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Revolutionizing Fibrosis Treatment: AI-Driven Discovery of TNIK Inhibitor INS018_055 Unveils New Horizons in Therapeutics

Researchers have encountered significant challenges in developing drugs for Idiopathic Pulmonary Fibrosis and renal fibrosis due to their complex pathogenesis and lack of effective treatments. However, utilizing AI, they identified TNIK as a promising anti-fibrotic target and developed the inhibitor INS018_055, showing favorable properties and efficacy in preclinical and clinical studies. This innovative approach offers promising prospects for fibrosis treatment.

 Revolutionizing Fibrosis Treatment: AI-Driven Discovery of TNIK Inhibitor INS018_055 Unveils New Horizons in Therapeutics

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Revolutionizing Fibrosis Treatment: AI-Driven Discovery of TNIK Inhibitor INS018_055 Unveils New Horizons in Therapeutics

Idiopathic Pulmonary Fibrosis (IPF) and renal fibrosis pose significant challenges in drug development due to their complex nature and lack of effective treatments. Despite extensive research, potential drug targets like TGF-β signaling pathways have not translated into viable therapies for clinical use. Addressing these unmet clinical needs requires innovative approaches to identify and develop effective anti-fibrotic medicines.

AI-Driven Drug Discovery

Researchers have identified TNIK as a promising anti-fibrotic target using AI. They have developed INS018_055, a TNIK inhibitor showing favorable drug properties and anti-fibrotic effects across various organs in vivo. The compound also exhibits anti-inflammatory effects and has been validated in clinical trials for safety and tolerability.

AI in Drug Development Process

The study utilized predictive AI to identify TNIK as an anti-fibrotic target. An AI-driven drug discovery pipeline, incorporating pathway analysis and multiomics data, generated INS018_055, a TNIK inhibitor. Its anti-fibrotic effects were assessed through various administration routes in vivo and validated for safety in clinical trials with healthy participants.

AI-Driven Target Discovery

Utilizing PandaOmics, an AI-driven platform, anti-fibrotic targets were discovered by integrating multiomics datasets, biological network analysis, and text data. TNIK emerged as the top candidate, unrecognized in IPF therapy, with potential implications for fibrosis and aging-related conditions.

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For AI KPI management advice, connect with us at hello@itinai.com. And for continuous insights into leveraging AI, stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom.

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Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

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