A method and apparatus for characterizing biomolecules and compositions thereof by measuring surface waves excited by applying a sample of a test material to its surface.

Abstract

Aspects of the present disclosure provide a method for biophysical characterization of a particular class of biomolecules or a composition thereof, the method comprising: i) acquiring wave data for a sample based on physical measurements of waves generated by providing contact between a droplet of the sample and the liquid system in a liquid system, wherein the waves have modes that encode characteristics relating to the interaction between the sample and the liquid system, and the sample contains a biomolecule or a composition thereof for analysis; and ii) providing a biophysical characterization of the biomolecule or a composition containing a biomolecule based on the wave data.

Description

Technical Field

[0001] The present invention relates to a method and an apparatus, and more particularly, to a method and an apparatus for determining the biophysical properties of biomolecules and compositions containing the same molecules.

Background Art

[0002] Biomolecules, such as proteins, fats, oils, and other biomolecules, have a series of properties that make the same biomolecules useful. With respect to food products, various compositions containing biomolecules may be used to achieve specific desired properties. Proteins and fats used in the food industry may have significant value.

[0003] With respect to personal care products and cosmetics, biomolecules may be used in various products, such as rinse-off products including shampoos, soaps, toothpastes, shower gels, etc. The same biomolecules may also be used in "leave-on" products, such as sanitizers, sunscreen lotions, cosmetics, body & face creams, insect repellents, perfumes, antiperspirants, etc.

[0004] Biomolecules may also be used in various therapeutic compositions for the treatment of humans and animals.

[0005] Determining the properties of biomolecules and the way the same biomolecules function in a specific usage situation may be difficult. The main field of research is the so-called "development potential assessment" regarding the evaluation of the properties of potential therapeutic candidates. In many cases, the purpose of the same evaluation is to avoid failures in the later stages of clinical research and to improve the manufacturability or safety profile of drugs.

[0006] The surface of a material has a thermodynamic potential independent of the volume of the material. The physical and chemical properties of a surface are derived from its thermodynamic potential. For example, the response of a surface to mechanical perturbations is given by properties such as surface tension and lateral compressibility. Similarly, the response of a surface to electromagnetic perturbations is given by properties such as surface dipole moment. As a result of these perturbations, different types of surface waves may be generated at the surface of a fluid (e.g., a liquid) forming an interface with another fluid (e.g., air). Some exemplary types of surface waves are Rayleigh waves, gravity waves, surface tension waves, and Lucassen waves. The physics of these waves is described in "Nonlinear fractional waves at elastic interfaces" by Julian Kappler, Shamit Shrivastava, Matthias F. Schneider, and Roland R. Netz in Phys. Rev. Fluids 2, 114804, published November 20, 2017. These waves may be hydrodynamically connected.

[0007] Rayleigh waves are characterized by the elliptical movement of hypothetical fluid particles in a plane that is perpendicular to the surface in equilibrium and parallel to the direction of wave propagation.

[0008] Gravity waves are characterized by the displacement of hypothetical fluid particles from their equilibrium state on a surface, and the displacement of these hypothetical particles is characterized by the presence of a restoring force of gravity or buoyancy.

[0009] Surface tension waves are characterized by the displacement of hypothetical fluid particles from an equilibrium state, possessing a restoring force of surface tension, where the displacement of the hypothetical fluid particles is transverse to the surface and transverse to the direction of wave propagation in the equilibrium state.

[0010] Lucassen waves are characterized by displacement due to vibrations from a hypothetical fluid particle's equilibrium state on the surface of a wave medium, in a direction parallel to the surface at equilibrium and parallel to the direction of wave propagation. In Lucassen waves, this hypothetical particle is subjected to a restoring force arising from the surface modulus of the wave medium's surface. In other words, Lucassen waves are compression-dilution waves that occur in the plane of the boundary (interface) between the wave medium and an adjacent medium such as air.

[0011] Lucassen waves have been observed in lipid monolayers and in other types of liquid systems.

[0012] Shamit Shrivastava and Matthias F. Schneider, "Opto-Mechanical Coupling in Interfaces under Static and Propagative Conditions and Its Biological Implications," describes how waves can be mechanically generated in a lipid monolayer using a dipper, and how the parameters of the generated waves, such as the intensity of fluorescent particles in the wave and the lateral pressure of the surface wave, can be measured, for example, using a photodetector and a Wilhemly balance, respectively.

[0013] Shrivastava S, Schneider MF. 2014, "Evidence for two-dimensional solitary sound waves in a lipid-controlled interface and its implications for biological signalling." (JRSoc.Interface11:20140098) describes how Lucassen waves can be generated in a lipid monolayer and how the parameters of these waves may be measured (e.g., fluorescence energy transfer (FRET) measurement, piezoelectric cantilever). The literature describes how the state of the lipid monolayer may be characterized by various thin-film parameters (e.g., surface density of lipid molecules, temperature, pH, lipid type, ion or protein absorption, solvent uptake, etc.) and how the state of the lipid monolayer may influence the parameters of waves propagating within it.

[0014] The article "Protons at the speed of sound: Predicting specific biological signaling" by Bernhard Fichtl, Shamit Shrivastava, and Matthias F. Schneider from the physics journal Nature Scientific Reports describes how Lucassen waves can be generated at lipid interfaces in response to changes in system pH, and how the velocity of these waves can be controlled by the compressibility of the interface. The article describes how the parameters of these waves depend on the degree of pH change. The article also describes how mechanical and electrical changes at lipid interfaces can be measured (e.g., using a Kelvin probe).

[0015] Lucassen waves may be described as interfacial compression waves and may be considered as two-dimensional sound waves (sound waves restricted to the boundary between two phases, e.g., the surface forming the fluid-air boundary). In an analogous manner to sound waves, shock waves may exist in the Lucassen wave system (e.g., two-dimensional shock waves). Lucassen shock waves may be characterized similarly to Lucassen waves, but with the additional constraint that the wave is characterized by a change in the wave medium that is nonlinear and / or discontinuous.

[0016] S. Shrivastava, "Shock and detonation waves at an interface and the collision of action potentials," in Progress in Biophysics and Molecular Biology, describes how Lucassen shock waves may propagate through lipid interfaces.

[0017] International Publication No. 2019 / 234437A1 describes how lipid interfaces may be used to transmit and receive signals. The document describes a signal processing device comprising: a first medium; a second medium; a lipid interface disposed between the first medium and the second medium, the lipid interface comprising a plurality of lipid molecules; an input transducer disposed to apply an input signal to the lipid interface, the input signal being disposed to generate a mechanical pulse at the lipid interface; and an output transducer disposed to receive an output signal by detecting a mechanical response at the lipid interface from the mechanical pulse generated at the lipid interface by the input transducer, the lipid interface being disposed to propagate the mechanical pulse from the input transducer through the lipid interface to the output transducer. [Overview of the project]

[0018] Aspects of the present invention are described in the independent claims, and optional features are described in the dependent claims. The aspects of this disclosure may be provided in relation to one another, and features of one aspect may be applied to other aspects. [Brief explanation of the drawing]

[0019] Embodiments of the present disclosure are described in detail here with reference to the accompanying drawings. [Figure 1] Figure 1 shows a flowchart illustrating the steps of a method for determining the biophysical properties of a biomolecule or a composition containing such biomolecule. [Figure 2] Figure 2 shows a diagram of an apparatus that may be used to measure physical waves in a liquid, as described in embodiments of this disclosure, comprising hardware components employed in a manner such as those described with reference to Figure 1 and / or Figure 2. [Figure 3] Figure 3 shows a flowchart illustrating the steps for identifying a candidate biomolecule within a class of biomolecules, or a composition of a candidate biomolecule, that has equivalent biophysical properties to a target biomolecule of the same class, or a composition of the same target biomolecule. [Figure 4] Figure 4 shows plots of liquid displacement at location (Y-axis) against time (X-axis) for two different samples. The first sample is a glyceride oil, and the second sample is the same glyceride oil mixed with 0.1% of a different glyceride oil. [Figure 5] Figure 5 shows a visual representation of the wave data array, where each row of the array corresponds to a waveform obtained by measuring a physical wave at a location in a liquid as a function of time. [Figure 6] Figure 6 shows a simplified visual representation of identifying separable contributions to the distribution of a data array, such as those shown in Figure 5. [Figure 7] Figure 7 shows the results of principal component analysis (PCA) from wave data acquired for glyceride oil selection.

[0020] In the drawings, like reference numerals are used to indicate like elements.

BEST MODE FOR CARRYING OUT THE INVENTION

[0021] FIG. 1 shows a method for biophysical characterization of a biomolecule of a particular class of biomolecules, or a composition of the same biomolecules. The characterization is based on wave data obtained from physical measurements of waves (or multiple waves) in a liquid generated by contact between the liquid and a droplet of the biomolecule or composition.

[0022] First, before starting the method shown in FIG. 1, a sample is prepared to contain a biomolecule or composition, as described in more detail below.

[0023] Next, a droplet of the prepared sample is brought into contact with the liquid. This may include dropping the droplet from a dispenser such that the droplet falls into the liquid and collides with the same liquid, or, for example, moving the nozzle of the dispenser that carries the droplet so as to lower the droplet to a position where the droplet contacts the liquid. The droplet may also be generated at a position where contact with the liquid occurs when the droplet reaches a given size. Thus, it will be understood that the contact may be made at a negligible net linear velocity.

[0024] The liquid may be provided in a trough such as a Langmuir trough or any similar system. The properties of the liquid are described in more detail below, but typically the liquid includes a liquid solution (such as an aqueous solution) and / or a liquid suspension. A suspension containing a colloidal suspension may be used. A thin film may be provided on the surface of the liquid.

[0025] Contact between a droplet and a liquid generates physical waves in the liquid. These waves are measured at a specific location, typically a short distance away from where the contact occurs. The measurement provides a time-series sample describing the turbulence of the liquid at that specific location. While the measurement is usually performed optically or electrically, regardless of how it is achieved, the measurement technique generally provides a physical wave measurement, including sensitivity to at least two of the following wave modes: Rayleigh waves, gravity waves, surface tension waves, and Lucassen waves. Sensitivity to Lucassen waves is usually desirable. These wave modes encode the characteristics of the interaction between the sample and the liquid system, and these characteristics themselves depend on the biophysical properties of the biomolecules or compositions in the sample. The wave data is stored in a data store, such as digital memory, for retrieval by a processor. Typically, the wave data associated with a droplet comprises a waveform, or set of waveforms, that provides a physical wave measurement in the liquid, with sufficient degrees of freedom to encode the relevant wave modes that constitute the physical wave. Visual representations of the two identical waveforms are shown in Figure 4.

[0026] The above procedure may be repeated for multiple droplets, so that for each droplet, the same waveform, or set of waveforms, is stored in the data store. The liquid conditions for each droplet may be identical to those of all the aforementioned droplets. For example, the composition of the liquid at the point of contact may be controlled so that the composition remains substantially the same for all droplets. One way to achieve this is to provide flow in the liquid, but other methods may be used. However, it is also possible to accumulate biomolecules or compositions in the liquid, so that the concentration of these biomolecules or compositions at the point of contact increases as the droplet is applied to the liquid. In any event, the final result of this procedure is a set of tuples of wave data. Each tuple corresponds to a waveform obtained by measuring the physical waves produced in the liquid by the corresponding droplet. This may be stored as an array of data where rows or columns correspond to the tuples. A visual representation of this array is provided in Figure 5.

[0027] Next, this array of wave data may be manipulated by a data processor, which may be configured to identify separable contributions to the waveforms described in the data. This may be done by dimensionality reduction techniques, for example, the processor may be configured to apply principal component analysis (PCA) to the wave data. In the context of this disclosure, it will be understood that the analysis generates a number of coefficients and a number of vectors. For example, the coefficients p may be eigenvalues, and the vectors P may be eigenvectors of the covariance matrix of the wave data. Other dimensionality reduction techniques may be used to identify one or more separable contributions to the variance in the wave data. A visual representation of dimensionality reduction of wave data is provided in Figure 6.

[0028] Next, the associated data is retrieved from a data store such as digital memory. The associated data may describe a predetermined relationship between the characteristics of the wave data and one or more biophysical properties of the sample. For example, the associated data may provide a mapping from separable contributions to specific values ​​relating to biophysical properties.

[0029] The method may include, for example, a data processor that obtains information defining the class of a biomolecule from the input and selects the relevant model from associated data according to the class of the biomolecule. The associated data may store a set of models for one or more of the following classes of biomolecules. • Classes of biomolecules, polymers and / or macromolecules, such as polypeptides, lipids, polysaccharides, and nucleic acids. • Root, for example • Antibodies, contractile proteins, enzymes, hormone proteins, structural proteins, storage proteins, transport proteins, or Plant proteins may be selected from algal proteins, black bean proteins, canola wheat proteins, chickpea proteins, broad bean proteins, lentil proteins, lupin bean proteins, mung bean proteins, oat proteins, pea proteins, potato proteins, rice proteins, soybean proteins, sunflower seed proteins, wheat proteins, white bean proteins, and their protein isolates or concentrates. Animal proteins may be selected from porcine protein, bovine protein, sheep protein, horse protein, poultry protein, and their protein isolates or concentrates. • Biomolecules selected from nonpolymeric and / or non-molecular-weight classes of biomolecules, e.g., vitamins, phytochemicals (e.g., carotenoids, flavonoids, isoflavonoids, phenolic acids), food seasonings (e.g., those based on plant-derived alcohols, esters, and / or ketone functional group compounds), amino acids, peptides, monosaccharides, disaccharides, oligosaccharides, free fatty acids, monoglycerides, diglycerides, triglycerides, sterols, steroids, nucleotides, nucleosides, etc. Edible glyceride oils selected from vegetable oils, marine oils, and animal oils, such as acai oil, almond oil, beech oil, cashew oil, coconut oil, rapeseed oil, corn oil, cottonseed oil, grapefruit seed oil, grapeseed oil, hazelnut oil, hemp oil, lemon oil, macadamia oil, mustard oil, olive oil, orange oil, peanut oil, pecan oil, pine nut oil, pistachio oil, poppy seed oil, rapeseed oil, rice bran oil, safflower oil, sesame oil, soybean oil, sunflower oil, walnut oil, and wheat germ oil.

[0030] In the context of this disclosure, it will be understood that these are merely examples of the class of biomolecules to which this disclosure may apply.

[0031] The data processor may also be configured to select from a set of models stored in the associated data according to biophysical parameters (or multiple parameters) determined for that class.

[0032] Next, association data relating to the relevant class of a biomolecule or composition and its relevant biophysical properties are used together with wave data to determine data exhibiting those biophysical properties. This may include performing actions defined in the association data on data exhibiting separable contributions.

[0033] An apparatus implementing this method is shown in Figure 2. In this example, the source of wave data in the apparatus shown in Figure 2 is analyzer 1, which is configured and positioned to perform physical measurements of waves in the liquid generated by contact between the liquid and a sample droplet.

[0034] As shown in the figure, the device 1 is configured to generate wave data as described later and to provide the wave data to the data processor.

[0035] The data processor is equipped with an input connection for acquiring wave data from a data source such as device 1. The data processor is also connected to a data store such as digital memory that stores associated data.

[0036] The associated data may also be derived from a prior analysis of other biomolecules of the same class as those present in the sample. As illustrated, the associated data may comprise multiple sets of models, each set of models relating to a particular class of biomolecules and their compositions. Each model comprises digital data that defines the data behavior performed by the processor to provide a mapping between wave data and one or more biophysical properties.

[0037] Various models may be used. As a first example, the associated data may comprise a set of coefficients that define a linear model of one or more biophysical properties as a function of selected separable properties relating to wave data. The data processor may be configured to determine the biophysical properties by scaling each of the selected contributions p according to the corresponding coefficients in the associated data, and then performing a linear sum of the scaled contributions. As a second example, the model may be nonlinear, defining one or more biophysical properties as a nonlinear function of the selected contributions p, and the data processor may apply the nonlinear function to the separable contributions p to determine the biophysical properties. As a third example, the associated data may define an adaptive model, for example, comprising machine learning elements in the form of a network of nodes and / or weights, and the data processor may be configured to apply the machine learning elements to wave data to determine the biophysical properties of biomolecules or compositions in a sample. Each node in the network may include linear and / or nonlinear operations as described above, and may also include a dimensionality reduction process to identify the separable components used in these operations. For example, the associated data and the data processor may be configured to collaboratively implement machine learning elements, such as neural networks or other machine learning systems.

[0038] The associated data may comprise a set of models for each class of biomolecules and for compositions containing biomolecules of the same class. For example, the associated data may comprise a set of models for proteins or a set for lipids. Each model in each set may associate one or more predetermined biophysical properties of the biomolecules or compositions of the same class with properties of wave data relating to biomolecules of the same class, such as selected separable components of the wave data.

[0039] The data processor may be configured, for example, to retrieve information that defines the class of a biomolecule from input or hardcoded data. The data processor may also be configured to select a relevant model from associated data according to the class of the biomolecule. The data processor may also be configured to select within a set according to biophysical parameters (or parameters) determined for that class.

[0040] As a first example, if the class of biomolecules is food proteins, the data processor may be configured to select associated data related to protein classification and to select models within that associated data that are associated with one or more perceptual properties exhibited by biomolecules used in food or compositions containing biomolecules.

[0041] As a second example, if the class of biomolecules, or compositions thereof, is a protein that is a therapeutic candidate, such as an antibody or a hormone protein, the data processor may be configured to select associated data related to protein classification and to select a model within that associated data that is associated with indicators of developmentability, such as manufacturability or safety profiles.

[0042] As a third example, the biomolecule may be an edible glyceride oil selected from vegetable oils, marine oils, and animal oils, and the data processor may be configured to select associated data related to perceptual properties associated with the application of the biomolecule, or a composition of the biomolecule, to the human or animal body. Examples of such perceptual properties include skin absorption, skin spreadability, skin stickiness, skin density, suppleness during use, shine during use, smoothness during use, softness during use, frizz after washing, shine after washing, smoothness after washing, and softness after washing.

[0043] As a fourth example, for any class of biomolecules or compositions thereof, the data processor may be configured to select associated data related to quantitative properties selected from viscosity, stability in a particular environment, hydrophobicity, binding affinity to a particular target, solubility in a particular solvent, surface charge, and specific gravity.

[0044] The data processor is further configured to perform data operations on wave data, determined by selected associated data relating to the relevant class of biomolecules or compositions, in order to determine data that exhibits biophysical properties.

[0045] As mentioned above, dimensionality techniques can be used to identify separable contributions to wave data, which can then be used to obtain inferences about the biophysical properties of biomolecules or compositions. Examples of dimensionality reduction methods include principal component analysis, singular value decomposition, independent component analysis, and blind signal separation (BSS) methods such as non-negative matrix factorization. Other examples of dimensionality reduction methods include spectral analysis methods and multi-resolution analysis methods such as time-frequency analysis (including analysis based on short-time Fourier transforms and wavelet analysis). However, it is not necessary to use a separate dimensionality reduction step, and in fact, it is not necessary to perform dimensionality reduction at all. Machine learning elements, such as neural networks or other machine learning systems, may include embedding to reduce the dimensionality of the machine learning element as part of its integrated data processing capabilities, rather than as a separate step as described above. In some embodiments, the machine learning element may be trained to attribute one or more biophysical properties of a particular class of biomolecules or compositions of those biomolecules to the wave data. In these embodiments, it is not necessary to determine separable contributions by dimensionality reduction or other methods. Associated data may define relationships between wave data and biophysical properties. Machine learning is just one way to do this, and associated data may define other mappings between wave data and biophysical properties to act on wave data to provide biophysical characterization of biomolecules or compositions containing biomolecules.

[0046] In the context of the above disclosure, it will be understood that the data processor may be configured to act on data obtained directly from apparatus 1, such as that shown in Figure 2. For example, the method of this disclosure may include performing physical measurements of a wave by preparing a sample for analysis, providing contact between a sample droplet and a liquid system to generate a wave, and then also performing physical measurements of the wave. However, the physical measurements may be performed separately by another apparatus, and the data processor may be configured to act on data obtained from other sources, such as data retrieved from memory or data received via network messages.

[0047] The physical wave may include a surface wave having modes that encode the characteristics of the interaction between the sample and the liquid. An example of such wave modes is the Lucassen wave mode. Regardless of the apparatus or measurement method used, the physical measurement may optionally include sensitivity to such modes, including the Lucassen wave mode.

[0048] Apparatus 1 is an example of an apparatus for characterizing the interaction between a sample droplet and a liquid system. The apparatus characterizes a wave 5 in the liquid system based on physical measurements of the wave. This may be done by an optical method, measuring the polarization of a light beam 7 reflected by the wave, and in particular, this may be done using the ratio between the s-polarization component and the p-polarization component of the reflected light beam 7 to sense variations in the refractive index (and therefore density) of the liquid in the presence of the wave. Time-series data of the same data at a specific location in the liquid may be used to characterize the wave.

[0049] The apparatus 1 shown in Figure 2 comprises a droplet supply unit 9 configured to bring a liquid into contact with a sample droplet, a light beam optical system 11, a light collector 13, a detector 15, and a wave measurement module 17. As shown in Figure 2, the apparatus 1 includes a storage unit 23 that holds the volume of liquid 21 (i.e., storage unit medium). The storage unit 23 may be provided by a trough such as a Langmuir trough.

[0050] As shown in Figure 2, a mechanical fixture 19 may be provided to hold the device in place relative to the storage unit 23, but it will be understood that the fixture 19 is not essential and may be manufactured and sold separately from the device 1 itself. The wave measurement module 17 is connected to the droplet dispenser 9 and to the detector 15 for communication of control signals and data, and the wave measurement module 17 may also be connected to the light beam optical system 11.

[0051] Examples of the type of liquid that may provide the volume of liquid 21 (i.e., storage medium) include aqueous solutions (e.g., deionized pure water) and suspensions.

[0052] The optical beam optics system 11 includes a polarization source positioned to illuminate a region of the liquid surface 3 with a beam 7 having a selected incidence angle α. The optical beam 7 may also be coherent. A preferred light source example includes a laser, and the optical beam optics system may include a polarizer.

[0053] The light collector 13 is positioned to receive the beam of light 7 reflected from the thin film region and to provide the reflected beam of light to the detector. The light collector 13 is positioned so that its optical axis is directed towards the region of the liquid surface 3 at the angle α of specular reflection of the light beam incident on the liquid surface from the light beam optical system.

[0054] The detector 15 is configured to sense the parameters of the light received from the light collector 13 and provide a signal containing these parameters to the wave measurement module 17. Typically, these parameters may include sensitivity to various wave modes, such as Lucassen waves and other types of waves, and may be parameters that can characterize the wave. For example, the parameters may indicate the polarization of the light beam 7 after interaction with the wave. For example, the parameters may include an index of the intensity of one or more polarization components of the received light, such as the intensity of (a) the first component of the second polarization and / or (b) the second component of the second polarization. The second component may be orthogonal to the first component. The first component may be the s component and the second component may be the p component.

[0055] The droplet provider 9 is positioned relative to the liquid 5 so that it can provide contact between the liquid and the sample droplet. For example, the droplet provider 9 may include a sample source and be configured to bring the surface of the liquid into contact with the sample droplet in order to generate waves in the liquid. Such stimulation may create waves 5 in the liquid exhibiting some or all of the above wave modes.

[0056] The wave measurement module 17 is configured to control the droplet dispenser 9 to apply a stimulus to the liquid, thereby operating the detector 15 and collecting a time series sample of the light received by the detector 15. The sample may include samples relating to the intensity of one or more polarization components as described above. Typically, the sampling rate is at least 1 MHz, for example, 10 MHz. The wave measurement module may also be configured to apply a low-pass filter to the simultaneous series before downsampling the data to approximately 20 kHz or 20 kHz. Typically, the sampling rate of the downsampled time series is selected based on the size of the irradiated area and the expected velocity of the waves on the liquid surface. For example, the expected velocity may be approximately 1 ms⁻¹, and the irradiated area may have a diameter of ~5 mm, in which case the upper limit on the frequency of surface waves that can be significantly sampled is 10 kHz. The sampling rate of the downsampled time series may be selected to ensure that the measurement stays sufficiently within this available bandwidth.

[0057] The wave measurement module 17 may be configured to control the timing of the sample based on the operation of the droplet dispenser 9, for example, so that the surface wave 5 in a region of the liquid surface irradiated by the beam 7 can be sampled at a selected time and for a selected period after contact with the droplet. The time and / or period is usually selected based on the distance from the portion of the liquid to which the droplet is applied to the irradiated region. The wave measurement module may further be configured to provide a specific sampling scheme for a particular type of measurement. The wave measurement module may be configured to implement a first sampling scheme for a first type of measurement and a different second sampling scheme for a different second type of measurement. For example, if a thin film is present on the surface of the storage unit, the wave measurement module may use a long sampling period (total time over which the sample is collected) to measure viscosity or hydrophobicity in a lipid thin film, or to measure binding in a protein thin film. Recording a single stimulus typically has a time resolution of microseconds and a duration of several seconds. This may be sufficient for measuring the properties of molecules that interact strongly and / or rapidly with the membrane, such as electrostatic interactions or hydrogen bonding. In some embodiments, multiple identical stimuli observed over a period of several seconds to several hours, with a repetition rate of several seconds, may provide improved measurements of the properties of molecules that interact weakly and / or slowly with the membrane, such as binding or reaction kinetics.

[0058] The wave measurement module may be configured to sample data at selected time intervals after stimulating the liquid surface of the storage section, and to repeat the same sampling at the same time interval after subsequent stimuli in order to provide repeated measurements. These measurements may be for relatively short periods.

[0059] During operation, the wave measurement module 17 operates the droplet provider 9 to provide contact between the liquid and a sample droplet. This triggers a wave 5 in the liquid. The wave propagates outward through the liquid from the point of contact. The light beam optics 11 illuminates the region of the liquid through which the wave is propagating, and the wave measurement module 17 operates the detector 15 to acquire a series of samples representing the light beam 7 after its interaction with the region. This series of samples may be provided to the detector 15 by the light collector 13. Thus, the disturbance of the liquid by the wave at the same location can be recorded as a function of time (time series) in the series of samples of data. Each sample in the simultaneous series may include polarization data, which may be in the form of the intensity of the s-polarization component and the intensity of the p-polarization component with respect to the reflected light. The wave measurement module 17 may be configured to determine, for each sample, an indication of the polarization angle of the reflected beam 7, for example, the ratio of the intensity of the s-component to the intensity of the p-component. The wave measurement module may derive Lucassen wave characteristics from this time series. Examples of Lucassen wave characteristics include the amplitude, frequency components, phase velocity, group velocity, and phase of the Lucassen wave. The wave measurement module 17 may then use these Lucassen wave characteristics to provide information about stimuli or information about an arbitrarily selected thin film on the storage surface, as described later. Other wave measurement methods may be used; this is just one example. Imaging methods and non-optical methods may include X-ray methods, electrical transducers, and mechanical transducers.

[0060] The apparatus may be controlled to provide a series of identical measurements of a series of identical waves, each wave being generated by contact between the liquid and a droplet of the test substance. This may generate wave data comprising multiple individual waveforms, each corresponding to a physical measurement of a single wave generated in the liquid by contact with the sample droplet.

[0061] In the context of this disclosure, it will be understood that the change in polarization resulting from reflection at an interface is related to the difference in refractive index at the interface. In this case, the inventors understand that the refractive index may be related to the surface properties of the liquid. Therefore, the wave measurement module may derive information about the variation of a time-series sample, such as the sp ratio, as a function of time. This may allow for the characterization of complex wave modes with many degrees of freedom. The storage surface may have a thin film, and the properties of the interface measured to generate wave data may be related to variations in the density and / or position of the thin film and the orientation of molecules within the film. As understood, after the deposition of biomolecules, the biomolecules themselves may be present in the existing thin film, or the thin film may be generated from the biomolecules themselves.

[0062] If a thin film is present, it typically contains a different type of liquid than that of the volume of liquid 21. Therefore, the liquid thin film and the volume of liquid 21 may have an interface between them, such as a liquid-liquid interface. The thin film may have viscoelastic properties. These types of thin films, and others, may exhibit various surface wave modes in response to stimuli. Examples of these wave modes include Rayleigh waves, gravity waves, surface tension waves, and Lucassen waves.

[0063] Examples of liquid types that provide a thin film include proteins, lipids, and other types of lipids. In the context of this disclosure, it will be understood that such materials may be held in a volume of liquid (for example, dispersed in a suspension, or otherwise), and that dynamic equilibrium may exist between the thin film and the material held in a volume of liquid 21.

[0064] The above apparatus may be used in other ways. One such method is shown in the flowchart in Figure 3.

[0065] Figure 3 shows a method for identifying candidate biomolecules within a class of biomolecules, or compositions of such candidate biomolecules, that have equivalent biophysical properties to a target biomolecule of the same class, or a composition of the same target biomolecule.

[0066] As previously mentioned with reference to Figure 1, a sample containing a candidate biomolecule within a class of biomolecules, or a composition of such candidate biomolecule, may be prepared, and contact may be provided between a droplet of the sample and a liquid. The physical waves generated in the liquid by this contact may be measured to obtain physical wave data, as previously mentioned.

[0067] However, the acquired same-wave data has a mode that encodes the characteristics of the interaction between the sample and the liquid system.

[0068] Next, the data processor, as described above with reference to Figure 2, provides a comparison of wave data obtained from the interaction of the sample with the surface of the liquid system with wave data associated with the target biomolecule or the target composition of the target biomolecule.

[0069] This comparison may be based on (a) biophysical characterization of a biomolecule or composition containing a biomolecule based on wave data, and (b) corresponding characterization of a target biomolecule or target composition of the target biomolecule. Any of the biophysical characterizations described above may be used.

[0070] The above steps may then be repeated for each of a number of different biomolecular candidates or compositions of the same biomolecular candidate until a biomolecular candidate or composition of the same biomolecular candidate is identified that has selected biophysical property values ​​that indicate the properties of the candidate biomolecular that match those of the target biomolecular.

[0071] The nature of this matching process may depend on the class of biomolecules in question. For example, the potential for developing a therapeutic candidate may be evaluated in this way. Similarly, the perceptual properties of candidate biomolecules used in food or personal care products may also be evaluated in this manner.

[0072] As described herein, in one embodiment, a method is provided for identifying a candidate biomolecule within a class of biomolecules, or a composition of a candidate biomolecule, that has equivalent biophysical properties to a target biomolecule of the same class, or a composition of the same target biomolecule. The method involves comparing wave data obtained from the interaction of a sample of the biomolecule or a composition of the same biomolecule with the surface of a liquid system, to wave data associated with the target biomolecule or a target composition of the same target biomolecule. In another embodiment, a method is provided in which the biophysical characterization of a biomolecule is performed based on wave data derived from the interaction of a biomolecule sample with a liquid system. The results of the biophysical characterization may be incorporated into a library as reference data, from which wave data for other biomolecules or compositions of other biomolecules may be compared.

[0073] To facilitate comparison between wave data obtained for target biomolecules or reference biomolecules held in a library, the biomolecule sample to be tested is selected such that the liquid carrier present in the sample is substantially the same as that used when obtaining corresponding wave data for the target biomolecule or reference biomolecule. Similarly, the viscosity of the liquid medium in the storage section of the liquid system, the height of the droplet dispenser, and the volumetric flow rate of the droplet dispenser, as described herein, also facilitate comparison. Nevertheless, this is not essential, as the comparison may still be performed with contributions from any variations in the method of wave data acquisition computationally (e.g., by multidimensional calibration). Furthermore, descriptions of specific biomolecules useful in the methods described herein, along with descriptions of sample preparation, are provided below.

[0074] Biomolecules The methods described herein enable the biophysical characterization of biomolecules or compositions thereof based on wave data obtained in connection with the methods. Biomolecules are well-known organic molecules, primarily composed of hydrogen and carbon, found in nature from ordinary organisms and / or as part of biological processes. There are no specific limitations on the properties of biomolecules or compositions thereof that may be used in the methods described herein.

[0075] Generally, biomolecules may be assigned to the polymeric and / or macromolecular class, or alternatively, the nonpolymeric and / or non-macromolecular class. The polymeric and / or macromolecular class of biomolecules includes those selected from polypeptides, lipids, polysaccharides, and nucleic acids.

[0076] A protein corresponds to a specific form of polypeptide, specifically one that possesses a tertiary structure that contributes to the function or use of that protein in a biological context. Examples of proteins include antibodies, contractile proteins, enzymes, hormone proteins, structural proteins, storage proteins, and transport proteins. As is understood, the protein class encompasses a wide range of uses / functions, including therapeutic, cosmetic, and nutritional applications.

[0077] Of course, one equivalent form of protein biomolecule for which substantial therapeutic value has been found is the antibody (e.g., monoclonal or polyclonal antibody). The methods of this disclosure may be used to investigate the biophysical properties of known antibodies or those under clinical development. This may then represent a means of determining the viability of a candidate antibody under development from the early stages based on a comparison of the candidate's biophysical properties with a target antibody (e.g., a commercially available antibody with proven therapeutic efficacy).

[0078] In some embodiments, the biomolecule (e.g., protein biomolecules), or compositions thereof, are therapeutic candidates such as antibodies or hormone proteins. In some embodiments, the biophysical properties provided by the methods described herein include indicators of developability, such as manufacturability or safety profile. For example, the indicators of developability are based on a plurality of biophysical properties, each of which is derived from one or more separable contributions of wave data.

[0079] The methods of this disclosure may be used to evaluate the biophysical properties of animal proteins, which may be selected from porcine proteins, bovine proteins, sheep proteins, horse proteins, poultry proteins, and their protein isolates or concentrates.

[0080] For example, the growing popularity of plant-based diets and their environmental benefits have attracted the interest of food manufacturers seeking to mimic the perceptual properties (e.g., sensory properties) of animal proteins with plant-based alternatives. The methods of the present disclosure may be used to evaluate the biophysical properties of plant proteins or compositions of plant proteins for the purpose of comparison with target biomolecules such as animal proteins or alternative plant proteins, or compositions of said target biomolecules. Examples of plant proteins include those selected from algal proteins, black bean proteins, canola wheat proteins, chickpea proteins, broad bean proteins, lentil proteins, lupin beans, mung bean proteins, oat proteins, pea proteins, potato proteins, rice proteins, soybean proteins, sunflower seed proteins, wheat proteins, white bean proteins, and protein isolates or concentrates thereof.

[0081] Accordingly, in some embodiments, the methods described herein provide biophysical characterization of a biomolecule or a composition of the biomolecule, including the perceptual properties of the biomolecule or a composition of the biomolecule. For example, the biomolecule or composition of the biomolecule may be selected from the protein class, and the method provides biophysical characterization including the perceptual properties that the biomolecule or composition containing the biomolecule exhibits when used in food.

[0082] Examples of nonpolymeric and / or non-molecular classes of biomolecules that may be characterized using the methods of the present disclosure include vitamins, phytochemicals (e.g., carotenoids, flavonoids, isoflavonoids, phenolic acids), food seasonings (e.g., those based on plant-derived alcohols, esters, and / or ketone functional group compounds), amino acids, peptides, monosaccharides, disaccharides, oligosaccharides, free fatty acids, monoglycerides, diglycerides, triglycerides, sterols, steroids, nucleotides, and nucleosides.

[0083] A class of biomolecules of particular interest to the food and cosmetic industries includes glyceride oils, which may be incorporated into food or applied to the human or animal body. As used herein, the term “glyceride oil” refers to an oil or fat containing triglycerides as its main component. For example, the triglyceride component may be at least 50% by weight of the glyceride oil. Glyceride oils may also contain monoglycerides and / or diglycerides. Glyceride oils are obtained at least partially from natural sources (e.g., plant, animal, or fish / crustacean sources). In its raw form, glyceride oil typically encompasses vegetable oils, marine oils, and animal oils / fats, which contain free fatty acids and phospholipid components.

[0084] Vegetable oils include oils from all plants, nuts, and seeds. Examples of suitable vegetable oils that may be used in the manner described herein include acai oil, almond oil, beech oil, cashew oil, coconut oil, rapeseed oil, corn oil, cottonseed oil, grapefruit seed oil, grapeseed oil, hazelnut oil, hemp oil, lemon oil, macadamia oil, mustard oil, olive oil, orange oil, palm oil, peanut oil, pecan oil, pine nut oil, pistachio oil, poppy seed oil, rapeseed oil, rice bran oil, safflower oil, sesame oil, soybean oil, sunflower oil, walnut oil, and wheat germ oil. Suitable marine oils include oils derived from the tissues of oil-containing fish or crustaceans (e.g., krill). Suitable examples of animal oils / fats include pork fat (lard), duck fat, goose fat, tallow, and butter.

[0085] Free fatty acids that may be present in glyceride oil include monounsaturated free fatty acids, polyunsaturated free fatty acids, and saturated free fatty acids. Examples of unsaturated free fatty acids include myristoleic acid, palmitoleic acid, sapienic acid, oleic acid, elaidic acid, vaccenic acid, linoleic acid, linoleidic acid, alpha-linolenic acid, arachidonic acid, eicosapentaenoic acid, erucic acid, and docosahexaenoic acid. Examples of saturated free fatty acids include caprylic acid, capric acid, undecylic acid, lauric acid, tridecylic acid, myristic acid, palmitic acid, margaric acid, stearic acid, nonadecylic acid, arachidic acid, henicosyl acid, behenic acid, lignoceric acid, and cerotic acid.

[0086] Accessing the biophysical signature of a biomolecule, such as a glyceride oil, or a composite composition of such biomolecule, through the method of this disclosure may enable comparison with a target biomolecule of the same class (e.g., another glyceride oil) having a specific biophysical property of interest, such as a desired sensory profile.

[0087] Therefore, in some embodiments, the biomolecule, or composition thereof, is an edible glyceride oil, and the method provides biophysical characterization of the biomolecule, or composition thereof, including the sensory properties of the biomolecule, or composition thereof. For example, the sensory properties may represent properties associated with the application of the biomolecule, or composition thereof, to the body of a human or animal. For example, the sensory properties represent at least one of the following: skin absorption, skin spreadability, skin stickiness, skin density, suppleness in use, shine in use, smoothness in use, softness in use, curliness after washing, shine after washing, smoothness after washing, and softness after washing.

[0088] As should be understood, the methods described herein may also be used to assess any potential contamination levels across a series of biomolecular samples, and the degree of contamination may be assessed based on the effect of the contamination on the dominant biophysical properties.

[0089] Biomolecular samples The method of the present invention involves generating wave data in a liquid system employing a liquid sample in which a biomolecule, or a composition thereof, is incorporated into a liquid carrier. As understood, certain properties relating to the biomolecule, or a composition thereof, influence the most preferred form that the sample may take, i.e., the particular way in which the biomolecule is arranged within the liquid carrier. For example, the biomolecule may be incorporated into the solution as a suspension or as part of an emulsion, depending on the miscibility of the biomolecule in a given liquid carrier or the melting point of the biomolecule. Those skilled in the art can easily select a liquid carrier based on a particular class of biomolecule being investigated.

[0090] For example, for polar biomolecules, aqueous gas carriers such as water (e.g., deionized water), glycerol-water mixtures, physiological saline (e.g., phosphate-buffered saline (PBS)), or medical buffer solutions (e.g., tris(hydroxymethyl)aminomethane buffer (THAM)) are generally the most suitable. Alternative non-aqueous liquid carriers that may also be used to solubilize amphiphilic and hydrophobic biomolecules include alcohols (e.g., methanol, ethanol, 2-propanol), chloroform, acetone, and combinations thereof (e.g., methanol-chloroform combinations).

[0091] Furthermore, biomolecules that are inherently amphiphilic (e.g., glycerides, free fatty acids, or lipids) may preferably be incorporated into the sample as an emulsion (e.g., an oil-in-water emulsion) which may be produced by known methods. For example, such an emulsion may be readily prepared by mixing the biomolecule, or a composition thereof, with an aqueous solvent (e.g., deionized water), stirring the mixture (e.g., using sonication), and applying heat (e.g., from 30°C to 60°C) until an emulsion is formed, as indicated by a change to an opaque liquid.

[0092] Table 1 below lists suitable liquid carriers for different classes of biomolecules, suitable concentration ranges (w / v) of biomolecules or compositions thereof in the liquid carriers, and indications of whether the sample can suitably form an emulsion. [Table 1]

[0093] Storage media As briefly described above, the storage medium employed in the methods described herein is a stable liquid, usually a solution or suspension, on the experimental timescale, in which surface waves can propagate when a sample is deposited in the storage medium. For ease of operation and reproducibility, simple aqueous solutions are usually employed in the methods described herein. For example, deionized water or saline / alkali metal halide salt solutions (e.g., NaCl) may be used, the latter having the potential advantage of promoting the retention / formation of a thin film on the surface of the liquid storage if present or desired. Surfactants may also be included in the liquid medium for the purpose of forming a thin film on the surface of the liquid storage, as a means of modifying or optimizing the waveform response after sample deposition. Examples of suitable surfactants include anionic surfactants (e.g., those containing sulfate or sulfonic acid groups).

[0094] Biophysical characterization The methods described herein can provide biophysical characterization of biomolecules or compositions thereof based on wave data generated in connection with the methods. These characterizations include several distinct categories of properties, including those that directly affect droplet formation and propagation in liquid systems (i.e., those relating to hydrodynamic parameters) and properties of compressible fluids that play a role in wave propagation, such as compressibility, bulk (second) viscosity, thermal conductivity, and heat capacity. In addition, there are enthalpies of the interaction between the droplet and the liquid in the reservoir, particularly interface-related enthalpies such as the droplet's surface charge, partition coefficient (hydrophobicity), and surface pKa, which directly contribute energy to the waves and are therefore derivable from the wave data. Furthermore, the reaction kinetics and timescale of this interaction are accessible (some of the above properties are time- and timescale-dependent). The timescale may include, for example, information regarding conformational changes in the system. Short timescales (i.e., <microseconds) may correspond to the enthalpy of conformational changes within a molecule; intermediate timescales (i.e., from microseconds to milliseconds) may correspond to collective intramolecular changes of large molecules (e.g., allosteric changes); and long timescales (i.e., >milliseconds) may correspond to collective intramolecular and intermolecular changes (e.g., phase changes from fluid to gel).

[0095] Furthermore, the wave data can be used to evaluate the indirect properties of biomolecules based on comparison with the wave data of a hypothetical standard biomolecule, where stepwise compositional changes deviating from the hypothetical standard composition may provide indirect insights into biophysical properties such as freezing point depression, vapor pressure (volatility), osmotic pressure, boiling point elevation, critical micelle concentration, and surface pressure.

[0096] Therefore, in another embodiment, a method is provided for determining the enthalpy of the interaction between a liquid system and a sample of a particular class of biomolecules, or a composition thereof. i) A step of acquiring wave data for a sample based on physical measurements of waves generated by providing contact between a sample droplet and the liquid system in a liquid system, wherein the waves have a mode that encodes characteristics relating to the interaction between the sample and the liquid system, and the sample contains a biomolecule for analysis or a composition of the biomolecule. ii) A step of determining the enthalpy, reaction kinetics, and / or time scale of the interaction between the sample and the liquid system based on wave data, Includes.

[0097] In yet another embodiment, a method is provided for identifying a candidate biomolecule within a class of biomolecules, or a composition of a candidate biomolecule, which has equivalent enthalpy, reaction kinetics, and / or timescales for interactions with a target biomolecule of the same class as the target biomolecule or a composition of the target biomolecule, wherein the method is (i) With respect to a sample, a step of acquiring wave data based on physical measurements of the generated wave by providing contact between a sample droplet and the liquid system in a liquid system, wherein the wave has a mode that encodes characteristics relating to the interaction between the sample and the liquid system, The sample contains a specific class of biomolecular candidates for analysis, or a composition of those biomolecular candidates, and the steps are as follows: (ii) A step of providing a comparison of wave data obtained from the interaction of a sample with the surface of a liquid system with wave data associated with a target biomolecule or a target composition of the target biomolecule, The process involves repeating steps i) to ii) until, optionally, for different biomolecule candidates of the same class, or compositions of the same different biomolecule candidates, a biomolecule candidate or composition of the same biomolecule candidate is identified that has equivalent values ​​for at least one form of interaction enthalpy exhibiting equivalent biophysical properties between the candidate biomolecule and the target biomolecule. Includes.

[0098] Herein, the present invention is illustrated by the following example with reference to the figures.

[0099] Outline method for sample preparation, deposition, and wave data acquisition 1. Obtain a biomolecule or composition thereof for testing and prepare a solution, suspension, or emulsion of the biomolecule in a liquid carrier at w / v concentrations ranging from 0.1 mg / ml to 0.5 mg / ml in a sample tube (e.g., a LowBind Eppendorf tube). 2. Prepare the liquid medium for the storage section and optionally degas it under vacuum. 3. Incorporate the storage medium into a storage unit (e.g., a Langmuir-Tlaff) into which the sample may accumulate. 4. Using a droplet dispenser (e.g., a single-channel pipette), one or more droplets of a sample in a set volume (e.g., from 1 μl to 100 μl) are deposited into the storage unit from a fixed location above the air-water interface (at a height of 2 mm to 5 mm above the air-water interface) at a fixed volumetric flow rate (e.g., from 0.1 μl / s to 20 μl / s) that provides a fixed droplet frequency, i.e., the number of droplets generated per second (e.g., from 0.1 droplets / s to 1000 droplets / s). 5. Using an apparatus (for example, an apparatus shown in Figure 2 and described in more detail herein) that incorporates: i) a light beam optical system with a polarization source positioned to illuminate a region of liquid on the surface of the storage section with a beam having a selected incidence angle α; ii) a light collector positioned to receive the beam of light after reflection by a region of thin film; and iii) a detector configured to sense the parameters of the light received from the light collector in order to provide a signal to a wave measurement module 17, the response of the air-water interface of the storage section to a first droplet and any consecutive droplets, which are administered into the storage section from a fixed location, is measured.

[0100] As can be understood, sample deposition may be automated, and an Opentrons(RTM) system (e.g., OT-1 or OT-2 manufactured by Opentrons Labworks) may preferably be used for this purpose. The Opentrons(RTM) system may contain multiple independently prepared samples in a separate sample holder positioned inside the Opentrons system. In particular, the system may be as follows: A. Sample locations within the Opentrons (RTM) system, B. The location where the sample is administered into the storage area, C. The height of the droplet dispensing unit before administering the sample into the storage unit, D. Volumetric flow rate of the sample to be administered (microliters / second and E. The volume (microliters) of the sample administered by aspiration, It may be programmed to specify this.

[0101] The ability to control the height of the droplet dispenser and the volumetric flow rate of the sample being administered allows for control of droplet size and droplet frequency. During operation, a sample placed inside the Opentrons(RTM) system at a designated location is aspirated and then moved to a designated location (x, y) before being moved in the z-direction to a designated z-location. The system pauses and then begins dispensing the sample at a designated volumetric flow rate, for example, using a 1000 microliter single-channel pipette of the Opentrons(RTM), until the designated volume is completely administered.

[0102] Example 1: Evaluation of glyceride oil The properties of numerous glyceride oils were investigated after the schematic method described above. Samples of various oils, including i) glyceride oil 1, ii) glyceride oil 2, iii) glyceride oil 3, iv) glyceride oil 4, v) glyceride oil 5, and vi) glyceride oil 6, were prepared and tested. Each oil is a composite mixture of different glyceride oils having saturated fatty acid chains, monounsaturated fatty acid chains, polyunsaturated fatty acids, and small amounts of free fatty acids.

[0103] Samples containing different oils were prepared in the same manner at the same concentration of 1 mg / mL. For each oil investigated, 50 mg of each oil was weighed into each 50 mL glass beaker, deionized, and pure water was poured into each beaker to constitute a maximum weight of 50 g of mixture. Each glass beaker was then placed in a heated sonicator bath (50°C) and sonicated for 10 minutes with stirring (operating at 40 kHz with a sonic power output of 120 W). After 10 minutes, the mixture appeared white and opaque, showing self-assembly of micelles in the continuous water phase in response to heating and stirring. Then, 1.5 ml of each mixture was divided equally into separate Eppendorf tubes, equilibrated at room temperature for a period of 10 minutes, and placed in an Opentrons (RTM) OT-2 system for testing.

[0104] The storage medium used in the tests in the experiment was the same storage salt solution. Each storage unit used in the experiment measured 120 mm × 80 mm and was filled with salt water. The liquid level of the storage units was adjusted optically before the start of each experiment (using a trigger laser intensity monitored as the storage medium was extracted from the storage unit until a fixed trigger laser intensity was reached) to ensure consistency. An Opentrons(RTM)OT2 workflow with an Opentrons(RTM) 1000 microliter single-channel pipette (polypropylene) was used for sample deposition, and the apparatus, substantially as shown in Figure 2 and described herein, was used to acquire surface wave data.

[0105] The Opentrons (RTM) system was configured to provide a deposition volume of 100 μL of sample (as a series of droplets) from a height of less than 1 mm, and to include washing with isopropanol and water between each oil sample test. During the experiment, the surface pressure of the film at the air-water interface was measured using a Whilhelmy plate to independently verify the increase in surface pressure as the sample deposition occurred, before the final saturation of the surface by the sample forming the thin film.

[0106] Subsequently, the surface wave data generated from the experiment was used to generate biophysical characterizations for each of the oils, incorporating principal component analysis (PCA). The results, visually shown in Figure 7, demonstrate that the present invention allows for the acquisition of unique signatures for different biomolecules (in this case, glyceride oils), or compositions of the same different biomolecules, to represent combinations of biophysical properties of oil samples. This can be recognized in the plots of Figure 7, which show different clusterings for individual oil samples. These data may be used to fill a library of signatures for comparison purposes and / or as a means of comparison against corresponding data for target biomolecules to determine whether the biomolecules investigated have equivalent biophysical properties.

[0107] PCA was found to reveal a direct correlation between the measurement signature and the measurement surface pressure for the oil samples in the experiment, and the correlation between principal components and given perceptual data for each of the oils investigated (as evaluated by a panel of human inspectors). The correlation between principal components from wave data and given perceptual data indicates that different principal components are correlated with different perceptual properties, including those selected from skin absorption, skin spreadability, skin stickiness, skin density, suppleness in use, shine in use, smoothness in use, softness in use, frizz after washing, shine after washing, smoothness after washing, and softness after washing. For example, it was found that the 0th principal component was correlated with skin absorption (in Pearson correlation), the 1st principal component was correlated with smoothness / softness after washing, and the 3rd principal component was correlated with shine in use.

[0108] The correlation in PCA was verified by the correlation between independently determined biophysical properties and perceptual properties of the oil, including the correlation between oil density, viscosity, iodine value, molecular weight, monounsaturated fatty acid content (MUFA), polyunsaturated fatty acid content (PUFA), and surface tension. Thus, the present invention enables the readily derivation of a biophysical signature (as opposed to a series of other biophysical assessments) by a single test, where the correlation may be derived from perceptual properties, and enables an understanding of whether it may exhibit equivalent performance in comparison to a target molecule having a particular perceptual profile, for example.

[0109] Example 2: Antibody Evaluation The properties of numerous different monoclonal antibodies (mAbs) were investigated after the schematic method described above. Samples of different mAbs were prepared and tested and included absiximab, bevacizumab, adalimumab, and 20 anonymized mAb samples obtained from third parties.

[0110] Absiximab, bevacizumab, and adalimumab were procured from Absolute Antibody Ltd (Cleveland, UK) as 1 mg / mL suspensions in PBS, together with 0.02 vol% ProClin(RTM) 300 (a widely available, unsalted, intrinsic glycol containing alkyl carboxylate stabilizers, containing 3% 5-chloro-2-methyl-4-isothiazolin-3-one (CMIT) and 2-methyl-4-isothiazolin-3-one (MIT)). The commercially procured suspensions of absiximab, bevacizumab, and adalimumab were removed from the refrigerator, equilibrated at room temperature before opening, and diluted to the desired w / v concentration using PBS in LowBind Eppendorf tubes. The final concentrations of the diluted finished samples were 0.5 mg / mL or 0.1 mg / mL. Anonymized mAb samples were provided by a third-party supplier as 0.1 mg / mL suspensions in PBS.

[0111] The storage medium used in the tests in the same experiment was the same storage solution containing an anionic surfactant with a sulfate base.

[0112] The storage chamber used in the experiment measured 40 mm × 80 mm and was filled with a storage aqueous solution. The liquid level of the storage chamber was adjusted optically before the start of each experiment (using a trigger laser intensity monitored as the storage medium was extracted from the storage chamber until a fixed trigger laser intensity was reached) to ensure consistency. An Opentrons(RTM)OT2 workflow with an Opentrons(RTM) 1000 microliter single-channel pipette (polypropylene) was used for sample deposition, and the apparatus substantially as shown in Figure 2 and described herein was used to acquire surface wave data.

[0113] The Opentrons (RTM) system was configured to provide a deposition volume of 100 μL of sample (as a series of droplets) from a height of less than 1 mm, and to include washing with isopropanol and water between each oil sample test. During the experiment, the surface pressure of the film at the air-water interface was measured using a Whilhelmy plate to independently verify the increase in surface pressure as sample deposition occurred, before the final saturation of the surface by the sample.

[0114] Subsequently, the surface wave data generated from the experiment was used to generate biophysical characterizations for each of the mAbs, including three commercially available mAbs and 20 anonymized mAbs from a third-party supplier. This allowed for comparison of the wave data for the three commercially available mAbs with the wave data for the 20 anonymized samples, enabling an assessment of the comparability between each of the samples and the commercially available mAbs.

[0115] Aspects of the present disclosure provide an apparatus for biophysical characterization of a particular class of biomolecules or compositions thereof, comprising an analyzer configured and positioned to acquire wave data for a sample based on physical measurements of waves generated in a liquid system, by performing physical measurements of waves in a liquid system to provide contact between the liquid system and a droplet of a sample containing the biomolecules or compositions thereof, wherein the waves have modes that encode characteristics relating to the interaction between the sample and the liquid system, and the apparatus comprises a data processor configured to provide biophysical characterization of the biomolecules or compositions containing biomolecules based on the wave data. The analyzer may include a droplet dispenser configured to bring the liquid into contact with a droplet of the sample. The analyzer may include a wave measurement module configured to provide wave data based on wave measurements. Wave measurements may be based on the interaction of a light beam, such as a laser, with the liquid system. The analyzer may include a trough holding the liquid system, and the liquid system may include a thin film. The data processor may be connected to receive wave data from the analyzer. The data processor may also be connected to a data store, such as digital memory, that stores associated data. The analyzer may be configured to generate wave data comprising representations of multiple identical waves generated in a liquid system, each of which corresponds to contact of multiple droplets with respect to different aspects of the liquid system. The data processor may be configured to provide biophysical characterization by identifying multiple separable contributions to the dispersion of the wave data. For example, the data processor may be connected to communicate with data storage that stores associated data defining the relationship between at least one of the separable contributions and the biophysical properties. The data processor may be configured to determine data that exhibits the biophysical properties based on the separable contributions and the associated data. It is assumed that the apparatus described in this paragraph should be configured to perform any one of the methods described or claimed herein.

[0116] This disclosure provides a process for manufacturing a product comprising a biomolecule of a particular class of biomolecules, or a composition thereof, or a product made from such biomolecule or a composition thereof, wherein the process includes obtaining wave data for a sample based on physical measurements of waves generated in a liquid system by providing contact between a droplet of a sample and a liquid system, wherein the sample is a liquid containing a precursor or component of a product, the precursor or component comprising a biomolecule or a composition thereof, and the waves having modes that encode characteristics relating to the interaction between the sample and the liquid system; providing biophysical characterization of the biomolecule or composition containing a biomolecule based on the wave data; and manufacturing the product.

[0117] Biophysical characterization may be provided in accordance with any one of the methods described or claimed herein. Manufacturing may be carried out in accordance with the biophysical characterization. Components may include part or all of the product, or the materials or other components of the product. Precursors may include the substance from which the product is made, or the substance used in the manufacture of the product.

[0118] Biophysical characterization may provide information used in the production of a product or in conjunction with the product. For example, the information may be used to verify quality or purity, or to adjust steps in the manufacturing process. For example, the product may then be produced by utilizing the information to control or verify the quality of the manufacturing process. As another example, compositions and / or components or precursors may be adjusted based on the information. This may include changing the ratios of excipients or components of the composition and / or components or precursors.

[0119] Any feature relating to any one of the examples disclosed herein may be combined with any selected feature relating to any of the other examples described herein. For example, features of a method may be implemented in preferably configured hardware, and a particular hardware configuration described herein may be employed in a manner that is implemented using other hardware.

[0120] From the above description, it will be understood that the embodiments shown in the figures are merely illustrative and include features that may be generalized, omitted, or replaced as described herein and in the claims. Generally referring to the drawings, it will be understood that schematic functional block diagrams are used to illustrate the functions of the systems and devices described herein. However, it will be understood that functions do not need to be divided in this way and should not be interpreted as suggesting any specific hardware structure other than those described and claimed below. One or more functions of the elements shown in the drawings may be further subdivided and / or distributed throughout the entire device of this disclosure. In some embodiments, the functions of one or more elements shown in the drawings may be integrated into a single functional unit.

[0121] In some examples, the functions of the controller and data processor described herein may be provided by a general-purpose processor which may be configured to perform any one of the methods described herein. In some examples, the functions may comprise digital logic, such as a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), or any other suitable hardware. In some examples, one or more memory elements may store data and / or program instructions used to implement the operations described herein. Embodiments of this disclosure provide a tangible non-temporary storage medium containing program instructions that can operate to program a processor to perform any one or more of the methods described and / or claimed herein, and / or to provide a data processing device described and / or claimed herein. The controller and data processor may comprise an analog control circuit that provides at least a portion of this control function. Embodiments provide an analog control circuit configured to perform any one or more of the methods described herein.

[0122] The embodiments described above should be understood as illustrative examples. Further embodiments are conceivable. Any feature described in relation to any one embodiment may be used alone or in combination with other features described, or in combination with one or more features of any other embodiment or any combination of any other embodiments. Furthermore, equivalents and modifications not described herein may be adopted without departing from the scope of the invention as defined in the appended claims.

Claims

1. A method for determining the biophysical properties of a specific class of biomolecules, or a composition of said biomolecules, The aforementioned method, i) A step of acquiring wave data for a sample based on physical measurements of waves generated by providing contact between a droplet of the sample and the liquid system in a liquid system, wherein the waves have a mode that encodes characteristics relating to the interaction between the sample and the liquid system, and the sample contains the biomolecule or composition of the biomolecule for analysis. ii) A step of providing a biophysical property determination for the biomolecule or the composition containing the biomolecule based on the wave data, including, A method characterized by the following:

2. A method for identifying a target biomolecule of the same class as a biomolecule, or a candidate biomolecule within a class of biomolecules having biophysical properties equivalent to a composition of the target biomolecule, or a composition of the candidate biomolecule, The aforementioned method, (i) With respect to a sample, a step of acquiring wave data based on physical measurements of waves generated by providing contact between a droplet of the sample and the liquid system in a liquid system, wherein the waves have a mode of encoding characteristics relating to the interaction between the sample and the liquid system, and the sample contains a specific class of biomolecule candidates for analysis, or a composition of the biomolecule candidates, (ii) A step of providing a comparison of wave data obtained from the interaction of the sample with the surface of the liquid system with wave data associated with the target biomolecule or the target composition of the target biomolecule, The process involves repeating steps i) to ii) until, optionally, a biomolecule candidate or composition of the biomolecule candidate is identified among different biomolecule candidates of the same class, or among different biomolecule candidate compositions, that has an equivalent value for at least one biophysical property exhibiting equivalent biophysical properties between the candidate biomolecule and the target biomolecule. including, A method characterized by the following:

3. The comparison is based on the determination of biophysical properties of the biomolecule or the composition containing the biomolecule based on the wave data, and the corresponding determination of properties of the target biomolecule or the target composition of the target biomolecule. The method according to claim 2.

4. The aforementioned wave vector is Expressions relating to the plurality of waves generated in the liquid system, Equipped with, Each of the waves corresponds to the contact of different droplets with respect to the liquid system. The method according to claim 1 or 3.

5. Providing the determination of biophysical properties is To identify multiple separable contributions to the dispersion of the wave data, including, The method according to claim 1, 3, or 4.

6. Obtaining association data that defines the relationship between at least one of the separable contributions and the biophysical properties, Based on the separable contributions and the associated data, determine the data that exhibits the biophysical characteristics. Includes, The determination of the biophysical properties of the biomolecule or composition containing the biomolecule provided in step (iii) is as follows: The data showing the biophysical characteristics, Equipped with, The method according to claim 5.

7. To identify the separable contributions, a dimensionality reduction method is applied to the wave data. including, The method according to claim 5 or 6.

8. The aforementioned dimensionality reduction method is, Blind Signal Separation (BSS) method, including, The method according to claim 7.

9. The BSS method described above is Principal component analysis and, Singular value decomposition and, Independent component analysis and, Non-negative matrix factorization and Includes at least one method of selecting from a list that includes The method according to claim 8.

10. The aforementioned dimensionality reduction method is, multi-resolution analysis method, including, The method according to claim 9.

11. The aforementioned multi-resolution analysis method is Time-frequency analysis, such as analysis based on the short-time Fourier transform, Wavelet analysis, such as analysis based on the short-time Fourier transform, A method including at least one selected from the above list, The method according to claim 10.

12. The class of biomolecules is selected from the class of polymers and / or macromolecules. The method according to any one of claims 1 to 11.

13. The class of biomolecules is selected from polypeptides, lipids, polysaccharides, and nucleic acids. The method according to claim 12.

14. The aforementioned class of biomolecules is selected from proteins. The method according to claim 13.

15. The class of biomolecules is selected from antibodies, contractile proteins, enzymes, hormone proteins, structural proteins, storage proteins, and transport proteins. The method according to claim 14.

16. The biomolecule, candidate biomolecule, or composition of the biomolecule and candidate biomolecule is: i) a plant protein selected from algal protein, black bean protein, canola wheat protein, chickpea protein, broad bean protein, lentil protein, lupin bean protein, mung bean protein, oat protein, pea protein, potato protein, rice protein, soybean protein, sunflower seed protein, wheat protein, white bean protein, and their protein isolates or concentrates; or ii) an animal protein selected from pig protein, bovine protein, sheep protein, horse protein, poultry protein, and their protein isolates or concentrates. The method according to claim 14.

17. The class of biomolecules is selected from the nonpolymeric and / or nonpolymeric classes of biomolecules. The method according to any one of claims 1 to 11.

18. The class of biomolecules is selected from vitamins, phytochemicals, food seasonings, amino acids, peptides, monosaccharides, disaccharides, oligosaccharides, free fatty acids, monoglycerides, diglycerides, triglycerides, sterols, steroids, nucleotides, and nucleosides. The method according to claim 17.

19. The class of biomolecules is an edible glyceride oil selected from vegetable oils, marine oils, and animal oils. The method according to claim 18.

20. The biomolecule, candidate biomolecule, or composition of the biomolecule and candidate biomolecule is a vegetable oil selected from acai oil, almond oil, beech oil, cashew oil, coconut oil, rapeseed oil, corn oil, cottonseed oil, grapefruit seed oil, grapeseed oil, hazelnut oil, hemp oil, lemon oil, macadamia oil, mustard oil, olive oil, orange oil, peanut oil, pecan oil, pinenut oil, pistachio oil, poppy seed oil, rapeseed oil, rice bran oil, safflower oil, sesame oil, soybean oil, sunflower oil, walnut oil, and wheat germ oil. The method according to claim 19.

21. The aforementioned biophysical properties are The perceptual properties of the biomolecule or the composition of the biomolecule, including, The method according to any one of claims 1 to 20.

22. The biomolecule is an edible glyceride oil selected from vegetable oils / fats, marine oils / fats, and animal oils / fats. The aforementioned perceptual properties are those associated with the application of the biomolecule or a composition of the biomolecule to the body of a human or animal. The method according to claim 21.

23. The aforementioned perceptual properties indicate at least one of the following: skin absorption, skin spreadability, skin stickiness, skin density, suppleness during use, shine during use, smoothness during use, softness during use, curliness after washing, shine after washing, smoothness after washing, and softness after washing. The method according to claim 22.

24. The biomolecule is selected from the protein class. The method according to claim 21.

25. The aforementioned perceptual properties represent the properties exhibited by the biomolecule used in food, or a composition containing the biomolecule. The method according to claim 24.

26. The biomolecule, or the composition of the biomolecule, is a candidate for treatment. The method according to any one of claims 1 to 20.

27. The aforementioned biomolecule is a protein such as an antibody or hormone protein. The method according to claim 26.

28. The aforementioned biophysical properties are Indicators of development feasibility, such as manufacturability or safety profile. including, The method according to claim 26 or 27.

29. The aforementioned indicators of development feasibility are based on multiple biophysical characteristics, Each of the aforementioned multiple biophysical properties is derived from one or more separable contributions of the wave data. The method according to claim 28.

30. The biophysical properties relating to the biomolecule or composition thereof are selected from viscosity, stability in a specific environment, hydrophobicity, binding affinity to a specific target, solubility in a specific solvent, surface charge, and specific gravity. The method according to any one of claims 1 to 29.

31. To perform the physical measurement of the wave, including, The method according to any one of claims 1 to 30.

32. To generate the aforementioned wave, provide the contact between the droplet of the sample and the liquid system. including, The method according to any one of claims 1 to 31.

33. To prepare the aforementioned sample for analysis, including, The method according to any one of claims 1 to 32.

34. The aforementioned wave is surface waves, Equipped with, The method according to any one of claims 1 to 33.

35. The mode for encoding the characteristics relating to the interaction between the sample and the liquid system is, Equipped with Lucassen wave mode, The aforementioned physical measurement is Sensitivity to the Lucassen wave mode, including, The method according to claim 34.

36. A process for producing a product comprising a biomolecule of a specific class of biomolecules, or a composition of said biomolecules, or a product made from said biomolecules, The aforementioned process, The method involves acquiring wave data based on physical measurements of waves generated by providing contact between a sample droplet and the liquid system in a liquid system, wherein the sample is a liquid containing a precursor or component of the product, the precursor or component containing the biomolecule or a composition of the biomolecule, and the waves have modes that encode characteristics relating to the interaction between the sample and the liquid system. Based on the aforementioned wave data, the objective is to provide a method for determining the biophysical properties of the biomolecule or the composition containing the biomolecule. To manufacture the aforementioned product, including, A process characterized by the following:

37. The biophysical properties are provided by the method described in any one of claims 1 to 35. The process according to claim 36.

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