ReviewAngewandte Chemie (International ed. in English)2026
Stereoselective Biotransformation: Transfer of Learning to Advance Drug Metabolism and Biocatalysis.
Review in Angewandte Chemie (International ed. in English), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Chirality is an important determinant of drug action, as enantiomers can exhibit markedly different pharmacological and toxicological profiles. Although the importance of stereochemistry in drug efficacy is well established, its role in drug metabolism and disposition remains comparatively underexplored, despite the inherently stereoselective nature of drug metabolizing enzymes. Given the high prevalence of chiral drugs in clinical use and among newly approved drugs, a systematic evaluation of stereoselective drug metabolism is needed. Understanding stereoselective biotransformations has important implications for predicting drug disposition and response and may also inspire novel biocatalytic and biomimetic strategies to address challenges in enantioselective synthesis of chiral active pharmaceutical ingredients and their metabolites. In this Systematic Review, we examine current trends and practices in the investigation of stereoselectivity in drug metabolism, the key factors influencing stereoselective metabolism, and the associated challenges and opportunities. We highlight how biocatalytic approaches can improve stereoselective access to chiral metabolites, and how insights from drug metabolism and pharmacokinetics (DMPK) studies can inspire the development of novel biocatalytic and biomimetic synthesis routes. Transfer of learning and cross‑disciplinary collaboration between biocatalysis and DMPK scientists will be critical for accelerating progress in these areas and for addressing shared challenges, including stereoselectivity prediction.
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