Batefenterol

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.

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.

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.

The COVID-19 pandemic infection has claimed many lives and added to the social, economic, and psychological distress. The contagious disease has quickly spread to almost 218 countries and territories following the regional outbreak in China. As the number of infected populations increases exponentially, there is a pressing demand for anti-COVID drugs and vaccines. Virtual screening provides possible leads while extensively cutting down the time and resources required for ab-initio drug design. We report structure-based virtual screening of a hundred plus library of quinoline drugs with established antiviral, antimalarial, antibiotic or kinase inhibitor activity. In this study, targets having a role in viral entry, viral assembly, and viral replication have been selected. The targets include: 1) RBD of receptor-binding domain spike protein S 2) M^pro Chymotrypsin main protease 3) P^pro Papain protease 4) RNA binding domain of Nucleocapsid Protein, and 5) RNA Dependent RNA polymerase from SARS-COV-2. An in-depth analysis of the interactions and G-score compared to the controls like hydroxyquinoline and remdesivir has been presented. The salient results are (1) higher scoring of antivirals as potential drugs (2) potential of afatinib by scoring as better inhibitor, and (3) biological explanation of the potency of afatinib. Further MD simulations and MM-PBSA calculations showed that afatinib works best to interfere with the the activity of RNA dependent RNA polymerase of SARS-COV-2, thereby inhibiting replication process of single stranded RNA virus. Communicated by Ramaswamy H. Sarma.