IRWD

IronwoodA

Nasdaq / Pharmaceuticals, Biotechnology & Life Sciences

At close

2026-07-18

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AI scenario view

RankAlpha Sentiment Codex

B+

Bull case

25%

Probability

Target price

$8.50

+120.2% vs current

Most likely

Base case

50%

Probability

Target price

$5.80

+50.3% vs current

B-

Bear case

25%

Probability

Target price

$2.40

-37.8% vs current

AI sentiment snapshot

Latest data as of 2026-07-18

AI commentary

News tone is favorable, with strong Q1 results, pediatric label expansion, and the July 14 confirmation of STARS-2 initiation and active recruitment. However, social coverage and analyst revision data are unavailable, and the current price remains below the median target with an unknown target count. The setup is constructive but monitoring-oriented rather than a high-conviction rerating thesis.

RankAlpha Sentiment Codex - 2026-07-18

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Evidence flagged

No evidence quality warning is currently attached to this memo.

Impact

standard

Confidence

AI events

2026-05-07catalystQ1 results reset the earnings baseHigh impact

Q1 2026 revenue was $106.5 million, GAAP net income was $40.8 million, adjusted EBITDA was $76.7 million, and LINZESS U.S. net sales reached $273 million with 5% year-over-year demand growth; full-year guidance was maintained. [#SEC-8K-2026-05-07]

2026-07-14eventSTARS-2 Phase 3 trial is active and recruitingHigh impact

Ironwood reported that the confirmatory apraglutide STARS-2 trial was initiated in June and is actively recruiting patients; the company also appointed Jeffrey Silber as chief medical officer and head of research and development. [#IR-2026-07-14]

2027-07-18catalystApraglutide development remains the main rerating leverHigh impact

Successful STARS-2 enrollment, clinical execution, and eventual regulatory progress could diversify Ironwood beyond LINZESS, but no trial outcome or later enrollment metrics are available yet. [#IR-2026-07-14]

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Recommendation

N/A

No formal recommendation provided.

Open AI Memo

As of 2026-07-18 • Updated nightlySource: Internal modelMethodology