Assessment of genetically modified maize DP910521 (application GMFF-2021-2473).

Genetically modified (GM) maize DP910521 was developed to confer resistance against certain lepidopteran insect pests as well as tolerance to glufosinate herbicide; these properties were achieved by introducing the mo-pat, pmi and cry1B.34 expression cassettes. The molecular characterisation data and bioinformatic analyses did not identify issues requiring food/feed safety assessment. None of the identified differences in the agronomic/phenotypic and compositional characteristics tested between maize DP910521 and its conventional counterpart needs further assessment except for the levels of iron in grain, which do not raise safety and nutritional concerns. The GMO Panel does not identify safety concerns regarding the toxicity and allergenicity of the Cry1B.34, PAT and PMI proteins as expressed in maize DP910521. The GMO panel finds no evidence that the genetic modification impacts the overall safety of maize DP910521. In the context of this application, the consumption of food and feed from maize DP910521 does not represent a nutritional concern in humans and animals. The GMO Panel concludes that maize DP910521 is as safe as its conventional counterpart and non-GM maize varieties tested, and no post-market monitoring of food/feed is considered necessary. In the case of accidental release of maize DP910521 material into the environment, this would not raise environmental safety concerns. The post-market environmental monitoring plan and reporting intervals are in line with the intended uses of maize DP910521. The GMO Panel concludes that maize DP910521 is as safe as its conventional counterpart and the tested non-GM maize varieties with respect to potential effects on human and animal health and the environment.

Association study of NAFLD with pericoronary adipose tissue and pericardial adipose tissue: Diagnosis of stable CAD patients with NAFLD based on radiomic features.

BACKGROUND AND AIM: Nonalcoholic fatty liver disease (NAFLD) is prone to complicated cardiovascular disease, and we aimed to identify patients with NAFLD who are prone to developing stable coronary artery disease (CAD). METHODS AND RESULTS: We retrospectively recruited adults who underwent coronary computed tomography angiography (CTA). A total of 127 NAFLD patients and 127 non-NAFLD patients were included in this study. Clinical features and imaging parameters were analysed, mainly including pericardial adipose tissue (PAT), pericoronary adipose tissue (PCAT), and radiomic features of 6792 PCATs. The inflammatory associations of NAFLD patients with PAT and PCAT were analysed. Clinical features (model 1), CTA parameters (model 2), the radscore (model 3), and a composite model (model 4) were constructed to identify patients with NAFLD with stable CAD. The presence of NAFLD resulted in a greater inflammatory involvement in all three coronary arteries (all P < 0.01) and was associated with increased PAT volume (r = 0.178**, P < 0.05). In the presence of NAFLD, the mean CT value of the PAT was significantly correlated with the fat attenuation index (FAI) in all three vessels and had the strongest correlation with the RCA FAI (r = 0.55, p < 0.001). A total of 9 radiomic features were screened by LASSO regression to calculate radiomic scores. In the model comparison, model 4 had the best performance of all models (AUC 0.914 [0.863-0.965]) and the highest overall diagnostic value of the model (sensitivity: 0.814, specificity: 0.941). CONCLUSIONS: NAFLD correlates with PAT volume and PCAT inflammation. Furthermore, combining clinical features, CTA parameters, and radiomic scores can improve the efficiency of early diagnosis of stable CAD in patients with NAFLD.

Improving Compliance With Evidence-Based Laboratory Testing Recommendations and Monitoring Associated Patient Outcomes.

A team comprising nursing, medical staff, and administrative leaders at an urban academic orthopedic hospital in the northeastern United States sought to revise a preoperative laboratory testing protocol based on evidence and practice guidelines. The goal was to decrease unnecessary tests by 20% without negatively affecting patient outcomes. After adding the revised protocol to the electronic health record, audits revealed that the target goal was not met and additional strategies were implemented, including educational webinars for surgeon office personnel who ordered tests, additional webinars for advanced practice professionals, and the creation of scorecards to track surgeons' progress. Overall, a downward trend in the ordering of unnecessary laboratory tests for patients without identified risks was observed, but a 20% reduction was not achieved. Surgical complications during the project were not associated with laboratory tests. Clinicians continue to use the revised preoperative laboratory testing protocol at the facility.

Rational formulation and industrial manufacturing of lipid-based complex injectables: Landmarks and trends.

Lipid-based complex injectables are renowned for their effectiveness in delivering drugs, with many approved products. While significant strides have been made in formulating nanosystems for small molecular weight drugs, a pivotal breakthrough emerged with the recognition of lipid nanoparticles as a promising platform for delivering nucleic acids. This finding has paved the way for tackling long-standing challenges in molecular and delivery aspects (e.g., mRNA stability, intracellular delivery) that have impeded the clinical translation of gene therapy, especially in the realm of immunotherapy. Nonetheless, developing and implementing new lipid-based delivery systems pose significant challenges, as industrial manufacturing of these formulations often involves complex, multi-batch processes, giving rise to issues related to scalability, stability, sterility, and regulatory compliance. To overcome these obstacles, embracing the principles of quality-by-design (QbD) is imperative. Furthermore, adopting cutting-edge manufacturing and process analytical tools (PAT) that facilitate the transition from batch to continuous production is essential. Herein, the key milestones and insights derived from the development of currently approved lipid- nanosystems will be explored. Additionally, a comprehensive and critical overview of the latest technologies and regulatory guidelines that underpin the creation of more efficient, scalable, and flexible manufacturing processes for complex lipid-based nanoformulations will be provided.

Predicting tablet properties using In-Line measurements and evolutionary equation Discovery: A high shear wet granulation study.

High shear wet granulation (HSWG) is widely used in tablet manufacturing mainly because of its advantages in improving flowability, powder handling, process run time, size distribution, and preventing segregation. In line process analytical technology measurements are essential in capturing detailed particle dynamics and presenting real-time data to uncover the complexity of the HSWG process and ultimately for process control. This study presents an opportunity to predict the properties of the granules and tablets through torque measurement of the granulation bowl and the force exerted on a novel force probe within the powder bed. Inline force measurements are found to be more sensitive than torque measurements to the granulation process. The characteristic force profiles present the overall fingerprint of the high shear wet granulation, in which the evolution of the granule formation can improve our understanding of the granulation process. This provides rich information relating to the properties of the granules, identification of the even distribution of the binder liquid, and potential granulation end point. Data were obtained from an experimental high shear mixer across a range of key process parameters using a face-centred surface response design of experiment (DoE). A closed-form analytical model was developed from the DOE matrix using the discovery of evolutionary equations. The model is able to provide a strong predictive indication of the expected tablet tensile strength based only on the data in-line. The use of a closed form mathematical equation carries notable advantages over other AI methodologies such as artificial neural networks, notably improved interpretability/interrogability, and minimal inference costs, thus allowing the model to be used for real-time decision making and process control. The capability of accurately predicting, in real time, the required compaction force required to achieve the desired tablet tensile strength from upstream data carries the potential to ensure compression machine settings rapidly reach and are maintained at optimal values, thus maximising efficiency and minimising waste.

Process analytical technology in Downstream-Processing of Drug Substances- A review.

Process Analytical Technology (PAT) has revolutionized pharmaceutical manufacturing by providing real-time monitoring and control capabilities throughout the production process. This review paper comprehensively examines the application of PAT methodologies specifically in the production of solid active pharmaceutical ingredients (APIs). Beginning with an overview of PAT principles and objectives, the paper explores the integration of advanced analytical techniques such as spectroscopy, imaging modalities and others into solid API substance production processes. Novel developments in in-line monitoring at academic level are also discussed. Emphasis is placed on the role of PAT in ensuring product quality, consistency, and compliance with regulatory requirements. Examples from existing literature illustrate the practical implementation of PAT in solid API substance production, including work-up, crystallization, filtration, and drying processes. The review addresses the quality and reliability of the measurement technologies, aspects of process implementation and handling, the integration of data treatment algorithms and current challenges. Overall, this review provides valuable insights into the transformative impact of PAT on enhancing pharmaceutical manufacturing processes for solid API substances.

Application of in-line Raman spectroscopy to monitor crystallization and melting processes in milk fat.

Anhydrous milk fat (AMF) and its fractions are used as ingredients in a wide range of food applications. Obtaining the appropriate solid fat content (SFC) is essential to achieve the desired product texture. At present, in-line monitoring techniques to control milk fat crystallization and melting are largely unavailable. The thermal behaviour of milk fat (AMF and four of its fractions) was monitored in a temperature-controlled vessel using an in-line Raman analyser and compared with thermograms generated using differential scanning calorimetry (DSC). The major stages of milk fat crystallization and melting were identified using the in-line Raman analyser. Thermal data from DSC showed excellent linear correlations with Raman spectral data (R2 value of 0.97 for the onset of milk fat crystallisation). Partial least squares regression (PLSR) models were developed using Raman spectra to predict SFC with coefficient of determination (R2Cs) from 0.929 to 0.992 and root mean standard error of calibration (RMSECs) ranging from 3.20 to 10.36%. Results demonstrated Raman spectroscopy has significant potential as a way of monitoring milk fat crystallization and melting processes.

Rapid total sialic acid monitoring during cell culture process using a machine learning model based soft sensor.

Total sialic acid content (TSA) in biotherapeutic proteins is often a critical quality attribute as it impacts the drug efficacy. Traditional wet chemical assays to quantify TSA in biotherapeutic proteins during cell culture typically takes several hours or longer due to the complexity of the assay which involves isolation of sialic acid from the protein of interest, followed by sample preparation and chromatographic based separation for analysis. Here, we developed a machine learning model-based technology to rapidly predict TSA during cell culture by using typically measured process parameters. The technology features a user interface, where the users only have to upload cell culture process parameters as input variables and TSA values are instantly displayed on a dashboard platform based on the model predictions. In this study, multiple machine learning algorithms were assessed on our dataset, with the Random Forest model being identified as the most promising model. Feature importance analysis from the Random Forest model revealed that attributes like viable cell density (VCD), glutamate, ammonium, phosphate, and basal medium type are critical for predictions. Notably, while the model demonstrated strong predictability by Day 14 of observation, challenges remain in forecasting TSA values at the edges of the calibration range. This research not only emphasizes the transformative power of machine learning and soft sensors in bioprocessing but also introduces a rapid and efficient tool for sialic acid prediction, signaling significant advancements in bioprocessing. Future endeavors may focus on data augmentation to further enhance model precision and exploration of process control capabilities.

Proof of a LOD Prediction Model with Orthogonal PAT Methods in Continuous Wet Granulation and Drying.

Real-time monitoring of critical quality attributes, such as residual water in granules after drying which can be determined through loss-on-drying (LOD), during wet granulation and drying is essential in continuous manufacturing. Near-infrared (NIR) spectroscopy has been widely used as process analytical technology (PAT) for in-line LOD monitoring. This study aims to develop and apply a model for predicting the LOD based on process parameters. Additionally, the efficacy of an orthogonal PAT approach using NIR and mass balance (MB) for a vibrating fluidized bed dryer (VFBD) is demonstrated. An in-house-built, cost-effective NIR sensor was utilized for measurements and exhibited good correlation compared to standard method via infrared drying. The combination of NIR and MB, as independent methods, has demonstrated their applicability. A good correlation, with a Pearson r above 0.99, was observed for LOD up to 16 % (w/w). The use of an orthogonal PAT method mitigated the risk of false process adaption. In some experiments where the NIR sensor might have been covered by powder and therefore did not measure accurately, LOD monitoring via MB remained feasible. The developed model effectively predicted LOD or process parameters, resulting in an R2 of 0.882 and a RMSE of 0.475 between predicted and measured LOD using the standard method.

Blend uniformity monitoring in a continuous manufacturing mixing process for a low-dosage formulation using a stream sampler and near infrared spectroscopy.

Continuous manufacturing has the potential to offer several benefits for the production of oral solid dosage forms, including reduced costs, low-scale equipment, and the application of process analytical technology (PAT) for real-time process control. This study focuses on the implementation of a stream sampler to develop a near infrared (NIR) calibration model for blend uniformity monitoring in a continuous manufacturing mixing process. Feeding and mixing characterizations were performed for three loss-in-weight feeders and a commercial continuous mixer to prepare powder blends of 2.5-7.5 % w/w ibuprofen DC 85 W with a total throughput of 33 kg/h. The NIR spectral acquisition was performed after the mixing stage using a stream sampler for flowing powders. A continuous mixer shaft speed of 250 RPM was selected to operate the mixing process based on a variability analysis developed with in-line spectral data acquired using the stream sampler at 6 RPM. A partial least squares regression (PLS-R) model was performed and evaluated, yielding a root-mean-square error of prediction (RMSEP) of 0.39 % w/w and a bias of 0.05 % w/w. An independent experimental run conducted two days later revealed that the continuous mixing process and the NIR calibration model presented low day-to-day variation. The minimum practical error (MPE) and sill values through variographic analysis showed low variance associated with the sampling process using the stream sampler. Results demonstrated the promising capacity of the stream sampler coupled to an NIR probe to be implemented within continuous manufacturing processes for the real-time determination of API concentration.

Reducing Waiting Times and Admission Periods through Pre-Admission Testing: A Quality Improvement Study on In-Hospital Renal Biopsy.

Background: Admission for renal biopsy is considered the gold standard for diagnosing kidney disease. However, prolonged waiting times for admission can lead to delayed diagnosis. Despite this issue, there are currently no studies demonstrating how to improve the efficiency of renal biopsy procedures. Methods: We initiated a quality improvement project to implement pre-admission testing (PAT) for renal biopsy from 2016 to 2024 (until 15 April). Our evaluation focused on waiting times for admission, length of admission periods, hospitalization expenses, percentage of cases with no renal biopsy performed, incidence of severe bleeding due to renal biopsy, and percentage of cases with adequate tissue samples obtained. Additionally, we highlighted the time periods during the outbreak of SARS-CoV-2. Results: The highest annual case number was observed in time period 1 (168.3/year). Following the outbreak of SARS-CoV-2, there was a notable decrease in case numbers during time period 2 (119.8), which then increased to 143.0 in time period 3 (post-SARS-CoV-2 era). The mean waiting time was 13.72 ± 40.30 days for time period 1 and 10.00 ± 47.80 days for time period 2, without statistical significance. Following the implementation of PAT, patients now only need to wait approximately 0.76 days for admission, representing a significant reduction in waiting time. Subsequently, following the implementation of PAT, the waiting time decreased significantly to 2.09 ± 2.65 days. Additionally, hospitalization expenses per patient significantly decreased from approximately USD 69.62 ± 97.09 to USD 41.66 ± 52.82. The percentage of missed biopsy is significantly low (p < 0.001). Severe bleeding events (indicated as embolization and blood transfusion) were consistent across the three time periods (p = 0.617). Conclusions: The implementation of PAT can improve the pre-admission process for renal biopsy, resulting in decreased waiting times, fewer missed appointments, shorter admission durations, and reduced hospitalization expenses. We propose implementing PAT for outpatient individuals awaiting in-hospital renal biopsy procedures to mitigate delayed diagnosis, reduce pre-admission waiting periods, and streamline admission processes, thereby enhancing overall patient care efficiency.

Mechanical Characterization of Vial Strain During Freezing and Thawing Operations Using Amorphous Excipients.

The purpose of this study was to investigate the mechanical stresses and strains acting on pharmaceutical glass tubing vials during freezing and thawing of model pharmaceutical formulations. Strain measurements were conducted inside of a laboratory-scale freeze-dryer using a custom wireless sensor. In both sucrose and trehalose formulations at concentrations between 5 % and 20 % w/v, the strain measurements initially increased before peaking in magnitude at temperatures close to the respective glass transition temperatures of the maximally freeze concentrated solutes, Tg'. We attribute this behavior to a shift in the mechanical properties of the frozen system from a purely elastic glass below Tg' to a viscoelastic rubber-like material above Tg'. That is, when the interstitial region becomes mechanically compliant at temperature above Tg'. The outputs were less predictable below 5 % w/v and tended to exhibit two separate peaks in strain output, one near the equilibrium melting temperature of pure ice and the other near Tg'. The peaks merged at concentrations between 4 and 5 % w/v where the largest strain magnitude was observed. The strain on primary packaging has traditionally been applied to evaluate the risk of damage or breakage due to, for example, crystallization of excipients. However, data collected during this study suggest there may be utility in formulation design or as a process analytical technology to minimize potentially destabilizing stresses and strains in the frozen formulation.

Bioprocess monitoring applications of an innovative ATR-FTIR spectroscopy platform.

Pharmaceutical manufacturing is reliant upon bioprocessing approaches to generate the range of therapeutic products that are available today. The high cost of production, susceptibility to process failure, and requirement to achieve consistent, high-quality product means that process monitoring is paramount during manufacturing. Process analytic technologies (PAT) are key to ensuring high quality product is produced at all stages of development. Spectroscopy-based technologies are well suited as PAT approaches as they are non-destructive and require minimum sample preparation. This study explored the use of a novel attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy platform, which utilises disposable internal reflection elements (IREs), as a method of upstream bioprocess monitoring. The platform was used to characterise organism health and to quantify cellular metabolites in growth media using quantification models to predict glucose and lactic acid levels both singularly and combined. Separation of the healthy and nutrient deficient cells within PC space was clearly apparent, indicating this technique could be used to characterise these classes. For the metabolite quantification, the binary models yielded R 2 values of 0.969 for glucose, 0.976 for lactic acid. When quantifying the metabolites in tandem using a multi-output partial least squares model, the corresponding R 2 value was 0.980. This initial study highlights the suitability of the platform for bioprocess monitoring and paves the way for future in-line developments.

Raman and NIR spectroscopy-based real-time monitoring of the membrane filtration process of a recombinant protein for the diagnosis of SARS-CoV-2.

This research shows the detailed comparison of Raman and near-infrared (NIR) spectroscopy as Process Analytical Technology tools for the real-time monitoring of a protein purification process. A comprehensive investigation of the application and model development of Raman and NIR spectroscopy was carried out for the real-time monitoring of a process-related impurity, imidazole, during the tangential flow filtration of Receptor-Binding Domain (RBD) of the SARS-CoV-2 Spike protein. The fast development of Raman and NIR spectroscopy-based calibration models was achieved using offline calibration data, resulting in low calibration and cross-validation errors. Raman model had an RMSEC of 1.53 mM, and an RMSECV of 1.78 mM, and the NIR model had an RMSEC of 1.87 mM and an RMSECV of 2.97 mM. Furthermore, Raman models had good robustness when applied in an inline measurement system, but on the contrary NIR spectroscopy was sensitive to the changes in the measurement environment. By utilizing the developed models, inline Raman and NIR spectroscopy were successfully applied for the real-time monitoring of a process-related impurity during the membrane filtration of a recombinant protein. The results enhance the importance of implementing real-time monitoring approaches for the broader field of diagnostic and therapeutic protein purification and underscore its potential to revolutionize the rapid development of biological products.

Spectroscopic insights into multi-phase protein crystallization in complex lysate using Raman spectroscopy and a particle-free bypass.

Protein crystallization as opposed to well-established chromatography processes has the benefits to reduce production costs while reaching a comparable high purity. However, monitoring crystallization processes remains a challenge as the produced crystals may interfere with analytical measurements. Especially for capturing proteins from complex feedstock containing various impurities, establishing reliable process analytical technology (PAT) to monitor protein crystallization processes can be complicated. In heterogeneous mixtures, important product characteristics can be found by multivariate analysis and chemometrics, thus contributing to the development of a thorough process understanding. In this project, an analytical set-up is established combining offline analytics, on-line ultraviolet visible light (UV/Vis) spectroscopy, and in-line Raman spectroscopy to monitor a stirred-batch crystallization process with multiple phases and species being present. As an example process, the enzyme Lactobacillus kefir alcohol dehydrogenase (LkADH) was crystallized from clarified Escherichia coli (E. coli) lysate on a 300 mL scale in five distinct experiments, with the experimental conditions changing in terms of the initial lysate solution preparation method and precipitant concentration. Since UV/Vis spectroscopy is sensitive to particles, a cross-flow filtration (cross-flow filtration)-based bypass enabled the on-line analysis of the liquid phase providing information on the lysate composition regarding the nucleic acid to protein ratio. A principal component analysis (PCA) of in situ Raman spectra supported the identification of spectra and wavenumber ranges associated with productspecific information and revealed that the experiments followed a comparable, spectral trend when crystals were present. Based on preprocessed Raman spectra, a partial least squares (PLS) regression model was optimized to monitor the target molecule concentration in real-time. The off-line sample analysis provided information on the crystal number and crystal geometry by automated image analysis as well as the concentration of LkADH and host cell proteins (HCPs) In spite of a complex lysate suspension containing scattering crystals and various impurities, it was possible to monitor the target molecule concentration in a heterogeneous, multi-phase process using spectroscopic methods. With the presented analytical set-up of off-line, particle-sensitive on-line, and in-line analyzers, a crystallization capture process can be characterized better in terms of the geometry, yield, and purity of the crystals.

Fermentation Analytical Technology (FAT): Monitoring industrial E. coli fermentations using absolute quantitative 1H NMR spectroscopy.

BACKGROUND: To perform fast, reproducible, and absolute quantitative measurements in an automated manner has become of paramount importance when monitoring industrial processes, including fermentations. Due to its numerous advantages - including its inherent quantitative nature - Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy provides an ideal tool for the time-resolved monitoring of fermentations. However, analytical conditions, including non-automated sample preparation and long relaxation times (T1) of some metabolites, can significantly lengthen the experimental time and make implementation in an industrial set up unfeasible. RESULTS: We present a high throughput method based on Standard Operating Procedures (SOPs) and 1H NMR, which lays the foundation for what we call Fermentation Analytical Technology (FAT). Our method was developed for the accurate absolute quantification of metabolites produced during Escherichia coli industrial fermentations. The method includes: (1) a stopped flow system for non-invasive sample collection followed by sample quenching, (2) automatic robot-assisted sample preparation, (3) fast 1H NMR measurements, (4) metabolites quantification using multivariate curve resolution (MCR), and (5) metabolites absolute quantitation using a novel correction factor (k) to compensate for the short recycle delay (D1) employed in the 1H NMR measurements. The quantification performance was tested using two sample types: buffer solutions of chemical standards and real fermentation samples. Five metabolites - glucose, acetate, alanine, phenylalanine and betaine - were quantified. Absolute quantitation ranged between 0.64 and 3.40 mM in pure buffer, and 0.71-7.76 mM in real samples. SIGNIFICANCE: The proposed method is generic and can be straight forward implemented to other types of fermentations, such as lactic acid, ethanol and acetic acid fermentations. It provides a high throughput automated solution for monitoring fermentation processes and for quality control through absolute quantification of key metabolites in fermentation broth. It can be easily implemented in an at-line industrial setting, facilitating the optimization of the manufacturing process towards higher yields and more efficient and sustainable use of resources.

Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning.

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology "fingerprints" extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Development of novel portable NIR spectroscopy process analytical technology (PAT) tool for monitoring the transition of ibuprofen to ibuprofen sodium during wet granulation process.

The aim of this research was to develop a process analytical technology (PAT) tool for monitoring the transformation of the active ingredient ibuprofen into the fast-dissolving salt ibuprofen sodium during the wet granulation process. Two near-infrared (NIR) spectrophotometers, portable and benchtop spectrophotometer, were compared. During the analysis with the built models, both demonstrated comparable accuracy and precision (R2X = 0.995, R2Y = 0.927, Q2 = 0.995, and R2X = 0.990, R2Y = 0.948, Q2 = 0.992, respectively). Considering the applicability, a model based on the portable NIR spectroscopic data was chosen for further development and application as a PAT tool for monitoring different steps during the wet granulation process. The evaluation of the model's predictive capability involved analyzing laboratory trial batches with varying amounts of sodium carbonate, resulting in different concentrations of ibuprofen sodium at the end of the wet granulation process. Subsequently, tablets were manufactured from each trial batch, followed by dissolution analysis. The dissolution rate assays were in good agreement with the NIR-predicted concentrations of ibuprofen sodium at the end of the wet granulation process. Based on the results, the proposed model provides an excellent tool to monitor the ibuprofen acid-salt transformation, to determine the end-point of the reaction, and to efficiently control the wet granulation process.

An Indocyanine Green-Based Nanoprobe for In Vivo Detection of Cellular Senescence.

There is an urgent need to improve conventional cancer-treatments by preventing detrimental side effects, cancer recurrence and metastases. Recent studies have shown that presence of senescent cells in tissues treated with chemo- or radiotherapy can be used to predict the effectiveness of cancer treatment. However, although the accumulation of senescent cells is one of the hallmarks of cancer, surprisingly little progress has been made in development of strategies for their detection in vivo. To address a lack of detection tools, we developed a biocompatible, injectable organic nanoprobe (NanoJagg), which is selectively taken up by senescent cells and accumulates in the lysosomes. The NanoJagg probe is obtained by self-assembly of indocyanine green (ICG) dimers using a scalable manufacturing process and characterized by a unique spectral signature suitable for both photoacoustic tomography (PAT) and fluorescence imaging. In vitro, ex vivo and in vivo studies all indicate that NanoJaggs are a clinically translatable probe for detection of senescence and their PAT signal makes them suitable for longitudinal monitoring of the senescence burden in solid tumors after chemotherapy or radiotherapy.

Development of iterative optimization technology: Selecting pure component spectra using a small-scale feed frame simulator.

The application of spectroscopic process analytical technology (PAT) for in-line data collection offers advantages to modern pharmaceutical manufacturing. Partial least squares (PLS) models are the preferred approach for predicting API potency from PAT data, particularly near-infrared (NIR) spectra. However, the calibration burden of PLS models is sometimes considered prohibitive. Pure component approaches, such as iterative optimization technology (IOT), have a reduced calibration burden for PAT applications. The IOT algorithm is dependent on several assumptions, including the harmonization of spectral collection conditions for pure component and mixture spectra. Collecting pure components under identical conditions to mixture spectra does not guarantee accurate predictions, and not all pure components are suitable for individual processing. This IOT assumption must be addressed to facilitate IOT application in PAT systems. In this work, IOT predicted API potency from in-line NIR spectra using combinations of stagnant and dynamic pure component spectra. A small number of mixture samples called a development set guided the selection of representative pure component spectral sets. Several model performance metrics from the development set predictions identified optimal pure component spectral sets for prediction of test sets. The combination of IOT and a development set generated accurate API potency predictions and potentiates the application of IOT in challenging pharmaceutical manufacturing settings. The IOT assumption of similar collection conditions should not be regarded as an assumption, but rather a consideration that the pure component spectral collection conditions should be representative of the mixture spectra to ensure appropriate predictions.