Shoya Iwanami1,2, Keisuke Ejima3, Kwang Su Kim1,2, Koji Noshita1, Yasuhisa Fujita1,2, Taiga Miyazaki4, Shigeru Kohno5, Yoshitsugu Miyazaki6, Shimpei Morimoto7, Shinji Nakaoka8, Yoshiki Koizumi9, Yusuke Asai10, Kazuyuki Aihara11, Koichi Watashi12,13,14, Robin N Thompson15,16, Kenji Shibuya17, Katsuhito Fujiu18,19, Alan S Perelson20,21, Shingo Iwami1,2,22,23,24,25, Takaji Wakita12. 1. Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan. 2. interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan. 3. Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Indiana, United States of America. 4. Department of Infectious Diseases, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan. 5. Nagasaki University, Nagasaki, Japan. 6. Department of Chemotherapy & Mycoses and Leprosy Research Center, National Institute of Infectious Diseases, Tokyo, Japan. 7. Institute of Biomedical Sciences, Nagasaki University, Japan. 8. Faculty of Advanced Life Science, Hokkaido University, Sapporo, Japan. 9. National Center for Global Health and Medicine, Tokyo, Japan. 10. Disease Control and Prevention Center, National Center for Global Health and Medicine, Tokyo, Japan. 11. International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan. 12. Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan. 13. Department of Applied Biological Science, Tokyo University of Science, Noda, Japan. 14. Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan. 15. Mathematics Institute, University of Warwick, Coventry, United Kingdom. 16. Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom. 17. Institute for Population Health, King's College London, London, United Kingdom. 18. Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 19. Department of Advanced Cardiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 20. Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America. 21. New Mexico Consortium, Los Alamos, New Mexico, United States of America. 22. Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan. 23. Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan. 24. NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan. 25. Science Groove Inc., Fukuoka, Japan.
Abstract
BACKGROUND: Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. METHODS AND FINDINGS: A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d-1 (95% CI: 1.06 to 1.27 d-1), 0.777 d-1 (0.716 to 0.838 d-1), and 0.450 d-1 (0.378 to 0.522 d-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. CONCLUSIONS: In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.
BACKGROUND: Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. METHODS AND FINDINGS: A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d-1 (95% CI: 1.06 to 1.27 d-1), 0.777 d-1 (0.716 to 0.838 d-1), and 0.450 d-1 (0.378 to 0.522 d-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. CONCLUSIONS: In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.
Authors: James A Watson; Stephen M Kissler; Nicholas P J Day; Yonatan H Grad; Nicholas J White Journal: Antimicrob Agents Chemother Date: 2022-06-23 Impact factor: 5.938
Authors: Veronika I Zarnitsyna; Juliano Ferrari Gianlupi; Amit Hagar; T J Sego; James A Glazier Journal: Curr Opin Virol Date: 2021-08-24 Impact factor: 7.090