IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-64304-2.html
   My bibliography  Save this article

Artificial intelligence coupled to pharmacometrics modelling to tailor malaria and tuberculosis treatment in Africa

Author

Listed:
  • Gemma Turon

    (Ersilia Open Source Initiative)

  • Mwila Mulubwa

    (University of Cape Town)

  • Anna Montaner

    (Ersilia Open Source Initiative)

  • Mathew Njoroge

    (University of Cape Town)

  • Kelly Chibale

    (University of Cape Town)

  • Miquel Duran-Frigola

    (Ersilia Open Source Initiative)

Abstract

Africa’s vast genetic diversity poses challenges for optimising drug treatments in the continent, which is exacerbated by the fact that drug discovery and development efforts have historically been performed outside Africa. This has led to suboptimal therapeutic outcomes in African populations and overall scarcity of relevant pharmacogenetic data, including characteristic genotypes as well as drugs prescribed in the continent to treat infectious diseases. Here, we propose a general approach to identify drug-gene pairs with potential pharmacogenetic interest. Our pipeline couples machine learning and artificial intelligence with physiologically-based pharmacokinetic (PBPK) and non-linear mixed effects (NLME) modelling to hypothesize which pharmacogenes could be of potential clinical interest, and which dose adjustments could be made to provide better treatment outcomes for African populations. Drug-gene pairs are first ranked with the latest knowledge embedding techniques, based on public structural and bioactivity data for drugs and genes, followed by a large language model-based refinement. Selected genes are then evaluated for their sensitivity in PBPK analysis, and relevant variants subsequently inspected with NLME for dose optimization. The analysis is focused on genes with potential clinical relevance in Africa. We delve deeper into malaria and tuberculosis therapies, many of which remain uncharacterised from a pharmacogenetic perspective.

Suggested Citation

  • Gemma Turon & Mwila Mulubwa & Anna Montaner & Mathew Njoroge & Kelly Chibale & Miquel Duran-Frigola, 2025. "Artificial intelligence coupled to pharmacometrics modelling to tailor malaria and tuberculosis treatment in Africa," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-64304-2
    DOI: 10.1038/s41467-025-64304-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-64304-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-64304-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Martino Bertoni & Miquel Duran-Frigola & Pau Badia-i-Mompel & Eduardo Pauls & Modesto Orozco-Ruiz & Oriol Guitart-Pla & Víctor Alcalde & Víctor M. Diaz & Antoni Berenguer-Llergo & Isabelle Brun-Heath , 2021. "Bioactivity descriptors for uncharacterized chemical compounds," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Gemma Turon & Jason Hlozek & John G. Woodland & Ankur Kumar & Kelly Chibale & Miquel Duran-Frigola, 2023. "First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Adrià Fernández-Torras & Miquel Duran-Frigola & Martino Bertoni & Martina Locatelli & Patrick Aloy, 2022. "Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    4. Joshua T. Vogelstein & Eric W. Bridgeford & Minh Tang & Da Zheng & Christopher Douville & Randal Burns & Mauro Maggioni, 2021. "Supervised dimensionality reduction for big data," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    5. Sebastian Farquhar & Jannik Kossen & Lorenz Kuhn & Yarin Gal, 2024. "Detecting hallucinations in large language models using semantic entropy," Nature, Nature, vol. 630(8017), pages 625-630, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gemma Turon & Jason Hlozek & John G. Woodland & Ankur Kumar & Kelly Chibale & Miquel Duran-Frigola, 2023. "First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Arnau Comajuncosa-Creus & Guillem Jorba & Xavier Barril & Patrick Aloy, 2024. "Comprehensive detection and characterization of human druggable pockets through binding site descriptors," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    3. Xiaoyi Luo & Aiwen Wang & Xinling Zhang & Kunda Huang & Songyu Wang & Lixin Chen & Yejia Cui, 2025. "Toward Intelligent AIoT: A Comprehensive Survey on Digital Twin and Multimodal Generative AI Integration," Mathematics, MDPI, vol. 13(21), pages 1-44, October.
    4. Abramson, Corey & Li, Zhuofan & Prendergast, Tara & Dohan, Daniel, 2025. "Qualitative Research in an Era of AI: A Pragmatic Approach to Data Analysis, Workflow, and Computation," SocArXiv 7bsgy_v1, Center for Open Science.
    5. Yusong Ke & Hongru Lin & Yuting Ruan & Junya Tang & Li Li, 2025. "Correctness Coverage Evaluation for Medical Multiple-Choice Question Answering Based on the Enhanced Conformal Prediction Framework," Mathematics, MDPI, vol. 13(9), pages 1-17, May.
    6. Sheng Wang & Fangyuan Zhao & Dechao Bu & Yunwei Lu & Ming Gong & Hongjie Liu & Zhaohui Yang & Xiaoxi Zeng & Zhiyuan Yuan & Baoping Wan & Jingbo Sun & Yang Wu & Lianhe Zhao & Xirun Wan & Wei Huang & Ta, 2025. "LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
    7. Francesco Carli & Pierluigi Chiaro & Mariangela Morelli & Chakit Arora & Luisa Bisceglia & Natalia Oliveira Rosa & Alice Cortesi & Sara Franceschi & Francesca Lessi & Anna Luisa Stefano & Orazio Santo, 2025. "Learning and actioning general principles of cancer cell drug sensitivity," Nature Communications, Nature, vol. 16(1), pages 1-23, December.
    8. Li, Butong & Zhu, Junjie & Zhao, Xufeng, 2025. "A prior knowledge-guided predictive framework for LCF life and its implementation in shaft-like components under multiaxial loading," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    9. Aboubacry Gaye & Abdou Ka Diongue & Seydou Nourou Sylla & Maryam Diarra & Amadou Diallo & Cheikh Talla & Cheikh Loucoubar, 2024. "Supervised Classification of High-Dimensional Correlated Data: Application to Genomic Data," Journal of Classification, Springer;The Classification Society, vol. 41(1), pages 158-169, March.
    10. Igor Halperin, 2025. "Prompt-Response Semantic Divergence Metrics for Faithfulness Hallucination and Misalignment Detection in Large Language Models," Papers 2508.10192, arXiv.org.
    11. Zhou, Zhen & Gu, Ziyuan & Qu, Xiaobo & Liu, Pan & Liu, Zhiyuan & Yu, Wenwu, 2024. "Urban mobility foundation model: A literature review and hierarchical perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
    12. Xiaohan Lin & Yijie Xia & Yanheng Li & Yu-Peng Huang & Shuo Liu & Jun Zhang & Yi Qin Gao, 2025. "In-silico 3D molecular editing through physics-informed and preference-aligned generative foundation models," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    13. Sanchaita Hazra & Marta Serra-Garcia, 2025. "Understanding Trust in AI as an Information Source: Cross-Country Evidence," CESifo Working Paper Series 11954, CESifo.
    14. Beining Xu & Yongming Lu, 2025. "TECP: Token-Entropy Conformal Prediction for LLMs," Mathematics, MDPI, vol. 13(20), pages 1-14, October.
    15. Igor Halperin, 2025. "Topic Identification in LLM Input-Output Pairs through the Lens of Information Bottleneck," Papers 2509.03533, arXiv.org.
    16. Akampurira Paul & Mutebi Joe & Mugisha Brian & Muhaise Hussein & Kyomuhangi Rosette, 2024. "Exploring Dimensionality Reduction Techniques for Improved Breast Cancer Diagnosis," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(5), pages 808-824, May.
    17. Peng Zhang & Jiayu Shi & Maged N. Kamel Boulos, 2024. "Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’," Future Internet, MDPI, vol. 16(12), pages 1-21, December.
    18. Hui-Hung Yu & Wei-Tsun Lin & Chih-Wei Kuan & Chao-Chi Yang & Kuan-Min Liao, 2025. "GraphRAG-Enhanced Dialogue Engine for Domain-Specific Question Answering: A Case Study on the Civil IoT Taiwan Platform," Future Internet, MDPI, vol. 17(9), pages 1-22, September.
    19. Hyejoon Park & Hyunjoong Kim & Yung-Seop Lee, 2025. "Maximizing adjusted covariance: new supervised dimension reduction for classification," Computational Statistics, Springer, vol. 40(1), pages 573-599, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-64304-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.