A dynamic time-to-event model for prediction of acute graft-versus-host disease in patients after allogeneic hematopoietic stem cell transplantation.

BACKGROUND: Acute graft-versus-host disease (aGvHD) is a major cause of death for patients following allogeneic hematopoietic stem cell transplantation (HSCT). Effective management of moderate to severe aGvHD remains challenging despite recent advances in HSCT, emphasizing the importance of prophylaxis and risk factor identification. METHODS: In this study, we analyzed data from 1479 adults who underwent HSCT between 2005 and 2017 to investigate the effects of aGvHD prophylaxis and time-dependent risk factors on the development of grades II-IV aGvHD within 100 days post-HSCT. RESULTS: Using a dynamic longitudinal time-to-event model, we observed a non-monotonic baseline hazard overtime with a low hazard during the first few days and a maximum hazard at day 17, described by Bateman function with a mean transit time of approximately 11 days. Multivariable analysis revealed significant time-dependent effects of white blood cell counts and cyclosporine A exposure as well as static effects of female donors for male recipients, patients with matched related donors, conditioning regimen consisting of fludarabine plus total body irradiation, and patient age in recipients of grafts from related donors on the risk to develop grades II-IV aGvHD. Additionally, we found that higher cumulative hazard on day 7 after allo-HSCT are associated with an increased incidence of grades II-IV aGvHD within 100 days indicating that an individual assessment of the cumulative hazard on day 7 could potentially serve as valuable predictor for later grades II-IV aGvHD development. Using the final model, stochastic simulations were performed to explore covariate effects on the cumulative incidence over time and to estimate risk ratios. CONCLUSION: Overall, the presented model showed good descriptive and predictive performance and provides valuable insights into the interplay of multiple static and time-dependent risk factors for the prediction of aGvHD.

Efficacy and safety of MP1032 plus standard-of-care compared to standard-of-care in hospitalised patients with COVID-19: a multicentre, randomised double-blind, placebo-controlled phase 2a trial.

Background: SARS-CoV-2 infections still have a significant impact on the global population. The existing vaccinations have contributed to reducing the severe disease courses, decreasing hospitalisations, and lowering the mortality rate. However, due to the variability of COVID-19 symptoms, the emergence of new variants and the uneven global distribution of vaccines there is still a great need for new therapy options. One promising approach is provided by host-directed therapies. We assessed here the efficacy and safety of MP1032, a host-directed anti-viral/anti-inflammatory drug in hospitalised patients with moderate to severe COVID-19. Methods: In a randomised, double-blind, placebo-controlled, Phase IIa study, patients were randomised 2:1 to receive either 300 mg MP032 bid + Standard-of-Care (SoC) or placebo bid + SoC for 28 days. Eligible patients were ≥18 years old, tested positive for SARS-CoV-2, and had moderate to severe COVID-19 symptoms. The study spanned 20 sites in six countries (Bulgaria, France, Hungary, Italy, Romania, Spain), assessing disease progression according the NIAID scale as the primary outcome on day 14. Secondary objectives included disease progression (day 28), disease resolution (days 14 and 28), mortality rate, COVID-19 related parameters and safety. Exposure-response analyses were performed, linking MP1032 to COVID-19 biomarkers (eGFR, D-dimer). Findings: 132 patients were enrolled to receive MP1032 + SoC (n = 87) or placebo + SoC (n = 45). The patients were all white or Caucasian with a mean (median) age of 60.5 (63) years. Overall, only 10 patients were vaccinated, 5 in each group. No significant risk difference of disease progression could be detected between groups on both day 14 (9.8% MP1032 vs. 11.6% placebo) and day 28 with MH common risk differences of -0.276% (95% CI, -11.634 to 11.081; p = 0.962) and 1.722% (95% CI, -4.576 to 8.019; p = 0.592), respectively.The treatment with MP1032 + SoC was safe and well-tolerated. Overall, 182 TEAEs including 10 SAEs were reported in 53.5% (46/86) of patients of the verum group and in 57.8% (26/45) of patients of the placebo group; the SAEs occurred in 5.8% (5/86) and 6.7% (3/45) of verum and placebo patients, respectively. None of the SAEs was considered as related. Interpretation: Despite the study's limitation in size and the variation in concurrent SoCs, these findings warrant further investigation of MP1032 as a host-directed anti-viral drug candidate. Funding: The study was funded by the COVID-19 Horizon Europe work programme and MetrioPharm AG.

Model-Based Analysis of SARS-CoV-2 Infections, Hospitalization and Outcome in Germany, the Federal States and Districts.

The coronavirus disease 2019 (COVID-19) pandemic challenged many national health care systems, with hospitals reaching capacity limits of intensive care units (ICU). Thus, the estimation of acute local burden of ICUs is critical for appropriate management of health care resources. In this work, we applied non-linear mixed effects modeling to develop an epidemiological SARS-CoV-2 infection model for Germany, with its 16 federal states and 400 districts, that describes infections as well as COVID-19 inpatients, ICU patients with and without mechanical ventilation, recoveries, and fatalities during the first two waves of the pandemic until April 2021. Based on model analyses, covariates influencing the relation between infections and outcomes were explored. Non-pharmaceutical interventions imposed by governments were found to have a major impact on the spreading of SARS-CoV-2. Patient age and sex, the spread of variant B.1.1.7, and the testing strategy (number of tests performed weekly, rate of positive tests) affected the severity and outcome of recorded cases and could reduce the observed unexplained variability between the states. Modeling could reasonably link the discrepancies between fine-grained model simulations of the 400 German districts and the reported number of available ICU beds to coarse-grained COVID-19 patient distribution patterns within German regions.

XplOit: An Ontology-Based Data Integration Platform Supporting the Development of Predictive Models for Personalized Medicine.

Predictive models can support physicians to tailor interventions and treatments to their individual patients based on their predicted response and risk of disease and help in this way to put personalized medicine into practice. In allogeneic stem cell transplantation risk assessment is to be enhanced in order to respond to emerging viral infections and transplantation reactions. However, to develop predictive models it is necessary to harmonize and integrate high amounts of heterogeneous medical data that is stored in different health information systems. Driven by the demand for predictive instruments in allogeneic stem cell transplantation we present in this paper an ontology-based platform that supports data owners and model developers to share and harmonize their data for model development respecting data privacy.