Lipid-based nanoparticle characterization

Abstract

A method for density-based separation of lipid-based nanoparticles by centrifugation includes receiving a container having a solution and lipid-based nanoparticles. The method includes applying centrifugal force to the container to displace the lipid-based nanoparticles and form at least one cluster within the container. The method includes measuring the position of the at least one detectable cluster of lipid-based nanoparticles. In one embodiment, applying centrifugal force to the container also causes the solution to form a density gradient.

Description

[Background technology]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to and benefit of U.S. Provisional Application No. 63 / 476,048, filed December 19, 2022, which was filed as PCT International Application No. 192333, the disclosure of which is hereby incorporated by reference in its entirety.

[0002] Lipid nanoparticles have attracted attention throughout the pharmaceutical industry as a promising means for the delivery of various therapeutic agents, such as mRNA vaccines. Lipid nanoparticles play an important role in effectively protecting and transporting mRNA into cells. Lipid nanoparticles can be synthetically produced or derived from biological sources. Illustrative examples of lipid nanoparticles include extracellular vesicles, which are small lipid-encapsulated carriers of bioactive proteins, lipids, and nucleic acids.

[0003] Lipid nanoparticles consist of a lipid layer or shell. For example, lipid nanoparticles can be made of a lipid bilayer, which forms a spherical structure and provides an interior space where a therapeutic payload can be loaded.

[0004] Lipid nanoparticles can be loaded with a wide variety of therapeutic payloads, such as messenger ribonucleic acid (mRNA), small interfering RNA (siRNA), plasmids, proteins and peptides, and small molecules. Characterizing lipid nanoparticles containing therapeutic payloads presents significant challenges. Summary of the Invention [Means for solving the problem]

[0005] In general, the present disclosure relates to analyzing nanoparticles, including the loading efficiency of such particles. In one possible configuration, lipid-based nanoparticles are classified based on payload fullness. Various aspects are described in the present disclosure, including, but not limited to, the following aspects:

[0006] One aspect relates to a system for analyzing a sample of lipid-based nanoparticles designed to carry a payload, the system comprising processing circuitry having one or more non-transitory computer-readable storage media storing instructions that, when executed by the processing circuitry, cause the processing circuitry to rotate a rotor holding at least one container having a density gradient-forming material and a sample of lipid-based nanoparticles, measure the absorbance of light by the sample of lipid-based nanoparticles in the at least one container while the rotor is rotating, and assess the size of the payload carried in the sample of lipid-based nanoparticles based on the absorbance.

[0007] Another aspect relates to a method of analyzing a sample of lipid-based nanoparticles carrying a payload, the method including providing a density gradient-forming material; operating a centrifuge to rotate a rotor about an axis of rotation at a preset rotor speed to form a density gradient in a container having the density gradient-forming material and the sample of lipid-based nanoparticles; measuring light absorbance by the sample of lipid-based nanoparticles in the container; and determining, based on the light absorbance, a ratio of lipid-based nanoparticles having a filled payload to lipid-based nanoparticles having an empty or partially filled payload.

[0008] Another aspect relates to a centrifuge for providing quality control of a lipid-based nanoparticle sample, the centrifuge comprising: a rotor for holding the lipid-based nanoparticle sample; an optical system for measuring the absorbance of light by the lipid-based nanoparticle sample while spinning the rotor; and one or more non-transitory computer-readable storage media programmed to analyze the absorbance of the lipid-based nanoparticle sample once the lipid-based nanoparticle sample is suspended in a density gradient.

[0009] Another aspect relates to a system for analyzing a sample of lipid-based nanoparticles, the system comprising processing circuitry having one or more non-transitory computer-readable storage media storing instructions that, when executed by the processing circuitry, cause the processing circuitry to rotate a rotor about an axis of rotation at a preset rotor speed, the rotor holding at least one container having a density gradient-forming material to be added to the sample of lipid-based nanoparticles, measure light absorbance by the sample of lipid-based nanoparticles in the at least one container, and determine, based on the light absorbance, a ratio of lipid-based nanoparticles having a filled payload to lipid-based nanoparticles having an empty or partially filled payload.

[0010] Another aspect relates to a system for analyzing a sample of lipid-based nanoparticles, the system comprising processing circuitry having one or more non-transitory computer-readable storage media storing instructions that, when executed by the processing circuitry, cause the processing circuitry to rotate a rotor holding at least one container having a density gradient-forming material and a sample of lipid-based nanoparticles, measure a density gradient within the at least one container, measure a position of the lipid-based nanoparticles relative to the density gradient, and assess a size of the lipid-based nanoparticle payload based on the position of the lipid-based nanoparticles relative to the density gradient.

[0011] Another aspect relates to a method for density-based separation of lipid-based nanoparticles by centrifugation, the method including receiving a container having a solution and lipid-based nanoparticles, applying centrifugal force to the container to displace the lipid-based nanoparticles and form at least one detectable group within the container, and measuring the location of the at least one detectable group of lipid-based nanoparticles.

[0012] Various additional aspects will be described in the description that follows. Aspects can relate to individual features and combinations of features. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the broad inventive concept on which the embodiments disclosed herein are based. [Brief explanation of the drawings]

[0013] The following drawing figures, which form part of this application, are illustrative of the described technology and are not meant to limit the scope of the disclosure in any way.

[0014] [Figure 1] FIG. 1 is a schematic block diagram of an exemplary centrifuge that can be used to analyze lipid nanoparticles according to the present disclosure.

[0015] [Figure 2] FIG. 2 illustrates diagrammatically an example of a kit that can be used to analyze a sample of lipid nanoparticles using the centrifuge of FIG.

[0016] [Figure 3] FIG. 3 schematically illustrates an example method for operating the centrifuge of FIG. 1 and analyzing lipid nanoparticles according to the present disclosure.

[0017] [Figure 4] FIG. 4 illustrates an example of a container with a density gradient solution that is added to a sample of lipid nanoparticles prior to centrifugation in the centrifuge of FIG.

[0018] [Figure 5] FIG. 5 illustrates an example of a container with a density gradient solution added to a sample of lipid nanoparticles after centrifugation in the centrifuge of FIG.

[0019] [Figure 6] FIG. 6 illustrates another embodiment of a rotor holding multiple containers with density gradient solutions that are added to a sample of lipid nanoparticles for centrifugation in the centrifuge of FIG.

[0020] [Figure 7] FIG. 7 illustrates a chart of theoretical data, including absorbance measured from a container having a density gradient solution added to a sample of lipid nanoparticles during centrifugation in the centrifuge of FIG.

[0021] [Figure 8a] FIG. 8a illustrates an example of a chart showing the effect of rotor speed on the density gradient formed by the centrifuge of FIG.

[0022] [Figure 8b] FIG. 8b illustrates an example of a chart showing the effect of time on the density gradient formed by the centrifuge of FIG.

[0023] [Figure 9] FIG. 9 illustrates schematically an example of a method for sorting lipid nanoparticles that can be performed by the centrifuge of FIG.

[0024] [Figure 10] FIG. 10 schematically illustrates another example of a method for sorting lipid nanoparticles that can be performed using the centrifuge of FIG.

[0025] [Figure 11]FIG. 11 illustrates experimental data of light absorbance measured across the length of a container during centrifugation with the centrifuge of FIG.

[0026] [Figure 12] FIG. 12 illustrates a portion of the experimental data from FIG. 11 measured towards the end of the experiment after equilibrium had been achieved.

[0027] [Figure 13] FIG. 13 illustrates experimental data of Rayleigh interference fringes (RIF) during centrifugation with the centrifuge of FIG.

[0028] [Figure 14] FIG. 14 illustrates some of the experimental data from FIG. 13 measured towards the end of the experiment after equilibrium had been achieved.

[0029] [Figure 15] FIG. 15 illustrates the compartmentalization of the experimental data from FIG.

[0030] [Figure 16] FIG. 16 illustrates schematically an example of a method for processing the experimental data of FIG. 12 to assess the size of the payload carried in a sample of lipid nanoparticles based on absorbance.

[0031] [Figure 17] FIG. 17 illustrates the processing of the experimental data from FIG. 12 into two separate Gaussian curves by implementing the method of FIG.

[0032] [Figure 18] FIG. 18 illustrates the processing of the experimental data from FIG. 12 into three separate Gaussian curves by implementing the method of FIG.

[0033] [Figure 19] FIG. 19 illustrates the processing of the experimental data from FIG. 12 into four individual Gaussian curves by implementing the method of FIG.

[0034] [Figure 20] FIG. 20 illustrates the processing of the experimental data from FIG. 12 into five individual Gaussian curves by implementing the method of FIG.

[0035] [Figure 21] FIG. 21 illustrates schematically an example of a method for processing the experimental data of FIGS. 24 and 28 to assess the size of the payload carried in a sample of liposomes based on absorbance at multiple wavelengths.

[0036] [Figure 22] FIG. 22 illustrates experimental data of absorbance measured across the length of a container loaded with samples of empty liposomes as well as loaded liposomes during centrifugation in the centrifuge of FIG.

[0037] [Figure 23] FIG. 23 illustrates a portion of the experimental data of FIG. 22 measured towards the end of the experiment after equilibrium had been achieved.

[0038] [Figure 24] FIG. 24 illustrates a compartmentalization of some of the experimental data from FIG.

[0039] [Figure 25A] 25A and 25B illustrate the operation of the method of FIG. 21 on the experimental data of FIG. [Figure 25B] 25A and 25B illustrate the operation of the method of FIG. 21 on the experimental data of FIG.

[0040] [Figure 26] FIG. 26 illustrates experimental data of absorbance measured across the length of a container loaded with samples of empty liposomes and drug-loaded liposomes during centrifugation in the centrifuge of FIG. 1.

[0041] [Figure 27] FIG. 27 illustrates a portion of the experimental data of FIG. 26 measured towards the end of the experiment after equilibrium had been achieved.

[0042] [Figure 28] FIG. 28 illustrates a compartmentalization of some of the experimental data from FIG.

[0043] [Figure 29A] 29A and 29B illustrate the operation of the method of FIG. 21 on the experimental data of FIG. [Figure 29B] 29A and 29B illustrate the operation of the method of FIG. 21 on the experimental data of FIG. DETAILED DESCRIPTION OF THE INVENTION

[0044] Detailed Description Various embodiments will now be described in detail with reference to the drawings, in which like reference numerals represent like parts and assemblies throughout the several views. Reference to various embodiments does not limit the scope of the claims appended hereto. Additionally, any examples described herein are not intended to be limiting, but merely to describe some of the many possible embodiments for the appended claims.

[0045] Lipid nanoparticles exhibit properties that can vary significantly based on their composition and their payload. This can make it more difficult to characterize lipid nanoparticles than other types of particles, such as adeno-associated virus (AAV). As used herein, payload can include single-stranded or more than single-stranded mRNA, siRNA, plasmids, proteins and peptides, small molecules, and other materials.

[0046] The composition of lipid nanoparticles differs significantly from that of AAV. For example, AAV typically contains a protein capsid or shell loaded with single-stranded DNA (ssDNA). In contrast, lipid nanoparticles are made of lipids and can be loaded with more than one item, such as multi-stranded RNA.

[0047] AAVs are considered partially loaded when their capsids contain an incomplete single strand of DNA. In contrast, lipid nanoparticles may contain several copies of RNA, but not enough to fill the entire internal space formed by the lipid shell. This can make characterizing lipid nanoparticles more difficult than other types of particles, such as AAVs.

[0048] Lipid nanoparticles can be orders of magnitude larger than AAVs, and the size of each lipid nanoparticle can vary widely. Also, because lipid nanoparticles can be loaded with variable amounts of payload (e.g., multi-stranded RNA), lipid nanoparticles can exhibit a wider range of densities than other types of particles, such as AAVs. Differences in size and density present additional challenges for analyzing and classifying lipid nanoparticles.

[0049] In addition, lipid nanoparticles typically do not exhibit characteristic absorption wavelengths because they do not consist of a protein shell like AAV. Instead, the ultraviolet (UV) absorption spectrum exhibited by lipid nanoparticles is modulated by their payload, which can vary for the reasons discussed above. As an illustrative example, lipid nanoparticles carrying mRNA cargo may absorb at approximately 260 nm, while some small molecule drugs may absorb at higher wavelengths. This presents another challenge for analyzing and classifying lipid nanoparticles compared to other types of particles, such as AAV.

[0050] Although the following description is given with reference to lipid nanoparticles, it is contemplated that the techniques described herein may be applied to other types of lipid-based nanoparticles as well. As used herein, lipid-based nanoparticles can include mRNA-loaded lipid nanoparticles, solid lipid nanoparticles, nanostructured lipid carriers, liposomes, drug-loaded liposomes, targeted liposomes, stealth liposomes, and cubosomes.

[0051] FIG. 1 is a schematic block diagram of an exemplary centrifuge 100. As will be described in further detail, the centrifuge 100 can be used to analyze various types of particles, including lipid nanoparticles, containing payloads, in accordance with the techniques described herein. The centrifuge 100 generates centrifugal force to separate particles mixed in a sample while also measuring data from the particles during centrifugation. In the example illustrated in FIG. 1, the centrifuge 100 includes a housing 102, a rotor chamber 104, a rotor 106, a drive shaft 108, a motor 110, a processor 120, and an instrument interface 126.

[0052] The housing 102 protects and encloses at least some of the components of the centrifuge 100, such as the rotor 106. As shown in FIG. 1 , the rotor 106 is arranged within a rotor chamber 104 and holds the sample. The rotor chamber 104 defines an interior space within which the rotor 106 spins. In the illustrated embodiment, an opening 122 on the top of the rotor chamber 104 provides a user with access to the rotor 106. A door 116 covers the opening 122, and a latch 118 secures the door 116 in place. Preferably, the door 116 and rotor chamber 104 are reinforced to contain energy and debris that may be released in the event of a rotor failure.

[0053] A drive shaft 108 extends into the rotor chamber 104 and releasably connects to the rotor 106. The releasable connection between the drive shaft 108 and the rotor 106 allows the rotor 106 to be removed from the rotor chamber 104, facilitating the use of a different rotor or rotor replacement, as desired. A motor 110 connects to the drive shaft 108 and rotates the rotor 106 about an axis of rotation R that is generally parallel to the drive shaft 108 at a predetermined speed. An example of the motor 110 is an AC induction motor or other suitable drive mechanism, including, for example, a switched reluctance drive.

[0054] 1, the centrifuge 100 includes a vacuum pump 112 to regulate the ambient pressure within the rotor chamber 104. The vacuum pump 112 is coupled to the rotor chamber 104 through a hose, tube, pipe, or the like, and removes air from the rotor chamber 104.

[0055] Centrifuge 100 includes an optical system 114 that measures data from the sample retained by rotor 106 in real time during centrifugation. In some embodiments, optical system 114 enables centrifuge 100 to perform analytical ultracentrifugation (AUC), an analytical technique that combines optical monitoring with ultracentrifugation (i.e., centrifugal forces greater than 100,000×g). In some alternative embodiments, centrifuge 100 can perform optical monitoring at lower speeds (e.g., centrifugal forces as low as 32,000×g).

[0056] The optical system 114 includes a first sensor 114a that detects light absorption by particles of interest contained in the sample retained by the rotor 106. The particles of interest may include lipid nanoparticles having a payload such as multi-stranded mRNA. In some embodiments, depending on the density gradient medium (e.g., iodixanol), the light absorption can be used to measure a density gradient formed by the centrifuge 100 within the sample retained by the rotor 106 during and / or after centrifugation.

[0057] Optical system 114 includes a second sensor 114b that detects optical interference in the sample held by rotor 106. According to examples provided below, optical interference can be used by centrifuge 100 to measure density gradients formed in the sample held by rotor 106 during and / or after centrifugation. Also, in some examples, optical interference can be used by centrifuge 100 to monitor particles of interest.

[0058] In some embodiments, optical system 114 further includes a third sensor 114c that can be used to detect the degree of fluorescence from particles of interest contained in a sample retained by rotor 106 by centrifuge 100. In such embodiments, third sensor 114c includes an off-axis orientation and can detect a fluorescent signal from the particles of interest.

[0059] The processor 120 controls the components of the centrifuge 100, including the motor 110, the vacuum pump 112, the optical system 114, and the latch 118. The processor 120 also manages the information and graphics displayed on the instrument interface 126. The processor 120 is communicatively coupled to one or more computer-readable storage media 124, such as memory storage devices. The computer-readable storage media 124 encode data instructions. When the data instructions are processed by the processor 120, the instructions cause the processor 120 to perform functionality described herein and / or interact with other components of the centrifuge 100 to perform functionality. Some embodiments include a non-transitory computer-readable medium or one or more non-transitory computer-readable media.

[0060] Processor 120 may include one or more processing devices, including a microprocessor, microcontroller, computer, or other suitable device, that control the operation of the device and execute programs. Various other processor devices may also be used, including central processing units ("CPUs"), microcontrollers, programmable logic devices, field programmable gate arrays, digital signal processing ("DSP") devices, and the like. Processor 120 may include any of the general variety of devices, such as a reduced instruction set computing ("RISC") device, a complex instruction set computing ("CISC") device, or a specially designed processing device, such as an application-specific integrated circuit ("ASIC") device.

[0061] The instrument interface 126 is an example of an input / output device configured for interaction with a user. The instrument interface 126 may be part of the centrifuge console or may be an external device connected to the centrifuge 100, such as a personal computer. In the disclosed example, the instrument interface 126 includes an instrument display 130 and one or more input interfaces 132. The instrument display 130 can be any display device, such as a computer monitor or video screen. The input interface 132 can be any information input device, such as a keyboard, mouse, or touchpad. In some embodiments, the instrument display 130 and the input interface 132 are combined in a touch-sensitive display.

[0062] The parameters of the centrifugation operation include rotor speed, rotor run time, and rotor chamber temperature, as well as detection parameters such as wavelength (related to absorbance), scan frequency, and scan count. The rotor speed is the rotational speed of the rotor 106 during the centrifugation operation. The rotor run time is the duration the rotor 106 spins at the rotor speed. The rotor chamber temperature is the temperature inside the rotor chamber 104. Preset parameters are values ​​that the centrifuge 100 is prepared to apply. These may be default values, values ​​from a previous centrifuge operation, values ​​entered or modified by a user through the input interface 132, or programmed values. The processor 120 displays one or more preset parameters on the instrument display 130. During centrifugation, the processor 120 controls the motor 110 to spin the rotor 106 at a preset rotor speed for a preset run time, and regulate the temperature inside the rotor chamber 104 to match the preset rotor chamber temperature.

[0063] As an illustrative example, the centrifuge 100 can rotate the rotor 106 at a rotor speed ranging from about 3,000 rpm to about 60,000 rpm (about 500×g to 290,000×g). As another illustrative example, the rotor chamber temperature can range from about 4° C. to about 40° C.

[0064] In some embodiments, centrifuge 100 enables multi-stage experiments in which different combinations of parameters can be applied to a sample of lipid nanoparticles. In some embodiments, centrifuge 100 is configured to perform multi-speed experiments in which the rotor speed is gradually increased from a minimum speed to a maximum speed in multiple steps, each step for a predetermined duration. Also, centrifuge 100 is configured to perform multi-speed experiments in which the rotor speed is gradually decreased from a maximum speed to a minimum speed in multiple steps, each step for a predetermined duration. Detection parameters (e.g., wavelength for absorbance, scan frequency, and number of scans) can also be varied for each stage of a multi-stage experiment performed by centrifuge 100.

[0065] 2 illustrates an example of a kit 200 that can be used to analyze a sample of lipid nanoparticles using centrifuge 100. In one particular example, kit 200 is used to analyze a sample of lipid nanoparticles that contain a payload. In some examples, the payload carried by the lipid nanoparticles comprises single-stranded or more than single-stranded mRNA.

[0066] Kit 200 includes a density gradient solution 202 having distinct components. In this example, density gradient solution 202 includes a density gradient-forming material 204, a buffer solution 206, and, optionally, water 208. In alternative examples, density gradient solution 202 can include additional components or have fewer components. In some examples, the components of density gradient solution 202 are in liquid form. In alternative examples, at least some of the components of density gradient solution 202 are in dry form prior to mixing with a sample of particles of interest (e.g., lipid nanoparticles).

[0067] Increasing the amount of density gradient-forming material 204 relative to other components of density gradient solution 202 increases the density of the solution, while decreasing the amount of density gradient-forming material 204 relative to other components of density gradient solution 202 decreases the density of the solution. As illustrative examples, density gradient-forming material 204 can include one or more of the following: cesium chloride, cesium sulfate, cesium acetate, sodium iodide, sodium bromide, potassium tartrate, potassium bromide, iodixanol, Percoll®, Percoll Plus®, sucrose, glucose, glycerol, dextran, trehalose, epichlorohydrin, Ficoll®, and Ficoll-Paque®. In some embodiments, combinations of different density gradient-forming materials can be used for optimal resolution, density range, osmotic balance, and other desired properties. In some embodiments, density gradient solution 202 includes a crowding material, such as polyethylene glycol (PEG).

[0068] In some embodiments, density gradient solution 202 is premixed and delivered to a customer. In other embodiments, the components of density gradient solution 202 are delivered separately to a customer, and the customer mixes the components together to create density gradient solution 202. In some embodiments, a predetermined amount of each of the components of density gradient solution 202 is delivered to a customer, and the customer then mixes the components together to form density gradient solution 202. The components of density gradient solution 202 can be delivered in dry or liquid form.

[0069] In some examples, a customer generates and measures density gradients formed from density gradient solution 202 using the equipment and techniques described in U.S. Provisional Patent Application No. 63 / 369,299, entitled "Non-Destructive Measurement of Density Gradients," filed July 25, 2022; U.S. Provisional Patent Application No. 63 / 369,306, entitled "Automatic Dispense of Density Gradients," filed July 25, 2022; and U.S. Provisional Patent Application No. 63 / 369,318, entitled "Replication of Density Gradients," filed July 25, 2022 (which are incorporated by reference herein in their entireties). Additional techniques for generating and measuring density gradient solution 202 are also contemplated.

[0070] 3 diagrammatically illustrates an embodiment of a method 300 for operating the centrifuge 100 and analyzing lipid nanoparticles. The method 300 includes an operation 302 of obtaining a container that can be loaded onto a rotor for centrifugation by the centrifuge 100.

[0071] Next, method 300 includes an operation 304 of adding density gradient solution 202 to the container obtained in operation 302. It is contemplated that the amount of density gradient solution 202 added to the container may vary.

[0072] Next, method 300 includes adding a sample of lipid nanoparticles to the container, operation 306. The order of operations 304, 306 can be reversed in some alternative embodiments, such that the sample of lipid nanoparticles is added to the container first, and the density gradient solution 202 is then added to the container thereafter.

[0073] Also, in some embodiments, the density gradient solution 202 can be combined with the lipid nanoparticle sample in an outer vessel, and the solution is then transferred to a container that can be loaded onto a rotor for centrifugation by the centrifuge 100. In some embodiments, a density of about 1.0 to about 1.3 g / mL of the density gradient solution 202 is obtained after combining the density gradient solution 202 with the lipid nanoparticle sample.

[0074] Method 300 includes operation 308 of loading a container into rotor 106. Operation 308 can include loading a plurality of containers onto rotor 106, each containing a lipid nanoparticle sample and a density gradient solution 202. In some embodiments, operation 308 includes loading one or more containers onto rotor 106 that contain density gradient solution 202 without a lipid nanoparticle sample to serve as a reference for the containers that contain the lipid nanoparticle sample and the density gradient solution. Additionally, one or more containers that are free of both density gradient solution 202 and lipid nanoparticles can be loaded onto rotor 106 to serve as additional references for monitoring the density gradient within the containers without lipid nanoparticles.

[0075] Next, method 300 includes operation 310 of loading rotor 106 onto centrifuge 100. As shown in FIG. 1 , rotor 106 can be inserted into rotor chamber 104 by releasing latch 118, opening door 116, and gaining access to rotor chamber 104 through opening 122. Operation 310 can further include loading rotor 106 onto drive shaft 108 and enabling motor 110 to rotate rotor 106 about axis of rotation R.

[0076] Method 300 includes operation 312 of operating centrifuge 100 to spin rotor 106 about rotation axis R inside rotor chamber 104. In some embodiments, processor 120 controls motor 110 to rotate in one or more stages, each stage comprising a preset rotor speed and a preset run time. Centrifugation by centrifuge 100 causes the formation of a density gradient within the container containing the lipid nanoparticle sample, allowing optical system 114 to analyze and classify the lipid nanoparticles within the container based on their position along the density gradient and / or radial position within rotor chamber 104.

[0077] 4 illustrates an example of a rotor 106 holding a first container 134a having a density gradient solution 202 to be added to a sample of lipid nanoparticles 136 prior to centrifugation by the centrifuge 100. In this example, the sample of lipid nanoparticles 136 includes lipid nanoparticles that are empty or partially filled with a payload and lipid nanoparticles that are filled with a payload. The rotor 106 also holds a second container 134b having a density gradient solution 202 without the sample of lipid nanoparticles 136. In this example, the second container 134b acts as a reference for the first container 134a.

[0078] 5 illustrates another embodiment of the rotor 106 holding the first and second containers 134a, 134b after centrifugation by the centrifuge 100 such that a density gradient 138 is formed within the first and second containers 134a. During centrifugation, the lipid nanoparticles migrate to positions along the density gradient 138 where the density of the lipid nanoparticles matches the density of the surrounding medium within the first container 134a and form at least one detectable population. In FIGS. 4 and 5, the second container 134b is used to assess the density gradient 138 independently from the sample of lipid nanoparticles 136 within the first container 134a.

[0079] As shown in Figure 5, lipid nanoparticles with a full payload are grouped with other similarly loaded lipid nanoparticles and separated from lipid nanoparticles with empty or partially filled payloads. For example, lipid nanoparticles with a full payload have a lower position (more radially outward) along the density gradient 138, while lipid nanoparticles with empty or partially filled payloads have a higher position (more radially inward) along the density gradient 138. This is because lipid nanoparticles with a full payload have a higher density than empty or partially filled lipid nanoparticles. Lipid nanoparticles with a full payload are denser because the cargo payload in these lipid nanoparticles (e.g., nucleic acids) is denser than the water / buffer solution, which displaces the payload inside the interior space of empty or partially filled particles. The terms "full," "partially filled," and "empty" are used to refer to the unit amount of payload encapsulated by a lipid-based nanoparticle. For example, the term can refer to the number of copies per particle, where the copies themselves can be units of nucleic acid, mRNA, small molecule, or another type of payload. Additionally, "full," "partially full," or "empty" can be used to describe a range of copy quantities per particle that apply to the upper, middle, and lower portions, respectively, of a broader range of copy quantity per particle. In such cases, the broader range of quantities completely encompasses the upper, middle, and lower portions.

[0080] Given the foregoing, the position of lipid nanoparticles along density gradient 138 corresponds to the degree of payload fullness. The amount of lipid nanoparticles with full payloads can be determined based on the amount of light absorbed at a position along density gradient 138. Similarly, the amount of lipid nanoparticles with partially full or empty payloads can be determined based on the amount of light absorbed at other positions along density gradient 138.

[0081] The aforementioned techniques for analyzing lipid nanoparticles differ from sedimentation velocity analytical ultracentrifugation (SV-AUC), a technique that can be used to distinguish empty, partially filled, and filled AAV capsids by monitoring the individual sedimentation velocities (S values) in a sample. The techniques of the present disclosure differ from SV-AUC because, instead of measuring the pelleting or sedimentation velocity of lipid nanoparticles, the density of the particles is used to determine whether the particles are filled, partially filled, or empty. Advantageously, the density data used to classify lipid nanoparticles according to the techniques of the present disclosure is more easily interpretable and does not require specialized analytical software. The techniques of the present disclosure can also generate density gradients 138 in a shorter time than the period required to reach equilibrium in sedimentation equilibrium AUC (SE-AUC), another technique in which a centrifuge is run at low speed for several days to allow particles to reach an equilibrium point where their sedimentation and diffusion rates are balanced. In SE-AUC, the particles themselves form a concentration gradient; no density gradient material is added and no density gradient is formed.

[0082] FIG. 6 illustrates another embodiment of a rotor 106 for use with the centrifuge 100. In this embodiment, the rotor 106 holds multiple containers (e.g., six containers) for centrifugation by the centrifuge 100. The rotor 106 holds multiple first containers 134a, each containing a density gradient solution 202 to be added to a sample of lipid nanoparticles 136, and multiple second containers 134b, each containing only the density gradient solution 202 (i.e., without the lipid nanoparticle sample). In this embodiment, each second container 134b acts as a reference for the first container 134a. In this embodiment, the rotor 106 increases the throughput of the centrifuge 100 for analyzing the lipid nanoparticle sample. In further embodiments, alternative rotors, such as rotors configured to hold more or less than six containers, may be used with the centrifuge 100.

[0083] FIG. 7 illustrates a chart 700 of theoretical data, including light absorbance, measured along the density gradient 138 in the first container 134a after centrifugation by the centrifuge 100. According to the embodiment described above, the light absorbance is measured by the optical system 114 (i.e., the first sensor 114a) of the centrifuge 100. As shown in FIG. 7, a first peak in light absorbance 702 is detected at a radial distance of approximately 6.2 cm. The first peak in light absorbance 702 identifies lipid nanoparticles that are empty of payload because these lipid nanoparticles have a lower density. As further shown in FIG. 7, a second peak in light absorbance 704 is detected at a radial distance of approximately 6.3 cm. The second peak in light absorbance 704 identifies lipid nanoparticles that have a full payload because these lipid nanoparticles have a higher density. An advantage of the above technique is that the raw data collected from the first container 134a allows for simple observation and analysis of the lipid nanoparticles from the centrifuge 100 without requiring complex calculations. The theoretical data shown in Figure 7 is supported by the experimental data illustrated in Figures 11-29, which will be explained in more detail below.

[0084] 8a illustrates an example of a chart 800 showing the effect of rotor speed on a density gradient 802 formed during centrifugation of a density gradient solution 202 by the centrifuge 100. The rotor speed is controlled by the processor 120, which operates the motor 110 to drive the drive shaft 108 and cause rotation of the rotor 106 about the axis of rotation R (see FIG. 1). The rotor speed of the centrifuge 100 can be adjusted during centrifugation. In this example, the rotor speed is maintained at 42,000 rpm for approximately 12 hours until equilibrium is reached, then reduced to 35,000 rpm for approximately 12 hours, and finally reduced to 5,000 rpm in the same manner. As shown in FIG. 8a, a higher rotor speed causes the slope of the density gradient 802 (y-axis) to have a greater slope along the radial distance (x-axis) of the container, while a lower rotor speed causes the slope of the density gradient 802 to have a smaller slope.

[0085] 8b illustrates an example of a chart 804 showing the effect of time on a density gradient 806 formed during centrifugation of the density gradient solution 202 by the centrifuge 100. In one example, a second container 134b having only the density gradient solution 202 (i.e., without a sample of lipid nanoparticles 136) is monitored according to the example shown in FIG.

[0086] Chart 804 was generated by maintaining the rotor speed at 42,000 rpm. As time progresses, density gradient 806 becomes steeper, indicating that a density gradient is forming. In this illustrative example, the change in density gradient 806 stops at approximately 6-8 hours, indicating that the density gradient is fully equilibrated at this point. Thus, in this example, density gradient solution 202 reaches equilibrium within approximately 6-8 hours at 42,000 rpm.

[0087] 9 schematically illustrates an example of a method 900 for sorting lipid nanoparticles that may be performed by centrifuge 100. Method 900 includes operation 902, which involves rotating rotor 106 about rotation axis R at a preset rotor speed for a preset run time. During operation 902, rotor 106 holds at least one container having density gradient solution 202, which is added to a sample of lipid nanoparticles, which may be prepared according to the operations of method 300 described with respect to FIG. 3 above. As discussed above, the lipid nanoparticles in the sample are designed to carry a payload.

[0088] In some embodiments, the rotor 106 is rotated at a preset rotor speed ranging from about 3,000 rpm to about 60,000 rpm. The preset rotor speed is higher than the SE-AUC. In some embodiments, the preset run time ranges from about 1 hour to about 72 hours. The preset run time is less than the time required to measure the balance rate in the SE-AUC.

[0089] Rotation of the at least one container during operation 902 forms a continuous density gradient. Operation 902 can include increasing a preset rotor speed and increasing the slope of the continuous density gradient to detect a wider range of densities. Alternatively, operation 902 can include decreasing the preset rotor speed and decreasing the slope of the continuous density gradient to detect densities with higher resolution. In some examples, operation 902 can include both increasing the preset rotor speed and decreasing the preset rotor speed to adjust the slope of the continuous density gradient during centrifugation. In some examples, centrifuge 100 is programmed to adjust the slope of the density gradient based on optical absorbance and / or optical interference measured by optical system 114 during centrifugation.

[0090] Method 900 further includes operation 904 of measuring a density gradient. The density gradient can be measured using the optical system 114 of the centrifuge 100. For example, second sensor 114b detects optical interference in one or more second containers 134b (see FIGS. 4-6) in operation 904 to measure the density gradient.

[0091] In some embodiments, operation 904 is performed during centrifugation, such that the density gradient is measured while the rotor is rotating. In such embodiments, operation 904 occurs simultaneously with operation 902 and determines when the centrifugation should be stopped, such as when the density gradient reaches equilibrium. In other embodiments, operation 904 is performed after completion of operation 902, such as when a preset run time expires.

[0092] Next, method 900 includes operation 906, which detects and / or measures the position of the lipid nanoparticles relative to the density gradient measured in operation 904. Operation 906 may include measuring absorbance data within one or more first containers 134a (see FIGS. 4-6 ). The absorbance data may be measured using the optical system 114 of the centrifuge 100. For example, the first sensor 114a detects the absorbance data within the one or more first containers 134a. In some instances, the absorbance data measured in operation 906 may be similar to the data shown in chart 700 of FIG. 7.

[0093] Method 900 further includes operation 908 of assessing the size of the payload in the lipid nanoparticles. In some embodiments, operation 908 includes classifying the lipid nanoparticles based on their position relative to the density gradient. As an illustrative example, operation 908 can include determining the amount of lipid nanoparticles that have a loaded payload based on their position along the density gradient, and determining the amount of lipid nanoparticles that do not have a loaded payload based on their position along the density gradient.

[0094] As an illustrative example, the absolute amount of either filled, empty, or partially filled lipid nanoparticles can be determined from the absorbance detected at a range of radial positions, such as those known to correspond to filled, empty, or partially filled species of lipid nanoparticles. In Figure 7, the absolute amount of empty lipid nanoparticles is obtained from the total area under the curve at a radial position of approximately 6.2 cm. The absolute amount of filled lipid nanoparticles is obtained from the total area under the curve at a radial position of approximately 6.3 cm. The ratio of these two areas gives the percentage of empty and filled lipid nanoparticles. Therefore, the radial position of the absorbance peak can be used to classify lipid nanoparticles, and the area under the curve at the absorbance peak can be used to determine the amount of lipid nanoparticles for a given class of lipid nanoparticles.

[0095] In some embodiments, operation 908 includes sorting the lipid nanoparticles between a first group having a loaded payload and a second group not having a loaded payload. Additional embodiments for sorting lipid nanoparticles are also possible.

[0096] In some further embodiments, operation 908 includes calculating a ratio between the amount of lipid nanoparticles with a loaded payload and the amount of lipid nanoparticles without a loaded payload. Additional examples of ratios that can be calculated to classify lipid nanoparticles are also possible.

[0097] Method 900 has several advantages over SV-AUC and SE-AUC. For example, by forming a density gradient used to distinguish loaded, payload-containing lipid nanoparticles from those without, method 900 is less sensitive to sample inconsistencies and temperature fluctuations. Furthermore, the raw data (e.g., absorbance) collected from method 900 is easily understandable and can be managed using simple data visualization tools, instead of relying on extensive mathematical deconvolution provided by sophisticated and specialized software packages, which is typical for SV-AUC and SE-AUC. Furthermore, method 900 is not limited by particle size (as is the case with SV-AUC), thus enabling a versatile approach to characterize lipid nanoparticles of various sizes and payloads.

[0098] Also, method 900 (unlike SV-AUC) is not time-resolved, but rather an end-point analysis, which allows more wavelengths to be assessed without sacrificing more time points for wavelengths. This is particularly advantageous for larger, fast-settling particles such as lipid nanoparticles (another benefit over AAV, as lipid nanoparticles have higher S values, thus allowing less time for analysis via SV-AUC before they sediment). Another advantage over both SV-AUC and SE-AUC is that the sample, by the nature of method 900, is concentrated within a density gradient, thereby allowing fewer samples to be analyzed.

[0099] Method 900 can generate density gradients in a shorter time period than the equilibrium reached in SE-AUC. For example, method 900 can generate density gradients in about 1 to about 72 hours, while SE-AUC typically requires about 3 to about 7 days to reach equilibrium. Method 900 can also separate filled lipid nanoparticles from partially filled lipid nanoparticles with reduced sample volume requirements compared to SV-AUC.

[0100] 10 schematically illustrates an example of a method 1000 of sorting lipid nanoparticles that may be performed using centrifuge 100. Method 1000 includes operation 1002 of providing a density gradient solution 202. As described above, density gradient solution 202 provided in operation 1002 may include density gradient forming material 204, a buffer solution 206, and water 208. Density gradient forming material 204 may include one or more of the following: cesium chloride, cesium sulfate, cesium acetate, sodium iodide, sodium bromide, potassium tartrate, potassium bromide, iodixanol, Percoll®, Percoll Plus®, sucrose, glucose, glycerol, dextran, trehalose, epichlorohydrin, Ficoll®, and Ficoll-Paque®.

[0101] In some embodiments, density gradient solution 202 is premixed and delivered to a customer. In other embodiments, the components of density gradient solution 202 are delivered separately to a customer, who then mixes the components together to create density gradient solution 202. In some embodiments, a predetermined amount of each of the components of density gradient solution 202 is delivered to a customer, who then mixes the components together to form density gradient solution 202. In some embodiments, at least some of the components of density gradient solution 202 are in dry form.

[0102] Method 1000 includes operation 1004 of operating centrifuge 100 to analyze lipid nanoparticles. In some embodiments, operation 1004 includes at least some of the operations described above with respect to method 300. For example, operation 1004 can include obtaining a container, adding a sample of lipid nanoparticles carrying a payload and density gradient solution 202 to the container, loading the container onto rotor 106, inserting rotor 106 into centrifuge 100, and using centrifuge 100 to perform centrifugation of the container including the sample of lipid nanoparticles carrying a payload and density gradient solution 202. Centrifuge 100 is programmed to perform centrifugation by rotating rotor 106 about rotation axis R in one or more stages, each defined by a preset rotor speed and a preset run time, to generate a density gradient for separating lipid nanoparticles based on their payload fullness.

[0103] Next, method 1000 includes operation 1006, which measures the absorbance of light from the lipid nanoparticles suspended in the density gradient created in the container after centrifugation is complete. Operation 1006 can include generating a chart including the absorbance (y-axis) over the length of the container (x-axis).

[0104] Method 1000 further includes operation 1008 of classifying the lipid nanoparticles based on the measured absorbance. Lipid nanoparticles with a loaded payload will be denser than lipid nanoparticles without a loaded payload, such that there will be different peaks in the absorbance measured from the container after centrifugation is complete. The difference between the peaks in absorbance can be used to calculate the ratio of the amount of lipid nanoparticles with a loaded payload to the amount of lipid nanoparticles without a loaded payload.

[0105] FIG. 11 illustrates experimental data 1100 of absorbance (y-axis) measured across the length (x-axis) of a container during centrifugation by centrifuge 100. The experimental data 1100 is measured from an experiment using a lipid nanoparticle sample with a total volume of 75 μL, of which 50 μL contains lipid nanoparticles with an empty payload and 25 μL contains lipid nanoparticles with a filled payload. The lipid nanoparticle sample is mixed with a density gradient solution 202 having 3% sucrose as the density gradient forming material. The experiment was run by rotating the rotor 106 at a constant speed of 60 krpm and a constant temperature of 20° C. for a run time of 43 hours. The absorbance is measured at a wavelength of 230 nm. The experimental data 1100 is based on a total of 259 scans measured every 10 minutes over an experiment with a duration of 43 hours, with every fifth scan (i.e., one plot every 50 minutes) shown in the experimental data 1100 of FIG. 11.

[0106] FIG. 12 illustrates a portion 1200 of the experimental data 1100 of FIG. 11. Portion 1200 includes scans measured toward the end of the experiment, after equilibrium had been achieved inside the container. For example, portion 1200 includes the last 20 scans (scans 239-259) measured between hours 40 and 43 of the experiment. This corresponds to 200 minutes of plotted data (10 minutes between scans), when the lipid nanoparticle sample and density gradient solution 202 had achieved equilibrium inside the container.

[0107] 13 illustrates experimental data 1300 of Rayleigh interference fringes (RIF) (y-axis) measured across the length of a container (x-axis) during centrifugation by centrifuge 100. The experimental data 1300 was measured from the experiment of FIGS. 11 and 12, which uses a sample of lipid nanoparticles having a total volume of 75 μL (with 50 μL of lipid nanoparticles with an empty payload and 25 μL of lipid nanoparticles with a filled payload) mixed with a density gradient solution 202 having 3% sucrose as the density gradient forming material 204, as described above. The experiment was run by rotating the rotor 106 at a constant speed of 60 krpm and a constant temperature of 20° C. for a run time of 43 hours.

[0108] The experimental data 1300 shown in Figure 13 is based on a total of 518 scans measured every 5 minutes over a 43-hour duration experiment, with every 10th scan (i.e., one plot every 50 minutes) shown. The RIF (y-axis) monitors the density gradient forming material 204 (sucrose) during the experiment. The density gradient forming material 204 approaches equilibrium, as indicated by the increasing slope of the scans in the experimental data 1300.

[0109] FIG. 14 illustrates a portion 1400 of the experimental data 1300 of FIG. 13. Portion 1400 includes scans measured toward the end of the experiment, after equilibrium had been achieved inside the container. For example, portion 1400 includes scans 478-518, measured between times 40-43 of the experiment. This corresponds to 40 plots measured every 5 minutes. Portion 1400 indicates that the density gradient forming material 204 has achieved equilibrium because the curvature of the scans does not change.

[0110] FIG. 15 illustrates a compartmentalization of a portion 1200 of the experimental data 1100 from FIG. 12. As explained above, the experimental data 1100 includes absorbance (y-axis) measured across the length (x-axis) of a container during centrifugation by the centrifuge 100. Different regions of the container are shown along the x-axis in FIG. 15. A first region 502 on the x-axis includes data measured from the air gap above the lipid nanoparticle sample in the container (the container is not filled to the brim with the lipid nanoparticle sample so that a small air gap region exists). The absorbance data in the first region 502 is ignored during analysis of the experimental data 1100. A second region 504 is the meniscus formed in the container between the air gap and the lipid nanoparticle sample. The absorbance data in the second region 504 is similarly ignored during analysis. A third region 506 is the density gradient region. The absorbance data in the third region 506 is further analyzed to assess the size of the payload carried in the lipid nanoparticle sample based on absorbance, as will be explained in more detail below. The fourth region 508 is the bottom of the container. The absorbance data in the fourth region 508 is ignored during analysis.

[0111] 16 schematically illustrates an example method 1600 for assessing the size of payload carried in a sample of lipid nanoparticles based on absorbance. Method 1600 is an example of using density gradient equilibrium analytical ultracentrifugation to determine the relative populations of different lipid nanoparticle (LNP) species, such as (i) empty or control LNPs, (ii) partially loaded LNPs, (iii) fully loaded LNPs, (iv) disrupted LNPs, and (v) free-floating drug cargo. Note that while method 1600 is described with respect to sorting LNPs, at least some of the operations of method 1600 can also be performed to sort liposomes, as will be described below with respect to FIG. 21.

[0112] 16 , method 1600 includes operation 1602 of collecting absorbance data during centrifugation by centrifuge 100. The absorbance data is collected from a container having a sample of lipid nanoparticles mixed with density gradient solution 202. As an illustrative example, density gradient solution 202 may include sucrose at a concentration of 3% as density gradient forming material 204. Operation 1602 may include rotating rotor 106 at a constant speed and a constant temperature. For example, operation 1602 may include rotating rotor 106 at a constant speed of about 60 krpm and a constant temperature of about 20° C. Operation 1602 may include measuring the absorbance data at a predetermined wavelength, such as 230 nm. Operation 1602 may include measuring the absorbance data at a predetermined time interval, such as every 10 minutes.

[0113] Method 1600 includes operation 1604, which determines whether the sample of lipid nanoparticles mixed with density gradient solution 202 has reached equilibrium. Operation 1604 may include determining whether equilibrium has been reached by examining RIF data (see FIGS. 13 and 14), determining whether a constant curvature of the scan has been reached, or the like. Additionally, operation 1604 may include examining absorbance data (see FIGS. 11 and 12) to determine whether equilibrium has been reached. If operation 1604 determines that equilibrium has not been reached (i.e., "No" at operation 1604), method 1600 returns to operation 1602 and continues to collect absorbance data during centrifugation.

[0114] If operation 1604 determines that equilibrium has been reached (i.e., "yes" at operation 1604), method 1600 proceeds to operation 1606, which exports the absorbance data. In some embodiments, operation 1606 includes exporting a portion of the absorbance data, such as the last 10-20 scans of 200 scans of the experiment (see portion 1200 shown in FIG. 12 ). Operation 1606 may further include performing one or more data quality checks, such as checking for missing scans in the portion of the absorbance data that is exported. In some instances, operation 1606 includes an extract-transform-load (ETL) workflow.

[0115] Method 1600 includes an operation 1608 that averages the scans included in the extinction data exported in operation 1606. By averaging the scans, operation 1608 can provide a better signal-to-noise ratio for the extinction data.

[0116] Method 1600 includes operation 1610, which is partitioning the absorbance data. For example, operation 1610 may include partitioning the absorbance data into regions shown in FIG. 15, such as first region 1502, second region 1504, third region 1506, and fourth region 1508. Method 1600 then includes operation 1612, which is truncating the absorbance data. Operation 1612 may include removing first region 1502, second region 1504, and fourth region 1508 from third region 1506, which includes the density gradient formed by centrifugation. Operation 1612 may include truncating the absorbance data such that third region 1506 includes an axial length of the container between 6.2 cm and 7.1 cm (see FIG. 15).

[0117] The method 1600 includes an operation 1614 of fitting the extinction data to a Gaussian function, such as Equation 1. [ka] where n is the number of Gaussian fits and C nis the center position for the nth Gaussian fitting, and A n is the amplitude for the nth Gaussian fitting, and S n is the width for the nth Gaussian fit.

[0118] Method 1600 may include an operation 1616 of optimizing the Gaussian fit of the extinction data by increasing n until the fit quality is optimized, which in this example is when the value of the residual function is minimized.

[0119] FIGS. 17-20 each illustrate an example of the performance of acts 1608-1616 of method 1600 on experimental data 1100. For example, FIGS. 17-20 include charts 1702, 1802, 1902, and 2002, respectively, of extinction data after averaging has been completed in act 1608. FIGS. 17-20 include charts 1704, 1804, 1904, and 2004, respectively, of extinction data after truncation has been completed in act 1612. FIGS. 17-20 include charts 1706, 1806, 1906, and 2006, respectively, showing fitted and residual curves after fitting the extinction data to a Gaussian function has been completed in act 1614. FIGS. 17-20 include charts 1708, 1808, 1908, and 2008, respectively, showing individual Gaussian curves extracted from the fitted curves.

[0120] 17, n is set to 2 so that the absorbance data is fitted to two Gaussian curves in chart 1708. In this example, the residual curve in chart 1706 has a mean value of 1.346698. In this example, operation 1616 may include increasing n to 3 to further minimize the value of the residual function and optimize the fitting quality of the absorbance data.

[0121] In Figure 18, n is increased to 3 so that the absorbance data is fitted to three Gaussian curves in chart 1808. In this example, the residual curve in chart 1806 has an average value of 1.104727, which is lower than the average value of the residual curve shown in chart 1706 of Figure 17. Therefore, operation 1616 may further include increasing n to 4 to further minimize the value of the residual function and optimize the fitting quality of the absorbance data.

[0122] 19, n is increased to 4 so that the absorbance data is fitted to four Gaussian curves in chart 1908. In this example, the residual curve in chart 1906 has an average value of 0.300438, which is lower than the average value of the residual curve shown in chart 1806 of FIG. 18. Therefore, operation 1616 may further include increasing n to 5 to further minimize the value of the residual function and optimize the fitting quality of the absorbance data.

[0123] In Figure 20, n is increased to 5 so that the absorbance data is fitted to five Gaussian curves in chart 2008. In this example, the residual curve in chart 2006 has an average value of 0.843571, which exceeds the average value of the residual curve shown in chart 1906 of Figure 19. Therefore, operation 1616 includes determining that the fit quality is optimized when the absorbance data is fitted to four Gaussian curves. This suggests that there are four distinct particle populations in the lipid nanoparticle sample.

[0124] 16, method 1600 further includes operation 1618 of assigning each Gaussian curve to a particle population in the sample of lipid nanoparticles. Operation 1618 can include assigning an empty lipid nanoparticle population to a first Gaussian curve, assigning a partially loaded lipid nanoparticle population to a second Gaussian curve, assigning a fully loaded lipid nanoparticle population to a third Gaussian curve, assigning a fractured lipid nanoparticle shell population to a fourth Gaussian curve, assigning an mRNA cargo population to a fifth Gaussian curve, etc.

[0125] Referring now to chart 1908 in Figure 19, Gaussian curves with peaks to the right of the x-axis are associated with particle populations having heavier loadings than particle populations associated with Gaussian curves with peaks to the left of the x-axis. This is because the right side of the x-axis is located toward the bottom of the container, where the density values ​​in the density gradient are highest, and the left side of the x-axis is located toward the top of the container, where the density values ​​in the density gradient are lowest. Thus, Gaussian curve 3 is associated with the particle population having the highest density, while Gaussian curve 1 is associated with the particle population having the lowest density.

[0126] 16, method 1600 further includes operation 1620, which calculates one or more measurements of the Gaussian curves assigned to the particle population in operation 1618. Operation 1620 may include calculating an amplitude, a center, and an area for each Gaussian curve associated with the particle population.

[0127] Table 1 provides the measurements calculated in operation 1620 for the Gaussian curves shown in chart 1908 of FIG. 19. As discussed above, fitting quality is optimized when the absorbance data is fitted to the four Gaussian curves of FIG. 19. Referring now to FIG. 19 and Table 1, Gaussian curve 1 has a peak amplitude centered at 6.252278 cm, furthest from the left side of the x-axis. Gaussian curve 1 is assigned to the first particle population, associated with the lowest density of fractured lipid nanoparticle shells, etc. Gaussian curve 1 has an area of ​​0.074633, which accounts for approximately 11.95% of the total area of ​​0.624805. Therefore, 11.95% of the lipid nanoparticle sample contains the first particle population. [Table 1]

[0128] Gaussian curve 2 has a peak amplitude at a center location of 6.698702 cm, to the right of Gaussian curve 1. Gaussian curve 2 is assigned to a second particle population associated with a higher density, such as empty lipid nanoparticles. Gaussian curve 2 has an area of ​​0.471041, which accounts for approximately 75.39% of the total area of ​​the Gaussian curves, 0.624805. Therefore, 75.39% of the lipid nanoparticle sample contains the second particle population.

[0129] Gaussian curve 3 has a peak amplitude at a center location of 7.002682 cm, the farthest point from the right side of the x-axis. Gaussian curve 3 is assigned to the third particle population, which is associated with the highest density, such as fully loaded lipid nanoparticles. Gaussian curve 3 has an area of ​​0.034405, which accounts for approximately 5.51% of the total area of ​​0.624805. Therefore, 5.51% of the lipid nanoparticle sample contains the third particle population.

[0130] Gaussian curve 4 has a peak amplitude at a center location of 6.956564 cm, to the left of Gaussian curve 3. Gaussian curve 4 is assigned to a fourth particle population associated with lower density, such as partially loaded lipid nanoparticles. Gaussian curve 4 has an area of ​​0.044726, which accounts for approximately 7.16% of the total area of ​​the Gaussian curves (0.624805). Therefore, 7.16% of the lipid nanoparticle sample contains the fourth particle population.

[0131] 21 schematically illustrates an example method 2100 for assessing the size of payload carried in a sample of liposomes based on absorbance. Method 2100 is an example of using density gradient equilibrium analytical ultracentrifugation to determine the relative populations of different liposome species, such as (i) empty or control liposomes, (ii) partially loaded liposomes, (iii) fully loaded liposomes, (iv) disrupted liposomes, and (v) free-floating drug cargo.

[0132] 21 , method 2100 can include operation 2102, collecting absorbance data during centrifugation by centrifuge 100, operation 2104, determining whether a sample of liposomes mixed with density gradient solution 202 has reached equilibrium, operation 2106, exporting the absorbance data, operation 2108, averaging scans included in the absorbance data exported in operation 2106, operation 2110, segmenting the absorbance data, and operation 2112, truncating the absorbance data. Operations 2102-2112 of method 2100 are identical to operations 1602-1612 of method 1600, such that the description of operations 1602-1612 provided above applies to operations 2102-2112 of method 2100 as well.

[0133] 21 , method 2100 includes operation 2114, which identifies liposome species from the extinction data truncated in operation 2112. In contrast to method 1600 for assessing lipid nanoparticles, method 2100 can identify liposome species without fitting and deconvolving the extinction data using a Gaussian function (see Equation 1). Thus, operation 2114 can include simply identifying species that are visible in the extinction data without further processing. In some cases, operation 2114 can include drawing a bounding box corresponding to the region for each liposome species.

[0134] Method 2100 includes operation 2116, which calculates one or more measurements of the liposome species identified in operation 2114. Operation 2116 may include calculating an amplitude, a center, and an area under the curve for each liposome species. Operation 2116 is similar to operation 1620 of method 1600. Operation 2116 may include calculating an area under the curve for each bin box corresponding to a liposome species, such that the percentage of the area under the curve for the bin box to the total area under the curve corresponds to the percentage of that liposome species in the sample of liposomes.

[0135] In some cases, operations 2108-2116 can be performed with respect to different wavelengths of absorbance data captured from a liposome sample. For example, absorbance data at a wavelength of 230 nm indicates both empty and filled liposome species, as well as any partially loaded liposomes. The payload or cargo loaded into the liposome generates an absorbance signal at a different wavelength. For example, a payload such as doxorubicin absorbs light at 490 nm. The absorbance data at 490 nm directly monitors the drug doxorubicin, whether inside the liposome or floating freely in solution. The 490 nm absorbance directly monitors the drug payload. Thus, by combining the 230 nm absorbance data and the 490 nm absorbance data, species having a peak at 230 nm and a peak at 490 nm can be identified as fully loaded or partially loaded liposomes.

[0136] FIG. 22 illustrates experimental data 2200 of absorbance (y-axis) measured across the length of a container (x-axis) during centrifugation by centrifuge 100. The experimental data 2200 is measured from an experiment using a liposome sample with a total volume of 7 μL, of which 5 μL contained liposomes with an empty payload and 2 μL contained liposomes with a drug payload. The liposome sample was mixed with a density gradient solution 202 having 10% sucrose as the density gradient forming material. The experiment was run by rotating the rotor 106 at a constant speed of 40 krpm and a constant temperature of 20° C. for a run time of 42 hours. The absorbance is measured at a wavelength of 230 nm. The experimental data 1100 is based on a total of 250 scans measured every 10 minutes over the course of an experiment lasting 42 hours, with every fifth scan (i.e., one plot every 50 minutes) shown in experimental data 2200 of FIG. 22.

[0137] Figure 23 illustrates a portion 2300 of the experimental data 2200 of Figure 22. Portion 2300 includes scans measured toward the end of the experiment, after equilibrium had been achieved inside the container. For example, portion 2300 includes absorbance scans measured between times 36-42 of the experiment. All scans during this period are shown in Figure 23, which corresponds to one plot every 10 minutes.

[0138] 23 shows that when the liposome sample and density gradient solution 202 achieve equilibrium inside the container, two distinct species are present in the liposome sample: a first species 2302, which occurs at approximately 6.3 cm along the length of the container, and a second species 2304, which occurs at approximately 6.6 cm along the length of the container. The first species 2302 corresponds to liposomes with a full or partially full payload, and the second species 2304 corresponds to liposomes with an empty payload.

[0139] Figure 24 illustrates a partitioning of a portion 2300 of the experimental data 2200 from Figure 23. As shown in Figure 24, a first partitioning box 2402 is drawn around a first species 2302 and a second partitioning box 2404 is drawn around a second species 2304.

[0140] 25A and 25B illustrate examples of the performance of acts 2108-2116 of method 2100 on experimental data 2200. FIG. 25A includes a chart 2502 of absorbance data after averaging has been completed in act 2108. FIG. 25A further includes a chart 2504 of absorbance data after partitioning has been completed in act 211. FIG. 25B includes a chart 2506 showing absorbance data after truncation has been completed in act 2112. FIG. 25B further includes a chart 2508 showing partition boxes drawn around first species 2302 and second species 2304 identified within the liposome data in act 2114.

[0141] Table 2 provides the measurements calculated in operation 2116 for a first bin box 2402 drawn around the first species 2302 and a second bin box 2404 drawn around the second species 2304. The first bin box 2402 has an area under the curve of 1.22*10^(-1), which represents 44.82% of the total area under the curve. Thus, in this illustrative example, 44.82% of the sample of liposomes analyzed according to method 2100 contains drug-loaded liposomes. The second bin box 2404 has an area under the curve of 1.50*10^(-1), which represents 55.14% of the total area under the curve. Thus, in this illustrative example, 55.14% of the sample of liposomes contains empty liposomes. [Table 2]

[0142] FIG. 26 illustrates experimental data 2600 of absorbance (y-axis) measured across the length (x-axis) of a container during centrifugation by centrifuge 100. Experimental data 2200 is measured from the same experiment that collected experimental data 2200 shown in FIG. 22. For example, experimental data 2600 is from a sample of 5 μL liposomes with an empty payload and 2 μL liposomes with a drug payload mixed with a density gradient solution having 10% sucrose as the density gradient-forming material. The rotor 106 is rotated at a constant speed of 40 krpm and a constant temperature of 20° C. for a 42-hour run time. Experimental data 2600 has a total of 250 scans measured every 10 minutes over the 42-hour experiment, with every fifth scan (i.e., one plot every 50 minutes) shown.

[0143] Experimental data 2600 differs from experimental data 2200 shown in Figure 22 in that absorbance is measured at a wavelength of 490 nm (instead of 230 nm in Figure 22). As shown in Figure 26, only one distinct species is visible in experimental data 2600. This is because measuring absorbance at 490 nm directly monitors the drug (e.g., doxorubicin). A comparison of the experimental data in Figure 22 to that in Figure 26 shows that at equilibrium, the single distinct species seen in Figure 26 corresponds to the left-hand species seen in Figure 22.

[0144] Figure 27 illustrates a portion 2700 of the experimental data 2600 of Figure 26. Portion 2700 includes scans measured toward the end of the experiment, after equilibrium had been achieved inside the container. For example, portion 2700 includes absorbance scans measured between times 36-42 of the experiment. All scans during this period are shown in Figure 27, which corresponds to one plot every 10 minutes.

[0145] Figure 27 shows that when the liposome sample achieves equilibrium inside the container, a single species 2702 is visible at approximately 6.3 cm along the length of the container under a wavelength of 490 nm. The second species 2304, visible in the experimental data of Figure 23, captured under a wavelength of 230 nm, is no longer prominent. The single species 2702 corresponds to liposomes with a full or partially full payload.

[0146] Figure 28 illustrates the partitioning of a portion 2700 of the experimental data 2600 from Figure 27. In this example, a single partition box 2802 is drawn around a single species 2702.

[0147] 29A and 29B illustrate an example of the performance of acts 2108-2116 of method 2100 on experimental data 2600. Figure 29A includes a chart 2902 of absorbance data after averaging has been completed in act 2108. Figure 29A further includes a chart 2904 of absorbance data after partitioning has been completed in act 2110. Figure 29B includes a chart 2906 showing absorbance data after truncation has been completed in act 2112. Figure 29B further includes a chart 2908 showing a partition box drawn around a single species 2702 identified within the liposome data in act 2114, and another partition box drawn around where a second species 2304 occurs.

[0148] The various embodiments described above are provided by way of example only and should not be construed as limiting in any way. Various modifications can be made to the above-described embodiments without departing from the true spirit and scope of the present disclosure.

[0149] Embodiments of the present disclosure can be described with reference to the following numbered appendices, where preferred features are expanded in the dependent appendices. 1. A system for analyzing a sample of lipid-based nanoparticles designed to carry a payload, comprising: 1. A processing circuitry having one or more non-transitory computer-readable storage media that store instructions that, when executed by the processing circuitry, cause the processing circuitry to: rotating a rotor holding at least one container, the at least one container having a density gradient-forming material and a sample of lipid-based nanoparticles; measuring the absorbance of light by a sample of lipid-based nanoparticles in at least one container while rotating the rotor; assessing the size of the payload carried in a sample of lipid-based nanoparticles based on absorbance; A processing circuitry that performs A system comprising: 2. The one or more non-transitory computer-readable storage media, when executed by the processing circuitry, further cause the processing circuitry to: 10. The system of claim 1, further comprising: storing additional instructions for rotating the rotor at a preset rotor speed within a range of 3,000 rpm to 60,000 rpm. 3. The one or more non-transitory computer-readable storage media, when executed by the processing circuitry, further cause the processing circuitry to: 3. The system of claim 1 or 2, wherein the system stores additional instructions for rotating the rotor for a preset run time in the range of 1 to 72 hours. 4. The one or more non-transitory computer-readable storage media, when executed by the processing circuitry, further cause the processing circuitry to: 10. The system of claim 1, further comprising: storing additional instructions for measuring density gradients based on optical interference. 5. The one or more non-transitory computer-readable storage media, when executed by the processing circuitry, further cause the processing circuitry to: 5. The system of claim 4, further comprising: storing additional instructions to increase the speed of rotation of the rotor and increase the slope of the density gradient. 6. The one or more non-transitory computer-readable storage media, when executed by the processing circuitry, further cause the processing circuitry to: 5. The system of claim 4, further comprising: storing additional instructions to decrease the speed of rotation of the rotor and decrease the slope of the density gradient. 7. The one or more non-transitory computer-readable storage media, when executed by the processing circuitry, further cause the processing circuitry to: increasing the speed of rotation of the rotor and increasing the slope of the density gradient based on at least one of light extinction and light interference; decreasing the speed of rotation of the rotor and decreasing the slope of the density gradient based on at least one of light absorption and light interference; 5. The system of claim 4, further comprising: 8. The system of claim 1, wherein the density gradient forming material comprises one or more of cesium chloride, cesium sulfate, cesium acetate, sodium iodide, sodium bromide, potassium tartrate, potassium bromide, iodixanol, Percoll®, Percoll Plus®, sucrose, glucose, glycerol, dextran, trehalose, epichlorohydrin, Ficoll®, and Ficoll-Paque®. 9. A method for analyzing a sample of lipid-based nanoparticles, comprising: providing a density gradient-forming material; operating the centrifuge to rotate the rotor about the axis of rotation at a preset rotor speed to create a density gradient within the container having the density gradient-forming material and the lipid-based nanoparticle sample; measuring the absorbance of light by a sample of lipid-based nanoparticles in the container; determining a ratio of lipid-based nanoparticles having a filled payload to lipid-based nanoparticles having an empty or partially filled payload based on the absorbance; A method comprising: 10. The method of claim 9, wherein the density gradient forming material comprises at least one of cesium chloride, cesium sulfate, cesium acetate, sodium iodide, sodium bromide, potassium tartrate, potassium bromide, iodixanol, Percoll®, Percoll Plus®, sucrose, glucose, glycerol, dextran, trehalose, epichlorohydrin, Ficoll®, and Ficoll-Paque®. 11. The method of claim 9, wherein the rotor is rotated at a preset rotor speed in the range of 3,000 rpm to 60,000 rpm. 12. The method of claim 9, further comprising measuring a density gradient in the container based on optical interference. 13. The method of claim 12, further comprising increasing the speed of rotation of the rotor and increasing the slope of the density gradient to detect a wider range of densities for determining the proportion of lipid-based nanoparticles. 14. The method of claim 12, further comprising decreasing the speed of rotation of the rotor and decreasing the slope of the density gradient to detect higher resolution densities for determining the proportion of lipid-based nanoparticles. 15. Increasing the speed of rotation of the rotor and increasing the slope of the density gradient based on at least one of light absorption and light interference; decreasing the speed of rotation of the rotor and decreasing the slope of the density gradient based on at least one of light absorption and light interference; 13. The method of claim 12, further comprising: 16. A centrifuge for providing quality control of a sample of lipid-based nanoparticles, comprising: a rotor for holding a sample of lipid-based nanoparticles; an optical system for measuring the absorbance of light by a sample of lipid-based nanoparticles while the rotor is rotating; one or more non-transitory computer-readable storage media programmed to analyze the absorbance of the sample of lipid-based nanoparticles once the sample of lipid-based nanoparticles is suspended in the density gradient; A centrifuge comprising: 17. The centrifuge of claim 16, wherein the one or more non-transitory computer-readable storage media are further programmed to determine a ratio of lipid-based nanoparticles having a filled payload to lipid-based nanoparticles having an empty or partially filled payload. 18. The centrifuge of claim 16, wherein the lipid-based nanoparticle sample comprises a density gradient-forming material comprising at least one of cesium chloride, cesium sulfate, cesium acetate, sodium iodide, sodium bromide, potassium tartrate, potassium bromide, iodixanol, Percoll®, Percoll Plus®, sucrose, glucose, glycerol, dextran, trehalose, epichlorohydrin, Ficoll®, and Ficoll-Paque®. 19. The centrifuge of claim 16, wherein the one or more non-transitory computer-readable storage media are further programmed to measure a density gradient based on optical interference of a sample of lipid-based nanoparticles suspended in the density gradient. 20. The centrifuge of claim 16, wherein the one or more non-transitory computer-readable storage media are further programmed to rotate the rotor at a preset rotor speed for a preset operating time. 21. The centrifuge of claim 20, wherein the preset rotor speed is in the range of 3,000 rpm to 60,000 rpm. 22. The centrifuge described in Appendix 20 or 21, wherein the preset operating time is within the range of 1 hour to 72 hours. 23. A system for analyzing a sample of lipid-based nanoparticles, comprising: 1. A processing circuitry having one or more non-transitory computer-readable storage media that store instructions that, when executed by the processing circuitry, cause the processing circuitry to: rotating a rotor about an axis of rotation at a preset rotor speed, the rotor holding at least one container having a density gradient forming material to be added to a sample of lipid-based nanoparticles; measuring the absorbance of light by a sample of lipid-based nanoparticles in at least one container; determining a ratio of lipid-based nanoparticles having a filled payload to lipid-based nanoparticles having an empty or partially filled payload based on the absorbance; A processing circuitry that performs A system comprising: 24. The system of claim 23, wherein the preset rotor speed is in the range of 3,000 rpm to 60,000 rpm. 25. The system of claim 23 or 24, wherein the rotor is rotated for a period of time ranging from 1 hour to 72 hours. 26. The one or more non-transitory computer-readable storage media, when executed by the processing circuitry, further cause the processing circuitry to: 24. The system of claim 23, further storing additional instructions for measuring a density gradient in at least one container based on optical interference. 27. A system for analyzing a sample of lipid-based nanoparticles, comprising: 1. A processing circuitry having one or more non-transitory computer-readable storage media that store instructions that, when executed by the processing circuitry, cause the processing circuitry to: rotating a rotor holding at least one container, the at least one container having a density gradient-forming material and a sample of lipid-based nanoparticles; measuring a density gradient in at least one container; measuring the position of the lipid-based nanoparticles relative to the density gradient; assessing the payload size of the lipid-based nanoparticles based on their position relative to the density gradient; A processing circuitry that performs A system comprising: 28. A method for density-based separation of lipid-based nanoparticles by centrifugation, comprising: receiving a container having a solution and lipid-based nanoparticles; applying a centrifugal force to the container to displace the lipid-based nanoparticles and form at least one detectable group within the container; determining the location of at least one detectable group of lipid-based nanoparticles; and A method comprising: 29. The method of claim 28, wherein applying centrifugal force to the container also causes a density gradient to form in the solution. 30. The method of claim 28, wherein the location of at least one group of lipid-based nanoparticles is determined using absorbance data. 31. The method of claim 28, wherein at least the detectable population of lipid-based nanoparticles comprises lipid-based nanoparticles having a similar density. 32. The method of claim 28, wherein at least one group of lipid-based nanoparticles comprises lipid-based nanoparticles having similar payloads, wherein the payloads are filled, partially empty, or empty. 33. The method of claim 32, further comprising determining the proportion of lipid-based nanoparticles that are filled, partially empty, or empty. 34. The method of claim 28, wherein the position of at least one group of lipid-based nanoparticles is measured relative to a radial position inside the rotor chamber. 35. The method of claim 28, wherein the position of at least one group of lipid-based nanoparticles is measured relative to a density gradient. 36. The method of claim 28, wherein the solution comprises at least one of cesium chloride, cesium sulfate, cesium acetate, sodium iodide, sodium bromide, potassium tartrate, potassium bromide, iodixanol, Percoll®, Percoll Plus®, sucrose, glucose, glycerol, dextran, trehalose, epichlorohydrin, Ficoll®, and Ficoll-Paque®. 37. The method of claim 28, wherein the centrifugal force is applied within the range of 3,000 rpm to 60,000 rpm for 1 to 72 hours. 38. The method of claim 28, wherein the centrifugal force is applied in multiple stages, each stage having a different rotor speed operated for a predetermined period of time.

Claims

1. 1. A method for analyzing a sample of lipid-based nanoparticles, the method comprising: providing a density gradient-forming material; operating a centrifuge to rotate a rotor about an axis of rotation at a preset rotor speed to create a density gradient within a container having the density gradient-forming material and the lipid-based nanoparticle sample; measuring the absorbance of light by a sample of the lipid-based nanoparticles in the container; determining a ratio of the lipid-based nanoparticles having a filled payload to the lipid-based nanoparticles having an empty or partially filled payload based on the absorbance; A method comprising:

2. 10. The method of claim 1, wherein the density gradient forming material comprises at least one of cesium chloride, cesium sulfate, cesium acetate, sodium iodide, sodium bromide, potassium tartrate, potassium bromide, iodixanol, Percoll®, Percoll Plus®, sucrose, glucose, glycerol, dextran, trehalose, epichlorohydrin, Ficoll®, and Ficoll-Paque®.

3. The method of claim 1 , wherein the rotor is rotated at a preset rotor speed in the range of 3,000 rpm to 60,000 rpm.

4. The method of claim 1 , further comprising measuring a density gradient within the container based on optical interference.

5. 5. The method of claim 4, further comprising increasing the speed of rotation of the rotor and increasing the slope of the density gradient to detect a wider range of densities for determining the ratio of the lipid-based nanoparticles.

6. 5. The method of claim 4, further comprising decreasing the speed of rotation of the rotor and decreasing the slope of the density gradient to detect higher resolution densities for determining the proportion of lipid-based nanoparticles.

7. increasing a speed of rotation of the rotor to increase a slope of the density gradient based on at least one of the light extinction and the light interference; decreasing a speed of rotation of the rotor to decrease a slope of the density gradient based on at least one of the light extinction and the light interference; The method of claim 4 further comprising:

8. 1. A system for analyzing a sample of lipid-based nanoparticles designed to carry a payload, the system comprising: Processing circuitry having one or more non-transitory computer-readable storage media storing instructions Equipped with The instructions, when executed by the processing circuitry, cause the processing circuitry to: rotating a rotor holding at least one container, the at least one container having a density gradient-forming material and a sample of the lipid-based nanoparticles; measuring the absorbance of light by a sample of the lipid-based nanoparticles in the at least one container while rotating the rotor; assessing the size of the payload carried in the sample of lipid-based nanoparticles based on the absorbance; and A system that allows the following to be performed.

9. The one or more non-transitory computer-readable storage media store additional instructions that, when executed by the processing circuitry, further cause the processing circuitry to: rotating said rotor at a preset rotor speed in the range of 3,000 rpm to 60,000 rpm; The system of claim 8 .

10. The one or more non-transitory computer-readable storage media store additional instructions that, when executed by the processing circuitry, further cause the processing circuitry to: rotating the rotor for a preset operating time in the range of 1 to 72 hours; The system according to claim 8 or 9,

11. The one or more non-transitory computer-readable storage media store additional instructions that, when executed by the processing circuitry, further cause the processing circuitry to: Measuring density gradients based on optical interference The system of claim 8 .

12. The one or more non-transitory computer-readable storage media store additional instructions that, when executed by the processing circuitry, further cause the processing circuitry to: Increasing the speed of rotation of the rotor to increase the slope of the density gradient. The system of claim 11 .

13. The one or more non-transitory computer-readable storage media store additional instructions that, when executed by the processing circuitry, further cause the processing circuitry to: Decreasing the speed of rotation of the rotor to decrease the slope of the density gradient. The system of claim 11 .

14. The one or more non-transitory computer-readable storage media store additional instructions that, when executed by the processing circuitry, further cause the processing circuitry to: increasing a speed of rotation of the rotor to increase a slope of the density gradient based on at least one of the light extinction and the light interference; decreasing a speed of rotation of the rotor to decrease a slope of the density gradient based on at least one of the light extinction and the light interference; The system of claim 11 .

15. 9. The system of claim 8, wherein the density gradient forming material comprises at least one of cesium chloride, cesium sulfate, cesium acetate, sodium iodide, sodium bromide, potassium tartrate, potassium bromide, iodixanol, Percoll®, Percoll Plus®, sucrose, glucose, glycerol, dextran, trehalose, epichlorohydrin, Ficoll®, and Ficoll-Paque®.

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