Home An introduction to quantitative systems pharmacology for chemical engineers
Article
Licensed
Unlicensed Requires Authentication

An introduction to quantitative systems pharmacology for chemical engineers

  • Roberto A. Abbiati ORCID logo EMAIL logo and Cesar Pichardo
Published/Copyright: February 21, 2025
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Physical Sciences Reviews
From the journal Physical Sciences Reviews Volume 10 Issue 1-2

Abstract

Quantitative systems pharmacology (QSP) is a discipline that integrates experimental and mathematical modelling practice to perform a variety of analysis in the pharmaceutical research and development space. As the pharma industry strives for leaner product development, reduction of time and costs, and the implementation of the personalized medicine ambition, modeling and simulation approaches are recognized as pivotal components to achieve these goals. Since there are notable similarities between chemical engineering modelling approaches and those of QSP, our aspiration for this chapter is setting the stage for further contribution by engineers in this space. To this end, we provide a concise overview of the various modelling applications currently employed across the pharmaceutical research and development value chain. We then focus on QSP, detailing specific research areas that benefit from its use, the relevant mathematical modelling techniques, and emphasizing its parallels with chemical engineering modelling. Finally, we illustrate two concrete examples of QSP applications in oncological drug development.

Keywords: quantitative systems pharmacology; QSP; chemical engineering; modelling and simulation; drug development; pharma
Corresponding author: Roberto A. Abbiati, Roche Pharma Research and Early Development, Predictive Modeling and Data Analytics, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland, E-mail:

Acknowledgments

The authors would like to thank the editors Prof. D. Bogle and Prof. T. Sosnowski for their guidance and review of this article before its publication.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

References

1. Leil, TA, Bertz, R. Quantitative systems pharmacology can reduce attrition and improve productivity in pharmaceutical research and development. Front Pharmacol 2014;5:247. https://doi.org/10.3389/fphar.2014.00247.Search in Google Scholar PubMed PubMed Central

2. Bleicher, LS, van Daelen, T, Honeycutt, JD, Hassan, M, Chandrasekhar, J, Shirley, W, et al.. Enhanced utility of AI/ML methods during lead optimization by inclusion of 3D ligand information. Front Drug Discov 2022;2. https://doi.org/10.3389/fddsv.2022.1074797.Search in Google Scholar

3. Lin, X, Li, X, Lin, X. A review on applications of computational methods in drug screening and design. Molecules 2020;25:1375. https://doi.org/10.3390/molecules25061375.Search in Google Scholar PubMed PubMed Central

4. Temml, V, Kutil, Z. Structure-based molecular modeling in SAR analysis and lead optimization. Comput Struct Biotechnol J 2021;19:1431–44. https://doi.org/10.1016/j.csbj.2021.02.018.Search in Google Scholar PubMed PubMed Central

5. European Medicine Agency. ICH guideline M3(R2) on non-clinical safety studies for the conduct of human clinical trials and marketing authorisation for pharmaceuticals; 2009.Search in Google Scholar

6. Zou, P, Yu, Y, Zheng, N, Yang, Y, Paholak, HJ, Yu, LX, et al.. Applications of human pharmacokinetic prediction in first-in-human dose estimation. AAPS J 2012;14:262–81. https://doi.org/10.1208/s12248-012-9332-y.Search in Google Scholar PubMed PubMed Central

7. Kimko, H, Pinheiro, J. Model-based clinical drug development in the past, present and future: a commentary. Br J Clin Pharmacol 2015;79:108–16. https://doi.org/10.1111/bcp.12341.Search in Google Scholar PubMed PubMed Central

8. Lalonde, RL, Kowalski, KG, Hutmacher, MM, Ewy, W, Nichols, DJ, Milligan, PA, et al.. Model-based drug development. Clin Pharmacol Ther 2007;82:21–32. https://doi.org/10.1038/sj.clpt.6100235.Search in Google Scholar PubMed

9. Milligan, PA, Brown, MJ, Marchant, B, Martin, SW, van der Graaf, PH, Benson, N, et al.. Model-based drug development: a rational approach to efficiently accelerate drug development. Clin Pharmacol Therapeut 2013;93:502–14. https://doi.org/10.1038/clpt.2013.54.Search in Google Scholar PubMed

10. Azer, K, Kaddi, CD, Barrett, JS, Bai, JPF, McQuade, ST, Merrill, NJ, et al.. History and future perspectives on the discipline of quantitative systems pharmacology modeling and its applications. Front Physiol 2021;12. https://doi.org/10.3389/fphys.2021.637999.Search in Google Scholar PubMed PubMed Central

11. Sorger, PK, Allerheiligen, SRB, Abernethy, DR, Altman, RB, Brouwer, KLR, Califano, A, et al.. Quantitative and systems pharmacology in the post-genomic era: new approaches to discovering drugs and understanding therapeutic mechanisms; 2011.Search in Google Scholar

12. Zhang, L, Sinha, V, Forgue, ST, Callies, S, Ni, L, Peck, R, et al.. Model-based drug development: the road to quantitative pharmacology. J Pharmacokinet Pharmacodyn 2006;33:369–93. https://doi.org/10.1007/s10928-006-9010-8.Search in Google Scholar PubMed

13. Agoram, BM, Demin, O. Integration not isolation: arguing the case for quantitative and systems pharmacology in drug discovery and development. Drug Discov Today 2011;16:1031–6. https://doi.org/10.1016/j.drudis.2011.10.001.Search in Google Scholar PubMed

14. Allerheiligen, SRB. Next-generation model-based drug discovery and development: quantitative and systems pharmacology. Clin Pharmacol Ther 2010;88:135–7. https://doi.org/10.1038/clpt.2010.81.Search in Google Scholar PubMed

15. Manca, D, editor. Quantitative systems pharmacology: models and model-based systems with applications. In: Computer aided chemical engineering. Amsterdam: Elsevier; 2018, 42.Search in Google Scholar

16. Fogler, HS. Elements of chemical reaction engineering, 5th ed. London, UK: Prentice Hall; 2016.Search in Google Scholar

17. Aghamiri, SS, Amin, R, Helikar, T. Recent applications of quantitative systems pharmacology and machine learning models across diseases. J Pharmacokinet Pharmacodyn 2022;49:19–37. https://doi.org/10.1007/s10928-021-09790-9.Search in Google Scholar PubMed PubMed Central

18. Musante, C, Ramanujan, S, Schmidt, B, Ghobrial, O, Lu, J, Heatherington, A. Quantitative systems pharmacology: a case for disease models. Clin Pharmacol Ther 2017;101:24–7. https://doi.org/10.1002/cpt.528.Search in Google Scholar PubMed PubMed Central

19. Strimbu, K, Tavel, JA. What are biomarkers? Curr Opin HIV AIDS 2010;5:463–6. https://doi.org/10.1097/COH.0b013e32833ed177.Search in Google Scholar PubMed PubMed Central

20. Pletcher, MJ, Pignone, M. Evaluating the clinical utility of a biomarker. Circulation 2011;123:1116–24. https://doi.org/10.1161/CIRCULATIONAHA.110.943860.Search in Google Scholar PubMed PubMed Central

21. Visser, Sa G, de Alwis, DP, Kerbusch, T, Stone, JA, Allerheiligen, SRB. Implementation of quantitative and systems pharmacology in large pharma. CPT Pharmacometrics Syst Pharmacol 2014;3:142. https://doi.org/10.1038/psp.2014.40.Search in Google Scholar PubMed PubMed Central

22. Arulraj, T, Wang, H, Emens, LA, Santa-Maria, CA, Popel, AS. A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition. Sci Adv 2023;9:eadg0289. https://doi.org/10.1126/sciadv.adg0289.Search in Google Scholar PubMed PubMed Central

23. Allen, RJ, Rieger, TR, Musante, CJ. Efficient generation and selection of virtual populations in quantitative systems pharmacology models. CPT Pharmacometrics Syst Pharmacol 2016;5:140–6. https://doi.org/10.1002/psp4.12063.Search in Google Scholar PubMed PubMed Central

24. Cheng, Y, Straube, R, Alnaif, AE, Huang, L, Leil, TA, Schmidt, BJ. Virtual populations for quantitative systems pharmacology models. In: Bai, JPF, Hur, J, editors. Systems medicine. New York, NY: Springer US; 2022:129–79 pp.10.1007/978-1-0716-2265-0_8Search in Google Scholar PubMed

25. Wang, H, Arulraj, T, Kimko, H, Popel, AS. Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition. npj Precis Oncol 2023;7:1–14. https://doi.org/10.1038/s41698-023-00405-9.Search in Google Scholar PubMed PubMed Central

26. Matthews, RJ, Hollinshead, D, Morrison, D, van der Graaf, PH, Kierzek, AM. QSP designer: quantitative systems pharmacology modeling with modular biological process map notation and multiple language code generation. CPT Pharmacometrics Syst Pharmacol 2023;12:889–903. https://doi.org/10.1002/psp4.12972.Search in Google Scholar PubMed PubMed Central

27. Gong, C, Ruiz-Martinez, A, Kimko, H, Popel, AS. A spatial quantitative systems pharmacology platform spQSP-IO for simulations of tumor-immune interactions and effects of checkpoint inhibitor immunotherapy. Cancers 2021;13:3751. https://doi.org/10.3390/cancers13153751.Search in Google Scholar PubMed PubMed Central

28. Ruiz-Martinez, A, Gong, C, Wang, H, Sové, RJ, Mi, H, Kimko, H, et al.. Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model. PLoS Comput Biol 2022;18:e1010254. https://doi.org/10.1371/journal.pcbi.1010254.Search in Google Scholar PubMed PubMed Central

29. Michaelis, L, Menten, ML. Die kinetik der invertinwirkung. Biochem Z 1913;49:333–69. Find this article online.Search in Google Scholar

30. Hill, AV. Proceedings of the physiological society: January 22, 1910. J Physiol 1910;40. https://doi.org/10.1113/jphysiol.1910.sp001386.Search in Google Scholar

31. Au, JL-S, Abbiati, RA, Wientjes, MG, Lu, Z. Target site delivery and residence of nanomedicines: application of quantitative systems pharmacology. Pharmacol Rev 2019;71:157–69. https://doi.org/10.1124/pr.118.016816.Search in Google Scholar PubMed PubMed Central

32. Flessner, MF. The transport barrier in intraperitoneal therapy. Am J Physiol Ren Physiol 2005;288:F433–42. https://doi.org/10.1152/ajprenal.00313.2004.Search in Google Scholar PubMed

33. Jain, RK, Martin, JD, Stylianopoulos, T. The role of mechanical forces in tumor growth and therapy. Annu Rev Biomed Eng 2014;16:321–46. https://doi.org/10.1146/annurev-bioeng-071813-105259.Search in Google Scholar PubMed PubMed Central

34. Morris, PD, Narracott, A, von Tengg-Kobligk, H, Silva Soto, DA, Hsiao, S, Lungu, A, et al.. Computational fluid dynamics modelling in cardiovascular medicine. Heart 2016;102:18–28. https://doi.org/10.1136/heartjnl-2015-308044.Search in Google Scholar PubMed PubMed Central

35. Quarteroni, A, Veneziani, A, Vergara, C. Geometric multiscale modeling of the cardiovascular system, between theory and practice. Comput Methods Appl Mech Eng 2016;302:193–252. https://doi.org/10.1016/j.cma.2016.01.007.Search in Google Scholar

36. Zheng, S, Carugo, D, Mosayyebi, A, Turney, B, Burkhard, F, Lange, D, et al.. Fluid mechanical modeling of the upper urinary tract. WIREs Mech Dis 2021;13:e1523. https://doi.org/10.1002/wsbm.1523.Search in Google Scholar PubMed

37. Cao, Y, Balthasar, JP, Jusko, WJ. Second-generation minimal physiologically-based pharmacokinetic model for monoclonal antibodies. J Pharmacokinet Pharmacodyn 2013;40:597–607. https://doi.org/10.1007/s10928-013-9332-2.Search in Google Scholar PubMed PubMed Central

38. Harms, BD, Kearns, JD, Su, SV, Kohli, N, Nielsen, UB, Schoeberl, B. Optimizing properties of antireceptor antibodies using kinetic computational models and experiments. Methods Enzymol 2012;502:67–87. https://doi.org/10.1016/B978-0-12-416039-2.00004-5.Search in Google Scholar PubMed

39. Stein, A, Franklin, JL, Chia, VM, Arrindell, D, Kormany, W, Wright, J, et al.. Benefit–risk assessment of blinatumomab in the treatment of relapsed/refractory B-cell precursor acute lymphoblastic leukemia. Drug Saf 2019;42:587–601. https://doi.org/10.1007/s40264-018-0760-1.Search in Google Scholar PubMed PubMed Central

40. Wang, L. Trial suggests expanded role for blinatumomab in treating ALL. Cancer Currents Blog 2023. https://www.cancer.gov/news-events/cancer-currents-blog/2023/blincyto-leukemia-minimal-residual-disease [Accessed 13 Aug 2024].Search in Google Scholar

41. Betts, A, van der Graaf, PH. Mechanistic quantitative pharmacology strategies for the early clinical development of bispecific antibodies in oncology. Clin Pharmacol Ther 2020;108:528–41. https://doi.org/10.1002/cpt.1961.Search in Google Scholar PubMed PubMed Central

42. Flowers, D, Bassen, D, Kapitanov, GI, Marcantonio, D, Burke, JM, Apgar, JF, et al.. A next generation mathematical model for the in vitro to clinical translation of T-cell engagers. J Pharmacokinet Pharmacodyn 2023;50:215–27. https://doi.org/10.1007/s10928-023-09846-y.Search in Google Scholar PubMed

43. Li, R, Dere, E, Kwong, M, Fei, M, Dave, R, Masih, S, et al.. A bispecific modeling framework enables the prediction of efficacy, toxicity, and optimal molecular design of bispecific antibodies targeting MerTK. AAPS J 2024;26:11. https://doi.org/10.1208/s12248-023-00881-8.Search in Google Scholar PubMed

44. Weddell, J. Mechanistically modeling peripheral cytokine dynamics following bispecific dosing in solid tumors. CPT Pharmacometrics Syst Pharmacol 2023;12:1726–37. https://doi.org/10.1002/psp4.12928.Search in Google Scholar PubMed PubMed Central

45. Sanford, M. Blinatumomab: first global approval. Drugs 2015;75:321–7. https://doi.org/10.1007/s40265-015-0356-3.Search in Google Scholar PubMed

46. Chen, X, Kamperschroer, C, Wong, G, Xuan, D. A modeling framework to characterize cytokine release upon T-cell-engaging bispecific antibody treatment: methodology and opportunities. Clin Transl Sci 2019;12:600–8. https://doi.org/10.1111/cts.12662.Search in Google Scholar PubMed PubMed Central

47. Hosseini, I, Gadkar, K, Stefanich, E, Li, C-C, Sun, LL, Chu, Y-W, et al.. Mitigating the risk of cytokine release syndrome in a Phase I trial of CD20/CD3 bispecific antibody mosunetuzumab in NHL: impact of translational system modeling. NPJ Syst Biol Appl 2020;6:28. https://doi.org/10.1038/s41540-020-00145-7.Search in Google Scholar PubMed PubMed Central

48. Jiang, X, Chen, X, Jaiprasart, P, Carpenter, TJ, Zhou, R, Wang, W. Development of a minimal physiologically-based pharmacokinetic/pharmacodynamic model to characterize target cell depletion and cytokine release for T cell-redirecting bispecific agents in humans. Eur J Pharmaceut Sci 2020;146:105260. https://doi.org/10.1016/j.ejps.2020.105260.Search in Google Scholar PubMed

49. Li, R, Dere, E, Kwong, M, Fei, M, Dave, R, Masih, S, et al.. A bispecific modeling framework enables the prediction of efficacy, toxicity, and optimal molecular design of bispecific antibodies targeting MerTK. AAPS J 2024;26:11. https://doi.org/10.1208/s12248-023-00881-8.Search in Google Scholar PubMed

50. Zhu, M, Wu, B, Brandl, C, Johnson, J, Wolf, A, Chow, A, et al.. Blinatumomab, a bispecific T-cell engager (BiTE(®)) for CD-19 targeted cancer immunotherapy: clinical pharmacology and its implications. Clin Pharmacokinet 2016;55:1271–88. https://doi.org/10.1007/s40262-016-0405-4.Search in Google Scholar PubMed

51. Betts, A, Haddish-Berhane, N, Shah, DK, Van Der Graaf, PH, Barletta, F, King, L, et al., 2019. A translational quantitative systems pharmacology model for CD3 bispecific molecules: application to quantify T cell-mediated tumor cell killing by P-Cadherin LP DART®. AAPS J 21, 66. https://doi.org/10.1208/s12248-019-0332-z.Search in Google Scholar PubMed PubMed Central

52. Carpentier, B, Pierobon, P, Hivroz, C, Henry, N. T-cell artificial focal triggering tools: linking surface interactions with cell response. PLoS One 2009;4:e4784. https://doi.org/10.1371/journal.pone.0004784.Search in Google Scholar PubMed PubMed Central

53. Nicolas, L, Monneret, G, Debard, AL, Blesius, A, Gutowski, MC, Salles, G, et al.. Human gammadelta T cells express a higher TCR/CD3 complex density than alphabeta T cells. Clin Immunol 2001;98:358–63. https://doi.org/10.1006/clim.2000.4978.Search in Google Scholar PubMed

54. Ginaldi, L, De Martinis, M, Matutes, E, Farahat, N, Morilla, R, Catovsky, D. Levels of expression of CD19 and CD20 in chronic B cell leukaemias. J Clin Pathol 1998;51:364–9. https://doi.org/10.1136/jcp.51.5.364.Search in Google Scholar PubMed PubMed Central

55. Dreier, T, Lorenczewski, G, Brandl, C, Hoffmann, P, Syring, U, Hanakam, F, et al.. Extremely potent, rapid and costimulation-independent cytotoxic T-cell response against lymphoma cells catalyzed by a single-chain bispecific antibody. Int J Cancer 2002;100:690–7. https://doi.org/10.1002/ijc.10557.Search in Google Scholar PubMed

56. Jiang, X, Chen, X, Carpenter, TJ, Wang, J, Zhou, R, Davis, HM, et al.. Development of a Target cell-Biologics-Effector cell (TBE) complex-based cell killing model to characterize target cell depletion by T cell redirecting bispecific agents. mAbs 2018;10:876–89. https://doi.org/10.1080/19420862.2018.1480299.Search in Google Scholar PubMed PubMed Central

57. Carpio, C, Bouabdallah, R, Ysebaert, L, Sancho, J-M, Salles, G, Cordoba, R, et al.. Avadomide monotherapy in relapsed/refractory DLBCL: safety, efficacy, and a predictive gene classifier. Blood 2020;135:996–1007. https://doi.org/10.1182/blood.2019002395.Search in Google Scholar PubMed PubMed Central

58. Abbiati, RA, Pourdehnad, M, Carrancio, S, Pierce, DW, Kasibhatla, S, McConnell, M, et al.. Quantitative systems pharmacology modeling of avadomide-induced neutropenia enables virtual clinical dose and schedule finding studies. AAPS J 2021;23:103. https://doi.org/10.1208/s12248-021-00623-8.Search in Google Scholar PubMed PubMed Central

59. Wang, Y, Huang, SM. Commentary on fit-for-purpose models for regulatory applications. J Pharmaceut Sci 2019;108:18–20. https://doi.org/10.1016/j.xphs.2018.09.009.Search in Google Scholar PubMed

60. Chiu, H, Trisal, P, Bjorklund, C, Carrancio, S, Toraño, EG, Guarinos, C, et al.. Combination lenalidomide-rituximab immunotherapy activates anti-tumour immunity and induces tumour cell death by complementary mechanisms of action in follicular lymphoma. Br J Haematol 2019;185:240–53. https://doi.org/10.1111/bjh.15797.Search in Google Scholar PubMed PubMed Central

61. Felmlee, MA, Morris, ME, Mager, DE. Mechanism-based pharmacodynamic modeling. Methods Mol Biol 2012;929:583–600. https://doi.org/10.1007/978-1-62703-050-2_21.Search in Google Scholar PubMed PubMed Central

62. Goutelle, S, Maurin, M, Rougier, F, Barbaut, X, Bourguignon, L, Ducher, M, et al.. The Hill equation: a review of its capabilities in pharmacological modelling. Fund Clin Pharmacol 2008;22:633–48. https://doi.org/10.1111/j.1472-8206.2008.00633.x.Search in Google Scholar PubMed

63. Bassingthwaighte, JB, Butterworth, E, Jardine, B, Raymond, GM. Compartmental modeling in the analysis of biological systems. In: Reisfeld, B, Mayeno, AN, editors. Computational toxicology. Totowa, NJ: Humana Press; 2012, vol I:391–438 pp.10.1007/978-1-62703-050-2_17Search in Google Scholar PubMed

64. Mould, D, Upton, R. Basic concepts in population modeling, simulation, and model-based drug development. CPT Pharmacometrics Syst Pharmacol 2012;1:6. https://doi.org/10.1038/psp.2012.4.Search in Google Scholar PubMed PubMed Central

65. Upton, RN, Foster, DJR, Abuhelwa, AY. An introduction to physiologically-based pharmacokinetic models. Pediatr Anesth 2016;26:1036–46. https://doi.org/10.1111/pan.12995.Search in Google Scholar PubMed

Received: 2024-09-27
Accepted: 2024-12-03
Published Online: 2025-02-21

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Reviews
  3. Potential development of thermally stable polycrystalline photovoltaic modules utilizing biocomposite materials
  4. Mathematical modelling of hollow-fiber haemodialysis modules
  5. Computational modelling in liver system and liver disease
  6. An introduction to quantitative systems pharmacology for chemical engineers
  7. Macroscopic transport models for drugs and vehicles in cancer tissues
  8. Engaging pre-service teachers in an indigenous activity to investigate sustainability and green practices in palm oil production
  9. Performance of 6 × 6 CNT transistor array using composite nanomaterials for biomedical applications
  10. Functionalization and performance of hybrid nanocellulose from plant-based/metal oxide nanocomposites for sustainable energy applications
  11. Modelling drug permeation across the skin: a chemical engineering perspective
  12. Environmental life cycle analysis of natural fiber composites in energy sector
https://doi.org/10.1515/psr-2024-0066
Keywords for this article
quantitative systems pharmacology; QSP; chemical engineering; modelling and simulation; drug development; pharma

Articles in the same Issue

  1. Frontmatter
  2. Reviews
  3. Potential development of thermally stable polycrystalline photovoltaic modules utilizing biocomposite materials
  4. Mathematical modelling of hollow-fiber haemodialysis modules
  5. Computational modelling in liver system and liver disease
  6. An introduction to quantitative systems pharmacology for chemical engineers
  7. Macroscopic transport models for drugs and vehicles in cancer tissues
  8. Engaging pre-service teachers in an indigenous activity to investigate sustainability and green practices in palm oil production
  9. Performance of 6 × 6 CNT transistor array using composite nanomaterials for biomedical applications
  10. Functionalization and performance of hybrid nanocellulose from plant-based/metal oxide nanocomposites for sustainable energy applications
  11. Modelling drug permeation across the skin: a chemical engineering perspective
  12. Environmental life cycle analysis of natural fiber composites in energy sector
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 13.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/psr-2024-0066/html
Scroll to top button