A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial
Rattachement africain : cn, us, ae. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Despite substantial progress in artificial intelligence (AI) for generative chemistry, few novel AI-discovered or AI-designed drugs have reached human clinical trials. Here we present the results of the first phase 2a multicenter, double-blind, randomized, placebo-controlled trial testing the safety and efficacy of rentosertib (formerly ISM001-055), a first-in-class AI-generated small-molecule inhibitor of TNIK, a first-in-class target in idiopathic pulmonary fibrosis (IPF) discovered using generative AI. IPF is an age-related progressive lung condition with no current therapies available that reverse the degenerative course of disease. Patients were randomized to 12 weeks of treatment with 30 mg rentosertib once daily (QD, n = 18), 30 mg rentosertib twice daily (BID, n = 18), 60 mg rentosertib QD (n = 18) or placebo (n = 17). The primary endpoint was the percentage of patients who have at least one treatment-emergent adverse event, which was similar across all treatment arms (72.2% in patients receiving 30 mg rentosertib QD (n = 13/18), 83.3% for 30 mg rentosertib BID (n = 15/18), 83.3% for 60 mg rentosertib QD (n = 15/18) and 70.6% for placebo (n = 12/17)). Treatment-related serious adverse event rates were low and comparable across treatment groups, with the most common events leading to treatment discontinuation related to liver toxicity or diarrhea. Secondary endpoints included pharmacokinetic dynamics (Cmax, Ctrough, tmax, AUC0–t/τ/∞ and t1/2), changes in lung function as measured by forced vital capacity, diffusion capacity of the lung for carbon monoxide, forced expiry in 1 s and change in the Leicester Cough Questionnaire score, change in 6-min walk distance and the number and hospitalization duration of acute exacerbations of IPF. We observed increased forced vital capacity at the highest dosage with a mean change of +98.4 ml (95% confidence interval 10.9 to 185.9) for patients in the 60 mg rentosertib QD group, compared with −20.3 ml (95% confidence interval −116.1 to 75.6) for the placebo group. These results suggest that targeting TNIK with rentosertib is safe and well tolerated and warrants further investigation in larger-scale clinical trials of longer duration. ClinicalTrials.gov registration number: NCT05938920 . Preliminary results from a phase 2a trial involving 71 patients suggest that a new agent, discovered and designed with artificial intelligence assistance, is safe and effective for the treatment of idiopathic pulmonary fibrosis.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial
- Date Crossref
- 03/06/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Chinese Academy of Medical Sciences & Peking Union Medical College pays non établi dans la noticeUniversité ou école supérieure
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Peking Union Medical College Hospital Department of Pulmonary and Critical Care Medicine pays non établi dans la noticeÉtablissement de santé
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Tianjin Medical University General Hospital Department of Respiratory and Critical Care Medicine pays non établi dans la noticeÉtablissement de santé
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Capital Medical University Department of Pulmonary and Critical Care Medicine pays non établi dans la noticeUniversité ou école supérieure
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Beijing Friendship Hospital pays non établi dans la noticeÉtablissement de santé
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Qilu Hospital of Shandong University Department of Respiratory Disease pays non établi dans la noticeÉtablissement de santé
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China Medical University Department of Respiratory Medicine pays non établi dans la noticeUniversité ou école supérieure
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Nanjing Drum Tower Hospital pays non établi dans la noticeÉtablissement de santé
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Hainan General Hospital Department of Respiratory and Critical Care Medicine pays non établi dans la noticeÉtablissement de santé
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Anhui Provincial Hospital pays non établi dans la noticeÉtablissement de santé
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Tongji University Department of Respiratory Medicine pays non établi dans la noticeUniversité ou école supérieure
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Shanghai Pulmonary Hospital pays non établi dans la noticeÉtablissement de santé
Chinese Academy of Medical Sciences & Peking Union Medical College, Department of Pulmonary and Critical Care Medicine — Peking Union Medical College Hospital et Department of Respiratory and Critical Care Medicine — Tianjin Medical University General Hospital, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.