Abstract
Quantitative Systems Pharmacology (QSP) has emerged as a powerful ensemble of approaches aiming at developing integrated mathematical and computational models elucidating the complex interactions between pharmacology, physiology, and disease. As the field grows and matures its applications expand beyond the boundaries of research and development and slowly enter the decision making and regulatory arenas. However, widespread acceptance and eventual adoption of a new modeling approach requires assessment criteria and quantifiable metrics that establish credibility and increase confidence in model predictions. QSP aims to provide an integrated understanding of pathology in the context of therapeutic interventions. Because of its ambitious nature and the fact that QSP emerged in an uncoordinated manner as a result of activities distributed across organizations and academic institutions, high entropy characterizes the tools, methods, and computational methodologies and approaches used. The eventual acceptance of QSP model predictions as supporting material for an application to a regulatory agency will require that two key aspects are considered: (1) increase confidence in the QSP framework, which drives standardization and assessment; and (2) careful articulation of the expectations. Both rely heavily on our ability to rigorously and consistently assess QSP models. In this manuscript, we wish to discuss the meaning and purpose of such an assessment in the context of QSP model development and elaborate on the differentiating features of QSP that render such an endeavor challenging. We argue that QSP establishes a conceptual, integrative framework rather than a specific and well-defined computational methodology. QSP elicits the use of a wide variety of modeling and computational methodologies optimized with respect to specific applications and available data modalities, which exceed the data structures employed by chemometrics and PK/PD models. While the range of options fosters creativity and promises to substantially advance our ability to design pharmaceutical interventions rationally and optimally, our expectations of QSP models need to be clearly articulated and agreed on, with assessment emphasizing the scope of QSP studies rather than the methods used. Nevertheless, QSP should not be considered an independent approach, rather one of many in the broader continuum of computational models.
Similar content being viewed by others
Notes
In (Hosseini, Feigelman et al. [61]) the environment was MATLAB whereas the models were assumed to be expressed in the form of standard ODEs.
References
Azer K, Kaddi CD, Barrett JS, Bai JPF, McQuade ST, Merrill NJ, Piccoli B, Neves-Zaph S, Marchetti L, Lombardo R, Parolo S, Immanuel SRC, Baliga NS (2021) History and future perspectives on the discipline of quantitative systems pharmacology modeling and its applications. Front Physiol 12:637999. https://doi.org/10.3389/fphys.2021.637999
Chae D (2020) Introduction to dynamical systems analysis in quantitative systems pharmacology: basic concepts and applications. Transl Clin Pharmacol 28(3):109–125. https://doi.org/10.12793/tcp.2020.28.e12
Androulakis IP (2016) Quantitative systems pharmacology: a framework for context. Curr Pharmacol Rep. https://doi.org/10.1007/s40495-016-0058-x
Rao RT, Scherholz ML, Hartmanshenn C, Bae SA, Androulakis IP (2016) On the analysis of complex biological supply chains: from process systems engineering to quantitative systems pharmacology. Comput Chem Eng. https://doi.org/10.1016/j.compchemeng.2017.06.003
Androulakis IP (2015) Systems engineering meets quantitative systems pharmacology: from low-level targets to engaging the host defenses. Wiley Interdiscip Rev Syst Biol Med 7(3):101–112. https://doi.org/10.1002/wsbm.1294
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.
Vodovotz Y, An G, Androulakis IP (2013) A systems engineering perspective on homeostasis and disease. Front Bioeng Biotechnol 1:6. https://doi.org/10.3389/fbioe.2013.00006
Danhof M (2016) Systems pharmacology—towards the modeling of network interactions. Eur J Pharm Sci 94:4–14. https://doi.org/10.1016/j.ejps.2016.04.027
Kitano H (2010) Grand challenges in systems physiology. Front Physiol 1:3. https://doi.org/10.3389/fphys.2010.00003
van der Greef J, McBurney RN (2005) Rescuing drug discovery: in vivo systems pathology and systems pharmacology. Nat Rev Drug Discov 4(12):961–967. https://doi.org/10.1038/nrd1904
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. https://doi.org/10.1016/j.csbj.2016.09.002
Scheff JD, Kamisoglu K, Androulakis IP (2016) Mechanistic modeling of inflammation. In: Mager DE, Kimko HHC (eds) Systems pharmacology and pharmacodynamics. Springer International Publishing, Cham, pp 325–352. https://doi.org/10.1007/978-3-319-44534-2_15
Ayyar VS, Jusko W (2020) Transitioning from basic towards systems pharmacodynamic models: lessons from corticosteroids. Pharmacol Rev 72(1):25
Morrison TM, Hariharan P, Funkhouser CM, Afshari P, Goodin M, Horner M (2019) Assessing computational model credibility using a risk-based framework: application to hemolysis in centrifugal blood pumps. ASAIO J 65(4):349–360. https://doi.org/10.1097/MAT.0000000000000996
Ramanujan S, Chan JR, Friedrich CM, Thalhauser CJ (2019) A flexible approach for context-dependent assessment of quantitative systems pharmacology models. CPT Pharmacomet Syst Pharmacol 8(6):340–343. https://doi.org/10.1002/psp4.12409
Gross F, MacLeod M (2017) Prospects and problems for standardizing model validation in systems biology. Prog Biophys Mol Biol 129:3–12. https://doi.org/10.1016/j.pbiomolbio.2017.01.003
Stadter P, Schalte Y, Schmiester L, Hasenauer J, Stapor PL (2021) Benchmarking of numerical integration methods for ODE models of biological systems. Sci Rep 11(1):2696. https://doi.org/10.1038/s41598-021-82196-2
Degasperi A, Fey D, Kholodenko BN (2017) Performance of objective functions and optimisation procedures for parameter estimation in system biology models. NPJ Syst Biol Appl 3:20. https://doi.org/10.1038/s41540-017-0023-2
Mazzia F, Cash JR, Soetaert K (2012) A test set for stiff initial value problem solvers in the open source software R: package deTestSet. J Comput Appl Math 236(16):4119–4131. https://doi.org/10.1016/j.cam.2012.03.014
Floudas CA, Pardalos PM, Adjiman CS, Esposito WR, Gumus ZH, Harding ST, Klepeis JL, Meyer CA, Schweiger CA (1999) Handbook of test problems in local and global optimization. Springer, Berlin. https://doi.org/10.1023/A:1008328212973
Geistlinger L, Csaba G, Santarelli M, Ramos M, Schiffer L, Turaga N, Law C, Davis S, Carey V, Morgan M, Zimmer R, Waldron L (2021) Toward a gold standard for benchmarking gene set enrichment analysis. Brief Bioinform 22(1):545–556. https://doi.org/10.1093/bib/bbz158
Bouzom F, Ball K, Perdaems N, Walther B (2012) Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs? Biopharm Drug Dispos 33(2):55–71. https://doi.org/10.1002/bdd.1767
Derendorf H, Meibohm B (1999) Modeling of pharmacokinetic/pharmacodynamic (PK/PD) relationships: concepts and perspectives. Pharm Res 16(2):176–185. https://doi.org/10.1023/A:1011907920641
Meibohm B, Derendorf H (1997) Basic concepts of pharmacokinetic/pharmacodynamic (PK/PD) modelling. Int J Clin Pharmacol Ther 35(10):401–413
Agoram B (2014) Evaluating systems pharmacology models is different from evaluating standard pharmacokinetic-pharmacodynamic models. CPT Pharmacomet Syst Pharmacol 3:e101. https://doi.org/10.1038/psp.2013.77
Hosseini I, Gajjala A, Bumbaca Yadav D, Sukumaran S, Ramanujan S, Paxson R, Gadkar K (2018) gPKPDSim: a SimBiology((R))-based GUI application for PKPD modeling in drug development. J Pharmacokinet Pharmacodyn 45(2):259–275. https://doi.org/10.1007/s10928-017-9562-9
Diao L, Meibohm B (2015) Tools for predicting the PK/PD of therapeutic proteins. Expert Opin Drug Metab Toxicol 11(7):1115–1125. https://doi.org/10.1517/17425255.2015.1041917
Jusko WJ (2013) Moving from basic toward systems pharmacodynamic models. J Pharm Sci 102(9):2930–2940. https://doi.org/10.1002/jps.23590
Mager DE, Wyska E, Jusko WJ (2003) Diversity of mechanism-based pharmacodynamic models. Drug Metab Dispos 31(5):510–518. https://doi.org/10.1124/dmd.31.5.510
Ghosh S, Matsuoka Y, Asai Y, Hsin KY, Kitano H (2011) Software for systems biology: from tools to integrated platforms. Nat Rev Genet 12(12):821–832. https://doi.org/10.1038/nrg3096
Machado D, Costa RS, Rocha M, Ferreira EC, Tidor B, Rocha I (2011) Modeling formalisms in systems biology. AMB Express 1(1):45. https://doi.org/10.1186/2191-0855-1-45
Gadkar K, Kirouac DC, Mager DE, van der Graaf PH, Ramanujan S (2016) A six-stage workflow for robust application of systems pharmacology. CPT Pharmacomet Syst Pharmacol 5(5):235–249. https://doi.org/10.1002/psp4.12071
Ermakov S, Schmidt BJ, Musante CJ, Thalhauser CJ (2019) A survey of software tool utilization and capabilities for quantitative systems pharmacology: what we have and what we need. CPT Pharmacomet Syst Pharmacol 8(2):62–76. https://doi.org/10.1002/psp4.12373
Gadkar K, Budha N, Baruch A, Davis JD, Fielder P, Ramanujan S (2014) A mechanistic systems pharmacology model for prediction of LDL cholesterol lowering by pcsk9 antagonism in human dyslipidemic populations. CPT Pharmacomet Syst Pharmacol 3:e149. https://doi.org/10.1038/psp.2014.47
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 Biol 11:1177625017710941. https://doi.org/10.1177/1177625017710941
Pappalardo F, Musumeci S, Motta S (2008) Modeling immune system control of atherogenesis. Bioinformatics 24(15):1715–1721. https://doi.org/10.1093/bioinformatics/btn306
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). https://doi.org/10.3390/cancers13153751
Friedrich CM (2016) A model qualification method for mechanistic physiological QSP models to support model-informed drug development. CPT: Pharmacomet Syst Pharmacol 5(2):43–53. https://doi.org/10.1002/psp4.12056
Kirouac DC (2018) How do we “Validate” a QSP model? CPT Pharmacomet Syst Pharmacol 7(9):547–548. https://doi.org/10.1002/psp4.12310
Lee G, Park C, Ahn J (2019) Novel deep learning model for more accurate prediction of drug-drug interaction effects. BMC Bioinformatics 20(1):415. https://doi.org/10.1186/s12859-019-3013-0
Guthrie NL, Carpenter J, Edwards KL, Appelbaum KJ, Dey S, Eisenberg DM, Katz DL, Berman MA (2019) Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study. BMJ Open 9(7):e030710. https://doi.org/10.1136/bmjopen-2019-030710
Yang H, Sun L, Li W, Liu G, Tang Y (2018) In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts. Front Chem 6:30. https://doi.org/10.3389/fchem.2018.00030
Wu Y, Wang G (2018) Machine learning based toxicity prediction: from chemical structural description to transcriptome analysis. Int J Mol Sci. https://doi.org/10.3390/ijms19082358
Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in drug discovery. Drug Discov Today 23(6):1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039
Zhang Y, Wong YS, Deng J, Anton C, Gabos S, Zhang W, Huang DY, Jin C (2016) Machine learning algorithms for mode-of-action classification in toxicity assessment. BioData Min 9:19. https://doi.org/10.1186/s13040-016-0098-0
McComb M, Bies R, Ramanathan M (2021) Machine learning in pharmacometrics: opportunities and challenges. Br J Clin Pharmacol. https://doi.org/10.1111/bcp.14801
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. https://doi.org/10.1007/s10928-022-09805-z
Lazarou G, Chelliah V, Small BG, Walker M, van der Graaf PH, Kierzek AM (2020) Integration of omics data sources to inform mechanistic modeling of immune-oncology therapies: a tutorial for clinical pharmacologists. Clin Pharmacol Ther 107(4):858–870. https://doi.org/10.1002/cpt.1786
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. https://doi.org/10.1007/s10928-021-09797-2
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 34:11364–83
Uddin S, Khan A, Hossain ME, Moni MA (2019) Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak 19(1):281. https://doi.org/10.1186/s12911-019-1004-8
Topp B, Trujillo ME, Sinha V (2019) Industrialization of quantitative systems pharmacology. CPT: Pharmacomet Syst Pharmacol 8(6):356–358. https://doi.org/10.1002/psp4.12427
Gauthier J, Vincent AT, Charette SJ, Derome N (2019) A brief history of bioinformatics. Brief Bioinform 20(6):1981–1996. https://doi.org/10.1093/bib/bby063
Katayama T, Arakawa K, Nakao M, Ono K, Aoki-Kinoshita KF, Yamamoto Y, Yamaguchi A, Kawashima S, Chun HW, Aerts J, Aranda B, Barboza LH, Bonnal RJ, Bruskiewich R, Bryne JC, Fernandez JM, Funahashi A, Gordon PM, Goto N, Groscurth A, Gutteridge A, Holland R, Kano Y, Kawas EA, Kerhornou A, Kibukawa E, Kinjo AR, Kuhn M, Lapp H, Lehvaslaiho H, Nakamura H, Nakamura Y, Nishizawa T, Nobata C, Noguchi T, Oinn TM, Okamoto S, Owen S, Pafilis E, Pocock M, Prins P, Ranzinger R, Reisinger F, Salwinski L, Schreiber M, Senger M, Shigemoto Y, Standley DM, Sugawara H, Tashiro T, Trelles O, Vos RA, Wilkinson MD, York W, Zmasek CM, Asai K, Takagi T (2010) The DBCLS BioHackathon: standardization and interoperability for bioinformatics web services and workflows. The DBCLS BioHackathon Consortium*. J Biomed Semantics 1 (1):8. https://doi.org/10.1186/2041-1480-1-8
Wang Y, Huang SM (2019) Commentary on fit-for-purpose models for regulatory applications. J Pharm Sci 108(1):18–20. https://doi.org/10.1016/j.xphs.2018.09.009
Yang J, Mager DE, Straubinger RM (2010) Comparison of two pharmacodynamic transduction models for the analysis of tumor therapeutic responses in model systems. AAPS J 12(1):1–10. https://doi.org/10.1208/s12248-009-9155-7
Cucurull-Sanchez L, Chappell MJ, Chelliah V, Amy Cheung SY, Derks G, Penney M, Phipps A, Malik-Sheriff RS, Timmis J, Tindall MJ, van der Graaf PH, Vicini P, Yates JWT (2019) Best practices to maximize the use and reuse of quantitative and systems pharmacology models: recommendations from the United Kingdom quantitative and systems pharmacology network. CPT Pharmacomet Syst Pharmacol 8(5):259–272. https://doi.org/10.1002/psp4.12381
Duffull SB (2016) A philosophical framework for integrating systems pharmacology models into pharmacometrics. CPT: Pharmacomet Syst Pharmacol 5(12):649–655. https://doi.org/10.1002/psp4.12148
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. https://doi.org/10.1208/s12248-017-0100-x
Drager A, Palsson BO (2014) Improving collaboration by standardization efforts in systems biology. Front Bioeng Biotechnol 2:61. https://doi.org/10.3389/fbioe.2014.00061
Hosseini I, Feigelman J, Gajjala A, Susilo M, Ramakrishnan V, Ramanujan S, Gadkar K (2020) gQSPSim: a SimBiology-based GUI for standardized QSP model development and application. CPT: Pharmacomet Syst Pharmacol 9(3):165–176. https://doi.org/10.1002/psp4.12494
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: Pharmacomet Syst Pharmacol 8(6):380–395. https://doi.org/10.1002/psp4.12426
Peterson MC, Riggs MM (2015) FDA advisory meeting clinical pharmacology review utilizes a quantitative systems pharmacology (QSP) model: a watershed moment? CPT Pharmacomet Syst Pharmacol 4(3):e00020. https://doi.org/10.1002/psp4.20
FDA (2017) US FDA regulatory science priorities (FY 2017).
FDA (2011) Advancing regulatory science at FDA. A strategic plan.
FDA (2020) The use of physiologically based pharmacokinetic analyses — biopharmaceutics applications for oral drug product development, manufacturing changes, and controls. Guidance for Industry.
Morrison TM, Pathmanathan P, Adwan M, Margerrison E (2018) Advancing regulatory science with computational modeling for medical devices at the FDA’s office of science and engineering laboratories. Front Med (Lausanne) 5:241. https://doi.org/10.3389/fmed.2018.00241
Bai JPF, Schmidt BJ, Gadkar KG, Damian V, Earp JC, Friedrich C, van der Graaf PH, Madabushi R, Musante CJ, Naik K, Rogge M, Zhu H (2021) FDA-industry scientific exchange on assessing quantitative systems pharmacology models in clinical drug development: a meeting report, summary of challenges/gaps, and future perspective. AAPS J 23(3):60. https://doi.org/10.1208/s12248-021-00585-x
Bradshaw EL, Spilker ME, Zang R, Bansal L, He H, Jones RDO, Le K, Penney M, Schuck E, Topp B, Tsai A, Xu C, Nijsen M, Chan JR (2019) Applications of quantitative systems pharmacology in model-informed drug discovery: perspective on impact and opportunities. CPT Pharmacomet Syst Pharmacol 8(11):777–791. https://doi.org/10.1002/psp4.12463
Zineh I (2019) Quantitative systems pharmacology: a regulatory perspective on translation. CPT Pharmacomet Syst Pharmacol 8(6):336–339. https://doi.org/10.1002/psp4.12403
Leil TA, Bertz R (2014) Quantitative systems pharmacology can reduce attrition and improve productivity in pharmaceutical research and development. Front Pharmacol 5:247. https://doi.org/10.3389/fphar.2014.00247
Acknowledgements
IPA acknowledges support from NIH GM131800.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Androulakis, I.P. Towards a comprehensive assessment of QSP models: what would it take?. J Pharmacokinet Pharmacodyn (2022). https://doi.org/10.1007/s10928-022-09820-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10928-022-09820-0