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Cited by 7Computational Drug Discovery MethodsBiomedical Text Mining and OntologiesArtificial Intelligence in Healthcare and Education

QSP ‐Copilot: An AI ‐Augmented Platform for Accelerating Quantitative Systems Pharmacology Model Development

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Saini, Anuraag, Farnoud, Ali·2025·CPT: Pharmacometrics & Systems Pharmacology
DOI 10.1002/psp4.70127PMC PMC12625087
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Abstract

Quantitative Systems Pharmacology (QSP) is a powerful approach to provide decision‐making support throughout the drug development process. QSP comes with many challenges in model development, validation, and applications. Traditional QSP workflows are limited by slow knowledge integration, labor‐intensive model construction, inconsistent validation practices, and restricted scalability. In this work, we introduce QSP‐Copilot, the first end‐to‐end AI‐augmented solution designed to improve QSP modeling workflows by integrating a multi‐agent system utilizing large language models (LLMs). QSP‐Copilot provides modular support from project scoping and model structuring to model evaluation and reporting. Through the automation of routine tasks, QSP‐Copilot reduces model development time by approximately 40% and improves methodological transparency through systematic documentation of literature sources and modeling assumptions. We demonstrate QSP‐Copilot's application for two rare diseases of blood coagulation and Gaucher disease. In the blood coagulation case, automated extraction from ten peer‐reviewed articles yielded 179 biological entity interaction pairs; out of these, only 105 unique mechanisms were retained after standardization. For Gaucher disease, screening nine articles produced 151 pairs, which were consolidated into 68 distinct biological interactions following the same post‐processing workflow. The extraction precision for blood coagulation and Gaucher disease is 99.1% and 100.0%, respectively. QSP‐Copilot extractions can be incorporated into effect diagrams with minimal expert filtering, significantly reducing the manual curation burden. The integration of AI‐augmented workflows like QSP‐Copilot represents a pivotal shift toward enhanced scalability and impact for QSP across the drug development pipelines, especially in disease areas where biological knowledge is sparse, such as rare diseases.

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  • LLMs can identify relevant literature given mechanistic context, read scientific text, and extract information into structured templates.

    “LLMs can identify relevant literature given mechanistic context, read scientific text, and extract information into structured templates [14, 15, 16, 17, 18].”

    From Structured Schemas for Provenance‐Rich, <scp>LLM</scp> ‐Assisted <scp>QSP</scp> Model Calibration (2026) · cites this paper as [16]

  • LLMs can identify relevant literature given mechanistic context, read scientific text, and extract information into structured templates.

    “LLMs can identify relevant literature given mechanistic context, read scientific text, and extract information into structured templates14,15,16,17,18.”

    From Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration (2026) · cites this paper as [16]

  • MAPLE addresses a gap between general-purpose biomedical extraction frameworks17,18, which do not address QSP-specific downstream requirements (uncertainty quantification, forward model specification, code generation), and QSP-focused tools like QSP-Copilot16, which include literature extraction components but emphasize model structure discovery rather than quantitative parameter calibration with

    “MAPLE addresses a gap between general-purpose biomedical extraction frameworks17,18, which do not address QSP-specific downstream requirements (uncertainty quantification, forward model specification, code generation), and QSP-focused tools like QSP-Copilot16”

    From Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration (2026) · cites this paper as [16]

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Research works printed but not linked: 2

  1. [3]A. Vaswani, N. Shazeer, N. Parmar, et al., “ Attention Is All You Need (Version 7),” arXiv (2017), 10.48550/ARXIV.1706.03762. · DOI · Paper page
  2. [16]J. R. Chan, R. Allen, B. Boras, et al., “ Current Practices for QSP Model Assessment: An IQ Consortium Survey,” Journal of Pharmacokinetics and Pharmacodynamics 51, no. 5 (2024): 543– 555, 10.1007/s10928-022-09811-1. 35953664 PMC9371373 · DOI

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Authors
Saini, A., & Farnoud, A.
Affiliations
Boehringer Ingelheim (Germany) (DE)
Corresponding author
Farnoud, Ali
ORCID
Venue
CPT: Pharmacometrics & Systems Pharmacology
Publisher
Wiley
Published
2025-10-29
Volume
14
Issue
11
Pages
1775-1786
ISSN
2163-8306 (ISSN-L)
DOI
10.1002/psp4.70127
PMC
PMC12625087
OpenAlex
W4415673519
Cited by
7
References
52
Citations by year
2026: 6 · 2025: 1
FWCI
3.62
Type
journal article
Language
en
Open access
Licence
Locations
  • CPT Pharmacometrics & Systems Pharmacology (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • 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
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
quantitative systems pharmacology, large language models, multi-agent systems, model validation, rare diseases, coagulopathy, Gaucher disease, drug development
MeSH terms
Drug Development, Artificial Intelligence, Humans, Models, Biological, Systems Biology, Workflow

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