Over the years, approaches of the pharmaceutical industry to discover and develop drugs have changed rapidly due to new scientific trends. Among others, they have started to explore Machine Learning (ML), a subset of Artificial Intelligence (AI), as a promising tool to generate new hypotheses regarding drug candidate selections for clinical trials and to predict adverse side effects. Despite these recent developments, the possibilities of ML in pharmaceutical sciences have so far hardly penetrated the training of pharmaceutical science students. ^{1, 2} Therefore, as part of an elective course, an introductory module on ML was developed at Utrecht University, Department of Pharmaceutical Sciences. The aim of this study was to assess student' views on the module set-up, and their perspectives on ML within pharmaceutical science curricula. Semi-structured interviews over three years were conducted with 15 students participating in the module. The students valued the well-designed and effective delivered module. They were personally motivated to learn more about ML in a future master or research internship. The students now perceive a lack of possibilities for ML training in pharmaceutical sciences education and indicate the value of incorporating ML opportunities for their future career. Integrating ML training into pharmaceutical sciences curricula is needed to keep future drug researchers up to date with drug research advancements, enhancing their skills, academic development, and career prospects.

Background and Aims: To improve quality of life (QoL), patients with inflammatory bowel diseases (Crohn’s disease and ulcerative colitis) often self-experiment with lifestyle changes such as dietary modifications. The nature (e.g., type of interventions, expectations, perceived efficacy) of these single-subject experiments has not been systematically investigated. Method: We used Participatory Narrative Inquiry (PNI), a structured qualitative method, to obtain information about these experiments through patient stories. Results: We demonstrate that PNI can be a method to collect and analyze IBD patient ideas and experiences regarding lifestyle and nutritional factors in a structured manner to reveal valuable insights for personal and scientific follow-up research. Patients report rest, (psychological) balance, and a change in diet when describing times when they experienced a better QoL. When focusing on diet, patients reported a considerable number of food products that were experienced as beneficial by one person but detrimental by another. Conclusions: PNI is a suitable method to obtain information about self-experimentation. An insight that was attained was that personalized (dietary) guidance that supports the individual is needed.

Post-acute sequelae of SARS-CoV-2 infection (PASC), also referred to as Long COVID, affects millions worldwide and is characterized by persistent fatigue, reduced immune fitness, and mood disturbances. The aim of the current study was to identify if immune fitness, mood, fatigue, and quality of life prior to SARS-CoV-2 infection could predict PASC fatigue severity. A retrospective cross-sectional survey was conducted among 299 Dutch PASC patients. Participants completed validated measures of immune fitness, fatigue (assessed with both the Fatigue Severity Scale and a single-item scale), mood (including stress, anxiety, depression, hostility, loneliness, and happiness) and quality of life for the three months prior to SARS-CoV-2 infection. The same assessments were made for the month before survey completion (i.e., during PASC). Correlational and regression analyses were conducted to identify possible predictors of PASC fatigue severity. Participants were predominantly female (90%): mean age 44.1 (SD 11.2) years. Both assessments of PASC fatigue did not correlate significantly with the prior SARS-CoV-2 assessments of immune fitness, fatigue, mood, and quality of life. The regression analyses revealed no significant predictors for PASC fatigue severity. In conclusion, immune fitness, fatigue, mood and quality of life prior to SARS-CoV-2 infection were not identified as independent predictors of PASC fatigue severity.

Background/Objectives: Metered-dose inhalers (MDIs) and dry powder inhalers (DPIs) are common inhaled corticosteroid (ICS) inhaler devices. The difference in formulation and administration technique of these devices may influence oral cavity microbiota composition. We aimed to compare the saliva microbiome in children with moderate-to-severe asthma using ICS via MDIs versus DPIs. Methods: Saliva samples collected from 143 children (6–17 yrs) with moderate-to-severe asthma across four European countries (The Netherlands, Germany, Spain, and Slovenia) as part of the SysPharmPediA cohort were subjected to 16S rRNA sequencing. The microbiome was compared using global diversity (α and β) between two groups of participants based on inhaler devices (MDI (n = 77) and DPI (n = 65)), and differential abundance was compared using the Analysis of Compositions of Microbiomes with the Bias Correction (ANCOM-BC) method. Results: No significant difference was observed in α-diversity between the two groups. However, β-diversity analysis revealed significant differences between groups using both Bray–Curtis and weighted UniFrac methods (adjusted p-value = 0.015 and 0.044, respectively). Significant differential abundance between groups, with higher relative abundance in the MDI group compared to the DPI group, was detected at the family level [Carnobacteriaceae (adjusted p = 0.033)] and at the genus level [Granulicatella (adjusted p = 0.021) and Aggregatibacter (adjusted p = 0.011)]. Conclusions: Types of ICS devices are associated with different saliva microbiome compositions in moderate-to-severe pediatric asthma. The causal relation between inhaler types and changes in saliva microbiota composition needs to be further evaluated, as well as whether this leads to different potential adverse effects in terms of occurrence and level of severity.