| 1. |
- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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| 2. |
- Menden, MP, et al.
(författare)
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Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
- 2019
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 2674-
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Tidskriftsartikel (refereegranskat)abstract
- The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
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| 3. |
- Nettelbladt, C. G., et al.
(författare)
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Starvation increases the number of coliform bacteria in ceacum and induces bacterial adherence to caecal epithelium in rats
- 1997
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Ingår i: European Journal of Surgery. - Stockholm, Sweden : Taylor & Francis. - 1102-4151 .- 1741-9271. ; 163:2, s. 135-142
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To investigate the impact of starvation for 24 and 48 h on the number of coliform bacteria in the caecal contents, on the mucosal adherence of coliform bacteria, and on bacterial translocation in rats.Design: Open prospective study.Setting: University departments of surgery and microbiology, Sweden.Material: 46 adult male Sprague-Dawley rats.Interventions: 19 rats served as controls, and were fed until samples were taken. Six animals were starved for 24 h and another 15 for 48 h, with free access to water, and then anaesthetised before blood, mesenteric lymph nodes (MLN), caecum, and caecal contents were sampled. To verify bacterial translocation in this strain of rats, another six rats underwent controlled haemorrhage for 60 min to reduce the blood pressure to 55 mm Hg mean arterial pressure (MAP). These rats had free access to food and water before haemorrhage but were allowed only water until samples were taken 24 h after haemorrhage.Main Outcomes Measures: Presence and number of coliform bacteria in samples taken from caecal contents, caecal epithelium, MLN, and blood.Results: Starvation for 24 h increased the number of coliform bacteria (colony forming units (CFU)/g) in the caecal contents 25-fold (p < 0.05). Starvation for 48 h further increased the number by a factor of 100. The number of coliform bacteria that adhered to the caecal epithelium increased 3,000 times in rats that had been starved for 48 h (p < 0.001). There was no significant difference in translocation (as indicated by cultures from MLN) between rats that had been fed and those that had been starved for 48 h. In 4 of the 6 rats that were bled and then starved for 24 h there were signs of bacterial translocation, which was significantly more than the 1/19 in fed rats (p < 0.05).Conclusion: Starvation increases the number of bacteria in the caecal contents and increases bacterial adherence to the caecal epithelium. These changes may contribute to the previously reported increase in bacterial translocation in starved compared wit fed rats that were subjected to stress. The same changes in the gut were observed in animals subjected to haemorrhagic stress in addition to starvation, and in which bacterial translocation was evident.
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| 4. |
- Mansouri, Kamel, et al.
(författare)
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CoMPARA : Collaborative Modeling Project for Androgen Receptor Activity
- 2020
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Ingår i: Journal of Environmental Health Perspectives. - 0091-6765 .- 1552-9924. ; 128:2, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast (TM) metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast (TM)/Tox21 HTS in vitro assays.RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of similar to 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment.
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