# Modality Lifecycle > Place a therapeutic modality on its maturity arc and answer "is this deliverable yet?" using a live mature-vs-proving map keyed to the delivery unlock as the primary state variable. Produces a modality position read with the binding delivery bottleneck, the platform fix that converts the class, and a P(modality deliverable) term for the conviction scorer. Activate when assessing whether a modality is ready, what gates its next stage, or whether a stalled class is about to inflect — for clinicians learning the field and investors screening early opportunities alike. - Skill: `npbuilds/modality-lifecycle` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add npbuilds/modality-lifecycle` - Raw SKILL.md: https://api.skillmd.com/api/skills/npbuilds/modality-lifecycle/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: npbuilds (https://skillmd.com/u/npbuilds) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/npbuilds/modality-lifecycle --- # Modality Lifecycle — The Live Maturity Map of Drug Delivery Modalities, not just molecules, have life-cycles. A small molecule, a GalNAc-siRNA, and an in-vivo CRISPR editor are at radically different points on the same arc — and the single state variable that determines where a modality sits is not its biology but its **delivery unlock**: what is the delivery problem, and has the fix landed? Get this wrong and you either dismiss a class that just inflected (TPD before vepdegestrant) or over-credit one whose delivery wall is still standing (extrahepatic RNAi, in-vivo CAR in humans). The core principle: **the platform fix, not new biology, is what converts a stalled class.** siRNA was dead-on-delivery for a decade until GalNAc conjugation gave it the liver; KRAS was "undruggable" until the 2013 covalent switch-II discovery; degraders waited on E3-ligase recruitment; ADCs were left for dead after gemtuzumab until site-specific linkers and DXd payloads. A high-profile failure followed by a modality re-engineering is a buy signal, not a kill signal. And the arc runs both ways: a modality can **regress** — AAV gene therapy slid backward in 2025 after Elevidys was tied to ≥3 deaths and an FDA dosing pause. This skill is dual-use by design. For a clinician-scientist it is a map of where each modality actually stands and what unlocks the next tissue. For an investor it supplies the **P(modality deliverable)** term that the conviction scorer multiplies against P(biology holds), arc-position, and competitive timing — the discipline that stops a great target from being funded inside an undeliverable modality. > **Maturity map:** the per-modality S-curve placement, delivery-wall lookup, and platform-fix registry are versioned in `references/modality-maturity-map.md` (a 2026 point-in-time snapshot — the most perishable data in the pillar). ## How to Run ### Input | Parameter | Source | Required? | |---|---|---| | Modality (e.g., in-vivo CAR, GalNAc-siRNA, base editing, PROTAC) | User | Yes | | Target tissue / compartment (liver, muscle, CNS, T-cell, tumor) | User | Recommended — delivery is tissue-specific | | Specific asset / company (if scoring a candidate) | User | Optional | | Indication | User | Optional (gates which delivery route matters) | | Use mode: learn (field map) vs screen (conviction input) | User | Optional (default: screen) | ### Steps #### Step 1 — Locate the Modality on the Four-Stage Maturity Map Assign one of four stages. The boundary tests are concrete, not vibes: - **Emerging** — first-in-human to early Ph1; proof-of-mechanism not yet durable or safe at scale. (in-vivo CAR, prime editing, CAR-NK/allogeneic) - **Proving** — Ph1/2 efficacy signals replicating; ≤1 approval; platform risk still live. (TPD, base editing, in-vivo CRISPR nuclease, TCR-T/TIL, oral biologics, saRNA) - **Established** — multiple approvals; mechanism de-risked; scaling/access is the remaining work. (ADCs, ASO/siRNA, mRNA vaccines, ex-vivo CAR-T, ex-vivo CRISPR, RLT, lentiviral) - **Mature** — dozens-to-hundreds of approvals; commoditized; innovation is incremental or in delivery. (small molecules, mAbs, peptides/macrocycles) Start from the maturity-map table in `references/quick-reference.md`. Do not anchor on hype — anchor on **approvals shipped × mechanism de-risking**. #### Step 2 — Identify the Binding Delivery Bottleneck For the modality + target tissue, name the **one** delivery/enabling-technology constraint that gates the next stage. This is the load-bearing analytical step. The universal rate-limiter in 2026 is delivery, not biology: ``` DELIVERY-WALL LOOKUP siRNA / ASO → extrahepatic reach (CNS, muscle, lung, immune cells) in-vivo CRISPR/base → LNP tropism beyond liver in-vivo CAR → T-cell- (or HSC/NK-) tropic LNP / retargeted vector AAV → pre-existing immunity, no redosing, liver-detargeting capsids mRNA therapeutics → repeat-dose tolerability, extrahepatic LNP oral peptides/biologics→ gut proteolysis + epithelial permeability (often <2% F) radioligand therapy → isotope supply chain (Ac-225) + radiopharmacy sites cell therapies → "delivery" = manufacturing logistics + persistence/rejection prime editing → editor payload size (hard to package) ``` Verify the current state of the wall against live data: - **ClinicalTrials.gov** (c-trials MCP) — confirm phase/status/enrollment of the proof-point trials (e.g., HAELO for lonvo-z; CPTX2309 Ph1; RAG-17 NCT06556394 for SCAD/intrathecal siRNA). - **bioRxiv/medRxiv** — delivery-platform preprints (tLNP, antibody-siRNA conjugates, novel capsids) lead approvals by 6-18 months; this is where the next unlock surfaces first. - **ChEMBL** — bioactivity for the enabling chemistry (covalent KRAS warheads, E3-recruiting glues, payload IC50s). - **PubMed/PMC + Consensus** — mechanism/review literature and evidence synthesis on whether the delivery claim actually replicates. #### Step 3 — Determine Whether the Platform Fix Has Landed Convert "delivery wall" into a binary-with-evidence: **has the enabling-platform fix landed, partially landed, or not?** The known fixes that convert a stalled class: ``` PLATFORM-FIX REGISTRY (fix, not biology, converts the class) GalNAc conjugation → siRNA/ASO to liver (LANDED — inclisiran, vutrisiran) covalent switch-II → KRAS druggability (LANDED — sotorasib 2021) tri-complex / RAS(ON) → pan-RAS beyond G12C (LANDING — daraxonrasib Ph3/BTD) E3-ligase recruitment → targeted degradation (LANDED — vepdegestrant 2025) site-specific linker + DXd → ADC redemption (LANDED — T-DXd, Datroway, Emrelis) T-cell-tropic LNP (tLNP) → in-vivo CAR (PARTIAL — CPTX2309 Ph1, NHP data only) C16/antibody-siRNA conj. → extrahepatic RNAi (PARTIAL — muscle AOCs, SCAD preclinical) low-seroprevalence capsid → AAV redosing (NOT LANDED — class regressed post-Elevidys) multiplexed HLA editing → allogeneic persistence/rejection (NOT LANDED) ``` If the fix has LANDED → modality clears for that tissue. If PARTIAL → flag the specific trial whose readout closes it. If NOT LANDED → the modality is gated regardless of target merit. #### Step 4 — Check for Regression and Maturity Signals The arc is bidirectional and the late-arc tells matter: - **Regression flag** — a marquee safety event reversing a class. AAV is the index case: Elevidys (DMD) tied to ≥3 liver-injury deaths and an FDA dosing pause in 2025, with Sarepta itself pivoting toward siRNA for redosing. A regressed modality borrows a *worse* transition matrix, not its pre-event one. - **Maturity / commoditization flag** — convenience/cadence reformulation. When a class competes on **route and dosing cadence rather than efficacy**, the alpha has left: subcutaneous nivolumab/pembrolizumab (Keytruda Qlex, Sept 2025), oral GLP-1 orforglipron, oral PCSK9 enlicitide, twice-yearly inclisiran. Note this for the investor read — mature ≠ attractive entry. #### Step 5 — Emit the P(modality deliverable) Term Translate the position into a deliverability probability for the conviction scorer. Anchor to modality LOA base rates and apply delivery/CMC/novelty haircuts: ``` P(modality deliverable) = base_modality_LOA_proxy × delivery_state_multiplier × regression_penalty base proxies (LOA, BIO/Informa class reads): vaccines ~9.7% | biologics ~9.1% > small molecules ~5.7% | CGT ~5.3% bimodal (CAR-T / AAV historically 13.6% in their validated indications) delivery_state_multiplier: LANDED for this tissue = 1.0 PARTIAL (proof in NHP/Ph1) = 0.6-0.8 NOT LANDED (wall standing) = 0.3-0.5 zero-clinical-history class = borrow nearest validated class matrix, never de novo optimism regression_penalty: active safety reversal = ×0.5-0.7 (AAV post-Elevidys) none = ×1.0 ``` For a zero-clinical-history modality, **borrow the nearest validated class's transition matrix and apply explicit delivery/CMC/regulatory-novelty haircuts** — never an optimistic de novo estimate. Platform risk concentrates in Phase 1→2. ### Output ``` MODALITY LIFECYCLE READ — [Modality] × [Target tissue] Date: [assessment date] | Mode: [learn / screen] Maturity stage: [Emerging / Proving / Established / Mature] Representative drugs: [examples] Lead players: [companies] Binding delivery bottleneck: [the one constraint gating next stage] Current state: [wall standing / partially breached / breached] Proof-point trial(s): [NCT# / readout + date] Source: [ClinicalTrials.gov / preprint] Platform-fix status: [LANDED / PARTIAL / NOT LANDED] The fix: [GalNAc / tLNP / E3-recruitment / capsid / etc.] What closes it: [specific pending readout if PARTIAL] Arc-direction flags: Regression: [none / active — describe safety event] Maturity/commoditization: [none / reformulation underway — route+cadence competition] P(modality deliverable): [0.00-1.00] = base [%] × delivery_multiplier [x] × regression_penalty [x] Borrowed matrix (if zero-history): [nearest validated class + haircuts applied] Bottom line: Clinician read: [where it stands, what unlocks the next tissue] Investor read: [deliverable enough to fund? entry attractive or commoditized?] ``` ## Error Handling | Scenario | Response | |---|---| | Modality not on the map | Place it by analogy to the nearest mapped class; flag as zero-clinical-history and borrow that class's matrix with novelty haircuts | | Conflicting stage signals (approval but class regressing) | Maturity stage = approvals shipped; apply regression_penalty separately. Record both — an Established class can still be a bad entry (AAV) | | Delivery claim only in preprint / NHP | Mark PARTIAL; do not credit as LANDED until a human Ph1 dose-response confirms; name the trial that would close it | | "AI-discovered" / novel-platform framing | Down-weight unless the platform has independent wet-lab + clinical validation; AI compresses discovery, not Phase 2/3 attrition | | Tissue not specified | Default to the validated tissue (usually liver for oligo/LNP); flag that extrahepatic asks carry a separate, harder delivery state | | Single company / single asset = the whole class | Treat as Emerging regardless of hype; one Ph1 asset is not a de-risked modality | ## Cross-Domain Connections - **manufacturing-ip/modality-manufacturing** (depends_on): The CMC/COGS counterpart — where this skill asks "can it reach the tissue?", that skill asks "can it be made at scale and cost?" Autologous CAR-T COGS ~$100-220K/dose and ex-vivo CRISPR's ~$2.2M price are manufacturing walls that cap an otherwise-deliverable modality. The two terms multiply. - **mechanism-risk-adjuster** (depends_on): Supplies the modality-deliverability haircut that adjusts a target's base PoS. A genetically-clean target inside a NOT-LANDED modality must be discounted for platform risk, not credited at face value. - **frontier-discovery/frontier-conviction-scorer**: Primary consumer — this skill emits the **P(modality deliverable)** term in the decomposition P(biology holds) × P(modality deliverable) × arc-position × competitive-timing. - **frontier-discovery/moa-analog-engine**: Complementary — moa-analog-engine places the *mechanism* on its historical arc; this skill places the *modality*. A candidate needs both reads (KRAS the mechanism × small-molecule the modality). - **probability-of-success/pos-base-rates**: Shares the modality LOA base rates; this skill is where the qualitative delivery state turns those rates into a program-specific deliverability probability. - **Dual use**: A clinician-scientist runs this in *learn* mode to understand what gates each modality's next tissue; an investor runs it in *screen* mode to gate early opportunities before a clinical asset exists, then hands off to phase-weighted rNPV once one does.