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

医学 慢性阻塞性肺病 临床试验 药物开发 机器学习 人工智能 药理学 计算机科学 内科学 药品
作者
Luigino Calzetta,Elena Pistocchini,Alfredo Chetta,Paola Rogliani,Mario Cazzola
出处
期刊:Expert Opinion on Investigational Drugs [Informa]
卷期号:32 (6): 525-536 被引量:15
标识
DOI:10.1080/13543784.2023.2230138
摘要

ABSTRACTIntroduction 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 - β2-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.KEYWORDS: Artificial IntelligenceCOPDensifentrineexperimental drugsMABAmachine learningphosphodiesterase inhibitorprecision medicine Article highlights Artificial Intelligence via Machine Learning models is an effective tool to predict the clinical development of investigational agents.According to accurate Machine Learning models, bifunctional MABA and PDE3/4 inhibitors have a moderate to very high probability of being approved in COPD.In the best-case scenario, ensifentrine should be approved around in 2025, whereas navafenterol and batefenterol around in 2028.Declaration of interestL. Cazzola has participated as an advisor in scientific meetings under the sponsorship of Boehringer Ingelheim and Novartis, received nonfinancial support from AstraZeneca, a research grant partially funded by Chiesi Farmaceutici, Boehringer Ingelheim, Novartis, and Almirall, and is or has been a consultant to ABC Farmaceutici, Edmond Pharma, Zambon, Verona Pharma, and Ockham Biotech. His department was funded by Almirall, Boehringer Ingelheim, Chiesi Farmaceutici, Novartis, Zambon.Pistocchini reports there are no competing interests to declare.Chetta reports grants from Menarini and Astra Zeneca; personal fee from Chiesi.Rogliani has participated as a lecturer and advisor in scientific meetings and courses under the sponsorship of Almirall, AstraZeneca, Biofutura, Boehringer Ingelheim, Chiesi Farmaceutici, GlaxoSmithKline, Menarini Group, Mundipharma, and Novartis. Her department was funded by Almirall, Boehringer Ingelheim, Chiesi Farmaceutici Novartis, and Zambon. M.C. reports grants and personal fees from Boehringer Ingelheim, grants and personal fees from Novartis, personal fees from AstraZeneca, personal fees from Chiesi Farmaceutici, grants and personal fees from Almirall, personal fees from ABC Farmaceutici, personal fees from Edmond Pharma, grants and personal fees from Zambon, personal fees from Verona Pharma, personal fees from Ockham Biotech, personal fees from Biofutura, personal fees from GlaxoSmithKline, personal fees from Menarini, personal fees from Lallemand, personal fees from Mundipharma, personal fees from Pfizer. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Reviewer disclosuresA reviewer on this manuscript has disclosed Grants and/or honoraria from several manufacturers of treatments for COPD or related conditions, namely AstraZeneca, Chiesi, GSK, Boehringer Ingelheim, CSL Behring, Grifols Biotherapeutics. Peer reviewers on this manuscript have no other relevant financial relationships or otherwise to disclose.Supplemental dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/13543784.2023.2230138.Additional informationFundingThis work has been partially supported by the MUR-PNRR M4C2I1.3 PE6 project PE00000019 Heal Italia (to P.Rogliani)
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
爱听歌的冷安完成签到,获得积分10
刚刚
小菜瓜完成签到,获得积分10
刚刚
xxfsx应助sally采纳,获得10
2秒前
xiaoxin发布了新的文献求助10
2秒前
机灵饼干发布了新的文献求助150
3秒前
anna1992发布了新的文献求助10
3秒前
5秒前
6秒前
Jasper应助xiaoxin采纳,获得10
7秒前
9秒前
hbhbj应助小菜瓜采纳,获得20
10秒前
葵花籽完成签到,获得积分10
11秒前
keeeeeeeli发布了新的文献求助10
12秒前
12秒前
独特的初彤完成签到 ,获得积分10
15秒前
赘婿应助科研通管家采纳,获得10
16秒前
天天快乐应助科研通管家采纳,获得10
16秒前
小青椒应助科研通管家采纳,获得100
16秒前
科研通AI2S应助科研通管家采纳,获得10
16秒前
凤凰应助科研通管家采纳,获得30
16秒前
wanci应助松松松采纳,获得50
16秒前
酷波er应助科研通管家采纳,获得10
16秒前
CipherSage应助科研通管家采纳,获得10
16秒前
思源应助科研通管家采纳,获得10
16秒前
浮游应助科研通管家采纳,获得10
16秒前
科研通AI2S应助科研通管家采纳,获得10
16秒前
16秒前
WB87应助科研通管家采纳,获得10
17秒前
NexusExplorer应助科研通管家采纳,获得10
17秒前
WB87应助科研通管家采纳,获得10
17秒前
科研通AI6应助科研通管家采纳,获得10
17秒前
NiL应助科研通管家采纳,获得10
17秒前
17秒前
英俊的铭应助xxm采纳,获得10
17秒前
CodeCraft应助科研通管家采纳,获得10
17秒前
Hilda007应助科研通管家采纳,获得10
17秒前
FashionBoy应助科研通管家采纳,获得10
17秒前
cheese完成签到 ,获得积分10
17秒前
18秒前
18秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Social Work Ethics Casebook: Cases and Commentary (revised 2nd ed.).. Frederic G. Reamer 1070
Alloy Phase Diagrams 1000
Introduction to Early Childhood Education 1000
2025-2031年中国兽用抗生素行业发展深度调研与未来趋势报告 1000
List of 1,091 Public Pension Profiles by Region 891
Historical Dictionary of British Intelligence (2014 / 2nd EDITION!) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5425244
求助须知:如何正确求助?哪些是违规求助? 4539333
关于积分的说明 14166974
捐赠科研通 4456649
什么是DOI,文献DOI怎么找? 2444274
邀请新用户注册赠送积分活动 1435255
关于科研通互助平台的介绍 1412637