Applied Transcriptomic Logic for Anticancer Selectivity / ATLAS / - Interpretable prognosis for susceptibility to active anti-cancer molecular forms, based on amides/carboxylic acids - hydrolysis derivatives of natural nitrile glycosides, of transcriptome cell lines inherent in tumors
Description
Development Timeline: Selective Anticancer Logic of Glycoside Amides
/Second supplemented edition/
📘 ATLAS of Molecular Oncology:
From Entomedical Insight to Chronic Cancer Management
This ATLAS presents, in an appended and systematized form, the complete monographic study on a transformative concept in oncology. Its purpose is twofold: to liberate the findings from the dense didactic format required for academic presentation, and to organize the essential molecular and clinical data needed for future therapeutic application and clinical research.
🧪 Historical and Entomedical Foundations
The roots of this work lie in a long-standing entomedical tradition—where natural compounds are studied not only for their toxicity, but for their selective therapeutic potential. This approach, grounded in biochemical logic and ecological observation, provides a unique lens through which cancer can be re-examined.
The theoretical basis of the study is built upon a well-established metabolic dependency: cancer cells feed almost exclusively on carbohydrates. This rigidity in energy sourcing creates a biochemical vulnerability. In parallel, there is growing evidence that certain nitrile glycosides, when properly modified, exhibit selective anticancer activity.
Entomedicine, with its emphasis on natural molecular selectivity and minimal systemic disruption, offers a conservative yet visionary framework for rethinking cancer therapy—not as an acute battle, but as a long-term, manageable condition.
🎯 Structure of the Study: Four Strategic Goals
The research unfolds through four interconnected goals, each representing a critical step in the transition from molecular theory to clinical viability.
1️⃣ Molecular Specificity and Therapeutic Activation
The study begins with a detailed analysis of Amygdalin, a well-known nitrile glycoside. Contrary to popular belief, Amygdalin itself is highly toxic to physiologically active animal cells and lacks direct antitumor properties. Through rigorous biochemical modeling, the research identifies the active anticancer form as a stoichiometric mixture of its amide and carboxyl derivatives—a formulation that aligns with entomedical principles of selective targeting.
2️⃣ Biochemical Pathways and Systemic Integration
The second goal investigates the compound’s journey through the body. Using advanced biochemical and mathematical analysis, the study maps its passage through the digestive system, bloodstream, and tumor microenvironment, culminating in selective membrane penetration of cancer cells. This stage reveals a series of biochemical cycles that explain the compound’s affinity for malignant cells, including metastatic variants.
3️⃣ Pharmaceutical Modeling and Formulation
The third goal focuses on practical application. A total of 54 pharmaceutical and methodological models are reviewed, each with hundreds of variables. This exhaustive analysis ensures that the therapeutic agent can be safely formulated and clinically scalable, without compromising molecular integrity.
4️⃣ Transcriptomic Prediction and Clinical Targeting
The fourth goal—highlighted in this edition—introduces a predictive framework based on transcriptome cell line analysis. By mapping the molecular affinities of glycosidic amides to known cancer cell lines, the study demonstrates superior efficacy and dramatically reduced toxicity compared to conventional treatments. This section lays the foundation for personalized, conservative oncology, where treatment is tailored to molecular susceptibility rather than generalized protocols.
🩺 Clinical Control and Therapeutic Strategy
An additional section presents a generalized clinical control model. It emphasizes the importance of guiding therapy toward irreversible pathological pathways, ensuring long-term suppression of malignant activity. This approach does not oppose conventional oncology—it refines and complements it, offering a conservative molecular alternative grounded in entomedical logic and transcriptomic precision.
🔄 Key Dependencies in Chronic Cancer Management
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Metabolic rigidity of cancer cells → reliance on carbohydrates
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Transcriptomic consistency → predictable molecular susceptibility
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Selective membrane permeability → targeted cytotoxicity
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Stoichiometric formulation → minimized systemic toxicity
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Entomedical logic → natural compounds with therapeutic selectivity
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Clinical control → suppression without disruption
✅ Conclusion: Toward a Practically Curable Future
This ATLAS does not propose a radical cure. It offers a conservative, scientifically grounded pathway toward chronic management of cancer—where molecular selectivity, transcriptomic insight, and entomedical wisdom converge to redefine what is therapeutically possible.
Cancer, in certain forms, may become chronic, predictable, and practically curable—not through disruption, but through precision, patience, and molecular understanding.
Notes (English)
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ATLAS - Interpretable prognosis for susceptibility to active anti-cancer molecular forms _ Second supplemented edition.pdf
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Additional details
Additional titles
- Subtitle (English)
- Second supplemented edition
Related works
- Is published in
- Book: 978-619-91534-6-8 (ISBN)
Dates
- Issued
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2024-10-09
References
- Cadow, J., Born, J., Manica, M., Oskooei, A., & Martínez, M. R. (2020). PaccMann: a web service for interpretable anticancer compound sensitivity prediction. Nucleic Acids Research, 48(W1), W502-8. doi:10.1093/nar/gkaa327
- Christopher, A. (2017). Drawing conclusions from data: descriptive statistics, inferential statistics, and hypothesis testing, In Interpreting and using statistics in psychological research. SAGE Publications Inc. doi:10.4135/9781506304144
- Dimitrov, S. D., Diderich, R., Sobanski, T., Pavlov, T. S., Chankov, G., Chapkanov, A., . . . Mekenyan, O. (2016). QSAR Toolbox – workflow and major functionalities. SAR and QSAR in Environmental Research, 27(3), 203-19. doi:10.1080/1062936X.2015.1136680
- Gary, W. C., Zhengyin, Y., Wensheng, L., & Masucci, A. (2012). The IC50 Concept Revisited. Current Topics in Medicinal Chemistry, 12(11). doi:10.2174/156802612800672844
- Ghandi, M., Huang, F. W., & Jané-Valbuena, J. (2019). Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature, 569, 503-8. doi:10.1038/s41586-019-1186-3
- Hubert, M., & Vandervieren, E. (2008). An adjusted boxplot for skewed distributions. Computational Statistics & Data Analysis, 52(12), 5186-201. doi:10.1016/j.csda.2007.11.008
- Legras, J., Chuzel, G., Arnaud A., Galzy, P. (1990). Natural nitriles and their metabolism. World Journal of Microbiology and Biotechnology, 6, 83-108 , doi:10.1007/BF01200927
- Marmolejo-Ramos, F., & Tian, T. (2010). he shifting boxplot. A boxplot based on essential summary statistics around the mean. International Journal of Psychological Research, 3(1), 37-45. doi:10.21500/20112084.823
- Sebaugh, J. L. (2011). Guidelines for accurate EC50/IC50 estimation. Pharmaceutical Statistics, 10, 128-34. doi:10.1002/pst.426
- Soares, J., Greninger, P., Yang, W., Edelman, E. J., Lightfoot, H., Forbes, S., . . . Garnett, M. (2013). Genomics of Drug Sensitivity in Cancer: a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research, 41(D1), D955-61. doi:10.1093/nar/gks1111
- Tsanov, V., Tsanov, H., Theoretical Analysis for the Safe Form and Dosage of Amygdalin Product, Anti-Cancer Agents in Medicinal Chemistry 2020; 20(7), DOI: 10.2174/1871520620666200313163801
- Tsanov, V., Tsanov, H., Theoretical Analysis of Anticancer Cellular Effects of Glycoside Amides, Anti-Cancer Agents in Medicinal Chemistry 2022; 22(6), DOI: 10.2174/1871520621666210903122831
- Tsanov, V., Tsanov, H., Theoretical Study of the Process of Passage of Glycoside Amides through the Cell Membrane of Cancer Cell, Anti-Cancer Agents in Medicinal Chemistry 2021; 21(12), DOI: 10.2174/1871520620999201103201008
- Tsanov, V.; Tsanov, H.,Theoretical study of anticancer activity of glycoside amides [ third supplemented edition ], pp. 857, ISBN: 978-619-91534-4-4, DOI: 10.5281/zenodo.7295357
- Yordanova, D., Schultz, T., Kuseva, C., Tankova, K., Ivanova, H., Dermen, I., . . . Mekenyan, O. (2019). Automated and standardized workflows in the OECD QSAR Toolbox. Computational Toxicology, 10, 89-104. doi:10.1016/j.comtox.2019.01.006