Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
The development of second-generation antihistamines represents a significant milestone in allergy management, driven by a continuous effort to optimize efficacy while minimizing sedating side effects. Central to this evolution is cetirizine, which was traditionally administered as a 50:50 racemic mixture. While the therapeutic success of the racemate is driven by the (R)-enantiomer (levocetirizine), its mirror image, (S)-cetirizine—commonly known as dextrocetirizine—has historically been dismissed as an inactive "passenger" molecule. This comprehensive review deconstructs the structural biochemistry, stereoselective disposition, and overlooked environmental impact of dextrocetirizine.Applying the Cahn-Ingold-Prelog (CIP) priority rules confirms that the (S)-configuration of dextrocetirizine imposes a spatial obstruction that limits its binding affinity at the human H₁-receptor, rendering it 30-fold less potent than levocetirizine. However, despite its clinical inactivity, dextrocetirizine exhibits distinct pharmacokinetic behavior, characterized by a lower plasma protein binding profile, a broader volume of distribution, and accelerated renal tubular clearance. Furthermore, because it bypasses major human metabolism and resists traditional activated sludge wastewater treatment, multi-ton quantities of dextrocetirizine are continuously discharged into aquatic ecosystems.Alarmingly, recent data reveals that riverbed biofilms and microbial communities can induce biotic chiral inversion, transforming this "harmless" passenger molecule back into biologically active levocetirizine. This creates a continuous, unrecognized threat to non-target aquatic wildlife. This review highlights the need to shift from treating dextrocetirizine as a neglected entity to implementing rigorous enantiomeric environmental monitoring and transitioning fully to pure asymmetric synthesis.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.