Quantitative systems pharmacology (QSP) attempts to quantitatively resolve the complex mechanisms and interactions between physiology and medications. Systems biology-based mechanistic models describe biological pathways and their regulatory networks at molecular, cellular, tissue, organ, patient, and/or population levels. QSP models are developed at various biological scales to present various multiscale mechanisms of pharmacological responses. PK PD (Pharmacokinetic pharmacodynamic) and QSP modeling differ in technical aspects such as data requirements, model implementation, and model evaluation/qualification methods. Previous evaluations have not examined the technical differences between PKPD and QSP modeling. Data needs, model implementation, and model evaluation/qualification are key distinctions. Our review addresses the gap by comparing PKPD modeling to QSP modeling, revealing QSP's superiority. The final gap left by previous evaluations is a limited examination of the applications of QSP for comprehending combination cancer therapies and systems-level analysis. Not enough has been said about pathway analysis, network modeling, logical modeling, virtual patient simulations, multi-scale systems pharmacology platforms, and identifying non-identifiable models. Our review fills in these voids and offers valuable insights into the significance of these approaches for elucidating the complex behavior of biological systems and their responses to medications. The aim and objective of this review are to clarify QSP's unique contributions and correct earlier assessments' lack of comparability. Secondly, we study QSP modeling computational approaches such as molecular docking, molecular dynamics modeling, machine learning, and similarity analysis. These methods can model drug-target interactions, filling the gap left by earlier studies and improving pharmacological knowledge. Pathway analysis and network modeling are vital to understanding the complicated regulatory processes that develop from biological component interactions. We examine how network and route analysis might enhance cancer combination treatment, transcending previous evaluations' limited scope. This review improves on earlier reviews by analyzing QSP more thoroughly. It illuminates QSP's potential for personalized medicine, treatment optimization, and illness management

Reference

1. Gall VM, Biasetti L, Veroli Di JG, Almarza PC, Kimko H et al. Quantitative systems modeling approaches towards model-informed drug development: perspective through case studies research square. 2022;1:1-21.
2. Bradshaw EL, Spilker ME, Zang R, Bansal L, He H, Jones RDO et al. Applications of quantitative systems pharmacology in model-informed drug discovery: perspective on impact and opportunities. CPT Pharmacometrics Syst Pharmacol. 2019;8(11):777-91. doi: 10.1002/psp4.12463, PMID 31535440.
3. Loewe L, Hillston J. Computational models in systems biology. Genome Biol. 2008;9(12):328. doi: 10.1186/gb-2008-9-12-328, PMID 19090975.
4. Nijsen MJMA, Wu F, Bansal L, Bradshaw-Pierce E, Chan JR, Liederer BM et al. Preclinical QSP modeling in the pharmaceutical industry. An IQ consortium survey examining the current landscape. CPT Pharmacometrics Syst Pharmacol. 2018;7(3):135-46. doi: 10.1002/psp4.12282, PMID 29349875.
5. Gadkar K, Kirouac DC, Mager DE, van der Graaf PH, Ramanujan S. A six-stage workflow for robust application of systems pharmacology. CPT Pharmacometrics Syst Pharmacol. 2016;5(5):235-49. doi: 10.1002/psp4.12071, PMID 27299936.
6. Friedrich CM. A model qualification method for mechanistic physiological QSP models to support model-informed drug development. CPT Pharmacometrics Syst Pharmacol. 2016;5(2):43-53. doi: 10.1002/psp4.12056, PMID 26933515.
7. Gadkar K, Kirouac D, Parrott N, Ramanujan S. Quantitative systems pharmacology: a promising approach for translational pharmacology. Drug Discov Today Technol. 2016;21-22:57-65. doi: 10.1016/j.ddtec.2016.11.001, PMID 27978989.
8. Van der Graaf PH, Benson N. Systems pharmacology: bridging systems biology and pharmacokinetics pharmacodynamics (PKPD) in drug discovery and development. Pharm Res. 2011;28(7):1460-64. doi: 10.1007/s11095-011-0467-9, PMID 21560018.
9. Leil TA, Bertz R. Quantitative systems pharmacology can reduce attrition and improve productivity in pharmaceutical research and development. Front Pharmacol. 2014;5:247. doi: 10.3389/fphar.2014.00247, PMID 25426074.
10. Rao, Hartmanshenn C, Bae SA, Androulakis IP. On the analysis of complex biological supply chains: from process systems engineering to quantitative systems pharmacology. R.T., Scherholz, M. L. Comput Chem Eng. 2017;107:100-10.
11. Ribba B, Grimm HP, Agoram B, Davies MR, Gadkar K, Niederer S et al. Methodologies for quantitative systems pharmacology (QSP) models: design and estimation. CPT Pharmacometr Syst Pharmacol. 2017;6: 496–498:20.
12. Timmis J, Alden K, Andrews P, Clark E, Nellis A, Naylor B et al. Building confidence in quantitative systems pharmacology models: an engineer’s guide to exploring the rationale in model design and development. CPT Pharmacometrics Syst Pharmacol. 2017;6(3):156-67. doi: 10.1002/psp4.12157, PMID 27863172.
13. PLOS is a nonprofit publisher of open-access journals in science, technology, and medicine, and other scientific literature, under an open-content license. It was founded in 2000 and launched its first journal, Biology PLOS. In: October 2003.
14. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183794.
15. Rogers M, Lyster P, Okita R. NIH support for the emergence of quantitative and systems pharmacology. CPT Pharmacometrics Syst Pharmacol. 2013;2(4):e37. doi: 10.1038/psp.2013.13, PMID 23887687.
16. Wist AD, Berger SI, Iyengar R. Systems pharmacology and genome medicine: a future perspective. Genome Med. 2009;1(1):11. doi: 10.1186/gm11, PMID 19348698.
17. Zhou W, Wang Y, Lu A, Zhang G. Systems pharmacology in small molecule drug discovery. Int J Mol Sci. 2016;17(2):246. doi: 10.3390/ijms17020246, PMID 26901192.
18. Gu J, Zhang X, Ma Y, Li N, Luo F, Cao L, et al. Quantitative modeling of dose-response and drug combination based on pathway network. J Cheminform. 2015;7:19. doi: 10.1186/s13321-015-0066-6, PMID 26101547.
19. Spiros A, Roberts P, Geerts H. A computer-based quantitative systems pharmacology model of negative symptoms in schizophrenia: exploring glycine modulation of excitation inhibition balance. Front Pharmacol. 2014;5:229. doi: 10.3389/fphar.2014.00229, PMID 25374541.
20. Fang J, Wu Z, Cai C, Wang Q, Tang Y, Cheng F. Quantitative and systems pharmacology. 1. In silico prediction of drug-target interactions of natural products enables new targeted cancer therapy. J Chem Inf Model. 2017;57(11):2657-71. doi: 10.1021/acs.jcim.7b00216, PMID 28956927.
21. Fleisher B, Brown AN, Ait-Oudhia S. Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: the example of luminal a breast cancer. Pharmacol Res. 2017;124:20-33. doi: 10.1016/j.phrs.2017.07.015, PMID 28735000.
22. Chen B, Dong JQ, Pan WJ, Ruiz A. Pharmacokinetics/pharmacodynamics model-supported early drug development. Curr Pharm Biotechnol. 2012;13(7):1360-75. doi: 10.2174/138920112800624436, PMID 22201585.
23. Lave T et al. Translational PK/PD modeling to increase the probability of success in drug discovery and early development. Drug Discov Today Technol. 2016:21-2, 27-34.
24. Wong H, Bohnert T, Damian-Iordache V, Gibson C, Hsu CP, Krishnatry AS et al. Translational pharmacokinetic-pharmacodynamic analysis in the pharmaceutical industry: an IQ Consortium PK-PD Discussion Group perspective. Drug Discov Today. 2017;22(10):1447-59. doi: 10.1016/j.drudis.2017.04.015, PMID 28476536.
25. Schmidt BJ, Casey FP, Paterson T, Chan JR. Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis. BMC Bioinformatics. 2013;14:221. doi: 10.1186/1471-2105-14-221, PMID 23841912.
26. Allen RJ, Rieger TR, Musante CJ. Efficient generation and selection of virtual populations in quantitative systems pharmacology models. CPT Pharmacometrics Syst Pharmacol. 2016;5(3):140-46. doi: 10.1002/psp4.12063, PMID 27069777.
27. American Journal of Pharmaceutical Education, Gabrielsson J, Weiner D. Pharmacokinetic and pharmacodynamic data analysis: concepts and applications. 4th ed. Vol. 3. Sweden: Swedish Pharmaceutical Press; 2010. p. 74.
28. Bansal L, Neisen J, Nichols E-M, Damian V. Development and application of a quantitative systems pharmacology (QSP) model of complement pathway to evaluate treatments for autoimmune diseases. J Pharmacokinet Pharmacodyn. 2017;44:S91-2.
29. Wu F, Heimbach T, Hatsis P, Tang HM, Dugyala R, Yue Q et al. Integrated TK-TD modeling for drug-induced concurrent tachycardia and QT changes in beagle dogs. J Pharmacokinet Pharmacodyn. 2017;44(5):449-62. doi: 10.1007/s10928-017-9532-2, PMID 28643108.
30. Geerts H, Spiros A, Roberts P, Carr R. Quantitative systems pharmacology as an extension of PK/PD modeling in CNS research and development. J Pharmacokinet Pharmacodyn. 2013;40(3):257-65. doi: 10.1007/s10928-013-9297-1, PMID 23338980.
31. Rostami-Hodjegan A. Physiologically based pharmacokinetics joined with in vitro—in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin Pharmacol Ther. 2012;92(1):50-61. doi: 10.1038/clpt.2012.65, PMID 22644330.
32. Bloomingdale P, Housand C, Apgar JF, Millard BL, Mager DE, Burke JM et al. Quantitative systems toxicology. Curr Opin Toxicol. 2017;4:79-87. doi: 10.1016/j.cotox.2017.07.003, PMID 29308440.
33. Pichardo-Almarza C, Diaz-Zuccarini V. From PK/ PD to QSP: understanding the dynamic effect of cholesterol-lowering drugs on atherosclerosis progression and stratified medicine. Curr Pharm Des. 2016;22(46):6903-10. doi: 10.2174/1381612822666160905095402, PMID 27592718.
34. Snelder N, Ploeger BA, Luttringer O, Rigel DF, Fu F, Beil M et al. Drug effects on the CVS in conscious rats: separating cardiac output into heart rate and stroke volume using PKPD modeling. Br J Pharmacol. 2014;171(22):5076-92. doi: 10.1111/bph.12824, PMID 24962208.
35. Hansson EK, Amantea MA, Westwood P, Milligan PA, Houk BE, French J et al. PKPD modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as predictors of tumor dynamics and overall survival following sunitinib treatment in GIST. CPT Pharmacometrics Syst Pharmacol. 2013;2(11):e84. doi: 10.1038/psp.2013.61, PMID 24257372.
36. Ma H, Wang H, Sové RJ, Wang J, Giragossian C, Popel AS. Combination therapy with T cell engager and PD-L1 blockade enhances the antitumor potency of T cells as predicted by a QSP model. J Immunother Cancer. 2020;8(2):e001141. doi: 10.1136/jitc-2020-001141, PMID 32859743.
37. Wilson JL, Lu D, Corr N, Fullerton A, Lu J. An in vitro quantitative systems pharmacology approach for deconvolving mechanisms of drug-induced, multilineage cytopenias. PLOS Comput Biol. 2020;16(7):e1007620. doi: 10.1371/journal.pcbi.1007620, PMID 32701980.
38. Sové RJ, Jafarnejad M, Zhao C, Wang H, Ma H, Popel AS et al. A quantitative systems pharmacology toolbox Fo r mechanistic multiscale modeling for immuno-oncology applications. CPT Pharmacometr Syst Pharmacol. 2020;9(9):484-97. doi: 10.1002/psp4.12546, PMID 32618119.
39. Ma H, Wang H, Sove RJ, Jafarnejad M, Tsai CH, Wang J et al. A quantitative systems pharmacology model of T cell engager applied to solid tumor. AAPS J. 2020;22(4):[85]. doi: 10.1208/s12248-020-00450-3, PMID 32533270.
40. Bloomingdale P, Nguyen VA, Niu J, Mager DE. Boolean network modeling in systems pharmacology. J Pharmacokinet Pharmacodyn. 2018;45(1):159-80. doi: 10.1007/s10928-017-9567-4, PMID 29307099.
41. Irurzun-Arana I, Pastor JM, Trocóniz IF, Gómez-Mantilla JD. Advanced Boolean modeling of biological networks applied to systems pharmacology. Bioinformatics. 2017;33(7):1040-48. doi: 10.1093/bioinformatics/btw747, PMID 28073755.
42. Danhof M. Systems pharmacology—towards the modeling of network interactions. Eur J Pharm Sci. 2016;94:4-14. doi: 10.1016/j.ejps.2016.04.027, PMID 27131606.
43. Boran AD, Iyengar R. Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov Devel. 2010;13(3):297-309. PMID 20443163.
44. Berger SI, Ma’ayan A, Iyengar R. Systems pharmacology of arrhythmias. Sci Signal. 2010;3(118):ra30. doi: 10.1126/scisignal.2000723, PMID 20407125.
45. Boland MR, Jacunski A, Lorberbaum T, Romano JD, Moskovitch R, Tatonetti NP. Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms. Wiley Interdiscip Rev Syst Biol Med. 2016;8(2):104-22. doi: 10.1002/wsbm.1323, PMID 26559926.
46. Goldstein LH, Berlin M, Saliba W, Elias M, Berkovitch M. Founding an adverse drug reaction (ADR) network: a method for improving doctors spontaneous ADR reporting in a general hospital. J Clin Pharmacol. 2013;53(11):1220-5. doi: 10.1002/jcph.149, PMID 23852627.
47. Zhao S, Nishimura T, Chen Y, Azeloglu EU, Gottesman O, Giannarelli C, et al. Systems pharmacology of adverse event mitigation by drug combinations. Sci Transl Med. 2013;5(206):206ra140. doi: 10.1126/scitranslmed.3006548, PMID 24107779.
48. Wu Z, Cheng F, Li J, Li W, Liu G, Tang Y. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief Bioinform. 2017;18(2):333-47. doi: 10.1093/bib/bbw012, PMID 26944082.
49. Wu Z, Lu W, Wu D, Luo A, Bian H, Li J, et al. In silico prediction of chemical mechanism of action via an improved network-based inference method. Br J Pharmacol. 2016;173(23):3372-85. doi: 10.1111/bph.13629, PMID 27646592.
50. Huang C, Zheng C, Li Y, Wang Y, Lu A, Yang L. Systems pharmacology in drug discovery and therapeutic insight for herbal medicines. Brief Bioinform. 2014;15(5):710-33. doi: 10.1093/bib/bbt035, PMID 23736100.
51. Li Q, Li X, Li C, Chen L, Song J, Tang Y et al. A network-based multi-target computational estimation scheme for anticoagulant activities of compounds. PLOS ONE. 2011;6(3):e14774. doi: 10.1371/journal.pone.0014774, PMID 21445339.
52. Zhang X, Gu J, Cao L, Ma Y, Su Z, Luo F, et al. Insights into the inhibition and mechanism of compounds against LPS-induced PGE2 production: a pathway network-based approach and molecular dynamics simulations. Integr Biol (Camb). 2014;6(12):1162-9. doi: 10.1039/c4ib00141a, PMID 25228393.
53. Zhao YL, Yang T, Chen D, Hu A, Liu CT et al. Y. Tong, J. Wang,J.H. Lua,Z.B. Jiao,Y. Yang, J.-J. Kai, Heterogeneous precipitation behavior and stacking-fault-mediated deformation in a CoCrNi-based medium-entropy alloy. 2017;138:72-82.
54. Traynard P, Tobalina L, Eduati F, Calzone L, Saez-Rodriguez J. Logic modeling in quantitative systems pharmacology. CPT Pharmacometrics Syst Pharmacol. 2017;6(8):499-511. doi: 10.1002/psp4.12225, PMID 28681552.
55. Poltz R, Naumann M. Dynamics of p53 and NF-κB regulation in response to DNA damage and identification of target proteins suitable for therapeutic intervention. BMC Syst Biol. 2012;6:125. doi: 10.1186/1752-0509-6-125, PMID 22979979.
56. Le Novère N. Quantitative and logic modeling of molecular and gene networks. Nat Rev Genet. 2015;16(3):146-58. doi: 10.1038/nrg3885, PMID 25645874.
57. Chaouiya C, Remy E. Logical modeling of regulatory networks, methods, and applications. Bull Math Biol. 2013;75(6):891-5. doi: 10.1007/s11538-013-9863-0, PMID 23722297.
58. Rieger TR, Allen RJ, Bystricky L, Chen Y, Colopy GW, Cui Y, et al. Improving the generation and selection of virtual populations in quantitative systems pharmacology models. Prog Biophys Mol Biol. 2018;139:15-22. doi: 10.1016/j.pbiomolbio.2018.06.002, PMID 29902482.
59. Geerts H, Spiros A, Roberts P, Carr R. Towards the virtual human patient. quantitative systems pharmacology in Alzheimer’s disease. Eur J Pharmacol. 2017;817:38-45. doi: 10.1016/j.ejphar.2017.05.062, PMID 28583429.
60. Wi?niowska B, Polak S. Virtual clinical trial toward polytherapy safety assessment: a combination of physiologically based pharmacokinetic/pharmacodynamic-based modeling and simulation approach with drug-drug interactions involving terfenadine as an example. J Pharm Sci. 2016;105(11):3415-24. doi: 10.1016/j.xphs.2016.08.002, PMID 27640752.
61. Geerts H, Roberts P, Spiros A. Assessing the synergy between cholinomimetics and memantine as augmentation therapy in cognitive impairment in schizophrenia. A virtual human patient trial using quantitative systems pharmacology. Front Pharmacol. 2015;6:198. doi: 10.3389/fphar.2015.00198, PMID 26441655.
62. Ru J, Li P, Wang J, Zhou W, Li B, Huang C, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13. doi: 10.1186/1758-2946-6-13, PMID 24735618.
63. Chassagnole C, Jackson RC, Hussain N, Bashir L, Derow C, Savin J et al. Using a mammalian cell cycle simulation to interpret differential kinase inhibition in antitumor pharmaceutical development. Biosystems. 2006;83(2-3):91-7. doi: 10.1016/j.biosystems.2005.04.007, PMID 16236428.
64. Tao W, Li B, Gao S, Bai Y, Shar PA, Zhang W, et al. CancerHSP: anticancer herbs database of systems pharmacology. Sci Rep. 2015;5:11481. doi: 10.1038/srep11481, PMID 26074488.
65. Huang H, Wu X, Pandey R, Li J, Zhao G, Ibrahim S et al. C2 Maps: a network pharmacology database with comprehensive disease-gene-drug connectivity relationships. BMC Genomics. 2012;13;Suppl 6;Suppl 17:S17. doi: 10.1186/1471-2164-13-S6-S17, PMID 23134618.
66. Hu Z, Chang YC, Wang Y, Huang CL, Liu Y, Tian F et al. VisANT 4.0: integrative network platform to connect genes, drugs, diseases, and therapies. Nucleic Acids Res. 2013;41(Web Server issue);Web Server Issue:W225-31. doi: 10.1093/nar/gkt401, PMID 23716640.
67. Wang D, Gu J, Zhu W, Luo F, Chen L, Xu X et al. PDTCM: a systems pharmacology platform of traditional Chinese medicine for psoriasis. Ann Med. 2017;49(8):652-60. doi: 10.1080/07853890.2017.1364417, PMID 28782992.
68. Gu J, Gui Y, Chen L, Yuan G, Xu X. CVDHD: a cardiovascular disease herbal database for drug discovery and network pharmacology. J Cheminform. 2013;5(1):51. doi: 10.1186/1758-2946-5-51, PMID 24344970.
69. Musante CJ, Ramanujan S, Schmidt BJ, Ghobrial OG, Lu J, Heatherington AC. Quantitative systems pharmacology: a case for disease models. Clin Pharmacol Ther. 2017;101(1):24-7. doi: 10.1002/cpt.528, PMID 27709613.
70. Ghosh S, Matsuoka Y, Asai Y, Hsin KY, Kitano H. Toward an integrated software platform for systems pharmacology. Biopharm Drug Dispos. 2013;34(9):508-26. doi: 10.1002/bdd.1875, PMID 24150748.
71. Spiros A, Roberts P, Geerts H. Phenotypic screening of the Prestwick library for treatment of Parkinson’s tremor symptoms using a humanized quantitative systems pharmacology platform. J Parkinsons Dis. 2013;3(4):569-80. doi: 10.3233/JPD-130211, PMID 24192755.
72. Ming JE, Abrams RE, Bartlett DW, Tao M, Nguyen T, Surks H et al. 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. 2017;11:1177625017710941. doi: 10.1177/1177625017710941, PMID 28804243.
73. Zheng C, Pei T, Huang C, Chen X, Bai Y, Xue J et al. A novel systems pharmacology platform to dissect action mechanisms of traditional Chinese medicines for bovine viral diarrhea disease. Eur J Pharm Sci. 2016;94:33-45. doi: 10.1016/j.ejps.2016.05.018, PMID 27208435.
74. Kirouac DC, Du JY, Lahdenranta J, Overland R, Yarar D, Paragas V et al. Computational modeling of ERBB2- amplified breast cancer identifies combined ErbB2/3 blockade as superior to the combination of MEK and AKT inhibitors. Sci Signal. 2013;6(288):ra68. doi: 10.1126/scisignal.2004008, PMID 23943608.
75. Liu H, Wang J, Zhou W, Wang Y, Yang L. Systems approaches and polypharmacology for drug discovery from herbal medicines: an example using licorice. J Ethnopharmacol. 2013;146(3):773-93. doi: 10.1016/j.jep.2013.02.004, PMID 23415946.
76. Li P, Chen J, Wang J, Zhou W, Wang X, Li B, et al. Systems pharmacology strategies for drug discovery and combination with applications to cardiovascular diseases. J Ethnopharmacol. 2014;151(1):93-107. doi: 10.1016/j.jep.2013.07.001, PMID 23850710.
77. Li P, Fu Y, Ru J, Huang C, Du J, Zheng C, et al. Insights from systems pharmacology into cardiovascular drug discovery and therapy. BMC Syst Biol. 2014;8:141. doi: 10.1186/s12918-014-0141-z, PMID 25539592.
78. Edwards IR, Aronson JK. Adverse drug reactions: definitions, diagnosis, and management. Lancet. 2000;356(9237):1255-9. doi: 10.1016/S0140-6736(00)02799-9, PMID 11072960.
79. Giacomini KM, Krauss RM, Roden DM, Eichelbaum M, Hayden MR, Nakamura Y. When good drugs go bad. Nature. 2007;446(7139):975-7. doi: 10.1038/446975a, PMID 17460642.
80. Duran-Frigola M, Aloy P. Analysis of chemical and biological features yields mechanistic insights into drug side effects. Chem Biol. 2013;20(4):594-603. doi: 10.1016/j.chembiol.2013.03.017, PMID 23601648.
81. Garcia-Serna RG, Mestres J. Anticipating drug side effects by comparative pharmacology. Expert Opin Drug Metab Toxicol. 2010;6(10):1253-63. doi: 10.1517/17425255.2010.509343, PMID 20662552.
82. Schuck E, Bohnert T, Chakravarty A, Damian-Iordache V, Gibson C, Hsu CP et al. Preclinical pharmacokinetic/pharmacodynamic modeling and simulation in the pharmaceutical industry: an IQ consortium survey examining the current landscape. AAPS J. 2015;17(2):462-73. doi: 10.1208/s12248-014-9716-2, PMID 25630504.
83. Bejarano L, Jord?o MJC, Joyce JA. Therapeutic targeting of the tumor microenvironment. Cancer Discov. 2021;11(4):933-59. doi: 10.1158/2159-8290.CD-20-1808, PMID 33811125.
84. Abril-Rodriguez G, Ribas A. Snapshot: immune checkpoint inhibitors. Cancer Cell. 2017;31(6):848-848.e1. doi: 10.1016/j.ccell.2017.05.010, PMID 28609660.
85. Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell. 2015;27(4):450-61. doi: 10.1016/j.ccell.2015.03.001, PMID 25858804.
86. Milberg O, Gong C, Jafarnejad M, Bartelink IH, Wang B, Vicini P, et al. A QSP model for predicting clinical responses to monotherapy, combination and sequential therapy following CTLA-4, PD-1, and PD-L1 checkpoint blockade. Sci Rep. 2019;9(1):11286. doi: 10.1038/s41598-019-47802-4, PMID 31375756.
87. Burstein HJ. The distinctive nature of HER2-positive breast cancers. N Engl J Med. 2005;353(16):1652-4. doi: 10.1056/NEJMp058197, PMID 16236735.
88. Garrido-Castro AC, Lin NU, Polyak K. Insights into molecular classifications of triple-negative breast cancer: improving patient selection for treatment. Cancer Discov. 2019;9(2):176-98. doi: 10.1158/2159-8290.CD-18-1177, PMID 30679171.
89. Sui H, Ma N, Wang Y, Li H, Liu X, Su Y et al. Anti-PD-1/PD-L1. Therapy for non-small-cell lung cancer: toward personalized medicine and combination strategies. J Immunol Res. 2018;2018:6984948. doi: 10.1155/2018/6984948, PMID 30159341.
90. Jafarnejad M, Gong C, Gabrielson E, Bartelink IH, Vicini P, Wang B, et al. A computational model of neoadjuvant PD-1 inhibition in non-small cell lung cancer. AAPS J. 2019;21(5):79. doi: 10.1208/s12248-019-0350-x, PMID 31236847.
91. Coletti R, Leonardelli L, Parolo S, Marchetti L. A QSP model of prostate cancer immunotherapy to identify effective combination therapies. Sci Rep. 2020;10(1):9063. doi: 10.1038/s41598-020-65590-0, PMID 32493951.
92. Sokolov V, Yakovleva T, Chu L, Tang W, Greasley PJ, Johansson S et al. Differentiating the sodium-glucose cotransporter 1 inhibition capacity of canagliflozin vs. dapagliflozin and empagliflozin using quantitative systems pharmacology modeling. CPT Pharmacometrics Syst Pharmacol. 2020;9(4):222-9. doi: 10.1002/psp4.12498, PMID 32064793.
93. DeFronzo RA, Davidson JA, Del Prato S. The role of the kidneys in glucose homeostasis: a new path towards normalizing glycemia. Diabetes Obes Metab. 2012;14(1):5-14. doi: 10.1111/j.1463-1326.2011.01511.x, PMID 21955459.
94. Mori-Anai K, Tashima Y, Nakada T, Nakamaru Y, Takahata T, Saito R. Mechanistic evaluation of the effect of sodium-dependent glucose transporter 2 inhibitors on delayed glucose absorption in patients with type 2 diabetes mellitus using a quantitative systems pharmacology model of human systemic glucose dynamics. Biopharm Drug Dispos. 2020;41(8-9):352-66. doi: 10.1002/bdd.2253, PMID 33085977.
95. Shah M, Stolbov L, Yakovleva T, Tang W, Sokolov V, Penland RC et al. A model-based approach to investigating the relationship between glucose-insulin dynamics and dapagliflozin treatment effect in patients with type 2 diabetes. Diabetes Obes Metab. 2021;23(4):991-1000. doi: 10.1111/dom.14305, PMID 33368935.
96. Tiwari S, Atluri V, Kaushik A, Yndart A, Nair M. Alzheimer’s disease: pathogenesis, diagnostics, and therapeutics. Int J Nanomedicine. 2019;14:5541-54. doi: 10.2147/IJN.S200490, PMID 31410002.
97. Geerts H, Spiros A. Learning from amyloid trials in Alzheimer’s disease. A virtual patient analysis using a quantitative systems pharmacology approach. Alzheimers Dement. 2020;16(6):862-72. doi: 10.1002/alz.12082, PMID 32255562.