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Pharmacokinetics–Pharmacodynamics Modeling for Evaluating Drug–Drug Interactions in Polypharmacy: Development and Challenges

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Abstract

Polypharmacy is commonly employed in clinical settings. The potential risks of drug–drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

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References

  1. Sudsakorn S, Bahadduri P, Fretland J, Lu C. 2020 FDA drug-drug interaction guidance: a comparison analysis and action plan by pharmaceutical industrial scientists. Curr Drug Metab. 2020;21(6):403–26.

    Article  CAS  PubMed  Google Scholar 

  2. Krichbaum M, Miransky N, Perez A. Trends in pain medication use in patients with type 2 diabetes: NHANES 2005–2018. J Pain Palliat Care Pharmacother. 2023;37(3):223–33.

    Article  PubMed  Google Scholar 

  3. Hau RK, Wright SH, Cherrington NJ. Addressing the clinical importance of equilibrative nucleoside transporters in drug discovery and development. Clin Pharmacol Ther. 2023;114(4):780–94.

    Article  CAS  PubMed  Google Scholar 

  4. Mease J, Ramamoorthy A, Yang X, Madabushi R, Pfuma FE, Zineh I. Statin drug-drug interactions: pharmacokinetic basis of FDA labeling recommendations and comparison across common tertiary clinical resources. J Clin Pharmacol. 2024;64(4):704–12.

    Article  CAS  PubMed  Google Scholar 

  5. Su YY, Chiang NJ, Chang JS, Wang YW, Shen BN, Li YJ, et al. The association between UGT1a1 polymorphisms and treatment toxicities of liposomal irinotecan. Esmo Open. 2023;8(1): 100746.

    Article  PubMed  Google Scholar 

  6. Alatwi E, Bairam A. The role of genetic polymorphisms in the sulfation of pregnenolone by human cytosolic sulfotransferase SULT2b1a. Res Sq. 2023;rs.3.rs-3471389.

  7. Miao L, Wu F, Yang X, Mousa YM, Ramamoorthy A, Lee S, et al. Application of solubility and dissolution profile comparison for prediction of gastric Ph-mediated drug-drug interactions. AAPS J. 2022;24(1):35–35.

    Article  CAS  PubMed  Google Scholar 

  8. Wang CM, Fernez MT, Woolston BM, Carrier RL. Native gastrointestinal mucus: critical features and techniques for studying interactions with drugs, drug carriers, and bacteria. Adv Drug Deliv Rev. 2023;200: 114966.

    Article  CAS  PubMed  Google Scholar 

  9. Sun J, Li R, Zhang J, Huang Y, Lu Y, Liu C, et al. Analysis of compatibility mechanism of shenxiong glucose injection after multiple dosing based on differences of PK-PD correlation and cytochrome P450 enzyme. J Pharm Biomed Anal. 2024;239: 115899.

    Article  CAS  PubMed  Google Scholar 

  10. Zhang L, **e H, Wang Y, Wang H, Hu J, Zhang G. Pharmacodynamic parameters of pharmacokinetic/pharmacodynamic (PK/PD) integration models. Front Vet Sci. 2022;9: 860472.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Jang HY, Song J, Kim JH, Lee H, Kim IW, Moon B, et al. Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information. NPJ Digit Med. 2022;5(1):88.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Sall C, Argikar U, Fonseca K, Hilgendorf C, Lopes F, Riedel J, et al. Industry perspective on therapeutic peptide drug-drug interaction assessments during drug development: a European federation of pharmaceutical industries and associations white paper. Clin Pharmacol Ther. 2023;113(6):1199–216.

    Article  CAS  PubMed  Google Scholar 

  13. Shan Z, Yang X, Liu H, Yuan Y, **ao Y, Nan J, et al. Cryo-EM structures of human organic anion transporting polypeptide OATP1b1. Cell Res. 2023;33(12):940–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Daniel WA, Bromek E, Danek PJ, Haduch A. The mechanisms of interactions of psychotropic drugs with liver and brain cytochrome P450 and their significance for drug effect and drug-drug interactions. Biochem Pharmacol. 2022;199: 115006.

    Article  CAS  PubMed  Google Scholar 

  15. Hu W, Zhang W, Zhou Y, Luo Y, Sun X, Xu H, et al. MECDDI: clarified drug–drug interaction mechanism facilitating rational drug use and potential drug–drug interaction prediction. J Chem Inf Model. 2023;63(5):1626–36.

    Article  CAS  PubMed  Google Scholar 

  16. Huang L, Chen Q, Lan W. Predicting drug-drug interactions based on multi-view and multichannel attention deep learning. Health Inf Sci Syst. 2023;11(1):50.

    Article  PubMed  Google Scholar 

  17. Wang L, Shendre A, Chiang CW, Cao W, Ning X, Zhang P, et al. A pharmacovigilance study of pharmacokinetic drug interactions using a translational informatics discovery approach. Br J Clin Pharmacol. 2022;88(4):1471–81.

    Article  CAS  PubMed  Google Scholar 

  18. Chu X, Prasad B, Neuhoff S, Yoshida K, Leeder JS, Mukherjee D, et al. Clinical implications of altered drug transporter abundance/function and PBPK modeling in specific populations: an ITC perspective. Clin Pharmacol Ther. 2022;112(3):501–26.

    Article  CAS  PubMed  Google Scholar 

  19. Shukkoor M, Baharuldin M, Raja K. A text mining protocol for extracting drug-drug interaction and adverse drug reactions specific to patient population, pharmacokinetics, pharmacodynamics, and disease. Methods Mol Biol. 2022;2496:259–82.

    Article  CAS  PubMed  Google Scholar 

  20. Colon OR, Knerler S, Fridman LB, Mercado A, Price AS, Rosado-Franco JJ, et al. Cocaine regulates antiretroviral therapy CNS access through pregnane-x receptor-mediated drug transporter and metabolizing enzyme modulation at the blood brain barrier. Fluids Barriers Cns. 2024;21(1):5.

    Article  Google Scholar 

  21. Budagaga Y, Sabet Z, Zhang Y, Novotna E, Hanke I, Rozkos T, et al. Tazemetostat synergistically combats multidrug resistance by the unique triple inhibition of ABCB1, ABCC1, and ABCG2 efflux transporters in vitro and ex vivo. Biochem Pharmacol. 2023;216: 115769.

    Article  CAS  PubMed  Google Scholar 

  22. Tseng E, Lin J, Strelevitz TJ, Dasilva E, Goosen TC, Obach RS. Projections of drug–drug interactions caused by time-dependent inhibitors of cytochrome P450 1a2, 2b6, 2c8, 2c9, 2c19, and 2d6 using in vitro data in static and dynamic models. Drug Metab Dispos. 2024;52(5):422–31.

    Article  CAS  Google Scholar 

  23. Gomez-Mantilla JD, Huang F, Peters SA. Can mechanistic static models for drug–drug interactions support regulatory filing for study waivers and label recommendations? Clin Pharmacokinet. 2023;62(3):457–80.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Lai Y, Chu X, Di L, Gao W, Guo Y, Liu X, et al. Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development. Acta Pharm Sin B. 2022;12(6):2751–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Liu S, Sodhi JK, Benet LZ. Analyzing potential intestinal transporter drug–drug interactions: reevaluating ticagrelor interaction studies. Pharm Res. 2021;38(10):1639–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sabo JP, Kort J, Ballow C, Haschke M, Battegay M, Fuhr R, et al. Clinical assessment of potential drug interactions of faldaprevir, a hepatitis c virus protease inhibitor, with darunavir/ritonavir, efavirenz, and tenofovir. Clin Infect Dis. 2014;59(10):1420–8.

    Article  CAS  PubMed  Google Scholar 

  27. Lu J, Zhang Y, Sun M, Liu M, Wang X. Comprehensive assessment of cucurbitacin e related hepatotoxicity and drug–drug interactions involving CYP3A and p-glycoprotein. Phytomedicine. 2017;26:1–10.

    Article  CAS  PubMed  Google Scholar 

  28. Yong WP, Desai AA, Innocenti F, Ramirez J, Shepard D, Kobayashi K, et al. Pharmacokinetic modulation of oral etoposide by ketoconazole in patients with advanced cancer. Cancer Chemother Pharmacol. 2007;60(6):811–9.

    Article  CAS  PubMed  Google Scholar 

  29. Courlet P, Guidi M, Alves SS, Cavassini M, Stoeckle M, Buclin T, et al. Population pharmacokinetic modelling to quantify the magnitude of drug–drug interactions between amlodipine and antiretroviral drugs. Eur J Clin Pharmacol. 2021;77(7):979–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bechtold BJ, Lynch KD, Oyanna VO, Call MR, Graf TN, Oberlies NH, et al. Rifampin- and silymarin-mediated pharmacokinetic interactions of exogenous and endogenous substrates in a transgenic OATP1b mouse model. Mol Pharm. 2024;21(5):2284–97.

    Article  CAS  PubMed  Google Scholar 

  31. Sharma S, Mettu VS, Prasad B. Interplay of breast cancer resistance protein (BCRP/ABCG2), sex, and fed state in oral pharmacokinetic variability of furosemide in rats. Pharmaceutics. 2023;15(2):542.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wang X, Wang Y, Fang C, Gong Q, Huang J, Zhang Y, et al. Allicin affects the pharmacokinetics of sulfadiazine and florfenicol by downregulating the expression of jejunum p-GP and BCRP in broilers. Poult Sci. 2022;101(7): 101947.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Stanislawiak-Rudowicz J, Karbownik A, Szkutnik-Fiedler D, Otto F, Grabowski T, Wolc A, et al. Bidirectional pharmacokinetic drug interactions between olaparib and metformin. Cancer Chemother Pharmacol. 2024;93(1):79–88.

    Article  CAS  PubMed  Google Scholar 

  34. Ren T, Yang M, **ao M, Zhu J, **e W, Zuo Z. Time-dependent inhibition of carbamazepine metabolism by piperine in anti-epileptic treatment. Life Sci. 2019;218:314–23.

    Article  CAS  PubMed  Google Scholar 

  35. Asano S, Kurosaki C, Mori Y, Shigemi R. Quantitative prediction of transporter-mediated drug–drug interactions using the mechanistic static pharmacokinetic (MSPK) model. Drug Metab Pharmacokinet. 2024;54:100531.

    Article  CAS  PubMed  Google Scholar 

  36. Mitra P, Kasliwala R, Iboki L, Madari S, Williams Z, Takahashi R, et al. Mechanistic static model based prediction of transporter substrate drug–drug interactions utilizing atorvastatin and rifampicin. Pharm Res. 2023;40(12):3025–42.

    Article  CAS  PubMed  Google Scholar 

  37. Chu X, Chan GH, Houle R, Lin M, Yabut J, Fandozzi C. In vitro assessment of transporter mediated perpetrator DDIs for several hepatitis C virus direct-acting antiviral drugs and prediction of DDIs with statins using static models. AAPS J. 2022;24(3):45.

    Article  CAS  PubMed  Google Scholar 

  38. Elsby R, Coghlan H, Edgerton J, Hodgson D, Outteridge S, Atkinson H. Mechanistic in vitro studies indicate that the clinical drug–drug interactions between protease inhibitors and rosuvastatin are driven by inhibition of intestinal BCRP and hepatic OATP1b1 with minimal contribution from OATP1b3, NTCP and OAT3. Pharmacol Res Perspect. 2023;11(2): e01060.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Tanna RS, Tian DD, Cech NB, Oberlies NH, Rettie AE, Thummel KE, et al. Refined prediction of pharmacokinetic kratom–drug interactions: time-dependent inhibition considerations. J Pharmacol Exp Ther. 2021;376(1):64–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Elsby R, Hare V, Neal H, Outteridge S, Pearson C, Plant K, et al. Mechanistic in vitro studies indicate that the clinical drug–drug interaction between telithromycin and simvastatin acid is driven by time-dependent inhibition of CYP3A4 with minimal effect on OATP1b1. Drug Metab Dispos. 2019;47(1):1–8.

    Article  CAS  PubMed  Google Scholar 

  41. Yamada M, Ishizuka T, Inoue SI, Rozehnal V, Fischer T, Sugiyama D. Drug–drug interaction risk assessment of esaxerenone as a perpetrator by in vitro studies and static and physiologically based pharmacokinetic models. Drug Metab Dispos. 2020;48(9):769–77.

    Article  CAS  PubMed  Google Scholar 

  42. Cheong EJ, Goh JJ, Hong Y, Venkatesan G, Liu Y, Chiu GN, et al. Application of static modeling—in the prediction of in vivo drug–drug interactions between rivaroxaban and antiarrhythmic agents based on in vitro inhibition studies. Drug Metab Dispos. 2017;45(3):260–8.

    Article  CAS  PubMed  Google Scholar 

  43. Xu RJ, Ling T, Tang H, Ge WH, Jiang Q. Prediction of rivaroxaban–rifampin interaction after major orthopedic surgery: physiologically based pharmacokinetic modeling and simulation. Front Pharmacol. 2021;12: 706781.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Otsuka Y, Poondru S, Bonate PL, Rose RH, Jamei M, Ushigome F, et al. Physiologically-based pharmacokinetic modeling to predict drug–drug interaction of enzalutamide with combined p-GP and CYP3A substrates. J Pharmacokinet Pharmacodyn. 2023;50(5):365–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mulford DJ, Ramsden D, Zhang L, Michon I, Leifke E, Smith N, et al. Tiered approach to evaluate the CYP3A victim and perpetrator drug–drug interaction potential for vonoprazan using PBPK modeling and clinical data to inform labeling. CPT Pharmacometrics Syst Pharmacol. 2023;12(4):532–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lee JM, Yoon JH, Maeng HJ, Kim YC. Physiologically based pharmacokinetic (PBPK) modeling to predict CYP3A-mediated drug interaction between saxagliptin and nicardipine: bridging rat-to-human extrapolation. Pharmaceutics. 2024;16(2):280.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Rodriguez-Vera L, Yin X, Almoslem M, Romahn K, Cicali B, Lukacova V, et al. Comprehensive physiologically based pharmacokinetic model to assess drug–drug interactions of phenytoin. Pharmaceutics. 2023;15(10):2486.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bettonte S, Berton M, Stader F, Battegay M, Marzolini C. Management of drug–drug interactions between long-acting cabotegravir and rilpivirine and comedications with inducing properties: a modeling study. Clin Infect Dis. 2023;76(7):1225–36.

    Article  PubMed  Google Scholar 

  49. Cho CK, Kang P, Jang CG, Lee SY, Lee YJ, Bae JW, et al. PBPK modeling to predict the pharmacokinetics of venlafaxine and its active metabolite in different CYP2D6 genotypes and drug–drug interactions with clarithromycin and paroxetine. Arch Pharm Res. 2024. https://doi.org/10.1007/s12272-024-01495-0.

    Article  PubMed  Google Scholar 

  50. Sahasrabudhe SA, Cheng S, Al-Kofahi M, Jarnes JR, Weinreb NJ, Kartha RV. Physiologically-based pharmacokinetic model development, validation, and application for prediction of eliglustat drug–drug interactions. Clin Pharmacol Ther. 2022;112(6):1254–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Marques L, Vale N. Prediction of CYP-mediated drug interaction using physiologically based pharmacokinetic modeling: a case study of salbutamol and fluvoxamine. Pharmaceutics. 2023;15(6):1586.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Chen L, Li C, Bai H, Li L, Chen W. Use of modeling and simulation to predict the influence of triazole antifungal agents on the pharmacokinetics of zanubrutinib and acalabrutinib. Front Pharmacol. 2022;13: 960186.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. He Q, Bu F, Wang Q, Li M, Lin J, Tang Z, et al. Examination of the impact of CYP3A4/5 on drug–drug interaction between schizandrol a/schizandrol b and tacrolimus (FK-506): a physiologically based pharmacokinetic modeling approach. Int J Mol Sci. 2022;23(9):4485.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Kanefendt F, Dallmann A, Chen H, Francke K, Liu T, Brase C, et al. Assessment of the CYP3A4 induction potential by carbamazepine: insights from two clinical DDI studies and PBPK modeling. Clin Pharmacol Ther. 2024;115(5):1025–32.

    Article  CAS  PubMed  Google Scholar 

  55. Zheng L, Zhang W, Olkkola KT, Dallmann A, Ni L, Zhao Y, et al. Physiologically based pharmacokinetic modeling of ritonavir-oxycodone drug interactions and its implication for dosing strategy. Eur J Pharm Sci. 2024;194: 106697.

    Article  CAS  PubMed  Google Scholar 

  56. Hong E, Carmanov E, Shi A, Chung PS, Rao AP, Forrester K, et al. Application of physiologically based pharmacokinetic modeling to predict drug–drug interactions between elexacaftor/tezacaftor/ivacaftor and tacrolimus in lung transplant recipients. Pharmaceutics. 2023;15(5):1438.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Bi YA, Jordan S, King-Ahmad A, West MA, Varma M. Mechanistic determinants of daprodustat drug–drug interactions and pharmacokinetics in hepatic dysfunction and chronic kidney disease: significance of OATP1B-CYP2C8 interplay. Clin Pharmacol Ther. 2024;115(6):1336–45.

    Article  CAS  PubMed  Google Scholar 

  58. Ngo LT, Lee J, Yun HY, Chae JW. Development of a physiologically based pharmacokinetic model for tegoprazan: application for the prediction of drug–drug interactions with CYP3A4 perpetrators. Pharmaceutics. 2023;15(1):182.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. **e W, Li J, Kong C, Luo W, Zheng J, Zhou Y. Metformin-cimetidine drug interaction and risk of lactic acidosis in renal failure: a pharmacovigilance–pharmacokinetic appraisal. Diabetes Care. 2024;47(1):144–50.

    Article  PubMed  Google Scholar 

  60. Yu J, Wang Y, Ragueneau-Majlessi I. Strong pharmacokinetic drug-drug interactions with drugs approved by the US food and drug administration in 2021: mechanisms and clinical implications. Clin Ther. 2022;44(11):1536–44.

    Article  CAS  PubMed  Google Scholar 

  61. Ramsden D, Fullenwider CL. Characterization of correction factors to enable assessment of clinical risk from in vitro CYP3A4 induction data and basic drug–drug interaction models. Eur J Drug Metab Pharmacokinet. 2022;47(4):467–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Giacomini KM, Yee SW, Koleske ML, Zou L, Matsson P, Chen EC, et al. New and emerging research on solute carrier and atp binding cassette transporters in drug discovery and development: outlook from the international transporter consortium. Clin Pharmacol Ther. 2022;112(3):540–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Sadaf A, Dong M, Pfeiffer A, Latham T, Kalfa T, Vinks AA, et al. A population pharmacokinetic analysis of l-glutamine exposure in patients with sickle cell disease: evaluation of dose and food effects. Clin Pharmacokinet. 2024;63(3):357–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Vieira ML, Kirby B, Ragueneau-Majlessi I, Galetin A, Chien JY, Einolf HJ, et al. Evaluation of various static in vitro-in vivo extrapolation models for risk assessment of the CYP3A inhibition potential of an investigational drug. Clin Pharmacol Ther. 2014;95(2):189–98.

    Article  CAS  PubMed  Google Scholar 

  65. Kenny JR, Ramsden D, Buckley DB, Dallas S, Fung C, Mohutsky M, et al. Considerations from the innovation and quality induction working group in response to drug–drug interaction guidances from regulatory agencies: focus on CYP3A4 mRNA in vitro response thresholds, variability, and clinical relevance. Drug Metab Dispos. 2018;46(9):1285–303.

    Article  CAS  PubMed  Google Scholar 

  66. Miyake T, Mochizuki T, Nakagawa T, Nakamura M, Emoto C, Komiyama N, et al. Quantitative prediction of CYP3A-mediated drug–drug interactions by correctly estimating fraction metabolized using human liver chimeric mice. Br J Pharmacol. 2024;181(7):1091–106.

    Article  CAS  PubMed  Google Scholar 

  67. Peng Y, Cheng Z, **e F. Evaluation of pharmacokinetic drug–drug interactions: a review of the mechanisms, in vitro and in silico approaches. Metabolites. 2021;11(2):75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Nozaki Y, Izumi S. Preincubation time-dependent, long-lasting inhibition of drug transporters and impact on the prediction of drug–drug interactions. Drug Metab Dispos. 2023;51(9):1077–88.

    Article  CAS  PubMed  Google Scholar 

  69. Jala A, Ponneganti S, Vishnubhatla DS, Bhuvanam G, Mekala PR, Varghese B, et al. Transporter-mediated drug–drug interactions: advancement in models, analytical tools, and regulatory perspective. Drug Metab Rev. 2021;53(3):285–320.

    Article  CAS  PubMed  Google Scholar 

  70. Younis IR, Manchandani P, Hassan HE, Qosa H. Trends in FDA transporter-based post-marketing requirements and commitments over the last decade. Clin Pharmacol Ther. 2022;112(3):635–42.

    Article  PubMed  Google Scholar 

  71. Kayesh R, Tambe V, Xu C, Yue W. Differential preincubation effects of nicardipine on OATP1B1- and OATP1B3-mediated transport in the presence and absence of protein: implications in assessing OATP1B1- and OATP1B3-mediated drug–drug interactions. Pharmaceutics. 2023;15(3):1020.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Murray M. Mechanisms and clinical significance of pharmacokinetic drug interactions mediated by FDA and EMA-approved hepatitis C direct-acting antiviral agents. Clin Pharmacokinet. 2023;62(10):1365–92.

    Article  CAS  PubMed  Google Scholar 

  73. Shugg T, Powell NR, Marroum PJ, Skaar TC, Younis IR. Evaluation of us food and drug administration drug label recommendations for coadministration of antivirals and acid-reducing agents. Clin Pharmacol Ther. 2022;112(5):1088–97.

    Article  PubMed  Google Scholar 

  74. ** Z, He Q, Zhu X, Zhu M, Wang Y, Wu XA, et al. Application of physiologically based pharmacokinetic modelling for the prediction of drug–drug interactions involving anlotinib as a perpetrator of cytochrome P450 enzymes. Basic Clin Pharmacol Toxicol. 2022;130(5):592–605.

    Article  CAS  PubMed  Google Scholar 

  75. Foti RS. Utility of pbpk modeling in predicting and characterizing clinical drug interactions. Drug Metab Dispos. 2024. https://doi.org/10.1124/dmd.123.001384.

    Article  PubMed  Google Scholar 

  76. Jia G, Ren C, Wang H, Fan C. Prediction of drug–drug interactions between roflumilast and cyp3a4/1a2 perpetrators using a physiologically-based pharmacokinetic (PBPK) approach. BMC Pharmacol Toxicol. 2024;25(1):4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Liu XI, Leong R, Burckart GJ, Dallmann A. Physiologically based pharmacokinetic modeling of nilotinib for drug–drug interactions, pediatric patients, and pregnancy and lactation. J Clin Pharmacol. 2024;64(3):323–33.

    Article  CAS  PubMed  Google Scholar 

  78. Hsueh CH, Hsu V, Pan Y, Zhao P. Predictive performance of physiologically-based pharmacokinetic models in predicting drug–drug interactions involving enzyme modulation. Clin Pharmacokinet. 2018;57(10):1337–46.

    Article  CAS  PubMed  Google Scholar 

  79. Minto CF, Schnider TW, Short TG, Gregg KM, Gentilini A, Shafer SL. Response surface model for anesthetic drug interactions. Anesthesiology. 2000;92(6):1603–16.

    Article  CAS  PubMed  Google Scholar 

  80. Greco WR, Park HS, Rustum YM. Application of a new approach for the quantitation of drug synergism to the combination of cis-diamminedichloroplatinum and 1-beta-d-arabinofuranosylcytosine. Cancer Res. 1990;50(17):5318–27.

    CAS  PubMed  Google Scholar 

  81. Weinstein JN, Bunow B, Weislow OS, Schinazi RF, Wahl SM, Wahl LM, et al. Synergistic drug combinations in aids therapy. Dipyridamole/3’-azido-3’-deoxythymidine in particular and principles of analysis in general. Ann N Y Acad Sci. 1990;616:367–84.

    Article  CAS  PubMed  Google Scholar 

  82. Shapovalova Y, Heskes T, Dijkstra T. Non-parametric synergy modeling of chemical compounds with gaussian processes. BMC Bioinf. 2022;23(1):14.

    Article  Google Scholar 

  83. El HB, Mantini G, Li PG, Capula M, Boyd L, Weinstein H, et al. To combine or not combine: drug interactions and tools for their analysis. Reflections from the EORTC-PAMM course on preclinical and early-phase clinical pharmacology. Anticancer Res. 2019;39(7):3303–9.

    Article  Google Scholar 

  84. Gu H, Zhang M, Cai M, Liu J. Combined use of etomidate and dexmedetomidine produces an additive effect in inhibiting the secretion of human adrenocortical hormones. Med Sci Monit. 2015;21:3528–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Jimenez-Toro I, Rodriguez CA, Zuluaga AF, Otalvaro JD, Perez-Madrid H, Vesga O. Pharmacokinetic/pharmacodynamic index linked to in vivo efficacy of the ampicillin–ceftriaxone combination against enterococcus faecalis. Antimicrob Agents Chemother. 2023;67(2): e0096622.

    Article  PubMed  Google Scholar 

  86. Hannam JA, Anderson BJ, Potts A. Acetaminophen, ibuprofen, and tramadol analgesic interactions after adenotonsillectomy. Paediatr Anaesth. 2018;28(10):841–51.

    Article  PubMed  Google Scholar 

  87. Genestet C, Ader F, Pichat C, Lina G, Dumitrescu O, Goutelle S. Assessing the combined antibacterial effect of isoniazid and rifampin on four mycobacterium tuberculosis strains using in vitro experiments and response-surface modeling. Antimicrob Agents Chemother. 2017;62(1):01413–7.

    Google Scholar 

  88. Borges BS, Bueno GP, Tomiotto-Pellissier F, Figueiredo FB, Soares Medeiros LC. In vitro anti-leishmania activity of triclabendazole and its synergic effect with amphotericin b. Front Cell Infect Microbiol. 2023;12:1044665.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Drusano GL, Kim S, Almoslem M, Schmidt S, D’Argenio DZ, Myrick J, et al. The funnel: a screening technique for identifying optimal two-drug combination chemotherapy regimens. Antimicrob Agents Chemother. 2021;65(2):e02172–220.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Te Dorsthorst DT, Verweij PE, Meis JF, Punt NC, Mouton JW. Comparison of fractional inhibitory concentration index with response surface modeling for characterization of in vitro interaction of antifungals against itraconazole-susceptible and -resistant aspergillus fumigatus isolates. Antimicrob Agents Chemother. 2002;46(3):702–7.

    Article  Google Scholar 

  91. de Miranda SC, Hajihosseini A, Myrick J, Nole J, Louie A, Schmidt S, et al. Effect of moxifloxacin plus pretomanid against mycobacterium tuberculosis in log phase, acid phase, and nonreplicating-persister phase in an in vitro assay. Antimicrob Agents Chemother. 2018;63(1):e01695-e1718.

    Google Scholar 

  92. Liou JY, Tsou MY, Obara S, Yu L, Ting CK. Plasma concentration based response surface model predict better than effect-site concentration based model for wake-up time during gastrointestinal endoscopy sedation. J Formos Med Assoc. 2019;118(1 Pt 2):291–8.

    Article  PubMed  Google Scholar 

  93. Caballero U, Kim S, Eraso E, Quindós G, Vozmediano V, Schmidt S, et al. In vitro synergistic interactions of isavuconazole and echinocandins against Candida auris. Antibiotics (Basel). 2021;10(4):355.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Clewe O, Wicha SG, de Vogel CP, de Steenwinkel J, Simonsson U. A model-informed preclinical approach for prediction of clinical pharmacodynamic interactions of anti-tb drug combinations. J Antimicrob Chemother. 2018;73(2):437–47.

    Article  CAS  PubMed  Google Scholar 

  95. Aranzana-Climent V, Buyck JM, Smani Y, Pachón-Diaz J, Marchand S, Couet W, et al. Semi-mechanistic PK/PD modelling of combined polymyxin b and minocycline against a polymyxin-resistant strain of Acinetobacter baumannii. Clin Microbiol Infect. 2020;26(9):1254.e9-1254.e15.

    Article  CAS  PubMed  Google Scholar 

  96. Su H, Koomen JV, Eleveld DJ, Struys M, Colin PJ. Pharmacodynamic mechanism-based interaction model for the haemodynamic effects of remifentanil and propofol in healthy volunteers. Br J Anaesth. 2023;131(2):222–33.

    Article  CAS  PubMed  Google Scholar 

  97. Nøhr-Nielsen A, Bagger SO, Brünner N, Stenvang J, Lund TM. Pharmacodynamic modelling reveals synergistic interaction between docetaxel and sco-101 in a docetaxel-resistant triple negative breast cancer cell line. Eur J Pharm Sci. 2020;148: 105315.

    Article  PubMed  Google Scholar 

  98. Chen C, Wicha SG, de Knegt GJ, Ortega F, Alameda L, Sousa V, et al. Assessing pharmacodynamic interactions in mice using the multistate tuberculosis pharmacometric and general pharmacodynamic interaction models. CPT Pharmacometr Syst Pharmacol. 2017;6(11):787–97.

    Article  CAS  Google Scholar 

  99. Jiang Z, Liu Y, Zhang X, Ting CK, Wang X, Brewer LM, et al. Response surface model comparison and combinations for remifentanil and propofol in describing response to esophageal instrumentation and adverse respiratory events. J Formos Med Assoc. 2022;121(12):2501–11.

    Article  CAS  PubMed  Google Scholar 

  100. Gallay PA, Ramirez CM, Baum MM. Acute antagonism in three-drug combinations for vaginal HIV prevention in humanized mice. Sci Rep. 2023;13(1):4594.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Li Y, Fang M, Xu Z, Li X. Tetrathiomolybdate as an old drug in a new use: as a chemotherapeutic sensitizer for non-small cell lung cancer. J Inorg Biochem. 2022;233: 111865.

    Article  CAS  PubMed  Google Scholar 

  102. Zhou H, Yang Y, Kang Y, Guo T, Zhou Y, Zhang Y, et al. Synergistic toxicity induced by the co-exposure of tenuazonic acid and patulin in caenorhabditis elegans: daf-16 plays an important regulatory role. Ecotoxicol Environ Saf. 2024;270: 115871.

    Article  CAS  PubMed  Google Scholar 

  103. Lin H, Jia B, Wu A. Cytotoxicities of co-occurring alternariol, alternariol monomethyl ether and tenuazonic acid on human gastric epithelial cells. Food Chem Toxicol. 2023;171: 113524.

    Article  CAS  PubMed  Google Scholar 

  104. **ong Y, Kim HK, Özer ÖÇ, van Duijn B, Korthout H, Zi L, et al. Synergistic inhibiting effect of phytochemicals in rheum palmatum on tyrosinase based on metabolomics and isobologram analyses. Molecules. 2023;28(3):944.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Bridgeman L, Juan C, Juan-García A, Berrada H. Individual and combined effect of acrylamide, fumitremorgin c and penitrem a on human neuroblastoma sh-sy5y cells. Food Chem Toxicol. 2023;182: 114114.

    Article  CAS  PubMed  Google Scholar 

  106. Wang Y, Chen L, Liu H, **e J, Yin W, Xu Z, et al. Characterization of the synergistic inhibitory effect of cyanidin-3-o-glucoside and catechin on pancreatic lipase. Food Chem. 2023;404(Pt B): 134672.

    Article  CAS  PubMed  Google Scholar 

  107. Lyu S, Deng Q, Lin W, Wu X. Randomized controlled trial for anesthesia during gastroscopy: interactions between remimazolam and propofol in combination with sufentanil. Int J Clin Pharm. 2023;45(4):857–63.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Al Hagbani T, Rizvi S, Shakil S, Lila A. Nano-formulating besifloxacin and employing quercetin as a synergizer to enhance the potency of besifloxacin against pathogenic bacterial strains: a nano-synergistic approach. Nanomaterials (Basel). 2023;13(14):2083.

    Article  CAS  PubMed  Google Scholar 

  109. Nabili M, Aslani N, Shokohi T, Hedayati MT, Hassanmoghadam F, Moazeni M. In vitro interaction between glabridin and voriconazole against aspergillus fumigatus isolates. Rev Iberoam Micol. 2021;38(3):145–7.

    Article  PubMed  Google Scholar 

  110. Khandelwal Gilman KA, Han S, Won YW, Putnam CW. Complex interactions of lovastatin with 10 chemotherapeutic drugs: a rigorous evaluation of synergism and antagonism. BMC Cancer. 2021;21(1):356.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Schwarz P, Nikolskiy I, Bidaud AL, Sommer F, Bange G, Dannaoui E. In vitro activity of amphotericin B in combination with colistin against fungi responsible for invasive infections. J Fungi (Basel). 2022;8(2):112.

    Google Scholar 

  112. Mashele SA, Steel HC, Matjokotja MT, Rasehlo S, Anderson R, Cholo MC. Assessment of the efficacy of clofazimine alone and in combination with primary agents against mycobacterium tuberculosis in vitro. J Glob Antimicrob Resist. 2022;29:343–52.

    Article  CAS  PubMed  Google Scholar 

  113. Wiśniowska B, Lisowski B, Kulig M, Polak S. Drug interaction at herg channel: in vitro assessment of the electrophysiological consequences of drug combinations and comparison against theoretical models. J Appl Toxicol. 2018;38(4):450–8.

    Article  PubMed  Google Scholar 

  114. Duarte D, Cardoso A, Vale N. Synergistic growth inhibition of HT-29 colon and MCF-7 breast cancer cells with simultaneous and sequential combinations of antineoplastics and CNS drugs. Int J Mol Sci. 2021;22(14):7408.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Wild M, Kicuntod J, Seyler L, Wangen C, Bertzbach LD, Conradie AM, et al. Combinatorial drug treatments reveal promising anticytomegaloviral profiles for clinically relevant pharmaceutical kinase inhibitors (PKIs). Int J Mol Sci. 2021;22(2):575.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Mukherjee D, Wang P, Hooi L, Sandhu V, You K, Blasiak A, et al. Addressing antimicrobial resistance with the identif.ai platform: rapidly optimizing clinically actionable combination therapy regimens against nontuberculous mycobacteria. Theranostics. 2022;12(16):6848–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Hoffmann T, Rahfeld JU, Schenk M, Ponath F, Makioka K, Hutter-Paier B, et al. Combination of the glutaminyl cyclase inhibitor PQ912 (varoglutamstat) and the murine monoclonal antibody PBD-Co6 (m6) shows additive effects on brain aβ pathology in transgenic mice. Int J Mol Sci. 2021;22(21):11791.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Fujimura T, Yamashita-Kashima Y, Kawasaki N, Yoshiura S, Harada N, Yoshimura Y. Obinutuzumab in combination with chemotherapy enhances direct cell death in CD20-positive obinutuzumab-resistant non-hodgkin lymphoma cells. Mol Cancer Ther. 2021;20(6):1133–41.

    Article  CAS  PubMed  Google Scholar 

  119. Mohammadi M, Abbasalipourkabir R, Ziamajidi N. Fish oil and chicoric acid combination protects better against palmitate-induced lipid accumulation via regulating AMPk-mediated SREBP-1/FAS and PPARΑ/UCP2 pathways. Arch Physiol Biochem. 2023;129(1):1–9.

    Article  CAS  PubMed  Google Scholar 

  120. Gunda V, Ghosh C, Hu J, Zhang L, Zhang YQ, Shen M, et al. Combination brafv600e inhibition with the multitargeting tyrosine kinase inhibitor axitinib shows additive anticancer activity in BRAFv600e-mutant anaplastic thyroid cancer. Thyroid. 2023;33(10):1201–14.

    Article  CAS  PubMed  Google Scholar 

  121. Wu Q, Ma X, ** Z, Ni R, Pan Y, Yang G. Zhuidu formula suppresses the migratory and invasive properties of triple-negative breast cancer cells via dual signaling pathways of RHOA/ROCK and CDC42/MRCK. J Ethnopharmacol. 2023;315: 116644.

    Article  CAS  PubMed  Google Scholar 

  122. Kumazoe M, Fujimura Y, Yoshitomi R, Shimada Y, Tachibana H. Fustin, a flavanonol, synergically potentiates the anticancer effect of green tea catechin epigallocatechin-3-O-gallate with activation of the eNOS/cGMP axis. J Agric Food Chem. 2022;70(11):3458–66.

    Article  CAS  PubMed  Google Scholar 

  123. Elkateb AS, Nofal S, Ali SA, Atya HB. Camptothecin sensitizes hepatocellular carcinoma cells to sorafenib- induced ferroptosis via suppression of NRF2. Inflammation. 2023;46(4):1493–511.

    Article  CAS  PubMed  Google Scholar 

  124. Xu J, Nelson AG, Hondzinski JM. Passive static stretching alters the characteristics of the force–velocity curvature differently for fast and slow muscle groups—a practical application of hill’s equation. Hum Mov Sci. 2021;79: 102852.

    Article  PubMed  Google Scholar 

  125. Franco EJ, Drusano GL, Hanrahan KC, Warfield KL, Brown AN. Combination therapy with UV-4b and molnupiravir enhances SARS-cov-2 suppression. Viruses. 2023;15(5):1175.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Wicha SG, Chen C, Clewe O, Simonsson USH. A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions. Nat Commun. 2017;8(1):2129–211.

    Article  PubMed  PubMed Central  Google Scholar 

  127. Scherrer V, Lamoureux F, Chaventre C, Thill C, Demailly Z, Selim J, et al. Reliability of the Minto model for target-controlled infusion of remifentanil during cardiac surgery with cardiopulmonary bypass. Br J Anaesth. 2022;129(2):163–71.

    Article  PubMed  Google Scholar 

  128. Ashton JC. Drug combination studies and their synergy quantification using the Chou-Talalay method—letter. Cancer Res. 2015;75(11):2400.

    Article  CAS  PubMed  Google Scholar 

  129. Chou TC. Preclinical versus clinical drug combination studies. Leuk Lymphoma. 2008;49(11):2059–80.

    Article  PubMed  Google Scholar 

  130. Salmaggi A, Corno C, Maschio M, Donzelli S, D’Urso A, Perego P, et al. Synergistic effect of perampanel and temozolomide in human glioma cell lines. J Pers Med. 2021;11(5):390.

    Article  PubMed  PubMed Central  Google Scholar 

  131. Huang RY, Pei L, Liu Q, Chen S, Dou H, Shu G, et al. Isobologram analysis: a comprehensive review of methodology and current research. Front Pharmacol. 2019;10:1222.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Salinas-Restrepo C, Naranjo-Duran AM, Quintana J, Bueno J, Guzman F, Hoyos PL, et al. Short antimicrobial peptide derived from the venom gland transcriptome of pamphobeteus verdolaga increases gentamicin susceptibility of multidrug-resistant Klebsiella pneumoniae. Antibiotics (Basel). 2023;13(1):6.

    Article  PubMed  Google Scholar 

  133. Lafi Z, Alshaer W, Gharaibeh L, Alqudah DA, Alquaissi B, Bashaireh B, et al. Synergistic combination of doxorubicin with hydralazine, and disulfiram against MCF-7 breast cancer cell line. PLoS ONE. 2023;18(9): e0291981.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Anastasiadi M, Polizzi K, Lambert R. An improved model for the analysis of combined antimicrobials: a replacement for the Chou-Talalay combination index method. J Appl Microbiol. 2018;124(1):97–107.

    Article  CAS  PubMed  Google Scholar 

  135. Kifer D, Jaksic D, Segvic KM. Assessing the effect of mycotoxin combinations: which mathematical model is (the most) appropriate? Toxins (Basel). 2020;12(3):153.

    Article  CAS  PubMed  Google Scholar 

  136. Lederer S, Dijkstra T, Heskes T. Additive dose response models: explicit formulation and the Loewe additivity consistency condition. Front Pharmacol. 2018;9:31.

    Article  PubMed  PubMed Central  Google Scholar 

  137. Thas O, Tourny A, Verbist B, Hawinkel S, Nazarov M, Mutambanengwe K, et al. Statistical detection of synergy: new methods and a comparative study. Pharm Stat. 2022;21(2):345–60.

    Article  PubMed  Google Scholar 

  138. Van der Borght K, Tourny A, Bagdziunas R, Thas O, Nazarov M, Turner H, et al. Bigl: biochemically intuitive generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism. Sci Rep. 2017;7(1):17935.

    Article  PubMed  PubMed Central  Google Scholar 

  139. Zhao W, Sachsenmeier K, Zhang L, Sult E, Hollingsworth RE, Yang H. A new Bliss independence model to analyze drug combination data. J Biomol Screen. 2014;19(5):817–21.

    Article  PubMed  Google Scholar 

  140. Ronneberg L, Cremaschi A, Hanes R, Enserink JM, Zucknick M. Bayesynergy: flexible bayesian modelling of synergistic interaction effects in in vitro drug combination experiments. Brief Bioinf. 2021;22(6):bbab251.

    Article  Google Scholar 

  141. Goldoni M, Johansson C. A mathematical approach to study combined effects of toxicants in vitro: evaluation of the Bliss independence criterion and the Loewe additivity model. Toxicol In Vitro. 2007;21(5):759–69.

    Article  CAS  PubMed  Google Scholar 

  142. Demidenko E, Miller TW. Statistical determination of synergy based on Bliss definition of drugs independence. PLoS ONE. 2019;14(11): e0224137.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Liu Q, Yin X, Languino LR, Altieri DC. Evaluation of drug combination effect using a Bliss independence dose–response surface model. Stat Biopharm Res. 2018;10(2):112–22.

    Article  PubMed  PubMed Central  Google Scholar 

  144. Kashif M, Andersson C, Mansoori S, Larsson R, Nygren P, Gustafsson MG. Bliss and Loewe interaction analyses of clinically relevant drug combinations in human colon cancer cell lines reveal complex patterns of synergy and antagonism. Oncotarget. 2017;8(61):103952–67.

    Article  PubMed  PubMed Central  Google Scholar 

  145. Meletiadis J, Andes DR, Lockhart SR, Ghannoum MA, Knapp CC, Ostrosky-Zeichner L, et al. Multicenter collaborative study of the interaction of antifungal combinations against Candida spp. By Loewe additivity and Bliss independence-based response surface analysis. J Fungi (Basel). 2022;8(9):967.

    Article  CAS  PubMed  Google Scholar 

  146. Witkowski J, Polak S, Rogulski Z, Pawelec D. In vitro/in vivo translation of synergistic combination of MDM2 and MEK inhibitors in melanoma using PBPK/PD modelling: part II. Int J Mol Sci. 2022;23(19):11939.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Yang Y, Chen Y, Wang L, Xu S, Fang G, Guo X, et al. PBPK modeling on organs-on-chips: an overview of recent advancements. Front Bioeng Biotechnol. 2022;10: 900481.

    Article  PubMed  PubMed Central  Google Scholar 

  148. Mi K, Zhou K, Sun L, Hou Y, Ma W, Xu X, et al. Application of semi-mechanistic pharmacokinetic and pharmacodynamic model in antimicrobial resistance. Pharmaceutics. 2022;14(2):246.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Dudal S, Bissantz C, Caruso A, David-Pierson P, Driessen W, Koller E, et al. Translating pharmacology models effectively to predict therapeutic benefit. Drug Discov Today. 2022;27(6):1604–21.

    Article  CAS  PubMed  Google Scholar 

  150. Aksoydan B, Durdagi S. Molecular simulations reveal the impact of ramp1 on ligand binding and dynamics of calcitonin gene-related peptide receptor (CGRPR) heterodimer. Comput Biol Med. 2022;141: 105130.

    Article  CAS  PubMed  Google Scholar 

  151. Auerbach A. Dynamics of receptor activation by agonists. Biophys J. 2024. https://doi.org/10.1016/j.bpj.2024.01.003.

    Article  PubMed  Google Scholar 

  152. Maldonado H, Leyton L. CSK-mediated signalling by integrins in cancer. Front Cell Dev Biol. 2023;11:1214787.

    Article  PubMed  PubMed Central  Google Scholar 

  153. Hanke N, Turk D, Selzer D, Wiebe S, Fernandez E, Stopfer P, et al. A mechanistic, enantioselective, physiologically based pharmacokinetic model of verapamil and norverapamil, built and evaluated for drug–drug interaction studies. Pharmaceutics. 2020;12(6):556.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Wang Z, **ang X, Liu S, Tang Z, Sun H, Parvez M, et al. A physiologically based pharmacokinetic/pharmacodynamic modeling approach for drug–drug interaction evaluation of warfarin enantiomers with sorafenib. Drug Metab Pharmacokinet. 2021;39: 100362.

    Article  CAS  PubMed  Google Scholar 

  155. Takeda T, Hao M, Cheng T, Bryant SH, Wang Y. Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge. J Cheminform. 2017;9:16.

    Article  PubMed  PubMed Central  Google Scholar 

  156. Zhang Z, Tang W. Drug metabolism in drug discovery and development. Acta Pharm Sin B. 2018;8(5):721–32.

    Article  PubMed  PubMed Central  Google Scholar 

  157. D’Souza S, Prema KV, Balaji S, Shah R. Deep learning-based modeling of drug–target interaction prediction incorporating binding site information of proteins. Interdiscip Sci. 2023;15(2):306–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Tajaldini M, Poorkhani A, Amiriani T, Amiriani A, Javid H, Aref P, et al. Strategy of targeting the tumor microenvironment via inhibition of fibroblast/fibrosis remodeling new era to cancer chemo-immunotherapy resistance. Eur J Pharmacol. 2023;957: 175991.

    Article  CAS  PubMed  Google Scholar 

  159. 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.

    Article  CAS  PubMed  Google Scholar 

  160. Lu D, Yadav R, Holder P, Chiang E, Sanjabi S, Poon V, et al. Complex PK-PD of an engineered IL-15/IL-15Ralpha-Fc fusion protein in cynomolgus monkeys: QSP modeling of lymphocyte dynamics. Eur J Pharm Sci. 2023;186: 106450.

    Article  CAS  PubMed  Google Scholar 

  161. Derbalah A, Al-Sallami H, Hasegawa C, Gulati A, Duffull SB. A framework for simplification of quantitative systems pharmacology models in clinical pharmacology. Brit J Clin Pharm. 2022;88:1430–40.

    Article  PubMed  Google Scholar 

  162. Abbiati RA, Wientjes MG, Au JL. Is it time to use modeling of cellular transporter homeostasis to inform drug–drug interaction studies: theoretical considerations. AAPS J. 2021;23(5):102.

    Article  CAS  PubMed  Google Scholar 

  163. Rose R, Mitchell E, Van Der Graaf P, Takaichi D, Hosogi J, Geerts H. A quantitative systems pharmacology model for simulating off-time in augmentation trials for parkinson’s disease: application to preladenant. J Pharmacokinet Pharmacodyn. 2022;49(6):593–606.

    Article  CAS  PubMed  Google Scholar 

  164. Singla S, Block R. Effect compartment model for the evaluation of tolerance to psychological highness following smoking marijuana. J Clin Pharmacol. 2022;62(12):1539–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Liu X, Wang J, Huang YW. Quantifying the effect of nano-tio(2) on the toxicity of lead on c. Dubia using a two-compartment modeling approach. Chemosphere. 2021;263:127958.

    Article  CAS  PubMed  Google Scholar 

  166. Mehta P, Soliman A, Rodriguez-Vera L, Schmidt S, Muniz P, Rodriguez M, et al. Interspecies brain pbpk modeling platform to predict passive transport through the blood–brain barrier and assess target site disposition. Pharmaceutics. 2024;16(2):226.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Iwasaki S, Zhu A, Hanley M, Venkatakrishnan K, **a C. A translational physiologically based pharmacokinetics/pharmacodynamics framework of target-mediated disposition, target inhibition and drug–drug interactions of bortezomib. AAPS J. 2020;22(3):66.

    Article  CAS  PubMed  Google Scholar 

  168. Dudas B, Decleves X, Cisternino S, Perahia D, Miteva MA. Abcg2/bcrp transport mechanism revealed through kinetically excited targeted molecular dynamics simulations. Comput Struct Biotechnol J. 2022;20:4195–205.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Baillie R, Schelling C, Ridgewell R, Kudrycki K, Chung D, Friedrich C, et al. Modeling shows the NK3R antagonist, ACER-801, reduces treatment-induced vasomotor symptoms. J Clin Oncol. 2021;39:e13013–e13013.

    Article  Google Scholar 

  170. Imai T, Hayakawa M, Ohe K. Development of description framework of pharmacodynamics ontology and its application to possible drug–drug interaction reasoning. Stud Health Technol Inf. 2013;192:567–71.

    Google Scholar 

  171. Michinaga S, Nagata A, Ogami R, Ogawa Y, Hishinuma S. Differential regulation of histamine H(1) receptor-mediated ERK phosphorylation by G(q) proteins and arrestins. Biochem Pharmacol. 2023;213: 115595.

    Article  CAS  PubMed  Google Scholar 

  172. Lin LH, Ghasemi M, Burke SM, Mavis CK, Nichols JR, Torka P, et al. Population pharmacokinetics and pharmacodynamics of carfilzomib in combination with rituximab, ifosfamide, carboplatin, and etoposide in adult patients with relapsed/refractory diffuse large B cell lymphoma. Target Oncol. 2023;18(5):685–95.

    Article  PubMed  Google Scholar 

  173. Guo H, Liu C, Li J, Zhang M, Hu M, Xu P, et al. A mechanistic physiologically based pharmacokinetic-enzyme turnover model involving both intestine and liver to predict CYP3A induction-mediated drug–drug interactions. J Pharm Sci. 2013;102(8):2819–36.

    Article  CAS  PubMed  Google Scholar 

  174. Benson N, van der Graaf PH, Peletier LA. Use of mathematics to guide target selection in systems pharmacology; application to receptor tyrosine kinase (RTK) pathways. Eur J Pharm Sci. 2017;109S:S140–8.

    Article  PubMed  Google Scholar 

  175. Franco YL, Ramakrishnan V, Vaidya TR, Mody H, Perez L, Ait-Oudhia S. A quantitative systems pharmacological approach identified activation of jnk signaling pathway as a promising treatment strategy for refractory HER2 positive breast cancer. J Pharmacokinet Pharmacodyn. 2021;48(2):273–93.

    Article  CAS  PubMed  Google Scholar 

  176. Pei F, Li H, Henderson MJ, Titus SA, Jadhav A, Simeonov A, et al. Connecting neuronal cell protective pathways and drug combinations in a Huntington’s disease model through the application of quantitative systems pharmacology. Sci Rep. 2017;7(1):17803.

    Article  PubMed  PubMed Central  Google Scholar 

  177. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Ayyar VS, Jusko WJ. Transitioning from basic toward systems pharmacodynamic models: lessons from corticosteroids. Pharmacol Rev. 2020;72(2):414–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Li RJ, Ma L, Li F, Li L, Bi Y, Yuan Y, et al. Model-informed approach supporting drug development and regulatory evaluation for rare diseases. J Clin Pharmacol. 2022;62:S27–37.

    Article  CAS  PubMed  Google Scholar 

  180. Madabushi R, Seo P, Zhao L, Tegenge M, Zhu H. Review: role of model-informed drug development approaches in the lifecycle of drug development and regulatory decision-making. Pharm Res. 2022;39(8):1669–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Ni L, Khan AZ, Long A, Gao L, Toms N, Gonzalez-Gugel E, et al. Optimizing the dosing regimen of cetuximab and ramucirumab using the model-informed drug development paradigm. Clin Pharmacol Ther. 2023;114(1):77–87.

    Article  CAS  PubMed  Google Scholar 

  182. Dong J, Prieto GL, Huang Y, Tang W, Lundahl A, Elebring M, et al. Understanding statin-roxadustat drug–drug–disease interaction using physiologically-based pharmacokinetic modeling. Clin Pharmacol Ther. 2023;114(4):825–35.

    Article  CAS  PubMed  Google Scholar 

  183. Pfab C, Abgaryan A, Danzer B, Mourtada F, Ali W, Gessner A, et al. Ceftazidime and cefepime antagonize 5-fluorouracil’s effect in colon cancer cells. BMC Cancer. 2022;22(1):125.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Ambrosioni J, Diaz NA, Marzolini C, Dragovic G, Imaz A, Calcagno A, et al. Outcomes of drug interactions between antiretrovirals and co-medications, including over-the-counter drugs: a real-world study. Infect Dis Ther. 2024;13(3):609–17.

    Article  PubMed  PubMed Central  Google Scholar 

  185. Perazzolo S, Shen DD, Scott AM, Ho R. Physiologically based pharmacokinetic model validated to enable predictions of multiple drugs in a long-acting drug-combination nano-particles (DCNP): confirmation with 3 HIV drugs, lopinavir, ritonavir, and tenofovir in DCNP products. J Pharm Sci. 2024;113(6):1653–63.

    Article  CAS  PubMed  Google Scholar 

  186. Sang L, Zhou Z, Luo S, Zhang Y, Qian H, Zhou Y, et al. An in silico platform to predict cardiotoxicity risk of anti-tumor drug combination with hIPSC-CMS based in vitro study. Pharm Res. 2024;41(2):247–62.

    Article  CAS  PubMed  Google Scholar 

  187. Wang H, Zhao C, Santa-Maria CA, Emens LA, Popel AS. Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer. iScience. 2022;25(8):104702.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  188. Zhang C, Gao Z, Niu L, Chen X. Effects of triptolide on pharmacokinetics of amlodipine in rats by using lc-ms/ms. Pharm Biol. 2018;56(1):132–7.

    Article  PubMed  PubMed Central  Google Scholar 

  189. Savoj S, Esfahani M, Karimi A, Karamali F. Integrated stem cells from apical papilla in a 3D culture system improve human embryonic stem cell derived retinal organoid formation. Life Sci. 2022;291: 120273.

    Article  CAS  PubMed  Google Scholar 

  190. Tischler J, Swank Z, Hsiung HA, Vianello S, Lutolf MP, Maerkl SJ. An automated do-it-yourself system for dynamic stem cell and organoid culture in standard multi-well plates. Cell Rep Methods. 2022;2(7): 100244.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  191. Shinha K, Nihei W, Ono T, Nakazato R, Kimura H. A pharmacokinetic-pharmacodynamic model based on multi-organ-on-a-chip for drug–drug interaction studies. Biomicrofluidics. 2020;14(4): 044108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Lin C, Shi J, Moore A, Khetani SR. Prediction of drug clearance and drug–drug interactions in microscale cultures of human hepatocytes. Drug Metab Dispos. 2016;44(1):127–36.

    Article  CAS  PubMed  Google Scholar 

  193. Lin C, Khetani SR. Micropatterned co-cultures of human hepatocytes and stromal cells for the assessment of drug clearance and drug–drug interactions. Curr Protoc Toxicol. 2017. https://doi.org/10.1002/cptx.23.

    Article  PubMed  PubMed Central  Google Scholar 

  194. Lin N, Zhou X, Geng X, Drewell C, Hübner J, Li Z, et al. Repeated dose multi-drug testing using a microfluidic chip-based coculture of human liver and kidney proximal tubules equivalents. Sci Rep. 2020;10(1):8879.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  195. Zhang X, Wei X, Wu CX, Men X, Wang J, Bai JJ, et al. Multiplex profiling of biomarker and drug uptake in single cells using microfluidic flow cytometry and mass spectrometry. ACS Nano. 2024;18(8):6612–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  196. Zhao S, **e Y, Ding X, Zheng C, Chen J, Zhao N, et al. Exploring the causal relationship between antihypertensive drugs and glioblastoma by combining drug target Mendelian randomization study, EQTL colocalization, and single-cell RNA sequencing. Environ Toxicol. 2024;39(6):3425–33.

    Article  CAS  PubMed  Google Scholar 

  197. Chen B, Zou J, **e L, Cai Y, Li B, Tan W, et al. WNT-inhibitory factor 1-mediated glycolysis protects photoreceptor cells in diabetic retinopathy. J Transl Med. 2024;22(1):245.

    Article  PubMed  PubMed Central  Google Scholar 

  198. Xu Q, Jiang S, Kang R, Wang Y, Zhang B, Tian J. Deciphering the molecular pathways underlying dopaminergic neuronal damage in Parkinson’s disease associated with SARS-cov-2 infection. Comput Biol Med. 2024;171: 108200.

    Article  CAS  PubMed  Google Scholar 

  199. Dai SX, Li WX, Li GH, Huang JF. Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin. PeerJ. 2016;4: e1791.

    Article  PubMed  PubMed Central  Google Scholar 

  200. Zareef R, Diab M, Al ST, Makarem A, Younis NK, Bitar F, et al. Aspirin in COVID-19: pros and cons. Front Pharmacol. 2022;13: 849628.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Sakata N. The anti-inflammatory effect of metformin: the molecular targets. Genes Cells. 2024;29(3):183–91.

    Article  CAS  PubMed  Google Scholar 

  202. Yamamiya I, Hunt A, Takenaka T, Sonnichsen D, Mina M, He Y, et al. Evaluation of the cytochrome P450 3A and p-glycoprotein drug–drug interaction potential of futibatinib. Clin Pharmacol Drug Dev. 2023;12(10):966–78.

    Article  CAS  PubMed  Google Scholar 

  203. Zeuner S, Vollmer J, Sigaud R, Oppermann S, Peterziel H, Elharouni D, et al. Combination drug screen identifies synergistic drug interaction of BCL-xl and class I histone deacetylase inhibitors in MYC-amplified medulloblastoma cells. J Neurooncol. 2024;166(1):99–112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  204. Tan S, Willemin ME, Snoeys J, Shen H, Rostami-Hodjegan A, Scotcher D, et al. Development of 4-pyridoxic acid pbpk model to support biomarker-informed evaluation of OAT1/3 inhibition and effect of chronic kidney disease. Clin Pharmacol Ther. 2023;114(6):1243–53.

    Article  CAS  PubMed  Google Scholar 

  205. Watanabe R, Kawata T, Ueda S, Shinbo T, Higashimori M, Natsume-Kitatani Y, et al. Prediction of the contribution ratio of a target metabolic enzyme to clearance from chemical structure information. Mol Pharm. 2023;20(1):419–26.

    Article  CAS  PubMed  Google Scholar 

  206. Ai N, Fan X, Ekins S. In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond. Adv Drug Deliv Rev. 2015;86:46–60.

    Article  CAS  PubMed  Google Scholar 

  207. Ai D, Cai H, Wei J, Zhao D, Chen Y, Wang L. Deepcyps: a deep learning platform for enhanced cytochrome P450 activity prediction. Front Pharmacol. 2023;14:1099093.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  208. Zhou Y, Chen SJ. Advances in machine-learning approaches to rna-targeted drug design. Artif Intell Chem. 2024;2(1): 100053.

    Article  PubMed  PubMed Central  Google Scholar 

  209. Asfand-E-Yar M, Hashir Q, Shah AA, Malik H, Alourani A, Khalil W. Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events. Sci Rep. 2024;14(1):4076.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  210. Sun L, Mi K, Hou Y, Hui T, Zhang L, Tao Y, et al. Pharmacokinetic and pharmacodynamic drug–drug interactions: research methods and applications. Metabolites. 2023;13(8):897.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  211. Hasselgren C, Oprea TI. Artificial intelligence for drug discovery: are we there yet? Annu Rev Pharmacol Toxicol. 2024;64:527–50.

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310000, China

    Di Zhao, ** Huang & Yu He

  2. Henan University of Chinese Medicine, Zhengzhou, China

    Di Zhao

  3. School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China

    Li Yu

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  1. Di Zhao
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  2. ** Huang
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  3. Li Yu
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  4. Yu He
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Correspondence to Yu He.

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Funding

This work was supported by the National Natural Science Foundation of China (no. 82374326).

Conflict of Interest

Di Zhao, ** Huang, Li Yu, and Yu He declare that they have no potential conflicts of interest that might be relevant to the contents of this manuscript.

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All data generated or analyzed during this study are included in this published article (and its supplementary information files).

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Author Contributions

Di Zhao contributed to the drafting of the manuscript. Yu He obtained funding, designed, conceived, supervised the process, and revised the manuscript. Li Yu and ** Huang were involved in searching, screening the search results, translation, and data collection and revising the manuscript. All the authors have read and approved the final manuscript.

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Zhao, D., Huang, P., Yu, L. et al. Pharmacokinetics–Pharmacodynamics Modeling for Evaluating Drug–Drug Interactions in Polypharmacy: Development and Challenges. Clin Pharmacokinet (2024). https://doi.org/10.1007/s40262-024-01391-2

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