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Nanoscaffold-based 3D human liver spheroids for predictive hepatotoxicity screening of antimalarial compounds from the global health priority box

Lina Wu et al · BMC · 2026

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Abstract Background Drug-induced liver injury (DILI) remains a significant barrier to the safe and efficient use of antimalarial medicines. Many promising compounds fail in late-stage development or post-marketing owing to unforeseen toxicity, particularly DILI. Incorporating a predictive hepatotoxicity assessment is therefore critical to reduce clinical risk and development costs. The Medicines for Malaria Venture (MMV) Global Health Priority Box (GHPB) provides a library of compounds with demonstrated or potential antimalarial activity, yet their hepatotoxicity risk remains poorly defined. Our early work developed and validated a nanoscaffold-based three-dimensional (3D) liver spheroid platform. Here, we apply this validated model for the first time to assess the hepatotoxicity of clinically used and candidate antimalarial GHPB compounds. Methods Our validated nanoscaffold-based 3D liver spheroid platform was used to evaluate the hepatotoxicity of approved antimalarial drugs of known liver toxicity (quinine, primaquine, amodiaquine, sulfadoxine/pyrimethamine, and artemisinin) and six GHPB candidate compounds. Half maximal inhibitory concentration (IC50)-derived data from the approved antimalarial drugs were used to generate a reference framework on the basis of established DILI classifications, which was then applied to categorize the GHPB candidate compounds relative to hepatotoxic risk. Results Our nanoscaffold-based 3D liver spheroid platform accurately reproduced the known DILI rankings of the approved antimalarials, confirming its predictive validity. Using these referenced IC50-derived profiles, candidate compounds from the GHPB were classified into distinct hepatotoxicity categories, ranging from low: MMV1167451 (compound 01) and MMV020192 (compound 02), moderate: MMV1797658 (compound 03) and MMV1435700 (compound 04), to high: MMV006344 (compound 05) and MMV006931 (compound 06) risk, demonstrating the model’s capacity to support early-stage animal-free antimalarial hepatotoxicity screening. Conclusions This study demonstrates the translational application of a validated nanoscaffold-based 3D human liver spheroid model for antimalarial drug in vitro hepatotoxicity assessment. By establishing a reference framework from clinically approved antimalarials and applying it to candidate compounds from the MMV GHPB, our platform enabled early classification of hepatotoxicity risk using a human-relevant, non-animal method. The findings support the integration of advanced 3D in vitro systems into antimalarial drug discovery pipelines to improve safety prediction, reduce reliance on animal testing, and accelerate the development of safer, more effective antimalarial therapies. Graphical Abstract

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APA 7

al, L. W. E. (2026). Nanoscaffold-based 3D human liver spheroids for predictive hepatotoxicity screening of antimalarial compounds from the global health priority box. https://doi.org/10.1186/s13071-026-07324-1

MLA

al, Lina Wu et. "Nanoscaffold-based 3D human liver spheroids for predictive hepatotoxicity screening of antimalarial compounds from the global health priority box." 2026. https://doi.org/10.1186/s13071-026-07324-1.

Chicago

al, Lina Wu et. 2026. "Nanoscaffold-based 3D human liver spheroids for predictive hepatotoxicity screening of antimalarial compounds from the global health priority box.". https://doi.org/10.1186/s13071-026-07324-1.

Harvard

al, L. W. E. 2026, Nanoscaffold-based 3D human liver spheroids for predictive hepatotoxicity screening of antimalarial compounds from the global health priority box, BMC, available at: https://doi.org/10.1186/s13071-026-07324-1 [Accessed 10 Aug. 2026].

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Title
Nanoscaffold-based 3D human liver spheroids for predictive hepatotoxicity screening of antimalarial compounds from the global health priority box
Author / contributors
Lina Wu et al
Publisher
BMC
Publication year
2026
ISSN
1756-3305
ISSN
1756-3305
Language
English

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