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Cited by 22Computational Drug Discovery MethodsArtificial Intelligence in Healthcare and EducationStatistical Methods in Clinical Trials

The dawn of a new era: can machine learning and large language models reshape QSP modeling?

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Androulakis, Ioannis P., Cucurull-Sanchez, Lourdes, Kondic, Anna, Mehta, Krina, Pichardo, Cesar, Pryor, Meghan, Renardy, Marissa·2025·Journal of Pharmacokinetics and Pharmacodynamics
DOI 10.1007/s10928-025-09984-5PMC PMC12170689
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Abstract

Quantitative Systems Pharmacology (QSP) has emerged as a cornerstone of modern drug development, providing a robust framework to integrate data from preclinical and clinical studies, enhance decision-making, and optimize therapeutic strategies. By modeling biological systems and drug interactions, QSP enables predictions of outcomes, optimization of dosing regimens, and personalized medicine applications. Recent advancements in artificial intelligence (AI) and machine learning (ML) hold the potential to significantly transform QSP by enabling enhanced data extraction, fostering the development of hybrid mechanistic ML models, and supporting the introduction of surrogate models and digital twins. This manuscript explores the transformative role of AI and ML in reshaping QSP modeling workflows. AI/ML tools now enable automated literature mining, the generation of dynamic models from data, and the creation of hybrid frameworks that blend mechanistic insights with data-driven approaches. Large Language Models (LLMs) further revolutionize the field by transitioning AI/ML from merely a tool to becoming an active partner in QSP modeling. By facilitating interdisciplinary collaboration, lowering barriers to entry, and democratizing QSP workflows, LLMs empower researchers without deep coding expertise to engage in complex modeling tasks. Additionally, the integration of Artificial General Intelligence (AGI) holds the potential to autonomously propose, refine, and validate models, further accelerating innovation across multiscale biological processes. Key challenges remain in integrating AI/ML into QSP workflows, particularly in ensuring rigorous validation pipelines, addressing ethical considerations, and establishing robust regulatory frameworks to address the reliability and reproducibility of AI-assisted models. Moreover, the complexity of multiscale biological integration, effective data management, and fostering interdisciplinary collaboration present ongoing hurdles. Despite these challenges, the potential of AI/ML to enhance hybrid model development, improve model interpretability, and democratize QSP modeling offers an exciting opportunity to revolutionize drug development and therapeutic innovation. This work highlights a pathway toward a transformative era for QSP, leveraging advancements in AI and ML to address these challenges and drive innovation in the field.

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  1. [1]Knight-Schrijver VR, Chelliah V, Cucurull-Sanchez L, Le Novere N (2016) The promises of quantitative systems Pharmacology modelling for drug development. Comput Struct Biotechnol J 14:363–370. 10.1016/j.csbj.2016.09.002 27761201 PMC5064996 · DOI
  2. [2]Musante CJ, Abernethy DR, Allerheiligen SR, Lauffenburger DA, Zager MG (2016) GPS for QSP: A summary of the ACoP6 symposium on quantitative systems Pharmacology and a stage for Near-Term efforts in the field. CPT Pharmacometrics Syst Pharmacol 5(9):449–451. 10.1002/psp4.12109 PMC5036418 27639191 · DOI
  3. [3]Musante CJ, Ramanujan S, Schmidt BJ, Ghobrial OG, Lu J, Heatherington AC (2017) Quantitative systems pharmacology: A case for disease models. Clin Pharmacol Ther 101(1):24–27. 10.1002/cpt.528 27709613 PMC5217891 · DOI
  4. [4]Allerheiligen S, Abernethy D, Altman RB, Brouwer K, Califano A, David Z, D’argenio, Iyengar R, Jusko W, Lalonde R, Lauffenburger D, Shoichet B, Stevens J, Sorger P, Subramaniam S, Graaf PD, Vicini P, Ward RJ (2011) Quantitative and Systems Pharmacology in the Post-genomic Era: New Approaches to Discovering Drugs and Understanding Therapeutic. In: An NIH White Paper by the QSP Workshop Group
  5. [5]Androulakis IP (2016) Quantitative systems pharmacology: A framework for context. Curr Pharmacol Rep 1–9. 10.1007/s40495-016-0058-x PMC4996481 27570730 · DOI
  6. [6]Cheng Y, Thalhauser CJ, Smithline S, Pagidala J, Miladinov M, Vezina HE, Gupta M, Leil TA, Schmidt BJ (2017) QSP toolbox: computational implementation of integrated workflow components for deploying Multi-Scale mechanistic models. AAPS J 19(4):1002–1016. 10.1208/s12248-017-0100-x 28540623 · DOI · Paper page
  7. [8]Go N, Arsene S, Faddeenkov I, Galland T, Martis BS, Lefaudeux D, Wang Y, Etheve L, Jacob E, Monteiro C, Bosley J, Sansone C, Pasquali C, Lehr L, Kulesza A (2024) A quantitative systems Pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials. J Allergy Clin Immunol 153(5):1330–1343. 10.1016/j.jaci.2023.12.031 38369029 · DOI
  8. [9]Helmlinger G, Sokolov V, Peskov K, Hallow KM, Kosinsky Y, Voronova V, Chu L, Yakovleva T, Azarov I, Kaschek D, Dolgun A, Schmidt H, Boulton DW, Penland RC (2019) Quantitative systems pharmacology: an exemplar Model-Building workflow with applications in cardiovascular, metabolic, and oncology drug development. CPT Pharmacometrics Syst Pharmacol 8(6):380–395. 10.1002/psp4.12426 31087533 PMC6617832 · DOI
  9. [10]Ming JE, Abrams RE, Bartlett DW, Tao M, Nguyen T, Surks H, Kudrycki K, Kadambi A, Friedrich CM, Djebli N, Goebel B, Koszycki A, Varshnaya M, Elassal J, Banerjee P, Sasiela WJ, Reed MJ, Barrett JS, Azer K (2017) A quantitative systems Pharmacology platform to investigate the impact of Alirocumab and Cholesterol-Lowering therapies on lipid profiles and plaque characteristics. Gene Regul Syst Bio 11:1177625017710941. 10.1177/1177625017710941 28804243 PMC5484552 · DOI
  10. [11]Gong C, Ruiz-Martinez A, Kimko H, Popel AS (2021) A Spatial quantitative systems Pharmacology platform spQSP-IO for simulations of Tumor-Immune interactions and effects of checkpoint inhibitor immunotherapy. Cancers (Basel) 1513. 10.3390/cancers13153751 PMC8345161 34359653 · DOI
  11. [13]Bai JPF, Liu G, Zhao M, Wang J, Xiong Y, Truong T, Earp JC, Yang Y, Liu J, Zhu H, Burckart GJ (2024) Landscape of regulatory quantitative systems Pharmacology submissions to the U.S. Food and drug administration: an update report. CPT Pharmacometrics Syst Pharmacol 13(12):2102–2110. 10.1002/psp4.13208 39423143 PMC11646928 · DOI
  12. [14]Bai JPF, Wang J, Zhang Y, Wang L, Jiang X (2023) Quantitative systems Pharmacology for rare disease drug development. J Pharm Sci 112(9):2313–2320. 10.1016/j.xphs.2023.06.019 37422281 · DOI
  13. [17]Ribba B (2023) Quantitative systems Pharmacology in the age of artificial intelligence. CPT Pharmacometrics Syst Pharmacol 12(12):1823–1826. 10.1002/psp4.13047 37779349 PMC10725257 · DOI
  14. [18]Zhang T, Androulakis IP, Bonate P, Cheng L, Helikar T, Parikh J, Rackauckas C, Subramanian K, Cho CR, Working G (2022) Two heads are better than one: current landscape of integrating QSP and machine learning: an isop QSP SIG white paper by the working group on the integration of quantitative systems Pharmacology and machine learning. J Pharmacokinet Pharmacodyn 49(1):5–18. 10.1007/s10928-022-09805-z 35103884 PMC8837505 · DOI
  15. [19]Wojtyniak JG, Britz H, Selzer D, Schwab M, Lehr T (2020) CPT Pharmacometrics Syst Pharmacol 9(6):322–331. 10.1002/psp4.12511. Data Digitizing: Accurate and Precise Data Extraction for Quantitative Systems Pharmacology and Physiologically-Based Pharmacokinetic Modeling PMC7306621 32543786 · DOI
  16. [20]Hakenberg J, Nenadic G, Rebholz-Schuhmann D, Kim JD (2013) Literature mining solutions for life science research. Adv Bioinformatics 2013:320436. 10.1155/2013/320436 PMC3556398 23365570 · DOI
  17. [21]He F, Liu K, Yang Z, Hannink M, Hammer RD, Popescu M, Xu D (2023) Applications of cutting-edge artificial intelligence technologies in biomedical literature and document mining. Med Rev (2021) 3 (3):200–204. 10.1515/mr-2023-0011 PMC10542881 37789956 · DOI
  18. [22]Han H (2025) Challenges of reproducible AI in biomedical data science. BMC Med Genomics 18(Suppl 1):8. 10.1186/s12920-024-02072-6 39794788 PMC11724458 · DOI
  19. [23]Wang Z, Kim S, Quinney SK, Guo Y, Hall SD, Rocha LM, Li L (2009) Literature mining on pharmacokinetics numerical data: a feasibility study. J Biomed Inf 42(4):726–735. 10.1016/j.jbi.2009.03.010 PMC2737819 19345282 · DOI
  20. [24]Wu H-Y, Karnik S, Subhadarshini A, Wang Z, Philips S, Han X, Chiang C, Liu L, Boustani M, Rocha LM, Quinney SK, Flockhart D, Li L (2013) An integrated pharmacokinetics ontology and corpus for text mining. BMC Bioinformatics 14(1):35. 10.1186/1471-2105-14-35 23374886 PMC3571923 · DOI
  21. [25]Gonzalez Hernandez F, Carter SJ, Iso-Sipila J, Goldsmith P, Almousa AA, Gastine S, Lilaonitkul W, Kloprogge F, Standing JF (2021) An automated approach to identify scientific publications reporting Pharmacokinetic parameters. Wellcome Open Res 6:88. 10.12688/wellcomeopenres.16718.1 34381873 PMC8343403 · DOI
  22. [26]Hsu JC, Wu M, Kim C, Vora B, Lien YTK, Jindal A, Yoshida K, Kawakatsu S, Gore J, Jin JY, Lu C, Chen B, Wu B (2024) Applications of advanced natural Language processing for clinical Pharmacology. Clin Pharmacol Ther 115(4):786–794. 10.1002/cpt.3161 38140747 · DOI
  23. [27]Mavroudis PD, Teutonico D, Abos A, Pillai N (2023) Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules. Front Syst Biology 3. 10.3389/fsysb.2023.1180948 PMC12342024 40809487 · DOI
  24. [28]Pillai N, Abos A, Teutonico D, Mavroudis PD (2024) Machine learning framework to predict Pharmacokinetic profile of small molecule drugs based on chemical structure. Clin Transl Sci 17(5):e13824. 10.1111/cts.13824 38752574 PMC11097621 · DOI
  25. [29]Chou WC, Lin Z (2023) Machine learning and artificial intelligence in physiologically based Pharmacokinetic modeling. Toxicol Sci 191(1):1–14. 10.1093/toxsci/kfac101 36156156 PMC9887681 · DOI
  26. [30]Lecca P (2021) Machine learning for causal inference in biological networks: perspectives of this challenge. Front Bioinform 1:746712. 10.3389/fbinf.2021.746712 36303798 PMC9581010 · DOI
  27. [31]Albert R (2007) Network inference, analysis, and modeling in systems biology. Plant Cell 19(11):3327–3338. 10.1105/tpc.107.054700 18055607 PMC2174897 · DOI
  28. [32]Ghanat Bari M, Ung CY, Zhang C, Zhu S, Li H (2017) Machine Learning-Assisted network inference approach to identify a new class of genes that coordinate the functionality of Cancer networks. Sci Rep 7(1):6993. 10.1038/s41598-017-07481-5 28765560 PMC5539301 · DOI
  29. [33]Erdem C, Birtwistle MR (2023) MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets. Front Syst Biol 3. 10.3389/fsysb.2023.1099413 PMC10807051 38269333 · DOI
  30. [34]Putnins M, Campagne O, Mager DE, Androulakis IP (2022) From data to QSP models: a pipeline for using boolean networks for hypothesis inference and dynamic model Building. J Pharmacokinet Pharmacodyn 49(1):101–115. 10.1007/s10928-021-09797-2 34988912 PMC9876619 · DOI
  31. [35]Bloomingdale P, Nguyen VA, Niu J, Mager DE (2018) Boolean network modeling in systems Pharmacology. J Pharmacokinet Pharmacodyn 45(1):159–180. 10.1007/s10928-017-9567-4 29307099 PMC6531050 · DOI
  32. [36]Krumsiek J, Pölsterl S, Wittmann DM, Theis FJ (2010) Odefy - From discrete to continuous models. BMC Bioinformatics 11(1):233. 10.1186/1471-2105-11-233 20459647 PMC2873544 · DOI
  33. [37]Scheff JD, Calvano SE, Androulakis IP (2013) Predicting critical transitions in a model of systemic inflammation. J Theor Biol 338:9–15. 10.1016/j.jtbi.2013.08.011 23973206 PMC3870185 · DOI
  34. [38]Gross T, Feudel U (2006) Generalized models as a universal approach to the analysis of nonlinear dynamical systems. Phys Rev E Stat Nonlin Soft Matter Phys 73(1 Pt 2):016205. 10.1103/PhysRevE.73.016205 16486256 · DOI
  35. [39]Chakraborty S, Adhikari S, Ganguli R (2021) The role of surrogate models in the development of digital twins of dynamic systems. Appl Math Model 90:662–681. 10.1016/j.apm.2020.09.037 · DOI
  36. [40]Myers RC, Augustin F, Huard J, Friedrich CM (2023) Using machine learning surrogate modeling for faster QSP VP cohort generation. CPT Pharmacometrics Syst Pharmacol 12(8):1047–1059. 10.1002/psp4.12999 37328956 PMC10431055 · DOI
  37. [41]Allen RJ, Rieger TR, Musante CJ (2016) Efficient generation and selection of virtual populations in quantitative systems Pharmacology models. CPT Pharmacometrics Syst Pharmacol 5(3):140–146. 10.1002/psp4.12063 27069777 PMC4809626 · DOI
  38. [42]Rieger TR, Allen RJ, Bystricky L, Chen Y, Colopy GW, Cui Y, Gonzalez A, Liu Y, White RD, Everett RA, Banks HT, Musante CJ (2018) Improving the generation and selection of virtual populations in quantitative systems Pharmacology models. Prog Biophys Mol Biol 139:15–22. 10.1016/j.pbiomolbio.2018.06.002 29902482 · DOI · Paper page
  39. [43]Laubenbacher R, Adler F, An G, Castiglione F, Eubank S, Fonseca LL, Glazier J, Helikar T, Jett-Tilton M, Kirschner D, Macklin P, Mehrad B, Moore B, Pasour V, Shmulevich I, Smith A, Voigt I, Yankeelov TE, Ziemssen T (2024) Forum on immune digital twins: a meeting report. NPJ Syst Biol Appl 10(1):19. 10.1038/s41540-024-00345-5 38365857 PMC10873299 · DOI
  40. [44]Laubenbacher R, Niarakis A, Helikar T, An G, Shapiro B, Malik-Sheriff RS, Sego TJ, Knapp A, Macklin P, Glazier JA (2022) Building digital twins of the human immune system: toward a roadmap. NPJ Digit Med 5(1):64. 10.1038/s41746-022-00610-z 35595830 PMC9122990 · DOI
  41. [46]Moingeon P, Chenel M, Rousseau C, Voisin E, Guedj M (2023) Virtual patients, digital twins and causal disease models: paving the ground for in Silico clinical trials. Drug Discov Today 28(7):103605. 10.1016/j.drudis.2023.103605 37146963 · DOI
  42. [47]Gherman IM, Abdallah ZS, Pang W, Gorochowski TE, Grierson CS, Marucci L (2023) Bridging the gap between mechanistic biological models and machine learning surrogates. PLoS Comput Biol 19(4):e1010988. 10.1371/journal.pcbi.1010988 37079494 PMC10118077 · DOI
  43. [49]Venkatapurapu SP, Gibbs M, Kimko H (2024) Augmented intelligence in precision medicine: transforming clinical decision-making with AI/ML and/or quantitative systems Pharmacology models. Clin Transl Sci 17(12):e70112. 10.1111/cts.70112 39673165 PMC11645444 · DOI
  44. [50]Androulakis IP (2024) Towards a comprehensive assessment of QSP models: what would it take? J Pharmacokinet Pharmacodyn 51(5):521–531. 10.1007/s10928-022-09820-0 35962928 PMC9922790 · DOI
  45. [51]Folguera-Blasco N, Boshier F, Uatay A, Pichardo-Almarza C, Lai M, Biasetti J, Dearden R, Gibbs M, Kimko H (2024) Coupling quantitative systems Pharmacology modelling to machine learning and artificial intelligence for drug development: its pAIns and gAIns. Front Syst Biology 4. 10.3389/fsysb.2024.1380685 PMC12342010 40809115 · DOI · Paper page
  46. [52]Procopio A, Cesarelli G, Donisi L, Merola A, Amato F, Cosentino C (2023) Combined mechanistic modeling and machine-learning approaches in systems biology - A systematic literature review. Comput Methods Programs Biomed 240:107681. 10.1016/j.cmpb.2023.107681 37385142 · DOI
  47. [53]Noordijk B, Garcia Gomez ML, ten Tusscher KHWJ, de Ridder D, van Dijk ADJ, Smith RW (2024) The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology. Front Syst Biology 4. 10.3389/fsysb.2024.1407994 PMC12341957 40809147 · DOI
  48. [54]Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L (2021) Physics-informed machine learning. Nat Reviews Phys 3(6):422–440. 10.1038/s42254-021-00314-5 · DOI · Paper page
  49. [55]Ahmadi Daryakenari N, Wang S, Karniadakis G (2024) CMINNs: compartment model informed neural networks - Unlocking drug dynamics. Comput Biol Med 184:109392. 10.1016/j.compbiomed.2024.109392 39608036 PMC11701831 · DOI
  50. [56]Qian Z, Zame W, Fleuren L, Elbers P, van der Schaar M (2021) Integrating expert odes into neural odes: Pharmacology and disease progression. Adv Neural Inf Process Syst 34
  51. [57]Cheng L, Qiu Y, Schmidt BJ, Wei GW (2022) Review of applications and challenges of quantitative systems Pharmacology modeling and machine learning for heart failure. J Pharmacokinet Pharmacodyn 49(1):39–50. 10.1007/s10928-021-09785-6 34637069 PMC8837528 · DOI
  52. [58]Valderrama D, Ponce-Bobadilla AV, Mensing S, Frohlich H, Stodtmann S (2024) Integrating machine learning with Pharmacokinetic models: benefits of scientific machine learning in adding neural networks components to existing PK models. CPT Pharmacometrics Syst Pharmacol 13(1):41–53. 10.1002/psp4.13054 37843389 PMC10787197 · DOI
  53. [59]Butner JD, Dogra P, Chung C, Koay EJ, Welsh JW, Hong DS, Cristini V, Wang Z (2024) Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy. NPJ Syst Biol Appl 10(1):88. 10.1038/s41540-024-00415-8 39143136 PMC11324794 · DOI
  54. [60]Aghamiri SS, Amin R, Helikar T (2022) Recent applications of quantitative systems Pharmacology and machine learning models across diseases. J Pharmacokinet Pharmacodyn 49(1):19–37. 10.1007/s10928-021-09790-9 34671863 PMC8528185 · DOI
  55. [61]Keutzer L, You H, Farnoud A, Nyberg J, Wicha SG, Maher-Edwards G, Vlasakakis G, Moghaddam GK, Svensson EM, Menden MP, Simonsson USH, On Behalf Of The Unite Tb C (2022) Machine learning and pharmacometrics for prediction of Pharmacokinetic data: differences, similarities and challenges illustrated with rifampicin. Pharmaceutics 14(8). 10.3390/pharmaceutics14081530 PMC9330804 35893785 · DOI
  56. [62]Huang Z, Denti P, Mistry H, Kloprogge F (2024) Machine learning and artificial intelligence in PK-PD modeling: fad, friend, or foe?? Clin Pharmacol Ther 115(4):652–654. 10.1002/cpt.3165 38179832 PMC11146679 · DOI
  57. [67]Meskó B (2023) Prompt engineering as an important emerging skill for medical professionals: tutorial. J Med Internet Res 25:e50638. 10.2196/50638 37792434 PMC10585440 · DOI · Paper page
  58. [68]Cloesmeijer ME, Janssen A, Koopman SF, Cnossen MH, Mathot RAA, consortium S (2024) ChatGPT in pharmacometrics? Potential opportunities and limitations. Br J Clin Pharmacol 90(1):360–365. 10.1111/bcp.15895 37621112 · DOI
  59. [69]Shin E, Hartman M, Ramanathan M (2024) Performance of the ChatGPT large Language model for decision support in community pharmacy. Br J Clin Pharmacol. 10.1111/bcp.16215 39191671 · DOI
  60. [70]Shin E, Ramanathan M (2023) Evaluation of prompt engineering strategies for Pharmacokinetic data analysis with the ChatGPT large Language model. J Pharmacokinet Pharmacodyn. 10.1007/s10928-023-09892-6 37952004 · DOI
  61. [71]Shin E, Yu Y, Bies RR, Ramanathan M (2024) Evaluation of ChatGPT and gemini large Language models for pharmacometrics with NONMEM. J Pharmacokinet Pharmacodyn 51(3):187–197. 10.1007/s10928-024-09921-y 38656706 · DOI
  62. [72]Androulakis IP, Cheng L, Cho CR, Zhang T (2025) Leveraging large Language models to compare perspectives on integrating QSP and AI/ML. J Pharmacokinet Pharmacodyn 52(3):29. 10.1007/s10928-025-09976-5 40325298 · DOI · Paper page
  63. [74]Koller D, Beam A, Manrai A, Ashley E, Liu X, Gichoya J, Holmes C, Zou J, Dagan N, Wong TY, Blumenthal D, Kohane I (2024) Why we support and encourage the use of large Language models in NEJM AI submissions. Nejm Ai 1(1). 10.1056/AIe2300128 · DOI
  64. [75]Hou W, Ji Z (2024) Comparing large Language models and human programmers for generating programming code. Adv Sci (Weinh) e2412279. 10.1002/advs.202412279 PMC11848527 39736107 · DOI
  65. [77]Mesko B, Topol EJ (2023) The imperative for regulatory oversight of large Language models (or generative AI) in healthcare. NPJ Digit Med 6(1):120. 10.1038/s41746-023-00873-0 37414860 PMC10326069 · DOI · Paper page
  66. [78]Collins KM, Jiang AQ, Frieder S, Wong L, Zilka M, Bhatt U, Lukasiewicz T, Wu Y, Tenenbaum JB, Hart W, Gowers T, Li W, Weller A, Jamnik M (2024) Evaluating Language models for mathematics through interactions. Proc Natl Acad Sci U S A 121(24):e2318124121. 10.1073/pnas.2318124121 38830100 PMC11181017 · DOI
  67. [79]Li M, Sun J, Tan X (2024) Evaluating the effectiveness of large Language models in abstract screening: a comparative analysis. Syst Reviews 13(1):219. 10.1186/s13643-024-02609-x PMC11337893 39169386 · DOI
  68. [80]Oami T, Okada Y, Nakada T-a (2024) Performance of a large Language model in screening citations. JAMA Netw Open 7(7):e2420496–e2420496. 10.1001/jamanetworkopen.2024.20496 38976267 PMC11231796 · DOI
  69. [81]Lucas HC, Upperman JS, Robinson JR (2024) A systematic review of large Language models and their implications in medical education. Med Educ 58(11):1276–1285. 10.1111/medu.15402 38639098 · DOI · Paper page
  70. [82]Tam TYC, Sivarajkumar S, Kapoor S, Stolyar AV, Polanska K, McCarthy KR, Osterhoudt H, Wu X, Visweswaran S, Fu S, Mathur P, Cacciamani GE, Sun C, Peng Y, Wang Y (2024) A framework for human evaluation of large Language models in healthcare derived from literature review. Npj Digit Med 7(1):258. 10.1038/s41746-024-01258-7 39333376 PMC11437138 · DOI · Paper page
  71. [83]Emsley R (2023) ChatGPT: these are not hallucinations - they’re fabrications and falsifications. Schizophrenia (Heidelb) 9(1):52. 10.1038/s41537-023-00379-4 37598184 PMC10439949 · DOI · Paper page

Cited by

Sorted by citations

Authors
Androulakis, I. P., Cucurull-Sanchez, L., Kondic, A., Mehta, K., Pichardo, C., Pryor, M., & Renardy, M.
Affiliations
  • Rutgers, The State University of New Jersey (US)
  • Bristol-Myers Squibb (United States) (US)
  • Kyowa Kirin (United States) (US)
  • GlaxoSmithKline (United States) (US)
Corresponding author
Androulakis, Ioannis P.
ORCID
Venue
Journal of Pharmacokinetics and Pharmacodynamics
Publisher
Springer Science and Business Media LLC
Published
2025-06-16
Volume
52
Issue
4
Pages
36-36
ISSN
  • 1567-567X (ISSN-L)
  • 1573-8744
DOI
10.1007/s10928-025-09984-5
PMC
PMC12170689
OpenAlex
W4411330311
Cited by
22
References
8
Citations by year
2026: 16 · 2025: 6
FWCI
1.36
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Pharmacokinetics and Pharmacodynamics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
National Institute of General Medical Sciences (R35GM131800)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
quantitative systems pharmacology, large language models, machine learning, artificial general intelligence, surrogate models, digital twin, model validation, model interpretability
MeSH terms
Machine Learning, Drug Development, Large Language Models, Artificial Intelligence, Models, Biological, Systems Biology, Data Mining

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