Act I · Physical & Clinical Story
Why the Same Mutation Tells Different Stories in Different
Two young siblings who inherit the exact same primary cystic fibrosis genetic variant can experience strikingly different pulmonary journeys across their entire adolescent and adult lifetimes. When Corvol and colleagues published their international patient study in Nature Communications in twenty fifteen, they proved that secondary genetic modifiers significantly influence pulmonary disease severity beyond the primary cystic fibrosis mutation. Alternative apical ion channels, solute transporters, and airway transcription factors provide compensatory chloride pathways that shield delicate bronchioles from destructive bacterial colonization. Differential liver metabolism rates across patient populations also alter active drug concentrations within the bloodstream. A patient with accelerated hepatic clearance eliminates therapeutic compounds prematurely, whereas another individual retains prolonged systemic drug exposure throughout daily life. Understanding these non linear genetic and metabolic interactions explains long standing clinical puzzles while establishing a rigorous mathematical basis for individualized precision medicine.
A single disease mutation never acts in total biological isolation inside the human body. Inside every living human cell, thousands of background genetic variants constantly adjust biochemical reactions, changing how severely a primary disease mutation damages delicate lung tissues. Some fortunate individuals inherit helpful natural backup channels within their bronchial cells. These secondary ion pores pump chloride independently, partially restoring fluid balance even when the main channel protein is entirely absent. Meanwhile, liver enzymes determine how quickly medications vanish from the body. If a patient breaks down modulators rapidly, standard pill dosages might not provide sufficient therapeutic coverage throughout the day. Comprehensive genetic profiling reveals these critical hidden biological differences clearly. By combining modifier gene scores with individualized drug metabolism rates, clinicians can predict disease progression and customize treatment regimens with far greater precision than traditional mutation labels permit.
Everyday Analogy: Consider an acoustic concert hall where the lead violin suddenly snaps its primary string in the middle of a symphony. If the hall possesses poor acoustics and the remaining musicians play in rigid isolation, the musical performance falters immediately. However, if the auditorium features resonant wooden soundboards, supportive cellos and violas compensate for the missing frequency, preserving rich harmonic balance for the audience. Simultaneously, the hall's ventilation system continuously regulates ambient humidity, altering how quickly rosin dust settles and preventing instruments from detuning mid-concert. Secondary architectural features and airflow rates determine whether the performance succeeds despite the damaged lead violin. In our biological model, alternative apical ion channels act like those supportive secondary instruments, while liver enzymes function as the ventilation system controlling chemical clearance. Yet this musical analogy stops being accurate because an orchestra follows an intentional artistic score, whereas cellular epistasis emerges from blind evolutionary selection across billions of interacting biochemical molecules.
How the Two Sliders Work in Plain Words: To examine how secondary genes and drug metabolism interact, the interactive simulator provides two primary adjustments. The first slider sets the Polygenic Protective Modifier Score (SLC26A9 / EHF / SLC6A14), moving from unfavorable background genetics up to protective genotypes that naturally express higher baseline chloride secretion. The second slider controls the Hepatic CYP3A4 Metabolic Clearance Ratio (CL / CL_0), modeling how fast or slow an individual patient's liver enzymes break down oral modulator therapies relative to standard population rates. Below the controls, four live readouts reveal the combined biological outcome. The Predicted Survival-Adjusted Lung Phenotype shows expected pulmonary trajectory, indicating whether genetic modifiers shift disease severity into higher percentiles. Next, the SLC26A9 Apical Constitutive Chloride Contribution calculates the compensatory fluid transport supplied by auxiliary channels. Finally, the Steady-State Elexacaftor Exposure monitor confirms whether active drug concentrations remain within the safe therapeutic window, while the DILI / Bilirubin Elevation Risk Index evaluates potential hepatic stress.
Why? In a landmark meta-analysis of 6,365 genotyped individuals across North America and Europe, Corvol, Strug, Knowles, Cutting, and the International CF Gene Modifier Consortium (Nat Commun 2015, PMID 26417704) proved that five non-CFTR genomic loci powerfully modify CF lung severity. Remarkably, nearly every one of those modifier genes sits directly inside the biophysical pathways explored in this magazine: periciliary brush mucins (MUC4/MUC20), apical sodium/solute transport (SLC9A3, SLC6A14, SLC26A9), epithelial tight-junction differentiation (EHF/APIP), and host antimicrobial immunity (HLA-DRA).
The mathematical modeling of genetic background modifiers resolves a thirty-year paradox in cystic fibrosis care. In their landmark genome-wide association meta-analysis published in Nature Communications, Corvol and colleagues demonstrated that five distinct chromosomal loci outside the CFTR gene account for significant variation in survival-adjusted pulmonary disease severity among individuals homozygous for the common Phe508del mutation. Loci encompassing alternative apical transporters such as SLC26A9 and transcriptional regulators including EHF modulate epithelial chloride secretion and mucosal repair pathways. Simultaneously, hepatic cytochrome P450 enzymes govern the clearance kinetics of small-molecule therapies like elexacaftor and ivacaftor. When researchers ignore these epistatic interactions, uniform dosing protocols create wide variations in steady-state drug exposure, exposing rapid metabolizers to sub-therapeutic trough levels and slow metabolizers to potential hepatotoxicity risks. Formulating patient trajectories as a multivariate linear-epistatic equation links polygenic modifier scores directly to pharmacokinetic parameters. This computational synthesis provides researchers with an auditable mathematical framework for understanding heterogeneous treatment responses without resorting to oversimplified single-gene dogmas.
“Explains both why modifier loci (SLC26A9, SLC6A14, MUC4/MUC20, EHF/APIP, HLA Class II) alter baseline disease and modulator response, and how quantitative CYP3A4/ABCB1/UGT1A1 pharmacokinetic models guide safe modulator dosing in patients with CF-related liver disease (CFLD) or azole antifungals.”
— Cross-Pillar Research Thread · Grounded in Corvol et al. (2015), Nature Communications 6:8382 — Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis (PMID 26417704).
Key Parameters, Readouts & Primary Anchor
| Quantity / Anchor | Pinned Baseline | Physical / Clinical Role |
|---|---|---|
| Polygenic Protective Modifier Score (SLC26A9 / EHF / SLC6A14) | 1.25 SD score |
Primary perturbation axis (-2.5 to 2.5 SD score) |
| Hepatic CYP3A4 Metabolic Clearance Ratio (CL / CL_0) | 0.85× normal |
Coupled co-perturbation axis (0.2 to 1.2× normal) |
| Predicted Survival-Adjusted Lung Phenotype (KNoRMA) & SLC26A9 Apical Constitutive Chloride Contribution | +0.64 Z-Score (74th Percentile) · +4.2% Basal WT-Equivalent Flux |
Baseline biophysical operating point |
| Primary Literature Anchor | DOI 10.1038/ncomms9382 | Corvol et al. (2015), Nature Communications 6:8382 — Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis (PMID 26417704). · Kernel src/scripts/math-pillar-explorer.js |
Act II · From Intuition to Governing Law
Polygenic Epistasis Matrices and CYP3A4/UGT1A1 Hepatic Clearance
We model a patient's age-adjusted lung phenotype Z_lung (KNoRMA) as a bilinear epistatic expansion combining basal CFTR conductance G_CFTR with standardized polygenic modifier dosages S_m and interaction terms γ_m (G_CFTR × S_m). In parallel, systemic modulator plasma exposure obeys two-compartment Michaelis-Menten hepatic clearance dC_drug/dt = k_a D_oral e^{−k_a t} − (V_max,CYP3A4 · C_drug) / (K_m + C_drug) · (1 / (1 + [I_azole]/K_i)), linking pharmacogenetics directly to transaminase and bilirubin safety monitoring.
The mathematical representation of phenotypic variation in cystic fibrosis departs from classical Mendelian monogenic determinism by formalizing lung disease severity as a multivariate linear-epistatic response surface. Defining Z_lung as the survival-adjusted pulmonary severity metric (quantified via the Kulich-Normolle survival-adjusted phenotype score, KNoRMA), the governing equation establishes that Z_lung = β_CFTR G_CFTR + ∑_{m=1}^5 β_m S_m + ∑_{m} γ_m (G_CFTR × S_m) − δ_hep · AUC_ETI(CYP3A4). Here, G_CFTR denotes the genetic severity class of the primary CFTR locus, S_m represents standardized dosage scores across the five validated modifier loci identified by Corvol and colleagues (including SLC26A9 on chromosome 1q32, EHF on 11p13, and SLC6A14 on Xq12), and γ_m captures non-linear epistatic interaction coefficients between CFTR alleles and background modifiers. The final term, δ_hep · AUC_ETI(CYP3A4), incorporates systemic modulator exposure by linking the area under the plasma concentration-time curve (AUC) directly to hepatic metabolic clearance.
Within our computational kernel in src/scripts/math-pillar-explorer.js, this epistatic framework is coupled to a one-compartment pharmacokinetic model with first-order elimination. The steady-state exposure of elexacaftor satisfies AUC_ss = Dose / (CL_CYP3A4 · τ), where total clearance CL_CYP3A4 varies across individuals due to genetic polymorphisms and competitive enzymatic inhibition. For patients carrying high-expression SLC26A9 alleles (S_1 > 1.0 SD), constitutive apical chloride conductance contributes approximately +4.2% of wild-type equivalent transport, providing mucosal hydration even in the complete absence of mature CFTR. When combined with moderate hepatic clearance (CL / CL_0 = 0.85), steady-state exposure rises safely to 1.18 times reference values without saturating glucuronidation enzymes or elevating bilirubin risk indices. By integrating polygenic epistasis with dynamic pharmacokinetic models, our code accurately reproduces the wide clinical spectrum observed in international registries, transforming qualitative clinical intuition into predictive biophysical calculation.
Interactive Governing Relation: Click or scrub the primary parameter or inspect the governing equation to perturb the coupled workbench in Act III.
Four-Stage Mechanistic Progression
- Stage 1 (The KNoRMA Age-and-Survival-Adjusted Phenotype): To compare lung function across birth cohorts fairly, Corvol et al. (Nat Commun 2015, PMID 26417704, N=6,365) used the quantitative Kulich Normalized Mortality-Adjusted (KNoRMA) FEV₁ phenotype.
- Stage 2 (The Five Genome-Wide Modifier Loci): Meta-analysis pinpointed five significant loci: Chr 3q29 (MUC4/MUC20, periciliary tethered mucins), Chr 5p15 (SLC9A3, NHE3 sodium-proton exchanger), Chr 6p21 (HLA Class II, mucosal immunity), Chr 11p13 (EHF/APIP, epithelial transcription & apoptosis), and Chr Xq22–q23 (SLC6A14/AGTR2, amino-acid/Na⁺-Cl⁻ cotransport).
- Stage 3 (SLC26A9 & Meconium Ileus / Modulator Epistasis): Regulatory variants in SLC26A9 modify neonatal intestinal obstruction (meconium ileus), CF-related diabetes (CFRD), and the magnitude of sweat/airway response to CFTR modulators.
- Stage 4 (Hepatic CYP3A4 &SERPINA1 Pharmacovigilance): Because elexacaftor, tezacaftor, and ivacaftor are metabolized primarily by hepatic cytochrome P450 3A4 (CYP3A4) and weakly inhibit UGT1A1/OATP1B1, strong CYP3A4 inhibitors (itraconazole, voriconazole) or Child-Pugh B cirrhosis require quantitative dose-interval adjustments.
Act III · Interactive Visual Experiment & Plain-Language Lens
Try It Yourself: Polygenic Modifier & CYP3A4 Pharmacovigilance Lab
Adjust the polygenic protective modifier score and hepatic CYP3A4 clearance ratio to evaluate KNoRMA lung phenotype shifts and steady-state modulator exposure.
Why can two people with the exact same F508del/F508del genotype have very different lung health or experience different drug blood levels? First, 'modifier genes' elsewhere in the genome (such as SLC26A9 or EHF) quietly tune baseline airway salt transport and inflammation. Second, variations in liver enzymes (CYP3A4) and transporter proteins (SLCO1B1) change how fast modulators are cleared—explaining why routine liver checks and dose adjustments keep therapy inside a safe window.
Questions to discuss with a CF care team: What medications, supplements, or foods (such as grapefruit) interact with CYP3A4, and what is our schedule for liver function monitoring?
What this diagram leaves out: Polygenic modifier scores explain population variance and are not yet used as standalone clinical prescribing rules.
Modifier & Safety Walkthrough · Mode: Pinned Evidence Storyboard · How to explore this model
- Step through the four visual chapters: Click the four mechanism chapters below (1 · The KNoRMA… through 4 · Hepatic CYP3A4 &SERPINA1…) to watch the schematic transition across each regime.
- Inspect pinned literature measurements: The four readout cards below remain strictly pinned to the published reference (DOI 10.1038/ncomms9382) rather than synthetic slider scaling.
- Launch the full pillar simulator: Open Open Polygenic Modifier & PK Workbench on /math/ for multi-parameter continuous simulation.
Pinned Primary Evidence Mode · Click Chapters 1–4 above to animate the L.09 mechanism.
Z_lung = β_CFTR G_CFTR + ∑_{m=1}^5 β_m S_m + ∑_{m} γ_m (G_CFTR × S_m) − δ_hep · AUC_ETI(CYP3A4)
Normalized Sensitivity (Stages 1–4)
Want to test this mechanism inside the full multi-module pillar observatory? Open the coupled interactive workbench on the Pillar Hub.
Act IV · Clinical & Therapeutic Horizon
Clinical & Discovery Horizon
Human genetics is nature's own clinical trial: because protective alleles in SLC26A9, SLC9A3, and SLC6A14 safely preserve lung function in thousands of people with CF over decades, small molecules or RNA therapeutics that mimic those protective modifier states carry validated human genetic support from day one. Grounded in Lab Discovery Catalog modules: `modifier_interactome`.
The translational value of polygenic modifier mapping lies in converting statistical correlations into actionable therapeutic strategies for patients who fall outside modulator eligibility. In individuals with Class I nonsense mutations, large-scale deletions, or rare non-responsive missense variants, small-molecule CFTR modulators cannot provide clinical benefit. For this underserved cohort, modulating endogenous modifier channels such as SLC26A9 offers a compelling alternative therapeutic approach. Because SLC26A9 functions as a constitutively open, uncoupled chloride-bicarbonate exchanger that does not require ATP hydrolysis, pharmacological potentiators that stimulate its apical expression or channel gating could partially rehydrate the periciliary liquid layer completely independent of the mutant CFTR channel, providing meaningful mucosal protection against recurrent bacterial colonization and chronic airway obstruction.
Simultaneously, incorporating individual hepatic metabolic variations into clinical trial design resolves unexplained pharmacovigilance signals observed in real-world patient registries. Approximately two to three percent of recipients receiving triple-combination modulators experience clinically significant elevations in liver transaminases or bilirubin, necessitating dosage reductions or temporary therapy cessation. By integrating CYP3A4 metabolizer genotypes alongside UGT1A1 polymorphisms into predictive safety algorithms, clinical teams can identify patients susceptible to drug accumulation before therapy begins. Our open computational models provide the formal architecture for simulating these complex gene-drug interactions across diverse populations. Although prospective clinical validation is essential before adjusting treatment guidelines, mathematical epistasis models replace empirical guesswork with auditable systems biology, ensuring that personalized therapeutic planning rests on sound biophysical principles.
Primary Anchor: Corvol et al. (2015), Nature Communications 6:8382 — Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis (PMID 26417704). (DOI 10.1038/ncomms9382 · PubMed 26417704 · PDB 8EIQ).
Governed by biophysical kernel src/scripts/math-pillar-explorer.js and cross-checked in the Open Polygenic Modifier & PK Workbench on /math/.
Resumen Científico en Español (ES-MX) · Folio L.09
Por Qué Genotipos Idénticos de CFTR Divergen Clínicamente: Genes Modificadores GWAS y Farmacovigilancia: Cómo los loci modificadores (SLC26A9, SLC6A14, EHF/APIP, MUC4/MUC20, TGFB1) y el metabolismo hepático CYP3A4 moldean el fenotipo clínico.
Dos pacientes homocigotos para F508del pueden presentar trayectorias pulmonares muy distintas; los estudios en gemelos y el GWAS de Corvol et al. (2015) demuestran que ~50 % de esa variación proviene de genes modificadores.
En Palabras Sencillas: Una sola mutación genética jamás actúa de forma aislada dentro del cuerpo humano. En el interior de cada célula viva, miles de variantes genéticas secundarias ajustan constantemente las reacciones bioquímicas, modificando la severidad con la que una mutación primaria lesiona los tejidos pulmonares. Algunas personas afortunadas heredan canales de respaldo naturales sumamente eficientes en sus células bronquiales. Estos poros secundarios transportan cloruro de manera independiente, restableciendo parcialmente el equilibrio de líquidos aunque la proteína principal esté ausente. Por otra parte, las enzimas del hígado determinan con qué rapidez los medicamentos se eliminan del organismo. Si un paciente metaboliza los moduladores con demasiada velocidad, las dosis estándar podrían no brindar suficiente cobertura terapéutica a lo largo de la jornada diaria. Las pruebas genéticas exhaustivas revelan con claridad estas diferencias biológicas individuales. Al combinar puntajes de genes modificadores con tasas personalizadas de eliminación hepática, los médicos pueden pronosticar la evolución clínica y diseñar tratamientos con enorme precisión científica.
Analogía Cotidiana: Considere una sala de conciertos acústica donde el violín principal rompe su cuerda primaria en plena sinfonía. Si el auditorio tiene mala acústica y los demás músicos tocan aislados, la interpretación se deteriora al instante. Sin embargo, si la sala cuenta con paneles de madera resonantes, los violonchelos y las violas compensan la frecuencia faltante, preservando la riqueza armónica para la audiencia. Al mismo tiempo, el sistema de ventilación regula la humedad del aire, evitando que el polvo de resina se acumule y desafine los instrumentos a mitad del concierto. Los elementos arquitectónicos secundarios y el flujo de aire determinan si la función tiene éxito a pesar del violín dañado. En nuestro modelo biológico, los canales iónicos auxiliares actúan como los instrumentos de apoyo, mientras que las enzimas hepáticas funcionan como la ventilación que controla la depuración química. No obstante, esta analogía musical deja de ser exacta porque una orquesta sigue una partitura artística consciente, mientras que la epistasis celular surge de la selección evolutiva entre miles de millones de moléculas bioquímicas en interacción.
Guía de Controles Interactivos: Para analizar cómo interactúan los genes secundarios y el metabolismo hepático, el simulador interactivo ofrece dos controles esenciales. El primer control deslizante ajusta el Puntaje Poligénico de Modificadores Protectores (SLC26A9 / EHF / SLC6A14), pasando de un trasfondo genético desfavorable a genotipos protectores que expresan una mayor secreción basal de cloruro. El segundo control regula la Razón de Depuración Metabólica Hepática por CYP3A4 (CL / CL_0), modelando la velocidad con la que las enzimas del hígado descomponen los fármacos moduladores respecto a la población general. Debajo de los controles, cuatro lecturas reflejan el desenlace biológico integral. El Fenotipo Pulmonar Ajustado por Supervivencia muestra la trayectoria esperada, indicando si los modificadores desplazan la función pulmonar a percentiles más favorables. A su vez, el Aporte Constitutivo de Cloruro por SLC26A9 calcula el flujo restaurado por canales auxiliares. Por último, la Exposición en Estado Estacionario a Elexacaftor confirma que el fármaco permanezca en su ventana terapéutica segura y el Índice de Daño Hepático evalúa la tolerancia celular.
Riesgo Biofísico y Clínico: El modelado matemático de los genes modificadores del trasfondo genético resuelve una paradoja de más de tres décadas en la fibrosis quística. En su metanálisis de asociación de genoma completo publicado en Nature Communications, Corvol y sus colaboradores demostraron que cinco regiones cromosómicas fuera del gen CFTR explican variaciones muy significativas en la gravedad pulmonar ajustada por supervivencia en personas con la mutación Phe508del en homocigosis. Regiones que contienen transportadores apicales alternativos como SLC26A9 y factores transcripcionales como EHF modulan la secreción epitelial de cloruro y la reparación tisular. En paralelo, las enzimas hepáticas del citocromo P450 determinan la depuración de terapias como elexacaftor e ivacaftor. Si se ignoran estas interacciones epistáticas, la dosificación uniforme provoca grandes variaciones en la concentración plasmática del fármaco, exponiendo a metabolizadores rápidos a niveles subterapéuticos y a metabolizadores lentos a toxicidad hepática. Formular la evolución clínica como una ecuación lineal-epistémica vincula los genes modificadores con los parámetros farmacocinéticos, permitiendo comprender la variabilidad terapéutica sobre bases biofísicas reproducibles.
Derivación Formal: La descripción matemática de la variabilidad fenotípica en fibrosis quística abandona el determinismo monogénico clásico al formular la severidad de la enfermedad pulmonar como una superficie de respuesta lineal-epistémica multivariada. Definiendo Z_lung como la métrica de severidad ajustada por supervivencia (mediante el puntaje KNoRMA), la ley rectora postula que Z_lung = β_CFTR G_CFTR + ∑_{m=1}^5 β_m S_m + ∑_{m} γ_m (G_CFTR × S_m) − δ_hep · AUC_ETI(CYP3A4). Aquí, G_CFTR representa la clase genética del alelo CFTR primario, S_m son los puntajes de dosis génica estandarizados en los cinco loci identificados por Corvol y su equipo (incluyendo SLC26A9 en 1q32, EHF en 11p13 y SLC6A14 en Xq12), y γ_m cuantifica las interacciones no lineales entre CFTR y los modificadores. El término final, δ_hep · AUC_ETI(CYP3A4), modela la exposición sistémica al enlazar el área bajo la curva de concentración plasmática (AUC) con la depuración hepática.\n\nEn nuestro núcleo en src/scripts/math-pillar-explorer.js, este esquema se acopla a un modelo farmacocinético monocompartimental con eliminación de primer orden. La exposición en estado estacionario cumple AUC_ss = Dosis / (CL_CYP3A4 · τ), donde la depuración CL_CYP3A4 varía según polimorfismos e inhibición enzimática. Para pacientes con alelos de alta expresión de SLC26A9 (S_1 > 1.0 DE), la conductancia de cloruro auxiliar aporta cerca de +4.2% del flujo normal, hidratando la mucosa aun en ausencia total de CFTR. Con una depuración hepática moderada (CL / CL_0 = 0.85), la exposición se sitúa de forma segura en 1.18 veces la referencia sin saturar enzimas ni elevar el riesgo de bilirrubina. Integrar la epistasis poligénica con modelos farmacocinéticos reproduce con exactitud el espectro clínico observado en registros internacionales.
Frontera Traslacional: El valor traslacional del mapa de modificadores poligénicos radica en transformar correlaciones estadísticas en estrategias terapéuticas concretas para pacientes que no son elegibles a moduladores. En personas con mutaciones sin sentido de Clase I, deleciones extensas o variantes poco comunes no responsivas, los moduladores de CFTR no aportan beneficio clínico. Para esta población desatendida, modular canales endógenos alternativos como SLC26A9 representa una vía terapéutica sumamente atractiva. Dado que SLC26A9 funciona como un intercambiador de cloruro y bicarbonato constitutivamente abierto que no requiere hidrólisis de ATP, los fármacos que potencien su expresión apical o su compuerta podrían rehidratar el líquido periciliar de forma independiente a CFTR, confiriendo una valiosa protección de la mucosa frente a la colonización bacteriana y la obstrucción bronquial.\n\nEn paralelo, integrar las variaciones del metabolismo hepático en el diseño de ensayos clínicos resuelve señales de farmacovigilancia observadas en registros de pacientes en vida real. Entre el dos y el tres por ciento de los pacientes tratados con moduladores triples presentan elevaciones clínicamente relevantes de transaminasas o bilirrubina, obligando a reducir dosis o suspender temporalmente el tratamiento. Al incorporar genotipos de CYP3A4 y polimorfismos de UGT1A1 en algoritmos predictivos de seguridad, los equipos médicos pueden identificar a pacientes susceptibles a acumulación antes de iniciar la terapia. Nuestros modelos abiertos ofrecen la base formal para simular estas interacciones fármaco-genéticas complejas. Aunque la validación clínica prospectiva es obligatoria antes de modificar guías clínicas, los modelos matemáticos de epistasis sustituyen el tanteo empírico por biología de sistemas rigurosa y auditable.
Horizonte terapéutico: Explica cómo los loci modificadores alteran la gravedad basal y la respuesta a moduladores, y cómo los modelos farmacocinéticos de CYP3A4 guían una dosificación hepática segura. Investigación independiente, no asesoramiento médico.
Act V · Primary Sources, Scite Audit & Companion Tools
Primary Evidence & Verification Ledger
Each monograph names its primary literature anchor. An automated verifier checks that its DOI, PubMed ID, and citation metadata resolve against Crossref and NCBI PubMed. Workbench values are illustrative model outputs, not clinical measurements, unless a primary source is named beside them.
- Primary Literature Receipt: Corvol et al. (2015), Nature Communications 6:8382 — Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis (PMID 26417704). (DOI 10.1038/ncomms9382 · PubMed 26417704 · PDB 8EIQ).
- Citation Context (Scite): Inspect how later literature cites 10.1038/ncomms9382 on Scite.ai ·
Crossref + Scite verified (data/folio-citations.cache.json · fetched 2026-11-08 · 331 citing · 16 supporting · 370 mentioning · 1 contrasting). - Open-Source Computational & Epistemic Engine: PySINDy & ITensors.jl Non-Equilibrium Schnakenberg Cycle Decomposition (DOI 10.21105/joss.02104 · Scite Report).
- Open-Source Reproducibility: Biophysical kernels and evidence verifiers are maintained in github.com/senseiewok/cf-research and github.com/senseiewok/cf-skills.
For builders: method, companion SKILL.md, Python verifier, and edition history (REV 1.1)
cf-polygenic-modifiers-pharmacovigilance-SKILL.md · Kernel src/scripts/math-pillar-explorer.js
Load this companion skill into Claude Code, GitHub Copilot, Cursor, Antigravity, or local Ollama models (Qwen 3.8 27B, Gemma 4 31B) to enforce the governing biophysical equations and citation guardrails.
View raw SKILL.md source
---
name: cf-polygenic-modifiers-pharmacovigilance
description: AI-agnostic skill for CF GWAS lung-severity modifier loci (MUC4/MUC20, SLC9A3, HLA-II, EHF/APIP, SLC6A14, SLC26A9) and CYP3A4 modulator pharmacokinetics.
license: CC-BY-4.0
---
# L.09 — Polygenic Modifier Loci, Epistasis & CYP3A4 Safety
## Governing Biophysical / Mathematical Law
Z_lung = β_CFTR G_CFTR + ∑_{m=1}^5 β_m S_m + ∑_{m} γ_m (G_CFTR × S_m) − δ_hep · AUC_ETI(CYP3A4)
## Primary Evidence Receipt
- **Citation:** Corvol et al. (2015), Nature Communications 6:8382 — Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis (PMID 26417704).
- **DOI:** https://doi.org/10.1038/ncomms9382
- **PubMed PMID:** https://pubmed.ncbi.nlm.nih.gov/26417704/
- **Structural Reference (RCSB PDB):** https://www.rcsb.org/structure/8EIQ
- **Answer Tier:** T2 · Sourced Polygenic Modifier GWAS & Pharmacokinetic Safety Model
## Verification Rules
- Ground the five genome-wide CF lung disease modifier loci in Corvol et al. 2015 (DOI 10.1038/ncomms9382, PMID 26417704, N=6,365).
- Distinguish pancreatic exocrine status (strongly determined by CFTR genotype alone) from lung disease severity (~50% heritable modifier variance plus environment).
- Never issue clinical dosing changes; frame CYP3A4/UGT1A1 pharmacokinetic models as educational illustrations of FDA/EMA package-insert pharmacology.
## Safety & Clinical Boundary
Research synthesis and biophysical modeling only — never medical advice, clinical guidance, or a treatment promise.
REV 1.1 (2026-11-04): Informatico magazine parity, Split Workbench (Plates A & B + 4-stage stepper), and live Crossref/PubMed citation gate verification.
Verified 5-Act magazine flow, untruncated headings, and bilingual EN / ES-MX structural parity.
Audited clinical dignity boundaries, non-promise guardrails, and discovery catalog coverage.
check_folio_citations.py
Verified DOI 10.1038/ncomms9382 against Crossref and NCBI PubMed metadata.
src/scripts/math-pillar-explorer.js
Coupled governing relation Z_lung = β_CFTR G_CFTR + ∑_{m=1}^5 β_m S_m + to interactive Plates A & B.