Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: its pAIns and gAIns
Open accessAbstract
Quantitative Systems Pharmacology (QSP) has become a powerful tool in the drug development landscape. To facilitate its continued implementation and to further enhance its applicability, a symbiotic approach in which QSP is combined with artificial intelligence (AI) and machine learning (ML) seems key. This manuscript presents four case examples where the application of a symbiotic approach could unlock new insights from multidimensional data, including real-world data, potentially leading to breakthroughs in drug development. Besides the remarkable benefits (gAIns) that the symbiosis can offer, it does also carry potential challenges (pAIns) such as how to assess and quantify uncertainty, bias and error. Hence, to ensure a successful implementation, arising pAIns need to be acknowledged and carefully addressed. Successful implementation of the symbiotic QSP and ML/AI approach has the potential to serve as a catalyst, paving the way for a paradigm shift in drug development.
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As QSP increasingly integrates with machine learning, the bottleneck of manual parameter extraction becomes more acute.
“As QSP increasingly integrates with machine learning [6, 7, 8, 9, 10], the bottleneck of manual parameter extraction becomes more acute.”
From Structured Schemas for Provenance‐Rich, <scp>LLM</scp> ‐Assisted <scp>QSP</scp> Model Calibration (2026) · cites this paper as [6]
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Cites
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803 · see citing works
Christodoulou, E., Ma, J., Collins, G. S., Steyerberg, E. W., Verbakel, J. Y., & Van Calster, B. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.02.004 · Cited by 2,119 · see citing works
Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
Myszczynska, M. A., Ojamies, P. N., Lacoste, A. M. B., Neil, D., Saffari, A., Mead, R., Hautbergue, G. M., Holbrook, J. D., & Ferraiuolo, L. (2020) · Nature Reviews Neurology · doi:10.1038/s41582-020-0377-8 · Cited by 595 · see citing works
Mapping transcriptomic vector fields of single cells
Qiu, X., Zhang, Y., Martin-Rufino, J. D., Weng, C., Hosseinzadeh, S., Yang, D., Pogson, A. N., Hein, M. Y., Min, K. H., Wang, L., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.045 · Cited by 436 · see citing works
Machine Learning Applications for Mass Spectrometry-Based Metabolomics
Liebal, U. W., An, P. N. T., Sudhakar, M., Raman, K., & Blank, L. M. (2020) · Metabolites · doi:10.3390/metabo10060243 · Cited by 371 · see citing works
RNA velocity—current challenges and future perspectives
Bergen, V., Soldatov, R. A., Kharchenko, P. V., & Theis, F. J. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110282 · Cited by 271 · see citing works
Mitigating bias in machine learning for medicine
Vokinger, K. N., Feuerriegel, S., & Kesselheim, A. S. (2021) · Communications Medicine · doi:10.1038/s43856-021-00028-w · Cited by 269 · see citing works
Odusami, M., Maskeliūnas, R., Damaševičius, R., & Krilavičius, T. (2021) · Diagnostics · doi:10.3390/diagnostics11061071 · Cited by 245 · see citing works
Cardiac Resynchronization Therapy in Women
Zusterzeel, R., Selzman, K. A., Sanders, W. E., Caños, D., O’Callaghan, K. M., Carpenter, J., Piña, I. L., & Strauss, D. G. (2014) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2014.2717 · Cited by 213 · see citing works
Causal integration of multi‐omics data with prior knowledge to generate mechanistic hypotheses
Dugourd, A., Kuppe, C., Sciacovelli, M., Gjerga, E., Gabor, A., Emdal, K. B., Vieira, V., Bekker‐Jensen, D. B., Kranz, J., Bindels, E. M. J., et al. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209730 · Cited by 149 · see citing works
Machine Learning in Pharmacometrics: Opportunities and Challenges
McComb, M., Bies, R., & Ramanathan, M. (2022) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14801 · Cited by 128 · see citing works
Hosseini, I., Gadkar, K. G., Stefanich, E. G., Li, C., Sun, L. L., Chu, Y., & Ramanujan, S. (2020) · npj Systems Biology and Applications · doi:10.1038/s41540-020-00145-7 · Cited by 123 · see citing works
Procopio, A., Cesarelli, G., Donisi, L., Merola, A., Amato, F., & Cosentino, C. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107681 · Cited by 111 · see citing works
Bouke, M. A., & Abdullah, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120715 · Cited by 109 · see citing works
Azer, K., Kaddi, C. D., Barrett, J. S., Bai, J. P. F., McQuade, S. T., Merrill, N. J., Piccoli, B., Neves, S. R., Marchetti, L., Lombardo, R., et al. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.637999 · Cited by 85 · see citing works
Tackling bias in AI health datasets through the STANDING TOGETHER initiative
Ganapathi, S., Palmer, J., Alderman, J. E., Calvert, M., Espinoza, C., Gath, J., Ghassemi, M., Heller, K., Mckay, F., Karthikesalingam, A., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01987-w · Cited by 82 · see citing works
Sandberg, K. L., Umans, J. G., & Group the Georgetown Consensus Conference Work (2015) · The FASEB Journal · doi:10.1096/fj.14-269548 · Cited by 81 · see citing works
Standard requirements for GCP-compliant data management in multinational clinical trials
Ohmann, C., Kuchinke, W., Canham, S., et al. (2011) · Trials · doi:10.1186/1745-6215-12-85 · Cited by 78 · see citing works
Toward an Integrated Machine Learning Model of a Proteomics Experiment
Neely, B. A., Dorfer, V., Martens, L., Bludau, I., Bouwmeester, R., Degroeve, S., Deutsch, E. W., Gessulat, S., Käll, L., Palczynski, P., et al. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00711 · Cited by 74 · see citing works
Integrating knowledge and omics to decipher mechanisms via large‐scale models of signaling networks
Garrido‐Rodríguez, M., Zirngibl, K., Ivanova, O., Lobentanzer, S., & Sáez-Rodríguez, J. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211036 · Cited by 71 · see citing works
Shelmerdine, S. C., Arthurs, O. J., Denniston, A. K., & Sebire, N. J. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100385 · Cited by 67 · see citing works
Enablers and challenges of spatial omics, a melting pot of technologies
Alexandrov, T., Sáez-Rodríguez, J., & Saka, S. K. (2023) · Molecular Systems Biology · doi:10.15252/msb.202110571 · Cited by 64 · see citing works
Two heads are better than one: current landscape of integrating QSP and machine learning
Zhang, T., Androulakis, I. P., Bonate, P. L., Cheng, L., Helikar, T., Parikh, J., Rackauckas, C., Subramanian, K., Cho, C. R., on behalf of the Working Group, et al. (2022) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-022-09805-z · Cited by 62 · see citing works
Foo, K. S., Lehtinen, M. L., Leung, C. Y., Lian, X. L., Xu, J., Keung, W., Geng, L., Kolstad, T. R., Thams, S., Wong, A. O.-T., et al. (2018) · Molecular Therapy · doi:10.1016/j.ymthe.2018.02.012 · Cited by 55 · see citing works
Bai, J. P. F., Earp, J. C., Florian, J. A., Madabushi, R., Strauss, D. G., Wang, Y., & Zhu, H. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12709 · Cited by 53 · see citing works
Chen, X., Kamperschroer, C., Wong, G. Y., & Xuan, D. (2019) · Clinical and Translational Science · doi:10.1111/cts.12662 · Cited by 50 · see citing works
Poch, C. M., Foo, K. S., Angelis, M. T. D., Jennbacken, K., Santamaria, G., Bähr, A., Wang, Q., Reiter, F., Hornaschewitz, N., Zawada, D. M., et al. (2022) · Nature Cell Biology · doi:10.1038/s41556-022-00899-8 · Cited by 50 · see citing works
Hallmarks of neurodegenerative disease: A systems pharmacology perspective
Bloomingdale, P., Karelina, T., Ramakrishnan, V., Bakshi, S., Véronneau‐Veilleux, F., Moye, M., Sekiguchi, K., Meno‐Tetang, G. M. L., Mohan, A., Maithreye, R., et al. (2022) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12852 · Cited by 50 · see citing works
Methodologies for Quantitative Systems Pharmacology (QSP) Models: Design and Estimation
Ribba, B., Grimm, H., Agoram, B., Davies, M., Gadkar, K., Niederer, S., van Riel, N., Timmis, J., & van der Graaf, P. (2017) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12206 · Cited by 43 · see citing works
Kremers, R., Ninivaggi, M., Devreese, K. M. J., et al. (2020) · Journal of Thrombosis and Haemostasis · doi:10.1111/jth.14863 · Cited by 42 · see citing works
Lugg-Widger, F. V., Angel, L., Cannings-John, R. L., Hood, K., Hughes, K., Moody, G., & Robling, M. R. (2018) · International Journal for Population Data Science · doi:10.23889/ijpds.v3i3.432 · Cited by 41 · see citing works
Venkatapurapu, S. P., Iwakiri, R., Udagawa, E., Patidar, N., Qi, Z., Takayama, R., Kumar, K. D., Sato, Y., Behar, M., Offner, P. J., et al. (2022) · Advances in Therapy · doi:10.1007/s12325-022-02144-y · Cited by 36 · see citing works
Bogatu, A., Wysocka, M., Wysocki, O., Butterworth, H., Pillai, M. R., Allison, J., Landers, D., Kilgour, E., Thistlethwaite, F. C., & Freitas, A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104367 · Cited by 36 · see citing works
Geerts, H., Spiros, A., & Roberts, P. (2018) · Alzheimer's Research & Therapy · doi:10.1186/s13195-018-0343-5 · Cited by 33 · see citing works
A dynamical systems approach for multiscale synthesis of Alzheimer’s pathogenesis
Rollo, J. L., Crawford, J. W., & Hardy, J. (2023) · Neuron · doi:10.1016/j.neuron.2023.04.018 · Cited by 30 · see citing works
Mechanistically modeling peripheral cytokine dynamics following bispecific dosing in solid tumors
Weddell, J. C. (2023) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12928 · Cited by 27 · see citing works
Clausznitzer, D., Pichardo‐Almarza, C., Relo, A. L., van Bergeijk, J., van der Kam, E. L., Laplanche, L., Benson, N., & Nijsen, M. (2018) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12351 · Cited by 26 · see citing works
Self-citationMachine learning alternative to systems biology should not solely depend on data
Yeo, H. C., & Selvarajoo, K. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac436 · Cited by 23 · see citing works
Geerts, H., Walker, M., Rose, R., Bergeler, S., van der Graaf, P. H., Schuck, E. L., Koyama, A., Yasuda, S., Hussein, Z., Reyderman, L., et al. (2023) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12912 · Cited by 23 · see citing works
Disease progression model using the integrated Alzheimer's Disease Rating Scale
Gueorguieva, I., Chua, L., Willis, B. A., Sims, J. R., & Wessels, A. M. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12876 · Cited by 18 · see citing works
Stein, A. M., & Looby, M. (2018) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12311 · Cited by 16 · see citing works
Verma, M., Gall, L., Biasetti, J., Veroli, G. Y. D., Pichardo‐Almarza, C., Gibbs, M. A., & Kimko, H. H. C. (2023) · Frontiers in Systems Biology · doi:10.3389/fsysb.2022.1063308 · Cited by 11 · see citing works
Self-citationSingh, G., & Ramanathan, M. K. (2023) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2987 · Cited by 10 · see citing works
Modeling and analysis of RNA‐seq data: a review from a statistical perspective
Li, W. V., & Li, J. J. (2018) · Quantitative Biology · doi:10.1007/s40484-018-0144-7 · Cited by 10 · see citing works
Physiological Indirect Response Model to Omics-Powered Quantitative Systems Pharmacology Model
Uatay, A., Gall, L., Irons, L., Tewari, S. G., Zhu, X., Gibbs, M. A., & Kimko, H. H. C. (2023) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2023.10.032 · Cited by 7 · see citing works
Self-citationMokhtari, R., Hamidinekoo, A., Sutton, D., Lewis, A., Angermann, B., Gehrmann, U., Lundin, P., Adissu, H. A., Cairns, J., Neisen, J., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.12095 · Cited by 4 · see citing works
Defining the pathways of heart regeneration
Timmer, L. T., & van Rooij, E. (2022) · Nature Cell Biology · doi:10.1038/s41556-022-00914-y · Cited by 3 · see citing works
Fan, X., Wang, P., Li, J., & Yang, N. (2023) · Research in Astronomy and Astrophysics · doi:10.1088/1674-4527/ad0498 · Cited by 1 · see citing works
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Research works printed but not linked: 1
- Kaufman S. Rosset S. Perlich C. (2011). “ Leakage in data mining: formulation, detection, and avoidance,” in In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '11) (New York, NY, USA: Association for Computing Machinery), 556– 563. 10.1145/2020408.2020496 · DOI · Paper page
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Goryanin, I., Goryanin, I., & Demin, O. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104448 · Cited by 21 · see citing works
Saini, A., & Farnoud, A. (2025) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.70127 · Cited by 7 · see citing works
Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration
Eliason, J., & Popel, A. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.05.709623
“As QSP increasingly integrates with machine learning 6,7,8,9,10 , the bottleneck of manual parameter extraction becomes more acute.”
Structured Schemas for Provenance‐Rich, <scp>LLM</scp> ‐Assisted <scp>QSP</scp> Model Calibration
Eliason, J., & Popel, A. S. (2026) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.70306
- Authors
- Folguera-Blasco, N., Boshier, F. A. T., Uatay, A., Pichardo-Almarza, C., Lai, M., Biasetti, J., Dearden, R., Gibbs, M., & Kimko, H.
- Affiliations
- AstraZeneca (United Kingdom) (GB)
- AstraZeneca (Sweden) (SE)
- AstraZeneca (United States) (US)
- Corresponding author
- Folguera-Blasco, Núria
- ORCID
- Venue
- Frontiers in Systems Biology
- Publisher
- Frontiers Media SA
- Published
- 2024-07-12
- Volume
- 4
- Pages
- 1380685-1380685
- ISSN
- 2674-0702 (ISSN-L)
- PMC
- PMC12342010
- OpenAlex
- W4400591549
- Cited by
- 15
- References
- 48
- Citations by year
- 2026: 7 · 2025: 8
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Systems Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- quantitative systems pharmacology, machine learning, artificial intelligence, drug development, real-world data, uncertainty quantification, multidimensional data
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