (Sigma). After blocking the membrane with protein-free rapid blocking solution (ACE) for 30 minutes at room temperature, it was incubated with a rabbit anti-PLK1 antibody (Zenbio) at 4°C overnight. After washing with PBS containing 0.1% Tween-20, the membrane was incubated with a secondary antibody. Beta-actin (Affinity) was used as an internal reference.

[0124] 4) In vivo anti-glioblastoma efficacy

[0125] Bioluminescence imaging of the U87MG-Luc orthotopic glioblastoma mouse model was performed on day 7 after tumor implantation. The mice were then randomly divided into 2 groups (injected with PBS, free siPLK1). Each mouse was injected with siRNA intravenously every two days at a dose of 2.5 mg / kg for a total of 9 times, and body weight was recorded throughout the experiment ( Fig.15 Tumor progression was monitored by bioluminescent imaging using IVIS on days 13, 19, and 25. Fig.13 The data on glioma size during treatment are shown in Fig.14 As shown, it can be seen that the optimized formula can significantly inhibit the growth of tumors.

[0126] MRI experiments were performed on days 9 and 23 using a Bruker 9.4T MRI scanner with a custom-made mouse head coil. Anatomical images were acquired using the RARE sequence (TR = 5000 ms, TE = 27.52 ms, EchoSpacing = 6.881 ms, RAREFactor = 8, FOV = 16 mm × 16 mm, image size = 192 × 192, slice thickness = 0.5 mm, average times = 5). Animal survival was monitored throughout the treatment period. To assess biosafety, three additional mice from each group were sacrificed after three injections. Blood samples were collected for biochemical analysis, and major organs (brain, liver, heart, and kidney) were removed. Brain tumor tissues were stored at -80°C for subsequent Western blot analysis to assess the effect of PLK1 gene silencing. Liver, heart, and kidney tissues were fixed with 4% paraformaldehyde, embedded in paraffin, and sliced ​​at a thickness of 4 μm. The sections were mounted on slides and stained with hematoxylin-eosin (H&E). The results showed no obvious pathological damage in the heart, liver, and kidneys, demonstrating the biosafety of LNP ( Fig.13 ).

[0127] In addition, the amount of drug delivered to the brain relative to the total injected dose was determined. The major organs of the sacrificed mice were collected and homogenized, and the supernatant was subsequently analyzed to determine the Cy5-siNC content, expressed as a percentage of the injected dose per gram of tissue (% ID / g). Fig.17 As shown, the optimized C2 formula showed significant differences in brain siRNA delivery compared with the control group O2 formula. As a classic LNP formula currently widely used for liver delivery, the O2 formula has an siRNA delivery efficiency of only about 0.2% in the brain. In contrast, the optimized C2 formula in this application achieved a brain delivery efficiency of about 2%, an increase of 10 times. In addition, in the delivery of major uptake organs such as the liver and spleen, the efficiency of the C2 formula was lower than that of the O2 formula, indicating that its organ distribution pattern in the body has changed. In non-major delivery organs such as the heart and lungs, there was no significant difference in the delivery efficiency of the two formulas.

[0128] Comparative Example

[0129] Other formulations (see Table 2) were used to prepare LNPs.

[0130] Table 2

[0131] formula SM-102 NT-O14B cholesterol DSPC DMG-PEG Comparison formula 1 40 10 38.8 9.7 1.5 Comparison formula 2 45 5 40.42 8.083 1.5 Comparison formula 3 40 10 41.57 6.929 1.5 Comparison formula 4 35 15 40.42 8.083 1.5

[0132] The above formulation was prepared into LNP by the method in Example 1, and MRI and in vivo fluorescence synchronous verification were performed. The results were as follows Fig.18 As shown, comparative formula 1 to comparative formula 4 are several formulas that form a typical contrast with C2. In comparative formulas 1 to 4, SM-102 is high and cholesterol is low. Compared with formula C2, these comparative formulas 1-4 have significantly lower brain siRNA delivery efficiency, which fully demonstrates the key role of formula parameter ratio optimization in improving brain delivery efficiency.

[0133] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims, and the description and drawings may be used to interpret the contents of the claims.

Claims

1. A lipid composition, characterized in that It contains cationic lipid SM-102, cationic lipid NT-O14B, cholesterol, distearoylphosphatidylcholine (DSPC) and phospholipid polyethylene glycol (PEG) derivatives; The molar ratio of SM-102, NT-O14B, cholesterol, DSPC and phospholipid PEG derivative in the lipid composition is: (25-33): (5-15): (45-55): (8-15): (1-2); The phospholipid PEG derivatives include one or more of DMG-PEG, DSG-PEG, DMPE-PEG, DPPE-PEG and DSPE-PEG.

2. The lipid composition according to claim 1, wherein The molar ratio of SM-102, NT-O14B, cholesterol, DSPC and phospholipid PEG derivative in the lipid composition is (27.74-32.3):(5.7-10.26):(48.4-51.86):(8.64-12.1):(1.4-1.6); Optionally, the molar ratio of SM-102, NT-O14B, cholesterol, DSPC and phospholipid PEG derivative in the lipid composition is 30.4:7.6:50.42:10.08:1.5; The molar ratio of SM-102, NT-O14B, cholesterol, DSPC and phospholipid PEG derivative in the lipid composition is 27.74:10.26:48.4:12.1:1.5, or, The molar ratio of SM-102, NT-O14B, cholesterol, DSPC and phospholipid PEG derivative in the lipid composition is 32.3:5.7:51.86:8.64:1.

5.

3. A lipid nanoparticle prepared using the lipid composition as described in claim 1 or 2.

4. A method for preparing lipid nanoparticles containing nucleic acid, characterized in that: The preparation method comprises: Using microfluidic technology, an organic phase containing the lipid composition according to claim 1 or 2 and an aqueous phase containing nucleic acids are mixed to obtain lipid nanoparticles; wherein the flow ratio of the aqueous phase to the organic phase is (3-10):1, and the nitrogen-phosphorus (NP) ratio in the mixture is (6-8):1; optionally, the flow ratio is (5-7):1; Optionally, the nucleic acid comprises one or more of siPLK1, siFluc, siEGFR and siPDL1.

5. The preparation method according to claim 4, characterized in that: Preparation of lipid nanoparticles using a microfluidic mixer; The preparation method comprises: Mixing the organic phase and the aqueous phase in a microfluidic chip to obtain lipid nanoparticles; Optionally, the preparation method further comprises: using a third channel to introduce a buffer to dilute the lipid nanoparticles; the buffer may be one or more of a citric acid solution, a Tris buffer, a sodium acetate buffer and a PBS solution; Further optionally, the flow ratio of the buffer solution to the total flow of the aqueous phase and the organic phase is (0.5-5):

1.

6. The lipid nanoparticles prepared by the preparation method according to claim 4 or 5; Optionally, the lipid nanoparticles satisfy one or more of the following conditions 1) to 5): 1) The average hydrated particle size is 60 nm to 120 nm, and optionally, 90 nm to 120 nm; 2) The polydispersity coefficient is less than 0.1; 3) The zeta potential is about -30mV to -10mV; 4) an apparent pKa value of 6 to 7; and 5) Encapsulation efficiency is greater than or equal to 90%.

7. A pharmaceutical composition or pharmaceutical preparation comprising the lipid nanoparticles according to claim 3 or 6.

8. The pharmaceutical composition or pharmaceutical preparation according to claim 7, characterized in that: The pharmaceutical preparation is in the form of an injection; Optionally, the delivery dose of the pharmaceutical preparation is 1 mg to 2.5 mg siRNA / kg subject body weight; Optionally, the concentration of the lipid nanoparticles in the pharmaceutical preparation is 0.01 mg / mL to 8 mg / L.

9. Use of the lipid nanoparticles according to claim 3 or 6 or the pharmaceutical composition or pharmaceutical preparation according to claim 7 or 8 in the preparation of a medicament for treating a nervous system disease.

10. The use according to claim 9, characterized in that The nervous system disease includes at least one of glioma, Parkinson's disease, Alzheimer's disease and epilepsy.

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