Pharmacokinetic and Pharmacodynamic Drug–Drug Interactions: Research Methods and Applications
Abstract
:1. Introduction
2. Pharmacokinetic Interactions
2.1. Probe Drug Cocktails
2.1.1. In Vitro Cocktail
2.1.2. In Vivo Cocktail
2.2. Hepatic Microsomal and Hepatocyte Models
2.3. Static Model
2.4. Physiologically Based Pharmacokinetic Model
2.4.1. DDI-PBPK Associated with Metabolic Enzymes
2.4.2. Transporter-Associated DDI-PBPK
2.5. Machine Learning Model
3. Pharmacodynamic Interactions
3.1. In Vivo Comparative Efficacy Studies
3.2. In Vitro Static and Dynamic Testing
3.3. Other Methods
4. Prospects
5. Study Highlights
5.1. What Is the Current Knowledge on the Topic?
5.2. What Question Did This Study Address?
5.3. What Does This Study Add to Our Knowledge?
5.4. How Might This Change Clinical Pharmacology or Translational Science?
Author Contributions
Funding
Conflicts of Interest
References
- Butkiewicz, M.; Restrepo, N.A.; Haines, J.L.; Crawford, D.C. Drug-drug interaction profiles of medication regimens extracted from a de-identified electronic medical records system. AMIA Jt. Summits Transl. Sci. Proc. 2016, 2016, 33–40. [Google Scholar]
- Bories, M.; Bouzillé, G.; Cuggia, M.; Le Corre, P. Drug-Drug Interactions with Oral Anticoagulants as Potentially Inappropriate Medications: Prevalence and Outcomes in Elderly Patients in Primary Care and Hospital Settings. Pharmaceutics 2022, 14, 1410. [Google Scholar] [CrossRef]
- Vuorio, A.; Raal, F.; Kovanen, P.T. Drug-drug interaction with oral antivirals for the early treatment of COVID-19. Int. J. Infect. Dis. 2023, 127, 171–172. [Google Scholar] [CrossRef]
- Takeda, T.; Hao, M.; Cheng, T.; Bryant, S.H.; Wang, Y. Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge. J. Cheminform. 2017, 9, 16. [Google Scholar] [CrossRef] [Green Version]
- Holbeck, S.L.; Camalier, R.; Crowell, J.A.; Govindharajulu, J.P.; Hollingshead, M.; Anderson, L.W.; Polley, E.; Rubinstein, L.; Srivastava, A.; Wilsker, D.; et al. The National Cancer Institute ALMANAC: A Comprehensive Screening Resource for the Detection of Anticancer Drug Pairs with Enhanced Therapeutic Activity. Cancer Res. 2017, 77, 3564–3576. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- In vitro Drug Interaction Studies—Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions. Available online: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/in-vitro-drug-interaction-studies-cytochrome-p450-enzyme-and-transporter-mediated-drug-interactions (accessed on 23 January 2020).
- Clinical Drug Interaction Studies—Guidance for Industry on Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions. Available online: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-drug-interaction-studies-cytochrome-p450-enzyme-and-transporter-mediated-drug-interactions (accessed on 23 January 2020).
- Technical Guidelines for Drug Interaction Research (Trial). Available online: https://www.cde.org.cn/main/news/viewInfoCommon/5a15b727e605482c1cf594c689bb994b (accessed on 25 January 2021).
- Zientek, M.; Youdim, K. Simultaneous Determination of Multiple CYP Inhibition Constants using a Cocktail-Probe Approach. Methods Mol. Biol. 2013, 987, 11–23. [Google Scholar] [CrossRef] [PubMed]
- Narushima, K.; Maeda, H.; Shiramoto, M.; Endo, Y.; Ohtsuka, S.; Nakamura, H.; Nagata, Y.; Uchimura, T.; Kannami, A.; Shimazaki, R.; et al. Assessment of CYP-Mediated Drug Interactions for Evocalcet, a New Calcimimetic Agent, Based on In Vitro Investigations and a Cocktail Study in Humans. Clin. Transl. Sci. 2019, 12, 20–27. [Google Scholar] [CrossRef] [Green Version]
- Zientek, M.; Miller, H.; Smith, D.; Dunklee, M.B.; Heinle, L.; Thurston, A.; Lee, C.; Hyland, R.; Fahmi, O.; Burdette, D. Development of an in vitro drug-drug interaction assay to simultaneously monitor five cytochrome P450 isoforms and performance assessment using drug library compounds. J. Pharmacol. Toxicol. Methods 2008, 58, 206–214. [Google Scholar] [CrossRef] [PubMed]
- Kahma, H.; Aurinsalo, L.; Neuvonen, M.; Katajamäki, J.; Paludetto, M.-N.; Viinamäki, J.; Launiainen, T.; Filppula, A.M.; Tornio, A.; Niemi, M.; et al. An automated cocktail method for in vitro assessment of direct and time-dependent inhibition of nine major cytochrome P450 enzymes—Application to establishing CYP2C8 inhibitor selectivity. Eur. J. Pharm. Sci. 2021, 162, 105810. [Google Scholar] [CrossRef]
- Ahire, D.; Sinha, S.; Brock, B.; Iyer, R.; Mandlekar, S.; Subramanian, M. Metabolite Identification, Reaction Phenotyping, and Retrospective Drug-Drug Interaction Predictions of 17-Deacetylnorgestimate, the Active Component of the Oral Contraceptive Norgestimate. Drug Metab. Dispos. 2017, 45, 676–685. [Google Scholar] [CrossRef] [Green Version]
- Chen, A.; Zhou, X.; Cheng, Y.; Tang, S.; Liu, M.; Wang, X. Design and optimization of the cocktail assay for rapid assessment of the activity of UGT enzymes in human and rat liver microsomes. Toxicol. Lett. 2018, 295, 379–389. [Google Scholar] [CrossRef] [PubMed]
- Ebner, T.; Ishiguro, N.; Taub, M.E. The Use of Transporter Probe Drug Cocktails for the Assessment of Transporter-Based Drug-Drug Interactions in a Clinical Setting—Proposal of a Four Component Transporter Cocktail. J. Pharm. Sci. 2015, 104, 3220–3228. [Google Scholar] [CrossRef] [PubMed]
- Sane, R.S.; Ramsden, D.; Sabo, J.P.; Cooper, C.; Rowland, L.; Ting, N.; Whitcher-Johnstone, A.; Tweedie, D.J. Contribution of Major Metabolites toward Complex Drug-Drug Interactions of Deleobuvir: In Vitro Predictions and In Vivo Outcomes. Drug Metab. Dispos. 2015, 44, 466–475. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, Y.; Wang, C.; Wang, S.; Zhou, Q.; Dai, D.; Shi, J.; Xu, X.; Luo, Q. Cytochrome P450-Based Drug-Drug Interactions of Vonoprazan In Vitro and In Vivo. Front. Pharmacol. 2020, 11, 53. [Google Scholar] [CrossRef] [Green Version]
- Nguyen, J.T.; Tian, D.; Tanna, R.S.; Hadi, D.L.; Bansal, S.; Calamia, J.C.; Arian, C.M.; Shireman, L.M.; Molnár, B.; Horváth, M.; et al. Assessing Transporter-Mediated Natural Product-Drug Interactions via In vitro-In Vivo Extrapolation: Clinical Evaluation With a Probe Cocktail. Clin. Pharmacol. Ther. 2020, 109, 1342–1352. [Google Scholar] [CrossRef]
- Fuhr, U.; Jetter, A.; Kirchheiner, J. Appropriate Phenotyping Procedures for Drug Metabolizing Enzymes and Transporters in Humans and Their Simultaneous Use in the “Cocktail” Approach. Clin. Pharmacol. Ther. 2007, 81, 270–283. [Google Scholar] [CrossRef]
- Ryu, J.Y.; Song, I.S.; Sunwoo, Y.E.; Shon, J.H.; Liu, K.H.; Cha, I.J.; Shin, J.G. Development of the “Inje Cocktail” for High-throughput Evaluation of Five Human Cytochrome P450 Isoforms in vivo. Clin. Pharmacol. Ther. 2007, 82, 531–540. [Google Scholar] [CrossRef]
- Suenderhauf, C.; Berger, B.; Puchkov, M.; Schmid, Y.; Müller, S.; Huwyler, J.; Haschke, M.; Krähenbühl, S.; Duthaler, U. Pharmacokinetics and phenotyping properties of the Basel phenotyping cocktail combination capsule in healthy male adults. Br. J. Clin. Pharmacol. 2020, 86, 352–361. [Google Scholar] [CrossRef]
- Lenoir, C.; Daali, Y.; Rollason, V.; Curtin, F.; Gloor, Y.; Bosilkovska, M.; Walder, B.; Gabay, C.; Nissen, M.J.; Desmeules, J.A.; et al. Impact of Acute Inflammation on Cytochromes P450 Activity Assessed by the Geneva Cocktail. Clin. Pharmacol. Ther. 2021, 109, 1668–1676. [Google Scholar] [CrossRef]
- Duthaler, U.; Bachmann, F.; Suenderhauf, C.; Grandinetti, T.; Pfefferkorn, F.; Haschke, M.; Hruz, P.; Bouitbir, J.; Krähenbühl, S. Liver Cirrhosis Affects the Pharmacokinetics of the Six Substrates of the Basel Phenotyping Cocktail Differently. Clin. Pharmacokinet. 2022, 61, 1039–1055. [Google Scholar] [CrossRef]
- Bachmann, F.; Duthaler, U.; Krähenbühl, S. Effect of deglucuronidation on the results of the Basel phenotyping cocktail. Br. J. Clin. Pharmacol. 2021, 87, 4608–4618. [Google Scholar] [CrossRef] [PubMed]
- Rollason, V.; Mouterde, M.; Daali, Y.; Čížková, M.; Priehodová, E.; Kulichová, I.; Posová, H.; Petanová, J.; Mulugeta, A.; Makonnen, E.; et al. Safety of the Geneva Cocktail, a Cytochrome P450 and P-Glycoprotein Phenotyping Cocktail, in Healthy Volunteers from Three Different Geographic Origins. Drug Saf. 2020, 43, 1181–1189. [Google Scholar] [CrossRef]
- Wiebe, S.T.; Giessmann, T.; Hohl, K.; Schmidt-Gerets, S.; Hauel, E.; Jambrecina, A.; Bader, K.; Ishiguro, N.; Taub, M.E.; Sharma, A.; et al. Validation of a Drug Transporter Probe Cocktail Using the Prototypical Inhibitors Rifampin, Probenecid, Verapamil, and Cimetidine. Clin. Pharmacokinet. 2020, 59, 1627–1639. [Google Scholar] [CrossRef]
- Stopfer, P.; Giessmann, T.; Hohl, K.; Hutzel, S.; Schmidt, S.; Gansser, D.; Ishiguro, N.; Taub, M.E.; Sharma, A.; Ebner, T.; et al. Optimization of a drug transporter probe cocktail: Potential screening tool for transporter-mediated drug-drug interactions. Br. J. Clin. Pharmacol. 2018, 84, 1941–1949. [Google Scholar] [CrossRef] [PubMed]
- Stopfer, P.; Giessmann, T.; Hohl, K.; Sharma, A.; Ishiguro, N.; Taub, M.; Zimdahl-Gelling, H.; Gansser, D.; Wein, M.; Ebner, T.; et al. Pharmacokinetic Evaluation of a Drug Transporter Cocktail Consisting of Digoxin, Furosemide, Metformin, and Rosuvastatin. Clin. Pharmacol. Ther. 2016, 100, 259–267. [Google Scholar] [CrossRef]
- Kosa, R.E.; Lazzaro, S.; Bi, Y.-A.; Tierney, B.; Gates, D.; Modi, S.; Costales, C.; Rodrigues, A.D.; Tremaine, L.M.; Varma, M.V. Simultaneous Assessment of Transporter-Mediated Drug-Drug Interactions Using a Probe Drug Cocktail in Cynomolgus Monkey. Drug Metab. Dispos. 2018, 46, 1179–1189. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kim, J.-M.; Seo, S.-W.; Han, D.-G.; Yun, H.; Yoon, I.-S. Assessment of Metabolic Interaction between Repaglinide and Quercetin via Mixed Inhibition in the Liver: In Vitro and In Vivo. Pharmaceutics 2021, 13, 782. [Google Scholar] [CrossRef] [PubMed]
- Choi, W.-G.; Park, R.; Kim, D.K.; Shin, Y.; Cho, Y.-Y.; Lee, H.S. Mertansine Inhibits mRNA Expression and Enzyme Activities of Cytochrome P450s and Uridine 5′-Diphospho-Glucuronosyltransferases in Human Hepatocytes and Liver Microsomes. Pharmaceutics 2020, 12, 220. [Google Scholar] [CrossRef] [Green Version]
- MacKenzie, K.R.; Zhao, M.; Barzi, M.; Wang, J.; Bissig, K.-D.; Maletic-Savatic, M.; Jung, S.Y.; Li, F. Metabolic profiling of norepinephrine reuptake inhibitor atomoxetine. Eur. J. Pharm. Sci. 2020, 153, 105488. [Google Scholar] [CrossRef]
- Elsby, R.; Hare, V.; Neal, H.; Outteridge, S.; Pearson, C.; Plant, K.; Gill, R.U.; Butler, P.; Riley, R.J. 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, 1–8. [Google Scholar] [CrossRef]
- Ye, L.; Cheng, L.; Deng, Y.; Liu, H.; Wu, X.; Wang, T.; Chang, Q.; Zhang, Y.; Wang, D.; Li, Z.; et al. Herb-Drug Interaction between Xiyanping Injection and Lopinavir/Ritonavir, Two Agents Used in COVID-19 Pharmacotherapy. Front. Pharmacol. 2021, 12, 773126. [Google Scholar] [CrossRef] [PubMed]
- Niu, Z.; Qiang, T.; Lin, W.; Li, Y.; Wang, K.; Wang, D.; Wang, X. Evaluation of Potential Herb-Drug Interactions Between Shengmai Injection and Losartan Potassium in Rat and In Vitro. Front. Pharmacol. 2022, 13, 878526. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Lyu, C.; Fong, S.Y.K.; Wang, Q.; Li, C.; Ho, N.J.; Chan, K.S.; Yan, X.; Zuo, Z. Evaluation of potential herb-drug interactions between oseltamivir and commonly used anti-influenza Chinese medicinal herbs. J. Ethnopharmacol. 2019, 243, 112097. [Google Scholar] [CrossRef] [PubMed]
- Yamane, M.; Kawashima, K.; Yamaguchi, K.; Nagao, S.; Sato, M.; Suzuki, M.; Honda, K.; Hagita, H.; Kuhlmann, O.; Poirier, A.; et al. In vitro profiling of the metabolism and drug–drug interaction of tofogliflozin, a potent and highly specific sodium-glucose co-transporter 2 inhibitor, using human liver microsomes, human hepatocytes, and recombinant human CYP. Xenobiotica 2015, 45, 230–238. [Google Scholar] [CrossRef] [PubMed]
- Lawrence, S.K.; Nguyen, D.; Bowen, C.; Richards-Peterson, L.; Skordos, K.W. The Metabolic Drug-Drug Interaction Profile of Dabrafenib: In Vitro Investigations and Quantitative Extrapolation of the P450-Mediated DDI Risk. Drug Metab. Dispos. 2014, 42, 1180–1190. [Google Scholar] [CrossRef] [Green Version]
- Farasyn, T.; Pahwa, S.; Xu, C.; Yue, W. Pre-incubation with OATP1B1 and OATP1B3 inhibitors potentiates inhibitory effects in physiologically relevant sandwich-cultured primary human hepatocytes. Eur. J. Pharm. Sci. 2021, 165, 105951. [Google Scholar] [CrossRef]
- Eng, H.; Tseng, E.; Cerny, M.A.; Goosen, T.C.; Obach, R.S. Cytochrome P450 3A Time-Dependent Inhibition Assays Are Too Sensitive for Identification of Drugs Causing Clinically Significant Drug-Drug Interactions: A Comparison of Human Liver Microsomes and Hepatocytes and Definition of Boundaries for Inactivation Rate Constants. Drug Metab. Dispos. 2020, 49, 442–450. [Google Scholar] [CrossRef]
- Tseng, E.; Eng, H.; Lin, J.; Cerny, M.A.; Tess, D.A.; Goosen, T.C.; Obach, R.S. Static and Dynamic Projections of Drug-Drug Interactions Caused by Cytochrome P450 3A Time-Dependent Inhibitors Measured in Human Liver Microsomes and Hepatocytes. Drug Metab. Dispos. 2021, 49, 947–960. [Google Scholar] [CrossRef]
- Varma, M.V.; El-Kattan, A.F. Transporter-Enzyme Interplay: Deconvoluting Effects of Hepatic Transporters and Enzymes on Drug Disposition Using Static and Dynamic Mechanistic Models. J. Clin. Pharmacol. 2016, 56 (Suppl. S7), S99–S109. [Google Scholar] [CrossRef]
- Iga, K.; Kiriyama, A. Simulations of Cytochrome P450 3A4-Mediated Drug-Drug Interactions by Simple Two-Compartment Model-Assisted Static Method. J. Pharm. Sci. 2017, 106, 1426–1438. [Google Scholar] [CrossRef]
- Iga, K. Dynamic and Static Simulations of Fluvoxamine-Perpetrated Drug-Drug Interactions Using Multiple Cytochrome P450 Inhibition Modeling, and Determination of Perpetrator-Specific CYP Isoform Inhibition Constants and Fractional CYP Isoform Contributions to Victim Clearance. J. Pharm. Sci. 2016, 105, 1307–1317. [Google Scholar] [CrossRef]
- Cheong, E.J.Y.; Ni Goh, J.J.; Hong, Y.; Venkatesan, G.; Liu, Y.; Chiu, G.N.C.; Kojodjojo, P.; Chan, E.C.Y. 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, 260–268. [Google Scholar] [CrossRef] [Green Version]
- Tod, M.; Goutelle, S.; Bleyzac, N.; Bourguignon, L. A Generic Model for Quantitative Prediction of Interactions Mediated by Efflux Transporters and Cytochromes: Application to P-Glycoprotein and Cytochrome 3A4. Clin. Pharmacokinet. 2019, 58, 503–523. [Google Scholar] [CrossRef] [PubMed]
- Fermier, N.; Bourguignon, L.; Goutelle, S.; Bleyzac, N.; Tod, M. Identification of Cytochrome P450-Mediated Drug-Drug Interactions at Risk in Cases of Gene Polymorphisms by Using a Quantitative Prediction Model. Clin. Pharmacokinet. 2018, 57, 1581–1591. [Google Scholar] [CrossRef] [PubMed]
- Taguchi, T.; Masuo, Y.; Futatsugi, A.; Kato, Y. Static Model-Based Assessment of OATP1B1-Mediated Drug Interactions with Preincubation-Dependent Inhibitors Based on Inactivation and Recovery Kinetics. Drug Metab. Dispos. 2020, 48, 750–758. [Google Scholar] [CrossRef] [PubMed]
- Fahmi, O.A.; Shebley, M.; Palamanda, J.; Sinz, M.; Ramsden, D.; Einolf, H.J.; Chen, L.; Wang, H. Evaluation of CYP2B6 Induction and Prediction of Clinical Drug-Drug Interactions: Considerations from the IQ Consortium Induction Working Group—An Industry Perspective. Drug Metab. Dispos. 2016, 44, 1720–1730. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Peters, S.A.; Schroeder, P.E.; Giri, N.; Dolgos, H. Evaluation of the Use of Static and Dynamic Models to Predict Drug-Drug Interaction and Its Associated Variability: Impact on Drug Discovery and Early Development. Drug Metab. Dispos. 2012, 40, 1495–1507. [Google Scholar] [CrossRef] [Green Version]
- Sangana, R.; Gu, H.; Chun, D.Y.; Einolf, H.J. Evaluation of Clinical Drug Interaction Potential of Clofazimine Using Static and Dynamic Modeling Approaches. Drug Metab. Dispos. 2018, 46, 26–32. [Google Scholar] [CrossRef] [Green Version]
- Teorell, T. Kinetics of distribution of substances administered to the body. I: The extravascular modes of administration. Arch. Intern. Pharmacodyn. 1937, 57, 205–225. [Google Scholar]
- Vieira, L.T.; Kirby, B.; Ragueneau-Majlessi, I.; Galetin, A.; Chien, J.Y.L.; Einolf, H.J.; Fahmi, O.A.; Fischer, V.; Fretland, A.; Grime, K.; 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, 189–198. [Google Scholar] [CrossRef]
- Emoto, C.; Fukuda, T.; Cox, S.; Christians, U.; Vinks, A. Development of a Physiologically-Based Pharmacokinetic Model for Sirolimus: Predicting Bioavailability Based on Intestinal CYP3A Content. CPT Pharmacometrics Syst. Pharmacol. 2013, 2, e59. [Google Scholar] [CrossRef] [PubMed]
- Doki, K.; Darwich, A.S.; Achour, B.; Tornio, A.; Backman, J.T.; Rostami-Hodjegan, A. Implications of intercorrelation between hepatic CYP3A4-CYP2C8 enzymes for the evaluation of drug-drug interactions: A case study with repaglinide. Br. J. Clin. Pharmacol. 2018, 84, 972–986. [Google Scholar] [CrossRef] [Green Version]
- Mamidi, R.N.V.S.; Dallas, S.; Sensenhauser, C.; Lim, H.K.; Scheers, E.; Verboven, P.; Cuyckens, F.; Leclercq, L.; Evans, D.C.; Kelley, M.F.; et al. In vitro and physiologically-based pharmacokinetic based assessment of drug-drug interaction potential of canagliflozin. Br. J. Clin. Pharmacol. 2016, 83, 1082–1096. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wu, F.; Krishna, G.; Surapaneni, S. Physiologically based pharmacokinetic modeling to assess metabolic drug-drug interaction risks and inform the drug label for fedratinib. Cancer Chemother. Pharmacol. 2020, 86, 461–473. [Google Scholar] [CrossRef] [PubMed]
- Olafuyi, O.; Coleman, M.; Badhan, R.K. Development of a paediatric physiologically based pharmacokinetic model to assess the impact of drug-drug interactions in tuberculosis co-infected malaria subjects: A case study with artemether-lumefantrine and the CYP3A4-inducer rifampicin. Eur. J. Pharm. Sci. 2017, 106, 20–33. [Google Scholar] [CrossRef] [Green Version]
- Reddy, V.P.; Walker, M.; Sharma, P.; Ballard, P.; Vishwanathan, K. Development, Verification, and Prediction of Osimertinib Drug-Drug Interactions Using PBPK Modeling Approach to Inform Drug Label. CPT Pharmacomet. Syst. Pharmacol. 2018, 7, 321–330. [Google Scholar] [CrossRef]
- Yamada, M.; Ishizuka, T.; Inoue, S.-I.; 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. Biol. Fate Chem. 2020, 48, 769–777. [Google Scholar] [CrossRef]
- Zakaria, Z.; Badhan, R.K. The impact of CYP2B6 polymorphisms on the interactions of efavirenz with lumefantrine: Implications for paediatric antimalarial therapy. Eur. J. Pharm. Sci. 2018, 119, 90–101. [Google Scholar] [CrossRef] [Green Version]
- Yamada, M.; Inoue, S.-I.; Sugiyama, D.; Nishiya, Y.; Ishizuka, T.; Watanabe, A.; Watanabe, K.; Yamashita, S.; Watanabe, N. Critical Impact of Drug-Drug Interactions via Intestinal CYP3A in the Risk Assessment of Weak Perpetrators Using Physiologically Based Pharmacokinetic Models. Drug Metab. Dispos. 2020, 48, 288–296. [Google Scholar] [CrossRef]
- Li, X.; Frechen, S.; Moj, D.; Lehr, T.; Taubert, M.; Hsin, C.-H.; Mikus, G.; Neuvonen, P.J.; Olkkola, K.T.; Saari, T.I.; et al. A Physiologically Based Pharmacokinetic Model of Voriconazole Integrating Time-Dependent Inhibition of CYP3A4, Genetic Polymorphisms of CYP2C19 and Predictions of Drug-Drug Interactions. Clin. Pharmacokinet. 2019, 59, 781–808. [Google Scholar] [CrossRef]
- Cai, T.; Liao, Y.; Chen, Z.; Zhu, Y.; Qiu, X. The Influence of Different Triazole Antifungal Agents on the Pharmacokinetics of Cyclophosphamide. Ann. Pharmacother. 2020, 54, 676–683. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.; Ma, F.; Lu, T.; Budha, N.; Jin, J.Y.; Kenny, J.R.; Wong, H.; Hop, C.E.C.A.; Mao, J. Development of a Physiologically Based Pharmacokinetic Model for Itraconazole Pharmacokinetics and Drug-Drug Interaction Prediction. Clin. Pharmacokinet. 2016, 55, 735–749. [Google Scholar] [CrossRef]
- Chiney, M.S.; Ng, J.; Gibbs, J.P.; Shebley, M. Quantitative Assessment of Elagolix Enzyme-Transporter Interplay and Drug-Drug Interactions Using Physiologically Based Pharmacokinetic Modeling. Clin. Pharmacokinet. 2020, 59, 617–627. [Google Scholar] [CrossRef] [Green Version]
- Duan, P.; Zhao, P.; Zhang, L. Physiologically Based Pharmacokinetic (PBPK) Modeling of Pitavastatin and Atorvastatin to Predict Drug-Drug Interactions (DDIs). Eur. J. Drug Metab. Pharmacokinet. 2016, 42, 689–705. [Google Scholar] [CrossRef] [PubMed]
- Yamazaki, S.; Costales, C.; Lazzaro, S.; Eatemadpour, S.; Kimoto, E.; Varma, M.V. Physiologically-Based Pharmacokinetic Modeling Approach to Predict Rifampin-Mediated Intestinal P-Glycoprotein Induction. CPT: Pharmacometrics Syst. Pharmacol. 2019, 8, 634–642. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yoshikado, T.; Toshimoto, K.; Maeda, K.; Kusuhara, H.; Kimoto, E.; Rodrigues, A.D.; Chiba, K.; Sugiyama, Y. PBPK Modeling of Coproporphyrin I as an Endogenous Biomarker for Drug Interactions Involving Inhibition of Hepatic OATP1B1 and OATP1B3. CPT Pharmacomet. Syst. Pharmacol. 2018, 7, 739–747. [Google Scholar] [CrossRef]
- Asaumi, R.; Toshimoto, K.; Tobe, Y.; Hashizume, K.; Nunoya, K.-I.; Imawaka, H.; Lee, W.; Sugiyama, Y. Comprehensive PBPK Model of Rifampicin for Quantitative Prediction of Complex Drug-Drug Interactions: CYP3A/2C9 Induction and OATP Inhibition Effects. CPT Pharmacomet. Syst. Pharmacol. 2018, 7, 186–196. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Patel, A.; Wilson, R.; Harrell, A.W.; Taskar, K.S.; Taylor, M.; Tracey, H.; Riddell, K.; Georgiou, A.; Cahn, A.P.; Marotti, M.; et al. Drug Interactions for Low-Dose Inhaled Nemiralisib: A Case Study Integrating Modeling, In Vitro, and Clinical Investigations. Drug Metab. Dispos. 2020, 48, 307–316. [Google Scholar] [CrossRef]
- Chen, K.-F.; Chan, L.-N.; Lin, Y.S. PBPK modeling of CYP3A and P-gp substrates to predict drug-drug interactions in patients undergoing Roux-en-Y gastric bypass surgery. J. Pharmacokinet. Pharmacodyn. 2020, 47, 493–512. [Google Scholar] [CrossRef]
- Dai, Y.; Guo, C.; Guo, W.; Eickhoff, C. Drug-drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings. Brief. Bioinform. 2021, 22, 256. [Google Scholar] [CrossRef]
- Jang, H.Y.; Song, J.; Kim, J.H.; Lee, H.; Kim, I.-W.; Moon, B.; Oh, J.M. Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information. NPJ Digit. Med. 2022, 5, 88. [Google Scholar] [CrossRef]
- Deng, Y.; Qiu, Y.; Xu, X.; Liu, S.; Zhang, Z.; Zhu, S.; Zhang, W. META-DDIE: Predicting drug–drug interaction events with few-shot learning. Brief. Bioinform. 2022, 23, 514. [Google Scholar] [CrossRef] [PubMed]
- Nyamabo, A.K.; Yu, H.; Shi, J.-Y. SSI-DDI: Substructure-substructure interactions for drug-drug interaction prediction. Brief. Bioinform. 2021, 22, 133. [Google Scholar] [CrossRef] [PubMed]
- Nyamabo, A.K.; Yu, H.; Liu, Z.; Shi, J.-Y. Drug-drug interaction prediction with learnable size-adaptive molecular substructures. Brief. Bioinform. 2022, 23, 441. [Google Scholar] [CrossRef] [PubMed]
- Wang, N.-N.; Wang, X.-G.; Xiong, G.-L.; Yang, Z.-Y.; Lu, A.-P.; Chen, X.; Liu, S.; Hou, T.-J.; Cao, D.-S. Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes. J. Cheminform. 2022, 14, 23. [Google Scholar] [CrossRef]
- Hu, B.; Zhou, X.; Mohutsky, M.A.; Desai, P.V. Structure-Property Relationships and Machine Learning Models for Addressing CYP3A4-Mediated Victim Drug-Drug Interaction Risk in Drug Discovery. Mol. Pharm. 2020, 17, 3600–3608. [Google Scholar] [CrossRef]
- Bahar, M.A.; Hak, E.; Bos, J.H.J.; Borgsteede, S.D.; Wilffert, B. The burden and management of cytochrome P450 2D6 (CYP2D6)-mediated drug-drug interaction (DDI): Co-medication of metoprolol and paroxetine or fluoxetine in the elderly. Pharmacoepidemiol. Drug Saf. 2017, 26, 752–765. [Google Scholar] [CrossRef]
- Mattos-Junior, E.; Flaherty, D.; Nishimura, L.T.; de Carregaro, A.B.; Carvalho, L.L. Clinical effects of epidurally administered dexmedetomidine with or without lidocaine in sheep. Vet. Rec. 2020, 186, 534. [Google Scholar] [CrossRef]
- Marjani, M.; Akbarinejad, V.; Bagheri, M. Comparison of intranasal and intramuscular ketamine-midazolam combination in cats. Vet. Anaesth. Analg. 2015, 42, 178–181. [Google Scholar] [CrossRef]
- Stemmet, G.P.; Meyer, L.C.; Bruns, A.; Buss, P.; Zimmerman, D.; Koeppel, K.; Zeiler, G.E. Compared to etorphine-azaperone, the ketamine-butorphanol-medetomidine combination is also effective at immobilizing zebra (Equus zebra). Vet. Anaesth. Analg. 2019, 46, 466–475. [Google Scholar] [CrossRef]
- Bustamante, R.; Daza, M.A.; Canfrán, S.; García, P.; Suárez, M.; Trobo, I.; de Segura, I.G. Comparison of the postoperative analgesic effects of cimicoxib, buprenorphine and their combination in healthy dogs undergoing ovariohysterectomy. Vet. Anaesth. Analg. 2018, 45, 545–556. [Google Scholar] [CrossRef] [PubMed]
- Hu, Y.; Miller, M.; Zhang, B.; Nguyen, T.-T.; Nielsen, M.; Aroian, R.V. In vivo and in vitro studies of Cry5B and nicotinic acetylcholine receptor agonist anthelmintics reveal a powerful and unique combination therapy against intestinal nematode parasites. PLoS Negl. Trop. Dis. 2018, 12, e0006506. [Google Scholar] [CrossRef] [PubMed]
- Singh, B.; Jang, Y.; Maharjan, S.; Kim, H.-J.; Lee, A.Y.; Kim, S.; Gankhuyag, N.; Yang, M.-S.; Choi, Y.-J.; Cho, M.-H.; et al. Combination therapy with doxorubicin-loaded galactosylated poly(ethyleneglycol)-lithocholic acid to suppress the tumor growth in an orthotopic mouse model of liver cancer. Biomaterials 2017, 116, 130–144. [Google Scholar] [CrossRef]
- Salandari, S.; Shomali, T.; Mosleh, N.; Nazifi, S. A comparative study on anti-inflammatory drug combinations in domestic pigeons with experimentally induced acute arthritis. Acta Vet. Hung. 2019, 67, 588–601. [Google Scholar] [CrossRef]
- Yao, X.; Zhao, C.-R.; Yin, H.; Wang, K.; Gao, J.-J. Synergistic antitumor activity of sorafenib and artesunate in hepatocellular carcinoma cells. Acta Pharmacol. Sin. 2020, 41, 1609–1620. [Google Scholar] [CrossRef]
- Portell, C.A.; Wages, N.A.; Kahl, B.S.; Budde, L.E.; Chen, R.W.; Cohen, J.B.; Varhegyi, N.E.; Petroni, G.R.; Williams, M.E. Dose Finding Study of Ibrutinib and Venetoclax in Relapsed or Refractory Mantle Cell Lymphoma. Blood Adv. 2022, 6, 1490–1498. [Google Scholar] [CrossRef]
- Veloso, C.; Cardoso, C.; Vitorino, C. Topical Fixed-Dose Combinations: A Way of Progress for Pain Management? J. Pharm. Sci. 2021, 110, 3345–3361. [Google Scholar] [CrossRef]
- Leggio, M.; Fusco, A.; Loreti, C.; Limongelli, G.; Bendini, M.G.; Mazza, A.; Frizziero, A.; Coraci, D.; Padua, L. Fixed and Low-Dose Combinations of Blood Pressure-Lowering Agents: For the Many or the Few? Drugs 2019, 79, 1831–1837. [Google Scholar] [CrossRef]
- Yin, Z.; Deng, Z.; Zhao, W.; Cao, Z. Searching Synergistic Dose Combinations for Anticancer Drugs. Front. Pharmacol. 2018, 9, 535. [Google Scholar] [CrossRef]
- Jadhav, S.B.; Crass, R.L.; Chapel, S.; Kerschnitzki, M.; Sasiela, W.J.; Emery, M.G.; Amore, B.M.; Barrett, P.H.R.; Watts, G.F.; Catapano, A.L. Pharmacodynamic effect of bempedoic acid and statin combinations: Predictions from a dose-response model. Eur. Heart J. Cardiovasc. Pharmacother. 2022, 8, 578–586. [Google Scholar] [CrossRef] [PubMed]
- Yu, Y.; Fang, J.-T.; Zheng, M.; Zhang, Q.; Walsh, T.R.; Liao, X.-P.; Sun, J.; Liu, Y.-H. Combination Therapy Strategies against Multiple-Resistant Streptococcus Suis. Front. Pharmacol. 2018, 9, 489. [Google Scholar] [CrossRef]
- Vestergaard, M.; Paulander, W.; Marvig, R.L.; Clasen, J.; Jochumsen, N.; Molin, S.; Jelsbak, L.; Ingmer, H.; Folkesson, A. Antibiotic combination therapy can select for broad-spectrum multidrug resistance in Pseudomonas aeruginosa. Int. J. Antimicrob. Agents 2016, 47, 48–55. [Google Scholar] [CrossRef] [Green Version]
- Scotty, N.C.; Brooks, D.E.; Rose, C.D.S. In vitro efficacy of an ophthalmic drug combination against corneal pathogens of horses. Am. J. Vet. Res. 2008, 69, 101–107. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.; Xie, M.; Zhou, J.; Lin, H.; Xiao, T.; Wu, L.; Ding, H.; Fang, B. Increased Antimicrobial Activity of Colistin in Combination with Gamithromycin against Pasteurella multocida in a Neutropenic Murine Lung Infection Model. Front. Microbiol. 2020, 11, 511356. [Google Scholar] [CrossRef] [PubMed]
- Luan, W.; Liu, X.; Wang, X.; An, Y.; Wang, Y.; Wang, C.; Shen, K.; Xu, H.; Li, S.; Liu, M.; et al. Inhibition of Drug Resistance of Staphylococcus aureus by Efflux Pump Inhibitor and Autolysis Inducer to Strengthen the Antibacterial Activity of β-lactam Drugs. Pol. J. Microbiol. 2019, 68, 477–491. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bernardino, P.N.; Bersano, P.R.O.; Neto, J.F.L.; Sforcin, J.M. Positive effects of antitumor drugs in combination with propolis on canine osteosarcoma cells (spOS-2) and mesenchymal stem cells. Biomed. Pharmacother. 2018, 104, 268–274. [Google Scholar] [CrossRef] [Green Version]
- Takano, T.; Satoh, K.; Doki, T.; Tanabe, T.; Hohdatsu, T. Antiviral Effects of Hydroxychloroquine and Type I Interferon on In Vitro Fatal Feline Coronavirus Infection. Viruses 2020, 12, 576. [Google Scholar] [CrossRef]
- Broussou, D.C.; Lacroix, M.Z.; Toutain, P.-L.; Woehrlé, F.; El Garch, F.; Bousquet-Melou, A.; Ferran, A.A. Differential Activity of the Combination of Vancomycin and Amikacin on Planktonic vs. Biofilm-Growing Staphylococcus aureus Bacteria in a Hollow Fiber Infection Model. Front. Microbiol. 2018, 9, 572. [Google Scholar] [CrossRef] [Green Version]
- Broussou, D.C.; Toutain, P.-L.; Woehrlé, F.; El Garch, F.; Bousquet-Melou, A.; Ferran, A.A. Comparison of in vitro static and dynamic assays to evaluate the efficacy of an antimicrobial drug combination against Staphylococcus aureus. PLoS ONE 2019, 14, e0211214. [Google Scholar] [CrossRef]
- Keiser, J.; Tritten, L.; Silbereisen, A.; Speich, B.; Adelfio, R.; Vargas, M. Activity of Oxantel Pamoate Monotherapy and Combination Chemotherapy against Trichuris muris and Hookworms: Revival of an Old Drug. PLoS Negl. Trop. Dis. 2013, 7, e2119. [Google Scholar] [CrossRef] [Green Version]
- Kotze, A.C.; Ruffell, A.; Lamb, J.; Elliott, T.P. Response of drug-susceptible and -resistant Haemonchus contortus larvae to monepantel and abamectin alone or in combination in vitro. Vet. Parasitol. 2018, 249, 57–62. [Google Scholar] [CrossRef] [PubMed]
Cocktail | Probe Drug | Enzyme | Dosage (mg) |
---|---|---|---|
Geneva Cocktail | Caffeine | CYP1A2 | 50 |
Bupropion | CYP2B6 | 20 | |
Flurbiprofen | CYP2C9 | 10 | |
Omeprazole | CYP2C19 | 10 | |
Dextromethorphan | CYP2D6 | 10 | |
Midazolam | CYP3A4 | 1 | |
Fexofenadine | P-glycoprotein | 25 | |
Basel Cocktail | Caffeine | CYP1A2 | 10 |
Efavirenz | CYP2B6 | 50 | |
Flurbiprofen | CYP2C9 | 12.5 | |
Omeprazole | CYP2C19 | 10 | |
Metoprolol | CYP2D6 | 12.5 | |
Midazolam | CYP3A | 2 | |
Inje Cocktail | Caffeine | CYP1A2 | 93 |
Losartan | CYP2C9 | 30 | |
Omeprazole | CYP2C19 | 20 | |
Dextromethorphan | CYP2D6 | 30 | |
Midazolam | CYP3A | 2 |
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Sun, L.; Mi, K.; Hou, Y.; Hui, T.; Zhang, L.; Tao, Y.; Liu, Z.; Huang, L. Pharmacokinetic and Pharmacodynamic Drug–Drug Interactions: Research Methods and Applications. Metabolites 2023, 13, 897. https://doi.org/10.3390/metabo13080897
Sun L, Mi K, Hou Y, Hui T, Zhang L, Tao Y, Liu Z, Huang L. Pharmacokinetic and Pharmacodynamic Drug–Drug Interactions: Research Methods and Applications. Metabolites. 2023; 13(8):897. https://doi.org/10.3390/metabo13080897
Chicago/Turabian StyleSun, Lei, Kun Mi, Yixuan Hou, Tianyi Hui, Lan Zhang, Yanfei Tao, Zhenli Liu, and Lingli Huang. 2023. "Pharmacokinetic and Pharmacodynamic Drug–Drug Interactions: Research Methods and Applications" Metabolites 13, no. 8: 897. https://doi.org/10.3390/metabo13080897
APA StyleSun, L., Mi, K., Hou, Y., Hui, T., Zhang, L., Tao, Y., Liu, Z., & Huang, L. (2023). Pharmacokinetic and Pharmacodynamic Drug–Drug Interactions: Research Methods and Applications. Metabolites, 13(8), 897. https://doi.org/10.3390/metabo13080897