Evaluating the Improvement of Blend Potency Measurements in the Feed Frame of a Rotary Tablet Press Using Combined NIR and Raman Spectroscopy.

This study investigated the added value of combining both near-infrared (NIR) and Raman spectroscopy into a single NIRaman Combi Fiber Probe for in-line blend potency determination in the feed frame of a rotary tablet press. A five-component platform formulation was used, containing acetylsalicylic acid as the Active Pharmaceutical Ingredient (API). Calibration models for the determination of 1 and 5%w/w label claim tablets were developed using NIR and Raman spectra of powder blends ranging from 0.75 to 1.25%w/w and 3.75 to 6.25%w/w API, respectively. Step-change experiments with deliberate 10% deviation steps from the label claims were performed, from which the collected spectra were used for model validation. For model development and validation, low-level data fusion was explored through concatenation of preprocessed NIR and Raman spectra. Mid-level data fusion was also evaluated, based on extracted features of the preprocessed data. Herewith, score vectors were extracted by transforming preprocessed spectra through Principal Component Analysis, followed by critical feature selection through Elastic Net Regression. Partial Least Squares regression was applied to regress singular, low-level or mid-level fused data versus blend potency. It could be concluded that irrespective of the data fusion technique, an increase in Step-Change Sensitivity (SCS) and decrease in Root Mean Squared Error (RMSE) was observed when predicting the 5%w/w step-change experiment. For the prediction of the 1%w/w step-change experiment, no added benefit with regard to SCS and RMSE was observed due to the addition of the noisy NIR spectra.

Relevance of controlled cooling and freezing phases in T-cell cryopreservation.

When cells are cryopreserved, they go through a freezing process with several distinct phases (i.e., cooling until nucleation, ice nucleation, ice crystal growth and cooling to a final temperature). Conventional cell freezing approaches often employ a single cooling rate to describe and optimize the entire freezing process, which neglects its complexity and does not provide insight into the effects of the different freezing phases. The aim of this work was to elucidate the impact of each freezing phase by varying different process parameters per phase. Hereto, spin freezing was used to freeze Jurkat T cells in either a Me2SO-based or Me2SO-free formulation. The cooling rates before ice nucleation and after total ice crystallization impacted cell viability, resulting in viability ranging from 26.7% to 52.8% for the Me2SO-free formulation, and 22.5%-42.6% for the Me2SO-based formulation. Interestingly, the degree of supercooling upon nucleation did not exhibit a significant effect on cell viability in this work. However, the rate of ice crystal formation emerged as a crucial factor, with viability ranging from 2.4% to 53.2% for the Me2SO-free formulation, and 0.3%-53.2% for the Me2SO-based formulation, depending on the freezing rate. A morphological study of the cells post-cryopreservation was performed using confocal microscopy, and it was found that cytoskeleton integrity and cell volume were impacted, depending on the formulation-process parameter combination. These findings underscore the importance of scrutinizing all cooling and freezing phases, as each phase impacted post-thaw viability in a distinct way, depending of the specific formulation used.

A Colourful Way to Unravel the Important Fluidization-Related Granule Size Effect on Semi-Continuous Drying.

Continuous twin screw wet granulation (TSWG) systems are possible pathways for oral solid dosage manufacturing in the pharmaceutical industry. TSWG requires a drying step after granulation before the tableting process. Typically, semi-continuous fluidized bed dryers (FBDs) are used for this purpose. At the same time, the pharmaceutical sector is interested in mathematical prediction models to save resources during the early drug product development (DPD) stage or to control manufacturing. Several authors have already developed prediction models for semi-continuous drying processes. However, these model structures reported systematic prediction offsets, which could be related to the incomplete implementation of fluidization and granule segregation phenomena. This study evaluates the complex fluidization behavior of wet granules in industrially relevant semi-continuous FBDs. A transparent perspex version of the dryer was used for the analysis of bed height, pressure drop, porosity, segregation, and spatial heating patterns at varying process settings. The investigated behaviors of the fluidizing bed will be helpful to derive phenomenological (sub)models for the detailed description of segregation in the semi-continuous fluidized bed system. In this study, it was found that semi-continuous FBDs are characterized by a change in fluidization regime from plug flow to a bubbling bed at the moment that the granule bed slumps. Secondly, the presence of size-based vertical segregation phenomena as well as spatial temperature differences were proven. The experimental results suggest that larger granules are dried under more intense drying conditions than smaller granules.

Optimizing the secondary drying phase of a continuous Spin-Freeze Drying process: A semi-mechanistic modelling approach.

Over the past decade, continuous spin freeze-drying technology has emerged as a promising alternative to conventional batch freeze-drying, effectively addressing many of the latter's inherent disadvantages. Much of the focus during this period has been on controlling and optimizing the primary drying phase of this process. However, optimizing the secondary drying step is equally critical for the overall efficiency of the process. The primary aim of this study was to develop a comprehensive semi-mechanistic model for the secondary drying phase in continuous spin freeze-drying, accounting for the effects of process settings such as freezing rate and product temperature on desorption kinetics. Additionally, the study aimed to address discrepancies between conventional desorption models, typically applied in batch freeze-drying, and the observed data in this research. To achieve this, a residual moisture-dependent activation energy was introduced to improve the accuracy of the desorption model. Using NIR spectroscopy and IR-thermography, unknown model parameters could reliably be estimated using a simple and fast procedure. The calibrated model successfully predicted the final moisture content with an accuracy within 0.11% of the measured value under previously untested process conditions. Ultimately, the proposed semi-mechanistic model demonstrated its reliability in predicting the impact of new process conditions on both product temperature and residual moisture over time, enabling the development of a practical design space.

Cracking the code: Spatial heterogeneity as the missing piece for modeling granular fluidized bed drying.

Pharmaceutical twin-screw wet granulation is a multifaceted and intricate process pivotal to drug product development. Accurate modeling of this process is indispensable for optimizing manufacturing parameters and ensuring product quality. The fluid bed dryer, an integral component of this granulation process, significantly influences the granular critical quality attributes. This study builds upon prior research by integrating experimental findings on granule segregation during fluid bed drying into an existing compartmental model, enhancing its predictive capabilities. An additional model layer on granule segregation behavior is composed and integrated into the existing model structure in this study. The added model compartment describes probability distributions on the vertical position of granules within each granule size class considered. To beware of overfitting, predictions of both the moisture content after drying and the granule bed temperature throughout drying are discussed in this study relative to experimental data from earlier published studies. These independent analyses demonstrated a marked improvement in prediction accuracy compared to earlier published model structures. The refined model accurately predicts the residual moisture content after drying for an untrained formulation. Moreover, it simultaneously makes accurate predictions of the granular bed temperature, which emboldens its structural correctness. This advancement makes it a powerful tool for predicting the behavior of the pharmaceutical fluid bed drying, which holds significant promise to facilitate pharmaceutical product development.

A framework for the in silico assessment of the robustness of an MPC in a CDC line in function of process variability.

The shift from batch manufacturing towards continuous manufacturing for the production of oral solid dosages requires the development and implementation of process models and process control. Previous work focused mainly on developing deterministic models for the investigated system. Furthermore, the in silico tuning and analysis of a control strategy are mostly done based on deterministic models. This deterministic approach could lead to wrong actions in diversion strategies and poor transferability of the controller performance if the system behaves differently than the deterministic model. This work introduces a framework that explicitly includes the process variability which is characteristic of powder handling processes and tests it on a novel continuous feeding-blending unit (i.e., the FE continuous processing system (CPS)), followed by a tablet press (i.e., the FE 55). It employs a stochastic model by allowing the model parameters to have a probability distribution. The performance of a model predictive control (MPC), steering the feed rate of the main excipient feeder to compensate for the feed rate deviations of the active pharmaceutical ingredient (API) feeder to keep the API concentration close to the desired value, is evaluated and the impact of process variability is assessed in a Monte Carlo (MC) analysis. Next to the process variability, a model for the prediction error of the chemometric model and realistic feed rate disturbances were included to increase the transferability of the results to the real system. The obtained results show that process variability is inherently present and that wrong conclusions can be drawn if it is not taken into account in the in silico analysis.

Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products.

T-shaped partial least squares regression (T-PLSR) is a valuable machine learning technique for the formulation and manufacturing process development of new drug products. An accurate T-PLSR model requires experimental data with multiple formulations and process conditions. However, it is usually challenging to collect comprehensive experimental data using large-scale manufacturing equipment because of the cost, time, and large consumption of raw materials. This study proposes a hybrid modeling of T-PLSR and transfer learning (TL) to enhance the prediction performance of a T-PLSR model for large-scale manufacturing data by exploiting a large amount of small-scale manufacturing data for model building. The proposed method of T-PLSR+TL was applied to a practical case study focusing on scaling up the tableting process from an experienced compaction simulator to a less-experienced rotary tablet press. The T-PLSR+TL models achieved significantly better prediction performance for tablet quality attributes of new drug products than T-PLSR models without using large-scale manufacturing data with new drug products. The results demonstrated that T-PLSR+TL is more capable of addressing new drug products than T-PLSR by using small-scale manufacturing data to cover a scarcity of large-scale manufacturing data. Furthermore, T-PLSR+TL holds the potential to streamline formulation and manufacturing process development activities for new drug products using an extensive database.

A Tandem-Affinity Purification Method for Identification of Primary Intracellular Drug-Binding Proteins.

In the field of drug discovery, understanding how small molecule drugs interact with cellular components is crucial. Our study introduces a novel methodology to uncover primary drug targets using Tandem Affinity Purification for identification of Drug-Binding Proteins (TAP-DBP). Central to our approach is the generation of a FLAG-hemagglutinin (HA)-tagged chimeric protein featuring the FKBP12(F36V) adaptor protein and the TurboID enzyme. Conjugation of drug molecules with the FKBP12(F36V) ligand allows for the coordinated recruitment of drug-binding partners effectively enabling in-cell TurboID-mediated biotinylation. By employing a tandem affinity purification protocol based on FLAG-immunoprecipitation and streptavidin pulldown, alongside mass spectrometry analysis, TAP-DBP allows for the precise identification of drug-primary binding partners. Overall, this study introduces a systematic, unbiased method for identification of drug-protein interactions, contributing a clear understanding of target engagement and drug selectivity to advance the mode of action of a drug in cells.

Recruiting Persons With Dementia: A Systematic Review of Facilitators, Barriers, and Strategies.

Study recruitment of persons with dementia is challenging. We aimed to assess facilitators, barriers, and strategies to identify and approach persons with dementia for recruitment to dementia care studies. We systematically searched MEDLINE/PubMed, CINAHL, Web of Science, and other sources (ORRCA [Online Resource for Research in Clinical triAls]; pertinent evidence syntheses; citation searching) and narratively summarised the results (PROSPERO CRD42022342600). Facilitators and barriers consisted of "characteristics of participants, researchers, clinical contact persons", "study characteristics", and "communication with participants". The highest number of participants were recruited by study information in electronic and print formats, as well as by networking and collaboration. Advertisements proved to be the most expensive way of recruitment. There is limited evidence on the impact of recruitment strategies to identify persons with dementia for recruitment to dementia care studies. Our analysis of facilitators and barriers may inform research teams in designing strategies to identify persons with dementia for recruitment purposes.

DCAF15 control of cohesin dynamics sustains acute myeloid leukemia.

The CRL4-DCAF15 E3 ubiquitin ligase complex is targeted by the aryl-sulfonamide molecular glues, leading to neo-substrate recruitment, ubiquitination, and proteasomal degradation. However, the physiological function of DCAF15 remains unknown. Using a domain-focused genetic screening approach, we reveal DCAF15 as an acute myeloid leukemia (AML)-biased dependency. Loss of DCAF15 results in suppression of AML through compromised replication fork integrity and consequent accumulation of DNA damage. Accordingly, DCAF15 loss sensitizes AML to replication stress-inducing therapeutics. Mechanistically, we discover that DCAF15 directly interacts with the SMC1A protein of the cohesin complex and destabilizes the cohesin regulatory factors PDS5A and CDCA5. Loss of PDS5A and CDCA5 removal precludes cohesin acetylation on chromatin, resulting in uncontrolled chromatin loop extrusion, defective DNA replication, and apoptosis. Collectively, our findings uncover an endogenous, cell autonomous function of DCAF15 in sustaining AML proliferation through post-translational control of cohesin dynamics.