journal article·2026-06-22Open access

A Machine Vision‐Guided Microphysiological Platform With Automated Microfluidics Enables Longitudinal Biomarker Monitoring and Emulation of Translationally Relevant Exposure Scenarios

Jibbe Keulen, Yi Zhong, Laura Dangel, Sonia Youhanna, Reza Zandi Shafagh, Jinhye Ryu, Domnica‐Gabriela Gurau, Mathias Haag, Thomas E. Mürdter, Yingxin Liang, Iro Beligiani, Nicole Ziegler, Sabine Willems, Nayere Taebnia, Wouter van der Wijngaart, Volker M. Lauschke

Abstract

ABSTRACT Low success rates in clinical drug development can be largely attributed to the poor predictive power of existing preclinical models. Microphysiological systems (MPS) have greatly advanced in vitro modeling; however, current platforms do not adequately support long‐term sampling and often fail to recapitulate nutrient and drug exposure dynamics. To address these limitations, we established a machine vision‐guided MPS with real‐time fluidic control that enables fully automated periodic sampling with high temporal resolution, media replenishment, and programmable dosing, allowing for the simulation of dynamic nutritional or pharmacological exposure scenarios. We showcase the system's capability by emulating physiological insulin profiles and repeated‐dose pharmacokinetic exposures over multiple weeks. Furthermore, pharmacokinetically accurate acetaminophen exposure in 3D primary human liver spheroids mimicking an acute overdose rapidly induced liver toxicity, as evidenced by aminotransferase release, cytokine secretion and a drop in cellular ATP. In contrast, dose‐equivalent constant exposure patterns did not elicit detectable hepatotoxicity. Mechanistically, targeted proteomics of sampled supernatants and Cell Painting revealed that toxicity was paralleled by disrupted lipid homeostasis, loss of tight junctions and extracellular matrix remodeling. These results demonstrate the robustness and versatility of the machine vision‐guided automated microphysiological platform and underscore the importance of incorporating drug exposure dynamics for mechanistic toxicology.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: 3D Printing in Biomedical Research · Pharmacogenetics and Drug Metabolism · Drug-Induced Hepatotoxicity and Protection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Dr. Margarete Fischer‐Bosch Institute of Clinical Pharmacology Stuttgart Germany

University of Tübingen Tübingen Germany

Department of Physiology and Pharmacology and Center for Molecular Medicine Karolinska Institutet and University Hospital Stockholm Sweden

Division of Micro‐ and Nanosystems KTH Royal Institute of Technology Stockholm Sweden

Pharmaceutical Informatics Institute College of Pharmaceutical Sciences Zhejiang University Hangzhou China

Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) Frankfurt am Main Germany

Department of Pharmacy the Second Xiangya Hospital Central South University Changsha China

Funding

Vetenskapsrådet · Novo Nordisk Fonden · Cancerfonden · National Natural Science Foundation of China

References (80)

Show source-provided references
1. 10.1007/s40273‐021‐01065‐y10.1007/s40273‐021‐01065‐y ↗
2. 10.1016/j.drudis.2025.10429110.1016/j.drudis.2025.104291 ↗
3. 10.1038/s41467‐025‐64552‐210.1038/s41467‐025‐64552‐2 ↗
4. 10.1001/jamanetworkopen.2024.1544510.1001/jamanetworkopen.2024.15445 ↗
5. 10.1080/17460441.2021.197225510.1080/17460441.2021.1972255 ↗
6. 10.1001/jama.296.14.173110.1001/jama.296.14.1731 ↗
7. 10.1136/bmj.39048.407928.BE10.1136/bmj.39048.407928.BE ↗
8. 10.1016/j.yrtph.2012.09.00210.1016/j.yrtph.2012.09.002 ↗
9. 10.1177/02611929140420030610.1177/026119291404200306 ↗
10. 10.1038/nrd430910.1038/nrd4309 ↗
11. 10.1038/s41580‐020‐0259‐310.1038/s41580‐020‐0259‐3 ↗
12. 10.1124/pharmrev.120.00023810.1124/pharmrev.120.000238 ↗
13. 10.1124/dmd.123.00151010.1124/dmd.123.001510 ↗
14. 10.1093/toxsci/kfae12210.1093/toxsci/kfae122 ↗
15. 10.1016/j.amjmed.2023.03.03310.1016/j.amjmed.2023.03.033 ↗
16. 10.1038/s41551‐023‐01154‐710.1038/s41551‐023‐01154‐7 ↗
17. 10.1016/j.yrtph.2024.10568310.1016/j.yrtph.2024.105683 ↗
18. 10.1038/s41576‐022‐00466‐910.1038/s41576‐022‐00466‐9 ↗
19. 10.1021/acssensors.4c0000410.1021/acssensors.4c00004 ↗
20. 10.1038/msb.2013.1910.1038/msb.2013.19 ↗
21. 10.1002/advs.20220336810.1002/advs.202203368 ↗
22. E.Xie W.Wang Z.Yu A.Anandkumar J. M.Alvarez andP.Luo SegFormer: Simple and Efficient Design for Semantic Segmentation With Transformers preprint arXiv (2021) https://doi.org/10.48550/arXiv.2105.15203.
23. 10.1038/srep2518710.1038/srep25187 ↗
24. 10.1111/bcp.1590310.1111/bcp.15903 ↗
25. 10.1124/dmd.110.03388610.1124/dmd.110.033886 ↗
26. 10.1038/s41592‐025‐02595‐510.1038/s41592‐025‐02595‐5 ↗
27. 10.1038/s41592‐025‐02887‐w10.1038/s41592‐025‐02887‐w ↗
28. 10.1186/gb‐2006‐7‐10‐r10010.1186/gb‐2006‐7‐10‐r100 ↗
29. O.Siméoni H. V.Vo M.Seitzer et al. DINOv3 preprint arXiv (2025) https://doi.org/10.48550/arXiv.2508.10104.
30. 10.1038/s41586‐024‐08328‐610.1038/s41586‐024‐08328‐6 ↗
31. 10.1002/adhm.20230356110.1002/adhm.202303561 ↗
32. 10.7326/0003‐4819‐137‐12‐200212170‐0000710.7326/0003‐4819‐137‐12‐200212170‐00007 ↗
33. 10.1002/hep.2094810.1002/hep.20948 ↗
34. 10.3109/15563650.2015.107796810.3109/15563650.2015.1077968 ↗
35. 10.1016/j.cbi.2015.10.01910.1016/j.cbi.2015.10.019 ↗
36. 10.1038/srep3754110.1038/srep37541 ↗
37. 10.1016/j.jhepr.2020.10014510.1016/j.jhepr.2020.100145 ↗
38. 10.1007/s00204‐019‐02597‐110.1007/s00204‐019‐02597‐1 ↗
39. 10.1063/5.017944410.1063/5.0179444 ↗
40. 10.1146/annurev-pharmtox-010617-05272210.1146/annurev-pharmtox-010617-052722 ↗
41. 10.1039/D3LC00876B10.1039/D3LC00876B ↗
42. 10.1146/annurev‐pharmtox‐061724‐08062110.1146/annurev‐pharmtox‐061724‐080621 ↗
43. 10.1208/s12248‐020‐0430‐y10.1208/s12248‐020‐0430‐y ↗
44. 10.1371/journal.pbio.300162410.1371/journal.pbio.3001624 ↗
45. 10.1124/dmd.116.07436910.1124/dmd.116.074369 ↗
46. 10.1096/fj.201601375R10.1096/fj.201601375R ↗
47. 10.1124/dmd.120.00034010.1124/dmd.120.000340 ↗
48. 10.1124/dmd.123.00163810.1124/dmd.123.001638 ↗
49. 10.1002/cpt.186010.1002/cpt.1860 ↗
50. 10.1016/j.bcp.2023.11575510.1016/j.bcp.2023.115755 ↗
51. 10.1093/toxsci/kfy05810.1093/toxsci/kfy058 ↗
52. 10.1007/s00204‐017‐2002‐110.1007/s00204‐017‐2002‐1 ↗
53. 10.1002/advs.20200024810.1002/advs.202000248 ↗
54. 10.1097/HEP.000000000000064810.1097/HEP.0000000000000648 ↗
55. 10.1038/srep3543410.1038/srep35434 ↗
56. 10.3390/cells904096410.3390/cells9040964 ↗
57. 10.1096/fj.202001989RR10.1096/fj.202001989RR ↗
58. 10.1002/advs.20240757210.1002/advs.202407572 ↗
59. 10.1089/ten.tec.2018.013410.1089/ten.tec.2018.0134 ↗
60. 10.1002/adbi.20250033710.1002/adbi.202500337 ↗
61. 10.1016/j.tem.2024.07.01910.1016/j.tem.2024.07.019 ↗
62. 10.1016/j.celrep.2024.11391810.1016/j.celrep.2024.113918 ↗
63. 10.1002/SMMD.2022003010.1002/SMMD.20220030 ↗
64. 10.34133/research.089810.34133/research.0898 ↗
65. 10.1016/j.actbio.2023.09.02210.1016/j.actbio.2023.09.022 ↗
66. 10.1039/b612140c10.1039/b612140c ↗
67. 10.1021/acsami.1c0763910.1021/acsami.1c07639 ↗
68. 10.1016/j.drudis.2011.07.00310.1016/j.drudis.2011.07.003 ↗
69. 10.1007/s00204‐014‐1432‐210.1007/s00204‐014‐1432‐2 ↗
70. 10.1016/j.apsb.2021.09.02310.1016/j.apsb.2021.09.023 ↗
71. 10.1007/s00204‐012‐0968‐210.1007/s00204‐012‐0968‐2 ↗
72. 10.1093/toxsci/kfx28910.1093/toxsci/kfx289 ↗
73. 10.1007/s00204‐019‐02427‐410.1007/s00204‐019‐02427‐4 ↗
74. 10.1007/s00204‐020‐02682‐w10.1007/s00204‐020‐02682‐w ↗
75. 10.1093/toxsci/kfs12210.1093/toxsci/kfs122 ↗
76. 10.1016/j.biomaterials.2012.11.02910.1016/j.biomaterials.2012.11.029 ↗
77. 10.1002/jat.309010.1002/jat.3090 ↗
78. 10.1124/dmd.123.00128110.1124/dmd.123.001281 ↗
79. 10.1002/cpt.296910.1002/cpt.2969 ↗
80. Immune Mechanisms in Acetaminophen‐Induced Acute Liver Failure