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Martelli, M. P.

Publications and source records attributed to Martelli, M. P..

2 recordsLinked to original sources

SiCoDEA: a simple, fast and complete app for analyzing the effect of individual drugs and their combinations

The administration of combinations of drugs is a method widely used in the treatment of different pathologies as it can lead to an increase in the therapeutic effect and a reduction in the dose compared to the administration of the single drugs. For these reasons, it is of interest to study combinations of drugs and in particular to determine whether a specific combination has a synergistic, antagonistic or additive effect, i.e greater, less than or equal to the effect expected by the sum of the individual drugs. For this purpose, various mathematical models have been developed, which use different methods to evaluate the synergy of a combination of drugs. Most of these methods are based on the Loewe Additivity Principle (Loewe et al., 1953), which has its key step in choosing the model used for predicting the effect of individual drugs. Creating a model for this purpose and calculating its parameters, however, requires a certain level of mathematical and programming knowledge or the use of commercial software. For this purpose, therefore, we have developed an open access and easy to use app that allows to explore different models and to choose the most fitting for the specific experimental data: SiCoDEA (Single and Combined Drug Effect Analysis, https://sicodea.shinyapps.io/shiny/). The data used to test SiCoDEA comes from cell line samples treated with different drug combinations and analyzed through metabolic or viability assays. SiCoDEA is developed through a Shiny interactive and easy-to-use interface (R based). There are five models taken into consideration for the analysis of single drugs and the calculation of combination index. The first is one of the most used, that of the median-effect; while the others are different forms of the log-logistic equation, with two, three and four parameters. The purpose of SiCoDEA is, on the one hand, to provide an easy-to-use tool for analyzing drug combination data and, on the other hand, also to have a view of the various steps and to offer different results based on the model chosen. An important prerequisite in analyzing drug combinations is in fact the dose-response curve calculated for individual drugs. For this purpose, SiCoDEA allows you to view the plots of the individual drugs both to evaluate the distribution of the calculated points and therefore identify any outliers, and to view the curve of the different models taken into consideration and evaluate which one best fits the data. A table showing all the R2 values for the five different models is created with the curve plot. In addition to the type of model, it is also possible to choose between two different normalization methods, one based on the maximum or minimum value and the other on the value calculated at drug concentrations equal to zero. For the chosen options, a plot is then created that shows the trend of the combination index for the different drug combinations and consequently whether it is synergy, antagonism or additivity. Finally, it is possible to export the results in single png files or in a summary report in pdf. SiCoDEA is an open-source app among the most complete and offers more functions even than the famous CompuSyn (Chou et al., 2010), as it allows you to analyze drug curves with different models, rather than just one, and it also allows the analysis of single drug curves.

bioinformatics↗

Prolonged XPO1 inhibition is essential for optimal anti-leukemic activity in NPM1-mutated AML

NPM1 encodes for a nucleolar multifunctional protein and is the most frequently mutated gene in adult acute myeloid leukemia (AML). NPM1 mutations cause the aberrant accumulation of mutant NPM1 (NPM1c) in the cytoplasm of leukemic cells, that is mediated by the nuclear exporter Exportin-1 (XPO1). Recent work has demonstrated that the interaction between NPM1c and XPO1 promotes high homeobox (HOX) genes expression, which is critical for maintaining the leukemic state of NPM1-mutated cells. However, the XPO1 inhibitor Selinexor administered once or twice/week in early-phase clinical trials did not translate into clinical benefit for NPM1-mutated AML patients. Here, we demonstrate that this dosing strategy results in only temporary disruption of the XPO1-NPM1c interaction and transient HOX genes downregulation, limiting the efficacy of Selinexor in the context of NPM1-mutated AML. Since second-generation XPO1 inhibitors can be administered more frequently, we compared intermittent (twice/week) versus prolonged (5 days/week) XPO1 inhibition in NPM1-mutated AML models. Integrating in vitro and in vivo data, we show that only prolonged XPO1 inhibition results in stable HOX downregulation, cell differentiation and remarkable anti-leukemic activity. This study lays the groundwork for the accurate design of clinical trials with second-generation XPO1 inhibitors in NPM1-mutated AML.

cancer biology↗