Analysis Technology Research Articles

Implemention of Innovative Process Analytical Technologies to Characterize Critical Quality Attributes of Co‐Formulated Monoclonal Antibody Products

ABSTRACTCharacterizing co‐formulated monoclonal antibodies (mAbs) poses significant challenges in the pharmaceutical industry. Due to the high structural similarity of the mAbs, traditional analytical methods, compounded by the lengthy method development process, hinder product development and manufacturing efficiency. There is increasing critical need in the pharmaceutical industry to streamline analytical approaches, minimizing time and resources, ensuring a rapid clinical entry and cost‐effective manufacturing. This study investigates the application of process analytical technologies (PAT) to address such challenges. Our investigation introduces two complementary technologies, on‐line ultra‐performance liquid chromatography (online UPLC) and multimode fluorescence spectroscopy (MMFS), as potential PAT tools tailored for characterizing critical quality attributes (CQA) in co‐formulated mAb products. Specifically, the CQAs under evaluation include the total protein concentration of the mAbs within the co‐formulation and the ratio of mAb A to mAb B. Online UPLC enables direct and automated measurement of the CQAs through physical separation, while MMFS determines them in a non‐destructive and more swift manner based on chemometric modeling. We demonstrate these technologies' comparable performance to conventional methods, alongside substantial benefits such as reduced analytical turnaround time and decreased laboratory efforts. Ultimately, integrating them as innovative PAT tools expedites the delivery of therapeutic solutions to patients and enhances manufacturing efficiency, aligning with the imperative for swift translation of scientific discoveries into clinical benefits.

Light-emitting diode-based absorbance detectors for flow-through analysis in analytical chemistry: A tutorial

The development of light-emitting diodes (LEDs) has played a significant role supporting many recent advances made in chemical analysis. Their small size, low power consumption, and semi-monochromaticity make them ideal light sources for portable photometric devices used in field and on-site analysis, environmental monitoring, and industrial applications, such as process analytical technology (PAT) and continuous real-time analysis. This tutorial provides an overview and critical comparison of the key components in LED-based absorbance detectors, including light sources, optical fibres, flow cells, and photodetectors. Fundamental aspects of LEDs relating to their use in such simple detectors are discussed in detail, along with the development of multi-wavelength detectors that incorporate multiple LEDs. The tutorial also details the different methodologies for assessing and characterising the LED-based absorbance detectors. Finally, this tutorial presents some selected applications of LED based photometric detectors in gas sensing, flow injection analysis (FIA), and coupled with separation techniques such as ion chromatography, liquid chromatography, and capillary zone electrophoresis (CZE).

NIR-Guided Coating Optimization of Omega-3 Fatty Acid Mini Soft Capsules with Pitavastatin and Ezetimibe

Background: This study aimed to optimize the coating process of Omega-3 fatty acid (OM3-FA) mini soft capsules containing the active pharmaceutical ingredients (APIs) pitavastatin and ezetimibe using near-infrared (NIR) spectroscopy for in-process monitoring. Cardiovascular disease treatments benefit from combining OM3-FA with lipid-lowering agents, but formulating such combinations in mini soft capsules presents challenges in maintaining stability and mechanical integrity. Methods: The coating process was developed using a pan coater and real-time NIR monitoring to ensure uniformity and quality. NIR spectroscopy enabled precise control of coating thickness, ensuring consistent drug distribution across the capsule surface. Results: The optimized process minimized OM3-FA oxidation and preserved the mechanical integrity of the capsules, as confirmed by texture analysis and in-vitro dissolution testing. This integration of NIR spectroscopy as a process analytical technology (PAT) significantly improved coating quality control, resulting in a stable and effective combination therapy for pitavastatin and ezetimibe in a mini soft capsule form. Conclusion: This approach offers an efficient solution for enhancing patient adherence in cardiovascular disease management. The application of NIR spectroscopy for real-time monitoring highlights its broader significance in pharmaceutical manufacturing, where it can serve as a versatile tool for ensuring product quality and optimizing production efficiency in diverse formulation processes. By incorporating NIR-based PAT, manufacturers can not only achieve product-specific improvements but also establish a foundation for continuous manufacturing and automated quality assurance systems, ultimately contributing to a more streamlined and robust production environment.

Soft-sensor model development for CHO growth/production, intracellular metabolite, and glycan predictions.

Efficaciously assessing product quality remains time- and resource-intensive. Online Process Analytical Technologies (PATs), encompassing real-time monitoring tools and soft-sensor models, are indispensable for understanding process effects and real-time product quality. This research study evaluated three modeling approaches for predicting CHO cell growth and production, metabolites (extracellular, nucleotide sugar donors (NSD) and glycan profiles): Mechanistic based on first principle Michaelis-Menten kinetics (MMK), data-driven orthogonal partial least square (OPLS) and neural network machine learning (NN). Our experimental design involved galactose-fed batch cultures. MMK excelled in predicting growth and production, demonstrating its reliability in these aspects and reducing the data burden by requiring fewer inputs. However, it was less precise in simulating glycan profiles and intracellular metabolite trends. In contrast, NN and OPLS performed better for predicting precise glycan compositions but displayed shortcomings in accurately predicting growth and production. We utilized time in the training set to address NN and OPLS extrapolation challenges. OPLS and NN models demanded more extensive inputs with similar intracellular metabolite trend prediction. However, there was a significant reduction in time required to develop these two models. The guidance presented here can provide valuable insight into rapid development and application of soft-sensor models with PATs for ipurposes. Therefore, we examined three model typesmproving real-time product CHO therapeutic product quality. Coupled with emerging -omics technologies, NN and OPLS will benefit from massive data availability, and we foresee more robust prediction models that can be advantageous to kinetic or partial-kinetic (hybrid) models.

How Two‐Dimensional Heterocovariance Spectroscopy and Data Fusion Level out the Inadequacies of Compact Spectrometers

ABSTRACTOver the past decade, sensors and compact spectrometers have emerged as a powerful means for real‐time monitoring of chemical and biochemical processes in the field of process analytical technologies (PATs). This is largely attributed to cost‐efficiency and their robustness in near‐line applications. In comparison to more sophisticated laboratory instruments, these devices exhibit limited sensitivity and resolution. In this study, a combination of near‐infrared, low‐field 1H nuclear magnetic resonance and compact Raman spectrometers were employed for the process monitoring of the acid‐catalyzed esterification of isoamyl alcohol and acetic acid to isoamyl acetate. The resulting real‐time data were transformed and visualized for interpretation using two‐dimensional heterocovariance spectroscopy. The data were also subjected to pretreatment, concatenation, and multivariate analysis in accordance with low‐ and mid‐level data fusion. The spectral interpretation derived from heterocovariance spectroscopy provided support for the data pretreatment during data fusion. By applying these computational techniques, the inherent limitations of sensitivity and resolution associated with compact spectroscopic instruments could be overcome, thereby facilitating the interpretation of data and yielding further insights into the process under study. A comparison of the process parameters resulting from the applied methods indicated that consistent data could be obtained. This study demonstrates that heterocovariance spectroscopy and data fusion allow to enhance compact, less expensive analytical instruments for process monitoring and to acquire process knowledge. This, in turn, enables material, financial, and energy resources to be conserved.

Multi-omics analysis revealed significant metabolic changes in brain cancer cells treated with paclitaxel and/or topotecan

Glioblastoma (GBM) stands as the most common primary malignant brain tumor. Despite the best standard therapies, GBM survivors have a brief survival time, about 24 months on average. The treatment is troublesome because the cancer cells may not respond well to specific therapies as they grow within an extensive network of blood vessels. Our study aims to evaluate the impact of paclitaxel 5.3 μg/mL and topotecan 0.26 μM solely and in pairwise combination on the resultant metabolic and proteomic signatures of the U87 cell line while using the precise ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF) analytical technology. The U87 cells wear treated with DMSO, paclitaxel 5.3 μM, topotecan 0.26 μM, and their combinations. Using One-way ANOVA, we observed 14 significantly altered metabolites compared to those cells treated with DMSO. For combination treatment (paclitaxel and topotecan), 11 metabolites were significantly dysregulated. Sparse partial least squares-discriminant analysis (sPLS-DA) revealed minimal overlap, highlighting distinctions among the four groups. While for proteomics, a total of 79 proteins were significantly dysregulated among the groups. These findings can aid in identifying new biomarkers associated with the utilized drugs and creating a map for targeted therapy. EIF3F, GNB2L1, HINT2, and RPA3 were shown to be significantly upregulated in the combination group relative to the control. Moreover, ribosome, apoptosis, HIF-1 signaling, arginine and proline, glutathione, purine metabolism, apelin signaling pathway, and glycolysis were significantly altered in the combination group. Overall, this study underscores the effectiveness of multi-omics approaches in revealing the molecular mechanisms driving chemotherapy responses in cancer cells. Additionally, this work generates a comprehensive list of molecular alterations that can serve as a foundation for further investigations and inform personalized healthcare strategies to enhance patient outcomes.

Multi-scalar data integration decoding risk genes for chronic kidney disease

BackgroundChronic Kidney Disease (CKD) impacts over 10% of the global population, and recent advancements in high-throughput analytical technologies are uncovering the complex physiology underlying this condition. By integrating Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq/RNA array), and single-cell RNA sequencing (scRNA-seq) data, our study aimed to explore the genes and cell types relevant to CKD traits.MethodsGWAS summary data for end-stage renal failure (ESRD) and decreased eGFR (CKD) with or without diabetes and (micro)proteinuria were obtained from the GWAS Catalog and the UK Biobank (UKB) database. Two gene Expression Omnibus (GEO) transcriptome datasets were used to establish glomerular and tubular gene expression differences between CKD patients and healthy individuals. Two scRNA-seq datasets were utilized to obtain the expression of key genes at the single-cell level. The expression profile, differentially expressed genes (DEGs), gene-gene interaction, and pathway enrichment were analysed for these CKD risk genes.ResultsA total of 779 distinct SNPs were identified from GWAS across different CKD traits, involving 681 genes. While many of these risk genes are specific to the CKD traits of renal failure, decreased eGFR, and (micro)proteinuria, they share common pathways, including extracellular matrix (ECM). ECM modeling was enriched in upregulated glomerular and tubular DEGs from CKD kidneys compared to healthy controls, with the expression of relevant collagen genes, such as COL1A2, prevalent in fibroblasts/myofibroblasts. Additionally, immune responses, including T cell differentiation, were dysregulated in CKD kidneys. The late podocyte signature gene THSD7A was enriched in podocytes but downregulated in CKD. We also highlighted that the regulated risk genes of CKD are mainly expressed in tubular cells and immune cells in the kidney.ConclusionsOur integrated analysis highlight the genes, pathways, and relevant cell types associational with the pathogenesis of kidney traits, as a basis for further mechanistic studies to understand the pathogenesis of CKD.

Raman spectroscopy for monitoring free sulfhydryl formation during monoclonal antibody manufacturing

During production, harvested cell culture fluid (HCCF) can degrade due to reductases breaking interchain disulfide bonds, forming low molecular weight (LMW) impurities that contain free sulfhydryl and high molecular weight (HMW) impurities through disulfide shuffling. Thus, detecting and quantifying the free sulfhydryl increase in HCCF is critical. Herein, Raman spectroscopy is implemented as a process analytical technology, and multivariate data analysis is applied to characterize and quantify sulfhydryl formation in HCCF with disulfide-containing indicator molecules. Raman spectra qualitatively probe the presence or absence of disulfide bond breakage in antibodies, consistent with offline non-reduced capillary electrophoresis sodium dodecyl sulfate results. Between two antibodies studied, mAb A was identified for a higher risk of antibody reduction where sulfhydryl formation was observed within 16 h, while mAb B did not show similar concerns even after 1 week. The offline measurement of redox potential is below –100 mV in HCCF for mAb A, while the stable mAb B HCCF shows redox potentials above +20 mV. A multivariate partial least squares (PLS) model for quantification is developed using an offline free sulfhydryl assay, applying Raman spectra to predict free sulfhydryl concentration with high accuracy (R2 > 0.98) and expected mean error of 0.677 mM from the offline Ellman’s Assay. This work confirms the use of Raman PAT to monitor real-time disulfide reduction, enabling improvements to process understanding and product quality.

Exploring omics signature in the cardiovascular response to semaglutide: Mechanistic insights and clinical implications.

Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, is a widely used drug for the treatment of type 2 diabetes that offers significant cardiovascular benefits. This review systematically examines the proteomic and metabolomic indicators associated with the cardiovascular effects of semaglutide. A comprehensive literature search was conducted to identify relevant studies. The review utilizes advanced analytical technologies such as mass spectrometry and nuclear magnetic resonance (NMR) to investigate the molecular mechanisms underlying the effects of semaglutide on insulin secretion, weight control, anti-inflammatory activities and lipid metabolism. These "omics" approaches offer critical insights into metabolic changes associated with cardiovascular health. However, challenges remain such as individual variability in expression, the need for comprehensive validation and the integration of these data with clinical parameters. These issues need to be addressed through further research to refine these indicators and increase their clinical utility. Future integration of proteomic and metabolomic data with artificial intelligence (AI) promises to improve prediction and monitoring of cardiovascular outcomes and may enable more accurate and effective management of cardiovascular health in patients with type 2 diabetes. This review highlights the transformative potential of integrating proteomics, metabolomics and AI to advance cardiovascular medicine and improve patient outcomes.

Combinatorial Order Pre-processing Search (COPS): A new pre-processing strategy for large-scale interpretable data analysis in process analytical technologies

Combinatorial Order Pre-processing Search (COPS), a novel approach for optimizing data pre-processing is proposed in this work. Unlike simultaneous hyperparameter optimization, COPS employs a priori optimization to reduce computational time while refining the search space for preprocessing sequences and combinations. It allows for setting a maximum number of pre-processing methods, while efficiently searching through combinations of methods with chemically relevant knowledge. In this work, 67 calibration datasets across various analytical techniques, including fluorescence spectroscopy, gas chromatography (GC), near-infrared spectroscopy (NIR), mid-infrared spectroscopy (MIR), visible-near-infrared spectroscopy (Vis-NIR), Raman spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and voltammetry were evaluated. COPS yielded significant improvements over existing methodologies based on design of experiment and compounded pre-processing approaches. The COPS outperformed the other methods, resulting in an average root mean square error of prediction (RMSEP) reduction of 31.7%, while also reduced the complexity (number of latent variables) of the model which allows for easier interpretation. This underscores the importance of combinatorial order set theory for the search of pre-processing method combinations (without fixing the sequence of pre-processing methods) to enhance model performance and interpretation. The novel COPS approach can be employed in process analytical technology (such as inline, online or at-line chemical sensing analytics) to enhance predictive accuracy and operational efficiency, fundamentally transforming the quality and reliability of chemical process monitoring and control.

Inline Raman spectroscopy as process analytical technology for SARS-CoV-2 VLP production.

The present work focused on inline Raman spectroscopy monitoring of SARS-CoV-2 VLP production using two culture media by fitting chemometric models for biochemical parameters (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, ammonium, and viral titer). For that purpose, linear, partial least square (PLS), and nonlinear approaches, artificial neural network (ANN), were used as correlation techniques to build the models for each variable. ANN approach resulted in better fitting for most parameters, except for viable cell density and glucose, whose PLS presented more suitable models. Both were statistically similar for ammonium. The mean absolute error of the best models, within the quantified value range for viable cell density (375,000-1,287,500 cell/mL), cell viability (29.76-100.00%), glucose (8.700-10.500g/), lactate (0.019-0.400g/L), glutamine (0.925-1.520g/L), glutamate (0.552-1.610g/L), viral titer (no virus quantified-7.505 log10 PFU/mL) and ammonium (0.0074-0.0478g/L) were, respectively, 41,533 ± 45,273cell/mL (PLS), 1.63 ± 1.54% (ANN), 0.058 ± 0.065g/L (PLS), 0.007 ± 0.007g/L (ANN), 0.007 ± 0.006g/L (ANN), 0.006 ± 0.006g/L (ANN), 0.211 ± 0.221 log10 PFU/mL (ANN), and 0.0026 ± 0.0026g/L (PLS) or 0.0027 ± 0.0034g/L (ANN). The correlation accuracy, errors, and best models obtained are in accord with studies, both online and offline approaches while using the same insect cell/baculovirus expression system or different cell host. Besides, the biochemical tracking throughout bioreactor runs using the models showed suitable profiles, even using two different culture media.

Ergot alkaloid control in biotechnological processes and pharmaceuticals (a mini review).

The control of ergot alkaloids in biotechnological processes is important in the context of obtaining new strain producers and studying the mechanisms of the biosynthesis, accumulation and secretion of alkaloids and the manufacturing of alkaloids. In pharmaceuticals, it is important to analyze the purity of raw materials, especially those capable of racemization, quality control of dosage forms and bulk drugs, stability during storage, etc. This review describes the methods used for qualitative and quantitative chemical analysis of ergot alkaloids in tablets and pharmaceutic forms, liquid cultural media and mycelia from submerged cultures of ergot and other organisms producing ergoalkaloid, sclerotias of industrial Claviceps spp. parasitic strains. We reviewed analytical approaches for the determination of ergopeptines (including their dihydro- and bromine derivatives) and semisynthetic ergot-derived medicines such as cabergoline, necergoline and pergolide, including precursors for their synthesis. Over the last few decades, strategies and approaches for the analysis of ergoalkaloids for medical use have changed, but the general principles and objectives have remained the same as before. These changes are related to the development of new genetically improved strains producing ergoalkaloids and the development of technologies for the online control of biotechnological processes and pharmaceutical manufacturing ("process analytical technologies," PAT). Overall, the industry is moving toward "smart manufacturing." The development of approaches to production cost estimation and product quality management, manufacturing management, increasing profitability and reducing the negative impact on personnel and the environment are integral components of sustainable development. Analytical approaches for the analysis of ergot alkaloids in pharmaceutical raw materials should have high enough specificity for the separation of dihydro derivatives, enantiomers and R-S epimers of alkaloids, but low values of the quantitative detection limit are less frequently needed. In terms of methodology, detection methods based on mass spectrometry have become more developed and widespread, but NMR analysis remains in demand because of its high accuracy and specificity. Both rapid methods and liquid chromatography remain in demand in routine practice, with rapid analysis evolving toward higher accuracy owing to improved analytical performance and new equipment. New composite electrochemical sensors (including disposable sensors) have demonstrated potential for real-time process control.

Influence of Polymer Film Thickness on Drug Release from Fluidized Bed Coated Pellets and Intended Process and Product Control.

Coated drug pellets enjoy widespread use in hard gelatine capsules. In heterogeneous pellets, the drug substance is layered onto core pellets. Coatings are often applied to generate a retarded release or an enteric coating. In the present study, the thickness of a polymer coating layer on drug pellets was correlated to the drug release kinetics. The question should be answered whether it is possible to stop the coating process when a layer thickness referring to an intended drug release is achieved. Inert pellets were first coated with sodium benzoate and second with different amounts of water insoluble polyacrylate in a fluidized bed apparatus equipped with a Wurster inlet. The whole process was controlled in-line and at-line with process analytical technology by the measurement of the particle size and the layer thickness. The in-vitro sodium benzoate release was investigated, and the data were linearized by different standard models and compared with the polyacrylate layer thickness. With increasing polyacrylate layer thickness the release rate diminishes. The superposition of several processes influencing the release results in release profiles corresponding approximately to first order kinetics. The coating layer thickness corresponds to a determined drug release profile. The manufacturing of coated drug pellets with intended drug release is possible by coating process control and layer thickness measurement. Preliminary investigations are necessary for different formulations.

Recommendations for Accurate Lipid Annotation and Semi-absolute Quantification from LC-MS/MS Datasets.

Recent developments in LC-MS instrumentation and analytical technologies together with bioinformatics tools supporting high-throughput processing of large omics datasets significantly enhanced our capabilities and efficiency of identification and quantification of lipids in diverse biological materials. However, each biological matrix is characterized by its unique lipid composition, thus requiring optimization of analytical and bioinformatics workflows for each studied lipidome. Here, we describe an integrated workflow for deep lipidome profiling, accurate annotation, and semi-absolute quantification of complex lipidomes based on reversed phase chromatography and high resolution mass spectrometry. This chapter provides details on selection of the optimal extraction protocol, acquisition of LC-MS/MS data for accurate annotation of lipid molecular species, and design of lipidome-specific mixtures of internal standards to assist quantitative analysis of complex, native lipidomes.

(Pre)Clinical Metabolomics Analysis.

Metabolomics is the scientific field with the eager goal to comprehensively analyze the entirety of all small molecules of a biological system, i.e., the metabolome. Over the last few years, metabolomics has matured to become an analytical cornerstone of life science research across diverse fields, from fundamental biochemical applications to preclinical studies, including biomarker discovery and drug development. In this chapter, we provide an introduction to (pre)clinical metabolomics. We define key metabolomics aspects and provide the basis to thoroughly understand the relevance of this field in a biological and clinical context. We present and explain state-of-the-art analytical technologies devoted to metabolomic analysis as well as emerging technologies, discussing both strengths and weaknesses. Given the ever-increasing demand for handling complex datasets, the role of bioinformatics approaches in the context of metabolomic analysis is also illustrated.

Monitoring a batch crystallization process by acoustic emission

Crystallization is regarded as an important unit operation for separation and purification, as well as the production of solid particles with specified end-use properties. However, it is still difficult to control or to optimize the crystallization process due to the complexity of the coupled phenomena taking place simultaneously in the liquid and solid phases. In order to overcome such drawback, different analytical technologies have been implemented in the literature for monitoring (in situ, at best) the key crystallization parameters, e.g. solute concentration and crystal size distribution, which in turn enhances the comprehension about this operation. In our work, a multi-probe monitoring system composed of acoustic emission, ATR-FTIR and imaging probes was applied to the crystallization of a model system (an aqueous solution of adipic acid) in a semi-batch crystallizer. The crystallization was carried out under two different feed rates and under vacuum or atmospheric pressure. The goal was to demonstrate the usefulness of the multi-probe system for monitoring the crystallization process and to study the influence of the process (stirring rotation speed, cooling speed, etc.) on the acoustic emission. As a matter of fact, the multi-probe system tracked information about the dissolved adipic acid in the liquid phase with the ATR-FTIR spectrometry, while the crystal size, shape and number were monitored with the help of the imaging probe (with image treatment methods). The acoustic emission probes were used to detect the beginning of crystallization and the phenomena of crystal growth and agglomeration

Determination of multiple restricted and prohibited ingredients in hair dyes using a novel 3D stationary phase coupled with gas chromatography-mass spectrometry

Hair dyes (HDs) are mainly composed of various benzene series, amines, and phenolic compounds. These ingredients are well known to have allergenic, teratogenic, and carcinogenic properties. As such, the presence of these ingredients in HDs has received increased attention in recent years. At present, the applications of traditional analytical and detection methods and commercial chromatographic columns are limited by problems such as poor qualitative analysis and inaccurate quantification. Thus, the development of new analytical and detection technologies and stationary phases is an urgent endeavor. Moreover, HDs contain complex compounds and exhibit significant matrix interference. Hence, appropriate sample pretreatment methods are necessary to analyze HDs. In this study, the 3D nonpolar rigid structure of triptycene (TP) was combined with the polar flexible chains of polyethylene glycol (PEG) to design and synthesize a TP derivative, TP-PEG, as a stationary phase for chromatographic columns. The stationary phase enabled the expansion of the selection range for polar and nonpolar analytes. Subsequently, gas chromatography-mass spectrometry (GC-MS) was used to quantitatively analyze 22 ingredients in HDs. The experimental results demonstrated that analytes with different polarities exhibited sharp and symmetrical peak shapes on the stationary phase, and all 22 analytes achieved baseline separation on the chromatographic column. The 22 ingredients in HDs showed good linear relationships within their respective ranges, with correlation coefficients greater than 0.9985. The average recovery rates at three spiked levels were in the range of 89.2%-103.2%, and RSDs were less than 5%. Compared with traditional methods, the proposed method has higher efficiency and better accuracy, thus verifying the excellent separation performance of the new stationary phase and the effectiveness of the established GC-MS detection method. The findings indicated the applicability of the developed method to the detection and analysis of various compounds in HDs.

MLAPI: A framework for developing machine learning-guided drug particle syntheses in automated continuous flow platforms

Recently, machine learning (ML) models are increasingly being used in process analytical technology (PAT) frameworks for pharmaceutical manufacturing. Yet, the applications of ML-integrated PAT frameworks are limited by big data requirements. This work introduces a computational framework to develop data-efficient ML models to guide drug particle synthesis in an automated continuous flow precipitation platform. The framework incorporates classification algorithms to identify feasible (fouling-free) operating regions of the precipitation platform, a multiple-output Gaussian process (GP) regression model to relate key process parameters to the drug particle size, and active learning to optimally generate new data for training and validation of the GP model. The usefulness of the proposed framework is demonstrated on the synthesis of ibuprofen microparticles in an automated flow precipitation platform. We envision that properly trained GP models developed using the proposed framework can be employed to fine tune the drug particle size, targeting desired particle bioavailability and processability.

Process analytical technology based quality assurance of API concentration and fiber diameter of electrospun amorphous solid dispersions

In this study, a novel quality assurance system was developed utilizing Process analytical technology (PAT) tools and artificial intelligence (AI). Our goal was to monitor the critical quality attributes (CQAs) like drug concentration, morphology and fiber diameter of electrospun amorphous solid dispersion (ASD) formulations with fast at-line techniques. Doxycycline-hyclate (DOX), a tetracycline-type antibiotic was used as a model drug with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) as the matrix excipient. The water-based formulations were electrospun with high-speed electrospinning (HSES). Raman and NIR sensors and machine vision-based color measurement techniques were employed to accurately determine the drug concentration. Given that morphology can influence the solubility of the drug, a convolutional neural network (CNN)-based AI model was developed to examine this property and detect manufacturing defects. Additionally, the diameter of electrospun fibrous samples was measured using camera images and a trained AI model, enabling rapid analysis of fiber diameter with results similar to that of scanning electron microscopy (SEM). These methods and models demonstrate potential in-line analytical tools, offering rapid, cheap and non-destructive analysis of ASD formulations.

Peptide Biomarkers - An Emerging Diagnostic Tool and Current Applicable Assay.

In the past few decades, impressive progress achieved in technology development and improvementhasaccelerated the application of peptides as diagnostic biomarkers for various diseases. We outline the advantages of peptides as good diagnostic targets, since they serve as molecular surrogates of enzyme activities, much more specific biomarkers than proteins, and also play vital roles in many biological processes. On the basis of an extensive literature survey, peptide markers with high specificity and sensitivity that are currently applied in clinical tests, as well as recently identified, are summarized for the following four major categories of diseases: neurodegenerative disease, heart failure, infectious disease, and cancer. In addition, we summarize a few prevalent techniques used in peptide biomarker discovery and analysis, such as immunoassays, nanopore-based and nanoparticle-based peptide detection, and also MS-based peptide analysis techniques, and their pros and cons. Currently, there are plenty of analytical technologies available to achieve fast, sensitive and reliable peptide analyses, benefiting from the developments of hardware and instrumentation, as well as data analysis software and databases. Thus, with peptides emerging as sensitive, specific and reliable biomarkers for early detection of diseases, therapeutic monitoring, clinical treatment decisions and disease prognosis, the medical need for peptide biomarkers will increase strongly in the future.