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The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

Summary: Background: Early trials of long-term lenalidomide use reported an increased incidence of second primary malignancy (SPM), including acute myeloid leukaemia and myelodysplastic syndrome. Later, meta-analysis suggested the link to be secondary to lenalidomide in combination with melphalan. Methods: Myeloma XI is a large, phase III randomised trial in-which lenalidomide was used at induction and maintenance, in transplant eligible (TE) and non-eligible (TNE) newly diagnosed patients (NCT01554852). Here we present an analysis of SPM incidence and profile the SPM type to determine the impact of autologous stem cell transplantation (ASCT) and lenalidomide exposure in 4358 patients treated on study. Data collection took place from the start of the trial in May 2010, to May 2019, as per the protocol timeline. The Median follow-up following maintenance randomisation was 54.5 and 46.1 months for TE and TNE patients, respectively. Findings: In the TE pathway, the overall SPM incidence was 7.7% in lenalidomide maintenance patients compared to 3.2% in those being observed (p = 0.006). Although the TNE lenalidomide maintenance patients had the greatest SPM incidence (15.4%), this was not statistically significant when compared to the observed patients (10%, p = 0.10).The SPM incidence was higher in patients who received lenalidomide at induction and maintenance (double exposure), when compared to those treated with lenalidomide at one time point (single exposure). Again, this was most marked in TNE patients where the overall SPM incidence was 16.9% in double exposed patients, compared to 11.7% in single exposed patients, and 11.2% in patients who did not receive lenalidomide (p = 0.04). This is likely an effect of treatment duration, with the median number of cycles being 27 in the TNE double exposed patients, vs 6 in the single exposure patients.Haematological SPMs were uncommon, diagnosed in 50 patients (incidence 1.1%). The majority of cases were diagnosed in TE patients treated with lenalidomide maintenance (n = 25, incidence 2.8%), suggesting a possible link with melphalan. Non-melanoma skin cancer incidence was highest in patients receiving lenalidomide maintenance, particularly in TNE patients, where squamous cell carcinoma and basal cell carcinoma were diagnosed in 5.5% and 2.6% of patients, respectively. The incidence of most solid tumour types was higher in lenalidomide maintenance patients.Mortality due to progressive myeloma was reduced in patients receiving lenalidomide maintenance, noted to be 16.6% compared 22.6% in those observed in TE patients and 32.7% compared to 41.5% in TNE patients. SPM related mortality was low, 1.8% and 6.1% in TE and TNE lenalidomide maintenance patients, respectively, compared to 0.4% and 2.8% in those being observed. Interpretation: This provides reassurance that long-term lenalidomide treatment is safe and associated with improved outcomes in TE and TNE populations, although monitoring for SPM development should be incorporated into clinic review processes. Funding: Primary financial support was from Cancer Research UK [C1298/A10410].

IntroductionProteasome inhibition is first line therapy in multiple myeloma (MM). The immunological potential of cell death triggered by defects of the ubiquitin-proteasome system (UPS) and subsequent perturbations of protein homeostasis is, however, less well defined.MethodsIn this paper, we applied the protein homeostasis disruptors bortezomib (BTZ), ONX0914, RA190 and PR619 to various MM cell lines and primary patient samples to investigate their ability to induce immunogenic cell death (ICD).ResultsOur data show that while BTZ treatment triggers sterile type I interferon (IFN) responses, exposure of the cells to ONX0914 or RA190 was mostly immunologically silent. Interestingly, inhibition of protein de-ubiquitination by PR619 was associated with the acquisition of a strong type I IFN gene signature which relied on key components of the unfolded protein and integrated stress responses including inositol-requiring enzyme 1 (IRE1), protein kinase R (PKR) and general control nonderepressible 2 (GCN2). The immunological relevance of blocking de-ubiquitination in MM was further reflected by the ability of PR619-induced apoptotic cells to facilitate dendritic cell (DC) maturation via type I IFN-dependent mechanisms.ConclusionAltogether, our findings identify de-ubiquitination inhibition as a promising strategy for inducing ICD of MM to expand current available treatments.

BACKGROUND:Diabetes mellitus is one of the most common todays public health problems. According to a survey by the World Health Organization, this metabolic disorder has reached global epidemic proportions, with a worldwide prevalence of 8.5% in the adult population. OBJECTIVES:The present study aimed to investigate the hypoglycemic effect of aqueous extract of Mangifera indica (EAMI) leaves in streptozotocin-induced diabetic rats. METHODS:Sixty male rats were divided into 2 groups: Normoglycemic and Diabetic. Each group was subdivided into negative control, glibenclamide 3 or 10 mg/kg, EAMI 125, 250, 500, and 1000 mg/kg. Intraperitoneal injection of streptozotocin 100 mg/kg was used to DM induction. The hypoglycemic response was assessed acutely after two and four weeks of treatment. After a 6-hour fasting period, the fasting blood glucose of animals was verified, and 2.5 g/kg glucose solution was orally administered. The insulin tolerance test and plasma insulin levels assessment were performed in the morning after fasting of 12 to 14 hours. RESULTS AND CONCLUSION:The chemical analysis of EAMI showed high levels of phenolic compounds. There was no significant difference in fasting blood glucose between normoglycemic and diabetic groups, and that EAMI did not have an acute effect on diabetes. After two and four weeks of treatment, the extract significantly reduced blood glucose levels, exceeding glibenclamide effects. EAMI was effective in maintaining the long-term hypoglycemic effect, as well as, significantly increased the sensitivity of diabetic animals to insulin and the plasma insulin level.

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, datadriven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine. [ABSTRACT FROM AUTHOR]