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RAI HEALTH JOURNAL

AI drug discovery: from molecules to clinical evidence

Designing a drug candidate with AI does not establish patient benefit. We read a phase 2a study alongside EMA’s approach to evaluating AI.

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RAI Health · AI-assisted editorial contentDrug discovery · Biotechnology

Research: a candidate tested in people

A randomized, double-blind, placebo-controlled study published in 2025 evaluated rentosertib, an inhibitor of the AI-discovered target TNIK, in 71 people with idiopathic pulmonary fibrosis over 12 weeks. The primary endpoint concerned treatment-emergent adverse events; lung function was among the secondary endpoints. Liver toxicity and diarrhea contributed to treatment discontinuation. The authors called for larger, longer studies. This is early clinical research evidence; publication alone is neither marketing authorization nor a recommendation for routine use.

Regulatory approach: risk depends on intended use

EMA’s 2024 reflection paper recommends considering AI risk in relation to its context of use and influence on regulatory decisions. Responsibility for ensuring that models, datasets and processing pipelines are fit for purpose remains with the sponsor, applicant or manufacturer. Early regulatory interaction is advised for applications that could affect a medicine’s benefit-risk balance. This document is not marketing authorization for a particular medicine.

Our editorial interpretation: ask three separate questions

What did AI contribute: target selection, molecular design or data analysis? Which endpoint and time period did the study evaluate? What current authorization and evidence support clinical use? Separating these questions helps avoid confusing discovery success with established patient benefit. The 2025 study discussed here should not be used to determine current authorization status. This article does not provide personal treatment advice.

Sources

  1. Xu et al. — A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial (Nature Medicine, 2025) ↗
    Accessed: 2026-10-03
  2. EMA — Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle (2024) ↗
    Accessed: 2026-10-03

This article is for education and information. Referencing an organization does not imply its endorsement of RAI PACS Pro. This is not a claim of clinical effectiveness for a product or model.

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