Evaluating the Therapeutic Index & Selective Cytotoxicity of Synergistic Phytochemical Combinations

This study is a Phase II continuation that expands upon foundational research initiated in 2025. While the 2025 study established the in vitro melanoma efficacy of single agents and combinations, this 2026 phase focused on calculating synergy, mapping molecular mechanisms via multi-omic in silico validation, and performing vital safety assessments utilizing in vitro healthy melanocyte cultures and in silico safety profiling. The overall research was conducted between March 2025 and February 2026 (11 Months).

Unless otherwise indicated, all figures, molecular models, and diagrams were developed by the student researcher. All binding affinities and IC50 determinations were calculated using log-value and non-linear regression to ensure statistical precision. Figures were made with Biorender.com.

Purpose: Addressing Oncology’s Critical Flaws

The Problem:

Melanoma is an exceptionally aggressive skin cancer, responsible for 90% of skin cancer deaths due to its high metastatic potential. Current clinical therapies face three devastating hurdles:

Systemic Toxicity: Treatments fail to distinguish between melanoma and healthy melanocytes, causing severe damage to healthy tissue.

Multi-Drug Resistance (MDR): Repeated exposure triggers efflux pumps (P-gp), which actively eject drugs from the cell, rendering treatment ineffective.

Economic Burden: With costs exceeding $100,000 per course, effective treatment remains a "financial death sentence" for patients in resource-limited regions like Ethiopia.

The Inspiration:

My parents’ upbringing in Ethiopia inspired this project; they witnessed how the combination of a harsh sun and unaffordable medical care turned treatable conditions into fatalities. While my 2025 research established the efficacy of phytochemicals (natural compounds), the 2026 phase was driven by the urgent need for patient safety and global accessibility.

The Solution:

The research question sought to determine if a synergistic, multi-omic, polypharmacological approach could restore sensitivity to cancer cells while sparing healthy physiology.

By using a matrix of six compounds—including Piperine to block efflux-mediated resistance and Quercetin to inhibit proliferation—this study establishes a new Therapeutic Safety Benchmark.

Global Impact & Future Vision

This project eases the lives of patients by offering a selective, non-toxic alternative that is 54x to 814x cheaper than standard chemotherapy. It bridges the gap between high-level systems pharmacology and humanitarian medicine, ensuring that a patient’s survival is dictated by science, not their bank account.

Methodology

Phase 1: Cell Culture & In Vitro Testing

I cultured A375 Melanoma and HEMn-DP (normal) cells in L-15 and Medium 254 at 37°C. Using 96-well plates, I tested six phytochemicals (Curcumin, Salicylic Acid, Artesunate, Quercetin, Salinomycin, Piperine) across 18/24/48-hour exposures. Cell viability was quantified using WST-8 assays (mitochondrial activity via 450nm absorbance), CyQUANT/NucBlue (DNA/genomic content via 480/520nm fluorescence), and EVE Cell Counting.

Phase 2: Statistical & Synergy Modeling

Potency was determined using Non-linear Regression to calculate IC50 values and the Selectivity Index (SI). Synergy was mathematically validated using the Chou-Talalay Combination Index (CI), Bliss Independence, and Absolute Synergy Enhancement models to quantify multi-drug interactions:

Combination Index:

(where CI < 1 indicates synergy).

Bliss Independence:

to calculate additive efficacy.

Isobolograms, Independent/Paired T-Tests, One-Way ANOVA, and Tukey’s Post-Hoc tests confirmed statistical significance (p < 0.05) and pinpointed specific group pairings driving the results.

Phase 3: Systems Pharmacology & Interactome Discovery

I utilized SwissADME to predict gastrointestinal absorption and synthetic feasibility. Medscape screened for adverse drug-drug interactions, finding zero "red flags" between the matrix compounds or with common NSAIDs and chemotherapy, ensuring safety for joint therapy. SwissTargetPrediction screened the top 90 compound-protein interactions which, through STRING Database network mapping, first identified STAT3 (survival), EGFR (proliferation), and CASP3 (apoptosis) as primary targets. Toxicity risks were screened via ToxPred and ProTox.

Phase 4: Multi-Omic Validation & Benchmarking

To confirm clinical significance, I used GEO (GSE3189) and GEPIA 2 to evaluate ABCB1 expression levels in melanoma. TCGA (KM Plotter) survival analysis correlated expression with patient outcomes. DAVID/KEGG pathway analysis was used to identify the top enriched biological pathways. Cytoscape was then used to confirm ABCB1 as the "strategic gatekeeper" for the identified signaling hubs. Finally, AutoDock Vina and PyMOL were used for high-resolution docking to compare binding stability against FDA benchmarks.

Results and Analysis

The multi-targeted synergistic matrix demonstrated superior efficacy and safety compared to conventional chemotherapeutics across all testing phases.

1. Individual Efficacy and Selectivity (IC50 & SI)

Dose-response curves revealed that all six phytochemicals induced potent, dose-dependent inhibition of A375 Melanoma cells. Salinomycin was the most potent (IC50 = 7.74µM). Fluorescence microscopy confirmed selective toxicity, showing total membrane collapse in A375 cells while healthy HEMn-DP cells remained intact. Critically, the Selectivity Index (SI) at 48 hours showed that Quercetin (SI: 38.96) and Piperine (SI: 19.63) were significantly safer for normal HEMn-DP cells than traditional treatments. T-test and One-Way ANOVA (p < 0.001) confirmed that increasing doses significantly changed outcomes without affecting normal cell viability at low-to-medium concentrations.

2. Synergistic Interactions (CI, Bliss and ABS)

Synergy was mathematically validated through the Combination Index (CI), Bliss Independence, and Absolute Synergy Enhancement models. Group 8 (Most Efficient Combination) achieved the highest observed killing percentage (91%).

Combination Index: All combinations yielded CI < 1.0 indicating synergy, with the SA+PIP pairing showing the strongest synergy.

Bliss Effect: Comparisons of "Observed vs. Expected" cell death revealed that the combinations produced significantly greater lethality than the sum of their individual parts. Group 8 outperformed it's Bliss Expected Efficacy by 49.9%.

Isobolograms: Data points fell deep within the synergy quadrant, proving a reduction in the required dosage for each compound while maximizing therapeutic effect.

3. Systems Pharmacology & Mechanism

Medscape verified no adverse drug interactions. ToxPred confirmed compounds are bioactive yet non-toxic, while SwissADME (BOILED-Egg) predicted high GI absorption common in chemo. ProTox-3 categorized the phytochemicals in Class 4-5, making the matrix physically safer than household NSAIDs.

SwissTargetPrediction analyzed the top 90 compound-protein interactions, identifying CASP3 (apoptosis initiation), EGFR (cell proliferation), and STAT3 (survival signaling) as the primary synergistic targets in melanoma.

4. Multi-Omic & Systemic Validation

TCGA-SKCM clinical data via GEPIA2 confirmed that high expression of the ABCB1 (P-gp) efflux pump correlates with poor patient survival (p=0.0051). KEGG Pathway Enrichment identified the ABC Transporter and PI3K-Akt pathways as primary targets for synergistic reprogramming. Topological PPI Mapping confirmed STAT3, EGFR, and CASP3 as high-degree "hub" nodes critical for melanoma progression, providing the biological rationale for multi-targeted intervention.

5. High-Resolution In Silico Docking

Molecular docking via SwissDock and PyMOL validated the binding efficacy at the identified protein hubs:

Superior Binding: Phytochemicals consistently achieved binding affinities (ΔG) between -6.17 and -8.14 kcal/mol, whereas FDA benchmarks (Cisplatin, Dacarbazine, Cyclophosphamide) showed erratic or weak binding (as low as -0.72 kcal/mol).

Convergent Docking: Unlike conventional drugs, the phytochemicals exhibited "convergent docking" into high-affinity pockets. A difference of ~6.8 kcal/mol represents a 100,000-fold increase in binding potency, proving that these natural agents physically outcompete standard chemotherapy for target access.

6. Statistical Significance and Clinical Compatibility

Tukey’s Post-Hoc analysis pinpointed Group 8 as the statistically superior combination across all time points. ProTox-II and SwissADME screening (BOILED-Egg model) confirmed high gastrointestinal absorption and zero toxicity "red flags," validating the matrix's clinical compatibility.

Discussion & Clinical Applications: The New Oncology Benchmark

This research establishes a multi-targeted, synergistic matrix that disrupts the traditional "high-toxicity, high-cost" chemotherapy standard. By integrating cellular assays with clinical genomics, the study proves that natural compounds can outcompete synthetic benchmarks in both safety and precision.

The "Gatekeeper" Strategy: Why Results Matter

Unlike non-specific regimens, this matrix exploits a "Molecular Vulnerability" through Convergent Docking. Quercetin demonstrated a 100,000-fold increase in binding potency compared to Cisplatin on the EGFR growth hub. Furthermore, TCGA Clinical Analysis confirmed with 99.5% survival certainty (P=0.0051) that ABCB1 (P-gp) is the strategic gatekeeper of metastatic melanoma. Connectivity shows that exploiting the ABCB1 vulnerability, the matrix effectively shuts down the high-degree hubs STAT3, EGFR, and CASP3 with mathematical stability (RMSD proof).

Synergy vs. Systemic Toxicity

The Absolute Synergy Enhancement of 60% observed in Group 8 confirms that multi-drug interactions provide superior lethality (91% killing) while minimizing chemical burden. Phytochemicals are 40x more selective than chemotherapeutics. Clinical Synergy allows for reduced individual dosages, shifting the therapeutic profile from Class 3 (Toxic) chemotherapy to Class 5 (Safe) benchmarks like Curcumin (LD50: 2000mg/kg), making the matrix safer than household NSAIDs.

Global Economic Accessibility

Perhaps the most disruptive finding is the Global Economic Impact. At $0.0132/mL$, the matrix is 814x (Chemotherapy) – 11,000x (Immunotherapy) more cost-effective. This addresses the $150,000/course burden, offering a viable, selective alternative for resource-limited global regions. This study bridges the gap between high-resolution systems pharmacology and affordable clinical application.

Future Directions: Translational Precision & Systemic Optimization

3D Bio-Mimetic Spheroids: 3D co-cultures of A375 and fibroblasts will be utilized to replicate tumor physical barriers and hypoxia for accurate standardized sensitivity.

Extra-Cellular Matrix Interaction: Collagen can help study how the synergistic matrix interacts with skin proteins to ensure effective topical delivery.

Heterogeneous Panel Validation: Test synergy across a diverse panel of melanoma lines (SK-MEL-28, G361, FM6, COLO892) will ensure the high Selectivity Index is universal.

ADR Predictive Modeling: Machine Learning will be employed to predict adverse reactions and refine IC50 dosages for personalized, metabolic-tailored clinical treatment.

Acknowledgements

I am deeply grateful to Team Canada-ISEF (Youth Science Canada) for selecting me as a 2025-2026 candidate. Their months of providing webinars and mentorship along with their feedback were vital in refining my project and sharpening my presentation skills. Special thanks to my parents for their unwavering support and for funding the essential laboratory equipment, including the microplate reader and incubator.

I sincerely thank Dr. Brad Doble (Bihler Chair in Stem Cell Research) for welcoming me into the Doble Lab, overseeing my work. I also thank Dr. Stephen Cornish for his foundational guidance in cell culture and training during my initial research phases.

Finally, thank you to the University of Manitoba and the Max Rady College of Medicine for providing facility access, biosafety training, and the resources of the laboratory. This collective support transformed this research from a concept into a rigorous project.

*For a complete list of peer-reviewed literature in APA, see Section 3 below.

Section 1: Suppliers

Cayman Chemical. (2026). Source of high-quality biochemicals and research compounds used in the melanoma treatment experiments. https://www.caymanchem.com/

ChemScene. (2026). Source of high-quality biochemicals and research compounds used in the melanoma treatment experiments. https://www.chemscene.com/

Cedarlane Labs. (2026). Supplier of cells, media, and reagents used for the A375 melanoma cell culture experiments. https://www.cedarlanelabs.com/

Thermo Fisher Scientific. (2026). Global supplier of laboratory equipment, reagents, and analytical instruments used for cell culture and molecular validation. Supplier of cells, media, and reagents used for the HEMn-DP (Healthy Melanocyte Darkly Pigmented) cell culture experiments https://www.thermofisher.com/

Section 2: Materials and Tools (Multi-Omic Silico Validation)

AutoDock Vina. (2026). Molecular docking software used for high-resolution simulation of compound-protein binding stability against FDA benchmarks. https://vina.scripps.edu/

BioRender. (2026). Scientific illustration platform used to create biological diagrams and pathway illustrations for the melanoma treatment mechanisms. https://biorender.com/

Cytoscape. (2026). Open-source software platform for visualizing complex networks and integrating these with any type of attribute data, used to confirm ABCB1 as a strategic gatekeeper. https://cytoscape.org/

DAVID (Database for Annotation, Visualization and Integrated Discovery). (2026). Bioinformatics resource providing a comprehensive set of functional annotation tools for investigators to understand the biological meaning behind large lists of genes. https://david.ncifcrf.gov/

GEPIA 2 (Gene Expression Profiling Interactive Analysis). (2026). Web-based tool for delivering fast and customizable functionalities based on TCGA and GTEx data, used to evaluate ABCB1 expression levels. http://gepia2.cancer-pku.cn/

GEO (Gene Expression Omnibus). (2026). Public functional genomics data repository supporting MIAME-compliant data submissions, used to evaluate ABCB1 expression levels (GSE3189). https://www.ncbi.nlm.nih.gov/geo/

KEGG (Kyoto Encyclopedia of Genes and Genomes). (2026). Database resource for understanding high-level functions and utilities of the biological system, used for pathway enrichment analysis. https://www.genome.jp/kegg/

Medscape. (2026). Medical information platform used to screen for adverse drug-drug interactions between matrix compounds and common medications. https://www.medscape.com/

Online Calculator. (2026). µg/mL to µM Calculator used for precise concentration conversions in the experimental design and analysis.

ProTox-3.0. (2026). Webserver for the prediction of toxicity of small molecules, used to assess the safety profile of the multi-targeted therapy. https://tox.charite.de/protox_II/

PubChem. (2026). Open chemistry database at the National Institutes of Health (NIH) used for chemical structure information and property validation. https://pubchem.ncbi.nlm.nih.gov/

PyMOL. (2026). Molecular visualization system on an open-source foundation, used for high-resolution docking visualization and structural analysis. https://pymol.org/

STRING Database. (2026). Protein-protein interaction networks database used for network mapping to identify primary targets like STAT3, EGFR, and CASP3. https://string-db.org/

SwissADME. (2026). Web tool that allows the prediction of physicochemical properties, ADME parameters, pharmacokinetic profile, and drug-likeness. http://www.swissadme.ch/

SwissDock. (2026). Web service to predict the molecular interactions that may occur between a target protein and a small molecule. http://www.swissdock.ch/

SwissTargetPrediction. (2026). Web tool to predict the most probable macromolecular targets of a small molecule, used to screen compound-protein interactions. http://www.swisstargetprediction.ch/

TCGA (The Cancer Genome Atlas). (2026). Landmark cancer genomics program that molecularly characterized over 20,000 primary cancer and matched normal samples, used for survival analysis. https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga

ToxPred by ProteinIQ. (2026). AI-based toxicity prediction tool used for screening potential risks and safety profiles of the compounds. https://proteiniq.io/

Section 3: Research and References (APA)

Al Hmada, Y., Brodell, R. T., Kharouf, N., Flanagan, T. W., Alamodi, A. A., Hassan, S. Y., Shalaby, H., Hassan, S. L., Haikel, Y., Megahed, M., Santourlidis, S., & Hassan, M. (2024). Mechanisms of melanoma progression and treatment resistance: Role of cancer stem-like cells. Cancers, 16(2), 470. https://doi.org/10.3390/cancers16020470

Andres, S., Pevny, S., Ziegenhagen, R., Bakhiya, N., Schäfer, B., Hirsch‐Ernst, K. I., & Lampen, A. (2018) . Safety aspects of the use of quercetin as a dietary supplement. Molecular nutrition & food research, 62(1), 1700447. https://doi.org/10.1002/mnfr.201700447

Arif, T. (2015) . Salicylic acid as a peeling agent: a comprehensive review. Clinical, cosmetic and investigational dermatology, 455-461. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4554394/

Hasan Arif; Sandeep Aggarwal. (2023) . Salicylic Acid. In StatPearls. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK519032/

BC Ministry of Health. (2024) . Radiation therapy costs in British Columbia. As reported by CBC News. https://www.cbc.ca/news/canada/british-columbia/16-million-bellingham-cancer-program-1.7255934

Berger, T. G., Dieckmann, D., Efferth, T., Schultz, E. S., Funk, J. O., Baur, A., & Schuler, G. (2005) . Artesunate in the treatment of metastatic uveal melanoma—first experiences. Oncology reports, 14(6), 1599-1603. https://www.spandidos-publications.com/or/14/6/1599

Bill, M. A., et al. (2009) . Curcumin induces proapoptotic effects against human melanoma cells. Cancer Research, 69(Supplement 2), 5107. https://doi.org/10.1158/0008-5472.CAN-09-5107

BioRender. Scientific illustration platform used to create biological diagrams and pathway illustrations for the melanoma treatment mechanisms. https://biorender.com/

Bliss, C. I. (1939) . The toxicity of poisons applied jointly. Annals of Applied Biology, 26(3), 585-615. https://doi.org/10.1111/j.1744-7348.1939.tb06990.x

Brillinger, D. (1984) . The Collected Works of John W. Tukey.

Canadian Cancer Society. (2025). Average lifetime cost for cancer patients in Canada. Referenced in multiple 2024-2025 sources indicating average costs of $33,000-$40,000 CAD annually.

Cao, H. H., Chu, J. H., Kwan, H. Y., Su, T., Yu, H., Cheng, C. Y., ... & Yu, Z. L. (2015). Quercetin inhibits HGF/c-Met signaling and HGF-stimulated melanoma cell migration and invasion. Molecular cancer, 14(1), 1-13. https://doi.org/10.1186/s12943-015-0375-3

Cayman Chemical. Source of high-quality biochemicals and research compounds used in the melanoma treatment experiments. https://www.caymanchem.com/

Cedarlane Labs. Supplier of cells, media, and reagents used for the A375 melanoma cell culture experiments. https://www.cedarlanelabs.com/

ChemScene. Source of high-quality biochemicals and research compounds used in the melanoma treatment experiments. https://www.chemscene.com/

Cheng, J., Fan, D., Wang, G., Gao, S., Zhang, Y., & Zhou, Z. (2020) . Acetylsalicylic acid and salicylic acid present anticancer properties against melanoma. Scientific reports, 10(1), 18405. https://doi.org/10.1038/s41598-020-75416-1

Chou, T. C. (2010) . Drug combination studies and their synergy quantification using the Chou-Talalay method. Cancer Research, 70(2), 440-446. https://doi.org/10.1158/0008-5472.CAN-09-1947

CoherentMI. (2025) . Global TIL Therapy market valuation. Referenced in FDA approval data indicating market value of USD 0.13 Billion in 2025.

Cragg, G. M., & Pezzuto, J. M. (2016). Natural products as a vital source for the discovery of cancer chemotherapeutic and chemopreventive agents. Medical Principles and Practice, 25(Suppl. 2), 41-59. https://doi.org/10.1159/000443404

Crealey, G. E., Hackett, C., Harkin, K., Heckmann, P., Kelleher, F., Lyng, Á., McCarthy, T., McEnery, M., Meaney, C., Roche, D., et al. (2023) . Melanoma-related costs by disease stage and phase of management in Ireland. Journal of Public Health, 45(3), 714-722. https://doi.org/10.1093/pubmed/fdac154

Drugs.com. Online pharmaceutical information resource used to research potential drug interactions for artesunate and other compounds. https://www.drugs.com/drug-interactions/artesunate.html

Efferth, T., & Oesch, F. (2021) . Repurposing of artemisinin-type drugs for the treatment of acute leukemia, cancers, and COVID-19. Molecules, 26(9), 2531. https://pubmed.ncbi.nlm.nih.gov/33925311/

European Bioinformatics Institute (EMBL-EBI) . ChEMBL database used for chemical structure information and bioactivity data for the natural compounds studied. https://www.ebi.ac.uk/chembl/

Famenini, S., Wu, J. J., Cho, E., & Kim, D. J. (2015) . Aspirin use and melanoma: A UCLA pilot study. Journal of the American Academy of Dermatology, 72(5), AB249. https://doi.org/10.1016/j.jaad.2015.02.984

FDA. (2024) . Approval data for lifileucel (Amtagvi) TIL therapy. Single treatment cost USD $515,000, equivalent to approximately CAD $700,000.

Fofaria, N. M., Kim, S. H., & Srivastava, S. K. (2014). Piperine causes G1 phase cell cycle arrest and apoptosis in melanoma cells. PLoS One, 9(4), e94296. https://doi.org/10.1371/journal.pone.0094296

Grammarly. AI Grammar and Paraphrasing tool that helped with the proper grammar and writing of my project. https://www.grammarly.com/

Grinevicius, V. M. A. S., Kviecinski, M. R., Santos, A. R. S., Correia, J. F. G., Filho, D. W., & Pedrosa, R. C. (2023) . Piperine–Chlorogenic Acid Hybrid Inhibits the Proliferation of the SK-MEL-28 Human Melanoma Cell Line. Molecules, 28(15), 5728. https://doi.org/10.3390/molecules28155728

Gupta, P. B., Onder, T. T., Jiang, G., Tao, K., Kuperwasser, C., Weinberg, R. A., & Lander, E. S. (2009) . Identification of selective inhibitors of cancer stem cells by high-throughput screening. Cell, 138(4), 645-659. https://pubmed.ncbi.nlm.nih.gov/19682730/

Gupta, S. C., Patchva, S., & Aggarwal, B. B. (2013) . Therapeutic roles of curcumin: Lessons learned from clinical trials. The AAPS Journal, 15(1), 195-218. https://doi.org/10.1208/s12248-012-9432-8

Guy, G. P., Ekwueme, D. U., Tangka, F. K., & Richardson, L. C. (2015) . Melanoma treatment costs: A systematic review of the literature, 1990-2011. American Journal of Preventive Medicine, 43(5), 537-545. https://doi.org/10.1016/j.amepre.2012.07.031

Harris, Z., Donovan, M. G., Branco, G. M., Limesand, K. H., & Burd, R. (2016) . Quercetin as an emerging anti-melanoma agent: a four-focus area therapeutic development strategy. Frontiers in nutrition, 3, 48. https://doi.org/10.3389/fnut.2016.00048

Healio/Longwoods. (2025) . Treatment costs for advanced melanoma. Report indicating Stage IV treatment costs have tripled in recent years with new therapies.

Jiang, Z., Lim, S. O., Kang, Y., Kim, M., Kim, S. K., Lee, K., ... & Park, Y. M. (2024). Artesunate induces melanoma cell ferroptosis and augments immunotherapy through SLC7A11 degradation. Cell Communication and Signaling, 22(1), 1-19. https://doi.org/10.1186/s12964-023-01420-3

Kent State University Libraries. (2025, March 31) . SPSS Tutorials: Independent Samples t Test. https://libguides.library.kent.edu/spss/independentttest

Laerd Statistics. Independent t-test - Statistical guide. https://statistics.laerd.com/statistical-guides/independent-t-test-statistical-guide.php

Laerd Statistics. One-way ANOVA - Statistical guide. https://statistics.laerd.com/statistical-guides/one-way-anova-statistical-guide.php

Lian, C., Zuo, Y., Zuo, H., Zhang, Z., & Li, W. (2021) . Artesunate combined with verteporfin inhibits uveal melanoma by regulating MALAT1/YAP signaling. Oncology Letters, 22(6), 1-11. https://doi.org/10.3892/ol.2021.13112

Longwoods. (2025) . Advanced melanoma treatment cost analysis. Report indicating Stage IV treatment costs have increased significantly with new therapies.

Mao, L., Liu, J., Zhang, D., Chen, H., Jin, S., Zhao, N., ... & Tang, J. (2017). Salinomycin-loaded lipid-polymer nanoparticles with anti-CD20 aptamers selectively suppress human melanoma initiating cells. Scientific reports, 7(1), 14608. https://doi.org/10.1038/s41598-017-15135-y

Manus. Data visualization and graphing platform used to create the synergy analysis charts, isobolograms, and efficacy plots for the melanoma research project. https://manus.im/app

Mauro, A. G., Yazbeck, V., & Salloum, F. N. (2022) . Melanoma treatment: The heart has skin in the game. JACC: CardioOncology, 4(4), 549-551. https://doi.org/10.1016/j.jaccao.2022.09.001

Meghwal, M., & Goswami, T. K. (2013) . Piper nigrum and piperine: an update. Phytotherapy Research, 27(8), 1121-1130. https://doi.org/10.1002/ptr.4972

Mirzaei, H., Naseri, G., Rezaee, R., Mohammadi, M., Banikazemi, Z., Mirzaei, H. R., ... & Avan, A. (2016) . Curcumin: A new candidate for melanoma therapy?. International journal of cancer, 139(8), 1683-1695. https://doi.org/10.1002/ijc.30224

Mitrovic, M., & Schildknecht, E. G. (1974) . Anticoccidial activity of salinomycin (coxistac) in chicks. Poultry Science, 53(4), 1448-1455. https://doi.org/10.3382/ps.0531448

Moges, A. (2025). A Potentially Selective, and Cost-Effective Multi-Targeted Anti-Melanoma Drug Therapy. Youth Science Canada (YSC) ProjectBoard. https://partner.projectboard.world/ysc/project/a-potentially-selective-and-cost-effective-multitargeted-anti-melanoma-drug-therapy

Online Calculator. µg/mL to µM Calculator used for precise concentration conversions in the experimental design and analysis.

Patel, M., Eckburg, A., Gantiwala, S., Hart, Z., Dein, J., Lam, K., & Puri, N. (2021). Resistance to Molecularly Targeted Therapies in Melanoma. Cancers, 13(7), 1618. https://pmc.ncbi.nlm.nih.gov/articles/PMC7961479/

Piantelli, M., Rossi, C., Iezzi, T., Lattanzio, R., La Sorda, R., & Alberti, S. (2010) . Quercetin and sulforaphane in combination suppress the progression of melanoma through the down-regulation of matrix metalloproteinase-9. Oncology reports, 23(5), 1431-1436. https://doi.org/10.3892/or_00000788

QuillBot. AI Grammar and Paraphrasing tool that helped with the proper grammar and writing of my project. https://quillbot.com/

Russo, M., Spagnuolo, C., Tedesco, I., Bilotto, S., & Russo, G. L. (2012) . The flavonoid quercetin in disease prevention and therapy: Facts and fancies. Biochemical Pharmacology, 83(1), 6-15. https://doi.org/10.1016/j.bcp.2011.08.010

Science Direct. (2024) . Analysis of immune checkpoint inhibitor costs in Canada. Report indicating these treatments represent 17% of cancer medicine annual budgets with significantly increased spending on newer treatments.

Shoba, G., Joy, D., Joseph, T., Majeed, M., Rajendran, R., & Srinivas, P. S. S. R. (1998). Influence of piperine on the pharmacokinetics of curcumin in animals and human volunteers. Planta Medica, 64(04), 353-356. https://doi.org/10.1055/s-2006-957450

Song, W., Liu, Y., Zhang, H., Wu, N., & Li, F. (2019) . Antitumor and Apoptosis-inducing Effects of Piperine on Human Melanoma Cells. Tropical Journal of Pharmaceutical Research, 18(11), 2339-2344. https://doi.org/10.4314/tjpr.v18i11.14

Srinivasan, K. (2007) . Black pepper and its pungent principle-piperine: a review of diverse physiological effects. Critical reviews in food science and nutrition, 47(8), 735-748. https://doi.org/10.1080/10408390601062054

Stark, L. A., Reid, K., Sansom, O. J., Din, F. V., Guichard, S., Mayer, I., ... & Dunlop, M. G. (2007) . Aspirin activates the NF-κB signalling pathway and induces apoptosis in intestinal neoplasia in two in vivo models of human colorectal cancer. Carcinogenesis, 28(5), 968-976. https://doi.org/10.1093/carcin/bgl220

Statistics How To. Tukey Test: Honest Significant Difference. Statistical resource used for post-hoc analysis of the melanoma treatment data. https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/post-hoc/tukey-test-honest-significant-difference/

Statistics Solutions. Paired T-Test. Statistical resource used for analyzing time-dependent effects in the melanoma treatment experiments. https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/paired-sample-t-test/

Thyss, R., Pinto, S., Abbe, P., El Messaoudi, S., Hauspie, S., Vanacker, J., ... & Bertolotto, C. (2020) . Acetylsalicylic Acid Exerts Potent Antitumor and Antiangiogenic Effects in Melanoma. Cancers, 12(10), 2999. https://doi.org/10.3390/cancers12102999

Toronto Minor Surgery Center. (2025) . Mole removal cost analysis. Reporting that mole removal starts at $600 with more complex melanoma surgeries costing significantly more.

Xue, J., Zhu, X., Wang, L., Lin, C., Hong, Z., & Lin, J. (2020). Salinomycin induces autophagic cell death in salinomycin-sensitive melanoma cells. Oncology Letters, 19(3), 2450-2458. https://doi.org/10.3892/ol.2020.11354

Zhao, X., Xie, F., Zhang, L., Tang, Q., Wang, J., Li, L., & Xu, W. (2022) . Curcumin Inhibits the Growth and Metastasis of Melanoma via miR-138-5p. Journal of immunology research, 2022, 5860189. https://doi.org/10.1155/2022/5860189

Zhou, J., Wang, G., Chen, Y., Wang, H., Hua, Y., & Cai, Z. (2019) . Salinomycin effectively eliminates cancer stem-like cells and overcomes hepatic metastasis in uveal melanoma. Molecular cancer, 18(1), 1-16. https://doi.org/10.1186/s12943-019-1042-9