Analyzing atomic force microscopy images of virus-like particles by expectation-maximization.

Analysis of virus-like particles (VLPs) is an essential task in optimizing their implementation as vaccine antigens for virus-initiated diseases. Interrogating VLP collections for elasticity by probing with a rigid atomic force microscopy (AFM) tip is a potential method for determining VLP morphological changes. During VLP morphological change, it is not expected that all VLPs would be in the same state. This leads to the open question of whether VLPs may change in a continuous or stepwise fashion. For continuous change, the statistical distribution of observed VLP properties would be expected as a single distribution, while stepwise change would lead to a multimodal distribution of properties. This study presents the application of a Gaussian mixture model (GMM), fit by the Expectation-Maximization (EM) algorithm, to identify different states of VLP morphological change observed by AFM imaging.

Real-time in situ monitoring of CRM-197 and polysaccharide conjugation reaction by fluorescence spectroscopy.

Aims: Process analytical technology (PAT) is increasingly being adopted within the pharmaceutical industry to build quality into a process. Development of PAT that provides real-time in situ analysis of critical quality attributes are highly desirable for rapid, improved process development. Conjugation of CRM-197 with pneumococcal polysaccharides to produce a desired pneumococcal conjugate vaccine is a significantly intricate process that can tremendously benefit from real-time process monitoring. Methods: In this work, a fluorescence-based PAT methodology is described to elucidate CRM-197-polysacharide conjugation kinetics in real time. Results & conclusion: In this work, a fluorescence-based PAT methodology is described to elucidate CRM-197-polysacharide conjugation kinetics in real time.

Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis.

BACKGROUND: Enzyme immobilization is a beneficial component involved in biocatalytic strategies. Understanding and evaluating the enzyme immobilization system plays an important role in the successful development and implementation of the biocatalysis route. Ensuring the implementation of a successful enzyme immobilization process is vital for realizing a highly functioning and well suited biocatalytic process within pharmaceutical development. AIM: To develop a method which can accurately and objectively identify and classify differences within enzyme immobilization systems, sample preparation methods, and data collection parameters. METHODS: Raman hyperspectral imaging was used to obtain a total of eight spectral data sets from enzyme immobilization samples. Partial least squares discriminant analysis (PLS-DA) was used to classify and identify the samples based on their differences. RESULTS: Several two-class, four-class, and eight-class PLS-DA models were built to classify the different sample data sets. All models reached between 92-100% accuracy after cross-validation and external validation, illustrating great success of the models for identifying differences between the samples. CONCLUSION: Raman hyperspectral imaging with machine learning can be used to investigate, interpret, and classify different data collection parameters, sample preparation methods, and enzyme immobilization supports, providing crucial insight into enzyme immobilization process development.

Process analytical technology and its recent applications for asymmetric synthesis.

The development of a safe and effective active pharmaceutical ingredient (API) to be used for addressing a disease is of the utmost importance in the pharmaceutical industry. Oftentimes, the synthetic pathway required for API development involves the genesis of a chiral compound. Asymmetric syntheses are popular routes for generating these kinds of compounds; these reaction routes require a high level of attention for efficient and successful syntheses. Process analytical technology (PAT) provides significant advantages for monitoring, controlling, and assessing synthetic processes directly and in real time. In this review, PAT applications for investigating and improving asymmetric synthetic reactions are discussed. The totality of this effort provides a comprehensive and thorough repository of recent work which has advanced the pharmaceutical field for generating chiral compounds for industrial applications.

A Novel Method for Detecting Duchenne Muscular Dystrophy in Blood Serum of mdx Mice.

Duchenne muscular dystrophy (DMD) is the most common form of muscular dystrophy, typically affecting males in infancy. The disease causes progressive weakness and atrophy of skeletal muscles, with approximately 20,000 new cases diagnosed yearly. Currently, methods for diagnosing DMD are invasive, laborious, and unable to make accurate early detections. While there is no cure for DMD, there are limited treatments available for managing symptoms. As such, there is a crucial unmet need to develop a simple and non-invasive method for accurately detecting DMD as early as possible. Raman spectroscopy with chemometric analysis is shown to have the potential to fill this diagnostic need.

In situ real time monitoring of emulsification and homogenization processes for vaccine adjuvants.

Adjuvants are commonly employed to enhance the efficacy of a vaccine and thereby increase the resulting immune response in a patient. The activity and effectiveness of emulsion-based adjuvants has been heavily studied throughout pharmaceuticals; however, there exists a lack in research which monitors the formation of a stable emulsion in real time. Process analytical technology (PAT) provides a solution to meet this need. PAT involves the collection of in situ data, thereby providing real time information about the monitored process as well as increasing understanding of that process. Here, three separate PAT tools - optical particle imaging, in situ particle analysis, and Raman spectroscopy - were used to monitor two key steps involved in the formation of a stable emulsion product, emulsification and homogenization, as well as perform a stability assessment. The obtained results provided new insights-particle size decreases during emulsification and homogenization, and molecular changes do not occur during either the emulsification or homogenization steps. Further, the stability assessment indicated that the coarse emulsion product obtained from the emulsification step is stable over the course of 24 hours when mixed. To the best of our knowledge, this is the first report of an analytical methodology for in situ, real time analysis of emulsification and homogenization processes for vaccine adjuvants. Using our proposed analytical methodology, an improved understanding of emulsion-based vaccine adjuvants can now be achieved, ultimately impacting the ability to develop and deliver successful pharmaceuticals.

Process monitoring of polysaccharide deketalization for vaccine bioconjugation development using in situ analytical methodology.

Pneumococcal conjugate vaccines (PCVs) are formed by bioconjugation of a carrier protein to the purified capsular polysaccharide (Ps) from multiple serological strains of Streptococcus pneumoniae. The associated bioconjugation chemistry relies on initial selective modifications to the Ps backbone structure. Among these modifications, removal of a ketal functional group, termed deketalization, is one that is important for pharmaceutical PCV production. Herein, we report a process monitoring investigation into the deketalization of a polysaccharide relevant to PCV process development. We have applied process analytical technology (PAT) for in situ process monitoring to study the deketalization reaction in real time. We find that in situ FTIR spectroscopy elucidates multiple classes of reaction kinetics, one of which correlates strongly with the deketalization reaction of interest. This PAT approach to real time reaction monitoring offers the possibility of improved process monitoring in the pharmaceutical production of PCVs. To our knowledge, this report represents the first PAT investigation into Ps deketalization. Our findings suggest that broader application of PAT to the chemical modifications associated with PCV bioconjugation, as well as other pharmaceutically relevant bioconjugation processes, carries the power to enhance process understanding, control, and efficiency through real time process monitoring.

Utilizing in situ spectroscopic tools to monitor ketal deprotection processes.

The use of protection groups to shield a functional group during a synthesis is employed throughout many reactions and organic syntheses. The role of a protection group can be vital to the success of a reaction, as well as increase reaction yield and selectivity. Although much work has been done to investigate the addition of a protection group, the removal of the protection group is just as important - however, there is a lack of methods employed within the literature for monitoring the removal of a protection group in real time. In this work, the process of removing, or deprotecting, a ketal protecting group is investigated. Process analytical technology tools are incorporated for in situ analysis of the deprotection reaction of a small molecule model compound. Specifically, Raman spectroscopy and Fourier transform infrared spectroscopy show that characteristic bands can be used to track the decrease of the reactant and the increase of the expected products over time. To the best of our knowledge, this is the first report of process analytical technology being used to monitor a ketal deprotection reaction in real time. This information can be capitalized on in the future for understanding and optimizing pharmaceutically-relevant deprotection processes and downstream reactions.

Quantitation and speciation of residual protein within active pharmaceutical ingredients using image analysis with SDS-PAGE.

Recent advances in biocatalysis and directed enzyme evolution has led to a variety of enzymatically-driven, elegant processes for active pharmaceutical ingredient (API) production. For biocatalytic processes, quantitation of any residual protein within a given API is of great importance to ensure process robustness and quality, pure pharmaceutical products. Typical analytical methods for analyzing residual enzymes within an API, such as enzyme-linked immunosorbent assays (ELISA), colorimetric assays, and liquid chromatographic techniques, are limited for determining only the concentration of known proteins and require harsh solvents with high API levels for analysis. For the first time, total residual protein content in a small molecule API was quantitated using image analysis applied to SDS-PAGE. Herein, a proposed methodology for residual protein detection, quantitation, and size-based speciation is presented, in which an orthogonal technique is offered to traditional analysis methods, such as ELISA. Results indicate that our application of the analytical methodology is able to reliably quantitate both protein standards and the total residual protein present within a final API, with good agreement as compared to traditional ELISA results. Further, speciation of the residual protein within the API provides key information concerning the individual residual proteins present, including their molecular weight, which can lead to improved process development efforts for residual protein rejection and control. This analytical methodology thus offers an alternative tool for easily identifying, quantitating, and speciating residual protein content in the presence of small molecule APIs, with potential for wide applicability across industry for biocatalytic or directed enzyme evolution efforts within process development.

Simultaneous multielement imaging of liver tissue using laser ablation inductively coupled plasma mass spectrometry.

Analysis of the spatial distribution of metals, metalloids, and non-metals in biological tissues is of significant interest in the life sciences, helping to illuminate the function and roles these elements play within various biological pathways. Chemical imaging methods are commonly employed to address biological questions and reveal individual spatial distributions of analytes of interest. Elucidation of these spatial distributions can help determine key elemental and molecular information within the respective biological specimens. However, traditionally utilized imaging methods prove challenging for certain biological tissue analysis, especially with respect to applications that require high spatial resolution or depth profiling. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been shown to be effective for direct elemental analysis of solid materials with high levels of precision. In this work, chemical imaging using LA-ICP-MS has been applied as a powerful analytical methodology for the analysis of liver tissue samples. The proposed analytical methodology successfully produced both qualitative and quantitative information regarding specific elemental distributions within images of thin tissue sections with high levels of sensitivity and spatial resolution. The spatial resolution of the analytical methodology was innovatively enhanced, helping to broaden applicability of this technique to applications requiring significantly high spatial resolutions. This information can be used to further understand the role these elements play within biological systems and impacts dysregulation may have.

Machine Learning and Chemical Imaging to Elucidate Enzyme Immobilization for Biocatalysis.

Biocatalysis has rapidly become an essential tool in the scientific and industrial communities for the development of efficient, safe, and sustainable chemical syntheses. Immobilization of the biocatalyst, typically an engineered enzyme, offers significant advantages, including increased enzyme stability and control, resistance to environmental change, and enhanced reusability. Determination and optimization of the spatial and chemical distribution of immobilized enzymes are critical for proper functionality; however, analytical methods currently employed for doing so are frequently inadequate. Machine learning, in the form of multivariate curve resolution, with Raman hyperspectral imaging is presented herein as a potential method for investigating the spatial and chemical distribution of evolved pantothenate kinase immobilized onto two diverse, microporous resins. An exhaustive analysis indicates that this method can successfully resolve, both spatially and spectrally, all chemical species involved in enzyme immobilization, including the enzyme, both resins, and other key components. Quantitation of the spatial coverage of immobilized enzymes, a key parameter used for process development, was accomplished. Optimal analytical parameters were determined by the evaluation of different excitation wavelengths. Exploratory chemometric approaches, including principal component analysis, were utilized to investigate the chemical species embedded within the data sets and their relationships. The totality of this information can be utilized for an enhanced understanding of enzyme immobilization processes and can allow for the further implementation of biocatalysis within the scientific and pharmaceutical communities.

Vibrational Spectroscopy for Detection of Diabetes: A Review.

Type II diabetes mellitus (T2DM) is a metabolic disorder that is characterized by chronically elevated glucose caused by insulin resistance. Although T2DM is manageable through insulin therapy, the disorder itself is a risk factor for much more dangerous diseases including cardiovascular disease, kidney disease, retinopathy, Alzheimer's disease, and more. T2DM affects 450 million people worldwide and is attributed to causing over four million deaths each year. Current methods for detecting diabetes typically involve testing a person's glycated hemoglobin levels as well as blood sugar levels randomly or after fasting. However, these methods can be problematic due to an individual's levels differing on a day-to-day basis or being affected by diet or environment, and due to the lack of sensitivity and reliability within the tests themselves. Vibrational spectroscopic methods have been pursued as a novel method for detecting diabetes accurately and early in a minimally invasive manner. This review summarizes recent research, since 2015, which has used infrared or Raman spectroscopy for the purpose of developing a fast and accurate method for diagnosing diabetes. Based on critical evaluation of the reviewed work, vibrational spectroscopy has the potential to improve and revolutionize the way diabetes is diagnosed, thereby allowing for faster and more effective treatment of the disorder.

Determining the stages of cellular differentiation using deep ultraviolet resonance Raman spectroscopy.

Cellular differentiation is a fundamental process in which one cell type changes into one or more specialized cell types. Cellular differentiation starts at the beginning of embryonic development when a simple zygote begins to transform into a complex multicellular organism composed of various cell and tissue types. This process continues into adulthood when adult stem cells differentiate into more specialized cells for normal growth, regeneration, repair, and cellular turnover. Any abnormalities associated with this fundamental process of cellular differentiation are linked to life-threatening conditions, including degenerative diseases and cancers. Detection of undifferentiated and different stages of differentiated cells can be used for disease diagnosis but is often challenging due to the laborious procedures, expensive tools, and specialized technical skills which are required. Here, a novel approach, called deep ultraviolet resonance Raman spectroscopy, is used to study various stages of cellular differentiation using a well-known myoblast cell line as a model system. These cells proliferate in the growth medium and spontaneously differentiate in differentiation medium into myocytes and later into myotubes. The cellular and molecular characteristics of these cells mimic very well actual muscle tissue in vivo. We have found that undifferentiated myoblast cells and myoblast cells differentiated at three different stages are able to be easily separated using deep ultraviolet resonance Raman spectroscopy in combination with chemometric techniques. Our study has a great potential to study cellular differentiation during normal development as well as to detect abnormal cellular differentiation in human pathological conditions in future studies.

Towards development of a novel screening method for identifying Alzheimer's disease risk: Raman spectroscopy of blood serum and machine learning.

There is an urgent clinical need for a fast and effective method for diagnosing Alzheimer's disease (AD). The identification of AD in its most initial stages, at which point treatment could provide maximum therapeutic benefits, is not only likely to slow down disease progression but to also potentially provide a cure. However, current clinical detection is complicated and requires a combination of several methods based on significant clinical manifestations due to widespread neurodegeneration. As such, Raman spectroscopy with machine learning is investigated as a novel alternative method for detecting AD in its earliest stages. Here, blood serum obtained from rats fed either a standard diet or a high-fat diet was analyzed. The high-fat diet has been shown to initiate a pre-AD state. Partial least squares discriminant analysis combined with receiver operating characteristic curve analysis was able to separate the two rat groups with 100% accuracy at the donor level during external validation. Although further work is necessary, this research suggests there is a potential for Raman spectroscopy to be used in the future as a successful method for identifying AD early on in its progression, which is essential for effective treatment of the disease.

Raman spectroscopy and machine learning for biomedical applications: Alzheimer's disease diagnosis based on the analysis of cerebrospinal fluid.

Current Alzheimer's disease (AD) diagnostics is based on clinical assessments, imaging and neuropsychological tests that are efficient only at advanced stages of the disease. Early diagnosis of AD will provide decisive opportunities for preventive treatment and development of disease-modifying drugs. Cerebrospinal fluid (CSF) is in direct contact with the human brain, where the deadly pathological process of the disease occurs. As such, the CSF biochemical composition reflects specific changes associated with the disease and is therefore the most promising body fluid for AD diagnostic test development. Here, we describe a new method to diagnose AD based on CSF via near infrared (NIR) Raman spectroscopy in combination with machine learning analysis. Raman spectroscopy is capable of probing the entire biochemical composition of a biological fluid at once. It has great potential to detect small changes specific to AD, even at the earliest stages of pathogenesis. NIR Raman spectra were measured of CSF samples acquired from 21 patients diagnosed with AD and 16 healthy control (HC) subjects. Artificial neural networks (ANN) and support vector machine discriminant analysis (SVM-DA) statistical methods were used for differentiation purposes, with the most successful results allowing for the differentiation of AD and HC subjects with 84% sensitivity and specificity. Our classification models show high discriminative power, suggesting the method has a great potential for AD diagnostics. The reported Raman spectroscopic examination of CSF can complement current clinical tests, making early AD detection fast, accurate, and inexpensive. While this study shows promise using a small sample set, further method validation on a larger scale is required to indicate the true strength of the approach.

Analysis of individual red blood cells for Celiac disease diagnosis.

The field of medical diagnostics has endeavored to explore single species of biomolecules for sensitive and informative disease diagnostic applications. Here, Raman hyperspectroscopy is used to analyze red blood cells for identifying Celiac disease (CD). CD is a common autoimmune disorder which affects approximately 1% of the population. The ingestion of gluten by an individual with CD will result in the body initiating a violent immune response which causes severe damage to the small intestine. If the disease goes undiagnosed, substantial long-term health complications ranging in severity can arise. It is thus crucial to identify the disease as early on as possible to prevent additional problems from manifesting. However, current methods for detecting CD are expensive, invasive, and laborious. It was therefore the goal of this study to develop a better method for diagnosing CD which is noninvasive, inexpensive, accurate and definitive. Raman hyperspectroscopy was used to investigate individual red blood cells from donors with CD and from healthy controls who follow a gluten-free diet. Partial least squares discriminant analysis (PLS-DA) was used to evaluate the collected Raman spectral data for diagnostic purposes. Receiver operating characteristic (ROC) curve analysis was applied to evaluate the performance of the PLS-DA prediction algorithm, resulting in 100% successful external validation of the developed method at the donor level. Raman hyperspectroscopy in combination with chemometric analysis is shown herein to successfully evaluate red blood cells for the accurate detection of CD in a noninvasive, simple, and cost-effective manner.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning.

Many problems exist within the myriad of currently employed screening and diagnostic methods. Further, an incredibly wide variety of procedures are used to identify an even greater number of diseases which exist in the world. There is a definite unmet clinical need to improve diagnostic capabilities of these procedures, including improving test sensitivity and specificity, objectivity and definitiveness, and reducing cost and invasiveness of the test, with an interest in replacing multiple diagnostic methods with one powerful tool. There has been a recent surge in the literature which focuses on utilizing Raman spectroscopy in combination with machine learning analyses to improve diagnostic measures for identifying an assortment of diseases, including cancers, viral and bacterial infections, neurodegenerative and autoimmune disorders, and more. This review highlights the work accomplished since 2018 which focuses on using Raman spectroscopy and machine learning to address the need for better screening and medical diagnostics in all areas of disease. A critical evaluation considers both the benefits and obstacles of utilizing the method for universal diagnostics. It is clear based on the evidence provided herein Raman spectroscopy in combination with machine learning provides the first glimmer of hope for the development of an accurate, inexpensive, fast, and non-invasive method for universal medical diagnostics.

Diagnosis of a model of Duchenne muscular dystrophy in blood serum of mdx mice using Raman hyperspectroscopy.

Duchenne muscular dystrophy (DMD) is the most common and severe form of muscular dystrophy and affects boys in infancy or early childhood. Current methods for diagnosing DMD are often laborious, expensive, invasive, and typically diagnose the disease late in its progression. In an effort to improve the accuracy and ease of diagnosis, this study focused on developing a novel method for diagnosing DMD which combines Raman hyperspectroscopic analysis of blood serum with advanced statistical analysis. Partial least squares discriminant analysis was applied to the spectral dataset acquired from blood serum of a mouse model of Duchenne muscular dystrophy (mdx) and control mice. Cross-validation showed 95.2% sensitivity and 94.6% specificity for identifying diseased spectra. These results were verified via external validation, which achieved 100% successful classification accuracy at the donor level. This proof-of-concept study presents Raman hyperspectroscopic analysis of blood serum as an easy, fast, non-expensive, and minimally invasive detection method for distinguishing control and mdx model mice, with a strong potential for clinical diagnosis of DMD.

Screening for Alzheimer's Disease Using Saliva: A New Approach Based on Machine Learning and Raman Hyperspectroscopy.

BACKGROUND: Alzheimer's disease and related dementias (ADRDs) are being diagnosed at epidemic rates, with incidence to triple from 35 to 115 million cases worldwide. Most ADRDs are characterized by progressive neurodegeneration, and Alzheimer's disease (AD) is the sixth leading cause of death in the United States. The ideal moment for diagnosing ADRDs is during the earliest stages of its progression; however, current diagnostic methods are inefficient, expensive, and unsuccessful at making diagnoses during the earliest stages of the disease. OBJECTIVE: The aim of this project was to utilize Raman hyperspectroscopy in combination with machine learning to develop a novel method for the diagnosis of AD based on the analysis of saliva. METHODS: Raman hyperspectroscopy was used to analyze saliva samples collected from normative, AD, and mild cognitive impairment (MCI) individuals. Genetic Algorithm and Artificial Neural Networks machine learning techniques were applied to the spectral dataset to build a diagnostic algorithm. RESULTS: Internal cross-validation showed 99% accuracy for differentiating the three classes; blind external validation was conducted using an independent dataset to further verify the results, achieving 100% accuracy. CONCLUSION: Raman hyperspectroscopic analysis of saliva has a remarkable potential for use as a non-invasive, efficient, and accurate method for diagnosing AD.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer.

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.