Green-By-Design Pharmaceuticals: from Human Admet to Environmental Safety in AI-Enabled Sustainable Drug Discovery

Green Chemistry Sustainable Drug Discovery Green-By-Design Pharmaceuticals ADMET Environmental Fate Ecotoxicity Biodegradability Artificial Intelligence Multi-Objective Optimization

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September 3, 2026

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Objectives to analyze how the concept of pharmaceutical sustainability could be taken further into the upstream processes of candidate selection to combine human ADMET, synthetic efficiency, environmental fate, ecotoxicity, biodegradability and AI without affecting therapeutic performance. This is a critical narrative review, which gave priority to peer-reviewed literature published 2021 to 30 August 2026 (with preference to 2024-2026) and the older foundational studies were retained. Official EMA, EUR-Lex, European Commission SSbD, OECD and PREMIER sources were added to PubMed search and targeted publisher searches. The study design, relevance of the endpoints, validation, transparency and relevance to an actionable R&D decision were evaluated, and no PRISMA flow or meta-analysis will be asserted. Prevention by design is supported, with an uneven maturity. Process metrics, solvent selection, catalysis, biocatalysis and continuous processing are relatively developed; prediction of PBT-related endpoints, bioaccumulation and ecotoxicity are being developed, but biodegradability prediction is less certain. The multi-objective prioritization can be assisted by AI when the domains of applicability, uncertainty, and experimental validation are clear. Out of the sources found, no prospective program could support the entire therapeutic-process-environmental framework in its entirety; instead, a stage-gated structure of evidence is suggested. The green reaction or environment score alone does not characterize a Green-by-Design pharmaceutical. It is a medically plausible therapy whose molecular characteristics, manufacturing burden and post consumption ecological conduct are taken into account as long as judgments are changeable. AI must not replace transparent evidence, regulatory assessment or validation but reveal trade-offs and focus on experiments.