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Introduction: Primary refractory disease affects 30-40% of patients diagnosed with DLBCL and is a significant challenge in disease management due to its poor prognosis. Predicting refractory status could greatly inform treatment strategies, enabling early intervention. Various options are now available based on patient and disease characteristics. Supervised machine-learning techniques, which can predict outcomes in a medical context, appear highly suitable for this purpose., Design: Retrospective monocentric cohort study., Patient Population: Adult patients with a first diagnosis of DLBCL admitted to the hematology unit from 2017 to 2022., Aim: We evaluated in our Center five supervised machine-learning (ML) models as a tool for the prediction of primary refractory DLBCL., Main Results: One hundred and thirty patients with Diffuse Large B-cell lymphoma (DLBCL) were included in this study between January 2017 and December 2022. The variables used for analysis included demographic characteristics, clinical condition, disease characteristics, first-line therapy and PET-CT scan realization after 2 cycles of treatment. We compared five supervised ML models: support vector machine (SVM), Random Forest Classifier (RFC), Logistic Regression (LR), Naïve Bayes (NB) Categorical classifier and eXtreme Gradient Boost (XGboost), to predict primary refractory disease. The performance of these models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, false positive rate, sensitivity, and F1-score to identify the best model. After a median follow-up of 19.5 months, the overall survival rate was 60% in the cohort. The Overall Survival at 3 years was 58.5% (95%CI, 51-68.5) and the 3-years Progression Free Survival was 63% (95%CI, 54-71) using Kaplan-Meier method. Of the 124 patients who received a first line treatment, primary refractory disease occurred in 42 patients (33.8%) and 2 patients (1.6%) experienced relapse within 6 months. The univariate analysis on refractory disease status shows age (p = 0.009), Ann Arbor stage (p = 0.013), CMV infection (p = 0.012), comorbidity (p = 0.019), IPI score (p<0.001), first line of treatment (p<0.001), EBV infection (p = 0.008) and socio-economics status (p = 0.02) as influencing factors. The NB Categorical classifier emerged as the top-performing model, boasting a ROC-AUC of 0.81 (95% CI, 0.64-0.96), an accuracy of 83%, a F1-score of 0.82, and a low false positive rate at 10% on the validation set. The eXtreme Gradient Boost (XGboost) model and the Random Forest Classifier (RFC) followed with a ROC-AUC of 0.74 (95%CI, 0.52-0.93) and 0.67 (95%CI, 0.46-0.88) respectively, an accuracy of 78% and 72% respectively, a F1-score of 0.75 and 0.67 respectively, and a false positive rate of 10% for both. The other two models performed worse with ROC-AUC of 0.65 (95%CI, 0.40-0.87) and 0.45 (95%CI, 0.29-0.64) for SVM and LR respectively, an accuracy of 67% and 50% respectively, a f1-score of 0.64 and 0.43 respectively, and a false positive rate of 28% and 37% respectively., Conclusion: Machine learning algorithms, particularly the NB Categorical classifier, have the potential to improve the prediction of primary refractory disease in DLBCL patients, thereby providing a novel decision-making tool for managing this condition. To validate these results on a broader scale, multicenter studies are needed to confirm the results in larger cohorts., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Detrait et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Burkholderia cenocepacia is an opportunistic pathogen responsible for life-threatening infections in cystic fibrosis patients. B. cenocepacia is extremely resistant towards antibiotics and therapy is complicated by its ability to form biofilms. We investigated the efficacy of an alternative antimicrobial strategy for B. cenocepacia lung infections using in vitro and in vivo models. A screening of the NIH Clinical Collection 1&2 was performed against B. cenocepacia biofilms formed in 96-well microtiter plates in the presence of tobramycin to identify repurposing candidates with potentiator activity. The efficacy of selected hits was evaluated in a three-dimensional (3D) organotypic human lung epithelial cell culture model. The in vivo effect was evaluated in the invertebrate Galleria mellonella and in a murine B. cenocepacia lung infection model. The screening resulted in 60 hits that potentiated the activity of tobramycin against B. cenocepacia biofilms, including four imidazoles of which econazole and miconazole were selected for further investigation. However, a potentiator effect was not observed in the 3D organotypic human lung epithelial cell culture model. Combination treatment was also not able to increase survival of infected G. mellonella. Also in mice, there was no added value for the combination treatment. Although potentiators of tobramycin with activity against biofilms of B. cenocepacia were identified in a repurposing screen, the in vitro activity could not be confirmed nor in a more sophisticated in vitro model, neither in vivo. This stresses the importance of validating hits resulting from in vitro studies in physiologically relevant model systems.