Adam Geller ¹, Tom Fuller², Aaron Spec³, George Longshore⁴

Affiliations:

1 MDS Labs® Botanical Research Lab, Valencia, California
2 Reventek® Institute of Anti-aging and Longevity Research, Los Angeles, California

PMID: Pending. PMCID: Pending. DOI: Pending.

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Abstract

Cellular senescence is a primary hallmark of aging, and the selective removal or quieting of senescent cells has become one of the most actively pursued strategies for extending healthspan. The reference senolytic regimen — the tyrosine-kinase inhibitor dasatinib combined with the plant flavonol quercetin (D+Q) — was itself discovered by a target-first logic: mapping the pro-survival signaling nodes that senescent cells depend on, then selecting agents that disable those nodes. This paper asks whether the same target-first logic can be run in the botanical direction: starting from the documented molecular target profile of dasatinib and assembling a set of naturally occurring compounds, each reported to act on one or more of those targets, at doses already used in humans. We formalize this as a five-step framework — target deconvolution, phytochemical mapping, evidence grading, candidate assembly, and (optional) in-vitro confirmation — and apply it to dasatinib. The approach is distinct from, and complementary to, the gene-expression-similarity method recently used to identify natural dasatinib substitutes: target mapping is more mechanistically transparent (it names which node each compound engages) but is constrained by the heterogeneous quality of the underlying literature. We present the resulting target map, the candidate compounds and the evidence tier behind each, an explicit accounting of which dasatinib targets are and are not covered, and a single candidate formulation expressed in customary human doses. We close with a validation roadmap. The contribution is the framework and its honest application; the formulation is a design hypothesis that could exist within the limitations of a viable consumer product. The author notes that often the real breakthroughs are found when research is synthesized into usable protocols that can be made available to the general public, a primary goal of this study.

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1. Introduction

Aging is accompanied by the progressive accumulation of senescent cells — cells that have exited the cell cycle but persist, resisting apoptosis and secreting a pro-inflammatory mixture known as the senescence-associated secretory phenotype (SASP). The accumulation of these cells is now recognized as a core hallmark of aging [3], and their selective elimination (by senolytics) or the suppression of their secretory phenotype (by senomorphics) has produced striking improvements in healthspan across animal models [1, 2].

The defining senolytic regimen is the combination of dasatinib and quercetin (D+Q). Its discovery is instructive for what follows. Zhu and colleagues did not find D+Q by screening compounds at random; they first used transcriptomic analysis to identify the “senescent-cell anti-apoptotic pathways” (SCAPs) — pro-survival networks, including ephrin ligands/receptors, PI3Kδ, BCL-xL, and others — that senescent cells rely on to evade death, then searched for agents known to disable those specific nodes. Dasatinib and quercetin were selected precisely because they engage SCAP components [4]. The senolytic field, in other words, began with a target-first method: define the vulnerability, then find the molecule.

That logic sits within a broader shift in pharmacology. The classical aim of designing maximally selective, single-target drugs has been complemented by network pharmacology, which recognizes that many effective agents act through modulation of multiple proteins, and that multi-target engagement can yield robustness against a network’s redundancy. Hopkins argued that this polypharmacological mode is especially characteristic of — and well-suited to — plant-derived compounds, which natively act on many targets at once [5]. A botanical mixture deliberately assembled to cover several nodes of a validated drug-target network is therefore not a dilution of a clean pharmaceutical idea; it is a direct instance of the polypharmacology paradigm.

This paper applies that paradigm in reverse. Rather than discovering a target network from scratch, we take a network whose senolytic relevance is already established — the molecular target profile of dasatinib — and ask which naturally occurring compounds, at doses already used in humans, collectively engage those targets. The result is a candidate botanical analogue of dasatinib, intended to be paired with quercetin in parallel to D+Q.

A recent, independent effort pursued the same destination by a different road. Meiners and colleagues used computational gene-expression-similarity matching to identify natural compounds whose transcriptional signatures resemble dasatinib’s, surfacing piperlongumine, parthenolide, phloretin, and curcumin as candidates [14]. The two approaches are complementary, and their trade-offs are worth stating plainly. Gene-expression similarity is unbiased and systematic but mechanism-agnostic: it tells you a compound behaves like dasatinib without saying why. Target mapping, the approach used here, is mechanistically explicit — each inclusion is justified by a named molecular target — but it is only as good as the literature it draws on, which is heterogeneous in quality and, for some compounds, computational rather than experimental. Neither approach, on its own, demonstrates efficacy; both generate hypotheses. That an independent, peer-reviewed group converged on the same overarching question supports the legitimacy of the problem even as the methods and the resulting compound sets diverge almost entirely.

The contributions of this paper are: (1) a reproducible, five-step framework for designing a botanical analogue of a target-defined drug; (2) its application to dasatinib, with an explicit, honest map of target coverage and the gaps; and (3) a candidate formulation, expressed in customary human doses, presented as a falsifiable design hypothesis together with the experiments that would test it.

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2. Background: Senescence and Senotherapeutics

Senescence is a stress response that arrests the division of damaged cells, serving as a tumor-suppressive and wound-healing mechanism. Senescent cells typically develop the SASP, a secretory program that recruits immune clearance; when these cells are not cleared and instead accumulate — as they do with age and at sites of tissue pathology — the same secretions drive chronic, sterile inflammation implicated in numerous age-related conditions [1, 3]. Because senescent-cell burden is causally linked to functional decline in animal models, the cells are regarded as a rational therapeutic target.

Senotherapeutics divide into two classes. Senolytics reduce the viability of senescent cells, typically by disabling the anti-apoptotic defenses (SCAPs) that allow these cells to survive despite their damage [4]. Senomorphics (or senostatics) leave the cells alive but suppress the SASP, reducing the inflammatory output without the burst of cell death. Since 2015, multiple senolytic and senomorphic candidates — pharmaceutical and natural — have moved from identification toward clinical testing, and a recognized subset of the natural candidates are dietary flavonoids and polyphenols, among them quercetin and fisetin [16]. The framework below treats both senolytic and senomorphic activity as relevant, since a botanical mixture may contribute through either mechanism.

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3. The Dasatinib–Quercetin Regimen: Mechanistic and Clinical Context

The studies in this section concern the reference D+Q regimen and the senolytic field. They establish context and motivate the design; they are not studies of any botanical formulation, and no equivalence of outcome should be inferred from them.

Mechanistic origin. As described above, D+Q emerged from mapping the SCAP nodes of senescent cells and selecting agents that engage them; dasatinib was most effective against senescent fat-cell progenitors, quercetin against senescent endothelial cells, and the combination covered a broader range of senescent-cell types than either alone [4].

Clinical context. The first-in-human senolytic trial administered D+Q to patients with idiopathic pulmonary fibrosis and reported improvements in physical function in an open-label pilot focused on feasibility and tolerability [9]. A subsequent open-label human study in diabetic kidney disease provided the first direct evidence that D+Q reduces senescent-cell burden in human adipose and skin tissue [10]. A vanguard, open-label pilot in early-stage Alzheimer’s disease (five participants, twelve weeks) had the primary aim of establishing central-nervous-system penetrance and safety; dasatinib reached cerebrospinal fluid, and the trial was explicitly not designed to demonstrate efficacy [11]. Reviews of the field additionally discuss preclinical work on senescence in models relevant to Down syndrome and radiation exposure [12], and senescent-cell clearance in aged human brain organoids [13]. The consistent thread is that dasatinib’s senolytic activity is attributed to engagement of specific molecular targets — the target set the present framework reverse-engineers.

Motivation for a botanical analogue. Dasatinib is a potent pharmaceutical with recognized adverse effects, including pulmonary arterial hypertension in a subset of treated patients [8]. Naturally occurring compounds are generally associated with fewer adverse effects than synthetic agents, which is one reason they are studied as starting points for kinase-directed strategies [15]; “fewer reported adverse effects,” however, is not “proven safe at any dose,” and the dose and duration cautions in Sections 8–9 apply.

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4. Methods: A Kinase-Target–Mapping Framework

The framework is presented generally — it could be applied to any target-defined drug — and then applied to dasatinib. It comprises five steps.

Step 1 — Target deconvolution of the reference drug. Assemble the documented molecular target profile of the reference agent from the strongest available sources, prioritizing systematic experimental data over narrative review. For dasatinib, the empirical anchor is the kinome-wide profiling of Karaman et al., who measured the binding of 38 kinase inhibitors (dasatinib among them) across 317 kinases — more than half the human kinome — and introduced a quantitative selectivity score [6]. This is complemented by the focused review of Montero et al., which enumerates dasatinib’s most sensitive targets: ABL (and the BCR-ABL fusion); the SRC-family kinases SRC, LCK, HCK, FYN, YES, FGR, BLK, LYN, and FRK; and the receptor tyrosine kinases c-KIT, PDGFR-α/β, DDR1, c-FMS (CSF1R), and the ephrin receptors [7]. Crucially, several of these overlap the SCAP nodes independently identified as senolytic vulnerabilities — the ephrin axis and PI3Kδ in particular [4] — which links dasatinib’s kinase profile directly to the survival network that senolytics exploit.

Step 2 — Phytochemical mapping. For each target (and immediately connected node), identify naturally occurring compounds with reported inhibitory or modulatory activity, drawing on the primary literature and, where primary data are absent, on computational target prediction. Compounds toxic at relevant doses, or available only as research chemicals, are excluded at this step regardless of their target fit.

Step 3 — Evidence grading. Grade each compound–target association by the strongest available evidence, on an explicit hierarchy: computational/in-silico (weakest) < in-vitro < animal < human. This grading is carried through to the final table so that the strength of each inclusion is visible rather than implied.

Step 4 — Candidate assembly and dose selection. Assemble the graded compounds into a candidate set, retaining quercetin to parallel the D+Q pairing, and select doses that fall within ranges already used in human studies or customary supplementation (Section 8). Doses are chosen for established human tolerability and are not optimized as a combination; documented pairwise interactions among these compounds do exist and are discussed in Section 8, but the assembled formulation itself has not been optimized or tested as a unit.

Step 5 — In-vitro confirmation (optional, confirmatory only). Where feasible, compare individual candidate compounds against the reference drug on the targeted nodes using a direct assay (e.g., Western blot for target phosphorylation) to corroborate literature activity before inclusion. In the present application this step was used only as an internal screening aid on individual compounds; it has not been peer-reviewed and is not reported here as data. Any formal publication should include the underlying assay images and quantification, or omit the claim. No results from this step are asserted in this document.

Coverage and gap analysis. Honesty about coverage is integral to the method, not an afterthought. The mapped botanicals engage a subset of dasatinib’s targets (Section 5, Table 1). Several documented targets are not meaningfully addressed: most individual SRC-family members beyond SRC/FYN/LYN (i.e., HCK, YES, FGR, BLK, FRK, LCK), DDR1, the ephrin receptors as a class, and FLT-3; the BCR-ABL fusion itself is engaged only indirectly, through in-vitro work on a single compound. The framework therefore yields an analogue informed by the target profile, never a reproduction of it — a distinction Section 8 treats as a hard limitation.

Relation to gene-expression matching. The contrast with the Meiners gene-expression approach [14] is methodological, not adversarial. Target mapping offers interpretability (named mechanism per inclusion) and direct actionability (each target suggests its own confirmatory assay); it sacrifices the unbiased, systematic breadth of a transcriptomic screen and inherits the unevenness of the source literature. A mature program would use both: gene-expression similarity to nominate candidates without mechanistic prejudice, and target mapping to explain and prioritize them.

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5. Results: Target Map and Candidate Compounds

Applying Steps 1–3 to dasatinib yields the target set of Section 4 and the compound map of Table 1. Each row records the dasatinib-relevant target(s) a compound was mapped to, the reported activity, and the strongest evidence tier identified.

Table 1. Candidate compounds, mapped dasatinib-relevant target(s), reported activity, and strongest evidence tier.

Compound	Mapped target(s)	Reported activity	Strongest evidence tier	Ref
Quercetin	SRC-family (SRC/FYN/LYN), BCL-2, PI3K/AKT	Inhibition / modulation; SCAP engagement	Review of in-vitro data; component of D+Q (human pilots)	[4, 15]
Fisetin	CDK1/CDK4; AMPK/MAPK/mTOR; senescent-cell survival	CDK inhibition; pathway modulation; senolytic	Animal + human tissue (senolytic); human trials ongoing	[16, 19]
Spermidine	SRC; BCL-2; autophagy	Src modulation; autophagy induction	In-vitro / mechanistic; review (autophagy/longevity)	[21, 22]
Pterostilbene	BCR/ABL signaling; AMPK	Downregulation (in-vitro); AMPK activation	In-vitro (leukemic and hepatic cells)	[23, 24]
Lupeol (from Sophora flavescens)	MMP / inflammatory signaling	Reduced MMP expression in aged fibroblasts	In-vitro (UVA-aged fibroblasts)	[25]
Berberine	ROCK2, PIK3CD (PI3Kδ), KCNMA1, CSF1R, KIT; AMPK	High-affinity binding (predicted); AMPK activation	In-silico docking (vs. imatinib); animal/human (AMPK)	[26, 27]
Rutin	c-Met (linked to c-Src); SASP	Senomorphic (SASP suppression); c-Met inhibition	In-vitro (senomorphic + c-Met)	[28, 29, 30]
Senna (Sennoside B)	PDGFR-β	Inhibition of PDGF-BB–induced signaling	In-vitro (osteosarcoma cells)	[18]

Evidence tier is the single strongest line of support identified, not a summary of total weight. “In-silico” denotes computational prediction and is the weakest tier; most rows are in-vitro. A mapped target means reported activity on that node, not a confirmed in-vivo effect for this use. Note the mechanistic convergence: berberine’s predicted target PI3Kδ (PIK3CD) is one of the SCAP survival nodes whose silencing kills senescent cells [4], and quercetin engages the PI3K/AKT and BCL-2 arms of the same network — i.e., the strongest threads of this map run through the validated senolytic vulnerability, not merely through dasatinib’s catalogue.

It bears emphasizing that the map’s evidentiary weight is uneven, and the design does not pretend otherwise. The senolytic and senomorphic rationale rests most firmly on the well-supported compounds — quercetin, fisetin, and rutin — whose activity is corroborated in animal and/or human-tissue work and which engage the validated survival network directly. The remaining inclusions, notably berberine (computational), lupeol (a single in-vitro skin-fibroblast study), and senna (a single in-vitro report), extend the breadth of target coverage at materially lower evidentiary confidence and should be read as the weaker, more provisional arms of the design. A reader is entitled to weight the formulation accordingly.

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6. Pathway Rationale

The targets above are described here in terms of cell-cycle control, survival signaling, inflammation, and senescence — the processes relevant to a senotherapeutic rationale. Several of these kinases were first characterized in oncology; that origin is noted only where it aids understanding. None of the following is a claim that any compound or formulation treats a disease.

BCR-ABL / ABL. A constitutively active fusion kinase driving unregulated proliferation; relevant here because the ABL axis overlaps dasatinib’s range and the proliferation/apoptosis-resistance programs also seen in senescent-cell biology.

SRC-family kinases (SFKs). Tyrosine kinases (SRC, LCK, HCK, FYN, YES, FGR, BLK, LYN, FRK) relaying growth, differentiation, and survival signals. SFK signaling intersects the survival programs that let senescent cells resist apoptosis — a recurrent senolytic node.

c-KIT. A receptor tyrosine kinase activating PI3K/AKT, JAK/STAT, and MAPK on binding stem-cell factor; relevant as a dasatinib RTK target and a regulator of progenitor survival signaling.

CSF1R (c-FMS). Governs macrophage/monocyte development and survival; because chronic macrophage-driven inflammation (“inflammaging”) is a feature of aged tissue, its modulation is studied in age-associated inflammation.

BCL-2 family. Anti-apoptotic proteins that promote survival by restraining programmed cell death. Senescent cells frequently upregulate these proteins, and that dependence is among the best-characterized senolytic vulnerabilities (a SCAP) [4].

PI3Kδ (PIK3CD). A lipid-kinase node of the PI3K/AKT survival axis; identified directly as a SCAP whose silencing selectively kills senescent cells [4]. Its appearance among berberine’s predicted targets [26] is the most mechanistically pointed entry in the map.

CDK1 / CDK2 / CDK3 / CDK6 and Cyclin D1. Core cell-cycle kinases governing the G2→M, G1→S, and G0→G1 transitions, partnered with their cyclins (Cyclin D1 with CDK4/6 phosphorylates Rb to drive G1→S). Their regulation is tied to orderly division, genomic stability, and the cell-cycle-arrest state that defines senescence.

CDK5. Atypical and largely neuronal: activated by p35/p39 rather than cyclins, it governs cytoskeletal and synaptic function; dysregulation is studied in neuronal stress and age-related neurodegeneration.

PDGFR-α/β. Receptor tyrosine kinases signaling through PI3K-AKT, MAPK-ERK, and PLC-γ to regulate growth, repair, and migration, with prominence in connective tissue and the vasculature; sustained aberrant signaling is associated with fibrotic remodeling. PDGFR-β is a shared target of imatinib and dasatinib.

MAPK. A cascade transmitting growth-factor, cytokine, and stress signals. Its role in aging is dual — appropriate signaling supports stress adaptation and repair, chronic over-activation promotes inflammation — so the relevant aim is balance, not simple inhibition.

AMPK. The cellular energy sensor; on activation it restores energy balance, promotes autophagy, and limits inflammatory signaling, and it is among the most consistently longevity-associated pathways, with lifespan extension reported in invertebrate and rodent models [20]. Several mapped compounds (fisetin, pterostilbene, berberine) converge on AMPK activation.

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7. The Candidate Compounds

Each entry states the source, the mapped target, and the strength of the evidence. Reported activities are described as such and are not claims of clinical effect.

Quercetin. A flavonoid abundant in onions, apples, berries, and tea, reported to inhibit or modulate SRC-family kinases (SRC, FYN, LYN), BCL-2, and the PI3K/AKT axis [15] — nodes that are also senescent-cell survival pathways [4]. It is one of the two components of the reference D+Q regimen and has additionally been studied for neuroprotective activity across in-vitro, animal, and human work [17]. Its inclusion anchors the design to D+Q.

Fisetin. A flavonoid (strawberries, apples, persimmons, onions) and one of the most studied natural senotherapeutics: late-life dosing of aged mice reduced senescence markers across tissues and extended median and maximum lifespan, with activity confirmed in human tissue explants, and human trials are ongoing [19]. Mechanistically it is reported to inhibit CDK1/CDK4 and modulate AMPK, MAPK, and mTOR [15]; the field recognizes it (with quercetin) as a leading nutritional senolytic [16].

Spermidine. A polyamine (aged cheese, mushrooms, soy, whole grains) studied chiefly as an autophagy inducer, with autophagy maintenance associated with cardioprotection and longevity in model systems [22]. It is also reported to modulate SRC kinase activity via an allosteric mechanism (not straightforward inhibition) and is discussed in connection with BCL-2-family survival signaling [21]. The autophagy rationale is its strongest; the Src-modulation finding is mechanistic and in-vitro.

Pterostilbene. A more bioavailable structural relative of resveratrol; in leukemic cell models it downregulated BCR/ABL signaling and induced apoptosis [23], and in hepatic cells it activated AMPK [24]. Both findings are in-vitro.

Lupeol (from Sophora flavescens, “Ku Shen”). A pentacyclic triterpene studied for anti-inflammatory activity; in UVA-aged human fibroblasts it reduced matrix-metalloproteinase expression [25], relevant to the inflammatory and extracellular-matrix changes of tissue aging. The evidence is in-vitro. (Sophora flavescens extract also contains oxymatrine, which carries its own pharmacology and is one reason dose and standardization, Section 8, matter for this ingredient.)

Berberine. An alkaloid (e.g., Coptis chinensis) whose best-established action is AMPK activation, with metabolic effects shown in animal and human studies of insulin-resistant states [27]. A separate in-silico docking study predicted high-affinity binding to ROCK2, PI3Kδ (PIK3CD), KCNMA1, CSF1R, and KIT, framing berberine as a possible natural analogue of imatinib [26]; CSF1R and KIT overlap dasatinib’s targets and PI3Kδ overlaps the SCAP set, which is the mapping link. This is a computational prediction — the weakest tier — comparing berberine to imatinib rather than dasatinib, and the authors call for experimental validation.

Rutin. A bioflavonoid (buckwheat, apples, citrus) validated as a potent senomorphic in a screen of natural medicinal agents, dampening the full-spectrum SASP in senescent cells and improving outcomes in preclinical models [29]. Separately it was identified as a c-Met inhibitory lead [28]; c-Met is described as an early senescence marker linked to apoptosis resistance and the SASP, and because dasatinib reaches c-Met indirectly through inhibition of c-Src — a downstream transducer of c-Met signaling [30] — rutin’s reported activity is the mapping basis.

Senna (Sennoside B). Senna’s sennosides are stimulant laxatives; beyond that, Sennoside B was reported in vitro to inhibit PDGF-receptor signaling and PDGF-BB–induced proliferation in osteosarcoma cells [18], and PDGFR-β is a dasatinib target. The evidence is in-vitro. Senna is the one ingredient whose primary pharmacology imposes a real use-limitation (Section 8): stimulant laxatives are not appropriate for unrestricted chronic use, which constrains both its dose and the framing of its inclusion.

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8. A Candidate Formulation

The framework’s output is a candidate formulation: the set of compounds above, with quercetin retained for D+Q parallelism, at doses within ranges used in human studies or customary supplementation. The amounts below represent one rational instantiation of the design. They are not claimed to be optimal or clinically validated, and while documented interactions among some of these compounds make combination benefit plausible (discussed below), no pharmacokinetic or pharmacodynamic data exist for the assembled formulation as a unit; the proportions were chosen for established single-ingredient human tolerability, not optimized as a combination.

Compound	Candidate amount (per daily dose)	Dose rationale	Caution
Quercetin	400 mg	Within common supplement range (≈250 mg–1 g); the Q of D+Q	Generally well tolerated
Fisetin	400 mg	Within ranges used in supplementation; human senolytic trials use intermittent higher (mg/kg) dosing	Low oral bioavailability; intermittent use studied
Pterostilbene	400 mg	Upper end of customary range (human lipid studies use ≈50–250 mg)	400 mg exceeds doses in most human studies — flagged
Rutin	500 mg	Within customary range (≈500 mg–1 g)	Generally well tolerated
Berberine	800 mg	Within customary range (commonly 500 mg, 1–3×/day)	GI effects; drug interactions (CYP/P-gp)
Spermidine	20 mg	Selected for autophagy rationale	Exceeds typical dietary intake (≈1–15 mg/day) and most supplement doses — flagged
Sophora flavescens extract (std. ~40% oxymatrine / ~20% lupeol)	250 mg	Within customary extract range	Oxymatrine has independent pharmacology; standardization matters
Senna (std. 70% Sennoside B)	15 mg sennosides	Low relative to laxative dosing	Stimulant laxative — not for unrestricted chronic daily use; intended at sub-laxative levels only

Two of these amounts warrant explicit flags even within a design rationale: pterostilbene at 400 mg sits above the doses used in most human studies, and spermidine at 20 mg exceeds typical dietary and supplemental intake. Both should be revisited against current tolerability data before any use. Senna’s inclusion is the most constrained: as a stimulant laxative it is unsuitable for unrestricted chronic intake, and its presence is justified, if at all, only at the low, sub-laxative level shown and with that limitation stated to the user.

Design under constraint: the access rationale

The dose choices above should be read against the constraint that actually governs this design. The objective is not a theoretically optimal molecule but a formulation that can be made available to the public, and the only channel through which a senolytic-inspired botanical can reach the public today is the dietary-supplement market. That channel admits only generally-available, non-prescription ingredients, at amounts consistent with established human use and applicable safety limits — it does not admit dasatinib itself, nor bespoke pharmacokinetically-optimized doses of novel agents. The amounts in the table are therefore the best-supported instantiation achievable within those constraints, not the amounts a clean-sheet pharmacology would prescribe. Where a dose is acknowledged as non-ideal (pterostilbene, spermidine), it was set at the edge of the customary human-use range rather than optimized, accepting “good and available” over “ideal and unavailable.”

This reflects the premise underlying the whole effort: the rate-limiting step in translating senolytic science into public benefit is less the discovery of new compounds than the availability of two things — the synthesized research, and a usable formulation grounded in it. Given the timescales over which aging acts, an accessible, evidence-anchored, honestly-bounded formulation offered now carries a kind of value that a hypothetically optimal but unavailable one does not — provided its limitations are disclosed rather than hidden, which is the purpose of Sections 5, 8, and 9. The access argument justifies making an honestly-labeled, hypothesis-stage formulation available.

The rationale for combining these compounds, rather than offering them singly, rests on three supports that are themselves explicit about their status. The first is the long precedent of polyherbal formulation — traditional systems such as Chinese medicine routinely combine botanicals on the expectation of complementary or synergistic effects — which establishes precedent, not proof. The second is the network-pharmacology argument that multi-target engagement can outperform single-target action against a redundant survival network, a property Hopkins identified as characteristic of plant compounds [5]. The third, and the most concrete, is that documented pharmacological interactions already exist among members of this set: stilbenes (here, pterostilbene) and flavonoids such as quercetin both inhibit the phase-II enzymes (sulfotransferases, UDP-glucuronosyltransferases) and efflux transporters that limit polyphenol bioavailability, and resveratrol–quercetin combinations have shown enhanced antioxidant, anti-inflammatory, and metabolic effects in preclinical models [31]. Two honest qualifications attach to this support. First, the human translation is unsettled — at least one human pharmacokinetic study found no increase in resveratrol exposure when quercetin was co-administered, likely because neither reached enzyme-saturating concentrations in vivo [31]. Second, none of these documented interactions has been shown to amplify the dasatinib-relevant kinase-signaling endpoints specifically; potentiation of a compound’s core activities does not automatically extend to the kinase pathways this design targets. What the existing data do support is a directional inference: because stilbene–flavonoid combinations more often potentiate than antagonize shared activities, the prior for the assembled formulation sits toward additive-or-synergistic rather than antagonistic — the documented interactions raise, rather than lower, the plausibility of combination benefit. Synergy among these specific compounds at these doses therefore remains a design hypothesis rather than a demonstrated property of this blend, but it is a hypothesis the existing combination literature makes more, not less, credible, and it sits explicitly on the validation roadmap (Section 10). Rigorous dose refinement — relating each compound’s target-engagement potency to concentrations achievable in human plasma and tissue — is the proper next step and is likewise a roadmap item; it is not, however, a precondition for making an honestly-bounded formulation available under the constraints described.

This formulation is presented as hypothesis — the best single instantiation the available evidence supports — not as a finished product.

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9. Limitations

The limitations are substantial and are stated as plainly as the rationale:

1. Evidence tier. Much of the supporting data is in-vitro; one key inclusion (berberine’s kinase targets) is in-silico, the weakest tier, and compares to imatinib rather than dasatinib. In-vitro or computational activity does not establish an effect in a living human at an achievable oral dose.
2. Partial target coverage. The botanicals engage only a subset of dasatinib’s targets (Section 4); major targets — most SFK members, DDR1, the ephrin receptors, FLT-3 — are unaddressed, and BCR-ABL only indirectly. The design is an analogue, not a reproduction.
3. No formulation-level data. No controlled study of the assembled set exists. The human and animal evidence cited concerns individual compounds or the separate D+Q regimen. Inter-compound interactions (additive, synergistic, or antagonistic) are unknown, as are combination pharmacokinetics.
4. Heterogeneous, sometimes oncology-derived evidence. Several target activities were characterized in cancer-cell models; relevance to senescent-cell biology is inferred, not always directly demonstrated.
5. Dose selection is pragmatic, not optimized. Amounts reflect customary single-ingredient human use, not optimization for the combination; two exceed typical human study doses (Section 8).
6. Confirmatory assays are preliminary. Any in-vitro comparison to dasatinib performed during selection was on individual compounds and is not reported here as data.
7. Mechanistic ≠ clinical. Engaging a pathway implicated in senescence is a hypothesis for benefit, not a demonstration of one.

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10. Validation Roadmap

The framework’s value to other investigators is that each claim sits at a known evidence tier and has a defined experiment that would advance it:

• Confirm individual target engagement. Profile each candidate compound against the mapped kinases using a standardized panel (as in the kinome-profiling approach of [6]) or direct phospho-target Western blots, to replace literature and in-silico associations with primary data at the relevant tier.
• Test senolytic/senomorphic activity per compound. Use established senescent-cell assays (SA-β-gal, SASP-factor quantification, viability/selectivity index against non-senescent controls) to grade each compound’s actual senescent-cell activity, not just target binding.
• Test the formulation as a unit. Evaluate the assembled set in senescent-cell models for additivity/synergy/antagonism, then in an aged-animal model for senescent-cell-burden and healthspan endpoints, before any efficacy framing.
• Characterize combination pharmacokinetics. Determine whether the chosen doses produce co-exposure at relevant tissues, given the divergent bioavailabilities (notably fisetin and pterostilbene).
• Benchmark against the gene-expression approach. Cross-reference candidates with transcriptomic-similarity hits [14] to identify compounds that satisfy both mechanistic and signature-level criteria — the strongest nominations would survive both filters.

A formulation that passed these stages would have earned an efficacy claim. Until then, this is a design rationale.

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11. Conclusion

The senolytic field began by mapping the survival network of senescent cells and choosing agents to disable it; D+Q was the first product of that target-first logic [4]. This paper runs the same logic in the botanical direction, formalized as a reproducible five-step framework and grounded in the network-pharmacology paradigm that recognizes multi-target, plant-derived modulation as principled rather than imprecise [5]. Applied to the documented target profile of dasatinib [6, 7], the framework yields a set of naturally occurring compounds — each justified by a named target and graded by evidence tier — that collectively engage part of that profile, with the uncovered targets named as honestly as the covered ones. The output is a single candidate formulation in customary human doses, and a roadmap of the experiments that would test it. Compared with the gene-expression-similarity route to natural dasatinib substitutes [14], target mapping trades systematic breadth for mechanistic transparency, and the two are best used together. With the best evidence currently available, the formulation of Section 8 represents the most defensible single instantiation of a botanical dasatinib analogue intended for pairing with quercetin — offered as a hypothesis, not a therapy to be assumed.

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Methodology paper / design rationale. Evidence tiers are stated per claim; in-vitro and in-silico findings predominate and do not establish clinical effect. No formulation has been evaluated as a unit. Not medical advice; not evaluated by the FDA; not intended to diagnose, treat, cure, or prevent any disease.