Challenges with the discovery of RNA-based therapeutics for flaviviruses.

INTRODUCTION: Flaviviruses are emerging or reemerging pathogens that have caused several outbreaks throughout the world and pose serious threats on human health and economic development. RNA-based therapeutics are developing rapidly, and hold promise in the fight against flaviviruses. However, to develop efficient and safe therapeutics for flaviviruses, many challenges remain unsolved. AREAS COVERED: In this review, the authors briefly introduced the biology of flaviviruses and the current advances in RNA-based therapeutics for them. Furthermore, the authors list the challenges and possible solutions in this area. Finally, the authors give their opinion on the development and future of RNA-based therapeutics for flaviviruses. EXPERT OPINION: With the rapid development of structural biology, the crystal structures of flavivirus proteins may lay the foundation for future rational drug design. Studies regarding the interactions between the flavivirus and the host will also be invaluable to inhibitor design. Researchers should maintain the current momentum to bring about safe and effective anti-flavivirus drugs to licensure through joint efforts of academia, government, and industry.

Structure-Guided Design and Procognitive Assessment of a Potent and Selective Phosphodiesterase 2A Inhibitor.

Herein we describe the development of a series of pyrazolopyrimidinone phosphodiesterase 2A (PDE2) inhibitors using structure-guided lead identification and design. The series was derived from informed chemotype replacement based on previously identified internal leads. The initially designed compound 3, while potent on PDE2, displayed unsatisfactory selectivity against the other PDE2 isoforms. Compound 3 was subsequently optimized for improved PDE2 activity and isoform selectivity. Insights into the origins of PDE2 selectivity are described and verified using cocrystallography. An optimized lead, 4, demonstrated improved performance in both a rodent and a nonhuman primate cognition model.

Prediction of activity for nonnucleoside inhibitors with HIV-1 reverse transcriptase based on Monte Carlo simulations.

Results of Monte Carlo (MC) simulations for more than 200 nonnucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) representing eight diverse chemotypes have been correlated with their anti-HIV activities in an effort to establish simulation protocols and methods that can be used in the development of more effective drugs. Each inhibitor was modeled in a complex with the protein and by itself in water, and potentially useful descriptors of binding affinity were collected during the MC simulations. A viable regression equation was obtained for each data set using an extended linear response approach, which yielded r(2) values between 0.54 and 0.85 and an average unsigned error of only 0.50 kcal/mol. The most common descriptors confirm that a good geometrical match between the inhibitor and the protein is important and that the net loss of hydrogen bonds with the inhibitor upon binding is unfavorable. Other physically reasonable descriptors of binding are needed on a chemotype case-by-case basis. By including descriptors in common from the individual fits, combination regressions that include multiple data sets were also developed. This procedure led to a refined "master" regression for 210 NNRTIs with an r(2) of 0.60 and a cross-validated q(2) of 0.55. The computed activities show an rms error of 0.86 kcal/mol in comparison with experiment and an average unsigned error of 0.69 kcal/mol. Encouraging results were obtained for the predictions of 27 NNRTIs, representing a new chemotype not included in the development of the regression model. Predictions for this test set using the master regression yielded a q(2) value of 0.51 and an average unsigned error of 0.67 kcal/mol. Finally, additional regression analysis reveals that use of ligand-only descriptors leads to models with much diminished predictive ability.