Configurable handheld biological analyzer for identifying biological products based on Raman spectroscopy

Abstract

Configurable hand-held biological analyzers and related biological analysis methods for the identification of biological products based on Raman spectroscopy are described. The bioclassification model configuration is loaded into a computer memory of a configurable handheld bioanalyzer having a processor and a scanner. The bioclassification model configuration includes a bioclassification model configured to receive a Raman-based spectral data set defining a biological product sample as scanned by a scanner. A spectral pre-processing algorithm is performed to reduce spectral deviation of the Raman-based spectral data set. The bioclassification model identifies a biological product type based on the Raman-based spectral data set and further based on a classification component selected to reduce at least one of (1) a Q residual or (2) a fit summary value of the bioclassification model. The bioclassification model configuration is transferable and loadable onto other configurable handheld bioanalyzers.

Description

[0001] This application is a divisional application. The application date of the original application is October 23, 2020, the application number is 202080074568.1 (PCT / US2020 / 056961), and the name of the invention is "Configurable handheld bioanalyzer for identification of biological products based on Raman spectroscopy".

[0002] Citation of Related Applications

[0003] This application claims the benefit of U.S. Provisional Application No. 62 / 925,893 (filed on October 25, 2019) and U.S. Provisional Application No. 63 / 043,976 (filed on June 25, 2020). The entirety of each of the foregoing provisional applications is incorporated herein by reference. Technical Field

[0004] The present disclosure relates generally to configurable handheld bioanalyzers and, more particularly, to systems and methods for identifying or classifying biological products based on Raman spectroscopy using the configurable handheld bioanalyzer. Background Art

[0005] The development and manufacture of pharmaceutical and biotech products often requires the measurement or characterization of raw materials used to develop such products. The purpose of characterization testing of products is to ensure the identity of the product. Situations where characterization testing is necessary include distribution of products to clinical sites, import testing, and transfer between network sites. Additionally, the measurement or characterization of biological products may be important to ensure the quality of the development or manufacturing process, as well as the quality of the final product itself, for the purpose of meeting quality standards and / or regulatory requirements.

[0006] The use of Raman spectroscopy to measure and identify biological products is a relatively new concept. In general, Raman spectroscopy can be used to probe the chemical or biological structure of a raw material or product. Raman spectroscopy is a non-destructive chemical or biological analysis technique that measures the interaction of light with a product or material, such as the interaction of light with a biological property or chemical bond of the product or material. Raman spectroscopy provides a light scattering technique in which the molecules of a sample material or product scatter incident light from a high-intensity laser source. Typically, most of the scattered light has the same wavelength (color) as the laser source and does not provide useful information - this is called Rayleigh scattering. However, a small amount of light is scattered at a different wavelength (color), which is caused by the chemical or molecular structure of the material or product being analyzed - this is called Raman scattering, and this scattered light can be analyzed or scanned to generate Raman-based data of the material or product being analyzed.

[0007] Analysis of Raman scattering can yield detailed information about the properties of a material or product, including the chemical structure and / or identity of the material or product, contaminants and impurities, phases and polymorphisms, crystallinity, intrinsic stress / strain, and / or molecular interactions. Such detailed information can be found in the Raman spectrum of the material. The Raman spectrum can be visualized to show multiple peaks at various wavelengths of light. The Raman spectrum can show the intensity and wavelength position of the Raman scattered light. Each peak can correspond to a specific molecular bond vibration associated with the material or product being analyzed.

[0008] Typically, Raman spectroscopy provides a unique chemical or biological "fingerprint" for a particular material, molecule, or product, and can be used to verify the identity of a particular material, molecule, or product - and / or distinguish it from other substances. Additionally, materials are often identified based on their Raman spectra using a Raman spectral library. That is, a Raman spectral library containing thousands of spectra can be searched to find a match with the Raman spectrum of a given material or product being measured, thereby identifying the given product material or product.

[0009] There are currently analyzers that implement Raman spectroscopy to authenticate raw materials and products. For example, Thermo Fisher Scientific Inc. offers a handheld Raman-based analyzer identified as the TruScan TM RM handheld Raman analyzer. However, due to the scanning deviation of materials and / or products (such as pharmaceutical and biotech materials or products, especially materials or products with similar Raman spectra), the use of such existing scanners may be problematic. For example, the deviation between the Raman spectra of similar products may cause the existing Raman-based handheld analyzer to fail to correctly identify, such as outputting a type I error (false positive) or a type II error (false negative) for a pharmaceutical product or a biotech product. The main source of deviation or error is the difference between Raman-based analyzers, including differences such as variability in any one of software, manufacturing, age, (multiple) components, operating environment (e.g., temperature), or other such differences in Raman-based analyzers.

[0010] Known methods generally fail to account for errors caused by bias or variability between handheld analyzers. For example, in one known method, data from several analyzers can be used to develop a static mathematical equation for use between several analyzers. However, in general, the difficulty with this approach is that instrument performance may change over time. Many times, it is also impractical or impossible to routinely access all of these instruments. In particular, data for constructing static mathematical equations is generally not available, especially for new analyzers, and manufacturers may not provide new specifications for new analyzers in advance. This hinders the development and maintenance of static mathematical equations, especially as such new analyzers are developed over time, and considering that the development of static mathematical equations generally requires a large number of samples to keep different analyzer types accurate. In addition, if there are no such new specifications for the new analyzer, the static mathematical equations may be incompatible when executed on the new analyzer. In addition, for example, differences in the manufacture or quality control of the analyzers (especially between different manufacturers) can cause the static mathematical equations to be too tolerant of variability, resulting in the static mathematical equations themselves being created to be too variable for accurate measurement and / or identification of biological products.

[0011] In a second known approach, data from a given analyzer is normalized, where a parent-child instrument graph is created for a given set of analyzers. However, this approach is limited because constructing a parent-child instrument graph typically requires data from both the parent and child instruments, which is often difficult and / or computationally expensive to implement or maintain, especially as several generations of analyzers are newly developed over a long period of time, requiring many permutations and types of parent-child instrument graphs. Additionally, for the biopharmaceutical industry, user access to child instruments is limited, which also limits the parent-child instrument graph approach.

[0012] In a third known approach, data from a given analyzer is also normalized, but wherein the variability between analyzers is ignored or considered insignificant. However, this approach is not ideal, given that analyzer-to-analyzer variability often impacts accurate identification and measurement of raw materials and / or biological products and should therefore be taken into account.

[0013] For the foregoing reasons, there is a need for configurable handheld bioanalyzers and related methods for Raman spectroscopy-based authentication of biological products that are configured to reduce variability and increase compatibility between similarly configured configurable handheld bioanalyzers. Summary of the invention

[0014] The disclosure of the present application describes the use of Raman spectroscopy to identify biological products via (multiple) handheld analyzers. In addition, the disclosure of this specification describes the use of configurable handheld bioanalyzers, systems and methods to overcome the limitations commonly associated with known methods for measuring biological products using Raman spectroscopy. For example, the Raman spectra between certain biological products may be too similar to be distinguished by known methods using Raman spectroscopy (which methods generally depend on generalized statistical algorithms). Raman spectroscopy measurements may be particularly problematic when there is instrument-to-instrument variability resulting in, for example, type I and type II errors between various analyzers. As described herein, this variability may be caused by any one or more differences in software, manufacturing, age, components, operating environment (e.g., temperature), or other differences in Raman-based analyzers. This problem manifests itself particularly during the development or manufacture of biological products, because analyzer-to-analyzer variability may be a key factor affecting quality, robustness and / or transferability in the manufacturing or development process associated with a pharmaceutical product or biological product. Thus, in various embodiments disclosed herein, for example, a configurable handheld bioanalyzer is described that utilizes a configuration using specific preprocessing algorithms and / or multivariate data analysis to (1) ensure that the measurement and / or identification of a material or product is sensitive and / or unambiguous, and (2) ensure that compatibility and configurations as developed on a first set of analyzers are transferable and / or implementable to additional analyzers, such as a "network" of analyzers or a new analyzer within a set of analyzers.

[0015] Thus, in various embodiments herein, a configurable handheld bioanalyzer for identifying a biological product based on Raman spectroscopy is disclosed. The configurable handheld bioanalyzer may include a first housing suitable for handheld manipulation. Additionally, the configurable handheld bioanalyzer may include a first scanner carried by the first housing. The configurable handheld bioanalyzer may include a first processor communicatively coupled to the first scanner. The configurable handheld bioanalyzer may further include a first computer memory communicatively coupled to the first processor. In various embodiments, the first computer memory may be configured to load a biological classification model configuration. The biological classification model configuration may include a biological classification model. The biological classification model may be configured to execute on a first processor. The first processor may be configured to (1) receive a first Raman-based spectral data set defining a first biological product sample as scanned by a first scanner, and (2) identify a biological product type based on the first Raman-based spectral data set using the biological classification model. The biological classification model configuration may include a spectral preprocessing algorithm. The first processor may be configured to execute the spectral preprocessing algorithm to reduce spectral deviations of the first Raman-based spectral data set when the first processor receives the first Raman-based spectral data set. Additionally, the biological classification model can include a taxonomic component selected to reduce at least one of (1) a Q residual of the biological classification model, or (2) a fit summary value of the biological classification model, the biological classification model being configured to identify the biological product type of the first biological product sample based on the taxonomic component.

[0016] In another embodiment disclosed herein, a bioanalytical method for identifying a biological product based on Raman spectroscopy is disclosed. The bioanalytical method may include: loading a biological classification model configuration into a first computer memory of a first configurable handheld bioanalyzer having a first processor and a first scanner. The biological classification model configuration may include a biological classification model. Additionally, the bioanalytical method may include: the biological classification model receiving a first Raman-based spectral data set defining a first biological product sample as scanned by the first scanner. Further, the bioanalytical method may include: executing a spectral preprocessing algorithm of the biological classification model to reduce spectral deviations of the first Raman-based spectral data set. Still further, the bioanalytical method may include: using the biological classification model to identify a type of biological product based on the first Raman-based spectral data set. The biological classification model may include a classification component selected to reduce at least one of (1) a Q residual of the biological classification model, or (2) a fit summary value of the biological classification model, the biological classification model being configured to identify the biological product type of the first biological product sample based on the classification component.

[0017] In yet further additional embodiments disclosed herein, a tangible, non-transitory computer-readable medium (e.g., computer memory) storing instructions for identifying a biological product based on Raman spectroscopy is described. These instructions, when executed by one or more processors of a configurable handheld bioanalyzer, cause the one or more processors of the configurable handheld bioanalyzer to load a biological classification model configuration into a computer memory of the configurable handheld bioanalyzer having a scanner. The biological classification model configuration may include a biological classification model. The biological classification model may receive a Raman-based spectral dataset defining a biological product sample as scanned by the scanner. In addition, the one or more processors of the configurable handheld bioanalyzer may execute a spectral preprocessing algorithm of the biological classification model to reduce spectral bias of the Raman-based spectral dataset. The one or more processors of the configurable handheld bioanalyzer may utilize the biological classification model to identify the type of biological product based on the Raman-based spectral dataset. As described in various embodiments, the biological classification model may include a classification component selected to reduce at least one of (1) a Q residual of the biological classification model, or (2) a fit summary value of the biological classification model. The biological taxonomic model can be configured to identify the biological product type of the biological product sample based on the taxonomic component.

[0018] Benefits of the present application include developing (multiple) bio-classification models (e.g., (multiple) multivariate analysis models) that produce consistent results for the same drug product or biological product (e.g., therapeutic product / drug) between different analyzers (including different analyzers used to scan Raman-based data sets used to construct the bio-classification model). As described herein, a bio-classification model can be constructed using multiple analyzers or multiple Raman spectral data sets generated by such analyzers.

[0019] Further, as described herein, the bio-classification model is configurable and transferable between configurable handheld bio-analyzers, and can include Raman spectral pre-processing, classification component selection (e.g., via singular value decomposition (SVD) analysis), and discriminant statistics analysis to reduce variability between configurable handheld bio-analyzers. For example, the use of the bio-classification model as described herein is an improvement over existing analyzers because it reduces instrument / analyzer variability, does not require data from child instruments to develop, and can be used between different analyzers that implement different software, have different software or software versions, have different manufacture, age, operating environment (e.g., temperature), components, or other such differences.

[0020] In various embodiments, the Q residual can be used as a discriminant statistic for determining which biological taxonomy models can tolerate analyzer-to-analyzer variability. It can indicate which biological taxonomy model(s) to select for loading into a configurable handheld bioanalyzer.

[0021] In addition, the accuracy of the biological classification model can be improved by applying preprocessing techniques (e.g., spectral preprocessing algorithms as described herein) to minimize statistical Type I and / or Type II errors of the output of the biological classification model and thereby improve the output of (multiple) configurable handheld bioanalyzers on which the biological classification model is installed / configured.

[0022] Additionally, in some embodiments, the configurable handheld bioanalyzer(s) can use multivariate analysis (e.g., principal component analysis (PCA)) to determine the taxonomic components for the bio-classification model. This allows the configurable handheld bioanalyzer to differentiate between biological products / drugs with similar formulations. This provides a flexible approach because the bio-classification model can be generated with various, different, and / or additional taxonomic components (e.g., a second principal component of the PCA bio-classification model) to correspond to products with multiple strengths (e.g., products related to denosumab).

[0023] In light of the foregoing and the disclosure herein, the present disclosure includes improvements in computer functionality or improvements to other technologies, at least because the claims recite, for example, a configurable handheld bioanalyzer for identifying biological products based on Raman spectroscopy, which is an improvement over existing handheld bioanalyzers. That is, the present disclosure describes improvements in the functionality of a computer itself or "any other technology or field of technology" because the configurable handheld bioanalyzer is a computing device as described herein and provides reduced analyzer-to-analyzer variability compared to existing handheld bioanalyzers via its biological classification model configuration. This is an improvement over the prior art, at least because the configurable handheld bioanalyzer described herein provides improved accuracy in the measurement, identification, and / or classification of materials and / or products (e.g., therapeutic products), which is an important feature in the manufacture and development of pharmaceutical products and / or other such biological products.

[0024] Additionally, the configurable handheld bioanalyzers as described herein are further improved by using a bio-taxonomy model configuration that is transferable, optionally updateable (with new data) and loadable into the memory of compatible configurable handheld bioanalyzer(s), which can enable standardization and thereby reduce variability between a group of analyzers (i.e., a "network" of analyzers). This reduces maintenance and / or deployment time of configurable handheld bioanalyzers for a network of analyzers.

[0025] In addition, the configurable handheld bioanalyzer is further improved by using a biotaxonomy model configuration including a biotaxonomy model. As described herein, the biotaxonomy model improves the accuracy of identification and / or classification of biological products by eliminating or reducing type I errors (e.g., false positives) and / or type II errors (e.g., false negatives).

[0026] In addition, the present disclosure includes application of certain claim elements using or by use of a particular machine, such as a configurable handheld bioanalyzer for identifying biological products based on Raman spectroscopy, including identifying biological products during development or manufacture of such products.

[0027] Furthermore, the present disclosure includes enabling the conversion or reduction of a particular item into a different state or thing, for example, the conversion or reduction of a Raman spectroscopy dataset into different states for use in the identification of a biological product based on Raman spectroscopy.

[0028] The present disclosure includes specific features that are beyond the routine, conventional activities well known in the art, or that add non-routine steps that limit the claims to specific useful applications, including, for example, providing a biological classification model configuration for reducing variability among a group of configurable handheld bioanalyzers (i.e., a "network" of configurable handheld bioanalyzers), where each configurable handheld bioanalyzer can be used to identify a biological product based on Raman spectroscopy.

[0029] For those skilled in the art, the advantages will become more apparent from the following description of the preferred embodiment which has been shown and described by way of illustration. As will be appreciated, the embodiments of the present invention may have other and different embodiments, and the details thereof may be modified in various respects. Therefore, the drawings and description are to be regarded as illustrative and not restrictive in nature. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings described below depict various aspects of the systems and methods disclosed therein. It should be understood that each of the drawings depicts an embodiment of a specific aspect of the disclosed systems and methods, and that each of the drawings is intended to be consistent with its possible embodiments. Further, wherever possible, the following description refers to reference numerals included in the following drawings, wherein features depicted in multiple drawings are represented by consistent reference numerals.

[0031] The arrangements of the present discussion are shown in the accompanying drawings, however, it should be understood that embodiments of the invention are not limited to the precise arrangements and instrumentalities shown, wherein:

[0032] Figure 1 An example configurable handheld bioanalyzer for identifying biological products based on Raman spectroscopy according to various embodiments disclosed herein is presented.

[0033] Figure 2 An example flow chart of a bioanalytical method for identifying a biological product based on Raman spectroscopy according to various embodiments disclosed herein is presented.

[0034] Figure 3A Example visualizations of Raman-based spectral data sets as scanned by various handheld bioanalyzers are shown in accordance with various embodiments disclosed herein.

[0035] Figure 3B Shows how Figure 3A Example visualization of a modified Raman-based spectroscopy dataset modified from .

[0036] Figure 3C An example visualization of a normalized Raman-based spectroscopy dataset is shown as Figure 3B Normalized version of the modified Raman-based spectral dataset.

[0037] Figure 4A Showing example visualizations of Q residuals for a biological classification model.

[0038] Figure 4B Example visualization showing summary values ​​of fit (e.g., Hotelling T^2 values) for a taxonomic model.

[0039] Figure 5 Example visualizations of Raman spectra of bioproduct types according to various embodiments disclosed herein are shown.

[0040] FIG. 6A to FIG. 6C An exemplary computer program listing according to various embodiments disclosed herein is presented, including pseudo code for biological classification model configuration.

[0041] Figure 7 Shown are example visualizations of simplified Q-residuals according to various embodiments described herein.

[0042] FIG. 8A to FIG. 8D Each shows an example visualization of simplified Q-residuals for a product of interest as evaluated for eighteen different configurable handheld bioanalyzers according to various embodiments described herein.

[0043] Fig. 8E Shown are example visualizations of simplified fit summary values ​​for a product of interest as evaluated for eighteen different configurable handheld bioanalyzers in accordance with various embodiments described herein.

[0044] The drawings depict preferred embodiments for purposes of illustration only.Alternative embodiments of the systems and methods presented herein may be employed without departing from the principles of the invention described herein. DETAILED DESCRIPTION

[0045] Figure 1 An example configurable handheld bioanalyzer 102 for identifying a biological product 140 based on Raman spectroscopy is shown in accordance with various embodiments disclosed herein. Figure 1 In an embodiment of the present invention, the configurable handheld bioanalyzer 102 includes a first housing 101 that is molded or otherwise adapted for handheld manipulation. In addition, the configurable handheld bioanalyzer 102 includes a first scanner 106 carried by the first housing (e.g., such as directly or indirectly coupled or connected to the first housing). The configurable handheld bioanalyzer 102 also includes a first processor 110 communicatively coupled to the first scanner 106. The configurable handheld bioanalyzer 102 may further include a first computer memory 108 communicatively coupled to the first processor 110. In addition, the configurable handheld bioanalyzer 102 may include an input / output (I / O) component 109 for receiving input from the navigation wheel 105. For example, a user may manipulate the navigation wheel 105 to select or scroll through data or information of a particular sample of a biological product, such as data or information scanned from the scanned biological product 140. The input / output (I / O) component 109 may also control the display of measurement information, identification information, classification information, or other information as described herein on the display screen 104. Each of the display screen 104, the navigation wheel 105, the first scanner 106, the first computer memory 108, the I / O components 109, and / or the first processor 110 are communicatively coupled via an electronic bus 107, which is configured to send and / or receive electronic signals (e.g., control signals) or information between the various components (including 104 to 110). In some embodiments, the configurable handheld bioanalyzer 102 can be a Raman-based handheld analyzer, such as the TruScan provided by Thermo Fisher Scientific Inc. TM RM Handheld Raman Analyzer.

[0046] In various embodiments, the first computer memory 108 is configured to load a taxonomy model configuration, such as the taxonomy model configuration 103. The taxonomy model configuration 103 may be used to implement Figure 2 A bioanalytical method for identifying biological products based on Raman spectroscopy, as further described herein.

[0047] exist Figure 1In the embodiment of the present invention, the biological classification model configuration 103 is implemented as an XML file in the extensible markup language (XML) format. As described in various embodiments of this document, FIG. 6A to FIG. 6C An example computer program listing is shown that includes pseudo code for a taxonomy model configuration (e.g., taxonomy model configuration 103) in XML format. For example, in FIG. 6A to FIG. 6C In code segment 1 of the computer program listing of the embodiment, the format of the biological classification model configuration 103 is XML, wherein the biological classification model (" <model>”) is defined in the biological classification model configuration 103. The biological classification model configuration 103 can be transferred, installed, and / or otherwise implemented or executed on a similarly configured configurable handheld bioanalyzer (e.g., the configurable handheld bioanalyzers 112, 114, and / or 116). Each of the configurable handheld bioanalyzers 112, 114, and 116 includes the same components as the configurable handheld bioanalyzer 102, and therefore, the disclosure of the configurable handheld bioanalyzer 102 is equally applicable to each of the configurable handheld bioanalyzers 112, 114, and 116. The configurable handheld bioanalyzers 102, 114, and 116 are similarly configured. Each of 112, 114, and 116 may be part of the same analyzer group or set (i.e., comprising a "network" or group" of analyzers). In some embodiments, each of the configurable handheld bioanalyzers 102, 112, 114, and / or 116 may have the same, similar properties or characteristics, and / or a different mix of properties or characteristics, such as the same, similar software version(s) or type(s), manufacture(s), age(s), operating environment(s) (e.g., temperature), components, or other such similarities or differences of Raman-based analyzers and / or a different mix of such properties or characteristics.

[0048] The bio-classification model configuration 103 and its associated bio-classification model allow a network of configurable handheld networked bio-analyzers (e.g., configurable handheld bio-analyzers 102, 112, 114, and 116) to produce consistent results when measuring or identifying a pharmaceutical product or biological product (e.g., a therapeutic product / drug), regardless of the same, similar characteristics or features, and / or different mixes of characteristics or features between the configurable handheld bio-analyzers 102, 112, 114, and 116. That is, despite the similarities or differences among a given network of configurable handheld bio-analyzers, such configurable handheld bio-analyzers can accurately identify or measure a given pharmaceutical product or biological product when such configurable handheld bio-analyzers are configured with a bio-classification model configuration as described herein.

[0049] In various embodiments, a plurality of analyzers may be used to generate or construct the taxonomic model configuration 103 and its associated taxonomic model. For example, in some embodiments, any one or more of the configurable handheld bioanalyzers 102, 112, 114, and 116 and / or other analyzers (not shown) may be used to generate or construct the taxonomic model.

[0050] The generation of the biological classification model configuration 103 and its associated biological classification model generally requires a set of analyzers or a network of analyzers that scan samples (e.g., of the biological product 140) to generate Raman-based spectral data sets for those samples. For example, scanning the biological product 140, such as by any one of the configurable handheld bioanalyzers 102, 112, 114, and 116, can generate detailed information about the biological product 140. For example, the detailed information can include Raman-based spectral data sets defining (multiple) biological product samples (e.g., of the biological product 140). As described herein, examples of the biological product 140 can include any one of denosuzumab DP, panitumumab DP, etanercept DP, pegfilgrastim DP, lomosozumab DP, adalimumab DS, and / or enoximab DP (such as lomosozumab DP, adalimumab DS, and / or enoximab DP). However, it should be understood that additional biological products are contemplated herein, and the biological product 140 is not limited to any particular biological product or grouping thereof.

[0051] In some embodiments, the configurable handheld bioanalyzer 102 can define instrument- or analyzer-based spectral acquisition parameters (e.g., integration time, laser power, etc.) for scanning a sample (e.g., of a biological product 140). For example, a user can select spectral acquisition parameters to use for scanning a sample via the navigation wheel 105. In some embodiments, the configurable handheld bioanalyzer 102 can generate an output file (e.g., an output file of a ".acq" file type) specifying the spectral acquisition parameters.

[0052] In some embodiments, a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) can load an output file (e.g., a ".acq" file) to configure the configurable handheld bioanalyzer with spectral acquisition parameters for scanning a target product. As described herein, one or more configurable handheld bioanalyzers (e.g., configurable handheld bioanalyzer 102) can scan (multiple) Raman-based spectral data sets to generate a biological classification model configuration (e.g., biological classification model configuration 103). In some embodiments, (multiple) samples (e.g., batches of) a biological product (e.g., biological product 140) can be selected as representative target products for scanning. In general, a "target product" as described herein refers to a biological product for training or otherwise configuring a biological classification model configuration and its associated models. In general, the target product is selected based on the biological specifications of the target product. Once the spectral acquisition parameters are set for scanning the target product, the configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) can scan a sample of the target product (e.g., using the first scanner 106), in some cases multiple times (e.g., fourteen (14) times), where each scan generates detailed information, including (multiple) Raman-based spectral data sets of the target product.

[0053] In a similar embodiment, a plurality of configurable handheld bioanalyzers (configurable handheld bioanalyzers 102, 112, 114, and / or 116) may load an output file (e.g., a ".acq" file) to configure each configurable handheld bioanalyzer with spectral acquisition parameters for scanning a biological product sample. Once configured, each configurable handheld bioanalyzer (e.g., any one of the configurable handheld bioanalyzers 102, 112, 114, and / or 116) is configured to scan (e.g., using the first scanner 106), in some cases multiple times (e.g., fourteen (14) times), wherein each scan generates detailed information of the target product, including (multiple) Raman-based spectral datasets. By utilizing different / multiple scanners to scan a given target product, the (multiple) Raman-based spectral datasets captured by these scanners become robust because the (multiple) Raman-based spectral datasets capture any differences between scanners (e.g., caused by software, manufacturing, age, operating environment (e.g., temperature), etc.). In this manner, the Raman-based spectral dataset(s) provide an ideal training dataset for reducing variability between multiple scanners as described herein. For example, each of the Raman-based spectral dataset(s) as scanned by multiple scanners (e.g., any one of the configurable handheld bioanalyzers 102, 112, 114, and / or 116) can be output and / or saved as, for example, a Raman spectral file of file type ".spc".

[0054] It should be understood that Raman-based spectral data sets may also be captured for a challenge product in the same or similar manner as for a target product. As used herein, a "challenge product" describes a biological product (e.g., selected from biological product 140) that a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) is configured to identify, classify, or measure when loaded or otherwise configured with a biological taxonomy model configuration (e.g., biological taxonomy model configuration 103) and its associated biological taxonomy model as described herein.

[0055] (multiple) Raman-based spectral datasets of a challenge product may be captured in the same or similar manner as a target product, wherein the challenge product may be selected based on the biological specifications of the challenge product, and wherein the configurable handheld bioanalyzer (e.g., the configurable handheld bioanalyzer 102) may load an output file (e.g., a ".acq" file) to configure the configurable handheld bioanalyzer with spectral acquisition parameters for scanning the challenge product. Once set up, the configurable handheld bioanalyzer (e.g., the configurable handheld bioanalyzer 102) is configured to scan a sample of the challenge product (e.g., using the first scanner 106), in some cases multiple times (e.g., three (3) times), wherein each scan generates detailed information of the challenge product, including (multiple) Raman-based spectral datasets. For example, the (multiple) Raman-based spectral datasets as scanned by the configurable handheld bioanalyzer 102 may be output and / or saved as, for example, a Raman spectral file of file type ".spc".

[0056] In some embodiments, the Figure 1 A remote processor, such as a processor of the computer 130 shown, performs the generation of a biological taxonomy model configuration (e.g., biological taxonomy model configuration 103). For example, (multiple) Raman-based spectral data sets generated for a biological product (e.g., selected from biological product 140) as described herein can be imported into and / or analyzed by modeling software, which is executed on the computer 130 and is configured to analyze (multiple) Raman-based spectral data sets. An example of such modeling software includes SOLO (Standalone Chemometrics Software) provided by Eigenvector Research, Inc. (Feature Vector Research Company). However, it should be understood that other modeling software implemented to perform the features described herein, including custom software or proprietary software, can also be used. The modeling software can build or generate a biological taxonomy model based on (multiple) Raman-based spectral data sets. For example, in some embodiments, as described herein, a biological taxonomy model can be built or generated using (multiple) Raman-based spectral data sets such as scanned or captured for (multiple) target products. Furthermore, (multiple) Raman-based spectral datasets (e.g., of a target product or a challenge product) can also be used to cross-validate the biological classification model. For example, the type I error (e.g., false positive) and type II error (e.g., false negative) of the biological classification model can be evaluated using (multiple) Raman-based spectral datasets against a cross-validation dataset of (multiple) Raman-based spectral datasets.

[0057] In various embodiments, a biological taxonomy model and / or its associated biological taxonomy model configuration (e.g., biological taxonomy model configuration 103) may be generated to include algorithms (e.g., scripts) and parameters to be used by a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) to identify, classify, and / or measure biological products as described herein. Figure 2 , Fig. 6A and Figure 6B Examples of algorithms (e.g., scripts) and / or parameters are described. For example, a taxonomy model configuration (e.g., taxonomy model configuration 103) can include parameters that define details of the taxonomy model. For example, such parameters can infer the number, loadings, etc. of taxonomic components of the taxonomy model. For example, in one embodiment, the number of taxonomic components can be determined, for example, by the modeling software through a singular value decomposition (SVD) analysis, where the taxonomic components include one or more principal components of a PCA. The modeling software can be configured to set a statistical confidence level to determine the taxonomic components (e.g., principal components) to include in the taxonomy model. For example, in FIG. 6A to FIG. 6C In code segment 1 of the embodiment of the computer program listing, the biological classification model configuration indicates a biological classification model (e.g., defined as " <model>”) is a PCA type taxonomic model. This indicates that the taxonomic components of the taxonomic model will be principal components. For example, in FIG. 6A to FIG. 6C In the embodiment of the present invention, code segment 2 indicates that the number of principal components to be determined via the SVD analysis ("Algorithm: SVD") to be performed on the first processor 110 of the configurable handheld bioanalyzer 102 will be one (single) principal component ("Number of PCs: 1").

[0058] As another example, a taxonomic model configuration (e.g., taxonomic model configuration 103) may include computer code or scripts for defining or implementing (multiple) spectral preprocessing algorithms, such as FIG. 3A to FIG. 3C As described herein. In general, a computer code or script defining or implementing (multiple) spectral preprocessing algorithms can be executed on a processor (e.g., first processor 110), wherein the processor receives (multiple) Raman-based spectral data sets of a biological product (e.g., biological product 140). The handheld bioanalyzer can then be configured to execute the computer code or script defining or implementing (multiple) spectral preprocessing algorithms to prepare / preprocess the data for input into (multiple) classification components of a biological classification model to identify, measure, or classify the biological product (e.g., challenge product) as described herein. For example, in FIG. 6A to FIG. 6C In code segment 2 of the embodiment computer program listing, the biological classification model configuration includes an execution sequence of an example spectral preprocessing algorithm (e.g., "preprocessing: 1st derivative (order: 2, window: 21pt, only, tail: polyinterp), SNV, mean centering"), which execution sequence includes determining the first derivative of a Raman-based spectral data set scanned for a specific product (e.g., a target product or a challenge product), applying a standard normal variate (SNV) algorithm, and further applying a mean centering function. This article is about FIG. 3A to FIG. 3C and FIG. 6A to FIG. 6C Code segments 4 to 6 of FIG. 4 describe and visualize an example embodiment of this execution sequence.

[0059] As another example, a taxonomy model configuration (e.g., taxonomy model configuration 103) may include (a plurality of) Raman-based spectral data sets for generating a taxonomy model. FIG. 6A to FIG. 6C In code segment 3 of the computer program listing of an embodiment, the biological taxonomy model configuration (e.g., biological taxonomy model configuration 103) includes a method for generating FIG. 6A to FIG. 6C Examples of biological classification model(s) based on Raman spectroscopy datasets.

[0060] In some embodiments, the biological classification model configuration (e.g., biological classification model configuration 103) may also define a threshold (e.g., as a statistical acceptance criterion) to determine whether the configurable handheld bioanalyzer 102 has successfully identified or measured a biological product. For example, such a threshold may define a Q residual or a Hotelling T 2 The pass / fail thresholds of the values ​​(as described herein) may be used to determine whether the configurable handheld bioanalyzer 102 has successfully identified or measured the biological product. In other embodiments, the thresholds may be configured independently of the biological taxonomy model configuration (e.g., biological taxonomy model configuration 103), such as by a user manually configuring and / or defining the thresholds via the navigation wheel 105 and display screen 104 described herein.

[0061] As described herein, once generated, the taxonomic model and its associated taxonomic model configuration (e.g., taxonomic model configuration 103) can be exported to a file (e.g., an XML file as described herein) for transmission (e.g., via a computer network 120 or other device as described herein) to a configurable handheld bioanalyzer (e.g., any one or more of the configurable handheld bioanalyzers 102, 112, 114, and / or 116) and / or loaded into a memory of the configurable handheld bioanalyzer. In some embodiments, the output file(s) (e.g., a ".acq" file as described herein) can also be transmitted (e.g., via a computer network 120 or other device as described herein) to a configurable handheld bioanalyzer (e.g., any one or more of the configurable handheld bioanalyzers 102, 112, 114, and / or 116) and / or loaded into a memory of the configurable handheld bioanalyzer.

[0062] The biological classification model can be generated by a remote processor remote from a given configurable handheld bioanalyzer. Figure 1 In some embodiments, the computer 130 includes a remote processor remote from the configurable handheld bioanalyzer 102. The computer 130 can generate (e.g., as described herein) and store one or more taxonomic model configurations and / or taxonomic models in a database 132. In various embodiments, the computer 130 can transfer (multiple) taxonomic model configurations (e.g., any of the taxonomic model configurations 103, 113, 115, and / or 117) to the configurable handheld bioanalyzer (e.g., to the configurable handheld bioanalyzers 102, 112, 114, and / or 116, respectively) via the computer network 120. In some embodiments, each of the taxonomic model configurations 103, 113, 115, and / or 117 can be a copy of the same file (e.g., the same XML file). The computer network 120 can include a wired and / or wireless (e.g., 802.11 standard network) implementing a computer packet protocol (such as, for example, the Transmission Control Protocol (TCP) / Internet Protocol (IP)). In other embodiments, the taxonomic model configuration (e.g., taxonomic model configuration 103) may be transferred via a universal serial bus (USB) cable (not shown), a memory drive (e.g., flash memory or thumb drive) (not shown), a disk (not shown), or other transfer or storage device cable for transferring data files (such as the XML files disclosed herein). In still further embodiments, the taxonomic model configuration 103 may be transferred via wireless standards or protocols such as Bluetooth, WiFi, etc., or via cellular standards such as GSM, EDGE, CDMA, etc.

[0063] A biological taxonomy model configuration (e.g., biological taxonomy model configuration 103) can be transferred between configurable handheld bioanalyzers. Once transferred, the biological taxonomy model configuration can be loaded into a memory of the configurable handheld bioanalyzer to calibrate or configure the configurable handheld bioanalyzer to have reduced variability relative to other configurable handheld bioanalyzers that implement or execute the biological taxonomy model. For example, in one embodiment, the biological taxonomy model configuration 103 can include a biological taxonomy model. The biological taxonomy model of the biological taxonomy model configuration 103 can be configured to execute on the first processor 110. For example, the first processor 110 can be configured to (1) receive a first Raman-based spectral data set of a first biological product sample as defined by a first scanner (e.g., scan of the biological product 140), and (2) identify a biological product type based on the first Raman-based spectral data set using the biological taxonomy model. For example, in some embodiments, the biological product type can be a therapeutic product having a therapeutic product type.

[0064] The biological classification model of the biological classification model configuration 103 can be transferred electronically to the configurable handheld bioanalyzer 112, for example, via the computer network 120 through the biological classification model configuration 113. Similar to the configurable handheld bioanalyzer 102, the configurable handheld bioanalyzer 112 can include: a second housing adapted for handheld manipulation, a second scanner coupled to the second housing, a second processor communicatively coupled to the second scanner, and a second computer memory communicatively coupled to the second processor. The second computer memory of the configurable handheld bioanalyzer 112 is configured to load the biological classification model configuration 113. The biological classification model configuration 113 includes the biological classification model of the biological classification model configuration 103. When implemented or executed on the second processor of the configurable handheld bioanalyzer 112, the second processor is configured to (1) receive a second Raman-based spectral data set defining a second biological product sample (e.g., obtained by scanning a biological product 140) scanned by the second scanner of the second configurable handheld bioanalyzer 112, and (2) identify the biological product type based on the second Raman-based spectral data set using the biological classification model. In such an embodiment, the same biological product or product type is identified by using the same biological classification model transferred through the biological classification model configuration file, wherein the second biological product sample is a new sample of the biological product type (e.g., the same biological product type analyzed by the first configurable handheld bioanalyzer 102).

[0065] In various embodiments, new or additional Raman-based spectral data sets can be scanned by the configurable handheld bioanalyzer and used to update the biological classification model. In such an embodiment, as described herein, the updated biological classification model can be transferred to the configurable handheld bioanalyzer (e.g., the configurable handheld bioanalyzer 102).

[0066] In some embodiments, the computer memory (e.g., the first computer memory 108) of the configurable handheld bioanalyzer (e.g., the configurable handheld bioanalyzer 102) can be configured to load a new biological classification model, wherein the new biological classification model can include updated classification components. For example, new classification components can be generated or determined for the new biological classification model received together with a new biological classification model configuration (e.g., the biological classification model configuration 103).

[0067] As described in various embodiments herein, a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) can be configured by loading a logical classification model configuration and its associated biological classification model. Once configured, the configurable handheld bioanalyzer 102 can be used to identify, classify, or measure products of interest (e.g., challenge products and / or samples) as described herein.

[0068] Figure 2 An example flow chart of a bioanalysis method 200 for identifying a biological product (e.g., biological product 140) based on Raman spectroscopy according to various embodiments disclosed herein is shown. The bioanalysis method 200 begins (202) at block 204 by loading a biological taxonomy model configuration (e.g., biological taxonomy model configuration 103) into a first computer memory (e.g., first computer memory 108) of a first configurable handheld bioanalyzer having a first processor (e.g., first processor 110) and a first scanner (e.g., first scanner 106). Figure 2 In some embodiments, the bio-taxonomy model configuration (e.g., bio-taxonomy model configuration 103) includes a bio-taxonomy model as described herein. Additionally, in some embodiments, the configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) can load spectral acquisition parameters (e.g., of a ".acq" file) into the memory 108 for scanning the product(s).

[0069] At box 206, the bioanalysis method 200 includes the biotaxonomy model (e.g., of the biotaxonomy model configuration 103) receiving a first Raman-based spectral data set defining a first biological product sample (e.g., selected from the biological product 140) as scanned by a first scanner (e.g., the first scanner 106).

[0070] At block 208, the bioanalysis method 200 includes, for example, a processor (e.g., the first processor 110) executing a spectral preprocessing algorithm of a bio-classification model to reduce spectral deviations of the first Raman-based spectral dataset. Spectral deviations are analyzer-to-analyzer spectral deviations between the first Raman-based spectral dataset and one or more other Raman-based spectral datasets of one or more corresponding other handheld bioanalyzers. For example, there may be spectral deviations between a Raman-based spectral dataset scanned by the configurable handheld bioanalyzer 102 and a Raman-based spectral dataset scanned by the configurable handheld bioanalyzer 112. Even if each Raman-based spectral dataset scanned by each analyzer represents the same type of biological product, there may be spectral deviations. Such spectral deviations may be caused by analyzer-to-analyzer variability and / or differences (e.g., software) including differences in version, manufacture, age, operating environment (e.g., temperature), components, or other differences of Raman-based analyzers as described herein.

[0071] The spectral preprocessing algorithm is configured to reduce analyzer-to-analyzer spectral deviations between the first Raman-based spectral data set and one or more other Raman-based spectral data sets. For example, in various embodiments, the spectral preprocessing algorithm is implemented or executed (e.g., on the first processor 110) to minimize statistical type I (e.g., false positive) and / or type II errors (e.g., false negative) associated with the identification of a biological product (e.g., biological product 140). In various embodiments, the spectral preprocessing algorithm can reduce analyzer-to-analyzer spectral deviations between a plurality of configurable handheld bioanalyzers (e.g., any one of the configurable handheld bioanalyzers 102, 112, 114, and / or 116).

[0072] FIG. 3A to FIG. 3C An example execution sequence of a spectral pre-processing algorithm for a configurable handheld bioanalyzer (e.g., the configurable handheld bioanalyzer 102) is shown. The execution of the spectral pre-processing algorithm (e.g., by the first processor 110) mitigates and mitigates the effects of differences unique to each analyzer (e.g., the configurable handheld bioanalyzers 102, 112, 114, and / or 116) and reduces the bias between Raman-based spectral data sets generated by scans of these analyzers. Figure 3A A visualization 302 of example Raman-based spectral datasets (eg, including Raman-based spectral datasets 302a, 302b, and 302c) as scanned by one or more handheld bioanalyzers is shown in accordance with various embodiments disclosed herein. Figure 3A The Raman-based spectral datasets may include Raman-based spectral datasets (e.g., including Raman-based spectral datasets 302a, 302b, and 302c) used to generate a taxonomy model configuration (e.g., taxonomy model configuration 103) and its associated taxonomy model as described herein. For example, Figure 3A The Raman-based spectral dataset can be found in Fig. 6A Those datasets identified in code snippet 3.

[0073] In some embodiments, Figure 3A Each Raman-based spectral data set (e.g., including Raman-based spectral data sets 302a, 302b, and 302c) may represent a scan performed by a different configurable handheld bioanalyzer (e.g., any of the configurable handheld bioanalyzers 102, 112, 114, and / or 116). However, in other embodiments, Figure 3A Each Raman-based spectral data set (eg, including Raman-based spectral data sets 302a, 302b, and 302c) may represent multiple scans performed by the same configurable handheld bioanalyzer (eg, any configurable handheld bioanalyzer 102).

[0074] Figure 3A Several Raman-based spectral data sets (e.g., including Raman-based spectral data sets 302a, 302b, and 302c) are depicted that are visualized across Raman intensity values ​​(on Raman intensity axis 304) and light wavelength / frequency values ​​(on Raman shift axis 306). Raman intensity axis 304 indicates the intensity of scattered light at a given wavelength across Raman shift axis 306. Raman intensity axis 304 can show a number of photons scattered by a biological product sample as scanned by an analyzer (e.g., configurable handheld bioanalyzer 102) (e.g., where data / value 3 is a relative measure of the intensity of photons measured / scanned by first scanner 106). Raman shift axis 306 indicates the wave number (e.g., inverse wavelength) of the scattered light. The units of wave number (i.e., wave number per centimeter (cm)) are -1 )) provides an indication of the frequency difference or wavelength difference between the incident light and the scattered light. Figure 3A In visualization 302, displacement axis 306 includes 600 cm -1 Up to 1500cm -1 The Raman intensity axis 304 includes a Raman intensity range of 1 to 5. Figure 3A As shown, each Raman-based spectral data set (eg, including Raman-based spectral data sets 302a, 302b, and 302c) is a Raman-based spectral data set having a wavelength of 600 cm -1 Up to 1500cm -1 The Raman intensity values ​​measured within the spectral range are visualized.

[0075] In addition, in various embodiments, Figure 3A Each Raman-based spectral data set (e.g., including Raman-based spectral data sets 302a, 302b, and 302c) may represent a scan of the same biological product sample having the same biological product type. Figure 3A As shown, even though any one or more of the configurable handheld bioanalyzer(s) may have scanned the same biological product sample having the same biological product type, there is still variability in the Raman intensity values ​​(on the Raman intensity axis 304) of the Raman-based spectral datasets (e.g., including the Raman-based spectral datasets 302a, 302b, and 302c) across the optical wavelength / frequency values ​​(on the Raman shift axis 306). As described herein, the variability may be caused by differences in software, manufacturing, age, optical component(s), operating environment (e.g., temperature), or other aspects between the configurable handheld bioanalyzers (e.g., any of the configurable handheld bioanalyzers 102, 112, 114, and / or 116).

[0076] Figure 3B Shows how Figure 3A An example visualization 312 of a modified Raman-based spectroscopy dataset modified from a Raman-based spectroscopy dataset. For example, Figure 3B may represent the first stage of the execution sequence of the spectral preprocessing algorithm. Figure 3B Visualization 312 includes the following Figure 3A The same Raman intensity axis 304 and Raman shift axis 306 are described. Figure 3B In an embodiment, a processor (eg, first processor 110) applies a derivative transform to Figure 3A The Raman-based spectral data set (eg, including the Raman-based spectral data sets 302a, 302b, and 302c) is used to generate the following Figure 3B The modified Raman-based spectral data set depicted (eg, including Raman-based spectral data sets 312a, 312b, and 312c). Specifically, Figure 3B In an embodiment of , a first derivative with 11 to 15 point data smoothing is applied (i.e., a Raman weighted average of consecutive groups of 11 to 15 Raman shift values ​​is determined, and then a first derivative transform is applied to these groups). In other words, Figure 3B The derivative transform shown includes determining, by a processor (e.g., the first processor 110), Raman weighted averages of successive groups of 11 to 15 Raman shift values ​​(of the Raman intensity axis 304) on the Raman shift axis 306, and then determining, by the processor (e.g., the first processor 110), corresponding derivatives of those Raman weighted averages on the Raman shift axis 306. Application of the derivative transform mitigates the effects of background curvature due to, for example, Rayleigh scattering / suppression optics and / or other dispersive elements. Figure 3A Visualization 302 and Figure 3B This is graphically illustrated by a comparison of visualizations 312 of , where Raman-based spectral data sets (e.g., including Raman-based spectral data sets 302a, 302b, and 302c, as shown in FIG. Figure 3A ) are removed or reduced to produce Figure 3B Modified Raman-based spectroscopy data sets with fewer changes are depicted (eg, including Raman-based spectroscopy data sets 312a, 312b, and 312c).

[0077] pass FIG. 6A to FIG. 6C The computer program listing further illustrates the Figure 3B The application of the derivative transformation is visualized in FIG. 6A to FIG. 6C In code segment 4 of the computer program listing of an embodiment, the biological classification model configuration includes a script executable by the first processor 110 of the configurable handheld bioanalyzer 102, the script application as described herein for Figure 3B The derivative transformation algorithm described.

[0078] Figure 3C An example visualization of a normalized Raman-based spectroscopy dataset is shown322 as Figure 3B A normalized version of a modified Raman-based spectral dataset. For example, Figure 3C May represent the next one or more stages in the execution sequence of the spectral preprocessing algorithm. Figure 3C Visualization 322 includes the following Figure 3A and Figure 3B The same Raman intensity axis 304 and Raman shift axis 306 as described above. For example, in one embodiment, Figure 3B The depicted modified Raman-based spectral data sets (eg, including Raman-based spectral data sets 312a, 312b, and 312c) are aligned by a processor (eg, first processor 110) across the Raman shift axis 306 to produce the following: Figure 3C Aligned Raman-based spectral data sets are depicted (e.g., including Raman-based spectral data sets 322a, 322b, and 322c). This alignment corrects for slight y-axis offsets (i.e., Raman intensity axis 304) caused by analyzer-to-analyzer deviations / differences as described herein. FIG. 6A to FIG. 6C The computer program listing further illustrates the Figure 3C The application of the alignment algorithm is visualized. For example, in FIG. 6A to FIG. 6C In code segment 6 of the computer program listing of an embodiment of the present invention, the biological classification model configuration (e.g., the biological classification model configuration 103) includes a script that can be executed by the first processor 110 of the configurable handheld bioanalyzer 102 to apply a mean centering algorithm, the mean centering algorithm adjusts the Figure 3B The modified Raman-based spectral datasets (e.g., including the Raman-based spectral datasets 312a, 312b, and 312c) are aligned to remove or reduce spectral bias (e.g., vertical and / or horizontal bias) of these modified Raman-based spectral datasets. Such adjustment results in Figure 3C The aligned Raman-based spectral datasets depicted in (eg, including Raman-based spectral datasets 322a, 322b, and 322c).

[0079] Additionally or alternatively, in another embodiment, Figure 3B The depicted modified Raman-based spectral data sets (eg, including Raman-based spectral data sets 312a, 312b, and 312c) are normalized by a processor (eg, first processor 110) across the Raman intensity axis 304 to produce as shown. Figure 3C The aligned Raman-based spectral data sets (e.g., including Raman-based spectral data sets 322a, 322b, and 322c) are depicted. Such normalization applies a robust normalization algorithm to account for intensity axis variations (i.e., variations in intensity values ​​across the Raman intensity axis 304) caused by analyzer-to-analyzer deviations / differences as described herein. FIG. 6A to FIG. 6C The computer program listing further illustrates the Figure 3C The application of the normalization algorithm is visualized in Figure 2. FIG. 6A to FIG. 6C In code segment 5 of the computer program listing of an embodiment of the present invention, the biological classification model configuration includes a script that can be executed by the first processor 110 of the configurable handheld bioanalyzer 102 to apply a normalization algorithm, the normalization algorithm being such as Figure 3B The depicted modified Raman-based spectral datasets (e.g., including Raman-based spectral datasets 312a, 312b, and 312c) are normalized to remove or reduce spectral bias (e.g., vertical and / or horizontal bias) of these modified Raman-based spectral datasets. Such normalization results in Figure 3C , (eg, including Raman-based spectral datasets 322a, 322b, and 322c). In particular, FIG. 6A to FIG. 6C In the embodiment of FIG. 1 , for example, the first processor 110 applies a standard normal variable (SNV) algorithm to the following Figure 3B The modified Raman-based spectral data sets depicted (eg, including Raman-based spectral data sets 312a, 312b, and 312c) are used to generate the following Figure 3C Aligned Raman-based spectroscopy data sets are depicted (eg, including Raman-based spectroscopy data sets 322a, 322b, and 322c).

[0080] Application of alignment algorithms and / or normalization algorithms (e.g., Figure 3C described) removes or reduces Figure 3B spectral deviations of the modified Raman-based spectral datasets depicted in (eg, including Raman-based spectral datasets 312a, 312b, and 312c). Figure 3B Visualization 312 and Figure 3C This is graphically illustrated by a comparison of visualizations 322 of , where Raman-based spectral data sets (e.g., including Raman-based spectral data sets 312a, 312b, and 312c, as shown in FIG. Figure 3B spectral deviations (e.g., vertical and / or horizontal deviations) are removed or reduced to produce Figure 3C Less variably aligned and / or normalized Raman-based spectroscopy data sets are depicted (eg, including Raman-based spectroscopy data sets 322a, 322b, and 322c).

[0081] exist Figure 2 At block 210 of the present invention, the bioanalysis method 200 includes using a bio-classification model based on a first Raman-based spectral data set (e.g., FIG. 3A to FIG. 3C Visualization and description of the Raman-based spectral data set) to identify or classify the type of biological product. For example, in various embodiments, once, for example, as described herein FIG. 3A to FIG. 3C and / or FIG. 6A to FIG. 6C By executing the spectral preprocessing algorithm described above (e.g., by the first processor 110), the configurable handheld bioanalyzer (e.g., the configurable handheld bioanalyzer 102) can use the preprocessed Raman-based data set (e.g., Figure 3C The depicted aligned and / or normalized Raman-based spectral datasets (eg, including Raman-based spectral datasets 322a, 322b, and 322c)) are used to identify or classify a biological product (eg, biological product 140).

[0082] Figure 5 An example visualization 500 of Raman spectra of biological product types (e.g., biological product types 511, 512, and 513) is shown. Each biological product type (e.g., biological product types 511, 512, and 513) can be identified, classified, or otherwise distinguished based on a taxonomic component using a biological taxonomy model (e.g., a biological taxonomy model of biological taxonomy model configuration 103) according to various embodiments disclosed herein. Figure 5 In the embodiment, each of the biological product types 511, 512 and 513 is a different biological product type, including adalimumab DS (biological product type 511), inovizumab DP (biological product type 512) and lomosozumab DP (biological product type 513), respectively. Figure 5 The visualization 500 includes the Figure 3A and Figure 3B The same or similar Raman intensity axis 504 and Raman shift axis 506 are depicted. However, each of the bioproduct types 511, 512, and 513 depicts its own separate Raman shift axis, wherein each Raman shift axis indicates Raman intensity values ​​from 0 to about 3. Additionally, the Raman shift axis 506 depicts Raman intensity values ​​from about 0 to about 3000 cm -1 frequency / wavelength range.

[0083] like Figure 5 As shown, each of the bio-product types 511, 512, and 513 spans a Raman shift axis 506 (i.e., spans the same or similar Raman spectral range (e.g., from 0 to 3000 cm -1 range, such as Figure 5 511), enoximab DP (biological product type 512), and lomosozumab DP (biological product type 513). When attempting to identify, measure, or classify such biological product types, a typical analyzer (not implementing or executing the biological classification model configuration 103 as described herein) typically generates a large number of type I errors (e.g., false positives) and type II errors (e.g., false negatives).

[0084] However, a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) loaded with and executing a biological classification model configuration as described herein (e.g., biological classification model configuration 103) can be used to accurately identify, classify, measure, or otherwise distinguish between biological product types adalimumab DS (biological product type 511), inovizumab DP (biological product type 512), and lomosozumab DP (biological product type 513). Figure 5 , where, for example, each of the biological product types adalimumab DS (biological product type 511), inovizumab DP (biological product type 512), and lomosozumab DP (biological product type 513) are identified, classified, and / or measured as being different from each other by different local features (e.g., local features 511c, 512c, and 513c) of the Raman spectrum. Figure 5 In the embodiment of FIG. 5 , for example, each local feature 511 c, 512 c, and 513 c of each of the biological product types Adalimumab DS (biological product type 511), Inovizumab DP (biological product type 512), and Lomosozumab DP (biological product type 513) is located across the Raman shift axis 506 at 1000 cm -1 Up to 1100cm -1 In particular, at 1000 cm -1 Up to 1100cm -1 Each local feature 511c, 512c, and 513c has a different Raman intensity value (having a different shape, peak, or other unique / different relative intensity) within a range that is specific to each of the biological product types Adalimumab DS (biological product type 511), Inovizumab DP (biological product type 512), and Lomosozumab DP (biological product type 513). Therefore, the unique local features (e.g., local features 511c, 512c, and 513c) provide a source of product-specific information that can be used by the configurable handheld bioanalyzer 102 to identify, classify, or otherwise distinguish biological products as described herein.

[0085] Additionally or alternatively, with respect to Figure 5 Further illustrated is an identification or classification, where, for example, each of the biological product types Adalimumab DS (biological product type 511), Anovizumab DP (biological product type 512), and Lomosozumab DP (biological product type 513) are identified, classified, and / or measured as being different from one another (even if those biological products have similar and / or identical Raman spectra) by their respective Raman shift axes (i.e., across Raman shift axis 506). For example, Adalimumab DS (biological product type 511) has a first Raman intensity value 511a of about 1.9 (at a Raman shift value of about 2900) and a second Raman intensity value 511z of about 2.25 (at a Raman shift value of about 140). In contrast, Anovizumab DP (biological product type 512) has a first Raman intensity value 512a of about 2.1 (at a Raman shift value of about 2900) and a second Raman intensity value 512z of about 2.5 (at a Raman shift value of about 140). By further comparison, Lomosozumab DP (biological product type 513) has a first Raman intensity value 513a of about 1.5 (at a Raman shift value of about 2900) and a second Raman intensity value 513z of about 2.05 (at a Raman shift value of about 140).

[0086] Therefore, if Figure 5 As shown in visualization 500 of , a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) loaded and executed with a biological classification model configuration as described herein (e.g., biological classification model configuration 103) is sensitive to relative differences in Raman intensity values ​​(e.g., of Raman intensity axis 504) and overall shape of Raman features (i.e., Raman intensity distribution curves within a range of Raman shift values ​​(Raman shift axis 506)) between different analyzers. This is because, at least in part, the configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) loaded and executed with a biological classification model configuration as described herein (e.g., biological classification model configuration 103) has pre-processed scan data (Raman-based spectral data sets) for each of biological product types Adalimumab DS (biological product type 511), Inovizumab DP (biological product type 512), and Lomosozumab DP (biological product type 513) using a spectral pre-processing algorithm as described herein. Additionally, the biological classification model used by a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) is further configured to identify the biological product type of the first biological product sample based on a classification component (i.e., implement a model having a classification component), which also reduces bias, thereby improving the ability of the configurable handheld bioanalyzer 102 to identify the biological product type of the first biological product sample.

[0087] In various embodiments, a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) identifies, classifies, and / or measures a biological product type of a biological product (e.g., biological product 140), such as adalimumab DS (biological product type 511), inovimab DP (biological product type 512), and lomosozumab DP (biological product type 513), based on (multiple) classification components as loaded from a biological classification model configuration (e.g., biological classification model configuration 103). For example, a biological classification model as loaded into the configurable handheld bioanalyzer 102 via the biological classification model configuration 103 may include classification components selected to reduce at least one of (1) a Q residual of the biological classification model, or (2) a fit summary value of the biological classification model, each of which is described herein with respect to Figure 4A and Figure 4B Give a description.

[0088] As used herein, the term "classification component" may include principal components determined for principal component analysis (PCA). More generally, in other embodiments, the classification component may be a coefficient or variable of a multivariate model (such as a regression model or a machine learning model). The biological classification model is configured to identify the biological product type of a given biological product sample (e.g., selected from biological products 140) based on the classification component.

[0089] In some embodiments, the bio-classification model can be implemented as a PCA model. PCA implementations represent the use of multivariate analysis (e.g., as implemented by the configurable handheld bioanalyzer 102 configured with the bio-classification model configuration 103) to differentiate between biological products (e.g., biological product 140) such as therapeutic products / drugs with similar formulations (e.g., as described herein for Figure 5 As described herein). For example, biological products or pharmaceutical products are often associated with high-dimensional data. High-dimensional data may include multiple features, such as the expression of many genes measured on a given sample (e.g., a sample of a scanned biological product 140). PCA provides a technique for simplifying the complexity of high-dimensional data (e.g., (multiple) Raman spectroscopy data sets) while retaining trends and patterns useful for prediction and / or identification purposes (e.g., identification of biological products as described herein) as used by the configurable handheld bioanalyzer 102. For example, the application of PCA includes (e.g., by the first processor 110) transforming a data set (e.g., a Raman-based spectroscopy data set) into fewer dimensions. The transformed data set of fewer dimensions provides a summary or simplification of the original data set. In turn, when manipulated by the configurable handheld bioanalyzer described herein (e.g., the configurable handheld bioanalyzer 102), the transformed data set reduces the computational cost. Further, as described herein, by implementing PCA, (multiple) error rates can also be reduced, thereby eliminating the need to apply (multiple) test corrections to higher dimensional data when testing the association of each feature with a specific result.

[0090] Additionally, PCA as implemented by the configurable handheld bioanalyzer 102 reduces data complexity by geometrically projecting the data onto lower dimensions called principal components (PCs), and by targeting an optimal data profile (and thus PCs) using a limited number of PCs. The first PC is selected to minimize the overall distance between the data and the projection of the data onto the PCs. Any second (subsequent) PCs are selected similarly, with the additional requirement that they are uncorrelated with all previous PCs.

[0091] PCA is an unsupervised learning method and is similar to clustering in that it finds trends or patterns without reference to prior knowledge about whether samples come from different sources, such as different configurable handheld bioanalyzers (e.g., configurable handheld bioanalyzers 102, 112, 114, and / or 116). For example, in some embodiments, the classification component of the biological classification model can be a first principal component of the PCA model. In such embodiments, the first principal component can be determined by the first processor 110 based on a singular value decomposition (SVD) analysis. The use of the first principal component by the configurable handheld bioanalyzer 102 limits or reduces the amount of analyzer variability considered by its biological classification model. In some embodiments, the first principal component (PC) can be the only principal component. In other embodiments, the biological classification model can include a second classification component, wherein the biological classification model is configured to identify the biological product type(s) of a given biological product sample (e.g., biological product 140) based on multiple classification components (the first classification component and the second classification component).

[0092] exist FIG. 6A to FIG. 6C In code segment 7 of the embodiment computer program listing, the taxonomic model configuration (e.g., taxonomic model configuration 103) defines a set of PCA predictions specified for its taxonomic model. Code segment 7 also provides for defining a fit summary statistic (e.g., Hotelling T 2 The script of code segment 7 can be executed by the first processor 110 to calculate the Q residual / value based on the Hotelling T 2 A value to identify or classify a biological product (e.g., biological product 140), as described herein, for example, with respect to Figure 4A and Figure 4B as described.

[0093] Figure 4A Example visualization 400 of the Q residuals for a biological classification model is shown. Figure 4A Includes Q residual axis 404 and Hotelling T 2 axis 406. In general, Q residuals and Hotelling T 2 The values ​​are summary statistics that can be used to explain how well a model (eg, a taxonomic model of the taxonomic model configuration 103) describes a given biological product sample (eg, obtained by scanning the biological product 140). Figure 4A Plotting the Q residuals and Hotelling T for multiple handheld bioanalyzers 2 In general, the Q residual is zero (0) and Hotelling T 2 A handheld bioanalyzer value of zero (0) indicates that there were no errors in scanning the product.

[0094] The handheld bioanalyzer includes handheld bioanalyzers of bioanalyzer groups 411n, 412m1, 412m2, and 413n. Analyzer group 411n represents an analyzer that scans the first biological product type adalimumab DS. Analyzer groups 412m1 and 412m2 each represent an analyzer that scans the second biological product type anovimab DP. Analyzer group 413n represents an analyzer that scans the third biological product type lomosozumab DP. Analyzer groups 412m1 and 412m2 include configurable handheld bioanalyzers (e.g., any one of configurable handheld bioanalyzers 102, 112, 114, and / or 116) configured and enhanced using a biological classification model configuration as described herein (e.g., biological classification model configuration 103) and a corresponding biological classification model. Analyzer groups 411n and 413n include typical bioanalyzers that are not configured using a biological classification model configuration or a biological classification model.

[0095] Analyzer groups 411n and 413n serve as a control group that demonstrates the improvement of a configurable handheld bioanalyzer (e.g., any of the configurable handheld bioanalyzers 102, 112, 114, and / or 116) over a typical analyzer (e.g., the analyzers in analyzer groups 411n and 413n) through reduced error (e.g., along the Q-residual axis 404) when compared to analyzer groups 412m1 and 412m2. In particular, the Q-residual (e.g., of the Q-residual axis 404) provides a lack-of-fit statistic calculated as a sum of squares for each product sample. The Q-residual represents the magnitude of variation remaining in each sample after projection through a given model (e.g., a bio-classification model as described herein). More generally, as Figure 4A , the Q residual values ​​(along the Q residual axis 404) are used as discriminant statistics. The Q residual is a measure of the "residual" or unexplained portion of the taxonomic model. For example, in an embodiment where the taxonomic model is implemented as a PCA model (e.g., the spectra are projected onto the first principal component), Figure 4A The value of will show the remaining portion (residual) after the scan data (eg, of bioanalyzer group 411n, 412m1, 412m2, and / or 413n) is projected by the first principal component.

[0096] In various embodiments, a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) includes a biological classification model (e.g., of biological classification model configuration 103) configured to identify or classify a biological product type of a biological product sample (e.g., obtained from biological product 140) based on a classification component when a Q residual satisfies a threshold. In some embodiments, the biological classification model, e.g., as implemented or executed by the first processor 110 of the configurable handheld bioanalyzer 102, outputs a pass / fail decision based on the threshold. For example, in Figure 4A In an embodiment, a threshold value of "1" across the Q residual axis 404 is selected as the pass / fail threshold value 405. In such an embodiment, a configurable handheld bioanalyzer (e.g., the configurable handheld bioanalyzer 102) implementing the biological classification model will identify or classify (i.e., "pass") those biological products whose scan data (e.g., (multiple) Raman spectral data sets) across the Q residual axis 404 falls within (i.e., is below) the threshold value 1. Otherwise, the biological analyzer (e.g., the configurable handheld bioanalyzer 102) implementing the biological classification model will not identify or classify (i.e., "fail") those biological products.

[0097] exist Figure 4A In an embodiment of the present invention, analyzer groups 412m1 and 412m2 include configurable handheld bioanalyzers (e.g., any one of configurable handheld bioanalyzers 102, 112, 114, and / or 116) configured and enhanced with a biological classification model configuration as described herein (e.g., biological classification model configuration 103) and a corresponding biological classification model configuration. The configurable handheld bioanalyzers in analyzer groups 412m1 and 412m2 correctly identified or classified (i.e., "qualified") the biological product (i.e., Anovimab DP), wherein the associated scan data (e.g., (multiple) Raman spectral data sets) fell within (i.e., was below) a threshold value of 1 when pre-processed using a spectral pre-processing algorithm as described herein, as shown in visualization 400.

[0098] Thus, the biological classification model of a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) can include classification components selected to reduce the Q residual of the biological classification model. In this manner, the biological classification model is configured to identify the biological product type of a given biological product sample based on the classification components. In general, the Q residual is best suited for methods with biological products having a single specification, where batch-to-batch variability is the primary source of analyzer-to-analyzer bias. Accordingly, as Figure 4A As shown, the Q residual can be used as a discriminant statistic for determining models (eg, the biological classification models described herein) that can tolerate analyzer-to-analyzer variability.

[0099] Figure 4B Shows summary values ​​of fit for taxonomic models (e.g., Hotelling T 2 Example visualization of values ​​450. In general, Hotelling T 2 The value represents a measure of the variation within a model (e.g., a taxonomic model) for each sample. Hotelling T 2 The value indicates how far each sample is from the "center" of the model (value 0). In other words, Hotelling T 2 The value is an indicator of the distance from the center of the model. Due to analyzer-to-analyzer variability, the distance from the center often varies. 2 Values ​​are useful for characterizing biological products with multiple strengths. In these cases, batch-to-batch variability (as discussed above) Figure 4A Different concentrations of active ingredient, excipients, etc. lead to more significant variability in the Raman spectra compared to the Q residuals described in Figure 2.

[0100] exist Figure 4B In an embodiment, a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) includes a biotaxonomic model (e.g., of biotaxonomic model configuration 103) configured to identify or classify the type of a biological product sample (e.g., obtained from biological product 140) as a biological product based on a classification component when a fitting profile value (e.g., Hotelling T 2 ) meets a threshold. Figure 4B includes the same Q-residual axis 404 and Hotelling T Figure 4A axis 406 as described herein for 2 The analyzer group 452m represents analyzers that scan the first biological product type, denosumab DP (with 2 specifications). The analyzer group 454m represents analyzers that scan the second biological product type, denosumab DS (with 1 specification). The analyzer group 462n represents analyzers that scan the third biological product type, enbrel DP. In Figure 4B an embodiment, the threshold "1" across the Hotelling T 2 axis 406 is selected as the pass / fail determination threshold 407. In such an embodiment, a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) implementing the biotaxonomic model will identify or classify (i.e., "pass") those biological products for which the scanned data (e.g., (plural) Raman spectral data sets) fall within (i.e., below) the threshold 1 of the Hotelling T 2 axis 406. Otherwise, a bioanalyzer (e.g., configurable handheld bioanalyzer 102) implementing the biotaxonomic model will not identify or classify (i.e., "fail") those biological products.

[0101] In Figure 4B an embodiment, the biotaxonomic model of a configurable handheld bioanalyzer (e.g., configurable handheld bioanalyzer 102) may include being selected to reduce the fitting profile value of the biotaxonomic model (e.g., Hotelling T 2 Classification components of the values). In this way, the biological classification model is configured to identify the type of biological product of a given biological product sample based on the classification components. For example, the analyzer groups 452m and 454m include configurable handheld biological analyzers (e.g., any one of configurable handheld biological analyzers 102, 112, 114, and / or 116) configured with the biological classification model as described herein (e.g., biological classification model configuration 103) and the corresponding biological classification model configuration and enhanced. The configurable handheld biological analyzers in the analyzer groups 412m1 and 412m2 correctly identified or classified (i.e., "qualified") biological products (i.e., denosumab DP and DS), where the relevant scan data (e.g., (multiple) Raman spectral data sets) fell within (i.e., below) threshold 1 when preprocessed using the spectral preprocessing algorithm as described herein, as shown in visualization 450. In contrast, the analyzer group 462n may represent an analyzer not configured with the biological classification model configuration as described herein.

[0102] As Figure 4A and Figure 4B Each of the Q residuals (e.g., of the Q residual axis 404) and / or Hotelling T 2 values can be used alone or together to identify or classify biological products. That is, the configurable handheld biological analyzer 102 can be configured to select or implement classification components for reducing one or both of (1) the Q residuals of the biological classification model and / or (2) the fitting profile values of the biological classification model.

[0103] As described herein with respect to Figure 2 , Figure 3A , Figure 3B , Figure 3C , Figure 4A , Figure 4B and Figure 5 described, the biological classification model can be configured to identify, classify, measure, or otherwise distinguish a given biological product sample having a given biological product type (e.g., adalimumab DS (biological product type 511)) from a different or second biological product sample having a different or second biological product type (e.g., inotuzumab DP (biological product type 512)) based on the classification components. For example, as described with respect to Figure 4A , Figure 4B and Figure 5 As described herein, the configurable handheld bioanalyzer 102 can distinguish a first biological product type (e.g., adalimumab DS (biological product type 511)) from a different biological product type (e.g., inovimab DP (biological product type 512)). For example, as described herein, once configured with the biological classification model configuration 103, the configurable handheld bioanalyzer 102 can perform a spectral pre-processing algorithm (e.g., as described herein) on the Raman-based spectral data set received by the first scanner 106. FIG. 3A to FIG. 3C Once the Raman-based spectral data set has been preprocessed by the spectral preprocessing algorithm, the configurable handheld bioanalyzer 102 can identify or classify the biological product based on the Q residual and / or Hotelling T2 value (e.g., as described herein for Figure 4A and Figure 4B described).

[0104] During the development or manufacture of a biological product, the type of biological product, such as a biological product 140 having a given biological product type, such as any of Adalimumab DS (biological product type 511), Inovizumab DP (biological product type 512), and / or Lomosozumab DP (biological product type 513) as described herein, can be identified by the configurable handheld bioanalyzer 102 (e.g., by the first processor 110) executing the biological classification model and / or spectral preprocessing algorithm. However, it should be understood that these biological product types are merely examples, and other biological product types or biological products can be identified, classified, measured, or otherwise distinguished in the same or similar manner as described for the various embodiments herein.

[0105] Aspects of the Disclosure

[0106] 1. A configurable handheld bioanalyzer for identifying biological products based on Raman spectroscopy, the configurable handheld bioanalyzer comprising: a first housing adapted for handheld operation; a first scanner carried by the first housing; a first processor communicatively coupled to the first scanner; and a first computer memory communicatively coupled to the first processor, wherein the first computer memory is configured to load a biological classification model configuration, the biological classification model configuration including a biological classification model, wherein the biological classification model is configured to execute on the first processor, the first processor being configured to (1) receive a first Raman-based spectral data set defining a first biological product sample as scanned by the first scanner, and (2) identify the biological product type based on the first Raman-based spectral data set using the biological classification model, wherein the biological classification model configuration further includes a spectral preprocessing algorithm, the first processor being configured to execute the spectral preprocessing algorithm when the first processor receives the first Raman-based spectral data set to reduce the spectral deviation of the first Raman-based spectral data set, and wherein the biological classification model includes classification components selected to reduce at least one of (1) the Q residuals of the biological classification model, or (2) the fitting summary value of the biological classification model, the biological classification model being configured to identify the biological product type of the first biological product sample based on the classification components.

[0107] 2. The configurable handheld bioanalyzer according to aspect 1, wherein the biological classification model configuration is capable of being transferred electronically to a second configurable handheld bioanalyzer, the second configurable handheld bioanalyzer comprising: a second housing adapted for handheld operation; a second scanner coupled to the second housing; a second processor communicatively coupled to the second scanner; and a second computer memory communicatively coupled to the second processor, wherein the second computer memory is configured to load the biological classification model configuration, the biological classification model configuration including the biological classification model, wherein the biological classification model is configured to execute on the second processor, the second processor being configured to (1) receive a second Raman-based spectral data set defining a second biological product sample as scanned by the second scanner, and (2) identify the biological product type based on the second Raman-based spectral data set using the biological classification model, wherein the second biological product sample is a new sample of the biological product type.

[0108] 3. A configurable handheld bioanalyzer as described in any of the preceding aspects, wherein the spectral deviation is an analyzer-to-analyzer spectral deviation between the first Raman-based spectral dataset and one or more other Raman-based spectral datasets of one or more corresponding other handheld bioanalyzers, each of the one or more other Raman-based spectral datasets representing the biological product type, and wherein the spectral preprocessing algorithm is configured to reduce the analyzer-to-analyzer spectral deviation between the first Raman-based spectral dataset and the one or more other Raman-based spectral datasets.

[0109] 4. A configurable handheld bioanalyzer as described in aspect 3, wherein the spectral preprocessing algorithm includes: applying a derivative transform to the first Raman-based spectral dataset to generate a modified Raman-based spectral dataset, aligning the modified Raman-based spectral dataset on the Raman shift axis, and normalizing the modified Raman-based spectral dataset on the Raman intensity axis.

[0110] 5. The configurable handheld bioanalyzer of aspect 4, wherein the derivative transformation comprises: determining Raman weighted averages of consecutive groups of 11 to 15 Raman shift values ​​on the Raman shift axis, and determining corresponding derivatives of those Raman weighted averages on the Raman shift axis.

[0111] 6. The configurable handheld bioanalyzer of any of the preceding aspects, wherein the classification component is selected to reduce both (1) the Q residual of the biological classification model and (2) the fit summary value of the biological classification model.

[0112] 7. The configurable handheld bioanalyzer of any of the preceding aspects, wherein the biological classification model further comprises a second classification component, and the biological classification model is configured to identify the biological product type of the first biological product sample based on the classification component and the second classification component.

[0113] 8. The configurable handheld bioanalyzer of any of the preceding aspects, wherein the biological classification model is implemented as a principal component analysis (PCA) model.

[0114] 9. The configurable handheld bioanalyzer of aspect 8, wherein the classification component is the first principal component of the PCA model.

[0115] 10. The configurable handheld bioanalyzer of any of the preceding aspects, wherein the computer memory is configured to load a new biological taxonomy model, the new biological taxonomy model comprising updated taxonomy components.

[0116] 11. The configurable handheld bioanalyzer according to any one of the foregoing aspects, wherein the biological classification model is configured to be implemented in the form of Extensible Markup Language (XML).

[0117] 12. The configurable handheld bioanalyzer according to any one of the foregoing aspects, wherein the biological product type is a therapeutic product.

[0118] 13. The configurable handheld bioanalyzer according to any one of the foregoing aspects, wherein the biological product type is identified by the biological classification model during the manufacture of the biological product having the biological product type.

[0119] 14. The configurable handheld bioanalyzer according to any one of the foregoing aspects, wherein the biological classification model is configured to distinguish the first biological product sample having the biological product type from different biological product samples having different biological product types based on the classification components.

[0120] 15. The configurable handheld bioanalyzer according to aspect 14, wherein the biological product type and the different biological product types each have different local characteristics within the same or similar Raman spectral range.

[0121] 16. The configurable handheld bioanalyzer according to any one of the foregoing aspects, wherein the biological classification model is configured to: when the Q-residual or the fitting profile value meets a threshold, identify the biological product type of the first biological product sample based on the classification components.

[0122] 17. The configurable handheld bioanalyzer according to aspect 16, wherein the biological classification model outputs a pass / fail determination based on the threshold.

[0123] 18. The configurable handheld bioanalyzer according to any one of the foregoing aspects, wherein the biological classification model is generated by a remote processor remote from the configurable handheld bioanalyzer.

[0124] 19. A bioanalytical method for identifying a biological product based on Raman spectroscopy, the bioanalytical method comprising: loading a biotaxonomy model configuration into a first computer memory of a first configurable handheld bioanalyzer having a first processor and a first scanner, the biotaxonomy model configuration comprising a biotaxonomy model; the biotaxonomy model receiving a first Raman-based spectral data set defining a first biological product sample as scanned by the first scanner; executing a spectral preprocessing algorithm of the biotaxonomy model to reduce spectral bias of the first Raman-based spectral data set; and using the biotaxonomy model to identify a type of a biological product based on the first Raman-based spectral data set, wherein the biotaxonomy model comprises a taxonomic component selected to reduce at least one of (1) a Q residual of the biotaxonomy model or (2) a fit summary value of the biotaxonomy model, the biotaxonomy model being configured to identify the type of the biological product of the first biological product sample based on the taxonomic component.

[0125] 20. The bioanalytical method of aspect 19, wherein the biotaxonomic model configuration is electronically transferable to a second configurable handheld bioanalyzer, the bioanalytical method further comprising: loading the biotaxonomic model configuration into a second computer memory of a second configurable handheld bioanalyzer having a second processor and a second scanner, the biotaxonomic model configuration comprising the biotaxonomic model; the biotaxonomic model receiving a second Raman-based spectral data set defining a second biological product sample as scanned by the second scanner; executing a spectral preprocessing algorithm of the biotaxonomic model to reduce a second spectral deviation of the second Raman-based spectral data set; and identifying the biological product type using the biotaxonomic model based on the second Raman-based spectral data set, wherein the second biological product sample is a new sample of the biological product type.

[0126] 21. The bioanalysis method of any one or more of aspects 19 to 20, wherein the spectral deviation is an analyzer-to-analyzer spectral deviation between the first Raman-based spectral dataset and one or more other Raman-based spectral datasets of one or more corresponding other handheld bioanalyzers, each of the one or more other Raman-based spectral datasets representing the biological product type, and wherein the spectral preprocessing algorithm is configured to reduce the analyzer-to-analyzer spectral deviation between the first Raman-based spectral dataset and the one or more other Raman-based spectral datasets.

[0127] 22. The bioanalysis method of aspect 21, wherein the spectral preprocessing algorithm comprises: applying a derivative transform to the first Raman-based spectral dataset to generate a modified Raman-based spectral dataset, aligning the modified Raman-based spectral dataset on the Raman shift axis, and normalizing the modified Raman-based spectral dataset on the Raman intensity axis.

[0128] 23. The bioanalysis method of aspect 22, wherein the derivative transformation comprises: determining Raman weighted averages of consecutive groups of 11 to 15 Raman shift values ​​on the Raman shift axis, and determining corresponding derivatives of those Raman weighted averages on the Raman shift axis.

[0129] 24. The bioanalysis method of any one or more of aspects 19 to 23, wherein the taxonomic component is selected to reduce both (1) the Q residual of the taxonomic model and (2) the fit summary value of the taxonomic model.

[0130] 25. The bioanalytical method of any one or more of aspects 19 to 24, wherein the biological taxonomic model further comprises a second taxonomic component, and the biological taxonomic model is configured to identify the biological product type of the first biological product sample based on the taxonomic component and the second taxonomic component.

[0131] 26. The bioanalysis method of any one or more of aspects 19 to 25, wherein the biotaxonomic model is implemented as a principal component analysis (PCA) model.

[0132] 27. The bioanalysis method of aspect 26, wherein the classification component is the first principal component of the PCA model.

[0133] 28. The biological analysis method of any one or more of aspects 19 to 27, wherein the first and / or second computer memory is configured to load a new biological taxonomy model comprising updated taxonomic components.

[0134] 29. The biological analysis method of any one or more of aspects 19 to 28, wherein the biological taxonomy model configuration is implemented in an extensible markup language (XML) format.

[0135] 30. The bioanalytical method of any one or more of aspects 19 to 29, wherein the biological product type is a therapeutic product.

[0136] 31. The bioanalytical method of any one or more of aspects 19 to 30, wherein the biological product type is identified by the biotaxonomic model during manufacture of a biological product having the biological product type.

[0137] 32. The bioanalytical method of any one or more of aspects 19 to 31, wherein the biological classification model is configured to distinguish the first biological product sample having the biological product type from a different biological product sample having a different biological product type based on the classification component.

[0138] 33. The bioanalysis method of aspect 32, wherein the biological product type and the different biological product type each have the same or similar Raman spectral range.

[0139] 34. The bioanalytical method of any one or more of aspects 19 to 33, wherein the biological classification model is configured to identify the biological product type of the first biological product sample based on the classification component when the Q residual or the fit summary value meets a threshold.

[0140] 35. The biological analysis method of aspect 34, wherein the biological classification model outputs a pass / fail decision based on the threshold value.

[0141] 36. The bioanalysis method of any one or more of aspects 19 to 35, wherein the bio-taxonomic model is generated by a remote processor remote from the configurable handheld bioanalyzer.

[0142] 37. A tangible, non-transitory computer-readable medium storing instructions for identifying a biological product based on Raman spectroscopy, the instructions, when executed by one or more processors of a configurable handheld bioanalyzer, causing the one or more processors of the configurable handheld bioanalyzer to: load a biological taxonomy model configuration into a computer memory of the configurable handheld bioanalyzer having a scanner, the biological taxonomy model configuration comprising a biological taxonomy model; the biological taxonomy model receiving a Raman-based spectral dataset defining a biological product sample as scanned by the scanner; executing a spectral preprocessing algorithm of the biological taxonomy model to reduce spectral bias of the Raman-based spectral dataset; and identifying a biological product type based on the Raman-based spectral dataset using the biological taxonomy model, wherein the biological taxonomy model includes a taxonomic component selected to reduce at least one of (1) a Q residual of the biological taxonomy model, or (2) a fit summary value of the biological taxonomy model, the biological taxonomy model being configured to identify the biological product type of the biological product sample based on the taxonomic component.

[0143] The foregoing aspects of the present disclosure are merely exemplary and are not intended to limit the scope of the present disclosure.

[0144] Additional Examples

[0145] The following additional examples provide additional support according to various embodiments described herein. In particular, the following additional examples demonstrate Raman spectroscopy for rapid identity (ID) verification of biotherapeutic protein products in solution. These examples demonstrate unique combinations of Raman features associated with both therapeutic agents and excipients as a basis for product differentiation. As described herein, a product ID method (e.g., a bioanalytical method) includes acquiring Raman spectra of (multiple) target products on multiple Raman analyzers (e.g., a configurable handheld bioanalyzer as described herein). The spectra can then be reduced in dimension using principal component analysis (PCA) to define product-specific models (e.g., bio-classification models) that will serve as the basis for product ID determination for a configurable handheld bioanalyzer and bioanalytical method for identifying biological products based on Raman spectroscopy as described herein. The product-specific models (e.g., bio-classification models) can be transferred to a separate instrument (e.g., a configurable handheld bioanalyzer) that has been validated for use in product testing. These models can be used for a variety of purposes, including quality control, incoming quality assurance, and manufacturing. Such analyzers and methods can be used in different Raman devices (e.g., configurable handheld bioanalyzers) from different manufacturers. In this way, the additional examples further demonstrate that the Raman ID analyzers and methods (e.g., configurable handheld bioanalyzers and related methods) described herein provide a variety of uses and tests for solution-based protein products in the biopharmaceutical industry.

[0146] Additional Examples - Materials

[0147] In the development and testing of the configurable handheld bioanalyzer and related methods described herein, drug substances and drug products corresponding to more than 28 independent product specifications were analyzed. Table 1 lists the active pharmaceutical ingredient (API) concentrations and molecular classes for 14 product specifications (which represent a set of late-stage and commercial product specifications). The product solution was transferred to a 4 mL glass vial and used as a sample pool for Raman spectroscopy acquisition. Table 1 provides general properties of the evaluated products, which can be used as targets for ID methods (e.g., bioanalytical methods) or as specific challenges described herein. For Table 1, for simplicity, each product is labeled with a character code. Products with the same letters but different numbers (e.g., A1 and A2) represent products with the same active ingredient but may differ in protein concentration and / or formulation. The listed materials can be used to manufacture drug products. It should be understood that some drug products can be identified by, for example, a brand name as described herein.

[0148] Table 1 (API concentration and molecular class)

[0149]

[0150] Additional Examples - Raman Instruments (e.g., Configurable Handheld Bioanalyzers) and Measurements

[0151] Regarding additional examples, as described herein, Raman spectra are measured using a configurable handheld bioanalyzer. For example, in some embodiments, the configurable handheld bioanalyzer can be a Raman-based handheld analyzer, such as the TruScan provided by Thermo Fisher Scientific Inc. TM RM Handheld Raman Analyzer. In such an embodiment, the configurable handheld bioanalyzer may implement TruTools TM Chemometrics software package. However, it should be understood that other brands or types of Raman analyzers using additional and / or different software packages can be utilized in accordance with the disclosure herein. In some embodiments, the configurable handheld bioanalyzer can be configured with a 785nm grating-stabilized laser source (maximum output 250mW) coupled to focusing optics (e.g., 0.33NA, 18mm working distance, >0.2mm spot) for sample review. For additional examples, a product solution contained in a glass vial is secured in front of the focusing optics using a bottle adapter of the configurable handheld bioanalyzer. All spectra are collected using the following same spectral acquisition settings (although other settings may be used), e.g., laser power = 250mW, integration time = 1000ms, and number of spectral additions = 70. For additional examples, spectra of the product are collected over a period of time using three different configurable handheld bioanalyzers (hereinafter referred to as configurable handheld bioanalyzers 1-3) and / or instruments dedicated to configuring and / or developing (multiple) bioanalytical methods for identifying biological products based on Raman spectroscopy as described herein. It should be understood that more or fewer analyzers using the same or different settings may be used to set up, configure, or otherwise initialize the configurable handheld bioanalyzer and associated bioanalytical method(s) as described herein.

[0152] Additional Example - Development of Multivariate Raman ID Bioanalytical Methods

[0153] Raman spectroscopy models (e.g., biological classification models) based on, for example, principal component analysis (PCA) can be generated, developed, or loaded as described herein. For example, in some embodiments, SOLO software equipped with Model Exporter add-on (Solo+Model_Exporter version 8.2.1; Eigenvector Research, Inc.) can be used to generate, develop, or load Raman spectroscopy models (e.g., biological classification models). However, it should be understood that other software can be used to generate, develop, or load Raman spectroscopy models (e.g., biological classification models). Typically, spectra for building models can be collected as repeated scans of two or more different batches of materials using a configurable handheld bioanalyzer (e.g., three configurable handheld bioanalyzers). For the purpose of considering instrument drift, spectra are typically acquired over multiple days. In some embodiments, before incorporating a model (e.g., a biological classification model), the spectral range can be reduced to exclude detector noise of >1800cm-1 and background variability caused by Rayleigh line suppression optics of <400cm-1. As described herein, for each model, the spectra can be further preprocessed and mean-centered. The models can also be refined by cross-validation using a random subset procedure with reference to the Raman spectra of the target and challenge products shown in Table 1.

[0154] The bio-classification model configuration (e.g., PCA model configuration) and Raman spectroscopy acquisition parameters can be configured or loaded into a configurable handheld bioanalyzer and / or bioanalytical method(s) can be used to identify biological products based on Raman spectroscopy as described herein. Acceptance (e.g., pass / fail) criteria for each method can also be specified. As described herein, the pass / fail criteria can be based on a simplified Hotelling T 2 (T r 2 ) and Q residual (Q r ), which are two summary statistics that generally describe how well a biological classification model (e.g., a PCA model) describes a Raman spectrum. Equations (1) to (4) below provide example decision logic options that a user may select for making a positive identification or determination (e.g., a pass / fail criterion) by a biological classification model (e.g., a PCA model):

[0155] Q r ≤1.000000 (1)

[0156]

[0157] In the example equation above, Hotelling T is calculated by dividing the original value by the corresponding confidence interval. 2 The values ​​and Q residual values ​​are normalized (i.e., simplified to T r 2 and Q r ), thereby setting the value of the upper limit to the value 1.

[0158] Additional Examples - Configurable Handheld Bioanalyzer and Method Transfer Testing

[0159] For additional examples, such as this article for FIG. 8A to FIG. 8E As described, the demonstration of the performance of the configurable handheld bioanalyzers and associated methods described herein for five product-specific models (e.g., bio-classification models) was performed using a small subset of analyzers (e.g., fifteen configurable handheld bioanalyzers) that had not undergone development of the configurable handheld bioanalyzers and associated methods described herein (i.e., not previously configured or loaded with the bio-classification model configuration as described herein). Product ID methods (e.g., bioanalytical methods for identifying biological products based on Raman spectroscopy) were prepared on configurable handheld bioanalyzers 1-3, and these methods were tested four times for a single product specification (e.g., Q1, Q2, A1, and A2) and one time for three similar specifications suitable for identifying the same protein product (e.g., B1, B2, and B3). Each test involved the use of target product spectra obtained on fifteen additional instruments (analyzers 4-18), each of which varied in age and performance. Model specificity was measured by also evaluating the closest specificity challenge product and formulation buffer (i.e., no protein). The Raman spectra of the samples were collected using the same collection parameters (i.e., laser power, acquisition time, number of co-additions) as those used to build the model. Raman spectra were obtained as duplicate samples on different days, generating approximately 250 spectra for each product sample. The spectra acquired during the test were evaluated for each of the five PCA models (e.g., in Eigenvector Solo+Model_Exporter software) to assess the likelihood of false positive results (i.e., misidentification of the challenge product as the target product) and false negative results (i.e., the model incorrectly rejected the target product).

[0160] During testing of additional examples, e.g. FIG. 8A to FIG. 8E As described, there was not a single instance of a false positive result for any of the five models and associated tests. In general, the Q of the challenge products for analyzers 4-18 was significantly higher than that of the instrument used to develop the models. r or T r 2 As an extension of this observation, the ability of a biotaxonomic model (e.g., a PCA model) to consistently reject a given challenge product can be inferred with high confidence based solely on the Raman spectra acquired during method development. Figure 7 An example visualization 700 of a simplified Q residual 704 is shown according to various embodiments described herein. In particular, Figure 7 An example graph of simplified Q residual values ​​700 for product A1 of Table 1 (which is considered a challenge product sample) evaluated against a biological classification model (e.g., a PCA model) for product A2 of Table 1 is provided. A linear index 706 is provided for indexing Raman spectra in a data set and is not necessarily associated with a sample.

[0161] The Raman spectra are distinguished based on whether they were acquired on an analyzer (702) used to develop the model or on an analyzer (703) used strictly for testing. Figure 7 Due to known instrument performance issues, the Q r The values ​​(i.e., linear index values ​​of approximately 250-270) are abnormally high, as will be discussed below. However, even excluding the measurements from analyzer 8, the Q values ​​for analyzers 4-18 are negative based on the rejection of the Shapiro-Wilk null hypothesis (p = 0.0013). r The values ​​are not normally distributed. For this data set, the median Q r The value of 3.02 is significantly greater than the median Q of the developed instrument r 2.53 (Mann-Whitney U test p value < 0.0001). No false positives were observed. However, there were 33 false negative predictions that should have been positive identifications out of 1540 total measurements, which only equates to about 2% false negatives—a small fraction of the total number of analyses.

[0162] FIG. 8A to FIG. 8E The summary statistics Q for each target product evaluated by a biological classification model (e.g., PCA model) corresponding to each target product are plotted. r or T r 2 , presents the analyses of each analyzer 802 (i.e., configurable handheld bioanalyzers 1-3 and analyzers 4-18). For clarity, FIG. 8A to FIG. 8E The validation results in are organized by analyzer number. FIG. 8A to FIG. 8D 800, 810, 820, and 830 each show example visualizations of simplified Q residuals for a target product (e.g., of Table 1) as evaluated for eighteen different configurable handheld bioanalyzers (configurable handheld bioanalyzers 1-3 and analyzers 4-18) according to various embodiments described herein. In particular, FIG. 8A to FIG. 8D The visualization of is a scatter plot depicting the spread of the simplified Q residuals for the target product of each method evaluated on analyzers 1-18. Fig. 8A The spread of the simplified Q residuals for the target product A1 in Table 1 is shown. Figure 8B The spread of the simplified Q residuals for the target product A2 in Table 1 is shown. Figure 8C shows the spread of the simplified Q residuals for the target product Q1 in Table 1. And Fig.8D The spread of the simplified Q residuals for the target product Q2 in Table 1 is shown. FIG. 8A to FIG. 8D In each of the graphs, the dashed horizontal line in each graph (i.e., 805, 815, 825, and 835, respectively) represents a pass / fail criterion or threshold, such that a value greater than 1 would produce a failed result (i.e., a false negative). Each linear index (e.g., 806, 816, 826, and 836, respectively) is provided for indexing Raman spectra in a data set and is not necessarily associated with a sample.

[0163] Fig. 8E 1 shows simplified fitted summary values ​​(e.g., Hotelling T ) of a target product (e.g., B1, B2, and / or B3) as evaluated for eighteen different configurable handheld bioanalyzers 802 (configurable handheld bioanalyzers 1-3 and analyzers 4-18) according to various embodiments described herein. 2 ). A dashed horizontal line 845 represents a pass / fail criterion or threshold, such that a value greater than 1 would produce a failed result (ie, a false negative). A linear index 846 is provided for indexing Raman spectra in a dataset and is not necessarily associated with a sample.

[0164] for FIG. 8A to FIG. 8E Each of the analyzers 10-16 and 18 had no false negative determinations. In fact, in most cases the summary statistics were <0.6, indicating that the likelihood of a false negative for any of these instruments was extremely low. There were 33 erroneous results isolated to the remaining three analyzers (8, 9, and 17), each of which had identifiable performance issues that were hardware-based and / or instrument-specific. Analyzer 8 (an early pilot build instrument) produced the highest number of false negatives. For Method A1, 20 / 20 spectra produced an unacceptable Q r values ​​(e.g., those values ​​greater than 1). However, there were only 3 false negatives in total among the other four methods, suggesting that the different performance of Method A1 may be related to the weak Raman scattering signal of this product due to the low protein concentration of this product (10 mg / mL) and the weak excipient bands. However, examination of the residues of Analyzer 8 showed a peak at ∼1300 cm -1 centered at 3 cm (data not shown). The optics of analyzer 8 (an early pilot build instrument) were different from those of the production analyzers (1-7 and 9-18) and resulted in observable Raman bands, which are believed to have caused the high rejection rate. Significant instrument performance issues were also noted for the remaining analyzers (9 and 17). The original wavenumber calibration of analyzer 9 was known to be ~3 cm -1 , outside the manufacturer's specifications. For analyzer 17, a previously unknown laser power / stability issue was identified after further investigation. Despite these known issues, the true positive rate for all five models on both analyzers FIG. 8A to FIG. 8E exceeded 85%, providing evidence that the bioclassification model (e.g., PCA model) can even tolerate some degree of degradation of instrument functionality to a certain extent. In the Good Manufacturing Practice (GMP) testing of biopharmaceutical products, there are already program mechanisms (e.g., installation qualification and operational qualification, regular preventive maintenance) designed to ensure the fitness for use of the instrument. However, the fact that the laser power issue of analyzer 17 was not known before testing highlights the value of a rigorous assessment of instrument performance metrics to ensure the long-term performance of spectrometers and multivariate models. However, as described above, the (multiple) configurable handheld bioanalyzers and related bioanalysis methods described herein for identifying biological products based on Raman spectroscopy are robust and fault-tolerant, and as described herein, can still be operated and used despite instrument hardware-based and / or instrument-specific performance issues.

[0165] Additional description

[0166] The above description herein has described various devices, components, parts, subsystems, and methods related to the use of drug delivery devices. The devices, components, parts, subsystems, methods, or drug delivery devices may further include a drug or be used with a drug, which drugs include but are not limited to those indicated below and their generic counterparts and biosimilar counterparts. As used herein, the term drug may be used interchangeably with other similar terms and may be used to refer to any type of drug or therapeutic material, including traditional and non-traditional drugs, nutraceuticals, supplements, biologics, bioactive agents and compositions, macromolecules, biosimilars, bioequivalents, therapeutic antibodies, polypeptides, proteins, small molecules, and generics. Also included are non-therapeutic injectable materials. The drug may be in liquid form, in lyophilized form, or in a form that can be reconstituted from the lyophilized form. The following exemplary list of drugs should not be considered inclusive or restrictive.

[0167] The drug will be contained in a reservoir. In some cases, the reservoir is the primary container, which is filled or pre-filled with the drug for treatment. The primary container may be a vial, a cartridge, or a pre-filled syringe.

[0168] In some embodiments, the reservoir of the drug delivery device may be filled with a colony-stimulating factor (such as granulocyte colony-stimulating factor (G-CSF)), or the device may be used with a colony-stimulating factor. Such G-CSF agents include but are not limited to (pegfilgrastim, pegylated filgrastim, pegylated G-CSF, pegylated hu-Met-G-CSF) and (filgrastim, G-CSF, hu-MetG-CSF).

[0169] In other embodiments, the drug delivery device may contain or be used with an erythropoiesis stimulating agent (ESA), which may be in liquid or lyophilized form. ESA is any molecule that stimulates erythropoiesis. In some embodiments, ESA is an erythropoiesis stimulating protein. As used herein, "erythropoiesis stimulating protein" means any protein that directly or indirectly causes activation of the erythropoietin receptor (e.g., by binding to and causing dimerization of the receptor). Erythropoiesis stimulating proteins include erythropoietin and its variants, analogs or derivatives that bind to and activate the erythropoietin receptor; antibodies that bind to and activate the erythropoietin receptor; or peptides that bind to and activate the erythropoietin receptor. Erythropoiesis stimulating proteins include but are not limited to (Epoetin α), (darbepoetin alfa), (Epoetine δ), (methoxy polyethylene glycol-epoetin beta), MRK-2578, INS-22, (Epoetin ζ), (Epoetin β), (Epoetin ζ), (Epoetin α), Epoetin α Hexal, (Epoetin α), (Epoetine θ), (Epoetine θ), (Epoetin theta), Epoetin alpha, Epoetin beta, Epoetin iota, Epoetin omega, Epoetin delta, Epoetin zeta, Epoetin theta and Epoetin delta, pegylated erythropoietin, carbamylated erythropoietin, and molecules or variants or analogs thereof.

[0170] Specific illustrative proteins are the specific proteins set forth below, including fusions, fragments, analogs, variants or derivatives thereof: OPGL-specific antibodies, peptibodies, related proteins, etc. (also referred to as RANKL-specific antibodies, peptibodies, etc.), including fully humanized OPGL-specific antibodies and human OPGL-specific antibodies, particularly fully humanized monoclonal antibodies; myostatin binding proteins, peptibodies, related proteins, etc., including myostatin-specific peptibodies; IL-4 receptor-specific antibodies, peptibodies, related proteins, etc., particularly those that inhibit activity mediated by binding of IL-4 and / or IL-13 to the receptor; interleukin 1-receptor 1 ("IL1-R1")-specific antibodies, peptibodies, related proteins, etc.; An g2-specific antibodies, peptibodies, related proteins, etc.; NGF-specific antibodies, peptibodies, related proteins, etc.; CD22-specific antibodies, peptibodies, related proteins, etc., especially human CD22-specific antibodies, such as but not limited to humanized and fully human antibodies, including but not limited to humanized and fully human monoclonal antibodies, especially including but not limited to human CD22-specific IgG antibodies, such as a dimer of a human-mouse monoclonal hLL2γ-chain and a human-mouse monoclonal hLL2κ chain connected by a disulfide, for example, the human CD22-specific fully humanized antibody in Epratuzumab, CAS registration number 501423-23-0; IGF-1 receptor-specific antibodies, peptibodies and related proteins, etc., including but not limited to anti-IGF-1R antibodies; antibodies, peptibodies, related proteins, etc. specific for B-7 related protein 1 ("B7RP-1", also known as B7H2, ICOSL, B7h and CD275), including but not limited to B7RP-specific fully human monoclonal IgG2 antibodies, including but not limited to fully human IgG2 monoclonal antibodies that bind to an epitope in the first immunoglobulin-like domain of B7RP-1, including but not limited to those that inhibit the interaction of B7RP-1 with its natural receptor ICOS on activated T cells; antibodies, peptibodies, related proteins, etc. specific for IL-15, such as, in particular, humanized monoclonal antibodies, including but not limited to HuMaxIL-15 antibodies and related proteins, such as, for example, 146B7; IFNγ-specific antibodies, peptibodies, related proteins, etc., including but not limited to human IFNγ-specific antibodies, and including but not limited to fully human anti-IFNγ antibodies; TALL-1-specific antibodies, peptibodies, related proteins, etc., and other TALL-specific binding proteins; parathyroid hormone ("PTH")-specific antibodies, peptibodies, related proteins, etc.; thrombopoietin receptor ("TPO-R")-specific antibodies, peptibodies, related proteins, etc.; hepatocyte growth factor ("HGF")-specific antibodies, peptibodies, related proteins, etc., including those targeting the HGF / SF:cMet axis (HGF / SF:c-Met), such as fully human monoclonal antibodies that neutralize hepatocyte growth factor / dispersant (HGF / SF);TRAIL-R2 specific antibodies, peptibodies, related proteins, etc.; Activin A specific antibodies, peptibodies, proteins, etc.; TGF-β specific antibodies, peptibodies, related proteins, etc.; Amyloid-β protein specific antibodies, peptibodies, related proteins, etc.; c-Kit specific antibodies, peptibodies, related proteins, etc., including but not limited to proteins that bind to c-Kit and / or other stem cell factor receptors; OX40L specific antibodies, peptibodies, related proteins, etc., including but not limited to proteins that bind to OX40L and / or other ligands of the OX40 receptor; (Alteplase, tPA); (Inotersen); (Darbepoetin alfa); (Epoetin alfa, or erythropoietin); GLP-1, (Interferon beta-1a); (Tositumomab, anti-CD22 monoclonal antibody); (Interferon-β); (Alemtuzumab, anti-CD52 monoclonal antibody); (Epoetin delta); (Bortezomib); MLN0002 (anti-α4β7 mAb); MLN1202 (anti-CCR2 chemokine receptor mAb); (Etanercept, TNF receptor / Fc fusion protein, TNF blocker); (Epoetin alfa); (Cetuximab, anti-EGFR / HER1 / c-ErbB-1); (Lumiliximab) (Growth hormone, human growth hormone); (Trastuzumab, anti-HER2 / neu (erbB2) receptor mAb); (Growth hormone, human growth hormone); (Adalimumab); (Panitumumab), (Denosumab), (Denosumab), (Etanercept, TNF-receptor / Fc fusion protein, TNF blocker), (Romiplostim); Rituximab; Ganitumab; Conatumumab; Brodalumab; Insulin in solution; (Interferon alfacon-1); (Nesiritide; Recombinant human B-type natriuretic peptide (hBNP)); (Anakinra); (sargramostim, rhuGM-CSF); (epatuzumab, anti-CD22 mAb); Benlysta TM (lymphostat B, belimumab, anti-BlyS mAb); (tenecteplase, a t-PA analog); (methoxypolyethylene glycol-epoetin beta); (gemtuzumab ozogamicin); (efalizumab); (certolizumab pegol, CDP 870); Soliris TM (eculizumab); pexelizumab (anti-C5 complement); (MEDI-524); (ranibizumab); (17-1A, edrecolomab); (lerdelimumab); TheraCim hR3 (nimotuzumab); Omnitarg (pertuzumab, 2C4); (IDM-1); (B43.13); (viciluzumab); cantuzumab mertansine (huC242-DM1); (Epoetin beta); (Opreleukin, human interleukin-11); Orthoclone (Muromonab-CD3, anti-CD3 monoclonal antibody); (Epoetin alpha); (infliximab, anti-TNFα monoclonal antibody); (abciximab, anti-GP lIb / Ilia receptor monoclonal antibody); (anti-IL6 receptor mAb); (bevacizumab), HuMax-CD4 (zanolimumab); (rituximab, anti-CD20 mAb); (erlotinib); (interferon alfa-2a); (basiliximab); (lumiracoxib); (palivizumab); 146B7-CHO (anti-IL15 antibody, see U.S. Patent No. 7,153,507); (natalizumab, anti-α4 integrin mAb); (MDX-1303, anti-anthrax protective antigen mAb); ABthrax TM ; (omalizumab); ETI211 (anti-MRSA mAb); IL-1trap (Fc portion of human IgG1 and extracellular domain of IL-1 receptor components (type I receptor and receptor accessory protein)); VEGF trap (Ig domain of VEGFR1 fused to IgG1 Fc); (daclizumab); (daclizumab, anti-IL-2Rα mAb); (ibritumomab tiuxetan); (ezetimibe); (atacicept, TACI-Ig); anti-CD80 mAb (galiximab); anti-CD23 mAb (ruximab); BR2-Fc (huBR3 / huFc fusion protein, soluble BAFF antagonist); CNTO 148 (golimumab, anti-TNFα mAb); HGS-ETR1 (mapatumumab; human anti-TRAIL receptor-1 mAb); HuMax-CD20 (ocrelizumab, anti-CD20 human mAb); HuMax-EGFR (zalutumumab); M200 (volociximab, anti-α5β1 integrin mAb); MDX-010 (ipilimumab, anti-CTLA-4 mAb and VEGFR-1 (IMC-18F1); anti-BR3 mAbs; anti-C. difficile toxin A and toxin BC mAbs MDX-066 (CDA-1) and MDX-1388); anti-CD22 dsFv-PE38 conjugates (CAT-3888 and CAT-8015); anti-CD25 mAb (HuMax-TAC); anti-CD3 mAb (NI-0401); adecatumumab; anti-CD30 mAb (MDX-060); MDX-1333 (anti-IFNAR); anti-CD38 mAb (HuMax CD38); anti-CD40L mAb; anti-Cripto mAb; anti-CTGF idiopathic pulmonary fibrosis stage I fibrinogen (FG-3019); anti-CTLA4 mAb; anti-eotaxin1 mAb (CAT-213); anti-FGF8 mAb; anti-ganglioside GD2 mAb; anti-ganglioside GM2 mAb; anti-GDF-8 human mAb (MYO-029); anti-GM-CSF receptor mAb (CAM-3001); anti-HepC mAb (HuMax HepC); anti-IFNα mAb (MEDI-545, MDX-1103); anti-IGF1R mAb; anti-IGF-1R mAb (HuMax-Inflam); anti-IL12 mAb (ABT-874); anti-IL12 / IL23 mAb (CNTO 1275); anti-IL13 mAb (CAT-354); anti-IL2Ra mAb (HuMax-TAC); anti-IL5 receptor mAb; anti-integrin receptor mAb (MDX-018, CNTO95); anti-IP10 ulcerative colitis mAb (MDX-1100); BMS-66513; anti-mannose receptor / hCGβ mAb (MDX-1307); anti-mesothelin dsFv-PE38 conjugate (CAT-5001);Anti-PD1 mAb (MDX-1106 (ONO-4538)); anti-PDGFRα antibody (IMC-3G3); anti-TGFβ mAb (GC-1008); anti-TRAIL receptor-2 human mAb (HGS-ETR2); anti-TWEAK mAb; anti-VEGFR / Flt-1 mAb; and anti-ZP3 mAb (HuMax-ZP3). ;

[0171] In some embodiments, the drug delivery device may comprise or be used with a sclerostin antibody, such as, but not limited to, romosozumab, blosozumab, or BPS 804 (Novartis), and in other embodiments, a monoclonal antibody (IgG) that binds human proprotein convertase subtilisin / Kexin type 9 (PCSK9). Such PCSK9-specific antibodies include, but are not limited to (evolocumab) and (alirocumab). In other embodiments, the drug delivery device may contain or be used with rituximab, bixalomer, trebananib, ganitazumab, canatumumab, motesanib diphosphate, brodalumab, vidupiprant, or panitumumab. In some embodiments, the reservoir of the drug delivery device may be filled with a drug for treating melanoma or other cancers. (talimogenelaherparepvec) or another oncolytic HSV, or the device can be used with it, the other oncolytic HSV includes but is not limited to OncoVEXGALV / CD; OrienX010; G207; 1716; NV1020; NV12023; NV1034; and NV1042. In some embodiments, the drug delivery device can contain or be used with an endogenous tissue metalloproteinase inhibitor (TIMP), such as but not limited to TIMP-3. Antagonistic antibodies to human calcitonin gene-related peptide (CGRP) receptors (such as but not limited to anovimab) and bispecific antibody molecules targeting CGRP receptors and other headache targets can also be delivered using the drug delivery device of the present disclosure. In addition, bispecific T cell engagers Antibodies (such as but not limited to (blinatumomab)) can be used in or with the drug delivery device of the present disclosure. In some embodiments, the drug delivery device can contain or be used with an APJ macromolecular agonist, such as but not limited to apelin or its analogs. In some embodiments, a therapeutically effective amount of anti-thymic stromal lymphopoietin (TSLP) or TSLP receptor antibody is used in or with the drug delivery device of the present disclosure.

[0172] Although the drug delivery devices, assemblies, components, subsystems and methods have been described in terms of exemplary embodiments, they are not limited thereto. The detailed description is to be construed as merely exemplary and does not describe every possible embodiment of the present disclosure. Many alternative embodiments may be implemented using current technology or technology developed after the filing date of this patent application, which still fall within the scope of the claims defining the invention disclosed herein.

[0173] Those skilled in the art will appreciate that various modifications, changes and combinations may be made to the embodiments described above without departing from the spirit and scope of the invention disclosed herein, and that such modifications, changes and combinations are deemed to be within the scope of the inventive concept.

[0174] Additional considerations

[0175] Although the disclosure herein sets forth detailed descriptions of many different embodiments, it should be understood that the legal scope of this specification is defined by the text of the claims set forth at the end of this patent and their equivalents. This detailed description is to be construed as exemplary only and does not describe every possible embodiment, as it would be impractical to describe every possible embodiment. Many alternative embodiments may be implemented using current technology or technology developed after the filing date of this patent, which still fall within the scope of the claims.

[0176] The following additional considerations apply to the foregoing discussion. Throughout the entire specification, multiple instances may implement components, operations or structures described as a single instance. Although the individual operations of one or more methods are shown and described as separate operations, one or more of the individual operations may be performed simultaneously, and the operations do not need to be performed in the order shown. The structures and functions presented as separate components in the example configuration may be implemented as combined structures or components. Similarly, the structures and functions presented as separate components may be implemented as separate components. These and other variations, modifications, additions and improvements all fall within the scope of this paper theme.

[0177] In addition, some embodiments are described herein as including logic or a number of routines, subroutines, applications, or instructions. These may constitute software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware, routines, etc. are tangible units capable of performing certain operations and may be configured or arranged in some manner. In an example embodiment, one or more computer systems (e.g., independent client or server computer systems) or one or more hardware modules (e.g., a processor or a group of processors) of a computer system may be configured by software (e.g., an application or application portion) as hardware modules that operate to perform certain operations as described herein.

[0178] In various embodiments, the hardware modules may be implemented mechanically or electronically. For example, the hardware modules may include dedicated circuitry or logic that is permanently configured (e.g., as a dedicated processor, such as a field programmable gate array (FPGA), or as an application specific integrated circuit (ASIC)) to perform specific operations. The hardware modules may also include programmable logic or circuitry (e.g., as contained within a general purpose processor or other programmable processor) that is temporarily configured by software to perform specific operations. It will be understood that the decision to implement the hardware modules mechanically in dedicated and permanently configured circuitry or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

[0179] Accordingly, the term "hardware module" should be understood to include tangible entities, referring to entities that are physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a particular manner or perform specific operations described herein. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each hardware module need not be configured or instantiated at any one time. For example, where the hardware modules include a general-purpose processor configured using software, the general-purpose processor can be configured as different hardware modules at different times. The software can configure the processor accordingly, for example, to constitute a specific hardware module at one time and to constitute a different hardware module at a different time.

[0180] The term "coupled to" as used herein does not require direct coupling or connection, such that two items may be "coupled to" each other through one or more intermediate components or other elements, such as an electronic bus, wires, mechanical components, or other such indirect connections.

[0181] Hardware modules can provide information to other hardware modules and receive information from other hardware modules. Therefore, the described hardware modules can be considered to be communicatively coupled. When multiple such hardware modules exist simultaneously, communication can be achieved by signal transmission (e.g., through appropriate circuits and buses) connecting the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communication between such hardware modules can be achieved, for example, by storing and obtaining information in a memory structure accessible to multiple hardware modules. For example, a hardware module can perform an operation and store the output of the operation in a storage device to which the hardware module is communicatively coupled. Then, another hardware module can access the storage device at a later time to obtain and process the stored output. The hardware module can also initiate communication with an input or output device and can operate on a resource (e.g., a collection of information).

[0182] The various operations of the example methods described herein may be performed at least in part by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. In some example embodiments, the modules mentioned herein may include processor-implemented modules.

[0183] Similarly, the methods or routines described herein may be implemented at least in part by a processor. For example, at least some of the operations of the method may be performed by one or more processors or by a hardware module implemented by a processor. The execution of certain operations in the operation may be distributed among one or more processors, not only residing in a single machine, but also deployed across multiple machines. In some example embodiments, one or more processors may be located in a single location, while in other embodiments, the processors may be distributed across multiple locations.

[0184] The execution of certain operations in the operation can be distributed among one or more processors, not only residing in a single machine, but also deployed across multiple machines. In some example embodiments, one or more processors or processor-implemented modules can be located in a single geographic location (e.g., in a home environment, an office environment, or a server farm). In other embodiments, one or more processors or processor-implemented modules can be distributed across multiple geographic locations.

[0185] This detailed description is to be construed as exemplary only and does not describe every possible embodiment because describing every possible embodiment would be impractical, if not impossible. Numerous alternative embodiments may be implemented by one of ordinary skill in the art using current technology or technology developed after the filing date of this application.

[0186] Those skilled in the art will appreciate that various modifications, changes and combinations may be made to the embodiments described above without departing from the scope of the present invention, and such modifications, changes and combinations may be considered to be within the scope of the present invention.

[0187] The patent claims at the end of this patent application are not intended to be interpreted under 35 U.S.C. §112(f) unless conventional means-plus-function language is explicitly recited, such as "means for" or "step for" language explicitly recited in the claim(s). The systems and methods described herein are directed to improving computer functionality and improving the operation of conventional computers.< / model> < / model>

Claims

1. A configurable handheld bioanalyzer for identifying biological products based on Raman spectroscopy, the configurable handheld bioanalyzer include: a first housing adapted for handheld operation; a first scanner, the first scanner being carried by the first housing; a first processor communicatively coupled to the first scanner; as well as a first computer memory communicatively coupled to the first processor, wherein the first computer memory is configured to load a biological taxonomy model configuration, the biological taxonomy model configuration comprising a biological taxonomy model, wherein the biological taxonomy model is configured to execute on the first processor, the first processor being configured to (1) receive a first Raman-based spectral data set defining a first biological product sample as scanned by the first scanner, and (2) identify a biological product type based on the first Raman-based spectral data set using the biological taxonomy model, The biological classification model configuration further includes a spectral preprocessing algorithm, and the first processor is configured to execute the spectral preprocessing algorithm to reduce the spectral deviation of the first Raman-based spectral dataset when the first processor receives the first Raman-based spectral dataset, and The biological taxonomy model includes variables selected to reduce at least one of (1) an error of the biological taxonomy model, or (2) a fit summary value of the biological taxonomy model, and the biological taxonomy model is configured to identify the biological product type of the first biological product sample.

2. The configurable handheld bioanalyzer of claim 1, wherein the bio-classification model configuration is electronically transferable to a second configurable handheld bioanalyzer, the second configurable handheld bioanalyzer include: a second housing adapted for handheld operation; a second scanner coupled to the second housing; a second processor communicatively coupled to the second scanner; as well as a second computer memory communicatively coupled to the second processor, wherein the second computer memory is configured to load the biological taxonomy model configuration, the biological taxonomy model configuration comprising the biological taxonomy model, wherein the biological taxonomy model is configured to execute on the second processor, the second processor being configured to (1) receive a second Raman-based spectral dataset defining a second biological product sample as scanned by the second scanner, and (2) identify the biological product type based on the second Raman-based spectral dataset using the biological taxonomy model, Wherein, the second biological product sample is a new sample of the biological product type.

3. The configurable handheld bioanalyzer of claim 1 , wherein the spectral deviation is an analyzer-to-analyzer spectral deviation between the first Raman-based spectral dataset and one or more other Raman-based spectral datasets of one or more corresponding other handheld bioanalyzers, each of the one or more other Raman-based spectral datasets representing the biological product type, and in, The spectral pre-processing algorithm is configured to reduce analyzer-to-analyzer spectral deviations between the first Raman-based spectral data set and the one or more other Raman-based spectral data sets.

4. The configurable handheld bioanalyzer as claimed in claim 3, wherein the spectral preprocessing algorithm include: applying a derivative transform to the first Raman-based spectral dataset to generate a modified Raman-based spectral dataset; aligning the modified Raman-based spectral data set on a Raman shift axis; as well as This modified Raman-based spectral data set was normalized on the Raman intensity axis.

5. The configurable handheld bioanalyzer of claim 1, wherein the variables are selected to reduce both (1) an error of the biological classification model and (2) a fit summary value of the biological classification model.

6. The configurable handheld bioanalyzer of claim 1, wherein the biological classification model further comprises a second variable, the biological classification model being configured to identify the biological product type of the first biological product sample based on the variable and the second variable.

7. The configurable handheld bioanalyzer of claim 1, wherein the biological classification model is implemented as a multivariate model.

8. The configurable handheld bioanalyzer of claim 1, wherein the computer memory is configured to load a new biological classification model, the new biological classification model comprising updated variables.

9. The configurable handheld bioanalyzer of claim 1, wherein the biological product type is a therapeutic product.

10. The configurable handheld bioanalyzer of claim 1, wherein the biological classification model is configured to distinguish the first biological product sample having the biological product type from a different biological product sample having a different biological product type based on the variable.

11. The configurable handheld bioanalyzer of claim 10, wherein the biological product type and the different biological product type each have different local features within the same or similar Raman spectral range.

12. The configurable handheld bioanalyzer of claim 1, wherein the biological classification model is configured to identify the biological product type of the first biological product sample based on the variable when the error or the fit summary value satisfies a threshold.

13. The configurable handheld bioanalyzer of claim 12, wherein the bio-classification model outputs a pass / fail decision based on the threshold.

14. The configurable handheld bioanalyzer of claim 1, wherein the biological classification model is generated by a remote processor remote from the configurable handheld bioanalyzer.

15. A bioanalytical method for identifying biological products based on Raman spectroscopy, the bioanalytical method include: loading a biological taxonomy model configuration into a first computer memory of a first configurable handheld bioanalyzer having a first processor and a first scanner, the biological taxonomy model configuration including a biological taxonomy model; The biological classification model receives a first Raman-based spectral data set defining a first biological product sample as scanned by the first scanner; executing a spectral preprocessing algorithm of the biological classification model to reduce spectral bias of the first Raman-based spectral data set; as well as identifying a type of biological product based on the first Raman-based spectral data set using the biological classification model, The biological taxonomy model includes variables selected to reduce at least one of (1) an error of the biological taxonomy model, or (2) a fit summary value of the biological taxonomy model, and the biological taxonomy model is configured to identify the biological product type of the first biological product sample.

16. The bioanalysis method according to claim 15, in, The bio-classification model configuration is capable of being electronically transferred to a second configurable handheld bio-analyzer, the bio-analysis method further comprising: loading the biological taxonomic model configuration into a second computer memory of a second configurable handheld bioanalyzer having a second processor and a second scanner, the biological taxonomic model configuration including the biological taxonomic model; The biological classification model receives a second Raman-based spectral data set defining a second biological product sample as scanned by the second scanner; executing a spectral preprocessing algorithm of the biological classification model to reduce a second spectral bias of the second Raman-based spectral data set; and identifying the bio-product type based on the second Raman-based spectral data set using the bio-taxonomy model, Wherein, the second biological product sample is a new sample of the biological product type.

17. The bioanalysis method according to claim 15, in, The spectral deviation is an analyzer-to-analyzer spectral deviation between the first Raman-based spectral dataset and one or more other Raman-based spectral datasets of one or more corresponding other handheld bioanalyzers, each of the one or more other Raman-based spectral datasets representing the biological product type, and The spectral pre-processing algorithm is configured to reduce analyzer-to-analyzer spectral deviations between the first Raman-based spectral data set and the one or more other Raman-based spectral data sets.

18. The bioanalysis method according to claim 17, in, The spectral preprocessing algorithm includes: applying a derivative transform to the first Raman-based spectral data set to generate a modified Raman-based spectral data set; aligning the modified Raman-based spectral data set on a Raman shift axis; and This modified Raman-based spectral data set was normalized on the Raman intensity axis.

19. The bioanalysis method according to claim 15, in, This type of biological product is a therapeutic product.

20. A tangible, non-transitory computer readable medium storing instructions for identifying a biological product based on Raman spectroscopy, the instructions, when executed by one or more processors of a configurable handheld bioanalyzer, causing the one or more processors of the configurable handheld bioanalyzer to: loading a biological taxonomy model configuration into a computer memory of the configurable handheld bioanalyzer having a scanner, the biological taxonomy model configuration comprising a biological taxonomy model; The biological classification model receives a Raman-based spectral dataset defining a biological product sample as scanned by the scanner; executing a spectral preprocessing algorithm of the biological classification model to reduce spectral bias of the Raman-based spectral dataset; as well as using the bio-classification model to identify the type of biological product based on the Raman-based spectral dataset, The biological taxonomy model includes variables selected to reduce at least one of (1) an error of the biological taxonomy model, or (2) a fit summary value of the biological taxonomy model, and the biological taxonomy model is configured to identify the biological product type of the biological product sample.

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