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)