Evidence map›Paper›PMID 42027046›Full record

ReviewAngewandte Chemie (International ed. in English)2026

Stereoselective Biotransformation: Transfer of Learning to Advance Drug Metabolism and Biocatalysis.

Grace A Okunlola, Godwin A Aleku

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Grace A OkunlolaInstitute of Pharmaceutical Science, King's College London, London, UK.
Godwin A AlekuInstitute of Pharmaceutical Science, King's College London, London, UK.ORCID 0000-0003-0969-5526

Funding

Engineering and Physical Sciences Research Council UKRI129
6 · The paper itself

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.

Indexed as

BiocatalysisBiotransformationPharmaceutical PreparationsStereoisomerismPharmaceutical Preparationsbiocatalysisbiotransformationdrug metabolismdrug metabolizing enzymesstereoselectivity

Identifiers

PMID42027046
PMCPMC13266941

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.