A configurable handheld biological analyzer for identifying biological products based on Raman spectroscopy.

Abstract

To provide handheld biological analyzers and related biological analytics methods based on Raman spectroscopy.SOLUTION: A biological classification model configuration is loaded into a computer memory of a handheld biological analyzer. The biological classification model configuration includes a biological classification model configured to receive a Raman-based spectra dataset defining a biological product sample as scanned by a scanner. A spectral preprocessing algorithm is executed to reduce a spectral variance of the Raman-based spectra dataset. The biological classification model identifies a biological product type based on the Raman-based spectra dataset and further based on a classification component selected to reduce at least one of (1) a Q-residual error or (2) a summary-of-fit value of the biological classification model. The biological classification model configuration is transferrable to and loadable on other configurable handheld biological analyzers.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims the interests of U.S. Provisional Patent Application No. 62 / 925,893 (filed October 25, 2019) and U.S. Provisional Patent Application No. 63 / 043,976 (filed June 25, 2020). Each of the above provisional patent applications is incorporated herein by reference in its entirety.

[0002] This disclosure relates, more broadly, to a configurable handheld biological analyzer, and more specifically, to a system and method for using a configurable handheld biological analyzer for identifying or classifying biological products based on Raman spectroscopy. [Background technology]

[0003] The development and manufacture of pharmaceutical and biotechnology products generally require the measurement or identification of the raw materials used in the development of such products. The purpose of product identification testing is to guarantee the identity of the product. Situations requiring identification testing include the distribution of products to clinical settings, import testing, and transportation between network locations. Furthermore, the measurement or identification of biological products can be crucial to ensure the quality of the development or manufacturing process, and consequently the quality of the final product itself, in order to meet quality standards and / or legal requirements.

[0004] The use of Raman spectroscopy for the measurement and identification of biological products is a relatively new concept. Generally, Raman spectroscopy can be used to examine the chemical or biological structure of raw materials or products. Raman spectroscopy is a non-destructive chemical or biological analytical technique that measures the interaction between light and a product or material, for example, the interaction between light and the biological properties or chemical bonds of the product or material. Raman spectroscopy provides a light scattering technique in which the molecules of the sample substance or product scatter incident light from a high-intensity laser source. Generally, most of the scattered light is 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 various wavelengths (colors) resulting from the chemical or molecular structure of the material or product being analyzed. This is called Raman scattering and can be analyzed or scanned to generate Raman-based data of the material or product being analyzed.

[0005] Raman scattering analysis can provide detailed information about the properties of a material or product, including its chemical structure and / or identity, contamination and impurities, phase and polymorphism, crystallinity, intrinsic stress / strain, and / or intermolecular interactions. This detailed information can be present in the Raman spectrum of the material. The Raman spectrum can be visualized to reveal numerous peaks across various wavelengths. The Raman spectrum can indicate the intensity and wavelength position of the Raman scattered light. Each peak may correspond to a specific molecular bond vibration related to the material or product being analyzed.

[0006] Generally, Raman spectra provide a different chemical or biological "fingerprint" of a particular material, molecule, or product, and can be used to verify the identity of a specific material, molecule, or product and / or distinguish it from others. Furthermore, Raman spectral libraries are often used to identify materials based on their Raman spectra. 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 material or product.

[0007] Analytical instruments that implement Raman spectroscopy exist for the identification of raw materials and products. For example, Thermo Fisher Scientific Inc. offers a Raman-based handheld analyzer, which may be identified as the TruScan® RM Handheld Raman Analyzer. However, the use of such existing scanners can be problematic due to inconsistencies in scanning materials and / or products, such as pharmaceutical and biotechnology materials or products, particularly those with similar Raman spectra. For example, due to inconsistencies between the Raman spectra of similar products, existing Raman-based handheld analyzers may misidentify products by outputting a Type I error (false positive) or a Type II error (false negative), for example, pharmaceutical or biotechnology products. The main causes of inconsistencies or errors are differences between Raman-based analyzers, including differences in software, manufacturing, age, components, operating environment (e.g., temperature), or other such differences in Raman-based analyzers.

[0008] Known approaches generally fail to address errors arising from inconsistencies or variability between handheld analytical instruments. For example, one known approach involves using data from several analytical instruments to develop a static mathematical equation for use across several instruments. However, a common problem with this approach is that instrument performance can change over time. Furthermore, having routines to access all of these instruments is often impractical or impossible. In particular, data for constructing static mathematical equations is generally unavailable, especially for new analytical instruments, as manufacturers cannot provide new specifications for the new instruments in advance. This hinders the development and maintenance of static mathematical equations, especially since these new analytical instruments are developed over time, and because the development of static mathematical equations generally requires a large number of samples, given the precision required for various types of analytical instruments. Moreover, without these new specifications for new analytical instruments, static mathematical equations cannot be made compatible when run on the new analytical instruments. Furthermore, differences in the manufacturing or quality control of analytical instruments, particularly among various manufacturers, can lead to excessive tolerance for variability in static mathematical equations, thereby generating static mathematical equations that themselves exhibit excessive variability in the accurate measurement and / or identification of biological products.

[0009] In the second known approach, data from a given analytical instrument is standardized, and a child-parent instrument map is generated for a given group of analytical instruments. However, constructing a child-parent instrument map generally requires data from both parent and child instruments, and this approach is limited, especially when developing new generations of analytical instruments, which necessitates numerous reorders and types of child-parent instrument maps over long periods, as it is typically difficult to implement and maintain, and / or computationally expensive. Furthermore, in the biopharmaceutical industry, user access to child instruments is limited, which also restricts the child-parent instrument map approach.

[0010] In a third known approach, data from a given analyzer is standardized, but here variations between analyzers are either ignored or treated as trivial. However, considering that variations between analyzers typically affect the accurate identification and measurement of raw materials and / or biological products and thus should be taken into account, such an approach is undesirable.

[0011] For the reasons above, there is a need for a configurable handheld biological analyzer for identifying biological products based on Raman spectroscopy and related methods that are configured to reduce variations and increase compatibility between similarly configured configurable handheld biological analyzers. Summary of the Invention Means for Solving the Problems

[0012] The disclosure of this application describes the use of Raman spectroscopy via a handheld analyzer for identifying biological products. Furthermore, the disclosure of this specification describes the use of configurable handheld biological analyzers, systems, and methods that overcome the limitations generally associated with the known uses of Raman spectra for measuring biological products. For example, Raman spectra of certain biological products may be so similar that they cannot be distinguished by known methods using Raman spectra, which generally rely on general-purpose statistical algorithms. Raman spectral measurements can be particularly problematic when instrument-to-instrument variability is introduced, resulting in, for example, Type I and Type II errors between different analyzers. As described herein, such variability may arise from any one or more of the following: differences in software, manufacturing, age, components, operating environment (e.g., temperature), or other differences in Raman-based analyzers. This problem is particularly apparent during the development or manufacture of biological products, as instrument-to-instrument variability can be a major factor affecting quality, robustness, and / or transmissibility in the manufacture or development of processes related to pharmaceutical or biological products. Accordingly, various embodiments disclosed herein describe configurable handheld biological analyzers that use configurations employing specific preprocessing algorithms and / or multivariate data analysis to ensure that (1) the measurement and / or identification of materials or products is highly sensitive and / or specific, and (2) the compatibility and configuration are transferable and / or implementable to additional analyzers, such as new analyzers within a “network” or group of analyzers, when deployed on a first set of analyzers.

[0013] Thus, in various embodiments of the present specification, a configurable handheld biological analyzer for identifying biological products based on Raman spectroscopy is disclosed. The configurable handheld biological analyzer may include a first housing adapted for handheld operation. Further, the configurable handheld biological analyzer may include a first scanner held by the first housing. The configurable handheld biological analyzer may include a first processor communicatively coupled to the first scanner. The configurable handheld biological analyzer 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 the first processor. The first processor may be configured to: (1) receive a dataset of a first Raman-based spectrum defining a first biological product sample scanned by the first scanner, and (2) identify a biological product type based on the dataset of the first Raman-based spectrum 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 when the dataset of the first Raman-based spectrum is received by the first processor to reduce spectral inconsistencies in the dataset of the first Raman-based spectrum. Further, the biological classification model may include a classification component selected to reduce at least one of: (1) the Q residual error of the biological classification model, or (2) the fitting summary value of the biological classification model, and the biological classification model may be configured to identify the biological product type of the first biological product sample based on the classification component.

[0014] In additional embodiments disclosed herein, a biological analysis method for identifying biological products based on Raman spectroscopy is disclosed. The biological analysis method may include loading a biological classification model configuration into the first computer memory of a first configurable handheld biological analyzer having a first processor and a first scanner. The biological classification model configuration may include a biological classification model. Furthermore, the biological analysis method may include receiving a first dataset of Raman-based spectra that define a first biological product sample scanned by the first scanner, via the biological classification model. Furthermore, the biological analysis method may include running a spectral preprocessing algorithm of the biological classification model to reduce spectral inconsistencies in the first dataset of Raman-based spectra. Moreover, the biological analysis method may include using the biological classification model to identify a biological product type based on the first dataset of Raman-based spectra. A biological classification model may include a classification component selected to reduce at least one of (1) the Q residual error of the biological classification model, or (2) the summary value of the fit of the biological classification model, and the biological classification model may be configured to identify the biological product type of a first biological product sample based on the classification component.

[0015] Further additional embodiments disclosed herein describe a tangible, non-temporary, computer-readable medium (e.g., computer memory) that stores instructions for identifying biological products based on Raman spectroscopy. These instructions, when executed by one or more processors of a configurable handheld biological analyzer, cause one or more processors of the configurable handheld biological analyzer to load a biological classification model configuration into the computer memory of the configurable handheld biological analyzer having a scanner. The biological classification model configuration may include a biological classification model. The biological classification model can receive a dataset of Raman-based spectra defining a biological product sample scanned by the scanner. Furthermore, one or more processors of the configurable handheld biological analyzer may execute spectral preprocessing algorithms for the biological classification model to reduce spectral inconsistencies in the dataset of Raman-based spectra. Using the biological classification model, one or more processors of the configurable handheld biological analyzer can identify biological product types based on the dataset of Raman-based spectra. As described in various embodiments, the biological classification model may include classification components selected to reduce at least one of (1) the Q residual error of the biological classification model, or (2) the summary value of the fit of the biological classification model. The biological classification model may be configured to identify the biological product type of a biological product sample based on the classification components.

[0016] The advantages of this application include the development of biological classification models (e.g., multivariate analysis models) that yield consistent results for the same pharmaceutical or biological product (e.g., therapeutic product / agent) across various analytical instruments, including various analytical instruments used to scan Raman-based datasets used to construct biological classification models. As described herein, multiple analytical instruments, or multiple datasets of Raman spectra generated by such analytical instruments, may be used to construct biological classification models.

[0017] Furthermore, as described herein, the biological classification model is configurable and transferable among configurable handheld biological analyzers, which may include Raman spectral preprocessing, classification component selection (e.g., via singular value decomposition (SVD) analysis), and discriminating statistical analysis to reduce variability among configurable handheld biological analyzers. For example, the use of the biological classification model described herein can improve existing analyzers by reducing variability between instruments / analytes, without requiring data from sub-instruments for development, and can be used across various analyzers with different software or software versions, different manufacturing, age, operating environment (e.g., temperature), components, or other such differences, as it does not require data from sub-instruments for development.

[0018] In various embodiments, the Q-residual can be used as a discriminative statistic to determine which biological classification models can withstand variability between analytical instruments. This can provide an indicator for selecting which biological classification models to load into a configurable handheld biological analyzer.

[0019] Furthermore, the accuracy of a biological classification model can be increased by applying preprocessing techniques (e.g., spectral preprocessing algorithms described herein) to minimize statistical type I and / or type II errors in the output of the biological classification model, and thus improve the output of a configurable handheld biological analyzer on which the biological classification model is implemented / configured.

[0020] Furthermore, in some embodiments, the configurable handheld biological analyzer can use multivariate analysis (e.g., major component analysis (PCA)) to determine the classification components of a biological classification model. This allows the configurable handheld biological analyzer to identify biological products / agents having similar formulations. This provides a flexible approach, as it can generate biological classification models that include various, different, and / or additional classification components (e.g., a second major component of the PCA biological classification model) corresponding to products with multiple specifications (e.g., products related to denosumab).

[0021] As described above and in accordance with the disclosures herein, since the claims enumerate configurable handheld biological analyzers for identifying biological products based on Raman spectroscopy, which are, for example, improvements to existing handheld biological analyzers, the disclosure includes improvements to the functionality of computers or other technologies. That is, since configurable handheld biological analyzers are computing devices and, through their biological classification model configurations, provide reduced variability between analyzers compared to existing handheld biological analyzers, the disclosure describes improvements to the functionality of computers themselves or "any other technology or field of technology." This means that, at the very least, the configurable handheld biological analyzers described herein improve upon the prior art to improve accuracy in the measurement, identification, and / or classification of materials and / or products (e.g., therapeutic products), which are important features in the manufacture and development of pharmaceutical and / or other such biological products.

[0022] Furthermore, the configurable handheld biological analyzers described herein are further improved by using a biological classification model configuration that is transferable to the memory of compatible configurable handheld biological analyzers, optionally updatable (with new data), and loadable, enabling standardization among sets (i.e., "networks") of analyzers, thereby reducing variability. This reduces the maintenance and / or deployment time of configurable handheld biological analyzers for analyzer networks.

[0023] Furthermore, the configurable handheld biological analyzer can be further improved by using a biological classification model configuration, which includes a biological classification model. The biological classification 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), as described herein.

[0024] Furthermore, this disclosure includes applying certain claim elements by using or employing certain instruments, such as configurable handheld biological analyzers, to identify biological products based on Raman spectroscopy (including the identification of biological products during the development or manufacture of such products).

[0025] Furthermore, this disclosure includes transforming or reducing a particular article into a different state or object, for example, transforming or reducing a Raman spectral dataset into a different state used to identify biological products based on Raman spectroscopy.

[0026] This disclosure includes certain features beyond routine conventional activities well understood in the art, or includes adding non-conventional steps that limit the scope of the claims to specific beneficial uses (for example, including providing a biological classification model configuration used to reduce variability among a set of configurable handheld biological analyzers (i.e., a “network”) that could each be used to identify biological products based on Raman spectroscopy.

[0027] Those skilled in the art will find the advantages more apparent from the description of the following preferred embodiments, which are shown and described as examples. As will be understood, other different embodiments are possible, and these details are modifiable in various ways. Therefore, the drawings and description are illustrative and not limiting.

[0028] The drawings described below depict various aspects of the systems and methods disclosed therein. It should be understood that each drawing depicts an embodiment of a particular aspect of the systems and methods of this disclosure, and that each drawing is intended to correspond to its possible embodiment. Furthermore, wherever possible, the following description refers to the reference numerals included in the drawings below, and features depicted in multiple figures are indicated by consistent reference numerals.

[0029] While the figures show the configurations discussed herein, it should be understood that these embodiments are not limited to the exact configurations and means shown. [Brief explanation of the drawing]

[0030] [Figure 1] Examples of configurable handheld biological analyzers for identifying biological products based on Raman spectroscopy, according to various embodiments disclosed herein, are shown. [Figure 2] This specification provides illustrative flowcharts of various embodiments of biological analytical methods for identifying biological products based on Raman spectroscopy. [Figure 3A] This specification presents exemplary visualizations of datasets of Raman-based spectra scanned by various handheld biological analyzers according to various embodiments disclosed herein. [Figure 3B] Figure 3A shows an illustrative visualization of a modified Raman-based spectral dataset, corrected from the original Raman-based spectral dataset. [Figure 3C] Figure 3B shows an illustrative visualization of the normalized Raman-based spectrum dataset, as a normalized version of the modified Raman-based spectrum dataset. [Figure 4A] This shows an illustrative visualization of the Q-residual error in a biological classification model. [Figure 4B] This shows an exemplary visualization of summary values ​​(e.g., Hotelling T^2 values) for fitting a biological classification model. [Figure 5] This specification shows exemplary visualizations of Raman spectra of biological product types according to various embodiments disclosed herein. [Figure 6A] This specification provides a list of exemplary computer programs containing pseudocode for biological classification model configurations according to various embodiments disclosed herein. [Figure 6B] This specification provides a list of exemplary computer programs containing pseudocode for biological classification model configurations according to various embodiments disclosed herein. [Figure 6C] This specification provides a list of exemplary computer programs containing pseudocode for biological classification model configurations according to various embodiments disclosed herein. [Figure 7] This specification shows exemplary visualizations of the reduced Q residual error according to various embodiments described herein. [Figure 8A] This specification presents exemplary visualizations of the reduced Q residual error of the target product, evaluated for 18 different configurable handheld biological analyzers according to various embodiments described herein. [Figure 8B]This specification presents exemplary visualizations of the reduced Q residual error of the target product, evaluated for 18 different configurable handheld biological analyzers according to various embodiments described herein. [Figure 8C] This specification presents exemplary visualizations of the reduced Q residual error of the target product, evaluated for 18 different configurable handheld biological analyzers according to various embodiments described herein. [Figure 8D] This specification presents exemplary visualizations of the reduced Q residual error of the target product, evaluated for 18 different configurable handheld biological analyzers according to various embodiments described herein. [Figure 8E] This specification presents illustrative visualizations of reduced fit summary values ​​of target products evaluated for 18 different configurable handheld biological analyzers according to various embodiments described herein. [Modes for carrying out the invention]

[0031] The drawings illustrate preferred embodiments for illustrative purposes only. Alternative embodiments of the systems and methods illustrated herein may be adopted without departing from the principles of the invention as described herein.

[0032] Figure 1 shows examples of a configurable handheld biological analyzer 102 for identifying biological products 140 based on Raman spectroscopy, according to various embodiments disclosed herein. In the embodiment of Figure 1, the configurable handheld biological analyzer 102 includes a first housing 101 molded or adapted for handheld operation. Furthermore, the configurable handheld biological analyzer 102 includes a first scanner 106 held by the first housing (e.g., directly or indirectly coupled or connected). The configurable handheld biological analyzer 102 also includes a first processor 110 communicatively coupled to the first scanner 106. The configurable handheld biological analyzer 102 may further include a first computer memory 108 communicatively coupled to the first processor 110. Furthermore, the configurable handheld biological analyzer 102 may include an input / output (I / O) component 109 for receiving input from a navigation wheel 105. For example, the user can operate the navigation wheel 105 to select or scroll through data or information on a specific sample of a scanned biological product, for example, by scanning a biological product 140. The input / output (I / O) component 109 can also control the display of measurements, identifications, classifications, or other information described herein on the display screen 104. The display screen 104, the navigation wheel 105, the first scanner 106, the first computer memory 108, the I / O component 109, and / or the first processor 110 are each communicably connected over an electronic bus 107 configured to transmit and / or receive electronic signals (e.g., control signals) or information between various components including 104-110. In some embodiments, the configurable handheld biological analyzer 102 may be a Raman-based handheld analyzer such as the TruScan® RM Handheld Raman Analyzer provided by Thermo Fisher Scientific Inc.

[0033] In various embodiments, the first computer memory 108 is configured to load a biological classification model configuration, for example, a biological classification model configuration 103. The biological classification model configuration 103 may be used to implement the biological analysis method shown in Figure 2 for identifying biological products based on Raman spectroscopy, as further described herein.

[0034] In the embodiment shown in Figure 1, the biological classification model configuration 103 is implemented as an XML file in the Extended Markup Language (XML) format. As described in various embodiments herein, Figures 6A–6C show exemplary computer program listings in XML format containing pseudocode for a biological classification model configuration (e.g., biological classification model configuration 103). In the computer program listings of the embodiments shown in Figures 6A–6C, for example, in code section 1, the biological classification model configuration 103 is formatted in XML, where the biological classification model (" <model>The term ")" is defined within the biological classification model configuration 103. The biological classification model configuration 103 is transferable, installable, and / or implementable or executable on similarly configured configurable handheld biological analyzers (e.g., configurable handheld biological analyzers 112, 114, and / or 116). Since each of the configurable handheld biological analyzers 112, 114, and 116 contains the same components as the configurable handheld biological analyzer 102, the disclosure of the configurable handheld biological analyzer 102 applies equally to each of the configurable handheld biological analyzers 112, 114, and 116. Each of the configurable handheld biological analyzers 102, 112, 114, and 116 may be part of the same group or set of analyzers (i.e., including a "network" or group of analyzers). In some embodiments, the configurable handheld biological analyzers 102, 112, 114, and / or 116 may each have the same, similar, and / or different mix of characteristics or features, for example, the same, similar, and / or different software versions or types, manufacture, age, operating environment (e.g., temperature), components, or a mix of similarities or differences among other such Raman base analyzers.

[0035] Regardless of the mix of the same, similar, and / or different properties or features among the configurable handheld biological analyzers 102, 112, 114, and 116, the biological classification model configuration 103 and its associated biological classification model enable a network of configurable handheld biological analyzers (e.g., configurable handheld biological analyzers 102, 112, 114, and 116) to produce consistent results when measuring or identifying pharmaceutical or biological products (e.g., therapeutic products / agents). In other words, regardless of the similarities or differences in a given network of configurable handheld biological analyzers, when such configurable handheld biological analyzers are configured with the biological classification model configuration described herein, such configurable handheld biological analyzers can accurately identify or measure a given pharmaceutical or biological product.

[0036] In various embodiments, a biological classification model configuration 103 and its associated biological classification models can be generated or constructed using multiple analytical instruments. For example, in some embodiments, a biological classification model can be generated or constructed using any one or more of the configurable handheld biological analytical instruments 102, 112, 114, and 116, and / or other analytical instruments (not shown).

[0037] To generate the biological classification model configuration 103 and its associated biological classification models, it is generally necessary for a group or network of analytical instruments to scan samples (e.g., biological products 140) to create a dataset of Raman-based spectra of these samples. For example, detailed information about the biological product 140 can be obtained by scanning the biological product 140 with, for example, one of the configurable handheld biological analyzers 102, 112, 114, and 116. For example, detailed information may include a dataset of Raman-based spectra that define the biological product sample (e.g., biological product 140). Examples of biological products 140 include any of the denosumab DP, panitumumab DP, etanercept DP, pegfilgrastim DP, romosozumab DP, adalimumab DS, and / or erenumab DP described herein (e.g., romosozumab DP, adalimumab DS, and / or erenumab DP). However, further biological products are also considered in this specification, and it should be understood that biological product 140 is not limited to any specific biological product or its subcategory.

[0038] In some embodiments, the configurable handheld biological analyzer 102 can define instrumental or analytical spectral acquisition parameters (e.g., integration time, laser power, etc.) for use in scanning a sample of, for example, a biological product 140. For example, the user can select spectral acquisition parameters to be used for scanning the sample via the navigation wheel 105. In some embodiments, the configurable handheld biological analyzer 102 can generate an output file (e.g., an output file in ".acq" file format) specifying the spectral acquisition parameters.

[0039] In some embodiments, a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) can be configured with spectral acquisition parameters to be used for scanning target products by loading an output file (e.g., a ".acq" file). As described herein, a dataset of Raman-based spectra can be scanned by one or more configurable handheld biological analyzers (e.g., configurable handheld biological analyzers 102) to generate a biological classification model configuration (e.g., biological classification model configuration 103). In some embodiments, samples (e.g., multiple lots) of a biological product (e.g., biological product 140) can be selected as representative target products for scanning. Generally, as described herein, “target product” refers to a biological product used to train or configure a biological classification model configuration and its associated models. Generally, target products are selected based on their biological specifications. Once set up with spectral acquisition parameters for use in scanning the target product, a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) can scan the target product sample multiple times (e.g., 14 times) (e.g., with the first scanner 106), where each scan generates detailed information including a dataset of Raman-based spectra of the target product.

[0040] In a similar embodiment, multiple configurable handheld biological analyzers (configurable handheld biological analyzers 102, 112, 114, and / or 116) can be loaded with an output file (e.g., a ".acq" file) and set up with spectral acquisition parameters for use in scanning a biological product sample. Once set up, each configurable handheld biological analyzer (e.g., any of the configurable handheld biological analyzers 102, 112, 114, and / or 116) is configured to scan the sample (e.g., with the first scanner 106) possibly multiple times (e.g., 14 times), with each scan generating detailed information including a dataset of Raman-based spectra of the target product. By scanning a given target product with different / multiple scanners, the dataset of Raman-based spectra acquired by these scanners becomes robust in that it captures any differences between scanners (e.g., arising from software, manufacturing, age, operating environment (e.g., temperature)). In this way, the dataset of Raman-based spectra provides an ideal training dataset for reducing the variability between multiple scanners described herein. For example, each of the datasets of Raman-based spectra scanned by multiple scanners (e.g., any of the configurable handheld biological analyzers 102, 112, 114, and / or 116) can be output and / or saved as a Raman spectrum file having, for example, the ".spc" file format.

[0041] It should be understood that Raman-based spectral datasets may also be obtained for challenge products in the same or similar manner as for target products. As used herein, “challenge product” refers to a biological product (e.g., selected from biological products 140) that a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) is configured to identify, classify, or measure when a biological classification model configuration (e.g., biological classification model configuration 103) and its associated biological classification model are loaded or configured using such configuration, as described herein.

[0042] A dataset of Raman-based spectra of the problem product can be acquired in the same or similar manner as the target product, where the problem product can be selected based on its biological specifications, and a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) can be loaded with an output file (e.g., a ".acq" file) and configured with spectral acquisition parameters to be used for scanning the problem product. Once set up, the configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) is configured to scan the problem product sample (e.g., with the first scanner 106) multiple times (e.g., three times), where each scan generates detailed information including a dataset of Raman-based spectra of the problem product. For example, the dataset of Raman-based spectra scanned by the configurable handheld biological analyzer 102 can be output and / or saved as a Raman spectrum file having, for example, a ".spc" file format.

[0043] In some embodiments, the generation of a biological classification model configuration (e.g., biological classification model configuration 103) may be performed by a remote processor, such as the processor of computer 130 shown in Figure 1. For example, a dataset of Raman-based spectra generated for a biological product described herein (e.g., selected from biological product 140) may be imported and / or analyzed by modeling software running on computer 130, configured to analyze the dataset of Raman-based spectra. An example of such modeling software is SOLO (stand-alone chemo-metrics software) provided by Eigenvector Research, Inc. However, it should be understood that other modeling software, including custom or proprietary software implemented to perform the functions described herein, may also be used. The modeling software can construct or generate a biological classification model based on the dataset of Raman-based spectra. For example, in some embodiments, a biological classification model may be constructed or generated using a dataset of Raman-based spectra scanned or acquired for a target product described herein. Furthermore, a dataset of Raman-based spectra (e.g., of a target product or a problem product) may be used for cross-validation of the biological classification model. For example, using a Raman-based spectral dataset, type I errors (e.g., false positives) and type II errors (e.g., false negatives) of biological classification models can be evaluated against a cross-confirmed dataset of Raman-based spectral datasets.

[0044] In various embodiments, a biological classification model, and / or its associated biological classification model configuration (e.g., biological classification model configuration 103), may be generated including algorithms (e.g., scripts) and parameters used by a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) for identifying, classifying, and / or measuring the biological products described herein. Examples of algorithms (e.g., scripts) and / or parameters are described herein in relation to Figures 2, 6A, and 6B. For example, a biological classification model configuration (e.g., biological classification model configuration 103) may include parameters that define the details of the biological classification model. For example, such parameters can determine the number of classification components, loadings, etc., of the biological classification model. For example, in one embodiment, the number of classification components may be determined by modeling software, for example, via singular value decomposition (SVD) analysis, where the classification components include one or more major components of PCA. The modeling software may be configured to set a statistical confidence level for determining which classification components (e.g., major components) to include in the biological classification model. For example, in the embodiments of the computer program listings in Figures 6A to 6C, in code section 1, the biological classification model configuration is defined as a biological classification model (for example, a defined " <model>This indicates that it is a PCA-type biological classification model. This indicates that the classification components of the biological classification model are the primary components. For example, in the embodiments of Figures 6A-6C, code section 2 indicates that the number of primary components is one (single) primary component ("Num.PCs:1"), which is determined, for example, via SVD analysis ("Algorithm:SVD") performed on the first processor 110 of the configurable handheld biological analyzer 102.

[0045] As a further example, a biological classification model configuration (e.g., biological classification model configuration 103) may include computer code or scripts for defining or implementing a spectral preprocessing algorithm, as described, for example, in relation to Figures 3A-3C. Generally, the computer code or scripts for defining or implementing the spectral preprocessing algorithm may be executed on a processor (e.g., a first processor 110), which receives a dataset of Raman-based spectra of biological products (e.g., biological product 140). The configurable handheld biological analyzer then executes the computer code or scripts for defining or implementing the spectral preprocessing algorithm to identify, measure, or classify the biological products described herein (e.g., problem products) to prepare / preprocess the data for input into the classification component of the biological classification model. For example, in the embodiments of the computer program listings in Figures 6A-6C, code section 2 includes an execution sequence of an exemplary spectral preprocessing algorithm (e.g., "Preprocessing:1st Derivative(order:2,window:21pt,incl only,tails:polyinterp),SNV,Mean Center") which includes determining a first derivative, applying a standard normal variate (SNV) algorithm, and further applying a mean centering function to a dataset of Raman-based spectra scanned with respect to a specific product (e.g., a target product or a problem product). Exemplary embodiments of this execution sequence are described and visualized herein in relation to code sections 4-6 of Figures 3A-3C and 6A-6C.

[0046] As a further example, a biological classification model configuration (e.g., biological classification model configuration 103) may include a dataset of Raman-based spectra used to generate a biological classification model. For example, in the embodiments of the computer program listings in Figures 6A-6C, in code section 3, the biological classification model configuration (e.g., biological classification model configuration 103) includes an exemplary dataset of Raman-based spectra used to generate the biological classification models of Figures 6A-6C.

[0047] In some embodiments, the biological classification model configuration (e.g., biological classification model configuration 103) may also define thresholds as statistical acceptance criteria for determining whether a biological product was successfully identified or measured by, for example, a configurable handheld biological analyzer 102. For example, such thresholds may be Q residuals or Hotelling T values ​​to determine whether a biological product was successfully identified or measured by the configurable handheld biological analyzer 102. 2 Pass / fail thresholds for values ​​(as described herein) can be defined. In other embodiments, thresholds may be configured independently of the biological classification model configuration (e.g., biological classification model configuration 103) by the user manually configuring and / or defining the thresholds via, for example, the navigation wheel 105 and display screen 104 as described herein.

[0048] Once generated, the biological classification model and its associated biological classification model configuration (e.g., biological classification model configuration 103) can be exported to a file (e.g., an XML file as described herein) for transmission (e.g., via computer network 120 or in any other manner as described herein) to a configurable handheld biological analyzer described herein (e.g., any one or more of configurable handheld biological analyzers 102, 112, 114, and / or 116), and / or for loading into the memory of a configurable handheld biological analyzer described herein (e.g., any one or more of configurable handheld biological analyzers 102, 112, 114, and / or 116). In some embodiments, the output file (e.g., the ".acq" file described herein) may also be transmitted to a configurable handheld biological analyzer (e.g., any one or more of the configurable handheld biological analyzers 102, 112, 114, and / or 116) (e.g., via a computer network 120, or in any other manner described herein) and / or loaded into the memory of the configurable handheld biological analyzer (e.g., any one or more of the configurable handheld biological analyzers 102, 112, 114, and / or 116).

[0049] A biological classification model may be generated by a remote processor that is remote to a given configurable handheld biological analyzer. For example, in the embodiment of Figure 1, computer 130 includes a remote processor that is remote to a configurable handheld biological analyzer 102. Computer 130 can generate one or more biological classification model configurations and / or biological classification models (e.g., as described herein) and store them in a database 132. In various embodiments, computer 130 can transfer a biological classification model configuration (e.g., any of the biological classification model configurations 103, 113, 115, and / or 117) over a computer network 120 to a configurable handheld biological analyzer (e.g., to configurable handheld biological analyzers 102, 112, 114, and / or 116, respectively). In some embodiments, each of the biological classification model configurations 103, 113, 115, and / or 117 may be a copy of the same file (e.g., the same XML file). The computer network 120 may include wired and / or wireless (e.g., an 802.11 standard network) implementing computer packet protocols such as the Transmission Control Protocol (TCP) / Internet Protocol (IP). In other embodiments, the biological classification model configuration (e.g., the biological classification model configuration 103) may be transmitted via a Universal Serial Bus (USB) cable (not shown), a memory drive (e.g., a flash or thumb drive) (not shown), a disk (not shown), or other transfer or memory device capable of transferring data files such as XML files disclosed herein. In further embodiments, the biological classification model configuration 103 may be transmitted via wireless standards or protocols, such as Bluetooth, WiFi, or cellular standards such as GSM, EDGE, or CDMA.

[0050] A biological classification model configuration (e.g., biological classification model configuration 103) can be transferred between configurable handheld biological analyzers. Once transferred, the biological classification model configuration is loaded into the memory of the configurable handheld biological analyzer so that the handheld biological analyzer can be calibrated or configured to reduce variability with respect to other configurable handheld biological analyzers that implement or run the biological classification model. For example, in one embodiment, the biological classification model configuration 103 may include a biological classification model. The biological classification model of the biological classification model configuration 103 may be configured to run on a first processor 110. For example, the first processor 110 may be configured to (1) receive a dataset of first Raman-based spectra defining a first biological product sample scanned by a first scanner (e.g., a scan of biological product 140), and (2) use the biological classification model to identify a biological product type based on the dataset of first Raman-based spectra. For example, in some embodiments, the biological product type may be that of a therapeutic product having a therapeutic product type.

[0051] The biological classification model of the biological classification model configuration 103 can be electronically transferred, for example, over a computer network 120 via the biological classification model configuration 113 to the configurable handheld biological analyzer 112. With respect to the configurable handheld biological analyzer 102 only, it may include a second housing adapted for handheld operation, a second scanner connected to the second housing, a second processor communicatively connected to the second scanner, and a second computer memory communicatively connected to the second processor. The second computer memory of the configurable handheld biological analyzer 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 run on the second processor of the configurable handheld biological analyzer 112, the second processor is configured to (1) receive a dataset of second Raman-based spectra defining a second biological product sample scanned by the second scanner of the configurable handheld biological analyzer 112 (e.g., taken from a scan of biological product 140), and (2) identify a biological product type based on the dataset of second Raman-based spectra using a biological classification model. In such embodiments, the same biological product or product type is identified by using the same biological classification model transferred by the biological classification model configuration file, where the second biological product sample is a novel sample of this biological product type (e.g., the same biological product type analyzed by the first configurable handheld biological analyzer 102).

[0052] In various embodiments, a new or additional dataset of Raman-based spectra may be scanned by a configurable handheld biological analyzer and used to update a biological classification model. In such embodiments, the updated biological classification model may be transferred to a configurable handheld biological analyzer described herein (e.g., configurable handheld biological analyzer 102).

[0053] In some embodiments, the computer memory (e.g., first computer memory 108) of a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) may be configured to load a novel biological classification model, which may include updated classification components. The novel classification components may be generated or determined with respect to a novel biological classification model received, for example, along with a novel biological classification model configuration (e.g., biological classification model configuration 103).

[0054] As described in various embodiments herein, a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) may be configured by loading a biological classification model configuration and its associated biological classification model. Once configured, the configurable handheld biological analyzer 102 may be used to identify, classify, or measure products of interest (e.g., problem products and / or samples) as described herein.

[0055] Figure 2 shows an exemplary flowchart of a biological analysis method 200 for identifying biological products (e.g., biological product 140) based on Raman spectroscopy, according to various embodiments disclosed herein. The biological analysis method 200 begins in block 204 by loading a biological classification model configuration (e.g., biological classification model configuration 103) into the first computer memory (e.g., first computer memory 108) of a first configurable handheld biological analyzer having a first processor (e.g., first processor 110) and a first scanner (e.g., first scanner 106) (202). In the embodiment of Figure 2, the biological classification model configuration (e.g., biological classification model configuration 103) includes a biological classification model described herein. Furthermore, in some embodiments, the configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) may load spectral acquisition parameters (e.g., in an ".acq" file) for use in scanning products into, for example, memory 108.

[0056] In block 206, the biological analysis method 200 includes receiving a dataset of first Raman-based spectra that define a first biological product sample (e.g., selected from biological products 140) scanned by a first scanner (e.g., first scanner 106) by a biological classification model (e.g., biological classification model configuration 103).

[0057] In block 208, the biological analysis method 200 includes, for example, using a processor (e.g., a first processor 110) to perform a spectral preprocessing algorithm for a biological classification model to reduce spectral inconsistencies in a first Raman-based spectral dataset. Spectral inconsistencies refer to spectral inconsistencies between analyzers between a first Raman-based spectral dataset and one or more other Raman-based spectral datasets of one or more corresponding other handheld biological analyzers. For example, spectral inconsistencies may exist between a Raman-based spectral dataset scanned by a configurable handheld biological analyzer 102 and a Raman-based spectral dataset scanned by a configurable handheld biological analyzer 112. Spectral inconsistencies may exist even if each Raman-based spectral dataset scanned by each analyzer represents the same biological product type. Such spectral inconsistencies may result from variations between analyzers and / or differences such as different versions of software, manufacturing, age, operating environment (e.g., temperature), components, or other differences in Raman-based analyzers as described herein.

[0058] The spectral preprocessing algorithm is configured to reduce spectral inconsistencies between analyzers between a first Raman-based spectral dataset and one or more other Raman-based spectral datasets. For example, in various embodiments, implementing or running the spectral preprocessing algorithm (e.g., on a first processor 110) minimizes statistical type I (e.g., false positives) and / or type II errors (e.g., false negatives) associated with the identification of biological products (e.g., biological product 140). In various embodiments, the spectral preprocessing algorithm can reduce spectral inconsistencies between analyzers between multiple configurable handheld biological analyzers (e.g., any of the configurable handheld biological analyzers 102, 112, 114, and / or 116).

[0059] Figures 3A–3C show an exemplary execution sequence of spectral preprocessing algorithms for a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102). By executing the spectral preprocessing algorithm (e.g., by the first processor 110), the effects of differences inherent in each analyzer (e.g., configurable handheld biological analyzers 102, 112, 114, and / or 116) are mitigated or reduced, and the inconsistencies between datasets of Raman-based spectra generated by scans from these analyzers are reduced. Figure 3A shows a visualization 302 of exemplary datasets of Raman-based spectra scanned by one or more handheld biological analyzers (e.g., including datasets 302a, 302b, and 302c of Raman-based spectra) according to various embodiments disclosed herein. The Raman-based spectral dataset in Figure 3A may include the biological classification model configurations described herein (e.g., biological classification model configuration 103) and the Raman-based spectral datasets used to generate the associated biological classification models (e.g., including Raman-based spectral datasets 302a, 302b, and 302c). For example, the Raman-based spectral dataset in Figure 3A may be identified in code section 3 of Figure 6A.

[0060] In some embodiments, each of the Raman-based spectral datasets in Figure 3A (e.g., including Raman-based spectral datasets 302a, 302b, and 302c) may represent scans from different configurable handheld biological analyzers (e.g., any of configurable handheld biological analyzers 102, 112, 114, and / or 116). However, in other embodiments, each of the Raman-based spectral datasets in Figure 3A (e.g., including Raman-based spectral datasets 302a, 302b, and 302c) may represent multiple scans from the same configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102).

[0061] Figure 3A shows several datasets of Raman-based spectra (including, for example, datasets 302a, 302b, and 302c) visualized across Raman intensity values ​​(on the Raman intensity axis 304) and optical wavelength / frequency values ​​(on the Raman shift axis 306). The Raman intensity axis 304 shows the intensity of scattered light at a given wavelength across the Raman shift axis 306. When scanned by an analytical instrument (e.g., a configurable handheld biological analyzer 102), the Raman intensity axis 304 can show many photons scattered by a biological product sample (e.g., the data / value 3 is a relative indicator of the intensity of photons measured / scanned by the first scanner 106). The Raman shift axis 306 shows the wavenumber of scattered light (e.g., inverse wavelength). The unit of wavenumber (i.e., the number of waves per centimeter (cm), cm) -1 ) indicates the difference in frequency or wavelength between the incident light and the scattered light. In visualization 302 of Figure 3A, the shift axis 306 is 600-1500 cm -1 This includes the range. The Raman intensity axis 304 includes the Raman intensity range of 1 to 5. As shown in Figure 3A, each of the Raman-based spectral datasets (e.g., including Raman-based spectral datasets 302a, 302b, and 302c) is 600 to 1500 cm⁻¹. -1 Visualize Raman intensity values ​​measured across the optical spectral range.

[0062] Furthermore, in various embodiments, each of the Raman-based spectral datasets in Figure 3A (e.g., including Raman-based spectral datasets 302a, 302b, and 302c) may represent a scan of the same biological product sample having the same biological product type. In such embodiments, as shown in Figure 3A, even if any one or more of the configurable handheld biological analyzers may have scanned the same biological product sample having the same biological product type, there will be variability in the Raman intensity values ​​(on the Raman intensity axis 304) of the Raman-based spectral datasets (e.g., including Raman-based spectral datasets 302a, 302b, and 302c) across optical wavelength / frequency values ​​(on the Raman shift axis 306). As described herein, variability may arise from differences in software, manufacturing, age, optical components, operating environment (e.g., temperature), or other factors between configurable handheld biological analyzers (e.g., any of the configurable handheld biological analyzers 102, 112, 114, and / or 116).

[0063] Figure 3B shows an exemplary visualization 312 of a modified Raman-based spectrum dataset, which is modified from the Raman-based spectrum dataset of Figure 3A. For example, Figure 3B may represent the first step in the execution sequence of a spectral preprocessing algorithm. Visualization 312 of Figure 3B includes the same Raman intensity axis 304 and Raman shift axis 306 described herein with respect to Figure 3A. In the embodiment of Figure 3B, a processor (e.g., a first processor 110) applies a derivative transform to the Raman-based spectrum dataset of Figure 3A (e.g., including Raman-based spectrum datasets 302a, 302b, and 302c) to generate the modified Raman-based spectrum dataset shown in Figure 3B (e.g., including Raman-based spectrum datasets 312a, 312b, and 312c). Specifically, in the embodiment shown in Figure 3B, a first derivative is applied that includes data smoothing of points 11-15 (i.e., a Raman-weighted average of a consecutive group of Raman shift values ​​11-15 is determined, and then the first derivative transformation is applied to this group). In other words, the derivative transformation shown in Figure 3B involves a processor (e.g., the first processor 110) determining a Raman-weighted average (of the Raman intensity axis 304) of a consecutive group of Raman shift values ​​11-15 across the entire Raman shift axis 306, and then the processor (e.g., the first processor 110) determining the corresponding derivative of these Raman-weighted averages across the entire Raman shift axis 306. By applying the derivative transformation, the effects of background curvature are mitigated, for example, due to Rayleigh scattering / rejection optics and / or other dispersive elements. This is illustrated by comparing visualization 302 in Figure 3A with visualization 312 in Figure 3B, where inconsistencies (e.g., vertical and / or horizontal inconsistencies) in the Raman-based spectral dataset (e.g., Raman-based spectral datasets 302a, 302b, and 302c as shown in Figure 3A) are eliminated or reduced, resulting in a less variable modified Raman-based spectral dataset (e.g., Raman-based spectral datasets 312a, 312b, and 312c as shown in Figure 3B).

[0064] The application of the derivative transform visualized in Figure 3B is further illustrated by the computer program listings in Figures 6A–6C. For example, in an embodiment of the computer program listings in Figures 6A–6C, in code section 4, the biological classification model configuration includes a script executable by the first processor 110 of the configurable handheld biological analyzer 102, which applies the derivative transform algorithm as described with respect to Figure 3B herein.

[0065] Figure 3C shows an exemplary visualization 322 of a normalized Raman-based spectrum dataset as a normalized version of the modified Raman-based spectrum dataset in Figure 3B. For example, Figure 3C may represent one or more subsequent steps in the execution sequence of a spectral preprocessing algorithm. Visualization 322 in Figure 3C includes the same Raman intensity axis 304 and Raman shift axis 306 described herein with respect to Figures 3A and 3B. For example, in one embodiment, the modified Raman-based spectrum dataset shown in Figure 3B (e.g., including Raman-based spectrum datasets 312a, 312b, and 312c) is aligned across the Raman shift axis 306 by a processor (e.g., a first processor 110) to produce the aligned Raman-based spectrum dataset shown in Figure 3C (e.g., including Raman-based spectrum datasets 322a, 322b, and 322c). Such alignment applies a correction for slight y-axis deviations (i.e., of the Raman intensity axis 304) resulting from discrepancies / differences between the analytical instruments described herein. The application of the alignment algorithm visualized in Figure 3C is further illustrated by the computer program listings in Figures 6A-6C. For example, in embodiments of the computer program listings in Figures 6A-6C, code section 6 includes a script executable by the first processor 110 of a configurable handheld biological analyzer 102, which applies an average centering algorithm to adjust the alignment of the modified Raman-based spectral datasets shown in Figure 3B (including, for example, Raman-based spectral datasets 312a, 312b, and 312c) to eliminate or reduce spectral discrepancies (e.g., vertical and / or horizontal discrepancies) in these modified Raman-based spectral datasets. This adjustment yields the aligned Raman-based spectrum dataset shown in Figure 3C (including, for example, Raman-based spectrum datasets 322a, 322b, and 322c).

[0066] Furthermore, or in another embodiment, the modified Raman-based spectrum dataset shown in Figure 3B (including, for example, Raman-based spectrum datasets 312a, 312b, and 312c) is normalized across the Raman intensity axis 304 by a processor (e.g., a first processor 110) to generate the aligned Raman-based spectrum dataset shown in Figure 3C (including, for example, Raman-based spectrum datasets 322a, 322b, and 322c). Such normalization applies a robust normalization algorithm that compensates for the intensity-axis variability (i.e., variability in intensity values ​​across the Raman intensity axis 304) resulting from discrepancies / differences between the analytical instruments described herein. The application of the normalization algorithm visualized in Figure 3C is further illustrated by the computer program listings in Figures 6A-6C. For example, in the embodiments of the computer program listings in Figures 6A-6C, code section 5 includes a script executable by the first processor 110 of a configurable handheld biological analyzer 102, which applies a normalization algorithm to normalize the modified Raman-based spectral datasets shown in Figure 3B (including, for example, Raman-based spectral datasets 312a, 312b, and 312c) to eliminate or reduce spectral inconsistencies (e.g., longitudinal and / or transverse inconsistencies) in these modified Raman-based spectral datasets. This normalization results in the normalized Raman-based spectral datasets shown in Figure 3C (including, for example, Raman-based spectral datasets 322a, 322b, and 322c). Specifically, in the embodiments shown in Figures 6A to 6C, for example, the first processor 110 applies a standard normalized variable (SNV) algorithm to the modified Raman-based spectrum dataset shown in Figure 3B (e.g., including Raman-based spectrum datasets 312a, 312b, and 312c) to generate the aligned Raman-based spectrum dataset shown in Figure 3C (e.g., including Raman-based spectrum datasets 322a, 322b, and 322c).

[0067] By applying an alignment and / or normalization algorithm (for example, as described with respect to Figure 3C), spectral inconsistencies in the modified Raman-based spectrum dataset (e.g., including Raman-based spectrum datasets 312a, 312b, and 312c) are eliminated or reduced, as shown in Figure 3B. This is illustrated by a comparison of visualization 312 in Figure 3B and visualization 322 in Figure 3C, which shows that spectral inconsistencies (e.g., vertical and / or horizontal inconsistencies) in the Raman-based spectrum dataset (e.g., including Raman-based spectrum datasets 312a, 312b, and 312c as shown in Figure 3B) are eliminated or reduced, resulting in a less variable aligned and / or normalized Raman-based spectrum dataset (e.g., including Raman-based spectrum datasets 322a, 322b, and 322c) as shown in Figure 3C.

[0068] In block 210 of Figure 2, the biological analysis method 200 includes identifying or classifying biological product types based on a first dataset of Raman-based spectra (e.g., the dataset of Raman-based spectra visualized and described with respect to Figures 3A-3C) using a biological classification model. For example, in various embodiments, once the execution sequence of the spectral preprocessing algorithm is performed (e.g., by the first processor 110), as described herein with respect to Figures 3A-3C and / or 6A-6C, the preprocessed Raman-based dataset, e.g., the aligned and / or normalized Raman-based spectrum dataset shown in Figure 3C (e.g., including Raman-based spectrum datasets 322a, 322b, and 322c), can be used by a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) to identify or classify biological products (e.g., biological product 140).

[0069] Figure 5 shows an exemplary visualization 500 of the Raman spectra of biological product types (e.g., biological product types 511, 512, and 513). Each of the biological product types (e.g., biological product types 511, 512, and 513) can be identified, classified, or distinguished using a biological classification model (e.g., the biological classification model of the biological classification model configuration 103) based on classification components, according to various embodiments disclosed herein. In the embodiment of Figure 5, each of the biological product types 511, 512, and 513 is a different biological product type, comprising adalimumab DS (biological product type 511), erenumab DP (biological product type 512), and romosozumab DP (biological product type 513), respectively. The visualization 500 of Figure 5 includes the same or similar Raman intensity axis 504 and Raman shift axis 506 as described herein with respect to Figures 3A and 3B. However, each biological product type 511, 512, and 513 exhibits its own distinct Raman shift axis, where each Raman shift axis exhibits Raman intensity values ​​from 0 to approximately 3. Furthermore, Raman shift axis 506 exhibits values ​​from approximately 0 to 3000 cm⁻¹. -1 This indicates the frequency / wavelength range.

[0070] As shown in Figure 5, each of the biological product types 511, 512, and 513 has the same or similar Raman spectral range across the Raman shift axis 506 (e.g., 0–3000 cm⁻¹ as shown in Figure 5). -1 They have similar patterns or “signatures” across the range of 511, 103

[0071] However, a configurable handheld biological analysis device (e.g., configurable handheld biological analysis device 102) that loads and executes the biological classification model configuration described herein (e.g., biological classification model configuration 103) can be used to accurately identify, classify, measure, or discriminate biological product types such as adalimumab DS (biological product type 511), enulumab DP (biological product type 512), and romosozumab DP (biological product type 513). This is shown in FIG. 5, where, for example, each of the biological product types adalimumab DS (biological product type 511), enulumab DP (biological product type 512), and romosozumab DP (biological product type 513) is identified, classified, and / or measured as being different from one another by different local features of the Raman spectrum (e.g., local features 511c, 512c, and 513c). In the embodiment of FIG. 5, for example, each of the local features 511c, 512c, and 5I3c of each of the biological product types adalimumab DS (biological product type 511), enulumab DP (biological product type 512), and romosozumab DP (biological product type 513) is different across the Raman shift axis 506 over the range of 1000 cm -1 ~1100 cm -1 Specifically, over the range of 1000 cm -1 ~1100 cm -1 each of the local features 511c, 512c, and 513c, which are specific to each of the biological product types adalimumab DS (biological product type 511), enulumab DP (biological product type 512), and romosozumab DP (biological product type 513), respectively, has various Raman intensity values (has various shapes, peaks, or other different / various relative intensities). Thus, the different local features (e.g., local features 511c, 512c, and 513c) provide a product-specific source of information that can be used by the configurable handheld biological analysis device 102 to identify, classify, or discriminate the biological products described herein.

[0072] Furthermore, or in connection with Figure 5, identification or classification is further shown, here, for example, that each of the biological product types, adalimumab DS (biological product type 511), erenumab DP (biological product type 512), and romosozumab DP (biological product type 513), is identified, classified, and / or measured as distinct from one another by their respective Raman shift axes, i.e., across Raman shift axis 506 (even if these biological products have similar and / or the same Raman spectrum). 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, erenumab DP (biological product type 512) has a first Raman intensity value of approximately 2.1 (at a Raman shift value of approximately 2900) and a second Raman intensity value of approximately 2.5 (at a Raman shift value of approximately 140). Further in contrast, romosozumab DP (biological product type 513) has a first Raman intensity value of approximately 1.5 (at a Raman shift value of approximately 2900) and a second Raman intensity value of approximately 2.05 (at a Raman shift value of approximately 140).

[0073] Therefore, as shown by visualization 500 in Figure 5, a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) loaded with and performing a biological classification model configuration (e.g., biological classification model configuration 103) described herein is highly sensitive to relative differences in Raman intensity values ​​(e.g., Raman intensity axis 504) between various analyzers, and to the overall shape of the Raman features (i.e., the Raman intensity profile over a range of Raman shift values ​​(Raman shift axis 506)). This is because a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102), which is loaded with and runs a biological classification model configuration (e.g., biological classification model configuration 103) described herein, has scan data (a dataset of Raman-based spectra) preprocessed with the spectral preprocessing algorithm described herein for each of the biological product types: adalimumab DS (biological product type 511), erenumab DP (biological product type 512), and romosozumab DP (biological product type 513). Furthermore, the biological classification model used by the configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) is further configured to identify the biological product type of a first biological product sample based on classification components (i.e., to implement a model having classification components), which also reduces discrepancies and thereby improves the ability of the configurable handheld biological analyzer 102 to identify the biological product type of a first biological product sample.

[0074] In various embodiments, a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) identifies, classifies, and / or measures the biological product type of a biological product (e.g., biological product 140) such as adalimumab DS (biological product type 511), erenumab DP (biological product type 512), and romosozumab DP (biological product type 513) based on classification components loaded from a biological classification model configuration (e.g., biological classification model configuration 103). For example, a biological classification model, such as one loaded into the configurable handheld biological analyzer 102 via a biological classification model configuration 103, may include classification components selected to reduce at least one of (1) the Q residual error of the biological classification model, or (2) the summary value of the fit of the biological classification model, as further described herein in relation to Figures 4A and 4B, respectively.

[0075] As used herein, the term “classification component” may include the main component determined for a major component analysis (PCA). In other embodiments, more generally, the classification component may be a coefficient or variable of a multivariate model (such as a regression model or a machine learning model). Based on the classification component, a biological classification model is configured to identify the biological product type of a given biological product sample (selected, for example, from biological products 140).

[0076] In some embodiments, the biological classification model may be implemented as a PCA model. Implementing PCA means using multivariate analysis implemented by a configurable handheld biological analyzer 102 configured by a biological classification model configuration 103 to identify biological products (e.g., biological product 140) such as therapeutic products / agents having similar formulations (e.g., as described herein with respect to Figure 5, for example). For example, biological or pharmaceutical products generally involve 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 scans of biological product 140). PCA provides a technique used by the configurable handheld biological analyzer 102 to simplify the complexity of high-dimensional data (e.g., a dataset of Raman spectra) while simultaneously retaining trends and patterns useful for predictive and / or identification purposes (e.g., identification of biological products described herein). For example, the application of PCA includes transforming the dataset (e.g., a dataset of Raman-based spectra) into a lower-dimensional dataset (e.g., by a first processor 110). The lower-dimensional transformed dataset provides a summary or simplification of the original dataset. The transformed dataset then reduces the computational cost when operated by the configurable handheld biological analyzer described herein (e.g., configurable handheld biological analyzer 102). Furthermore, the error rate described herein is also reduced by implementing PCA, thereby eliminating the need to apply test corrections to higher-dimensional data when testing each feature to correlate with specific results.

[0077] Furthermore, when implemented by a configurable handheld biological analyzer 102, PCA reduces the complexity of the data by geometrically projecting the data into lower dimensions called primary components (PCs), and by using a limited number of PCs, thereby targeting the data and, consequently, the best summary of the PCs. The first PC is selected to minimize the total distance between the data and its projection onto the PC. Any second (subsequent) PC is similarly selected, with the additional requirement that it does not correlate with any previous PCs.

[0078] PCA is an unsupervised learning method, similar to clustering. That is, it discovers trends or patterns without referring to prior knowledge about whether samples came from different sources, such as different configurable handheld biological analyzers (e.g., configurable handheld biological analyzers 102, 112, 114, and / or 116). For example, in some embodiments, the classification component of a biological classification model may be the first primary component of the PCA model. In such embodiments, the first primary component may be determined by the first processor 110 based on singular value decomposition (SVD) analysis. The use of the first primary component by the configurable handheld biological analyzer 102 limits or reduces the amount of analyzer variability compensated for by its biological classification model. In some embodiments, the first primary component (PC) may be the only primary component. In other embodiments, the biological classification model may include a second classification component, where the biological classification model is configured to identify the biological product type of a given biological product sample (e.g., biological product 140) based on a plurality of classification components (e.g., a first classification component and a second classification component).

[0079] In the embodiments of the computer program listings shown in Figures 6A-6C, in code section 7, a biological classification model configuration (e.g., biological classification model configuration 103) defines a set of PCA predictions specified for that biological classification model. Code section 7 also defines statistics for the fit summary (e.g., Hotelling T). 2 A script is also provided that defines the calculations for the Q residual / value and the Hotelling T (Q residual / value). The script in Code Section 7 is executed by the first processor 110 to calculate the Q residual / value and the Hotelling T (Q residual / value) as described herein, for example with respect to Figures 4A and 4B. 2 Based on the values, biological products (e.g., biological product 140) can be identified or classified.

[0080] Figure 4A shows an exemplary visualization of the Q residual error of a biological classification model. Figure 4A shows the Q residual error axis and Hotelling T. 2 Includes axis 406. Generally, Q residual error and Hotelling T 2 The values ​​are summary statistics that can be used to explain how well a model (e.g., a biological classification model of the biological classification model configuration 103) describes a given biological product sample (e.g., taken from a scan of biological product 140). Figure 4A shows the Q residual error and Hotelling T for a number of handheld biological analyzers. 2 Plot the values. Generally, the Q residual error is zero (0) and the Hotelling T is zero (0). 2 A handheld biological analyzer with a value represents an error-free product scan.

[0081] The handheld biological analyzers include handheld biological analyzers of biological analyzer groups 411n, 412m1, 412m2, and 413n. Analyzer group 411n represents an analyzer that scans adalimumab DS, a first biological product type. Analyzer groups 412m1 and 412m2 each represent analyzers that scan erenumab DP, a second biological product type. Analyzer group 413n represents an analyzer that scans romosozumab DP, a third biological product type. Analyzer groups 412m1 and 412m2 include configurable handheld biological analyzers (e.g., any of configurable handheld biological analyzers 102, 112, 114, and / or 116) that are configured and enhanced with the biological classification model configurations described herein (e.g., biological classification model configuration 103) and the respective biological classification models. Analytical instrument groups 411n and 413n include typical biological analytical instruments that are either configured with a biological classification model or are not configured with a biological classification model.

[0082] The analyzer groups 411n and 413n serve as a control group, demonstrating an improvement over typical analyzers of analyzer groups 411n and 413n, for example, through error reduction (e.g., along the Q residual error axis 404) of configurable handheld biological analyzers (e.g., any of the configurable handheld biological analyzers 102, 112, 114, and / or 116) when compared with analyzer groups 412m1 and 412m2. Specifically, the Q residual (e.g., along the Q residual error axis 404) provides a degree of misfit statistic calculated as the sum of squares for each product sample. The Q residual represents the magnitude of the variability remaining in each sample after projection through a given model (e.g., the biological classification model described herein). More generally, as shown in the embodiment of Figure 4A, the Q residual value (along the Q residual error axis 404) serves as a discriminative statistic. The Q residual is an indicator of what is “left behind,” i.e., what is not explained, by a given biological classification model. For example, in one embodiment where the biological classification model is implemented as a PCA model (e.g., the spectrum is projected onto a first major component), the values ​​in Figure 4A represent what is left behind (residuals) after the scanned data (e.g., of biological analyzer groups 411n, 412m1, 412m2, and / or 413n) has been projected onto the first major component.

[0083] In various embodiments, a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) includes a biological classification model (e.g., a biological classification model configuration 103) configured to identify or classify the biological product type of a biological product sample (e.g., taken from a biological product 140) based on classification components when the Q residual error satisfies a threshold. In some embodiments, for example, a biological classification model implemented or executed by a first processor 110 of the configurable handheld biological analyzer 102 outputs a pass / fail decision based on a threshold. For example, in the embodiment of Figure 4A, a threshold of "1" across the Q residual error axis 404 is selected as the pass / fail determination element (determinant) threshold 405. In these embodiments, a configurable handheld biological analyzer implementing a biological classification model (e.g., configurable handheld biological analyzer 102) identifies or classifies (i.e., "passes") these biological products using scanned data (e.g., a Raman spectral dataset) that are within (i.e., less than) a threshold of 1 across the Q residual error axis 404. Otherwise, the biological analyzer implementing the biological classification model (e.g., configurable handheld biological analyzer 102) neither identifies nor classifies (i.e., "fails") these biological products.

[0084] In the embodiment shown in Figure 4A, the analyzer groups 412m1 and 412m2 include a configurable handheld biological analyzer (e.g., any of the configurable handheld biological analyzers 102, 112, 114, and / or 116) configured and enhanced with the biological classification model configuration (e.g., biological classification model configuration 103) described herein. The configurable handheld biological analyzers of analyzer groups 412m1 and 412m2 accurately identify or classify (i.e., "pass") the biological product (i.e., erenumab DP), where the relevant scanned data (e.g., a dataset of Raman spectra) is preprocessed with the spectral preprocessing algorithm described herein so that it falls within a threshold of 1 (i.e., less than 1), as shown by visualization 400.

[0085] Therefore, a biological classification model of a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) may include classification components selected to reduce the Q residual error of the biological classification model. In this way, 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 use with single-standard biological products when lot-to-lot variability is the main cause of discrepancies between analyzers. Thus, as shown in Figure 4A, the Q residual can be used as a discriminative statistic to determine a model that can withstand variability between analyzers (e.g., the biological classification models described herein).

[0086] Figure 4B shows the summary values ​​of the fit of a biological classification model (e.g., Hotelling T). 2 An exemplary visualization of the value 450 is shown. In general, Hotelling T 2 The value represents an index of variability in each sample within the model (e.g., a biological classification model). (Hotelling T) 2 The value indicates how far each sample is from the model's "midpoint" (value of 0). In other words, Hotelling T 2 The value is an indicator of the distance from the model midpoint. The distance from the midpoint can often be due to variations between analytical instruments. (Hotelling T) 2 Using values ​​is advantageous for identifying biological products using multiple standards. In these cases, varying concentrations of active ingredients, excipients, etc., cause greater variability in the Raman spectra than lot-to-lot variability (as described above with respect to the Q residual, with reference to Figure 4A).

[0087] In the embodiment shown in Figure 4B, a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) provides a summary value for the fit (e.g., Hotelling T). 2 The diagram includes a biological classification model (e.g., a biological classification model configuration 103) configured to identify or classify the biological product type of a biological product sample (e.g., taken from biological product 140) based on a classification component when the threshold is met. Figure 4B shows the same Q residual error axis 404 and Hotelling T as described herein with respect to Figure 4A. 2 Includes axis 406. Analytical instrument group 452m represents an analytical instrument that scanned for the first biological product type, denosumab DP (having two specifications). Analytical instrument group 454m represents an analytical instrument that scanned for the second biological product type, denosumab DS (having one specification). Analytical instrument group 462n represents an analytical instrument that scanned for the third biological product type, Enbrel DP. In the embodiment of Figure 4B, Hotelling T 2 A threshold of "1" across axis 406 is selected as the pass / fail determination element threshold 407. In such embodiments, a configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) implementing the biological classification model is used. 2 These biological products are identified or classified (i.e., "passed") using scanned data (e.g., Raman spectral dataset) that falls within the threshold range of axis 406 (i.e., is below this threshold). Otherwise, a biological analyzer implementing a biological classification model (e.g., a configurable handheld biological analyzer 102) neither identifies nor classifies these biological products (i.e., "fails").

[0088] In the embodiment shown in Figure 4B, the biological classification model of the configurable handheld biological analyzer (e.g., configurable handheld biological analyzer 102) is a summary value of the fit of the biological classification model (e.g., Hotelling T). 2 The classification components may be selected to reduce the value. In this way, the biological classification model is configured to identify the biological product type of a given biological product sample based on the classification components. For example, analyzer groups 452m and 454m include the biological classification model configurations described herein (e.g., biological classification model configuration 103) and configurable handheld biological analyzers (e.g., any of configurable handheld biological analyzers 102, 112, 114, and / or 116) configured and enhanced with the respective biological classification models. The configurable handheld biological analyzers of analyzer groups 412m1 and 412m2 accurately identify or classify (i.e., "pass") the biological products (i.e., denosumab DP and DS), where the relevant scanned data (e.g., a dataset of Raman spectra) is preprocessed with the spectral preprocessing algorithm described herein and falls within a threshold range of 1 (i.e., less than 1), as shown by visualization 450. In contrast, analytical instrument group 462n may represent analytical instruments that are not configured with the biological classification model configurations described herein.

[0089] As shown in Figures 4A and 4B respectively, the Q residual error (e.g., Q residual error axis 404) and / or Hotelling T 2 Each of the values ​​can be used alone or together to identify or classify biological products. In other words, the configurable handheld biological analyzer 102 can be configured to select or implement classification components to reduce one or both of (1) the Q residual error of the biological classification model and / or (2) the summary value of the fit of the biological classification model.

[0090] As described herein, in relation to Figures 2, 3A, 3B, 3C, 4A, 4B, and 5, a biological classification model may be configured to identify, classify, measure, or 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., erenumab DP (biological product type 512)) based on classification components. For example, as described in relation to Figures 4A, 4B, and 5, a configurable handheld biological analyzer 102 can distinguish between a first biological product type (e.g., adalimumab DS (biological product type 511)) and a different biological product type (e.g., erenumab DP (biological product type 512)). For example, as described herein, the configurable handheld biological analyzer 102, once configured using the biological classification model configuration 103, can run a spectral preprocessing algorithm (e.g., as described in Figures 3A-3C herein) on a dataset of Raman-based spectra received by the first scanner 106. Once the dataset of Raman-based spectra has been preprocessed by the spectral preprocessing algorithm, the configurable handheld biological analyzer 102 can identify or classify biological products based on Q residuals and / or Hotelling T2 values ​​(e.g., as described herein with respect to Figures 4A and 4B).

[0091] During the development or production of a biological product such as biological product 140 having a given biological product type, for example, adalimumab DS (biological product type 511), erenumab DP (biological product type 512), and / or romosozumab DP (biological product type 513) as described herein, the biological product type can be identified by a configurable handheld biological analyzer 102 (e.g., by a first processor 110) that performs a 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 identified in the same or similar ways as described with respect to various embodiments herein.

[0092] The nature of this disclosure 1. A configurable handheld biological analyzer for identifying biological products based on Raman spectroscopy, comprising: a first housing adapted for handheld operation; a first scanner held 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 includes a biological classification model, the biological classification model is configured to run on the first processor, the first processor (1) receives a dataset of first Raman-based spectra defining a first biological product sample scanned by the first scanner, and (2) uses the biological classification model to determine the first Raman-based A configurable handheld biological analyzer configured to identify a biological product type based on a dataset of Raman base spectra, wherein the biological classification model configuration further includes a spectral preprocessing algorithm, the first processor is configured to execute the spectral preprocessing algorithm when a dataset of first Raman base spectra is received by the first processor to reduce spectral inconsistencies in the dataset of first Raman base spectra, the biological classification model includes classification components selected to reduce at least one of (1) the Q residual error of the biological classification model, or (2) the summary value of the fit of the biological classification model, and the biological classification model is configured to identify a biological product type of a first biological product sample based on the classification components.

[0093] 2. The configurable handheld biological analyzer according to Embodiment 1, wherein the biological classification model configuration is electronically transferable to a second configurable handheld biological analyzer, the second configurable handheld biological analyzer includes 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, the second computer memory being configured to load the biological classification model configuration, the biological classification model configuration including a biological classification model, the biological classification model being configured to run on the second processor, the second processor being configured to (1) receive a dataset of second Raman-based spectra defining a second biological product sample scanned by the second scanner, and (2) use the biological classification model to identify a biological product type based on the dataset of second Raman-based spectra, the second biological product sample being a novel sample of a biological product type.

[0094] 3. A configurable handheld biological analyzer according to either embodiment 1 or 2, wherein spectral mismatch is a spectral mismatch between an analyzer and one or more datasets of other Raman-based spectra of one or more corresponding other handheld biological analyzers, each of which datasets represents a biological product type, and the spectral preprocessing algorithm is configured to reduce the spectral mismatch between the analyzer and one or more datasets of other Raman-based spectra.

[0095] 4. A configurable handheld biological analyzer according to Embodiment 3, wherein the spectral preprocessing algorithm includes: applying a derivative transform to a first Raman-based spectral dataset to generate a modified Raman-based spectral dataset; aligning the modified Raman-based spectral dataset across the entire Raman shift axis; and normalizing the modified Raman-based spectral dataset across the entire Raman intensity axis.

[0096] 5. The configurable handheld biological analyzer according to Embodiment 4, wherein the derivative transformation includes determining a Raman-weighted mean of consecutive groups of 11–15 Raman shift values ​​across the entire Raman shift axis, and determining the corresponding derivative of these Raman-weighted means across the entire Raman shift axis.

[0097] 6. A configurable handheld biological analyzer according to any one of embodiments 1 to 5, wherein the classification component is selected to reduce both (1) the Q residual error of the biological classification model and (2) the summary value of the fit of the biological classification model.

[0098] 7. A configurable handheld biological analyzer according to any one of embodiments 1 to 6, 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 a first biological product sample based on the classification component and the second classification component.

[0099] 8. A configurable handheld biological analyzer as described in any of embodiments 1 to 7, in which the biological classification model is implemented as a major component analysis (PCA) model.

[0100] 9. The classification component is the first main component of the PCA model, a configurable handheld biological analyzer as described in Embodiment 8.

[0101] 10. A configurable handheld biological analyzer according to any one of embodiments 1 to 9, wherein the computer memory is configured to load a new biological classification model, and the new biological classification model includes updated classification components.

[0102] 11. A configurable handheld biological analyzer according to any of embodiments 1 to 10, the biological classification model configuration being implemented in Extended Markup Language (XML) format.

[0103] 12. A configurable handheld biological analyzer according to any one of embodiments 1 to 11, wherein the type of biological product is a therapeutic product.

[0104] 13. A configurable handheld biological analyzer according to any one of embodiments 1 to 12, wherein the biological product type is identified by a biological classification model during the production of a biological product having a biological product type.

[0105] 14. A configurable handheld biological analyzer according to any one of embodiments 1 to 13, wherein the biological classification model is configured to distinguish a first biological product sample having a biological product type from different biological product samples having different biological product types, based on a classification component.

[0106] 15. A configurable handheld biological analyzer according to embodiment 14, wherein the biological product type and different biological product types each have distinct local characteristics within the same or similar Raman spectral ranges.

[0107] 16. A configurable handheld biological analyzer according to any one of embodiments 1 to 15, wherein the biological classification model is configured to identify the biological product type of a first biological product sample based on a classification component when the Q residual error or the fitting summary value satisfies a threshold.

[0108] 17. A configurable handheld biological analyzer according to embodiment 16, wherein the biological classification model outputs a pass / fail decision based on a threshold.

[0109] 18. A configurable handheld biological analyzer according to any one of embodiments 1 to 17, wherein the biological classification model is generated by a remote processor that is remote to the configurable handheld biological analyzer.

[0110] 19. A biological analysis method for identifying biological products based on Raman spectroscopy, comprising: loading a biological classification model configuration, including a biological classification model, into a first computer memory of a first configurable handheld biological analyzer having a first processor and a first scanner; receiving a first dataset of first Raman-based spectra defining a first biological product sample scanned by a first scanner using the biological classification model; running a spectral preprocessing algorithm of the biological classification model to reduce spectral inconsistencies in the first dataset of Raman-based spectra; and identifying a biological product type based on the first dataset of Raman-based spectra using the biological classification model, wherein the biological classification model includes classification components selected to reduce at least one of (1) the Q residual error of the biological classification model, or (2) the summary value of the fit of the biological classification model, and the biological classification model is configured to identify a biological product type of a first biological product sample based on the classification components.

[0111] 20. The biological classification model configuration is electronically transferable to a second configurable handheld biological analyzer, and the biological analysis method further comprises loading the biological classification model configuration, including the biological classification model, into the second computer memory of a second configurable handheld biological analyzer having a second processor and a second scanner; receiving a second dataset of second Raman-based spectra that define a second biological product sample scanned by the second scanner using the biological classification model; executing a spectral preprocessing algorithm of the biological classification model to reduce the second spectral mismatch of the second dataset of second Raman-based spectra; and identifying a biological product type based on the second dataset of second Raman-based spectra using the biological classification model, wherein the second biological product sample is a novel sample of the biological product type, the biological analysis method according to aspect 19.

[0112] 21. The biological analysis method according to embodiment 19 or 20, wherein spectral mismatch is an instrument-to-instrument spectral mismatch between a first Raman-based spectral dataset and one or more other Raman-based spectral datasets of one or more corresponding other handheld biological analyzers, each of which represents a biological product type, and the spectral preprocessing algorithm is configured to reduce the instrument-to-instrument spectral mismatch between the first Raman-based spectral dataset and one or more other Raman-based spectral datasets.

[0113] 22. The biological analysis method according to embodiment 21, wherein the spectral preprocessing algorithm includes: applying a derivative transform to a dataset of first Raman-based spectra to generate a dataset of modified Raman-based spectra; aligning the dataset of modified Raman-based spectra across the entire Raman shift axis; and normalizing the dataset of modified Raman-based spectra across the entire Raman intensity axis.

[0114] 23. The biological analysis method according to embodiment 22, wherein the derivative transformation includes determining a Raman-weighted mean of a successive group of 11–15 Raman shift values ​​across the entire Raman shift axis, and determining the corresponding derivative of these Raman-weighted means across the entire Raman shift axis.

[0115] 24. A biological analysis method according to one or more embodiments 19 to 23, wherein the classification component is selected to reduce both (1) the Q residual error of the biological classification model and (2) the summary value of the fit of the biological classification model.

[0116] 25. A biological analytical method according to any one or more embodiments 19 to 24, 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 a first biological product sample based on the classification component and the second classification component.

[0117] 26. A biological classification model implemented as a major component analysis (PCA) model, as described in one or more of embodiments 19 to 25.

[0118] 27. The classification component is the first major component of the PCA model, according to the biological analysis method described in aspect 26.

[0119] 28. A biological analysis method according to any one or more embodiments 19 to 27, wherein a first and / or second computer memory is configured to load a new biological classification model, and the new biological classification model includes updated classification components.

[0120] 29. A biological classification model configuration is implemented in the Extended Markup Language (XML) format, and is a biological analysis method described in one or more of embodiments 19 to 28.

[0121] 30. A biological analytical method according to one or more of embodiments 19 to 29, wherein the type of biological product is a therapeutic product.

[0122] 31. A biological analytical method according to one or more embodiments 19 to 30, wherein the biological product type is identified by a biological classification model during the production of a biological product having a biological product type.

[0123] 32. A biological analysis method according to any one of embodiments 19 to 31, wherein the biological classification model is configured to distinguish a first biological product sample having a biological product type from different biological product samples having different biological product types, based on classification components.

[0124] 33. The biological analysis method according to embodiment 32, wherein the biological product type and different biological product types each have the same or similar Raman spectral ranges.

[0125] 34. A biological analysis method according to any one of embodiments 19 to 33, wherein the biological classification model is configured to identify the biological product type of a first biological product sample based on a classification component when the Q residual error or the fitting summary value satisfies a threshold.

[0126] 35. A biological analysis method according to embodiment 34, wherein the biological classification model outputs a pass / fail decision based on a threshold.

[0127] 36. A biological analysis method according to any one of embodiments 19 to 35, wherein the biological classification model is generated by a remote processor that is remote to a configurable handheld biological analyzer.

[0128] 37. A tangible, non-temporary, computer-readable medium storing instructions for identifying biological products based on Raman spectroscopy, wherein, when performed by one or more processors of a configurable handheld biological analyzer, the biological classification model configuration is loaded into the computer memory of a configurable handheld biological analyzer having a scanner, the biological classification model configuration includes a biological classification model; the biological classification model receives a dataset of Raman-based spectra defining a biological product sample scanned by the scanner; the biological classification model executes a spectral preprocessing algorithm to reduce spectral inconsistencies in the dataset of Raman-based spectra; and the biological classification model identifies biological product types based on the dataset of Raman-based spectra, wherein the biological classification model includes classification components selected to reduce at least one of (1) the Q residual error of the biological classification model, or (2) the summary value of the fit of the biological classification model, and the biological classification model is configured to identify biological product types of biological product samples based on the classification components.

[0129] The above-described aspects of this disclosure are illustrative and do not limit the scope of this disclosure.

[0130] Additional examples The following additional examples provide further assistance to the various embodiments described herein. Specifically, the following additional examples demonstrate Raman spectroscopy for rapid identification (ID) of biotherapeutic protein products in solution. These examples demonstrate unique combinations of Raman features relevant to both therapeutic and excipients as criteria for product differentiation. The product ID methods described herein (e.g., biological analysis methods) include obtaining Raman spectra of target products on multiple Raman analyzers (e.g., configurable handheld biological analyzers described herein). These spectra may subsequently be subjected to primary component analysis (PCA) to define a product-specific model (e.g., a biological classification model) that serves as a criterion for product ID determination on the configurable handheld biological analyzer, and dimensionality reduction using the biological analysis methods to identify biological products based on the Raman spectroscopy described herein. The product-specific model (e.g., a biological classification model) may be transferred to individual instruments (e.g., configurable handheld biological analyzers) whose effectiveness for product testing has been confirmed. These can be used for a variety of purposes, including quality control, supply chain quality assurance, and manufacturing. These analytical instruments and methods can be used across a variety of Raman instruments from various manufacturers (e.g., configurable handheld biological analyzers). Thus, additional examples further demonstrate that the Raman ID analyzers and methods described herein (e.g., configurable handheld biological analyzers and related methods) provide a variety of applications and testing for solution-based protein products in the biopharmaceutical industry.

[0131] Additional Examples - Materials The active pharmaceutical ingredients and formulations corresponding to more than 28 individual product specifications were analyzed during the development and testing of the configurable handheld biological analyzers and associated methods described herein. Table 1 shows the active pharmaceutical ingredient (API) concentrations and molecular classes for 14 product specifications representing a set of late-stage and commercial product specifications. Product solutions serving as sample cells for Raman spectroscopy were transferred to 4 mL glass vials. Table 1 provides general properties of the evaluated products as targets of ID methods (e.g., biological analysis methods) or as any of the specificity challenges described herein. For clarity, each product in Table 1 is labeled with a character code. Products with the same character letter but different numbers (e.g., A1 and A2) represent products with the same active ingredient, but with different protein concentrations and / or formulations. The listed materials can be used in the manufacture of formulations. It will be understood that some formulations may be identified by, for example, the trademark names mentioned herein.

[0132] [Table 1]

[0133] Additional examples - Raman instruments (e.g., configurable handheld biological analyzers) and measurement In additional embodiments, Raman spectra were measured using the configurable handheld biological analyzer described herein. For example, in certain embodiments, the configurable handheld biological analyzer may be a Raman-based handheld analyzer such as the TruScan® RM Handheld Raman Analyzer provided by Thermo Fisher Scientific Inc. In such embodiments, the configurable handheld biological analyzer may implement the TruTools® software package for metrology. However, it should be understood that other brands or types of Raman analyzers using additional and / or different software packages may be used in accordance with the disclosure herein. In some embodiments, for sample integration, the configurable handheld biological analyzer may consist of a 785 nm diffraction grating-stabilized laser source (maximum output 250 mW) coupled with a focusing optics element (e.g., 0.33 NA, working distance 18 mm, spot > 0.2 mm). For additional embodiments, a product solution contained in a glass vial was fixed to the front of the focusing optics element using the vial adapter of the configurable handheld biological analyzer. For example, all spectra were collected using the same spectral acquisition settings: laser power = 250 mW, integration time = 1000 ms, and number of spectral co-additions = 70 (however, other settings may be used). For additional embodiments, product spectra were collected over a predetermined period using three different configurable handheld biological analyzers (hereinafter referred to as configurable handheld biological analyzers 1-3) and / or instruments dedicated to configuring and / or developing biological analytical methods for identifying biological products based on Raman spectroscopy as described herein. It should be understood that additional or fewer analyzers using the same or different settings may be used to set up, configure, or initialize the configurable handheld biological analyzers and associated biological analytical methods as described herein.

[0134] Additional Examples - Development of Multivariate Raman ID Biological Analysis Methods For example, Raman spectral models (e.g., biological classification models) based on major component analysis (PCA) can be generated, developed, or loaded as described herein. For example, in some embodiments, Raman spectral models (e.g., biological classification models) can be generated, developed, or loaded using SOLO software (Solo+Model_Exporter version 8.2.1; Eigenvector Research, Inc.) equipped with the Model Exporter add-on. However, it should be understood that other software may be used to generate, develop, or load Raman spectral models (e.g., biological classification models). The spectra used for model construction can generally be collected as replicate scans on two or more separate batches of material using a configurable handheld biological analyzer (e.g., three configurable handheld biological analyzers). The spectra are generally acquired over several days to include instrument drift. In some embodiments, the spectral range may be reduced to exclude detector noise at >1800 cm⁻¹ and background variability resulting from optical elements that remove Rayleigh lines at <400 cm⁻¹ before being incorporated into a model (e.g., a biological classification model). The spectra may be further preprocessed and mean-centered for each model as described herein. The models may be further refined by cross-verification using a random subset procedure by referring to the Raman spectra of target and problem products shown in Table 1.

[0135] A biological classification model configuration (e.g., a PCA model configuration) may be configured or loaded into a configurable handheld biological analyzer, along with Raman spectral acquisition parameters, and / or a biological analytical method for identifying biological products based on Raman spectroscopy as described herein may be used. Acceptance (e.g., pass / fail) criteria for each method may also be specified. As described herein, the pass / fail criteria are two summary statistics that generally describe how well the Raman spectrum is described by the biological classification model (e.g., a PCA model): the reduced Hotelling T2. 2 (T r 2 ) and Q residual (Q r This can be obtained based on a threshold. The following equations (1) to (4) provide exemplary user-selectable decision logic options for reliable identification or determination (e.g., pass / fail criteria) by a biological classification model (e.g., a PCA model).

number

[0136] In the example equation above, by dividing the original value by the corresponding confidence interval and thereby setting the upper bound to a value of 1, we obtain the Hotelling T 2 And the Q residual values ​​are normalized (i.e., each T is reduced). r 2 and Q r ).

[0137] Additional Examples - Configurable Handheld Biological Analyzer and Transfer Test Method In connection with additional examples, the performance of the configurable handheld biological analyzers and associated methods described herein with respect to the five product-specific models (e.g., biological classification models) described herein with respect to Figures 8A–8E was demonstrated using a small group of analyzers (e.g., 15 configurable handheld biological analyzers) that had not been used in the development of the configurable handheld biological analyzers and associated methods described herein, i.e., had not previously been configured with or loaded with the biological classification model configurations described herein. Product ID methods (e.g., biological analytical methods for identifying biological products based on Raman spectroscopy) were prepared on configurable handheld biological analyzers 1–3, and four tests were performed for a single product specification (e.g., Q1, Q2, A1, and A2), and one test (e.g., B1, B2, and B3) suitable for identifying three similar specifications of the same protein product. Each test included the use of target product spectra acquired on 15 additional instruments (analyzers 4–18), each with different ages and performance levels. Model specificity was also measured by evaluating the closest specificity challenge product and formulation buffer (i.e., without protein). Raman spectra of the samples were acquired using the same acquisition parameters (i.e., laser power, acquisition time, and number of co-additions) as those used in model construction. Raman spectra were acquired as replications over various days, yielding approximately 250 spectra per product sample. Spectra acquired during the experiment were evaluated for each of the five PCA models (e.g., in Eigenvector Solo and Model_Exporter software) to assess the likelihood of false positives (i.e., misidentification of the challenge product as the target) and false negatives (i.e., inaccurate rejection of the target product by the model).

[0138] During testing of additional examples, for example, no instances of false positive results were observed in any of the five models and related tests described with respect to Figures 8A-8E. Generally, the Q of the problem product... r or T r 2 The values ​​were larger for analyzers 4–18 than for the instruments used in the development of the model. Extending this observation, the ability of a biological classification model (e.g., a PCA model) to consistently reject a given problem product can be estimated with high confidence based solely on the Raman spectra obtained during method development. Figure 7 shows exemplary visualizations 700 of the reduced Q residual error 704 according to various embodiments described herein. Specifically, Figure 7 provides exemplary plots of the reduced Q residual error values ​​700 for product A1 of Table 1 treated as a problem product sample, evaluated against a biological classification model (e.g., a PCA model) for product A2 of Table 1. Linear indices 706 are provided for Raman spectral indices in the dataset and are not necessarily related to the sample.

[0139] Each point in Figure 7 is differentiated based on whether the corresponding Raman spectrum was acquired on the analytical instrument used for model development (702) or used solely for testing (703). Q of analytical instrument 8 r The values ​​(i.e., linear exponential values ​​of approximately 250-270) were abnormally high due to known instrument performance issues, which will be discussed below in this specification. Nevertheless, even excluding the measurements made on analyzer 8, the Q values ​​of analyzers 4-18 remain high. r The values ​​did not distribute normally based on the rejection of the Shapiro-Wilk null hypothesis (p=0.0013). In this dataset, the central Q of 3.02 for analyzers 4-18. r The value is the central Q of the development equipment at 2.53. r The results were significantly larger than (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 across 1540 total measurements, which is equivalent to only about 2% false negatives and represents only a small fraction of the total number of analyses.

[0140] Figures 8A–8E show the summary statistics Q for each target product evaluated against its corresponding biological classification model (e.g., the PCA model). r or T r 2 The analysis of various analytical instruments 802 (i.e., configurable handheld biological analyzers 1-3 and analyzers 4-18) is shown by plotting the results. For clarity, the verification results in Figures 8A-8E are organized according to the analyzer number. Figures 8A-8D show exemplary visualizations 800, 810, 820, and 830 of the reduced Q residual error of the target product (e.g., Table 1) evaluated for 18 different configurable handheld biological analyzers (configurable handheld biological analyzers 1-3 and analyzers 4-18) according to the various embodiments described herein. Specifically, the visualizations in Figures 8A-8D are represented as scattering plots showing the spread of the reduced Q residual of the target product for each method evaluated on analyzers 1-18. Figure 8A shows the spread of the reduced Q residual for target product A1 in Table 1. Figure 8B shows the spread of the reduced Q residual for target product A2 in Table 1. Figure 8C shows the spread of the reduced Q residual for target product Q1 in Table 1. Figure 8D shows the spread of the reduced Q residual for target product Q2 in Table 1. In each of Figures 8A-8D, the horizontal dashed lines in each graph (i.e., 805, 815, 825, and 835, respectively) represent the pass / fail criteria or thresholds, and values ​​greater than 1 result in a failing result (i.e., a false negative). Each linear exponent (e.g., 806, 816, 826, and 836, respectively) is provided for the Raman spectral indices in the dataset and is not necessarily related to the sample.

[0141] Figure 8E shows the reduced fit summary values ​​(e.g., Hotelling T) of target products (e.g., B1, B2, and / or B3) evaluated for 18 different configurable handheld biological analyzers 802 (configurable handheld biological analyzers 1-3 and analyzers 4-18) according to various embodiments described herein. 2 An exemplary visualization 840 is shown. The horizontal dashed line 845 represents the pass / fail criterion or threshold, so a value greater than 1 results in a failing result (i.e., a false negative). A linear index 846 is provided for the Raman spectral index in the dataset and is not necessarily related to the sample.

[0142] For each of Figures 8A–8E, no false negatives were determined on analyzers 10–16 and 18. In fact, the summary statistic was <0.6 in most cases, suggesting that the possibility of false negatives is extremely low for any of these instruments. For the remaining three analyzers (8, 9, and 17), there were 33 isolated erroneous results, each of which had an identifiable hardware-based and / or instrument-specific performance issue. Analyst 8, the first instrument to be tested, resulted in the highest number of false negatives. For Method A1, 20 / 20 spectra failed Q r This produced a value (e.g., a value greater than 1). However, the other four methods produced only three false negatives in total, suggesting that the differing performance of method A1 is likely related to the weak Raman scattering signal resulting from its low protein concentration (10 mg / mL) and weak excipient band. Nevertheless, the residual analysis of analyzer 8 was performed at approximately 1300 cm⁻¹. -1 A wide range of characteristics, primarily focusing on [specific aspect], were revealed (data not shown). Analytical instrument 8, an early test-built instrument, had different optical components than the production analytical instruments (1-7 and 9-18) that produced observable Raman bands, which was thought to be the cause of the high rejection rate. Significant instrument performance issues were also noted for the remaining analytical instruments (9 and 17). The original wavenumber calibration of analytical instrument 9 was approximately 3 cm from the manufacturer's specifications. -1 It was known that the instrument was out of range. Further investigation of analyzer 17 identified previously unknown laser power / stability issues. Despite these known issues, the true positive rate for all five models in Figures 8A–8E exceeded 85% on both analyzers, providing evidence that biological classification models (e.g., PCA) can tolerate even a degree of instrument performance degradation. Procedural mechanisms designed to ensure instrument fit-for-use (e.g., installation and operational performance qualification, periodic preventive maintenance) are already documented in Good Manufacturing Practice (GMP) testing for biopharmaceuticals. However, the fact that the laser power issue of analyzer 17 was unknown before testing highlights the value of rigorous evaluation of instrument performance metrics to ensure the long-term performance of spectrometers and multivariate models. However, as demonstrated above, the configurable handheld biological analyzers and associated biological analytical methods described herein for identifying biological products based on Raman spectroscopy are robust and fault-tolerant as described herein and remain operational and usable despite hardware-based and / or instrument-specific performance issues of the instruments.

[0143] Additional explanation The above descriptions in this specification describe various devices, assemblies, components, subsystems, and uses related to drug delivery devices. Devices, assemblies, components, subsystems, methods, or drug delivery devices may further include, or be used in conjunction with, drugs identified below, and their generic and biosimilar equivalents, but not limited to those drugs. As used herein, the term "drug" may be interchangeable with other similar terms and may be used to refer to any type of drug or therapeutic material, including traditional and non-traditional medicines, dietary supplements, supplements, biological preparations, biological activators and compositions, large molecules, biosimilars, bioequivalents, therapeutic antibodies, polypeptides, proteins, small molecules, and generic drugs. Non-therapeutic injectable materials are also included. Drugs may be in liquid form, lyophilized form, or reconstituted from lyophilized form. The following list of exemplary drugs should not be considered exhaustive or restrictive.

[0144] The drug is contained in a reservoir. In some cases, the reservoir is a primary container that is either filled with or pre-filled with the drug for therapeutic purposes. The primary container may be a vial, cartridge, or pre-filled syringe.

[0145] In some embodiments, colony-stimulating factors such as granulocyte colony-stimulating factor (G-CSF) may be packed into the reservoir of a drug delivery device, or the device may be used together with them. Examples of such G-CSF agents include, but are not limited to, Neulasta® (pegfilgrastim, PEGylated filgastrim, PEGylated G-CSF, PEGylated hu-Met-G-CSF) and Neupogen® (filgrastim, G-CSF, hu-MetG-CSF).

[0146] 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. The ESA is any molecule that stimulates erythrocyte production. In some embodiments, the ESA is an erythropoiesis-stimulating protein. As used herein, “erythropoiesis-stimulating protein” means any protein that directly or indirectly activates the erythropoietin receptor, for example, by binding to the receptor and causing its dimerization. Examples of 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. Examples of red blood cell production stimulating proteins include Epogen® (epoetin alfa), Aranesp® (darbepoetin alfa), Dynepo® (epoetin delta), Mircera® (methoxypolyethylene glycol-epoetin beta), Hematide®, MRK-2578, INS-22, Retacrit® (epoetin zeta), Neorecormon® (epoetin beta), Silapo® (epoetin zeta), and Binocrit® (epoetin alfa). Examples include, but are not limited to, epoetin alpha Hexal, Abseamed® (epoetin alpha), Ratioepo® (epoetin theta), Eporatio® (epoetin theta), Biopoin® (epoetin theta), epoetin alpha, epoetin beta, epoetin iota, epoetin omega, epoetin delta, epoetin zeta, epoetin theta, and epoetin delta, PEGylated erythropoietin, carbamylated erythropoietin, and their molecules, variants, or analogues.

[0147] Among certain exemplary proteins, there are specific proteins described below, including their fusions, fragments, analogs, variants, or derivatives: OPGL-specific antibodies, peptide bodies, and related proteins, including fully humanized and human OPGL-specific antibodies, particularly fully humanized monoclonal antibodies (also referred to as RANKL-specific antibodies, peptide bodies, etc.); myostatin-binding proteins, peptide bodies, and related proteins, including myostatin-specific peptide bodies; IL-4 receptor-specific antibodies, peptide bodies, and related proteins. Proteins, in particular those that suppress the activity mediated by the binding of IL-4 and / or IL-13 to their receptors; interleukin 1-receptor 1 ("IL1-R1") specific antibodies, peptide bodies, and related proteins; Ang2 specific antibodies, peptide bodies, and related proteins; NGF specific antibodies, peptide bodies, and related proteins; CD22 specific antibodies, peptide bodies, and related proteins, in particular those that bind human-mouse monoclonal hLL2κ chains to human-mouse monoclonal hLL2γ chain disulfides. Human CD22-specific antibodies, including but not limited to human CD22-specific IgG antibodies, such as human CD22-specific fully humanized antibodies like epratuzumab (CAS registry number 501423-23-0); human CD22-specific antibodies, including but not limited to humanized and fully human monoclonal antibodies; humanized and fully human antibodies; IGF-1 receptor-specific antibodies, peptide bodies, and related proteins, including but not limited to anti-IGF-1R antibodies; B7RP-1 and its natural properties on activated T cells This includes, but is not limited to, antibodies that inhibit interaction with the receptor ICOS, fully human IgG2 monoclonal antibodies that bind to the epitope of the first immunoglobulin-like domain of B7RP-1, fully human IgG2 monoclonal antibodies that include, but are not limited to, B7RP-specific antibodies, peptide bodies, and related proteins (also referred to as "B7RP-1", B7H2, ICOSL, B7h, and CD275); e.g., 146B7, HuMaxIL-15 specific antibodies, peptide bodies, and related proteins, including but not limited to IL-15 antibodies and related proteins, particularly humanized monoclonal antibodies; IFN-γ specific antibodies, peptide bodies, and related proteins, including but not limited to human IFN-γ specific antibodies and fully human anti-IFN-γ antibodies; TALL-1 specific antibodies, peptide bodies, and related proteins, as well as other TALL-specific binding proteins; parathyroid hormone ("PTH") specific antibodies, peptide bodies, and related proteins; Thrombopoietin receptor ("TPO-R") specific antibodies, peptide bodies, and related proteins; hepatocyte growth factor ("HGF") specific antibodies, peptide bodies, and related proteins, including those targeting HGF / SF; cMet axis (HGF / SF:c-Met) such as fully human monoclonal antibodies that neutralize hepatocyte growth factor / scattering (HGF / SF) specific antibodies; TRAIL-R2 specific antibodies, peptide bodies, and related proteins; activin A specific antibodies, peptide bodies, and proteins; TGF-β specific antibodies, peptide bodies, and related proteins; Amyloid-beta protein-specific antibodies, peptide bodies, and related proteins; c-Kit-specific antibodies, peptide bodies, and related proteins, including but not limited to proteins that bind to c-Kit and / or other stem cell factor receptors; OX40L-specific antibodies, peptide bodies, and related proteins, including but not limited to proteins that bind to OX40L and / or other ligands of the OX40 receptor; Activase® (alteplase, tPA), Aimovig® (erenumab), Aranesp® (Darbepoetin alfa), Epogen® (Epoetin alfa, or erythropoietin), GLP-1, Avonex® (Interferon β-1a), Bexxar® (Tositumomab, anti-CD22 monoclonal antibody), Betaseron® (Interferon-β), Campath® (Alemtuzumab, anti-CD52 monoclonal antibody), Dynepo® (Epoetin delta), Velcade® (Bortezomib), MLN0002 (Anti-α4β7)mAb), MLN1202 (anti-CCR2 chemokine receptor mAb), Enbrel® (etanercept, TNF receptor / Fc fusion protein, TNF blocker), Eprex® (epoetin alfa), Erbitux® (cetuximab, anti-EGFR / HER1 / c-ErbB-1), Evenity® (romosozumab), Genotropin® (somatropin, human growth hormone), Herceptin® (trastuzumab, anti-HER2 / neu(erbB2) receptor mAb), Humatrope® (somatropin, human growth hormone), Humira® (adalimumab), Vectibix® (panic acid) Tummumab), Xgeva® (denosumab), Prolia® (denosumab), Enbrel® (etanercept, TNF receptor / Fc fusion protein, TNF blocker), Nplate® (romiplostim), rilotumumab, ganitumumab, conatumumab, brodalumab, insulin in solution, Infergen® (interferon alpha-1), Natrecor® (nesiritide, recombinant human type B natriuretic peptide (hBNP)), Kineret® (anakinra), Leukine® (sargamostim, rhuGM-CSF), LymphoCide® (epratuzumab, anti-CD22 mAb), Benlysta (trademark) (lymphostat B, belimumab, anti-BlyS mAb), Metalyse (registered trademark) (tenecteplase, t-PA analog), Mircera (registered trademark) (methoxypolyethylene glycol-epoetin β), Mylotarg (registered trademark) (gemtuzumab ozogamicin), Raptiva (registered trademark) (efalizumab), Cimzia (registered trademark) (certolizumab pegol, CDP870), Soliris (trademark) (eculizumab), paxerizumab (anti-C5 complement), Numax (registered trademark) (MEDI-524), Lucentis (registered trademark) (ranibizumab), Panorex (registered trademark) (17-1A, edrecolomab), Trabio (registered trademark) (reldelimumab), TheraCimhR3 (nimotuzumab), Omnitarg (pertuzumab, 2C4), Osidem (registered trademark) (IDM-1), OvaRex (registered trademark) (B43.13), Nuvion (registered trademark) (vizilizumab), cantuzumab meltansine (huC242-DM1), NeoRecormon (registered trademark) (epoetin beta), Neumega (registered trademark) (oprelbequin, human interleukin-11), Orthoclone OKT3 (registered trademark) (muromonab-CD3, anti-CD3 monoclonal antibody), Procrit (registered trademark) (epoetin alfa), Remicade (registered trademark) (infliximab, anti-TNFα monoclonal antibody), Reopro (registered trademark) (absiximab, anti-GP) (Ib / Ilia receptor monoclonal antibody), Actemra® (anti-IL6 receptor mAb), Avastin® (bevacizumab), HuMax-CD4 (zanorimumab), Rituxan® (rituximab, anti-CD20 mAb), Tarceva® (erlotinib), Roferon-A® (interferon α-2a), Simulect® (basiliximab), Prexige® (lumiracoxib), Synagis® (palivizumab), 146B7-CHO (anti-IL15 antibody, see U.S. Patent No. 7,153,507), Tysabri® (natalizumab, anti-α4 integrin mAb), Valortim® (MDX-1303, anti-anthrax protective antigen mAb), ABthrax®, Xolair® (omalizumab), ETI211 (anti-MRSA mAb), IL-1 trap (Fc portion of human IgG1 and extracellular domains of both IL-1 receptor components (type I receptor and receptor co-protein)), VEGF trap (IgG1 VEGFR1 Ig domain fused with Fc), Zenapax® (daclizumab), Zenapax® (daclizumab, anti-IL-2Rα mAb), Zevalin® (ibritumomab tiuxetan), Zetia® (ezetimibe), Orencia® (atacicept, TACI-Ig), anti-CD80 monoclonal antibody (galiximab), anti-CD23mAb (lumiliximab), BR2-Fc (huBR3 / huFc fusion protein, soluble BAFF antagonist), CNTO148 (golimumab, anti-TNFα mAb), HGS-ETR1 (mapatumumab, human anti-TRAIL receptor-1 mAb), HuMax-CD20 (ocrelizumab, anti-CD20 human mAb), HuMax-EGFR (saltumumab), M200 (boroxiximab, anti-α5β1 integrin mAb), MDX-010 (ipilimumab, anti-CTLA-4 mAb, and VEGFR-1 (IMC-18F1), anti-BR3 mAb, anti-C. difficile toxin A and toxin BC mAbs MDX-066 (CDA-1) and MDX-1388), anti-CD22 dsFv-PE38 conjugate (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 fibrogen for idiopathic pulmonary fibrosis stage 1 (FG-3019), anti-CTLA4 mAb, anti-eotaxin 1 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 (CNTO1275), 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-TGFβmAb (GC-1008), TRAIL-2 mAb (HGS-ETR2), TWEAK mAb, VEGFR / Flt-1 mAb, ZP3 mAb (HuMax-3)

[0148] In some embodiments, the drug delivery device may contain, or be used in conjunction with, a sclerostin antibody such as, but not limited to, romosozumab, brosozumab, or BPS 804 (Novartis), and in other embodiments, a monoclonal antibody (IgG) that binds to human proprotein convertase subtilisin / kexin type 9 (PCSK9). Examples of such PCSK9-specific antibodies include, but are not limited to, Repatha® (evolocumab) and Praluent® (alirocumab). In other embodiments, the drug delivery device may contain, or be used in conjunction with, rilotumumab, bixalomer, trevananib, ganitumab, conatumumab, motesanib diphosphate, brodalumab, vispiprant, or panitumumab. In some embodiments, the reservoir of the drug delivery device may be filled with IMLYGIC® (tarimodine rherparepvec) or another oncolytic HSV for the treatment of melanoma or other cancers, including but not limited to OncoVEXGALV / CD;OrienX010;G207, 1716;NV1020;NV12023;NV1034; and NV1042, or the device may be used in conjunction with such. In some embodiments, the drug delivery device may contain or be used in conjunction with an endogenous tissue inhibitor (TIMP) of metalloproteinases, such as but not limited to TIMP-3. Antagonistic antibodies of the human calcitonin gene-related peptide (CGRP) receptor, such as erenumab and bispecific antibody molecules targeting the CGRP receptor and other headache targets, but not limited to such bispecific antibody molecules, may also be delivered using the drug delivery device of the Disclosure. In addition, bispecific T cell engager (BiTE®) antibodies, such as but not limited to BLINCYTO® (blinatumomab), may be used in or with the drug delivery device of this disclosure. In some embodiments, the drug delivery device may contain or be used with APJ macromolecule agonists, such as but not limited to apelin or its analogues.In some embodiments, a therapeutically effective amount of anti-thymocrine interstitial lymphocyte generating factor (TSLP) or TSLP receptor antibody is used in or in conjunction with the drug delivery device of the Disclosure.

[0149] Drug delivery devices, assemblies, components, subsystems, and methods have been described in terms of exemplary embodiments, but are not limited thereto. The embodiments for carrying out this invention should be construed as illustrative only and do not describe all possible embodiments of this disclosure. Various alternative embodiments can be carried out using either the current art or art developed after the filing date of this patent, but such embodiments are still included within the claims defining the invention as disclosed herein.

[0150] Those skilled in the art will understand that a wide variety of modifications, changes, and combinations can be made to the above embodiments without departing from the spirit and scope of the invention disclosed herein, and that such modifications, changes, and combinations will be interpreted as falling within the scope of the concept of the present invention.

[0151] Other considerations While the disclosure herein provides a detailed description of numerous different embodiments, it should be understood that the legal scope of the description is defined by the language of the claims and their equivalents, as set forth at the end of this patent. The embodiments for carrying out this invention should be interpreted as illustrative only, and not all conceivable embodiments are described, as it would be impractical to describe them all. Various alternative embodiments can be carried out using either the current art or art developed after the filing date of this patent, and such embodiments are still within the scope of the claims.

[0152] The following additional considerations also apply to the above description. Throughout this specification, multiple examples may implement a component, operation, or structure described as a single example. While individual operations of one or more methods are illustrated and described as separate operations, one or more of these operations may be performed simultaneously, and there is no requirement that the operations be performed in the illustrated order. Structures and functions presented as separate components in an exemplary configuration can also be implemented as a combined structure or component. Similarly, structures and functions presented as a single component can also be implemented as separate components. These and other changes, improvements, additions, and enhancements are also within the scope of this specification.

[0153] Furthermore, certain embodiments described herein include logic circuits, or a plurality of routines, subroutines, applications, or instructions. These can constitute either software (e.g., code embodied on a machine-readable medium or in a transmitted signal) or hardware. In hardware, routines, etc., are tangible units capable of performing specific operations and can be configured or arranged in a particular manner. In exemplary embodiments, one or more computer systems (e.g., standalone, client, or server computer systems), or one or more hardware modules of a computer system (e.g., processors or groups of processors), may be configured by software (e.g., applications or application portions) as hardware modules that operate to perform specific operations described herein.

[0154] In various embodiments, hardware modules may be implemented mechanically or electronically. For example, a hardware module may include dedicated circuits or logic circuits permanently configured to perform a specific operation (e.g., as a dedicated processor such as a field-programmable gate array (FPGA) or application-specific integrated circuit (ASIC)). A hardware module may also include programmable logic or circuits temporarily configured to perform a specific operation by software (e.g., as incorporated into a general-purpose processor or other programmable processor). It will be understood that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuits, or in temporarily configured circuits (e.g., configured by software) may be determined by considering costs and time.

[0155] Therefore, the term “hardware module” should be understood to encompass tangible objects, whether physically constructed, permanently configured (e.g., by wiring), or temporarily configured (e.g., by programming), that operate in a particular manner as described herein or perform a particular operation. In embodiments where a hardware module is temporarily configured (e.g., by programming), each hardware module does not need to be configured or instantiated in any instance within a given time. For example, if a hardware module includes a general-purpose processor configured using software, the general-purpose processor may be configured as different hardware modules at different points in time. Thus, the software may configure the processor to configure a particular hardware module in one instance and different hardware modules in other instances.

[0156] As used herein, the term “join” does not require a direct bond or connection; therefore, two items may be “joined” to one another via one or more intermediate components or other elements, such as an electronic bus, electrical wiring, mechanical components, or other such indirect connections.

[0157] 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 linked. When multiple such hardware modules exist simultaneously, communication can be achieved by signal transmission (e.g., via appropriate circuits and buses) connecting the hardware modules. In embodiments where multiple hardware modules are configured or instantiated at different points in time, communication between these hardware modules can be achieved, for example, through the storage and retrieval of information in memory structures accessed by the multiple hardware modules. For example, one hardware module can perform an operation and store the output of that operation in a memory device to which it is communicatively linked. Further hardware modules can then, at a later point in time, access the memory device to read and process the stored output. Hardware modules can also initiate communication with input or output devices and operate on resources (e.g., a set of information).

[0158] Various operations of the exemplary methods described herein may be performed, at least in part, by one or more processors that are temporarily (e.g., by software) or permanently configured to perform the operations in question. Whether temporarily or permanently configured, such processors may constitute a processor implementation module that operates to perform one or more operations or functions. Modules referred to herein may, in some exemplary embodiments, include processor implementation modules.

[0159] Similarly, any methods or routines described herein may be implemented by a processor, at least in part. For example, at least some of the operations of a method may be performed by one or more processors or processor-implementing hardware modules. The implementation of a particular part of an operation may be distributed among one or more processors deployed across multiple machines, rather than being located within a single machine. In some exemplary embodiments, one or more processors may be located in one place, while in other embodiments, processors may be distributed across multiple locations.

[0160] The implementation of specific parts of the operation can be distributed across one or more processors deployed across multiple machines, rather than being located within a single machine. In some exemplary embodiments, one or more processors or processor implementation modules may be located in a single geographical location (e.g., a home environment, an office environment, or a server farm). In other embodiments, one or more processors or processor implementation modules may be distributed across multiple geographical locations.

[0161] The embodiments for carrying out this invention should be interpreted as illustrative only, and not all conceivable embodiments are described, as it would be impractical, if not impossible, to describe all conceivable embodiments. Those skilled in the art can carry out various alternative embodiments using either the current art or art developed after the filing date of this application.

[0162] Those skilled in the art will understand that, in relation to the embodiments described above, various improvements, modifications, and combinations can be made without departing from the scope of the present invention, and that such improvements, modifications, and combinations are also considered to fall within the scope of the concept of the present invention.

[0163] The claims at the end of this patent application are not intended to be construed under Section 112(f) of the United States Patent Act unless they explicitly contain traditional means-plus-function language, such as "means for" or "step for." The systems and methods described herein are intended to improve the functionality of computers and are intended to improve the functionality of conventional computers.< / model> < / model>

Claims

1. A configurable handheld biological analyzer for identifying biological products based on Raman spectroscopy, First housing adapted for handheld operation, The first scanner held by the first housing, A first processor, which is communicably connected to the first scanner, and Includes a first computer memory that is communicably connected to the first processor, The first computer memory is configured to load a biological classification model configuration, the biological classification model configuration includes a biological classification model, the biological classification model is configured to run on the first processor, the first processor is configured to (1) receive a first Raman-based spectral dataset defining a first biological product sample scanned by the first scanner, and (2) use the biological classification model to identify a biological product type based on the first Raman-based spectral dataset. The biological classification model configuration further includes a spectral preprocessing algorithm, wherein the first processor is configured to execute the spectral preprocessing algorithm when the first Raman-based spectral dataset is received by the first processor to reduce spectral inconsistencies between the first Raman-based spectral dataset and one or more other Raman-based spectral datasets. A configurable handheld biological analyzer comprising a biological classification model comprising a variable selected to reduce at least one of (1) the error of the biological classification model or (2) the summary value of the fit of the biological classification model, wherein the biological classification model is configured to identify the biological product type of the first biological product sample.

2. The aforementioned biological classification model configuration can be electronically transferred to a second configurable handheld biological analyzer, and the second configurable handheld biological analyzer is A second housing adapted for handheld operation, A second scanner connected to the second housing, A second processor, which is communicably connected to the second scanner, and Includes a second computer memory that is communicably connected to the second processor, The second computer memory is configured to load the biological classification model configuration, the biological classification model configuration includes the biological classification model, the biological classification model is configured to run on the second processor, the second processor is configured to (1) receive a second Raman-based spectral dataset defining a second biological product sample scanned by the second scanner, and (2) use the biological classification model to identify the biological product type based on the second Raman-based spectral dataset. The configurable handheld biological analyzer according to claim 1, wherein the second biological product sample is a novel sample of the biological product type.

3. The spectral mismatch is an instrument-to-instrument spectral mismatch between the first Raman-based spectral dataset and one or more other Raman-based spectral datasets of one or more corresponding other handheld biological analyzers, where each of the one or more other Raman-based spectral datasets represents the biological product type. The configurable handheld biological analyzer according to claim 1, wherein the spectral preprocessing algorithm is configured to reduce spectral mismatches between the analyzer and the dataset of the first Raman-based spectrum and the dataset of one or more other Raman-based spectra.

4. The spectral preprocessing algorithm described above is: Applying a derivative transformation to the first Raman-based spectrum dataset generates a modified Raman-based spectrum dataset, Aligning the modified Raman-based spectrum dataset across the entire Raman shift axis, A configurable handheld biological analyzer according to claim 3, comprising normalizing the modified Raman-based spectral dataset across the entire Raman intensity axis.

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

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

7. The configurable handheld biological analyzer according to claim 1, wherein the biological classification model is implemented as a multivariate model.

8. The configurable handheld biological analyzer according to claim 1, wherein the computer memory is configured to load a new biological classification model, and the new biological classification model includes updated variables.

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

10. The configurable handheld biological analyzer according to claim 1, 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 variables.

11. The configurable handheld biological analyzer according to claim 10, wherein the biological product type and the different biological product types each have distinct local features within the same or similar Raman spectral ranges.

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

13. The configurable handheld biological analyzer according to claim 12, wherein the biological classification model outputs a pass / fail decision based on the threshold.

14. The configurable handheld biological analyzer according to claim 1, wherein the biological classification model is generated by a remote processor that is remote to the configurable handheld biological analyzer.

15. A biological analytical method for identifying biological products based on Raman spectroscopy, Loading a biological classification model configuration, including a biological classification model, into the first computer memory of a first configurable handheld biological analyzer having a first processor and a first scanner, The biological classification model receives a dataset of first Raman-based spectra that define the first biological product sample scanned by the first scanner, The spectral preprocessing algorithm of the biological classification model is executed to reduce spectral inconsistencies between the first Raman-based spectral dataset and one or more other Raman-based spectral datasets. This includes identifying the biological product type based on the first Raman-based spectral dataset using the biological classification model, A biological analysis method wherein the biological classification model includes a variable selected to reduce at least one of (1) the error of the biological classification model or (2) the summary value of the fit of the biological classification model, and the biological classification model is configured to identify the biological product type of the first biological product sample.

16. The aforementioned biological classification model configuration can be electronically transferred to a second configurable handheld biological analysis device, and the aforementioned biological analysis method is Loading the biological classification model configuration, including the biological classification model, into the second computer memory of a second configurable handheld biological analyzer having a second processor and a second scanner, The biological classification model receives a second dataset of Raman-based spectra that define the second biological product sample scanned by the second scanner, The spectral preprocessing algorithm of the biological classification model is executed to reduce the discrepancies in the second spectrum of the second Raman-based spectral dataset, The method further includes identifying the biological product type based on the second Raman-based spectral dataset using the biological classification model described above. The biological analysis method according to claim 15, wherein the second biological product sample is a novel sample of the biological product type.

17. The spectral mismatch is an instrument-to-instrument spectral mismatch between the first Raman-based spectral dataset and one or more other Raman-based spectral datasets of one or more corresponding other handheld biological analyzers, where each of the one or more other Raman-based spectral datasets represents the biological product type. The biological analysis method according to claim 15, wherein the spectral preprocessing algorithm is configured to reduce spectral mismatch between the analyzer and the dataset of the first Raman-based spectrum and the dataset of one or more other Raman-based spectra.

18. The spectral preprocessing algorithm described above is: Applying a derivative transformation to the first Raman-based spectrum dataset generates a modified Raman-based spectrum dataset, Aligning the modified Raman-based spectrum dataset across the entire Raman shift axis, The biological analysis method according to claim 17, comprising normalizing the modified Raman-based spectral dataset across the entire Raman intensity axis.

19. The biological analysis method according to claim 15, wherein the type of biological product is a therapeutic product.

20. When performed by one or more processors of a configurable handheld biological analyzer, the one or more processors of the configurable handheld biological analyzer, Loading a biological classification model configuration into the computer memory of the configurable handheld biological analyzer having a scanner, wherein the biological classification model configuration includes a biological classification model, The biological classification model receives a dataset of Raman-based spectra that define the biological product sample scanned by the scanner, The spectral preprocessing algorithm of the biological classification model is executed to reduce spectral inconsistencies between the Raman-based spectral dataset and one or more other Raman-based spectral datasets. Using the aforementioned biological classification model, the biological product type is identified based on the Raman-based spectral dataset, and the following is performed: The biological classification model includes a variable selected to reduce at least one of (1) the error of the biological classification model, or (2) the summary value of the fit of the biological classification model, and the biological classification model is configured to identify the biological product type of the biological product sample, in a tangible, non-temporary, computer-readable medium that stores instructions for identifying biological products based on Raman spectroscopy.

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