Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling.

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs' protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective combinations with the least toxicity and the best synergistic effect on blast cells. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment.

Power Dependence of the Magnetic Field Effect on Triplet Fusion: A Quantitative Model.

Two strategies for improving solar energy efficiencies, triplet fusion and singlet fission, rely on the details of triplet-triplet interactions. In triplet fusion, there are several steps, each of which is a possible loss mechanism. In solution, the parameters describing triplet fusion collisions are difficult to inspect. Here we show that these parameters can be determined by examining the magnetic field dependence of triplet fusion upconversion. We show that there is a reduction of the magnetic field effect for perylene triplet fusion as the system moves from the quadratic to linear annihilation regimes with an increase in laser power. Our data are modeled with a small set of parameters that characterize the triplet fusion dynamics. These parameters are cross-validated with molecular dynamics simulations. This approach can be applied to both solution and solid state materials, providing a tool for screening potential annihilators for photon upconversion.

A systems biology analysis of protein-protein interaction of digestive disorders and Covid-19 virus based on comprehensive gene information.

Aim: Analysis of networks of digestive disorder and their relationship with Covid-19 based on systems biology methods, evaluation similarity, and usefulness of networks to give a new treatment approach. Background: Digestive disorders are typically complex diseases associated with high treatment costs. They are related to the immune system and inflammation. With the outbreak of Covid-19, this disease was shown to have signs like diarrhea. Some signs of Covid-19 are similar to those of digestive disorders, like IBD and diarrhea. Both of them are accompanied by inflammation and induce disorders in the digestive system. Methods: DisGeNET and STRING databases were sources of disease genes and constructing networks and were used to construct the network of digestive diseases and Covid-19. Three plugins of Cytoscape software, namely ClusterONE, ClueGO, and CluePedia, were used to analyze cluster networks and enrichment pathways. To describe the interaction of proteins, information from KEGG pathway and Reactome was used. Results: According to the results, IBD, gastritis, and diarrhea have common pathways. The CXCL8, IL-6, IL-1ß, TNF-α, TLR4, and MBL2 molecules were identified as inflammatory molecules in all networks. Conclusion: It seems that detecting genes and pathways can be useful in applying new approaches for treating these diseases.

Identification of Celecoxib-Targeted Proteins Using Label-Free Thermal Proteome Profiling on Rat Hippocampus.

Celecoxib, or Celebrex, a nonsteroidal anti-inflammatory drug, is one of the most common medicines for treating inflammatory diseases. Recently, it has been shown that celecoxib is associated with implications in complex diseases, such as Alzheimer disease and cancer as well as with cardiovascular risk assessment and toxicity, suggesting that celecoxib may affect multiple unknown targets. In this project, we detected targets of celecoxib within the nervous system using a label-free thermal proteome profiling method. First, proteins of the rat hippocampus were treated with multiple drug concentrations and temperatures. Next, we separated the soluble proteins from the denatured and sedimented total protein load by ultracentrifugation. Subsequently, the soluble proteins were analyzed by nano-liquid chromatography tandem mass spectrometry to determine the identity of the celecoxib-targeted proteins based on structural changes by thermal stability variation of targeted proteins toward higher solubility in the higher temperatures. In the analysis of the soluble protein extract at 67°C, 44 proteins were uniquely detected in drug-treated samples out of all 478 identified proteins at this temperature. Ras-associated binding protein 4a, 1 out of these 44 proteins, has previously been reported as one of the celecoxib off targets in the rat central nervous system. Furthermore, we provide more molecular details through biomedical enrichment analysis to explore the potential role of all detected proteins in the biologic systems. We show that the determined proteins play a role in the signaling pathways related to neurodegenerative disease-and cancer pathways. Finally, we fill out molecular supporting evidence for using celecoxib toward the drug-repurposing approach by exploring drug targets. SIGNIFICANCE STATEMENT: This study determined 44 off-target proteins of celecoxib, a nonsteroidal anti-inflammatory and one of the most common medicines for treating inflammatory diseases. It shows that these proteins play a role in the signaling pathways related to neurodegenerative disease and cancer pathways. Finally, the study provides molecular supporting evidence for using celecoxib toward the drug-repurposing approach by exploring drug targets.

Showing NAFLD, as a key connector disease between Alzheimer's disease and diabetes via analysis of systems biology.

AIM: This study was designed to perform network analysis of Alzheimers҆ disease and diabetes and to find their correlation with each other and other diseases/pathways. BACKGROUND: Alzheimer's disease (AD) as a neurodegenerative disease and diabetes as a metabolic disease are two major health problems in the recent years. The recent studies have reported their correlation and same spreading pathways of these two diseases together, but details of this relation are not well known yet at molecular level.. METHODS: In thermal proteome profiling (TPP) technique, after treatment of the extracted proteins by heat and drug concentration, the resulting proteins were analyzed by mass spectrometry. Enrichment analysis of these proteins led to development of AD and diabetes. First, corresponding genes for each disease were extracted from DisGeNET database and then, protein-protein interaction network was constructed for each of them using the search tool for retrieval of interacting genes and proteins (STRING). After analyzing these networks, hub-bottleneck nodes of networks were evaluated. Also, common nodes between two networks were extracted and used for further analysis. RESULTS: High correlation was found between AD and diabetes based on the existence of 40 common genes. Results of analyses revealed 14 genes in AD and 12 genes in diabetes as hub-bottleneck 7 of which were common including caspase 3 (CASP3), insulin-like growth factor 1 (IGF1), catalase (CAT), tumor necrosis factor (TNF), leptin (LEP), vascular endothelial growth factor A (VEGFA), and interleukin 6 ( IL-6). CONCLUSION: Our results revealed a direct correlation between AD and diabetes and also a correlation between these two diseases and non-alcoholic fatty liver disease (NAFLD), suggesting that a small change in each of these three diseases can lead to development of any other diseases in the patients. Also, the enrichments exhibited the existence of common pathways between AD, diabetes, NAFLD, and male infertility.

Photochemical upconversion is suppressed by high concentrations of molecular sensitizers.

Photochemical upconversion uses sensitized triplet-triplet annihilation in bimolecular compositions to convert lower energy photons to higher energy photons. For high efficiency under low illumination, usually a high sensitizer concentration is desirable. However, here we show that the upconversion sensitizer can reduce the emitter triplet lifetime by dynamic quenching, with rate constants on the order 106 M-1 s-1, leading to diminishing returns beyond a certain concentration. These results serve as a warning to designers of photochemical upconvertors that higher concentrations of sensitizers are not necessarily beneficial to upconversion performance.