Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312069724> ?p ?o ?g. }
- W4312069724 endingPage "30" @default.
- W4312069724 startingPage "21" @default.
- W4312069724 abstract "The goal of this project was to utilize mechanistic simulation to demonstrate a methodology that could determine drug combination dose schedules and dose intensities that would be most effective in eliminating multidrug resistant cancer cells in early-stage colon cancer. An agent-based model of cell dynamics in human colon crypts was calibrated using measurements of human biopsy specimens. Mutant cancer cells were simulated as cells that were resistant to each of two drugs when the drugs were used separately. The drugs, 5-flurouracil and sulindac, have different mechanisms of action. An artificial neural network was used to generate nearly two hundred thousand two-drug dose schedules. A high-performance computer simulated each dose schedule as a in silico clinical trial and evaluated each dose schedule for its efficiency to cure (eliminate) multidrug resistant cancer cells and its toxicity to the host, as indicated by continued crypt function. Among the dose schedules that were generated, 2430 dose schedules were found to cure all multidrug resistant mutants in each of the 50 simulated trials and retained colon crypt function. One dose schedule was optimal; it eliminated multidrug resistant cancer cells with the minimum toxicity and had a time schedule that would be practical for implementation in the clinic. These results demonstrate a procedure to identify which combination drug dose schedules could be most effective in eliminating drug resistant cancer cells. This was accomplished using a calibrated agent-based model of a human tissue, and a high-performance computer simulation of clinical trials." @default.
- W4312069724 created "2023-01-04" @default.
- W4312069724 creator A5005224275 @default.
- W4312069724 creator A5046890578 @default.
- W4312069724 date "2023-01-09" @default.
- W4312069724 modified "2023-09-30" @default.
- W4312069724 title "Combination Chemotherapy of Multidrug-resistant Early-stage Colon Cancer: Determining Optimal Dose Schedules by High-performance Computer Simulation" @default.
- W4312069724 cites W1840481440 @default.
- W4312069724 cites W1846418786 @default.
- W4312069724 cites W1968913969 @default.
- W4312069724 cites W1973137168 @default.
- W4312069724 cites W1977523316 @default.
- W4312069724 cites W1979435383 @default.
- W4312069724 cites W2003437884 @default.
- W4312069724 cites W2009857926 @default.
- W4312069724 cites W2025221196 @default.
- W4312069724 cites W2025678613 @default.
- W4312069724 cites W2026387101 @default.
- W4312069724 cites W2042619042 @default.
- W4312069724 cites W2050672815 @default.
- W4312069724 cites W2061061337 @default.
- W4312069724 cites W2071735670 @default.
- W4312069724 cites W2075672121 @default.
- W4312069724 cites W2087541083 @default.
- W4312069724 cites W2088479878 @default.
- W4312069724 cites W2089148745 @default.
- W4312069724 cites W2092262835 @default.
- W4312069724 cites W2097235831 @default.
- W4312069724 cites W2099787654 @default.
- W4312069724 cites W2103980474 @default.
- W4312069724 cites W2109740395 @default.
- W4312069724 cites W2110650955 @default.
- W4312069724 cites W2123431502 @default.
- W4312069724 cites W2126228632 @default.
- W4312069724 cites W2127681478 @default.
- W4312069724 cites W2130392703 @default.
- W4312069724 cites W2137408681 @default.
- W4312069724 cites W2139183953 @default.
- W4312069724 cites W2149022033 @default.
- W4312069724 cites W2153606352 @default.
- W4312069724 cites W2154980968 @default.
- W4312069724 cites W2155582899 @default.
- W4312069724 cites W2169055426 @default.
- W4312069724 cites W2170720047 @default.
- W4312069724 cites W2342376494 @default.
- W4312069724 cites W2516244230 @default.
- W4312069724 cites W2553897545 @default.
- W4312069724 cites W2600231501 @default.
- W4312069724 cites W2600452401 @default.
- W4312069724 cites W2603434056 @default.
- W4312069724 cites W2618035748 @default.
- W4312069724 cites W2625961828 @default.
- W4312069724 cites W2641505551 @default.
- W4312069724 cites W2769426045 @default.
- W4312069724 cites W2772168100 @default.
- W4312069724 cites W2789271913 @default.
- W4312069724 cites W2803028751 @default.
- W4312069724 cites W2807637204 @default.
- W4312069724 cites W2902395974 @default.
- W4312069724 cites W2949415937 @default.
- W4312069724 cites W2951611630 @default.
- W4312069724 cites W2998744531 @default.
- W4312069724 cites W3021909056 @default.
- W4312069724 cites W3033199721 @default.
- W4312069724 cites W3035350645 @default.
- W4312069724 cites W3036137756 @default.
- W4312069724 cites W3078799708 @default.
- W4312069724 cites W3094540299 @default.
- W4312069724 cites W3099461365 @default.
- W4312069724 cites W3112044717 @default.
- W4312069724 cites W3113769443 @default.
- W4312069724 cites W3117536145 @default.
- W4312069724 cites W3120564033 @default.
- W4312069724 cites W3122336120 @default.
- W4312069724 cites W3181413488 @default.
- W4312069724 cites W3187309064 @default.
- W4312069724 cites W3191440158 @default.
- W4312069724 cites W3204811815 @default.
- W4312069724 cites W4210297318 @default.
- W4312069724 cites W4210584452 @default.
- W4312069724 cites W2952774707 @default.
- W4312069724 doi "https://doi.org/10.1158/2767-9764.crc-22-0271" @default.
- W4312069724 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36685168" @default.
- W4312069724 hasPublicationYear "2023" @default.
- W4312069724 type Work @default.
- W4312069724 citedByCount "0" @default.
- W4312069724 crossrefType "journal-article" @default.
- W4312069724 hasAuthorship W4312069724A5005224275 @default.
- W4312069724 hasAuthorship W4312069724A5046890578 @default.
- W4312069724 hasBestOaLocation W43120697241 @default.
- W4312069724 hasConcept C114851261 @default.
- W4312069724 hasConcept C121608353 @default.
- W4312069724 hasConcept C126322002 @default.
- W4312069724 hasConcept C133936738 @default.
- W4312069724 hasConcept C143998085 @default.
- W4312069724 hasConcept C2780035454 @default.
- W4312069724 hasConcept C526805850 @default.
- W4312069724 hasConcept C71924100 @default.