Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention
Cancer, which is currently rated as the second-leading cause of mortality across the globe, is one of the most hazardous disease groups that has plagued humanity for centuries. The experiments presented here span over two decades and were conducted on a specific species of mice, aiming to neutralize...
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MDPI AG
2024-02-01
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| Series: | BioMedInformatics |
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| Online Access: | https://www.mdpi.com/2673-7426/4/1/21 |
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| author | Dimitrios G. Boucharas Chryssa Anastasiadou Spyridon Karkabounas Efthimia Antonopoulou George Manis |
| author_facet | Dimitrios G. Boucharas Chryssa Anastasiadou Spyridon Karkabounas Efthimia Antonopoulou George Manis |
| author_sort | Dimitrios G. Boucharas |
| collection | DOAJ |
| description | Cancer, which is currently rated as the second-leading cause of mortality across the globe, is one of the most hazardous disease groups that has plagued humanity for centuries. The experiments presented here span over two decades and were conducted on a specific species of mice, aiming to neutralize a highly carcinogenic agent by altering its chemical structure when combined with certain compounds. A plethora of growth models, each of which makes use of distinctive qualities, are utilized in the investigation and explanation of the phenomena of chemically induced oncogenesis and prevention. The analysis ultimately results in the formalization of the process of locating the growth model that provides the best descriptive power based on predefined criteria. This is accomplished through a methodological workflow that adopts a computational pipeline based on the Levenberg–Marquardt algorithm with pioneering and conventional metrics as well as a ruleset. The developed process simplifies the investigated phenomena as the parameter space of growth models is reduced. The predictability is proven strong in the near future (i.e., a 0.61% difference between the predicted and actual values). The parameters differentiate between active compounds (i.e., classification results reach up to 96% in sensitivity and other performance metrics). The distribution of parameter contribution complements the findings that the logistic growth model is the most appropriate (i.e., 44.47%). In addition, the dosage of chemicals is increased by a factor of two for the next round of trials, which exposes parallel behavior between the two dosages. As a consequence, the study reveals important information on chemoprevention and the cycles of cancer proliferation. If developed further, it might lead to the development of nutritional supplements that completely inhibit the expansion of cancerous tumors. The methodology provided can be used to describe other phenomena that progress over time and it has the power to estimate future results. |
| first_indexed | 2024-04-24T18:31:24Z |
| format | Article |
| id | doaj.art-8dcb997c944842b29fad0c677d31cdc2 |
| institution | Directory Open Access Journal |
| issn | 2673-7426 |
| language | English |
| last_indexed | 2024-04-24T18:31:24Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj.art-8dcb997c944842b29fad0c677d31cdc22024-03-27T13:27:29ZengMDPI AGBioMedInformatics2673-74262024-02-014136038410.3390/biomedinformatics4010021Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and ChemopreventionDimitrios G. Boucharas0Chryssa Anastasiadou1Spyridon Karkabounas2Efthimia Antonopoulou3George Manis4Department of Computer Science and Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, GreeceHellenic Agricultural Organization, Fisheries Research Institute, 64007 Nea Peramos, GreeceLaboratory of Experimental Physiology, School of Medicine, University of Ioannina, 45110 Ioannina, GreeceDepartment of Zoology, School of Biology, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Computer Science and Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, GreeceCancer, which is currently rated as the second-leading cause of mortality across the globe, is one of the most hazardous disease groups that has plagued humanity for centuries. The experiments presented here span over two decades and were conducted on a specific species of mice, aiming to neutralize a highly carcinogenic agent by altering its chemical structure when combined with certain compounds. A plethora of growth models, each of which makes use of distinctive qualities, are utilized in the investigation and explanation of the phenomena of chemically induced oncogenesis and prevention. The analysis ultimately results in the formalization of the process of locating the growth model that provides the best descriptive power based on predefined criteria. This is accomplished through a methodological workflow that adopts a computational pipeline based on the Levenberg–Marquardt algorithm with pioneering and conventional metrics as well as a ruleset. The developed process simplifies the investigated phenomena as the parameter space of growth models is reduced. The predictability is proven strong in the near future (i.e., a 0.61% difference between the predicted and actual values). The parameters differentiate between active compounds (i.e., classification results reach up to 96% in sensitivity and other performance metrics). The distribution of parameter contribution complements the findings that the logistic growth model is the most appropriate (i.e., 44.47%). In addition, the dosage of chemicals is increased by a factor of two for the next round of trials, which exposes parallel behavior between the two dosages. As a consequence, the study reveals important information on chemoprevention and the cycles of cancer proliferation. If developed further, it might lead to the development of nutritional supplements that completely inhibit the expansion of cancerous tumors. The methodology provided can be used to describe other phenomena that progress over time and it has the power to estimate future results.https://www.mdpi.com/2673-7426/4/1/21benzopyrenecancerchemopreventionchemotherapymathematical modelingpolyamines |
| spellingShingle | Dimitrios G. Boucharas Chryssa Anastasiadou Spyridon Karkabounas Efthimia Antonopoulou George Manis Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention BioMedInformatics benzopyrene cancer chemoprevention chemotherapy mathematical modeling polyamines |
| title | Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention |
| title_full | Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention |
| title_fullStr | Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention |
| title_full_unstemmed | Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention |
| title_short | Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention |
| title_sort | toward cancer chemoprevention mathematical modeling of chemically induced carcinogenesis and chemoprevention |
| topic | benzopyrene cancer chemoprevention chemotherapy mathematical modeling polyamines |
| url | https://www.mdpi.com/2673-7426/4/1/21 |
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