INTRODUCTION:

Cancer can occur in practically any organ or tissue in the body when aberrant cells proliferate unchecked, cross their boundaries, and spread to other parts of the body and to other organs.¹ Patients who have cancer are more likely to die if the disease spreads. Metastasizing is the term for this. Two more words for cancer are neoplasm and malignant tumor. Cancer claimed the lives of 9.6 million people around the world in 2018, or one in every six people. Most common cancers in men include lung, prostrate, colon, stomach, and liver. There are people worldwide whose health, mental health, and financial well-being are being adversely affected by the rising incidence of cancer². Many cancer patients worldwide are unable to receive timely and appropriate medical care because of underdeveloped health systems in low and middle-income countries. In 2019, predicted that the year 2000 would see 20 incidences of breast cancer and 10 million deaths from cancer. World Health Organization In the next two decades, the burden of cancer is expected to rise by nearly 60 percent, putting significant strain on health systems, individuals, and communities³.There will be an estimated 30 million additional cancer cases worldwide by 2040. The most considerable growth will be seen among developing countries, particularly in low and medium-income countries. If no other measures are taken to prevent and control cancer, the number of cancer diagnoses in the Americas will rise by 55% to 6.23 million by 2040. Common cancers are also one of the most hazardous because they can go undetected at various stages of the disease. Early detection of tumors is expected to improve patient survival rates due to this new technology. True positives are generated in great numbers by this method. In 2020, Low-dose CT radiation exposure may also raise a patient's cancer risk^4,5.

Due to the enormous population that needs to be screened, a new, less invasive screening method is needed. To ensure that such a broad spectrum of healthcare professionals can operate the technology effectively, as of Sensors and microelectronics can be used in various industries to reduce the size of complex equipment⁵. Medical technology has led to the development of diagnostic sensors over time. Sensor development in this field focuses on precisely identifying biomarkers and volatile metabolites associated with lung cancer. Their approaches to discovering cancer-related chemicals differed substantially as a result⁵. It is necessary to have sensors that can identify specific molecules.

METHODOLOGY:

Researchers searched literature from August 2011 to August 2021 using methods utilized in prior study initiatives abroad. Each database was searched. E-research archives contain data from peer-reviewed articles, books, research papers, patent filings, and other anticancer plant studies. These records date back to August 2021. The most common cancers that affect both men and women include prostate and breast cancer, as well as Kaposi sarcoma and Burkitt's lymphoma.

RESULT AND DISCUSSION:

Evaluation of Cancer Target Medication:

Proteins are ideal for cancer treatment because they are natural and readily available. Incorporating protein into a patient's regular diet is simple due to its high absorption rate. Compared to synthetic substances, proteins are less toxic to human cells and are more tolerated by the body⁶. Some exceptions include cyanogenetic glycosides and lignans, and some taxanes. Clinical trials may be possible for plant-derived medicines if the study is selective, nontoxic to regular cell lines, and cytotoxic to cancer cells. A few years ago, Inhibitors of histone deacetylase (HDA), methyltransferase (MT), or mitotic disruption (MD) were all examples of plant-derived medicines⁷. As seen in Figure 1, the numerous substances under review include their origin, anticancer activity, and development toward clinical trials in 1997.

Figure 1: Natural bioactive components discovery from 18th to 19th Century.

Sulforaphane, isothiocyanates, isoflavones, and pomiferin are all HDAC inhibitors. They block carcinogenic proteins from causing cancer⁸. Sulforaphane, for example, blocks critical targets in breast cancer development. Sulforaphane reduced ER, EGFR, and HER-2 expression in breast cancer cell lines. Apoptotic cells can enter apoptosis when HDAC inhibitors reactivate epigenetically suppressed acetylation-dependent genes (apoptosis)⁸. Plant-derived HDAC inhibitors can cure human malignancies more successfully. Vinca alkaloid derivatives include vincristine, vinblastine, vinorelbine, vindesine, and vinflunine target microtubule dynamics by binding to tubulin. Taxanes like these, including Paclitaxel and docetaxel, destroy microtubules. These chemicals hinder metaphase-anaphase transition, causing growth arrest and death. Paclitaxel works by stabilizing or polymerizing microtubules within cancer cells. Paclitaxel changed cancer treatment, and vincristine and vinblastine, two of its variants, were identified first. Plant extracts combining vinca alkaloids, Urtica membranaceous,Taxus diterpenes, Podophyllum lignans, and Camptotheca alkaloids may have improved anticancer effects⁹. The study indicated that anticancer plant extracts might destroy cancer cells without affecting normal human lymphocytes and fibroblasts. Using plant extracts instead of chemical ones minimizes the chance of hazardous side effects. The plant extracts improved the number of G1 cells with decreased DNA content and chromatin condensation.

Traditional Concept for cancer management:

Although the public has spent billions on cancer research, no medications have been shown to lessen the mortality rate from the disease¹⁰. Doctors often assume that only synthetic treatments like chemotherapy or surgery can cure the disease. In contrast, these drugs encourage the growth of cancer cells. In contrast, ancient metaphysics and systems had a firm grasp of balance, harmony, and life concepts. In the fight against cancer, modern medicine has fallen short due to its failure to recognize the role of immunity. In 2012, traditional healers were aware of it, but contemporary science was unaware of it at the time. Cancer-induced imbalances have not been addressed by existing treatment, according to experts. Smoking and drinking are the two things that can stabilize your body and lower your risk of cancer. Modern drugs and medicines can become carcinogens when used for an extended period because of their ability to generate mutations¹¹. Re-energizing the cells requires a sattvic diet rich in green and rainbow foods. One of the most popular myths about veganism is that it can heal and prevent cancer. Cancer cells are said to be energized and balanced by several modalities, including yoga, acupuncture, and crystal treatment. Controlling or regulating one's anger, emotions, and desires can be beneficial. It is possible to achieve inner peace and harmony via compassionate life¹².

Methods for identifying cancer drivers based on gene mutations.:

A gene known as a "driver" must be changed in order for a tumor to grow. Cell division, checkpoints, and other genomic alterations accumulate in cancer cells as a result of driver mutations, which are somatic mutations that contribute to abnormal cell proliferation and carcinogenesis (Figure 2). Mutations that have no effect on the cells may be possible¹³. In 2007, due to clonal proliferation, all final cancer cells will have attendant mutations, making them just as important as driving mutations¹⁴.

Figure 2: Mutations in the driver genes in the genome induce tumorigenicity in the cell.

Proto-oncogenes and tumor suppressor genes are two cancer-causing genes. Oncogene genes like BRAF, KRAS, or MYC must have a gain of function mutation to become cancerous. Two copies of the tumor suppressor gene must be inactivated for the tumor to form. This includes TP53, PTEN, and CDKN2A. Finding driver genes can be done by looking for DNA sequences that affect a particular protein location¹⁵. These genes can be used to classify mutations that cause disease. Those genes that have changed more frequently than expected are known as driver genes. In multi-cancer patients, driver changes are more common.

In 2009, mutational processes have caused an overstock of mutations relative to the projected base rate, leading to a new class of approaches for finding areas of DNA. It has been discovered that 568 genes are responsible for 28,000 tumors and 66 malignancies. Only 2% of driver mutations in proteins are accountable for the more than 20 different malignancies caused¹⁶. Knowledge of driver genes and their tumorigenic potential is necessary for cancer patients' antibody or other inhibitor treatment. There are still many unanswered questions about driving genes. In contrast to rare genes, those that often occur in the human population are discovered. A rare genetic mutation drives less than 1% of all malignancies¹⁷.

Figure 3: Classification of drive gene.

To understand why particular driver mutations are so rare, researchers used a theoretical framework. The protein is activated by tissue-specific driver mutations with high or low frequency, according to the explanation. Because just a few driver mutations happen, the protein activation is insufficient, according to the theory of this study. Even while clinically unusual drivers may be found, computational^18,19 software may not, which creates an entirely different set of complications¹⁸. It is becoming more and more common knowledge that non-coding genes contribute to the occurrence of driving mutations, but their total number in the human genome is still very small. From a study stated that These components generate extraordinarily lengthy sequences, notwithstanding our discovery²⁰.

Nano-medication for cancer drug development:

Nanoparticles have unique physical and chemical properties because of their small size and high surface-to-volume ratio. In the 2017, Tinkle S nanoparticles, made of biocompatible materials, are used in cancer therapy to solve issues such as limited specificity and bioavailability²¹. Increased solubility/biocompatibility, stability in physiological fluids, and retention duration in tumor vasculature benefit nanoparticle encapsulation. As an alternative, nanoparticles can be designed to release medications in reaction to specific triggers. A study stated that when the temperature changes, ThermoDox releases doxorubicin from the liposomes. Gold nanoparticles are attractive because of their optical, electrical, and toxicological properties. They are used in X-ray, CT, photoacoustic, and photodynamic therapy as contrast agents. The FDA approved AuroShell (Nanospectra) in 2012 to treat breast cancer with photodynamic therapy²¹. Drugs can deliver by using organic nanoparticles. There are two types of phospholipids: micelles and liposomes, both of which have the same structure. Similar to cell membranes, liposomes are spheres that contain at least one layer of lipid. Hydrophobic drugs can also be incorporated into the bilayer or chemically attached particles. The hydrophobic core of micelles allows them to encapsulate medications. Doxorubicin-loaded PEGylated liposomes were licensed by the FDA in 1995 for use in the treatment of AIDS-associated Kaposi's sarcoma²². There are fewer adverse effects associated with Doxorubicin use. Many liposomal cancer medicines, such as Myocet and DaunoXome, have been approved by the FDA since that time. They are highly flexible since their architecture can easily manipulated. Poly-L-lysine dendrimers loaded with doxorubicin have been shown to decrease angiogenesis in a tumor model²³.

Virtual Screening and Molecular docking of Cancer drugs:

By utilizing computer-aided drug design, you may save money, time, and effort to create new medications. In 2020, Chemotherapy has long targeted DNA replication because cancer cells expand unchecked compared to non-cancerous ones. DNA replication-licensing complex component MCM7 is a possible therapeutic target in some malignancies. We computationally tested 452 biogenic chemicals for their ability to bind to the MCM7 protein using the UEFS Natural Products dataset. According to three of them (UEFS99, UEFS137, and UEFS428) were shown to be very MCM7-specific (see below). Twelve active site amino acids in MCM7 are targeted by UEFS99 (Pro383, Gln384, Val 385, Ala386, Lys387, Ser388, Gln389, Asp445, Glu446, Lys449, Ala487, and Asn489). Van der Waals interactions between UEFS99 and MCM7 include Asp445, Gln389, and Lys449 of MCM7 (Figure 4).

Figure 4: UEFS99-binding residues in MCM7 Residual interactions are denoted by a variety of colors.

Digital drug assignment development for Artificial Intelligence:

Machine learning is commonly referred to as "artificial intelligence" (AI) (ML). When it comes to artificial intelligence (AI), computer science and the FDA's action plan use the term "intelligent computer programs²⁴." In 2019, it is difficult to find good predictive algorithms for treatment decisions based on ML because of the various variables that influence MTA efficacy. An advanced then rule-based expert system was employed to build the DDA system based on the experimental data. Transparency, openness, and consistency are all hallmarks of rule-based expert systems. reduced the system's complexity. The Realtime Oncology Treatment CalculatorTM uses TMB, MSI, druggable targets, MTAs, and tumor type in its calculations²⁵ (Figure 5). In addition, COSMIC was incorporated. A gene's molecular changes and druggable target genes and MTAs are all considered by the DDA algorithm during scoring²⁶. In 2020, the MDAP will be implemented. Then, based on their link to all of the tumor's putative driver genes, it assigns each gene a score. Based on how many "driver genes" they have, how many "target genes," and their ratings, MTAs are ranked. A study by According to evidence, biological parameters in one tumor can be compared to those in other tumors to compute weights²⁷.

Figure 5: Schematic representation of a digital medication assignment system (DDA). Druggable molecular targets (TARGET) and cancer-promoting genes (DRIVER) have been linked (MTA). A DDA score is produced for each driver gene present in the tumour, together with their targets and MTAs. In the AEL score computation for DRIVERS, TARGETS, and MTAs, the lines represent evidence-based functional associations, not physical correlations.

CONCLUSION:

Overexpression of MCM7, a DNA replication license complex component, is found in several types of human malignancies^28-31. To discover MCM7 inhibitors, biogenic chemicals were screened against the MCM7 protein. DDA was used in this investigation to determine which MTAs should be prioritized for precision oncology. DDA is a sophisticated artificial intelligence (AI) system. Each case's unique algorithm, evidence database, and comprehensive explanations of reasoning and action procedure are all accessible to human quality assurance. If–then links and techniques abound in the expert system. These devices are included in the FDA's AI/ML Action Plan for Medical Devices^32-36. The advantages of using an electronic nose versus a biosensor are numerous. In this way, the sensor can be utilized for multiple readings without any degradation in performance. Large-scale manufacture of these items is now possible. Modern science must first grasp and support traditional principles like harmony, healing, and compassion³⁰. Balance, repair, and compassion can only be supported by modern science. Anticancer drugs produced from plants are becoming more popular because of their efficacy. Regulating these agents is necessary to accommodate demand and ensure their long-term viability. Sections of this report detail some of the leading players in the Cancer Cognitive Computing market at the end of the study^37-40.' These companies include IBM; Microsoft; Google; Apple; Palantir; Alacris; and others. Financial information, product and service offerings, and recent developments are all included in this report^41,42. Additionally, the big players in the industry are discussed both favorably and unfavorably in this part. As a result of this research, companies can use the data to develop business plans and identify new market opportunities in the cancer cognitive computing industry worldwide⁴².

REFERENCES:

1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010; 127(12): 2893–917. doi:10.1002/ijc.25516.

2. Malnic B, Hirono J, Sato T, Buck LB. Combinatorial receptor codes for odors. Cell. 1999; 96(5): 713–23. doi:10.1016/s0092-8674(00)80581-4.

3. Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol. 2016; 11(1): 39–51. doi:10.1016/j.jtho.2015.09.009.

4. World Health Organization (WHO). Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000–2019. Geneva: WHO; 2020.

5. Rahman Bhuiyan MK, Rahman DM, Kumar Das S, Shahidul Islam DSM, Mahmud DF, Mukit A, et al. Current aspects of non Hodgkin lymphoma (NHL) in Bangladesh: A mini review. Clin Med Health Res J [Internet]. 2023; 3(01): 312–6. Available from: https://doi.org/10.18535/cmhrj.v3i01.130

6. Geras KJ, Mann RM, Moy L. Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives. Radiology. 2019; 293(2): 246–59. doi:10.1148/radiol.2019182627.

7. Bray F, Laversanne M, Weiderpass E, Soerjomataram I. The ever-increasing importance of cancer as a leading cause of premature death worldwide. Cancer. 2021; 127(16): 3029–30. doi:10.1002/cncr.33587.

8. Hu L, Bell D, Antani S, Xue Z, Yu K, Horning MP, et al. An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening. J Natl Cancer Inst. 2019; 111(9): 923–32. doi:10.1093/jnci/djy225.

9. Tschandl P, Rinner C, Apalla Z, Argenziano G, Codella N, Halpern A, et al. Human-computer collaboration for skin cancer recognition. Nat Med. 2020; 26(8): 1229–34. doi:10.1038/s41591-020-0942-0.

10. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542(7639): 115–8. doi:10.1038/nature21056.

11. Liu Q, Lim SY, Soo RA, Park MK, Shin Y. A rapid MZI-IDA sensor system for EGFR mutation testing in non-small cell lung cancer (NSCLC). Biosens Bioelectron. 2015; 74: 865–71. doi:10.1016/j.bios.2015.07.055.

12. Bayley JP, Devilee P. Warburg tumours and the mechanisms of mitochondrial tumour suppressor genes. Curr Opin Genet Dev. 2010;20(3):324–9. doi:10.1016/j.gde.2010.02.008.

13. Shah U, Shah R, Acharya S, Acharya N. Novel anticancer agents from plant sources. Chin J Nat Med. 2013; 11(1): 16–23. doi:10.1016/s1875-5364(13)60002-3.

14. Phillipson JD. Medicinal plants. J Bio Educ. 1997; 31(2): 109–15.

15. Khazir J, Mir BA, Pilcher L, Riley DL. Role of plants in anticancer drug discovery. Phytochem Lett. 2014; 7: 173–81. doi:10.1016/j.phytol.2013.11.010.

16. Kumar R, Saha P, Nyarko RO, Lokare P, Boateng ADE, Kahwa I, et al. Effect of covid-19 in management of lung cancer disease: A review. Asian J Pharm Res Dev. 2022; 10(3): 58–64. doi:10.22270/ajprd.v10i3.1131.

17. Solowey E, Lichtenstein M, Sallon S, Paavilainen H, Solowey E, Lorberboum-Galski H. Evaluating medicinal plants for anticancer activity. Sci World J. 2014; 2014: 721402. doi:10.1155/2014/721402.

18. Afrin S, Pillai HPJS, Emmanuel ESC, Viktor P, Al Hafiz M, Aboqade Al-Aouadi RF, et al. A systematic review of bioinformatics’ influence on drug developments. Res J Pharm Technol. 2024; 17(6): 6158–64. Available from: https://doi.org/10.52711/0974-360x.2024.00934

19. Mahedi RA, Iqbal H, Azmee R, Azmee M, Jakir F, Nishan MA, et al. Current trends and future prospects of artificial intelligence in transforming radiology. J Curr Health Sci. 2024; 4(2): 95–104. Available from: https://doi.org/10.47679/jchs.202487

20. Bhatnagar P, Pant AB, Shukla Y, Chaudhari B, Kumar P, Gupta KC. Bromelain nanoparticles protect against 7,12-dimethylbenz[a]anthracene induced skin carcinogenesis in mouse model. Eur J Pharm Biopharm. 2015; 91: 35–46. doi:10.1016/j.ejpb.2015.01.015.

21. Zschocke S, Rabe T, Taylor JL, Jager AK, van Staden J. Plant part substitution--a way to conserve endangered medicinal plants? J Ethnopharmacol. 2000; 71(1-2): 281–92. doi:10.1016/s0378-8741(00)00186-0.

22. Stagos D, Amoutzias GD, Matakos A, Spyrou A, Tsatsakis AM, Kouretas D. Chemoprevention of liver cancer by plant polyphenols. Food Chem Toxicol. 2012; 50(6): 2155–70. doi:10.1016/j.fct.2012.04.002.

23. Zajicek G. Cancer and metaphysics. Med Hypotheses. 2001; 57(2): 243–8. doi:10.1054/mehy.2001.1348.

24. Starwyn D. The Psycho-Emotional Roots of Cancer. Acupunct Today. 2012;13.

25. Kearney KG, Hicks RE. Self-compassion and breast cancer in 23 cancer respondents: Is the way you relate to yourself a factor in disease onset and progress? Psychology (Irvine). 2017; 8(1): 14–26. doi:10.4236/psych.2017.81002.

26. Rahnama M, Khoshknab MF, Maddah SS, Ahmadi F. Iranian cancer patients' perception of spirituality: a qualitative content analysis study. BMC Nurs. 2012; 11: 19. doi:10.1186/1472-6955-11-19.

27. Chauhan A, Semwal DK, Mishra SP, Semwal RB. Ayurvedic concept of Shatkriyakala: a traditional knowledge of cancer pathogenesis and therapy. J Integr Med. 2017; 15(2): 88–94. doi:10.1016/S2095-4964(17)60311-X.

28. Haber DA, Settleman J. Cancer: drivers and passengers. Nature. 2007; 446(7132): 145–6. doi:10.1038/446145a.

29. DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB. The biology of cancer: Metabolic reprogramming fuels cell growth and proliferation. Cell Metab [Internet]. 2008; 7(1): 11–20. Available from: http://doi.org/10.1016/j.cmet.2007.10.002

30. McFarland CD, Mirny LA, Korolev KS. Tug-of-war between driver and passenger mutations in cancer and other adaptive processes. Proc Natl Acad Sci U S A [Internet]. 2014; 111(42): 15138–43. Available from: http://doi.org/10.1073/pnas.1404341111

31. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009; 458(7239): 719–24. doi:10.1038/nature07943.

32. Salichos L, Meyerson W, Warrell J, Gerstein M. Estimating growth patterns and driver effects in tumor evolution from individual samples. Nat Commun [Internet]. 2020; 11(1): 732. Available from: http://dx.doi.org/10.1038/s41467-020-14407-9

33. Kumar S, Gerstein M. Cancer genomics: Less is more in the hunt for driver mutations. Nature. 2017; 547(7661): 40–1. doi:10.1038/nature23085.

34. Albanese A, Tang PS, Chan WC. The effect of nanoparticle size, shape, and surface chemistry on biological systems. Annu Rev Biomed Eng. 2012; 14: 1–16. doi:10.1146/annurev-bioeng-071811-150124.

35. Gerlowski LE, Jain RK. Microvascular permeability of normal and neoplastic tissues. Microvasc Res. 1986; 31(3): 288–305. doi:10.1016/0026-2862(86)90018-x.

36. Kim EM, Jeong HJ. Current Status and Future Direction of Nanomedicine: Focus on Advanced Biological and Medical Applications. Nucl Med Mol Imaging. 2017; 51(2): 106–17. doi:10.1007/s13139-016-0435-8.

37. Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N, et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res. 2019; 47(D1): D941–7. doi:10.1093/nar/gky1015.

38. Patidar A, Shivhare SC, Ateneriya U, Choudhary S. A Comprehensive Review on Breast Cancer. Asian J Nurs Educ Res. 2012; 2(1): 28–32.

39. Iranmanesh S, Shamsi A. The Relationship between Type of Cancer and Parent's Psychosocial Risks. Asian J Nurs Educ Res. 2014; 4(4): 495–501.

40. Raju CP, Babu GR, Sowjanya M, Ramayyappa M. Evaluation of Cancer Bio-markers through Hyphenated Analytical Techniques. Asian J Pharm Anal. 2021; 11(3): 235–42.

41. Honale VS, Muneshwar SD, Thipe SS, Sawale AV. Formulation and Evaluation of Herbal Anticancer Chewing Gum. Asian J Pharm Anal. 2023; 13(4): 278–84.

42. Kumbhar S, Salunkhe V, Magdum C. Targeted Drug Delivery: A Backbone for Cancer Therapy. Asian J Pharm Res. 2013; 3(1): 40–6.

Received on 20.04.2025 Revised on 04.08.2025

Accepted on 16.10.2025 Published on 20.05.2026

Available online from May 25, 2026

Research J. Pharmacy and Technology. 2026;19(5):2343-2348.

DOI: 10.52711/0974-360X.2026.00336

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.