Structure-Activity Studies of 1H-Imidazo[4,5-c]quinolin-4-amine Derivatives as A3 Adenosine Receptor Positive Allosteric Modulators.

We previously reported 1H-imidazo[4,5-c]quinolin-4-amines as A3 adenosine receptor (A3AR) positive allosteric modulators (PAMs). A3AR agonists, but not PAMs, are in clinical trials for inflammatory diseases and liver conditions. We synthesized new analogues to distinguish 2-cyclopropyl antagonist 17 (orthosteric interaction demonstrated by binding and predicted computationally) from PAMs (derivatives with large 2-alkyl/cycloalkyl/bicycloalkyl groups). We predicted PAM binding at a hydrophobic site on the A3AR cytosolic interface. Although having low Caco-2 permeability and high plasma protein binding, hydrophobic 2-cyclohept-4-enyl-N-3,4-dichlorophenyl, MRS7788 18, and 2-heptan-4-yl-N-4-iodophenyl, MRS8054 39, derivatives were orally bioavailable in rat. 2-Heptan-4-yl-N-3,4-dichlorophenyl 14 and 2-cyclononyl-N-3,4-dichlorophenyl 20 derivatives and 39 greatly enhanced Cl-IB-MECA-stimulated [35S]GTPγS binding Emax, with only 12b trending toward decreasing the agonist EC50. A feasible route for radio-iodination at the p-position of a 4-phenylamino substituent suggests a potential radioligand for allosteric site binding. Herein, we advanced an allosteric approach to developing A3AR-activating drugs that are potentially event- and site-specific in action.

A Machine Learning Algorithm Predicts Duration of hospitalization in COVID-19 patients.

The COVID-19 pandemic has placed unprecedented strain on the healthcare system, particularly hospital bed capacity in the setting of large variations in patient length of stay (LOS). Using electronic health record data from 966 COVID-19 patients at a large academic medical center, we developed three machine learning algorithms to predict the likelihood of prolonged LOS, defined as >8 days. The models included 353 variables and were trained on 80% of the cohort, with 20% used for model validation. The three models were created on hospital days 1, 2 and 3, each including information available at or before that point in time. The models' predictive capabilities improved sequentially over time, reaching an accuracy of 0.765, with an AUC of 0.819 by day 3. These models, developed using readily available data, may help hospital systems prepare for bed capacity needs, and help clinicians counsel patients on their likelihood of prolonged hospitalization.