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临床试验中的 COPD 实验药物:通过机器学习方法的人工智能预测从早期开发到获得批准的成功进展。

Experimental drugs in clinical trials for COPD: artificial intelligence via machine learning approach to predict the successful advance from early-stage development to approval.

机构信息

Department of Medicine and Surgery, University of Parma, Parma, Italy.

Department of Experimental Medicine, University of Rome "Tor Vergata", Rome, Italy.

出版信息

Expert Opin Investig Drugs. 2023 Jan-Jun;32(6):525-536. doi: 10.1080/13543784.2023.2230138. Epub 2023 Jul 6.

DOI:10.1080/13543784.2023.2230138
PMID:37364225
Abstract

INTRODUCTION

Therapeutic advances in drug therapy of chronic obstructive pulmonary disease (COPD) really effective in suppressing the pathological processes underlying the disease deterioration are still needed. Artificial Intelligence (AI) via Machine Learning (ML) may represent an effective tool to predict clinical development of investigational agents.

AREAL COVERED

Experimental drugs in Phase I and II development for COPD from early 2014 to late 2022 were identified in the ClinicalTrials.gov database. Different ML models, trained from prior knowledge on clinical trial success, were used to predict the probability that experimental drugs will successfully advance toward approval in COPD, according to Bayesian inference as follows: ≤25% low probability, >25% and ≤50% moderate probability, >50% and ≤75% high probability, and >75% very high probability.

EXPERT OPINION

The Artificial Neural Network and Random Forest ML models indicated that, among the current experimental drugs in clinical trials for COPD, only the bifunctional muscarinic antagonist - β-adrenoceptor agonists (MABA) navafenterol and batefenterol, the inhaled corticosteroid (ICS)/MABA fluticasone furoate/batefenterol, and the bifunctional phosphodiesterase (PDE) 3/4 inhibitor ensifentrine resulted to have a moderate to very high probability of being approved in the next future, however not before 2025.

摘要

简介

在慢性阻塞性肺疾病(COPD)的药物治疗方面,仍需要有真正能抑制疾病恶化的病理过程的治疗进展。人工智能(AI)通过机器学习(ML)可能是预测研究药物临床开发的有效工具。

涵盖领域

从 2014 年初到 2022 年底,在 ClinicalTrials.gov 数据库中确定了处于 I 期和 II 期开发阶段的 COPD 实验药物。根据贝叶斯推理,不同的 ML 模型从临床试验成功的先验知识中进行训练,用于预测实验药物在 COPD 中成功获得批准的概率,如下所示:≤25%为低概率,>25%和≤50%为中概率,>50%和≤75%为高概率,>75%为非常高概率。

专家意见

人工神经网络和随机森林 ML 模型表明,在 COPD 的当前临床试验中的实验药物中,只有双功能毒蕈碱拮抗剂-β-肾上腺素能受体激动剂(MABA)navafenterol 和 batefenterol、吸入性皮质类固醇(ICS)/MABA 氟替卡松富马酸盐/batefenterol 以及双功能磷酸二酯酶(PDE)3/4 抑制剂 ensifentrine 具有中度到非常高的批准概率,然而,这不会在 2025 年之前发生。

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