Janssen (Belgium)

Microfluidics offers a reproducible approach to liposome manufacture; however, the impact of solvent choice on formulation performance remains underexplored. Here, we investigated whether solvent selection during microfluidic manufacturing influences liposome performance by assessing its effect on the drug release and biodistribution of liposomal doxorubicin, using rotary evaporation as a conventional method of comparison. PEGylated liposomes (DSPC:Chol:DSPE-PEG2000, 3:1:1 w/w) were prepared using a staggered herringbone microfluidic mixer, with lipids dissolved in either ethanol or Transcutol, and compared with liposomes produced by rotary evaporation and extrusion. All formulations were actively loaded with doxorubicin via an ammonium sulphate gradient, purified by tangential flow filtration, and characterised for size, polydispersity, and drug loading. The three formulations (two microfluidic (ethanol vs Transcutol) and one rotary evaporation control) were intravenously administered to Sprague-Dawley rats (1mg/kg), and doxorubicin concentrations in plasma and tissues were quantified using LC-MS. Although solvent choice produced liposomes with broadly comparable physicochemical properties (~ 100-120nm, PDI < 0.25, encapsulation > 90%), Transcutol-based microfluidic liposomes showed prolonged and higher concentrations of doxorubicin within the plasma and tissue samples compared with liposomes produced by ethanol microfluidics and rotary evaporation. By contrast, the manufacturing method alone (microfluidics using ethanol as the solvent versus rotary evaporation) did not significantly influence biodistribution. These findings highlight solvent selection as an important parameter in microfluidic liposome manufacture, demonstrating that matching standard critical quality attributes and in vitro release behaviour alone may be insufficient to ensure comparable in vivo performance.

Purpose:To investigate the responsiveness of the patient-reported outcome measure pediatric physical therapy (PROM-ppt), a questionnaire used to also stimulate shared decision making in Dutch pediatric physical therapy practices.Methods:Children completed the PROM-ppt at intake and 3 months after intervention or post-intervention. Reported problems were categorized as motor or pain related goals for intervention. Responsiveness was examined to test the a priori hypotheses and area under the curve (AUC). The Global Perceived Effect scale was used for comparison.Results:Overall, the hypotheses were confirmed in 60% of the cases with adequate AUCs. In children with motor problems, 80% of the hypotheses were matched, with adequate AUCs. In children with pain related problems, 40% of the hypotheses were matched, with inadequate AUCs.Conclusion:The PROM-ppt had moderate responsiveness in the pediatric population, good responsiveness in children with interventions for motor problems, and poor responsiveness to pain related problems.

Process development and optimization of multiphase reactions is often challenging due to the complex interplay between reaction kinetics and mass transfer. This work analyzes an industrial-scale pharmaceutical debenzylation of an amine-containing precursor catalyzed by a Pd/C catalyst. Using historical lab-scale data, a kinetic model was constructed that reveals competitive adsorption between hydrogen and the precursor, resulting in a reduced debenzylation rate at higher dissolved hydrogen concentrations. When accounting for gas–liquid mass transfer at the pilot scale, lower pressures and stirring speeds limit hydrogen availability due to mass transfer, while higher pressures and stirring speeds increase the dissolved hydrogen concentration, inducing more pronounced adsorption competition and, hence, reducing the reaction rate. These findings indicate the existence of an optimal intermediate operational window that minimizes conversion times by balancing hydrogen availability against catalyst inhibition. Methodologically, this work highlights how multiscale modeling based on historical lab and pilot data can improve industrial-scale predictions and supports more efficient, better-understood pharmaceutical manufacturing.

Successful fibroblast growth factor receptor alterations (FGFRalt) testing is essential for identifying patients eligible for erdafitinib. This analysis of the global ANNAR biomarker study assessed the proportion of valid fibroblast growth factor receptor (FGFR) test results, reasons for test failure, and proportion of FGFRalt in locally advanced/metastatic urothelial cancer (mUC) and non-muscle-invasive bladder cancer (NMIBC). Archival tumor tissue was tested using the QIAGEN therascreen FGFR real-time polymerase chain reaction kit, an approved companion diagnostic for erdafitinib. A total of 2706 mUC and 962 NMIBC samples were tested. The proportion of valid test results was significantly different between mUC and NMIBC (86% and 66%, respectively; p<0.001), which declined with older archival sample age for both mUC (<1yr 89% and ≥3yr 77%; p<0.001) and NMIBC (<1yr 72% and ≥3yr 43%; p<0.001). For mUC, the proportion of valid test results was higher for the primary tumor than for metastatic samples (92% and 55%, respectively; p<0.001) but similar between upper and lower tract disease (87% and 86%, respectively; p=0.7). Common reasons for test failure were low tumor content and insufficient RNA. FGFRalt were found in 19% of mUC (83% mutations; 15% fusions) and 53% of NMIBC (92% mutations; 6.3% fusions) samples. Limitations include the lack of data on sample collection method. This is the first and largest report on factors affecting FGFR test results. To ensure valid FGFR test results, adequate tumor sample amount, RNA quality, short archival sample age, and primary tumor samples are important factors.

BackgroundBlood biomarkers represent the next generation of Alzheimer's disease (AD) diagnostics, enabling noninvasive, inexpensive, and scalable monitoring of amyloid‐beta (Aβ) plaque (A) and tau neurofibrillary tangles (T) pathologies and neurodegeneration (N). Plasma p‐tau217 has emerged as perhaps the most promising AD blood biomarker, prompting the development of several technologies to evaluate its prognostic and diagnostic utility. However, cross‐cohort validation studies in community‐based cohorts are lacking.MethodsHere, we assessed the Janssen plasma p‐tau217+ assay in three community‐based cohorts: the Monongahela Youghiogheny Healthy Aging Team‐Neuroimaging (MYHAT‐NI) with 113 participants (Aβ‐PET positivity=24.8%), the Human Connectome Project (HCP) comprising 234 participants (Aβ‐PET positivity= 15.0%) and the Heart Strategies Concentrating on Risk Evaluation study (Heart SCORE) made up of 154 participants (Aβ‐PET positivity=18.2%), all recruited from southwestern Pennsylvania, USA. We employed [11C] Pittsburgh Compound B (PiB) positron emission tomography (PET) imaging for brain Aβ load. We utilized receiver operating characteristic (ROC) curves to evaluate p‐tau217+ accuracies in detecting Aβ pathology, adjusting for age, sex, and APOE4 carrier status.ResultsThe Janssen p‐tau217+ assay exhibited high performance in identifying Aβ PET positivity in all three cohorts, with AUCs of 91% for MYHAT‐NI, 93% for HCP, and 82% for Heart SCORE. Plasma p‐tau217+ showed high specificity: MYHAT‐NI (57%), HCP (81%), Heart SCORE (87%) but poor sensitivity to Aβ PET: MYHAT‐NI (75%), HCP (70%), Heart SCORE (59%). p‐tau217+ was strongly correlated with Aβ‐PET SUVR and was stronger in the Aβ‐PET‐positive sub‐groups MYHAT‐NI (r = ‐0.1292; p = 0.2443), HCP (r = ‐0.1617; p = 0.0347), Heart SCORE (r = 0.1586; p = 0.0896). Importantly, correlation with Aβ‐PET was strongest in MYHAT‐NI which had the highest proportion of Aβ‐PET‐positive participants.ConclusionsThe Janssen p‐tau217+ assay identifies Aβ pathology in cognitively normal older adults in the community, underscoring its potential utility as a diagnostic tool for investigating AD at the population‐level.

[This corrects the article DOI: 10.1016/j.jhepr.2025.101516.].

Our DNA carries the blueprint of life, but it is not left unprotected. Instead, it is packaged into chromatin, which can be organized in a loose form (euchromatin) or a compact form (heterochromatin). This packaging not only regulates which genes are active, but also plays a key role in protecting and repairing our DNA. Heterochromatin, however, poses a special challenge: its repetitive nature makes it more difficult to repair DNA damage, increasing the risk of errors that can lead to cancer and other diseases. In my thesis, I investigated how heterochromatin is organized and regulated in different contexts: healthy human cells, cancer, and the evolution of organisms. I developed a new system to induce precise DNA breaks, allowing us to compare repair processes in eu- and heterochromatin. We found that breaks in heterochromatin often move away from this domain in order to be repaired. I also studied how cancer-related changes in heterochromatin proteins impact genome stability. Our results showed that misregulation of these proteins correlates with genomic instability in tumors, highlighting their importance in cancer development. Finally, I explored the evolution of heterochromatin repair mechanisms in different fruit fly species, uncovering rapid changes in sequence in key repair proteins that shape how heterochromatin damage is handled, while function remained consistent. Together, these findings reveal that heterochromatin is a dynamic yet fragile environment. Its proper regulation is essential to safeguard genome stability and may hold the key to preventing diseases such as cancer.

Recent advancements in Large Language Models (LLMs) present promising opportunities for applying these technologies to aid the detection and monitoring of Major Depressive Disorder. However, demographic biases in LLMs may present challenges in the extraction of key information, where concerns persist about whether these models perform equally well across diverse populations. This study investigates how demographic factors, specifically age and gender affect the performance of LLMs in classifying depression symptom severity across multilingual datasets. By systematically balancing and evaluating datasets in English, Spanish, and Dutch, we aim to uncover performance disparities linked to demographic representation and linguistic diversity. The findings from this work can directly inform the design and deployment of more equitable LLM-based screening systems. Gender had varying effects across models, whereas age consistently produced more pronounced differences in performance. Additionally, model accuracy varied noticeably across languages. This study emphasizes the need to incorporate demographic-aware models in health-related analyses. It raises awareness of the biases that may affect their application in mental health and suggests further research on methods to mitigate these biases and enhance model generalization.