JHTV Public-Data Portfolio Scoring

The cohort comprises approximately 602 inventions listed under the Therapeutic Modalities category on the JHU Technology Ventures public technology publisher at jhu.technologypublisher.com. Data were extracted via the Algolia search API on 2026-05-06, capturing only publicly disclosed listing fields: title, description, technology category, development stage (where listed), and JHU reference number. No PII, no internal IP filings, and no non-public data were persisted. Inventions without a Therapeutic Modalities category tag were excluded from the scored cohort.

The extracted listing data represent the public-facing invention disclosure only — not the full patent text, internal research status, or licensing activity. The development stage field is often absent or listed at preclinical; where not specified, the scoring pipeline defaults to Preclinical as the assumed stage for rNPV envelope construction. This is a conservative default: actual stage may be more advanced, which would reduce time-to-market and raise the rNPV envelope.

The composite score for each invention is computed as a weighted sum across three dimensions. Weights are locked at the values below for the methodology@2026-05-07 rubric. Future revisions will be documented in Section 10 with a version bump.

What is not in the rubric, and why. Earlier methodology versions (through methodology@2026-06) included an “IRA exposure” dimension at 20% — a modality-derived proxy for Medicare Drug Price Negotiation risk under the Inflation Reduction Act. As of methodology@2026-05-07 this dimension was removed because it double-counts: IRA cliff risk is already modeled inside the rNPV engine via the Year 9 / Year 13 MFP discount on terminal-value cash flows, so weighting it again in a scouting-stage rubric counts the same effect twice (once in the composite score, once in the dollar number). The dimension also correlated with the modality-fit dimension already in the rubric, and provided ~zero discrimination across the JHTV cohort (every top-10 asset clustered at 0.85–0.90). The 20% weight was redistributed across the three retained dimensions; see Section 10 for changelog and substrate-guarantee details.

The 50 highest-scoring inventions receive asset-specific engine treatment. Each deep-dive includes named real-world comparator programs anchoring stage costs and PoS, a per-asset stage profile with each value cited to comparator evidence, an evidence-register table linking every load-bearing assumption to a verifiable public source, a deterministic rNPV envelope (low / base / high) plus a 1,000-trial Monte Carlo distribution, tornado sensitivity ranking the top six drivers of rNPV variance, revenue trajectories of comparable launched programs where applicable, and a thesis narrative plus key-risks list specific to that asset. The deep-dive payload is exposed at /api/v1/case-studies/jhtv-2026/deep-dive.

Comparator selection criteria, in priority order:

• Same target / mechanism class — mechanism identity beats unrelated launched comparators for stage calibration. POLθ inhibition is anchored to Artios ART558/ART4215 and Repare RP-3467 even when those programs are still preclinical or Phase 1.
• Same indication + modality combination with a launched program — AAV gene therapy for ultra-rare neurological disease is anchored to Zolgensma economics regardless of the specific target.
• Same regulatory pathway (orphan, fast-track, RMAT, accelerated approval) — used when target and indication-modality comparators don’t exist.
• Same modality with comparable patient population size — last-resort comparator class.

Engine input perturbation for the rNPV envelope. Low case applies costs ×1.25 and peak revenue ×0.75; high case applies costs ×0.75 and peak revenue ×1.25; base case uses the comparator-anchored values directly. WACC is 12% base case unless an asset-specific WACC is justified in the deep-dive narrative.

Each deep-dive carries a funding-path archetype validated against the engine output:

• VC-fundable — base rNPV is positive at industry-median WACC and standalone-equity assumptions. Standard biotech VC structures apply.
• Partnership candidate — base rNPV is negative or near-zero under standalone-equity assumptions but the asset is a real strategic prospect under co-development or pharma-deal economics. The narrative explains what deal structure or platform context would clear the asset.
• Grant / non-commercial — non-traditional revenue model (Gates, GAVI, NIH, product-development partnership). rNPV doesn’t apply meaningfully; valuation is a different exercise (cost-effectiveness, public-health impact).

The archetype is shown alongside the rNPV envelope on every deep-dive sub-page so that a negative envelope can be honestly classified as “needs partnership” or “needs grant funding” rather than “not investable.” This is intentional: a sophisticated reader should be able to distinguish a structurally non-VC-fundable asset (e.g., a malaria vaccine) from a VC-fundable one with execution risk.

The PhaseFolio methodology defines seven evidence-based PoS multipliers (genetic validation 2.6×, biomarker 1.7×, orphan 1.4×, CAR-T 1.73×, gene therapy 1.41×, fast-track / RMAT 1.3×, pediatric voucher 1.2×). For the JHTV deep-dive, the comparator research pass already calibrates per-stage PoS values against asset-specific comparator programs that themselves carry the relevant evidence (orphan-validated genetic medicines, biomarker-stratified oncology, etc.). Re-applying the formal multiplier stack on top of the comparator-calibrated PoS would double-count that evidence and produce implausibly high cumulative LoA for preclinical assets.

Empirical sanity check. Post-deep-dive cumulative LoA across the 50 assets ranges from 0.1% to 10.8%; the genuine therapeutic assets cluster ~5–11%, consistent with published cumulative success rates for orphan-validated genetic medicines (~10–15%) and biomarker-stratified oncology (~5–10%). The lowest values are enabling-technology, research-tool, and basic-science-discovery entries whose conservative illustrative profiles are shown for cohort consistency, not as forecasts. Pre-fix values (with multipliers stacked) ranged from 59% to 73%, which is implausible for any preclinical asset and would have been correctly challenged by any academic-rigor reviewer.

• The composite score ranks each invention by scientific and strategic merit — clinical unmet need, modality-indication fit, and competitive whitespace. It does not depend on market size, cost structure, or commercial-policy risk (the latter is captured in the engine, not the rubric).
• The rNPV envelope measures expected financial value under standard pharma economics — comparator-anchored development costs, indication-specific peak revenue, cumulative PoS, WACC discounting, and IRA terminal-value adjustment per the modality cliff schedule.

An invention can rubric-rank high but rNPV-rank low (e.g., an ultra-rare-disease platform with strong unmet need but small market) and vice versa. Within the top 50 under methodology@2026-05-07, four show positive base rNPV; seven are tagged VC-fundable (the gap reflects assets where the high-input scenario is positive but the base case is not); 26 are honestly classified as partnership candidates and 17 as grant / non-commercial. Many of the ranks 11–50 are enabling technologies, research tools, or basic-science discoveries rather than standalone drug candidates — for these the rNPV envelope is shown for cohort consistency, not as the decision criterion, and the asset is labelled with its true class on its deep-dive page. The 552 lower-ranked inventions are rubric-scored only — full-portfolio engine treatment is a separate engagement, not part of the public artifact.

Cohort selection vs display order. Top-10 membership is locked to the highest composite-score inventions — the rubric, not rNPV, decides who enters the deep-dive cohort. Display order on the index page is then sorted by base rNPV (descending) for investor readability, so the asset with the strongest standalone economics appears first. Composite-score rank is preserved on every deep-dive sub-page header (a footer line reads “composite-score rank #X of 50 deep-dive inventions · page header uses rNPV rank #Y”), so a reader who wants to follow the rubric’s strict ordering can do so. Under methodology@2026-05-07, Personalized Allogeneic Neoantigen CAR-T is rank #1 by composite at 0.770 but rank #11 by rNPV (−$16M) — its CAR-T modality-baseline PoS = 1.00 lifts the composite while platform-breadth assumptions hold the standalone-equity rNPV down. The override condition for dropping an asset from the top 50 is narrow: high-case rNPV below −$50M and no fitting funding-path archetype. In the 2026-Q2 build this condition triggered zero times — every asset has a defensible archetype framing even when the rNPV envelope is fully negative.

• Public-data-only scope. Each listing is described in approximately 350 characters of public disclosure text. No internal IP filing details, preclinical data packages, or licensing activity are available. Clinical relevance scores based on this text are coarser than scores derived from a full data room.
• Comparator-anchored cost calibration. Top-50 stage profiles are anchored to named real-world comparator programs, but those comparators may differ from the JHU asset on details not captured in public listings (specific patient population definitions, manufacturing scale-up risk, IP overlap). Treat each stage profile as the closest defensible reference, not an asset-specific forecast.
• Top-50 only. Asset-specific deep-dive treatment is applied to the 50 highest-composite-score inventions only. The remaining 552 are rubric-scored without engine output. Full-portfolio engine treatment is a separate engagement and out of scope for this public artifact.
• Preclinical-stage default. When the development stage is not specified in the public listing, the pipeline defaults to Preclinical. If an invention is actually in Phase 1 or later, the envelope understates the rNPV by embedding additional preclinical cost and time that has already been incurred.
• LLM classification noise. Clinical relevance subscores are generated by an LLM and subject to classification noise — particularly for novel modalities or indications with limited training data representation. The structured prompt reduces but does not eliminate variability across runs.
• IRA risk lives in the engine, not the rubric. The composite score deliberately does not weight Inflation Reduction Act exposure (see Section 2). The IRA effect is captured in the rNPV envelope through a Year 9 / Year 13 MFP discount on terminal-value cash flows. The cliff schedule is binary by modality class and does not model partial-lifecycle exposure, indication-specific negotiation probability, or potential exemptions (orphan, pediatric). Treat the engine’s IRA adjustment as a proxy, not a legal or regulatory determination.

Inventions are sorted by composite score and assigned to one of three tiers:

Total cohort: ~602 inventions. All counts are approximate — the Algolia extract may include or exclude listings on the boundary of the Therapeutic Modalities category depending on tagging at time of extraction.

01JHU Technology Ventures — Technology Publisher — Johns Hopkins University, 2026

02Clinical Development Success Rates 2011–2020 — BIO, QLS Advisors, Informa Pharma Intelligence, 2021

03Durable Cell and Gene Therapies Have Substantially Higher Clinical Success Rates than Other Treatments — NEWDIGS at Tufts Medical Center (FoCUS Project), 2023

04Inflation Reduction Act of 2022 — Drug Pricing Provisions — U.S. Congress, 2022

05Probability of Success Calibration — PhaseFolio Methodology