Conditions: Suspected or Confirmed COVID-19
Keywords: COVID-19 NBT-NM108 Notitia Biotechnologies Company Notitia Biotechnologies Microbiome Microbiota Gut microbiome Gut microbiota Corona Virus Novel Corona Virus SARS-CoV-2
Allocation: RANDOMIZED | Intervention model: PARALLEL
Enrollment: 42
Healthy volunteers: no

Brief summary:
This open-label, randomized, and controlled clinical trial aims to determine the feasibility and effectiveness of using NBT-NM108, a novel botanical-based fixed-combination drug, to modulate the gut microbiota and treat early-stage suspected or confirmed symptomatic COVID-19 patients.

Detailed description:

Arms:
arm 1: None arm 2: None

Interventions: intervention 1: NBT-NM108 intervention 2: Usual Care Only
Intervention descriptions: intervention 1: Taken in the form of drinks four times a day (before each main meal and 2 hours after dinner) for 28 days. Each drink is prepared by mixing one sachet (30 g) with 500 ml of water. intervention 2: Control group will also follow the same schedule except they will drink the same volume of water without NBT-NM108.

Protocol document:
Prot_000.pdf: 













































SAP_001.pdf: 
4:?58?68

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1 
COVGUT20 
Study Analysis Plan 
Sequencing Data Analysis 
DNA, protein, and metabolites will be extracted from fecal samples. 16S rRNA gene V4 
region sequencing and metagenomic shotgun sequencing with different platforms such as 
Ilumina and PacBio will be done using the extracted DNA. Metaproteome profiling will be 
achieved using mass spectrometry-based proteome analysis based on extracted protein. 
Metabolomics will be measured using fecal sample supernatant. Multi-omics data will be 
combined with clinical data for statistical analysis to explore the overall changes during 
COVID-19 course and changes induced by the treatment. 
  
16S rRNA gene sequencing data will be analyzed based on the amplicon sequence variants 
(ASVs) [1] to determine the gut microbiota composition using QIIME 2 [2]. Shannon index 
and ASV richness will be used to assess alpha diversity. The principal coordinate analysis 
will be used to compare and visualize dissimilarity in gut microbiota structure between 
samples based on beta diversity (Weighted and Unweighted UniFrac distance). Statistical 
differences will be tested using permutational multivariate analysis of variance 
(PERMANOVA). PICRUSt 2 will be used to predict the functions of individual ASVs and the 
collective functions of the gut microbiota at the gene content and pathway levels [3]. Global 
functional profiles of the gut microbiota will be compared and visualized by principal 
component analysis. 
  
Metagenomic sequencing data will be analyzed in a genome-centric way. The raw 
sequencing 
data 
will 
be 
processed 
with 
KneadData 
(https://huttenhower.sph.harvard.edu/kneaddata) for quality control. High-quality draft 
genomes will be de novo assembled from the high-quality sequencing data. The quality 
assessment, taxonomic assignment, and functional annotation of the genomes will be 
conducted by using CheckM [4], GTDB-Tk [5], and Prokka [6] respectively. The principal 
coordinate analysis will be used to compare and visualize dissimilarity in gut microbiota 
structure between samples based on beta diversity, and statistical differences will be tested 
using permutational multivariate analysis of variance (PERMANOVA). 
 
The metaproteomes will be analyzed by using Unified Human Gastrointestinal Genome 
(UHGG) v2.0 as the reference for functional annotation and protein sequences database.  
The predicted protein sequences from metagenomic dataset will be aligned with the 
measured protein sequences from metaproteomic dataset for integrative analysis. Global 
level protein changes of the gut microbiota will be compared and visualized by principal 
component analysis. The metabolome will measure both polar metabolites and lipids. 
Global level of compound changes of will be compared and visualized by principal 
component analysis. 
  
Repeat measures correlation will be used to assess the relationships between ASVs/high-
quality draft genomes [7], followed by ASV/high-quality draft genome clustering based on 
co-abundance patterns. Multivariate methods such as MaAsLin2, that allow adjustment for 


 
 
 
2 
COVGUT20 
confounding variables and model the covariates as random effects, will be applied to 
interpret the relationship between ASVs / ASV co-abundance groups/functions/high-quality 
draft genomes/high-quality draft genome co-abundance groups and the clinical 
metadata/proteins/metabolites. 
 
Clinical Data Analysis  
To analyze the clinical data, Aalen’s additive survival model will be used to test whether the 
impact of covarites on the cumulative hazard is time-dependent [8]. If the covariates do 
not vary with time, cox proportional hazards model will be subsequently used to adjust for 
age, gender, race/ethnicity, baseline COVID-19 severity, and whether the participant has 
taken treatments that may reduce COVID-19 severity (vaccination and monoclonal 
antibody treatment) as covariates. Survival analysis Kaplan-Meyer curves and log-rank test 
will be used to estimate and compare the risk of death and the risk of recovery, as a 
function of time, in participants with COVID-19-like symptoms [9, 10]. To compare the time 
to hospitalization, the time to recovery, the time to complete resolution of subjective 
symptoms, and the time to complete resolution of objective symptoms  between treatment 
groups, we will use competing risks survival analysis (treating death or self-reported illness 
severity at Days 1, 14, 28 and 56 as a competing risk) [10, 11], use survival analysis without 
competing risk, and explore joint survival analysis and Bayesian survival model [12]. 
Speciﬁcally, cumulative incidence functions will be estimated and tested using Gray’s test 
[13]. The Fine-and-Gray subdistribution hazard regression analysis [11] will be used to 
further adjust for age, gender, race/ethnicity, baseline COVID-19 severity, and whether the 
participant has taken treatments that may reduce COVID-19 severity (vaccination and 
monoclonal antibody treatment) as covariates. Proportion of participants who are “alive 
and not admitted to the hospital”, proportions of participants who visit the emergency 
room at Days 1, 14, 28 and 56, have complete resolution of objective symptoms, have 
complete resolution of subjective symptoms and have complete resolution of subjective 
symptoms except cough and fatigue at Days 1, 14, 28 and 56 will be compared between 
groups using separate logistic regression analysis at each time point. Time for participants 
to have complete resolution of objective symptoms, complete resolution of subjective 
symptoms and complete resolution of subjective symptoms except cough and fatigue will 
be compared between groups using competing risks survival analysis described previously, 
treating death or self-reported illness severity at Days 1, 14, 28 and 56 as a competing risk 
[9, 11]. To compare the longitudinally (repeatedly) measured illness severity based on the 
Ordinal Scale for Clinical Improvement from the World Health Organization, body 
temperature (oral), oxygen saturation level, pulse rate, respiratory rate and fasting blood 
glucose between treatment groups, accounting for the eiect of death, reported adverse 
event, or other appropirate competing risk, we will compare the treatment eiect using the 
method of joint survival and longitudinal data analysis [14, 15], where each of the clinical 


 
 
 
3 
COVGUT20 
outcomes (e.g., illness severity, body temperature (oral), and oxygen saturation level, etc.) 
will be modeled using linear mixed models, and death, reported adverse event, or other 
appropirate competing risk will be modelled using Weibull proportional hazards model 
[16]. Age, gender, race/ethnicity, baseline COVID-19 severity, and whether the participant 
has taken treatments that may reduce COVID-19 severity (vaccination and monoclonal 
antibody treatment) will be controlled as covariates in these statistical analyses, where 
appropriate. Within-group changes of main outcomes between groups will be modeled as 
change from pre- to post-treatment usng ANOVA and modeled as the post-treatment value 
as the change itself with the pretreatment value entered as covariate using ANCOVA [17]. 
Lastly, alternative statistical methods will be explored to quantify the underlying 
diierences between groups with respect to a time-to-event end point, robust methods 
include calculating ratio/diierence of t-Year Survival Rates, ratio/diierence of percentiles 
of survival functions, and ratio/diierenceof restricted mean survival times or restricted 
mean time lost [18]. Statistical analyses described above will be conducted for both intent-
to-treat dataset and per-protocal dataset. For each test of an outcome, we deﬁne the 
statistical signiﬁcance by p<0.05. The false discovery rate [19] will be applied for multiple 
testing, where appropriate. Sensitivity analysis using multiple imputation methods [20] will 
be performed to handle the missing data. Other approaches, such as methods of selection 
models [21] or use the pattern-mixture models such as the control-based pattern 
imputation approach, or the tipping-point approach [22, 23] will also be considered. 
 
 


 
 
 
4 
COVGUT20 
Reference 
1. 
Callahan, B.J., et al., DADA2: High-resolution sample inference from Illumina amplicon data. Nat 
Methods, 2016. 13(7): p. 581-3. 
2. 
Bolyen, E., et al., QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data 
science. 2018, PeerJ Preprints. 
3. 
Douglas, G.M., et al., PICRUSt2: An improved and extensible approach for metagenome 
inference. bioRxiv, 2019: p. 672295. 
4. 
Parks, D.H., et al., CheckM: assessing the quality of microbial genomes recovered from isolates, 
single cells, and metagenomes. Genome Res, 2015. 25(7): p. 1043-55. 
5. 
Chaumeil, P.A., et al., GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy 
Database. Bioinformatics, 2019. 
6. 
Seemann, T., Prokka: rapid prokaryotic genome annotation. Bioinformatics, 2014. 30(14): p. 
2068-9. 
7. 
Bakdash, J.Z. and L.R. Marusich, Repeated Measures Correlation. Front Psychol, 2017. 8: p. 
456. 
8. 
Martinussen, T., et al., Estimation of direct effects for survival data by using the Aalen additive 
hazards model. 2011. 73(5): p. 773-788. 
9. 
Putter, H., M. Fiocco, and R.B. Geskus, Tutorial in biostatistics: competing risks and multi-state 
models. Stat Med, 2007. 26(11): p. 2389-430. 
10. 
Meira-Machado, L., et al., Multi-state models for the analysis of time-to-event data. Stat Methods 
Med Res, 2009. 18(2): p. 195-222. 
11. 
Fine, J.P. and R.J. Gray, A Proportional Hazards Model for the Subdistribution of a Competing 
Risk. Journal of the American Statistical Association, 1999. 94(446): p. 496-509. 
12. 
Klein, J.P., et al., Handbook of survival analysis. Chapman & Hall/CRC handbooks of modern 
statistical methods. 2014, Boca Raton, FL: CRC Press. 
13. 
Gray, R.J., A Class of K-Sample Tests for Comparing the Cumulative Incidence of a Competing 
Risk. The Annals of Statistics, 1988. 16(3): p. 1141-1154. 
14. 
Tsiatis, A.A. and M. Davidian, JOINT MODELING OF LONGITUDINAL AND TIME-TO-EVENT 
DATA: AN OVERVIEW. Statistica Sinica, 2004. 14(3): p. 809-834. 
15. 
Rizopoulos, D., JM: An R package for the joint modelling of longitudinal and time-to-event data. 
Journal of Statistical Software (Online), 2010. 35(9): p. 1-33. 
16. 
Rizopoulos, D., JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event 
Data. 2010, 2010. 35(9): p. 33 %J Journal of Statistical Software. 
17. 
French, A., et al., Multivariate analysis of variance (MANOVA). 2008. 
18. 
Uno, H., et al., Moving beyond the hazard ratio in quantifying the between-group difference in 
survival analysis. J Clin Oncol, 2014. 32(22): p. 2380-5. 
19. 
Benjamini, Y. and D. Yekutieli, The control of the false discovery rate in multiple testingunder 
dependency. 2001. 29 %J The Annals of Statistics(4): p. 1165-1188, 24. 
20. 
Siddique, J., O. Harel, and C.M. Crespi, Addressing Missing Data Mechanism Uncertainty using 
Multiple-Model Multiple Imputation: Application to a Longitudinal Clinical Trial. Ann Appl Stat, 
2012. 6(4): p. 1814-1837. 
21. 
Molenberghs, G. and G. Verbeke, A review on linear mixed models for longitudinal data, possibly 
subject to dropout. Statistical Modelling, 2001. 1(4): p. 235-269. 
22. 
Little, R.J.A., Modeling the Drop-Out Mechanism in Repeated-Measures Studies. Journal of the 
American Statistical Association, 1995. 90(431): p. 1112-1121. 
23. 
Roderick, J.A.L. and Y. Wang, Pattern-Mixture Models for Multivariate Incomplete Data with 
Covariates. Biometrics, 1996. 52(1): p. 98-111.