| 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. |
- Katouli, Mohammad, et al.
(författare)
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Selective translocation of coliform bacteria adhering to caecal epithelium of rats during catabolic stress
- 1997
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Ingår i: Journal of Medical Microbiology. - : The Microbiology Society. - 0022-2615 .- 1473-5644. ; 46:7, s. 571-578
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Tidskriftsartikel (refereegranskat)abstract
- Adult conventional rats were starved for 48 h with or without haemorrhage at 24 h, and translocation of caecal coliforms to mesenteric lymph nodes (MLNs) was measured. Translocation was detected in three of 11 rats without haemorrhage, in 6 of 11 starved and sham-operated rats and in 12 of 22 rats after haemorrhage. In contrast, only one of 13 non-instrumented and fed control rats showed translocation. Translocation was associated with more coliforms adhering to caecal epithelium in rats. Coliform isolates from caecum, caecal epithelium and MLNs were characterised and grouped into different biochemical phenotypes (BPTs) by a biochemical fingerprinting method. Of 291 BPTs detected in the caecum of all rats, 108 were also found on caecal epithelium; 36 BPTs were detected in MLNs, of which 17 were not detected either in the caecum or on the caecal epithelium of the corresponding rats. One isolate from each of these 36 BPTs was selected and compared to the others. Four common (C) BPTs (i.e., C1-C4) were identified among them. Strains of C1 formed the majority of isolates from the caecum (79%), caecal epithelium(71%) and MLNs (91%). In contrast, C2-C4 had a significantly lower incidence both in the caecum and on the caecal epithelium, but not in the MLNs. These findings indicate that not all caecal coliforms adhere to the epithelium during catabolic stress and that for translocation to occur, other bacterial properties besides adhesion are needed. It is also concluded that coliforms with a low incidence in the caecum can translocate with the same efficiency as those with a high incidence.
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| 4. |
- Muratov, E. N., et al.
(författare)
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QSAR without borders
- 2020
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Ingår i: Chemical Society Reviews. - : Royal Society of Chemistry (RSC). - 0306-0012 .- 1460-4744. ; 49:11, s. 3525-3564
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Tidskriftsartikel (refereegranskat)abstract
- Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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