Systematizing Washout Protocols in Integrative Oncology: A Pharmacokinetic Model Based on 5 × t½ and Hierarchical Evidence Weighting of Botanical Extracts and Off-Label Medications

Systematizing Washout Protocols in Integrative Oncology: A Pharmacokinetic Model Based on 5 × t½ and Hierarchical Evidence Weighting of Botanical Extracts and Off-Label Medications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Systematizing Washout Protocols in Integrative Oncology: A Pharmacokinetic Model Based on 5 × t½ and Hierarchical Evidence Weighting of Botanical Extracts and Off-Label Medications Hanne Kjær Uhlig, Tina Ingrid Horsted This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9654569/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The concurrent use of botanical extracts and off-label medications by oncology patients introduces significant pharmacokinetic risks, compounded by a prevalent clinical 'disclosure gap'. There is a critical need for a systematic, evidence-based framework to safely manage complementary biosupport without compromising conventional cancer therapies Purpose: To develop an objective decision-support model based on pharmacokinetics to safely separate oncological treatment from supplementary compounds through precise timing. Method: We propose a novel clinical decision-making model based on two pillars. First, a hierarchical evidence-weighting system that categorizes 55 commonly used compounds into four levels, operationalized as a visual 'traffic-light' tool for rapid clinical assessment. Second, a strict pharmacokinetic washout protocol utilizing the 5 × t½ standard. To account for inter-species variability and data scarcity in Level 4 evidence (in-vitro/animal data), an integrated Safety Factor (S = 2) is applied to washout calculations. Results: The model establishes standardized pre-treatment washout durations for 55 compounds. For highly lipophilic substances or those with prolonged terminal half-lives, a maximum clinical ceiling of 28 days is established. This ensures that plasma concentrations reach < 3.125% of steady-state levels prior to oncology treatment, providing a robust safety overlay Conclusion: By structuring the integration of botanical and off-label biosupport, this model bridges the disclosure gap and strengthens the therapeutic alliance. It facilitates a structured patient-clinician dialogue, operationalized via an open-access clinical decision-support website (https://jegharkraeft.dk/en/chemo-and-radiotherapy-support/), providing oncologists with a structured clinical rationale and actionable guidelines to maintain the integrity of primary oncological treatments while enhancing patient safety. Oncology Integrative Oncology Complementary Oncology Pharmacokinetics Herb-Drug Interactions (HDI) Washout Protocols Chemotherapy Support Radiotherapy Support Clinical Decision Support Cytochrome P450 Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Bridging the Disclosure Gap The integration of supplementary biosupport and off-label medications into conventional oncology is a complex clinical reality. Current data indicates that between 50% and 80% of cancer patients utilize some form of complementary intervention, yet a significant majority—fearing categorical prohibition—opt not to disclose this use to their oncologists [29]. This "disclosure gap" creates a clinical vacuum, leaving clinicians without objective tools to assess real-time interaction risks. The prevalent 'disclosure gap' in oncology - where patients refrain from informing their oncologists about complementary biosupport - presents a significant clinical challenge [29]. Without a systematic, evidence-based model to evaluate these interventions, the resulting lack of transparency creates professional insecurity for the clinician and potential safety risks for the patient [14, 28, 29]. This model aims to bridge that gap by providing a formal structure for these necessary conversations. Pharmacological interference typically manifests through two critical pathways: the neutralization of treatment efficacy, where exogenous antioxidants may mitigate the oxidative stress required for chemotherapy or radiation [3, 11], and the modulation of hepatic enzymes (CYP450) and transport proteins (P-gp), which can dangerously alter the systemic exposure of oncological drugs [14, 28]. To address these challenges, we propose a shift from reactive prohibition to a proactive, time-based risk management model. By applying standardized pharmacokinetic principles, specifically the "5 × t½ " rule, this model enables the safe separation of supplementary compounds and primary treatment, thereby preserving treatment integrity while respecting patient autonomy. The model provides a framework for integrating complementary oncology by ensuring that supplementary biosupport does not compromise the efficacy of conventional chemo- and radiotherapy. To ensure immediate clinical utility, this pharmacokinetic logic has been operationalized via an open-access decision-support website ( https://jegharkraeft.dk/en/chemo-and-radiotherapy-support/ ). This platform provides a searchable database of 55 standardized compounds, enabling oncologists and patients to access visual "traffic-light" washout protocols directly in a clinical setting. Importantly, the inclusion of specific botanical extracts or off-label medications in this framework does not constitute an endorsement of their safety, efficacy, or clinical validity. The protocols are strictly designed as a harm-reduction strategy to manage the pharmacokinetic risks associated with patient-led self-administration. Methods: Pharmacokinetic Framework 3.1 The 5 × t½ Safety Standard The model utilizes five terminal half-lives as the standard for clinical clearance [14, 27]. This interval ensures that plasma concentrations are reduced to a level where pharmacodynamic activity is clinically insignificant. For compounds with exceptionally rapid elimination, a mandatory 48-hour minimum washout period is applied as a conservative safety margin. Figure 1 : Summary of Pharmacokinetic Decision-Support Logic This flowchart provides a systematic framework for determining the appropriate washout timing of a supplementary biosupport agent prior to and between cycles of conventional oncological treatment. The systematic logic begins with compound identification (Step 1) and a formal assessment of the available evidence quality (Step 2) using a hierarchical taxonomy from Levels 1 to 4. In Step 3, this evidence quality is integrated with the pharmacokinetic (PK) standard for elimination (5 × t ½). This critical step uses the evidence to create a 'Safety Overlay' that determines the final confidence in the calculated elimination window and manages interaction risks. The process culminates in clear, risk-managed clinical guidance, presented via a traffic-light categorization system (Green, Yellow, Red). This system advises on the timing of the agent—allowing earlier timing with high-quality evidence (Level 1/2), requiring consultation for moderate evidence (Level 3), or mandating deferral when evidence is low (Level 4) or toxicity is high. 3.2 Hierarchical Evidence Taxonomy (Levels 1–4) To ensure methodological rigor across the 55 compounds, data validity is weighted as follows [1, 14]: Level 1 : Direct clinical evidence from human pharmacokinetics in vivo studies. Level 2 : Extrapolation based on primary active metabolites or biomarkers. Level 3 : Pharmacological analogy based on metabolic pathways (e.g., CYP450 affinity). Level 4 : Preclinical transition from in vitro or animal models, with an integrated safety factor (S-factor) to account for inter-species variation. To account for inter-individual variability and potential data gaps in Level 4 evidence, a theoretical heuristic margin (Safety Factor, S = 2) is applied to the calculated washout period. This effectively doubles the 5 × t½ duration to act as a preliminary risk-mitigation strategy in the absence of robust human pharmacokinetic data Figure 2 : Theoretical elimination curve based on terminal half-life. The graph demonstrates the exponential decay of plasma concentrations over time. The "5-half-lives" threshold is utilized as the clinical safety standard, ensuring that > 96.8\% of the parent compound is eliminated from systemic circulation. This margin minimizes the potential for pharmacodynamic interference with primary oncological interventions. 3.3 Risk Categorization and Color Coding The model categorizes compounds based on their interaction risk and required washout duration, visualized through a traffic-light system: White (◯) : Low interaction potential. No widely documented pharmacokinetic interference with standard chemotherapy or radiotherapy based on current literature. Green (◯) : Rapid elimination (terminal t½ 24 hours). Requires an extended washout of 5–28 days. Acute vs. Accumulated Doses : While half-lives are typically based on single-dose kinetics, prolonged use of lipophilic substances (e.g., Quercetin or fat-soluble vitamins) may lead to tissue sequestration, necessitating the conservative upper-limit washout protocols established in this model. Results Table 1 A: Representative Pharmacokinetic Washout Protocols Compound Intended Biosupport Role t½ (Terminal) Evidence Level Washout Protocol Primary Interaction Risk Melatonin Sleep / Antioxidant 40–60 minutes 1 (White) 1 day Competition for CYP1A2 metabolism. Astragalus Immune support 2.1–2.7 hours 1 (White) 1 day Rapid renal clearance; low baseline risk. Artemisinin Integrative support 1–5 hours 1 (White) 1 day Induction of CYP2B6 and CYP3A4 enzymes. Honokiol CYP modulation 2.5–5 hours 1 (White) 3 days Potent inhibition of hepatic CYP1A and 2C. Quercetin Flavonoid 1–3 hours 1 (White) 3 days Inhibition of CYP3A4 and P-glycoprotein. Resveratrol Polyphenol 2–9.7 hours 1 (White) 3 days Impact on growth factors and IGF-1 signaling. Nigella Sativa Thymoquinone N/A 3 (Yellow) 4 days Strong CYP3A4 inhibition (toxicity risk). Boswellia Anti-inflammatory 6.8 hours 1 (White) 4 days Lipophilic sequestration in minor depots. Metformin Metabolic (off-label) 1.5–6.2 hours 1 (White) 5 days Erythrocyte partitioning (reservoir effect). AHCC Immune support 12–24 hours 3 (Yellow) 5 days Interference with Phase II conjugation pathways. Summary of pharmacokinetic evidence and documentation The pharmacokinetic profiles of the 55 evaluated compounds—comprising botanical extracts and off-label medications—demonstrate a wide range of elimination kinetics. To ensure clinical safety, these compounds have been categorized based on their terminal half-lives (t½) and metabolic pathways. The following thematic summaries consolidate the evidence used to establish the washout protocols (see Appendix B, Section 12, for detailed individual compound documentation): Rapidly Eliminated Compounds (Washout: 1–2 days) : A significant portion of the dataset, including Astragalus [47], Artemisinin [44], and Ginger [87], exhibits rapid systemic clearance with half-lives under 5 hours. These compounds are typically cleared within 24 to 48 hours, posing a minimal risk of lingering metabolic interference. Hepatic Enzyme and Transport Modulators (Washout: 3–5 days) : Several bioactive polyphenols, such as Quercetin [141], Honokiol [83], and Nigella Sativa [126], act as potent inhibitors or inducers of Cytochrome P450 (especially CYP3A4) and P-glycoprotein. To prevent altered systemic exposure of chemotherapeutic agents, a conservative washout of 3–5 days is required to allow for enzymatic recovery. Lipophilic and Adipose-Sequestered Substances (Washout: 12–28 days) : Compounds with high lipophilicity, notably Cannabinoids (CBD/THC) [61, 63], Vitamin E [170], and CoQ10 [64], demonstrate prolonged terminal elimination phases due to sequestration in adipose tissue. For these substances, extended washout periods (up to 28 days) are established to ensure complete systemic depletion. Enterohepatic and Metabolic Recirculation (Washout: 14–28 days) : Specific compounds like TUDCA [156] and Omega-3 fatty acids [129] are subject to continuous physiological recycling or incorporation into cellular membranes. These require the longest recovery windows (up to 21–28 days) to achieve baseline normalization and minimize risks such as altered coagulation or unintended cytoprotection. Evidence Weighting and Uncertainty : For compounds categorized under Level 4 evidence (e.g., Apigenin [43] or Pao Pereira [133]), where human in vivo data is limited, the model incorporates an additional safety margin. This hierarchical approach ensures that potential inter-species variability is countered by a conservative temporal buffer, prioritizing oncological precision in the absence of definitive human trials, especially regarding unstandardized botanical preparations [28]. Figure 3 : Biochemical mechanism of Herb-Drug Interactions (HDI) at the Cytochrome P450 level. This illustration depicts how bioactive botanical compounds (e.g., flavonoids) can act as competitive inhibitors of hepatic enzymes like CYP3A4. Such inhibition alters the metabolic clearance of chemotherapeutic agents, underscoring the necessity of a structured washout period to restore enzymatic baseline activity. Figure 4 : Hierarchical Evidence Taxonomy A pyramidal weighting system is employed to manage varying degrees of data granularity. Level 1 represents the highest certainty via human in vivo studies, while Level 4 incorporates a conservative safety factor (S-factor) to account for inter-species variation in preclinical models. This hierarchy ensures that temporal safety margins are maintained despite gaps in clinical literature. Discussion: Safety, Transparency and Therapeutic Alliance The Disclosure Gap The integration of supplementary biosupport and off-label medications into conventional oncology is a complex clinical reality that requires a shift from reactive prohibition to proactive risk management. Current data suggests that between 50% and 80% of cancer patients utilize some form of complementary intervention, yet a significant majority do not disclose this use to their oncologists [29]. This "disclosure gap" is primarily driven by the patient's fear of categorical prohibition and the clinician's lack of objective tools to assess interaction risks. The primary objective of the proposed model is to transform this clinical dialogue from one of clandestine use to one of documented safety by providing a framework rooted in pharmacokinetic logic rather than a "zero-tolerance" policy [14, 29]. Protection of Treatment Integrity Pharmacological interference typically manifests in two critical ways: through the neutralization of treatment efficacy or the modulation of drug metabolism. Specifically, the risk of exogenous antioxidants mitigating the oxidative stress required for the cytotoxic effects of chemotherapy or radiation poses a significant risk [3, 11]. Furthermore, the modulation of hepatic enzymes (CYP450) and transport proteins (P-gp) by botanical extracts can dangerously alter the systemic exposure of oncological drugs, leading to either sub-therapeutic levels or increased toxicity [14, 28]. The implementation of the "5 × t½ " safety standard ensures that the liver’s enzymatic capacity is fully reserved for conventional protocols and prevents "unintended cytoprotection," where supplemental compounds might inadvertently shield malignant cells. Establishing Clinical Washout Ceilings For highly lipophilic substances with a terminal t½ exceeding 100 hours, or where chronic tissue accumulation is suspected (e.g., Cannabinoids or Vitamin E), a clinical ceiling of 28 days is established. This duration ensures that even for substances with extreme tissue sequestration, plasma concentrations are reduced to < 3.125% of steady-state levels (5 half-lives). This 28-day 'safety overlay' provides a robust buffer that accounts for terminal elimination phases that might otherwise be underestimated in standard clinical literature [14, 27] Clinical Implementation and Therapeutic Alliance By providing oncologists with a tool based on pharmacology rather than categorical dismissal, the model fosters a transparent clinical dialogue and increases overall patient safety. The associated WordPress platform translates complex pharmacokinetic variables into an intuitive, searchable interface. This allows for rapid clinical look-ups, providing documented evidence for the required safety windows. In a high-pressure clinical environment, the 'traffic light' system offers a practical decision-making tool. Rather than a time-consuming case-by-case literature review or a categorical prohibition of all supplements, the oncologist can use this hierarchical evidence weighing to make rapid, defensible decisions [14, 28]. This allows for a more nuanced and time-efficient approach to patient-led biosupport in daily practice. By creating these clear clinical windows for biosupport during the patient’s recovery phase, the model preserves treatment integrity while respecting patient autonomy. When patients are integrated into a structured framework of collaboration the need for secrecy diminishes, and the therapeutic alliance is significantly enhanced. By utilizing the concepts of informed disclosure and structured collaboration, this model strengthens the therapeutic alliance. It moves the clinical encounter away from a binary 'either-or' choice, empowering the patient to feel supported in their autonomy while ensuring that the oncologist maintains control over the primary treatment’s integrity [14, 29]. This alignment is crucial for patient retention and psychological well-being during treatment. Ultimately, this approach creates an evidence-based bridge between patient shared decision-making and oncological precision, ensuring that supplementary interventions do not compromise the efficacy of the primary treatment. Limitations While the proposed pharmacokinetic model provides a robust framework for clinical decision-making, it is subject to certain limitations. First, the model relies on a hierarchical evidence taxonomy; compounds categorized at Level 4 are based on preclinical data, where human 1:1 translation remains an estimate [1]. This translational uncertainty is compounded by the significant variability in quality assurance, species identification, and standardization within the botanical market [28]. Second, individual genetic variability—specifically polymorphisms in the Cytochrome P450 enzyme family—can significantly alter a patient’s "real-world" elimination rate compared to clinical averages. Third, the model primarily addresses acute systemic interference and does not fully account for long-term sequestration in deep tissue compartments (e.g., adipose tissue) for all compounds, although conservative margins have been established for highly lipophilic substances like Vitamin E and Cannabinoids [61, 170]. Finally, the 5 × t½ standard targets the elimination of 96.8% of the parent compound, but the persistence of active secondary metabolites may, in rare cases, require even longer recovery windows. Conclusion The model provides a framework for integrating complementary oncology by ensuring that chemo- and radiotherapy support does not interfere with the efficacy of conventional treatment. The implementation of a pharmacokinetic washout model in integrative oncology represents a shift from reactive prohibition to proactive risk management. By utilizing the 5 × t½ rule and hierarchical evidence weighting, clinicians can replace uncertainty with documented safety margins [14, 27]. The implementation of the 5 × t½ pharmacokinetic standard serves as a robust 'safety overlay' [14, 27]. By strictly adhering to these washout protocols, the model ensures that the integrity of conventional oncology treatment remains uncompromised. This standardized approach provides a practical framework and ethical rationale for managing the use of botanical extracts in a professional oncology setting. This approach not only minimizes the risk of treatment interference - such as unintended cytoprotection or metabolic competition - but also fosters a transparent therapeutic alliance. When patients are provided with a structured framework for collaboration the need for clandestine use of supplements diminishes. Ultimately, this model serves as a bridge between patient autonomy and oncological precision, ensuring that supplementary biosupport enhances the recovery phase without compromising the efficacy of the primary oncological intervention. Declarations Author Approval: All authors have seen and approved the manuscript. 8. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The development of the pharmacokinetic washout protocols is based solely on a review of available pharmacological literature and is intended for clinical decision support. Funding This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. 9. Data Availability Statement The theoretical pharmacokinetic model presented in this study is operationalized through an open-access clinical decision-support website (https://jegharkraeft.dk/en/chemo-and-radiotherapy-support/). The complete dataset supporting the conclusions of this article, including detailed compound-by-compound documentation, terminal half-lives ( t½ ), hierarchical evidence weighting (Levels 1–4), and the visual risk-managed washout protocols (Green, Yellow, Red) for all 55 compounds, is freely available on this platform. This database is searchable and updated regularly to facilitate rapid evidence-based decisions in a clinical oncology setting. References Ashrafpour, S, M. et al. The double-edged sword of nutraceuticals: comprehensive review of protective agents and their hidden risks. Frontiers in Nutrition . 2025; 12: 1524627. doi.org/10.3389/fnut.2025.1524627 Narimatsu, H. Yaguchi Y. et al. Nutraceuticals and Cancer: Potential for Natural Polyphenols. 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Pharmacokinetic Evaluation of Ursodeoxycholic Acid, Unconjugated and Conjugated, within Two Oral Formulations in Healthy Male Subjects. Journal of Biosciences and Medicines . 2026; 14(3): 112-126. DOI: 10.4236/jbm.2026.141023 Kusaczuk, M. Tauroursodeoxycholate—Bile Acid with Chaperoning Activity: Molecular and Cellular Effects and Therapeutic Perspectives. Cells . 2019; 8(12):1471. doi: 10.3390/cells8121471 Davis, T. M., et al. Pharmacokinetics of retinyl palmitate and retinol after intramuscular retinyl palmitate administration in severe malaria. Clinical Science . 2000; 99(5): 433-41. PMID: 11052924. https://pubmed.ncbi.nlm.nih.gov/11052924/ Reinersdorff, D. V., Bush, E. Liberato, D. J., et al. Plasma kinetics of vitamin A in humans after a single oral dose of [8,9,19-13C] retinyl palmitate. Journal of Lipid Research . 1996; 37(9): 1875-1885. PMID: 8895053 Furr, H. C., Amedee-Manesme, O., Clifford, A. J., et al. Vitamin A concentrations in liver determined by isotope dilution assay with tetradeuterated vitamin A and by biopsy in generally healthy adult humans. The American Journal of Clinical Nutrition . 1989; 49(4):713-6. doi: 10.1093/ajcn/49.4.713 Lindschinger, M., Tatzber, F., Schimetta, W., et al. A Randomized Pilot Trial to Evaluate the Bioavailability of Natural versus Synthetic Vitamin B Complexes in Healthy Humans and Their Effects on Homocysteine, Oxidative Stress, and Antioxidant Levels. Oxidative Medicine and Cellular Longevity . 2019; 2019: 6082613. doi: 10.1155/2019/6082613 Schellack, N., Yotsombut, K., Sabet, A., et al. Expert Consensus on Vitamin B6 Therapeutic Use for Patients: Guidance on Safe Dosage, Duration and Clinical Management. Drug, Healthcare and Patient Safety . 2025; 17: 97-108. DOI https://doi.org/10.2147/DHPS.S499941 Ali, M. A., Hafez, H. A., Kamel, M. A., et al. Dietary Vitamin B Complex: Orchestration in Human Nutrition throughout Life with Sex Differences. Nutrients . 2022; 14(19):3940. doi: 10.3390/nu14193940 FAO. Human Vitamin and Mineral Requirements. Chapter 3. Thiamin, riboflavin, niacin, vitamin B6, pantothenic acid and biotin . 2001. https://www.fao.org/4/y2809e/y2809e09.htm Charoenngam, N., Kalajian, T. A., Shirvani, A., et al. A pilot-randomized, double-blind crossover trial to evaluate the pharmacokinetics of orally administered 25-hydroxyvitamin D3 and vitamin D3 in healthy adults with differing BMI and in adults with intestinal malabsorption. The American Journal of Clinical Nutrition . 2021; 114(3): 1189-1199. doi: 10.1093/ajcn/nqab123 Uçar, N., Pickering, R. T., Mueller, P. M., et al. Vitamin D3, 25-Hydroxyvitamin D3, and 1,25-Dihydroxyvitamin D3 Uptake in Cultured Human Mature Adipocytes. Nutrients . 2025; 17(13): 2107. doi.org/10.3390/nu17132107 Fassio, A., Adami, G., Rossini, M., et al. Pharmacokinetics of Oral Cholecalciferol in Healthy Subjects with Vitamin D Deficiency: A Randomized Open-Label Study. Nutrients . 2020; 12(6): 1553. doi: 10.3390/nu12061553 Violet, P-C, Ebenuwa, I. C., Wang, Y., et al. Vitamin E sequestration by liver fat in humans. JCI Insight . 2020; 5(1): e133309. doi: 10.1172/jci.insight.133309 Handelman, G. J., Epstein, W. L., Peerson, J., et al. Human adipose alpha-tocopherol and gamma-tocopherol kinetics during and after 1 y of alpha-tocopherol supplementation. The American Journal of Clinical Nutrition . 1994; 59(5): 1025-1032. doi: 10.1093/ajcn/59.5.1025 Podszun, M., Frank, J. Vitamin E–drug interactions: molecular basis and clinical relevance. Nutrition Research Reviews . 2014; 27(2): 215-231. DOI: https://doi.org/10.1017/S0954422414000146 Zaffarin, M., A. S., et al. Pharmacology and Pharmacokinetics of Vitamin E: Nanoformulations to Enhance Bioavailability. International Journal of Nanomedicine . 2020; 15: 9961-9974. DOI https://doi.org/10.2147/IJN.S276355 Sato, T., Schurgers, L. J., Uenishi, K. Comparison of menaquinone-4 and menaquinone-7 bioavailability in healthy women. Nutrition Journal . 2012; 11: 93. doi: 10.1186/1475-2891-11-93 Du, F. Yan, M., Duan, L., et al. The study of bioavailability and endogenous circadian rhythm of menaquinone-7, a form of vitamin K2, in healthy subjects. British Journal of Nutrition . 2023; 130(11): 1885-1897. PMID: 37132123. DOI: 10.1017/S0007114523001034 Council for Responsible Nutrition. Vitamin K2 – Menaquinone-7. Vitamin and Mineral Safety . 4th Ed. 2025. https://crnusa.org/sites/default/files/pdfs/09.2-CRNVMS4-VITAMINK2_MK7_FINAL-EHedits.pdf Salhab, A. S., Zmeili, S. M., Gharaibeh, M. N., et al. The bioequivalence study of Folifer-Z®: a new formulation of sustained-release iron and zinc. International Journal of Pharmaceutics . 1999; 178(2): 171-181. doi.org/10.1016/S0378-5173(98)00368-8 Vale, S., Leite, L., Alves, C. X., et al. Zinc pharmacokinetic parameters in the determination of zinc status in children. Journal of Trace Elements in Medicine and Biology . 2013; 68(2): DOI:10.1038/ejcn.2013.250 Ranasinghe, P., Galappatthy, P., Katulanda, P., et al. Pharmacokinetics of zinc in pre-diabetes: a pilot study. Journal of Diabetes & Metabolic Disorders . 2018; 5(1): DOI:10.15406/jdmdc.2018.05.00131 Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9654569","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":636905965,"identity":"964a87f4-f8ac-4a90-991e-928053d8be0f","order_by":0,"name":"Hanne Kjær Uhlig","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACAzBZwcbAIMHAwNgAZLMDCckGglrOIGnhOUaMFsY2BhK0mLOfPfbg4zw+Ofno5mcfZ+6xk+ORb2C8OQOPFsuevHTDmdvYjA3vHDOeueFZsjEPGwOz5QZ8DjuQYybNu40tceOMBGPGBweYE/ezMbBJPsCn5fwbM+m/c9jqN85I/wzUUl/fQ1DLDaAtjA1sCfISOcaMGw4cTuABacHrsBtvzCR7jrEZbpDIKWacceC4YQ9bYrMlPu8bnM8xk/hRc0xefkb6ZsaeA9XyPMyHD97swaMFCo4BwwHOAUcPQVDDIE+UulEwCkbBKBiRAACcs08SNPXougAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0000-6522-402X","institution":"Jeg har Kræft","correspondingAuthor":true,"prefix":"","firstName":"Hanne","middleName":"Kjær","lastName":"Uhlig","suffix":""},{"id":636909207,"identity":"c3731910-c2f5-4751-896f-657ec4f425cd","order_by":1,"name":"Tina Ingrid Horsted","email":"","orcid":"","institution":"Horsted Institute","correspondingAuthor":false,"prefix":"","firstName":"Tina","middleName":"Ingrid","lastName":"Horsted","suffix":""}],"badges":[],"createdAt":"2026-05-08 13:12:56","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9654569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9654569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108979964,"identity":"a91c129f-02cf-4c04-b1e4-6e040a282e14","added_by":"auto","created_at":"2026-05-11 12:02:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1343491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of Pharmacokinetic Decision-Support Logic\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis flowchart provides a systematic framework for determining the appropriate washout timing of a supplementary biosupport agent prior to and between cycles of conventional oncological treatment. The systematic logic begins with compound identification (Step 1) and a formal assessment of the available evidence quality (Step 2) using a hierarchical taxonomy from Levels 1 to 4.\u003c/p\u003e\n\u003cp\u003eIn Step 3, this evidence quality is integrated with the pharmacokinetic (PK) standard for elimination (5 × \u003cem\u003et\u003c/em\u003e½). This critical step uses the evidence to create a 'Safety Overlay' that determines the final confidence in the calculated elimination window and manages interaction risks.\u003c/p\u003e\n\u003cp\u003eThe process culminates in clear, risk-managed clinical guidance, presented via a traffic-light categorization system (Green, Yellow, Red). This system advises on the timing of the agent—allowing earlier timing with high-quality evidence (Level 1/2), requiring consultation for moderate evidence (Level 3), or mandating deferral when evidence is low (Level 4) or toxicity is high.\u003c/p\u003e","description":"","filename":"Figure1SummaryofPharmacokineticDecisionSupportLogic.png","url":"https://assets-eu.researchsquare.com/files/rs-9654569/v1/7ec45836651fc89a4f195730.png"},{"id":108980079,"identity":"f8385ce7-cf21-4041-be12-8223bf3411f6","added_by":"auto","created_at":"2026-05-11 12:03:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":689411,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTheoretical elimination curve based on terminal half-life.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe graph demonstrates the exponential decay of plasma concentrations over time. The \"5-half-lives\" threshold is utilized as the clinical safety standard, ensuring that \u0026gt;96.8\\% of the parent compound is eliminated from systemic circulation. This margin minimizes the potential for pharmacodynamic interference with primary oncological interventions.\u003c/p\u003e","description":"","filename":"Figure2.Farmakokinetiskelimination5timest.png","url":"https://assets-eu.researchsquare.com/files/rs-9654569/v1/3a06b07d125497db6b9172dc.png"},{"id":108979971,"identity":"1e235749-acb2-49c1-bdbb-825c11ec9abc","added_by":"auto","created_at":"2026-05-11 12:02:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1332983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiochemical mechanism of Herb-Drug Interactions (HDI) at the Cytochrome P450 level.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis illustration depicts how bioactive botanical compounds (e.g., flavonoids) can act as competitive inhibitors of hepatic enzymes like CYP3A4. Such inhibition alters the metabolic clearance of chemotherapeutic agents, underscoring the necessity of a structured washout period to restore enzymatic baseline activity.\u003c/p\u003e","description":"","filename":"Figur3.MekanismeforleverinteraktionCYP450.png","url":"https://assets-eu.researchsquare.com/files/rs-9654569/v1/86d17e7b0acb06c49defa3a1.png"},{"id":108979975,"identity":"776dcea5-2f67-4435-8a15-36095d68005a","added_by":"auto","created_at":"2026-05-11 12:02:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1227226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHierarchical Evidence Taxonomy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA pyramidal weighting system is employed to manage varying degrees of data granularity. Level 1 represents the highest certainty via human \u003cem\u003ein vivo\u003c/em\u003e studies, while Level 4 incorporates a conservative safety factor (S-factor) to account for inter-species variation in preclinical models. This hierarchy ensures that temporal safety margins are maintained despite gaps in clinical literature.\u003c/p\u003e","description":"","filename":"Figure4.HierarchicalEvidenceTaxonomyforBotanicalandOfflabelCompounds.png","url":"https://assets-eu.researchsquare.com/files/rs-9654569/v1/2164fc60e220f5c4039606de.png"},{"id":108982287,"identity":"d6353e9f-1c48-472d-821b-69ab0c9fa0dc","added_by":"auto","created_at":"2026-05-11 12:24:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5000501,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9654569/v1/597a64aa-e631-4b73-bafa-831628580fcc.pdf"},{"id":108979980,"identity":"104d22d6-6b11-49fc-b003-b4e1bed03b26","added_by":"auto","created_at":"2026-05-11 12:02:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45156,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9654569/v1/ed566230be2365c70be1d297.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSystematizing Washout Protocols in Integrative Oncology: A Pharmacokinetic Model Based on 5 × \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003et½\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and Hierarchical Evidence Weighting of Botanical Extracts and Off-Label Medications\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cb\u003eBridging the Disclosure Gap\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe integration of supplementary biosupport and off-label medications into conventional oncology is a complex clinical reality. Current data indicates that between 50% and 80% of cancer patients utilize some form of complementary intervention, yet a significant majority\u0026mdash;fearing categorical prohibition\u0026mdash;opt not to disclose this use to their oncologists [29]. This \"disclosure gap\" creates a clinical vacuum, leaving clinicians without objective tools to assess real-time interaction risks.\u003c/p\u003e \u003cp\u003eThe prevalent 'disclosure gap' in oncology - where patients refrain from informing their oncologists about complementary biosupport - presents a significant clinical challenge [29]. Without a systematic, evidence-based model to evaluate these interventions, the resulting lack of transparency creates professional insecurity for the clinician and potential safety risks for the patient [14, 28, 29]. This model aims to bridge that gap by providing a formal structure for these necessary conversations.\u003c/p\u003e \u003cp\u003ePharmacological interference typically manifests through two critical pathways: the neutralization of treatment efficacy, where exogenous antioxidants may mitigate the oxidative stress required for chemotherapy or radiation [3, 11], and the modulation of hepatic enzymes (CYP450) and transport proteins (P-gp), which can dangerously alter the systemic exposure of oncological drugs [14, 28].\u003c/p\u003e \u003cp\u003eTo address these challenges, we propose a shift from reactive prohibition to a proactive, time-based risk management model. By applying standardized pharmacokinetic principles, specifically the \"5 \u0026times; \u003cem\u003et\u0026frac12;\u003c/em\u003e\" rule, this model enables the safe separation of supplementary compounds and primary treatment, thereby preserving treatment integrity while respecting patient autonomy.\u003c/p\u003e \u003cp\u003eThe model provides a framework for integrating complementary oncology by ensuring that supplementary biosupport does not compromise the efficacy of conventional chemo- and radiotherapy.\u003c/p\u003e \u003cp\u003eTo ensure immediate clinical utility, this pharmacokinetic logic has been operationalized via an open-access decision-support website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jegharkraeft.dk/en/chemo-and-radiotherapy-support/\u003c/span\u003e\u003cspan address=\"https://jegharkraeft.dk/en/chemo-and-radiotherapy-support/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This platform provides a searchable database of 55 standardized compounds, enabling oncologists and patients to access visual \"traffic-light\" washout protocols directly in a clinical setting.\u003c/p\u003e \u003cp\u003eImportantly, the inclusion of specific botanical extracts or off-label medications in this framework does not constitute an endorsement of their safety, efficacy, or clinical validity. The protocols are strictly designed as a harm-reduction strategy to manage the pharmacokinetic risks associated with patient-led self-administration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods: Pharmacokinetic Framework","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.1 The 5\u003c/b\u003e \u0026times; \u003cb\u003et\u0026frac12;\u003c/b\u003e \u003cb\u003eSafety Standard\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe model utilizes five terminal half-lives as the standard for clinical clearance [14, 27]. This interval ensures that plasma concentrations are reduced to a level where pharmacodynamic activity is clinically insignificant. For compounds with exceptionally rapid elimination, a mandatory 48-hour minimum washout period is applied as a conservative safety margin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cb\u003eSummary of Pharmacokinetic Decision-Support Logic\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis flowchart provides a systematic framework for determining the appropriate washout timing of a supplementary biosupport agent prior to and between cycles of conventional oncological treatment. The systematic logic begins with compound identification (Step 1) and a formal assessment of the available evidence quality (Step 2) using a hierarchical taxonomy from Levels 1 to 4.\u003c/p\u003e \u003cp\u003eIn Step 3, this evidence quality is integrated with the pharmacokinetic (PK) standard for elimination (5 \u0026times; \u003cem\u003et\u003c/em\u003e\u0026frac12;). This critical step uses the evidence to create a 'Safety Overlay' that determines the final confidence in the calculated elimination window and manages interaction risks.\u003c/p\u003e \u003cp\u003eThe process culminates in clear, risk-managed clinical guidance, presented via a traffic-light categorization system (Green, Yellow, Red). This system advises on the timing of the agent\u0026mdash;allowing earlier timing with high-quality evidence (Level 1/2), requiring consultation for moderate evidence (Level 3), or mandating deferral when evidence is low (Level 4) or toxicity is high.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Hierarchical Evidence Taxonomy (Levels 1\u0026ndash;4)\u003c/h2\u003e \u003cp\u003eTo ensure methodological rigor across the 55 compounds, data validity is weighted as follows [1, 14]:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLevel 1\u003c/b\u003e: Direct clinical evidence from human pharmacokinetics in vivo studies.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLevel 2\u003c/b\u003e: Extrapolation based on primary active metabolites or biomarkers.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLevel 3\u003c/b\u003e: Pharmacological analogy based on metabolic pathways (e.g., CYP450 affinity).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLevel 4\u003c/b\u003e: Preclinical transition from in vitro or animal models, with an integrated safety factor (S-factor) to account for inter-species variation.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo account for inter-individual variability and potential data gaps in Level 4 evidence, a theoretical heuristic margin (Safety Factor, S\u0026thinsp;=\u0026thinsp;2) is applied to the calculated washout period. This effectively doubles the 5 \u0026times; \u003cem\u003et\u0026frac12;\u003c/em\u003e duration to act as a preliminary risk-mitigation strategy in the absence of robust human pharmacokinetic data\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cb\u003eTheoretical elimination curve based on terminal half-life.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe graph demonstrates the exponential decay of plasma concentrations over time. The \"5-half-lives\" threshold is utilized as the clinical safety standard, ensuring that \u0026gt;\u0026thinsp;96.8\\% of the parent compound is eliminated from systemic circulation. This margin minimizes the potential for pharmacodynamic interference with primary oncological interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Risk Categorization and Color Coding\u003c/h2\u003e \u003cp\u003eThe model categorizes compounds based on their interaction risk and required washout duration, visualized through a traffic-light system:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWhite (◯)\u003c/b\u003e: Low interaction potential. No widely documented pharmacokinetic interference with standard chemotherapy or radiotherapy based on current literature.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGreen (◯)\u003c/b\u003e: Rapid elimination (terminal \u003cem\u003et\u0026frac12;\u003c/em\u003e \u0026lt; 4 hours). High clinical control.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eYellow (⬤)\u003c/b\u003e: Moderate elimination (terminal \u003cem\u003et\u0026frac12;\u003c/em\u003e between 4\u0026ndash;24 hours). Requires a 1\u0026ndash;5 day washout.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRed (▲✖)\u003c/b\u003e: Slow elimination (terminal \u003cem\u003et\u0026frac12;\u003c/em\u003e \u0026gt; 24 hours). Requires an extended washout of 5\u0026ndash;28 days.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAcute vs. Accumulated Doses\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eWhile half-lives are typically based on single-dose kinetics, prolonged use of lipophilic substances (e.g., Quercetin or fat-soluble vitamins) may lead to tissue sequestration, necessitating the conservative upper-limit washout protocols established in this model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA: \u003cb\u003eRepresentative Pharmacokinetic Washout Protocols\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntended Biosupport Role\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003et\u0026frac12;\u003c/em\u003e (Terminal)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvidence Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWashout Protocol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrimary Interaction Risk\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMelatonin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep / Antioxidant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u0026ndash;60 minutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCompetition for CYP1A2 metabolism.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAstragalus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u0026ndash;2.7 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRapid renal clearance; low baseline risk.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArtemisinin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntegrative support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;5 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInduction of CYP2B6 and CYP3A4 enzymes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHonokiol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP modulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u0026ndash;5 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePotent inhibition of hepatic CYP1A and 2C.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuercetin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlavonoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;3 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInhibition of CYP3A4 and P-glycoprotein.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResveratrol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolyphenol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026ndash;9.7 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImpact on growth factors and IGF-1 signaling.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNigella Sativa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThymoquinone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (Yellow)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrong CYP3A4 inhibition (toxicity risk).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBoswellia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnti-inflammatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLipophilic sequestration in minor depots.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetformin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolic (off-label)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;6.2 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eErythrocyte partitioning (reservoir effect).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAHCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u0026ndash;24 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (Yellow)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInterference with Phase II conjugation pathways.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSummary of pharmacokinetic evidence and documentation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe pharmacokinetic profiles of the 55 evaluated compounds\u0026mdash;comprising botanical extracts and off-label medications\u0026mdash;demonstrate a wide range of elimination kinetics. To ensure clinical safety, these compounds have been categorized based on their terminal half-lives \u003cem\u003e(t\u0026frac12;)\u003c/em\u003e and metabolic pathways. The following thematic summaries consolidate the evidence used to establish the washout protocols (see Appendix B, Section 12, for detailed individual compound documentation):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRapidly Eliminated Compounds (Washout: 1\u0026ndash;2 days)\u003c/b\u003e: A significant portion of the dataset, including Astragalus [47], Artemisinin [44], and Ginger [87], exhibits rapid systemic clearance with half-lives under 5 hours. These compounds are typically cleared within 24 to 48 hours, posing a minimal risk of lingering metabolic interference.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHepatic Enzyme and Transport Modulators (Washout: 3\u0026ndash;5 days)\u003c/b\u003e: Several bioactive polyphenols, such as Quercetin [141], Honokiol [83], and Nigella Sativa [126], act as potent inhibitors or inducers of Cytochrome P450 (especially CYP3A4) and P-glycoprotein. To prevent altered systemic exposure of chemotherapeutic agents, a conservative washout of 3\u0026ndash;5 days is required to allow for enzymatic recovery.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLipophilic and Adipose-Sequestered Substances (Washout: 12\u0026ndash;28 days)\u003c/b\u003e: Compounds with high lipophilicity, notably Cannabinoids (CBD/THC) [61, 63], Vitamin E [170], and CoQ10 [64], demonstrate prolonged terminal elimination phases due to sequestration in adipose tissue. For these substances, extended washout periods (up to 28 days) are established to ensure complete systemic depletion.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEnterohepatic and Metabolic Recirculation (Washout: 14\u0026ndash;28 days)\u003c/b\u003e: Specific compounds like TUDCA [156] and Omega-3 fatty acids [129] are subject to continuous physiological recycling or incorporation into cellular membranes. These require the longest recovery windows (up to 21\u0026ndash;28 days) to achieve baseline normalization and minimize risks such as altered coagulation or unintended cytoprotection.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEvidence Weighting and Uncertainty\u003c/b\u003e: For compounds categorized under Level 4 evidence (e.g., Apigenin [43] or Pao Pereira [133]), where human \u003cem\u003ein vivo\u003c/em\u003e data is limited, the model incorporates an additional safety margin. This hierarchical approach ensures that potential inter-species variability is countered by a conservative temporal buffer, prioritizing oncological precision in the absence of definitive human trials, especially regarding unstandardized botanical preparations [28].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u003cb\u003eBiochemical mechanism of Herb-Drug Interactions (HDI) at the Cytochrome P450 level.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis illustration depicts how bioactive botanical compounds (e.g., flavonoids) can act as competitive inhibitors of hepatic enzymes like CYP3A4. Such inhibition alters the metabolic clearance of chemotherapeutic agents, underscoring the necessity of a structured washout period to restore enzymatic baseline activity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: \u003cb\u003eHierarchical Evidence Taxonomy\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA pyramidal weighting system is employed to manage varying degrees of data granularity. Level 1 represents the highest certainty via human \u003cem\u003ein vivo\u003c/em\u003e studies, while Level 4 incorporates a conservative safety factor (S-factor) to account for inter-species variation in preclinical models. This hierarchy ensures that temporal safety margins are maintained despite gaps in clinical literature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion: Safety, Transparency and Therapeutic Alliance","content":"\u003cp\u003e \u003cb\u003eThe Disclosure Gap\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe integration of supplementary biosupport and off-label medications into conventional oncology is a complex clinical reality that requires a shift from reactive prohibition to proactive risk management. Current data suggests that between 50% and 80% of cancer patients utilize some form of complementary intervention, yet a significant majority do not disclose this use to their oncologists [29]. This \"disclosure gap\" is primarily driven by the patient's fear of categorical prohibition and the clinician's lack of objective tools to assess interaction risks. The primary objective of the proposed model is to transform this clinical dialogue from one of clandestine use to one of documented safety by providing a framework rooted in pharmacokinetic logic rather than a \"zero-tolerance\" policy [14, 29].\u003c/p\u003e \u003cp\u003e \u003cb\u003eProtection of Treatment Integrity\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePharmacological interference typically manifests in two critical ways: through the neutralization of treatment efficacy or the modulation of drug metabolism. Specifically, the risk of exogenous antioxidants mitigating the oxidative stress required for the cytotoxic effects of chemotherapy or radiation poses a significant risk [3, 11]. Furthermore, the modulation of hepatic enzymes (CYP450) and transport proteins (P-gp) by botanical extracts can dangerously alter the systemic exposure of oncological drugs, leading to either sub-therapeutic levels or increased toxicity [14, 28]. The implementation of the \"5 \u0026times; \u003cem\u003et\u0026frac12;\u003c/em\u003e\" safety standard ensures that the liver\u0026rsquo;s enzymatic capacity is fully reserved for conventional protocols and prevents \"unintended cytoprotection,\" where supplemental compounds might inadvertently shield malignant cells.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEstablishing Clinical Washout Ceilings\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor highly lipophilic substances with a terminal \u003cem\u003et\u0026frac12;\u003c/em\u003e exceeding 100 hours, or where chronic tissue accumulation is suspected (e.g., Cannabinoids or Vitamin E), a clinical ceiling of 28 days is established.\u003c/p\u003e \u003cp\u003eThis duration ensures that even for substances with extreme tissue sequestration, plasma concentrations are reduced to \u0026lt;\u0026thinsp;3.125% of steady-state levels (5 half-lives). This 28-day 'safety overlay' provides a robust buffer that accounts for terminal elimination phases that might otherwise be underestimated in standard clinical literature [14, 27]\u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical Implementation and Therapeutic Alliance\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBy providing oncologists with a tool based on pharmacology rather than categorical dismissal, the model fosters a transparent clinical dialogue and increases overall patient safety. The associated WordPress platform translates complex pharmacokinetic variables into an intuitive, searchable interface. This allows for rapid clinical look-ups, providing documented evidence for the required safety windows. In a high-pressure clinical environment, the 'traffic light' system offers a practical decision-making tool. Rather than a time-consuming case-by-case literature review or a categorical prohibition of all supplements, the oncologist can use this hierarchical evidence weighing to make rapid, defensible decisions [14, 28]. This allows for a more nuanced and time-efficient approach to patient-led biosupport in daily practice.\u003c/p\u003e \u003cp\u003eBy creating these clear clinical windows for biosupport during the patient\u0026rsquo;s recovery phase, the model preserves treatment integrity while respecting patient autonomy. When patients are integrated into a structured framework of collaboration the need for secrecy diminishes, and the therapeutic alliance is significantly enhanced. By utilizing the concepts of informed disclosure and structured collaboration, this model strengthens the therapeutic alliance. It moves the clinical encounter away from a binary 'either-or' choice, empowering the patient to feel supported in their autonomy while ensuring that the oncologist maintains control over the primary treatment\u0026rsquo;s integrity [14, 29]. This alignment is crucial for patient retention and psychological well-being during treatment.\u003c/p\u003e \u003cp\u003eUltimately, this approach creates an evidence-based bridge between patient shared decision-making and oncological precision, ensuring that supplementary interventions do not compromise the efficacy of the primary treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eWhile the proposed pharmacokinetic model provides a robust framework for clinical decision-making, it is subject to certain limitations.\u003c/p\u003e \u003cp\u003eFirst, the model relies on a hierarchical evidence taxonomy; compounds categorized at Level 4 are based on preclinical data, where human 1:1 translation remains an estimate [1]. This translational uncertainty is compounded by the significant variability in quality assurance, species identification, and standardization within the botanical market [28].\u003c/p\u003e \u003cp\u003eSecond, individual genetic variability\u0026mdash;specifically polymorphisms in the Cytochrome P450 enzyme family\u0026mdash;can significantly alter a patient\u0026rsquo;s \"real-world\" elimination rate compared to clinical averages.\u003c/p\u003e \u003cp\u003eThird, the model primarily addresses acute systemic interference and does not fully account for long-term sequestration in deep tissue compartments (e.g., adipose tissue) for all compounds, although conservative margins have been established for highly lipophilic substances like Vitamin E and Cannabinoids [61, 170].\u003c/p\u003e \u003cp\u003eFinally, the 5 \u0026times; \u003cem\u003et\u0026frac12;\u003c/em\u003e standard targets the elimination of 96.8% of the parent compound, but the persistence of active secondary metabolites may, in rare cases, require even longer recovery windows.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe model provides a framework for integrating complementary oncology by ensuring that chemo- and radiotherapy support does not interfere with the efficacy of conventional treatment.\u003c/p\u003e \u003cp\u003eThe implementation of a pharmacokinetic washout model in integrative oncology represents a shift from reactive prohibition to proactive risk management. By utilizing the 5 \u0026times; \u003cem\u003et\u0026frac12;\u003c/em\u003e rule and hierarchical evidence weighting, clinicians can replace uncertainty with documented safety margins [14, 27].\u003c/p\u003e \u003cp\u003eThe implementation of the 5 \u0026times; \u003cem\u003et\u0026frac12;\u003c/em\u003e pharmacokinetic standard serves as a robust 'safety overlay' [14, 27]. By strictly adhering to these washout protocols, the model ensures that the integrity of conventional oncology treatment remains uncompromised. This standardized approach provides a practical framework and ethical rationale for managing the use of botanical extracts in a professional oncology setting.\u003c/p\u003e \u003cp\u003eThis approach not only minimizes the risk of treatment interference - such as unintended cytoprotection or metabolic competition - but also fosters a transparent therapeutic alliance.\u003c/p\u003e \u003cp\u003eWhen patients are provided with a structured framework for collaboration the need for clandestine use of supplements diminishes. Ultimately, this model serves as a bridge between patient autonomy and oncological precision, ensuring that supplementary biosupport enhances the recovery phase without compromising the efficacy of the primary oncological intervention.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Approval:\u003c/p\u003e\n\u003cp\u003eAll authors have seen and approved the manuscript.\u003c/p\u003e\u003cp\u003e8. Conflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The development of the pharmacokinetic washout protocols is based solely on a review of available pharmacological literature and is intended for clinical decision support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e9. Data Availability Statement \u003c/p\u003e\n\u003cp\u003eThe theoretical pharmacokinetic model presented in this study is operationalized through an open-access clinical decision-support website (https://jegharkraeft.dk/en/chemo-and-radiotherapy-support/). The complete dataset supporting the conclusions of this article, including detailed compound-by-compound documentation, terminal half-lives (\u003cem\u003et\u0026frac12;\u003c/em\u003e), hierarchical evidence weighting (Levels 1\u0026ndash;4), and the visual risk-managed washout protocols (Green, Yellow, Red) for all 55 compounds, is freely available on this platform. This database is searchable and updated regularly to facilitate rapid evidence-based decisions in a clinical oncology setting.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAshrafpour, S, M. et al. The double-edged sword of nutraceuticals: comprehensive review of protective agents and their hidden risks. \u003cem\u003eFrontiers in Nutrition\u003c/em\u003e. 2025; 12: 1524627. doi.org/10.3389/fnut.2025.1524627\u003c/li\u003e\n\u003cli\u003eNarimatsu, H. Yaguchi Y. et al. 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Pharmacokinetics of zinc in pre-diabetes: a pilot study. \u003cem\u003eJournal of Diabetes \u0026amp; Metabolic Disorders\u003c/em\u003e. 2018; 5(1): DOI:10.15406/jdmdc.2018.05.00131\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Integrative Oncology, Complementary Oncology, Pharmacokinetics, Herb-Drug Interactions (HDI), Washout Protocols, Chemotherapy Support, Radiotherapy Support, Clinical Decision Support, Cytochrome P450","lastPublishedDoi":"10.21203/rs.3.rs-9654569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9654569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe concurrent use of botanical extracts and off-label medications by oncology patients introduces significant pharmacokinetic risks, compounded by a prevalent clinical 'disclosure gap'. There is a critical need for a systematic, evidence-based framework to safely manage complementary biosupport without compromising conventional cancer therapies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo develop an objective decision-support model based on pharmacokinetics to safely separate oncological treatment from supplementary compounds through precise timing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe propose a novel clinical decision-making model based on two pillars. First, a hierarchical evidence-weighting system that categorizes 55 commonly used compounds into four levels, operationalized as a visual 'traffic-light' tool for rapid clinical assessment. Second, a strict pharmacokinetic washout protocol utilizing the 5 × \u003cem\u003et½\u003c/em\u003e standard. To account for inter-species variability and data scarcity in Level 4 evidence (in-vitro/animal data), an integrated Safety Factor (S = 2) is applied to washout calculations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe model establishes standardized pre-treatment washout durations for 55 compounds. For highly lipophilic substances or those with prolonged terminal half-lives, a maximum clinical ceiling of 28 days is established. This ensures that plasma concentrations reach \u0026lt; 3.125% of steady-state levels prior to oncology treatment, providing a robust safety overlay\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy structuring the integration of botanical and off-label biosupport, this model bridges the disclosure gap and strengthens the therapeutic alliance. It facilitates a structured patient-clinician dialogue, operationalized via an open-access clinical decision-support website (https://jegharkraeft.dk/en/chemo-and-radiotherapy-support/), providing oncologists with a structured clinical rationale and actionable guidelines to maintain the integrity of primary oncological treatments while enhancing patient safety.\u003c/p\u003e","manuscriptTitle":"Systematizing Washout Protocols in Integrative Oncology: A Pharmacokinetic Model Based on 5 × t½ and Hierarchical Evidence Weighting of Botanical Extracts and Off-Label Medications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 10:59:16","doi":"10.21203/rs.3.rs-9654569/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1024528c-1962-4429-92df-11f5651d0215","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67793030,"name":"Oncology"}],"tags":[],"updatedAt":"2026-05-11T10:59:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 10:59:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9654569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9654569","identity":"rs-9654569","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}