1. Ferkol, T, Schraufnagel, D. The global burden of respiratory disease. Ann Am Thorac Soc 2014; 11: 404–406.
Google Scholar | Crossref | Medline2. World Health Organization . Global tuberculosis report 2020, https://apps.who.int/iris/bitstream/handle/10665/336069/9789240013131-eng.pdf (2020, accessed 5 November 2020).
Google Scholar3. Hallett, T, Hogan, A, Jewell, B, et al. Potential impact of the COVID-19 pandemic on HIV, TB and malaria in low-and middle-income countries: a modelling study. Lancet Glob Health 2020; 8: e1132–e1141.
Google Scholar | Crossref | Medline4. Stop, TB Partnership . The potential impact of the COVID-19 response on tuberculosis in high-burden countries: a modelling analysis, http://www.stoptb.org/assets/documents/news/Modeling%20Report_1%20May%202020_FINAL.pdf (2020, accessed 2 November 2020).
Google Scholar5. Sumner, A, Hoy, C, Ortiz-Juarez, E. Estimates of the impact of COVID-19 on global poverty. Helsinki: UNU-WIDER, 2020, pp. 1–14.
Google Scholar | Crossref6. Carter, DJ, Glaziou, P, Lönnroth, K, et al. The impact of social protection and poverty elimination on global tuberculosis incidence: a statistical modelling analysis of sustainable development goal 1. Lancet Glob Health 2018; 6: e514–e522.
Google Scholar | Crossref | Medline7. Saunders, MJ, Evans, CA. COVID-19, tuberculosis, and poverty: preventing a perfect storm. Eur Respir J 2020; 56: 2001348.
Google Scholar | Crossref | Medline8. Tamuzi, JL, Ayele, BT, Shumba, CS, et al. Implications of COVID-19 in high burden countries for HIV/TB: a systematic review of evidence. BMC Infect Dis 2020; 20: 744.
Google Scholar | Crossref | Medline9. Liu, Y, Bi, L, Chen, Y, et al. Active or latent tuberculosis increases susceptibility to COVID-19 and disease severity. medRxiv 2020, https://www.medrxiv.org/content/10.1101/2020.03.10.20033795v1
Google Scholar10. Tadolini, M, Codecasa, LR, García-García, JM, et al. Active tuberculosis, sequelae and COVID-19 co-infection: first cohort of 49 cases. Eur Respir J 2020; 56: 2001398.
Google Scholar | Crossref | Medline11. Miroševi, ć, Skvrce, N, Macolić, Š, arinić, V, Mucalo, I, et al. Adverse drug reactions caused by drug-drug interactions reported to Croatian Agency for Medicinal Products and Medical Devices: a retrospective observational study. Croat Med J 2011; 52: 604–614.
Google Scholar | Crossref | Medline12. Khandeparkar, A, Rataboli, PV. A study of harmful drug–drug interactions due to polypharmacy in hospitalized patients in Goa Medical College. Perspect Clin Res 2017; 8: 180–186.
Google Scholar | Crossref | Medline13. Lazarou, J, Pomeranz, BH, Corey, PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 1998; 279: 1200–1205.
Google Scholar | Crossref | Medline | ISI14. Al-Arifi, M, Abu-Hashem, H, Al-Meziny, M, et al. Emergency department visits and admissions due to drug related problems at Riyadh military hospital (RMH), Saudi Arabia. Saudi Pharm J 2014; 22: 17–25.
Google Scholar | Crossref | Medline15. Taylor, R, Pergolizzi, JV, Puenpatom, RA, et al. Economic implications of potential drug–drug interactions in chronic pain patients. Expert Rev Pharmacoecon Outcomes Res 2013; 13: 725–734.
Google Scholar | Crossref | Medline16. Baburaj, G, Thomas, L, Rao, M. Potential drug interactions of repurposed COVID-19 drugs with lung cancer pharmacotherapies. Arch Med Res 2021; 52: 261–269.
Google Scholar | Crossref | Medline17. Cattaneo, D, Pasina, L, Maggioni, AP, et al. Drug–drug interactions and prescription appropriateness in patients with COVID-19: a retrospective analysis from a Reference Hospital in Northern Italy. Drugs Aging 2020; 37: 925–933.
Google Scholar | Crossref | Medline18. Roblek, T, Vaupotic, T, Mrhar, A, et al. Drug-drug interaction software in clinical practice: a systematic review. Eur J Clin Pharmacol 2015; 71: 131–142.
Google Scholar | Crossref | Medline19. Kheshti, R, Aalipour, M, Namazi, S. A comparison of five common drug–drug interaction software programs regarding accuracy and comprehensiveness. J Res Pharm Pract 2016; 5: 257–263.
Google Scholar | Crossref | Medline20. Safdari, R, Ferdousi, R, Aziziheris, K, et al. Computerized techniques pave the way for drug-drug interaction prediction and interpretation. Bioimpacts 2016; 6: 71–78.
Google Scholar | Crossref | Medline21. Ferdousi, R, Safdari, R, Omidi, Y. Computational prediction of drug-drug interactions based on drugs functional similarities. J Biomed Inform 2017; 70: 54–64.
Google Scholar | Crossref | Medline22. Sotgiu, G, Centis, R, D’ambrosio, L, et al. Tuberculosis treatment and drug regimens. Cold Spring Harb Perspect Med 2015; 5: a017822.
Google Scholar | Crossref | Medline23. Ogu, CC, Maxa, JL. Drug interactions due to cytochrome P450. Proc (Bayl Univ Med Cent) 2000; 13: 421–423.
Google Scholar | Crossref | Medline24. Kim, RB. Drugs as P-glycoprotein substrates, inhibitors, and inducers. Drug Metab Rev 2002; 34: 47–54.
Google Scholar | Crossref | Medline | ISI25. Horita, Y, Doi, N. Comparative study of the effects of antituberculosis drugs and antiretroviral drugs on cytochrome P450 3A4 and P-glycoprotein. Antimicrob Agents Chemother 2014; 58: 3168–3176.
Google Scholar | Crossref | Medline26. Chen, J, Raymond, K. Roles of rifampicin in drug-drug interactions: underlying molecular mechanisms involving the nuclear pregnane X receptor. Ann Clin Microbiol Antimicrob 2006; 5: 3.
Google Scholar | Crossref | Medline27. Guglielmetti, L, Tiberi, S, Burman, M, et al. QT prolongation and cardiac toxicity of new tuberculosis drugs in Europe: a Tuberculosis Network European Trialsgroup (TBnet) study. Eur Respir J 2018; 52: 1800537.
Google Scholar | Crossref | Medline28. Harausz, E, Cox, H, Rich, M, et al. QTc prolongation and treatment of multidrug-resistant tuberculosis. Int J Tuberc Lung Dis 2015; 19: 385–391.
Google Scholar | Crossref | Medline29. Crotti, L, Arbelo, E. COVID-19 treatments, QT interval and arrhythmic risk: the need for an international Registry on Arrhythmias. Heart Rhythm 2020; 17: 1423–1424.
Google Scholar | Crossref | Medline30. van den Broek, MPH, Möhlmann, JE, Abeln, BGS, et al. Chloroquine-induced QTc prolongation in COVID-19 patients. Neth Heart J 2020; 28: 406–409.
Google Scholar | Crossref | Medline31. Vandenberg, JI, Perry, MD, Perrin, MJ, et al. hERG K+ channels: structure, function, and clinical significance. Physiol Rev 2012; 92: 1393–1478.
Google Scholar | Crossref | Medline32. Gutman, GA, Chandy, KG, Adelman, JP, et al. International Union of Pharmacology. XLI . Compendium of voltage-gated ion channels: potassium channels. Pharmacol Rev 2003; 55: 583–586.
Google Scholar | Crossref | Medline33. Recanatini, M, Poluzzi, E, Masetti, M, et al. QT prolongation through hERG K+ channel blockade: current knowledge and strategies for the early prediction during drug development. Med Res Rev 2005; 25: 133–166.
Google Scholar | Crossref | Medline | ISI34. Hancox, JC, McPate, MJ, El Harchi, A, et al. The hERG potassium channel and hERG screening for drug-induced torsades de pointes. Pharmacol Ther 2008; 119: 118–132.
Google Scholar | Crossref | Medline | ISI35. Alexandrou, AJ, Duncan, RS, Sullivan, A, et al. Mechanism of hERG K+ channel blockade by the fluoroquinolone antibiotic moxifloxacin. Br J Pharmacol 2006; 147: 905–916.
Google Scholar | Crossref | Medline36. Briasoulis, A, Agarwal, V, Pierce, WJ. QT prolongation and torsade de pointes induced by fluoroquinolones: infrequent side effects from commonly used medications. Cardiology 2011; 120: 103–110.
Google Scholar | Crossref | Medline | ISI37. Goldstein, EJ, Owens, RC, Nolin, TD. Antimicrobial-associated QT interval prolongation: pointes of interest. Clin Infect Dis 2006; 43: 1603–1611.
Google Scholar | Crossref | Medline38. Carpenter, A, Chambers, OJ, El Harchi, A, et al. COVID-19 management and arrhythmia: risks and challenges for clinicians treating patients affected by SARS-CoV-2. Front Cardiovasc Med 2020; 7: 85.
Google Scholar | Crossref | Medline39. American Society of Health-System Pharmacists . Assessment of evidence for COVID-19-related treatments, https://www.ashp.org/-/media/8CA43C674C6D4335B6A19852843C4052.ashx (accessed 23 March 2021).
Google Scholar40. RCSB PDB . https://www.rcsb.org/ (accessed 10 November 2020).
Google Scholar41. Sevrioukova, I. Interaction of human drug-metabolizing CYP3A4 with small inhibitory molecules. Biochemistry 2019; 58: 930–939.
Google Scholar | Crossref | Medline42. Farid, R, Day, T, Friesner, RA, et al. New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies. Bioorg Med Chem 2006; 14: 3160–3173.
Google Scholar | Crossref | Medline43. Wan, H, Selvaggio, G, Pearlstein, RA. Toward in vivo-relevant hERG safety assessment and mitigation strategies based on relationships between non-equilibrium blocker binding, three-dimensional channel-blocker interactions, dynamic occupancy, dynamic exposure, and cellular arrhythmia. PLoS ONE 2020; 15: e0234946.
Google Scholar | Crossref | Medline44. Schwartz, PJ, Woosley, RL. Predicting the unpredictable: drug-induced QT prolongation and torsades de pointes. J Am Coll Cardiol 2016; 67: 1639–1650.
Google Scholar | Crossref | Medline | ISI45. Woosley, RL, Heise, CW, Romero, KA. QT drugs list, Crediblemeds.org (2016, accessed 20 November 2020).
Google Scholar46. Brillault, J, De Castro, WV, Harnois, T, et al. P-glycoprotein-mediated transport of moxifloxacin in a Calu-3 lung epithelial cell model. Antimicrob Agents Chemother 2009; 53: 1457–1462.
Google Scholar | Crossref | Medline47. Alvarez, AI, Pérez, M, Prieto, JG, et al. Fluoroquinolone efflux mediated by ABC transporters. J Pharm Sci 2008; 97: 3483–3493.
Google Scholar | Crossref | Medline48. Sood, D, Kumar, N, Singh, A, et al. Antibacterial and pharmacological evaluation of fluoroquinolones: a chemoinformatics approach. Genomics Inform 2018; 16: 44–51.
Google Scholar | Crossref | Medline49. Wallis, RS. Cardiac safety of extensively drug-resistant tuberculosis regimens including bedaquiline, delamanid and clofazimine. Eur Respir J 2016; 48: 1526–1527.
Google Scholar | Crossref | Medline50. Shimokawa, Y, Sasahara, K, Yoda, N, et al. Delamanid does not inhibit or induce cytochrome p450 enzymes in vitro. Biol Pharm Bull 2014; 37: 1727–1735.
Google Scholar | Crossref | Medline51. Roberts, T, Beyers, N, Aguirre, A, et al. Immunosuppression during active tuberculosis is characterized by decreased interferon-γ production and CD25 expression with elevated forkhead box P3, transforming growth factor-β, and interleukin-4 mRNA levels. J Infect Dis 2007; 195: 870–878.
Google Scholar | Crossref |