Discovery of 5-Nitro-6-thiocyanatopyrimidines as Inhibitors of Cryptococcus neoformans and Cryptococcus gattii

Enantiocomplementary synthesis of β-adrenergic blocker precursors via biocatalytic nitration of phenyl glycidyl ethers

Enantiopure β-nitroalcohols, as an important class of nitro-containing compounds, are essential building blocks in pharmaceutical and organic chemistry, particularly for the synthesis of β-adrenergic blockers. In this study, we present the successful protein engineering of halohydrin dehalogenase HHDHamb for the enantioselective bio-nitration of various phenyl glycidyl ethers to the corresponding chiral β-nitroalcohols, using the inexpensive, commercially available, and safer nitrite as a nitrating agent. The chiral (R)- and (S)-1-nitro-3-phenoxypropan-2-ols were synthesized by the several enantiocomplementary HHDHamb variants through the whole-cell biotransformation, which showed good catalytic efficiency (up to 43% isolated yields) and high optical purity (up to >99% ee). In addition, we also demonstrated that the bio-nitration method was able to tolerate the substrate at a high concentration of 1000 mM (150 g/L). Furthermore, representative synthesis of two optically active enantiomers of the β-adrenergic blocker metoprolol was successfully achieved by utilizing the corresponding chiral β-nitroalcohols as precursors.

Drug design strategies for the treatment azole-resistant candidiasis

INTRODUCTION

Despite the availability of novel antifungals and therapeutic strategies, the rate of global mortality linked to invasive fungal diseases from fungal infection remains high. Candida albicans account for the most invasive mycosis produced by yeast. Thus, the current arsenal of medicinal chemists is focused on finding new effective agents with lower toxicity and broad-spectrum activity. In this review article, recent efforts to find effective agents against azole-resistant candidiasis, a common fungal infection, are covered.

AREAS COVERED

Herein, the authors outlined all azole-based compounds, dual target, and new scaffolds (non-azole-based compounds) which were effective against azole-resistant candidiasis. In addition, the mechanism of action and SAR studies were also discussed, if the data were available.

EXPERT OPINION

The current status of fungal infections and the drawbacks of existing drugs have encouraged scientists to find novel scaffolds based on different methods like virtual screening and fragment-based drug discovery. Machine learning and in-silico methods have found their role in this field and experts are hopeful to find novel scaffolds/compounds by using these methods.

Enhancing preclinical drug discovery with artificial intelligence

Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. In this review, we provide an overview of current AI technologies and a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact. Considering the excitement and hyperbole surrounding AI in drug discovery, we aim to present a realistic view by discussing both opportunities and challenges in adopting AI in drug discovery.