Design, Synthesis, and Investigation of Novel Nitric Oxide (NO)-Releasing Aromatic Aldehydes as Drug Candidates for the Treatment of Sickle Cell Disease.

Sickle cell disease (SCD) is caused by a single-point mutation, and the ensuing deoxygenation-induced polymerization of sickle hemoglobin (HbS), and reduction in bioavailability of vascular nitric oxide (NO), contribute to the pathogenesis of the disease. In a proof-of-concept study, we successfully incorporated nitrate ester groups onto two previously studied potent antisickling aromatic aldehydes, TD7 and VZHE039, to form TD7-NO and VZHE039-NO hybrids, respectively. These compounds are stable in buffer but demonstrated the expected release of NO in whole blood in vitro and in mice. The more promising VZHE039-NO retained the functional and antisickling activities of the parent VZHE039 molecule. Moreover, VZHE039-NO, unlike VZHE039, significantly attenuated RBC adhesion to laminin, suggesting this compound has potential in vivo RBC anti-adhesion properties relevant to vaso-occlusive events. Crystallographic studies show that, as with VZHE039, VZHE039-NO also binds to liganded Hb to make similar protein interactions. The knowledge gained during these investigations provides a unique opportunity to generate a superior candidate drug in SCD with enhanced benefits.

Targeted modification of furan-2-carboxaldehydes into Michael acceptor analogs yielded long-acting hemoglobin modulators with dual antisickling activities.

Sickle cell disease (SCD) is the most common genetic disorder, affecting millions of people worldwide. Aromatic aldehydes, which increase the oxygen affinity of human hemoglobin to prevent polymerization of sickle hemoglobin and inhibit red blood cell (RBC) sickling, have been the subject of keen interest for the development of effective treatment against SCD. However, the aldehyde functional group metabolic instability has severly hampered their development, except for voxelotor, which was approved in 2019 for SCD treatment. To improve the metabolic stability of aromatic aldehydes, we designed and synthesized novel molecules by incorporating Michael acceptor reactive centers into the previously clinically studied aromatic aldehyde, 5-hydroxymethylfurfural (5-HMF). Eight such derivatives, referred to as MMA compounds were synthesized and studied for their functional and biological activities. Unlike 5-HMF, which forms Schiff-base interaction with αVal1 nitrogen of hemoglobin, the MMA compounds covalently interacted with ßCys93, as evidenced by reverse-phase HPLC and disulfide exchange reaction, explaining their RBC sickling inhibitory activities, which at 2 mM and 5 mM, range from 0% to 21% and 9% to 64%, respectively. Additionally, the MMA compounds showed a second mechanism of sickling inhibition (12%-41% and 13%-62% at 2 mM and 5 mM, respectively) by directly destabilizing the sickle hemoglobin polymer. In vitro studies demonstrated sustained pharmacologic activities of the compounds compared to 5-HMF. These findings hold promise for advancing SCD therapeutics.

Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh.

Fine particulate matter (PM2.5) has become a prominent pollutant due to rapid economic development, urbanization, industrialization, and transport activities, which has serious adverse effects on human health and the environment. Many studies have employed traditional statistical models and remote-sensing technologies to estimate PM2.5 concentrations. However, statistical models have shown inconsistency in PM2.5 concentration predictions, while machine learning algorithms have excellent predictive capacity, but little research has been done on the complementary advantages of diverse approaches. The present study proposed the best subset regression model and machine learning approaches, including random tree, additive regression, reduced error pruning tree, and random subspace, to estimate the ground-level PM2.5 concentrations over Dhaka. This study used advanced machine learning algorithms to measure the effects of meteorological factors and air pollutants (NOX, SO2, CO, and O3) on the dynamics of PM2.5 in Dhaka from 2012 to 2020. Results showed that the best subset regression model was well-performed for forecasting PM2.5 concentrations for all sites based on the integration of precipitation, relative humidity, temperature, wind speed, SO2, NOX, and O3. Precipitation, relative humidity, and temperature have negative correlations with PM2.5. The concentration levels of pollutants are much higher at the beginning and end of the year. Random subspace is the optimal model for estimating PM2.5 because it has the least statistical error metrics compared to other models. This study suggests ensemble learning models to estimate PM2.5 concentrations. This study will help quantify ground-level PM2.5 concentration exposure and recommend regional government actions to prevent and regulate PM2.5 air pollution. Supplementary Information: The online version contains supplementary material available at 10.1007/s11869-023-01329-w.