pHOXWELL for COVID-19: real-time meta analysis of 1 study
Abstract
Significantly lower risk is seen for cases.
Meta analysis using the most serious outcome reported shows
47% [29‑62%] lower risk.
Currently there is very limited data, with only one study to date.
No treatment is 100%
effective. Protocols combine safe and effective options with individual
risk/benefit analysis and monitoring.
pHOXWELL may affect the natural microbiome, especially with prolonged use.
All data and sources to reproduce this analysis are in the appendix.
pHOXWELL for COVID-19 — Highlights
pHOXWELL reduces risk with low confidence for cases.
Real-time updates and corrections with a consistent protocol for 172 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.
Naso/oropharyngeal treatments
AlkalinizationCetylpyridin..
Chlorhexidine
Chlorphenira..
Hydrogen Per..
Iota-carragee..
Nitric Oxide
Phthalocyan..
Povidone-Iod..
Sentinox
Sodium Bicar..
pHOXWELL
SARS-CoV-2 infection typically starts in the upper respiratory
tract, and specifically the nasal respiratory epithelium. Entry via the eyes
and gastrointestinal tract is possible, but less common, and entry via other
routes is rare.
Infection may progress to the lower respiratory tract, other tissues, and the
nervous and cardiovascular systems. The primary initial route for entry into
the central nervous system is thought to be the olfactory nerve in the nasal
cavity2.
Progression may lead to cytokine storm, pneumonia, ARDS, neurological
injury3-15 and cognitive
deficits6,11, cardiovascular
complications16-20, organ failure, and death.
Even mild untreated infections may result in persistent cognitive
deficits21—the spike protein binds to fibrin leading to
fibrinolysis-resistant blood clots, thromboinflammation, and
neuropathology.
Systemic treatments may be insufficient to prevent
neurological damage10.
Minimizing replication as early as possible is recommended.
Logically, stopping replication in the upper respiratory tract should be
simpler and more effective.
Wu et al., using an airway organoid model incorporating many in
vivo aspects, show that SARS-CoV-2 initially attaches to cilia—hair-like
structures responsible for moving the mucus layer and where ACE2 is
localized in nasal epithelial cells24. The mucus layer and the
need for ciliary transport slow down infection, providing more time for
localized treatments22,23.
Early or prophylactic nasopharyngeal/oropharyngeal treatment may avoid the
consequences of viral replication in other tissues, and avoid the requirement
for systemic treatments with greater potential for side effects.
SARS-CoV-2 infection and replication involves the complex interplay of 100+
host and viral proteins and other factorsA,25-32, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 9,000 compounds may
reduce COVID-19 risk33, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
pHOXWELL
for COVID-19.
Search methods, inclusion criteria, effect extraction criteria (more serious
outcomes have priority), all individual study data, PRISMA answers, and
statistical methods are detailed in Appendix 1. We present random
effects meta-analysis results for all studies, individual outcomes, and Randomized Controlled Trials (RCTs).
Figure 4 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Figure 4. Treatment stages.
Table 1 summarizes the results for all studies and for Randomized Controlled Trials.
Figure 5 and 6
show forest plots for random effects meta-analysis of
all studies with pooled effects and cases.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 47% [29‑62%] **** | 1 | 556 | 15 |
| Randomized Controlled TrialsRCTs | 47% [29‑62%] **** | 1 | 556 | 15 |
Figure 6. Random effects meta-analysis for cases.
Figure 8.
Optimal spray angle may increase nasopharyngeal drug delivery 100x for nasal sprays,
adapted from Akash et al.
In addition to the dosage and frequency of administration,
efficacy for nasopharyngeal/oropharyngeal treatments may depend on many
other details. For example considering sprays, viscosity, mucoadhesion,
sprayability, and application angle are important.
Akash et al. performed a computational fluid dynamics study
of nasal spray administration showing 100x improvement in nasopharyngeal drug
delivery using a new spray placement protocol, which involves holding the spay
nozzle as horizontally as possible at the nostril, with a slight tilt towards
the cheeks. The study also found the optimal droplet size range for
nasopharyngeal deposition was ~7-17µm.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and
treatment may critically affect how well a treatment works. For example an
antiviral may be very effective when used early but may not be effective in
late stage disease, and may even be harmful. Oseltamivir, for example, is
generally only considered effective for influenza when used within 0-36 or
0-48 hours35,36. Baloxavir marboxil studies for influenza
also show that treatment delay is critical — Ikematsu et al. report
an 86% reduction in cases for post-exposure prophylaxis, Hayden et al.
show a 33 hour reduction in the time to alleviation of symptoms for treatment
within 24 hours and a reduction of 13 hours for treatment within 24-48 hours,
and Kumar et al. report only 2.5 hours improvement for inpatient
treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases37 |
| <24 hours | -33 hours symptoms38 |
| 24-48 hours | -13 hours symptoms38 |
| Inpatients | -2.5 hours to improvement39 |
Figure 9 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 172 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 9. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 172 treatments.
Details of the patient population including age and comorbidities may
critically affect how well a treatment works. For example, many COVID-19
studies with relatively young low-comorbidity patients show all patients
recovering quickly with or without treatment. In such cases, there is little
room for an effective treatment to improve results, for example as in
López-Medina et al.
Efficacy may depend critically on the distribution of
SARS-CoV-2 variants encountered by patients. Risk varies significantly across
variants41, for example the Gamma variant shows significantly
different characteristics42-45. Different
mechanisms of action may be more or less effective depending on variants, for
example the degree to which TMPRSS2 contributes to viral entry can differ
across variants46,47.
Effectiveness may depend strongly on the dosage and treatment regimen.
The quality of medications may vary significantly between
manufacturers and production batches, which may significantly affect efficacy
and safety. Williams et al. analyze ivermectin from 11 different sources,
showing highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
The
distribution of studies will alter the outcome of a meta analysis. Consider a
simplified example where everything is equal except for the treatment delay,
and effectiveness decreases to zero or below with increasing delay. If there
are many studies using very late treatment, the outcome may be negative, even
though early treatment is very effective.
All meta analyses combine heterogeneous studies, varying in population,
variants, and potentially all factors above, and therefore may obscure
efficacy by including studies where treatment is less effective. Generally, we
expect the estimated effect size from meta analysis to be less than that for
the optimal case.
Looking at all studies is valuable for providing an overview of all research,
important to avoid cherry-picking, and informative when a positive result is
found despite combining less-optimal situations. However, the resulting
estimate does not apply to specific cases such as
early treatment in high-risk populations.
While we present results for all studies, we also present treatment time and
individual outcome analyses, which may be more informative for specific use
cases.
Studies to date use a variety of administration methods to the
respiratory tract, including nasal and oral sprays, nasal irrigation, oral
rinses, and inhalation. Table 3 shows the relative efficacy for
nasal, oral, and combined administration. Combined administration shows the
best results, and nasal administration is more effective than oral. Precise
efficacy depends on the details of administration, e.g., mucoadhesion and
sprayability for sprays.
| Nasal/oral administration to the respiratory tract | Improvement | Studies |
| Oral spray/rinse | 38% [25‑49%] | 11 |
| Nasal spray/rinse | 58% [49‑65%] | 20 |
| Nasal & oral | 91% [74‑97%] | 7 |
Nasopharyngeal/oropharyngeal treatments may not be highly selective. In
addition to inhibiting or disabling SARS-CoV-2, they may also be harmful to
beneficial microbes, disrupting the natural microbiome in the oral cavity and
nasal passages that have important protective and metabolic roles67. This may be
especially important for prolonged use or overuse.
Table 4 summarizes the potential for common
nasopharyngeal/oropharyngeal treatments to affect the natural
microbiome.
| Treatment | Microbiome disruption potential | Notes |
|---|---|---|
| Iota-carrageenan | Low | Primarily antiviral, however extended use may mildly affect the microbiome |
| Nitric Oxide | Low to moderate | More selective towards pathogens, however excessive concentrations or prolonged use may disrupt the balance of bacteria |
| Alkalinization | Moderate | Increases pH, negatively impacting beneficial microbes that thrive in a slightly acidic environment |
| Cetylpyridinium Chloride | Moderate | Quaternary ammonium broad-spectrum antiseptic that can disrupt beneficial and harmful bacteria |
| Phthalocyanine | Moderate to high | Photodynamic compound with antimicrobial activity, likely to affect the microbiome |
| Chlorhexidine | High | Potent antiseptic with broad activity, significantly disrupts the microbiome |
| Hydrogen Peroxide | High | Strong oxidizer, harming both beneficial and harmful microbes |
| Povidone-Iodine | High | Potent broad-spectrum antiseptic harmful to beneficial microbes |
Publishing is often biased
towards positive results, however evidence suggests that there may be a negative bias for
inexpensive treatments for COVID-19. Both negative and positive results are
very important for COVID-19, media in many countries prioritizes negative
results for inexpensive treatments (inverting the typical incentive for
scientists that value media recognition), and there are many reports of
difficulty publishing positive results68-71.
For pHOXWELL, there is currently not
enough data to evaluate publication bias with high confidence.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. pHOXWELL for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 pHOXWELL trials have been run by
physicians on the front lines with the primary goal of finding the best
methods to save human lives and minimize the collateral damage caused by
COVID-19. While pharmaceutical companies are careful to run trials under
optimal conditions (for example, restricting patients to those most likely to
benefit, only including patients that can be treated soon after onset when
necessary, and ensuring accurate dosing), not all pHOXWELL trials
represent the optimal conditions for efficacy.
Summary statistics from
meta analysis necessarily lose information. As with all meta analyses, studies
are heterogeneous, with differences
in treatment delay, treatment regimen, patient demographics, variants,
conflicts of interest, standard of care, and other factors. We provide analyses for specific
outcomes and by treatment delay, and we aim to identify key characteristics in
the forest plots and summaries. Results should be viewed in the context of
study characteristics.
Some analyses classify treatment based on early or late
administration, as done here, while others distinguish between mild, moderate,
and severe cases. Viral load does not indicate degree of symptoms — for
example patients may have a high viral load while being asymptomatic. With
regard to treatments that have antiviral properties, timing of treatment is
critical — late administration may be less helpful regardless of
severity.
Details of treatment delay per patient is often not available.
For example, a study may treat 90% of patients relatively early, but the
events driving the outcome may come from 10% of patients treated very late.
Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in
the studies performed, for example dose, variants, and conflicts of interest.
Trials with conflicts of interest may use designs better suited to the
preferred outcome.
In some cases, the most serious outcome has very few events,
resulting in lower confidence results being used in pooled analysis, however
the method is simpler and more transparent. This is less critical as the
number of studies increases. Restriction to outcomes with sufficient power may
be beneficial in pooled analysis and improve accuracy when there are few
studies, however we maintain our pre-specified method to avoid any
retrospective changes.
Studies show that combinations of treatments can be highly
synergistic and may result in many times greater efficacy than individual
treatments alone50-66.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
SARS-CoV-2 infection and replication involves a complex
interplay of 100+ host and viral proteins and other
factors25-32, providing many therapeutic
targets.
Over 9,000 compounds have been predicted to reduce COVID-19
risk33, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 10 shows an overview of the results for pHOXWELL
in the context of multiple COVID-19 treatments, and Figure 11 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Figure 10.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 9,000+ proposed treatments show efficacy73.
Figure 11. Efficacy vs. cost for COVID-19 treatments.
SARS-CoV-2 infection typically starts in the upper respiratory tract.
Progression may lead to cytokine storm, pneumonia, ARDS, neurological issues,
organ failure, and death. Stopping replication in the upper respiratory tract,
via early or prophylactic nasopharyngeal/oropharyngeal treatment, can avoid
the consequences of progression to other tissues, and avoid the requirement
for systemic treatments with greater potential for side effects.
Studies to date show that pHOXWELL is
an effective treatment for COVID-19.
Significantly lower risk is seen for cases.
Meta analysis using the most serious outcome reported shows
47% [29‑62%] lower risk.
Currently there is very limited data, with only one study to date.
pHOXWELL may affect the natural microbiome, especially with prolonged use.
648 patient RCT pHOXWELL nasal spray in India, showing significantly lower IgGS+ and significantly lower symptomatic cases with treatment.
pHOXWELL includes a combination of natural virucidal agents and is designed to mimic the fluid surrounding healthy cells. The spray included xylitol, zinc chloride, polyethylene glycol 400, poloxamer, disodium hydrogen phosphate, sodium chloride, hydroxypropyl methylcellulose, ginger oil, eucalyptus oil, basil oil, clove oil, sodium hydrogen carbonate, potassium dihydrogen phosphate, ethylenediaminetetraacetic acid, sodium hyaluronate, calcium chloride dihydrate, benzalkonium chloride, magnesium chloride hexahydrate, potassium chloride, and glycerol. The spray was administered up to three times per day (TID) 140 μl/nostril for 45 days, with a gap of 6-8 hours between doses.
pHOXWELL includes a combination of natural virucidal agents and is designed to mimic the fluid surrounding healthy cells. The spray included xylitol, zinc chloride, polyethylene glycol 400, poloxamer, disodium hydrogen phosphate, sodium chloride, hydroxypropyl methylcellulose, ginger oil, eucalyptus oil, basil oil, clove oil, sodium hydrogen carbonate, potassium dihydrogen phosphate, ethylenediaminetetraacetic acid, sodium hyaluronate, calcium chloride dihydrate, benzalkonium chloride, magnesium chloride hexahydrate, potassium chloride, and glycerol. The spray was administered up to three times per day (TID) 140 μl/nostril for 45 days, with a gap of 6-8 hours between doses.
We perform ongoing searches of PubMed, medRxiv, Europe PMC,
ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research
Square, ScienceDirect, Oxford University Press, the reference lists of other
studies and meta-analyses, and submissions to the site c19early.org.
Search terms are pHOXWELL and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion.
All studies regarding the use of pHOXWELL for COVID-19 that report
a comparison with a control group are included in the main analysis.
Studies with major unexplained data issues, for example major outcome data that
is impossible to be correct with no response from the authors, are excluded.
This is a living analysis and is updated regularly.
Figure 12.
Mid-recovery results can more accurately reflect efficacy when almost all patients
recover. Mateja et al. confirm that intermediate viral load results more accurately
reflect hospitalization/death.
We extracted effect sizes and associated data from all studies.
If studies report multiple kinds of effects then the most serious
outcome is used in pooled analysis, while other outcomes are included in the
outcome specific analyses. For example, if effects for mortality and cases are
reported then they are both used in specific outcome analyses, while mortality
is used for pooled analysis.
If symptomatic
results are reported at multiple times, we use the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28
days have preference. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms are not used, the next most serious
outcome with one or more events is used. For example, in low-risk populations
with no mortality, a reduction in mortality with treatment is not possible,
however a reduction in hospitalization, for example, is still valuable.
Clinical outcomes are considered more important than viral outcomes.
When basically all patients recover in both treatment and control groups,
preference for viral clearance and recovery is given to results mid-recovery
where available. After most or all patients have recovered there is little or
no room for an effective treatment to do better, however faster recovery is
valuable.
An IPD meta-analysis confirms that intermediate viral load reduction
is more closely associated with hospitalization/death than later
viral load reduction74.
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
When results provide an odds ratio, we compute the relative risk when
possible, or convert to a relative risk according to Zhang et al.
Reported confidence intervals and p-values are used when available,
and adjusted values are used when provided. If multiple types of adjustments are
reported propensity score matching and multivariable regression has preference
over propensity score matching or weighting, which has preference over
multivariable regression. Adjusted results have preference over unadjusted
results for a more serious outcome when the adjustments significantly alter
results. When needed, conversion between reported p-values and
confidence intervals followed Altman, Altman (B), and Fisher's exact
test was used to calculate p-values for event data. If continuity
correction for zero values is required, we use the reciprocal of the opposite
arm with the sum of the correction factors equal to 178.
Results are expressed with RR < 1.0 favoring treatment, and using the risk of
a negative outcome when applicable (for example, the risk of death rather than
the risk of survival). If studies only report relative continuous values such
as relative times, the ratio of the time for the treatment group versus the
time for the control group is used. Calculations are done in Python
(3.13.4) with
scipy (1.15.3), pythonmeta (1.26), numpy (2.3.0), statsmodels (0.14.4), and plotly (6.1.2).
Forest plots are computed using PythonMeta79
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Results are presented with 95% confidence intervals. Heterogeneity among studies was
assessed using the I2 statistic.
Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor
(4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically significant.
Grobid 0.8.2 is used to parse PDF documents.
We have classified studies as early treatment if most patients
are not already at a severe stage at the time of treatment (for example based
on oxygen status or lung involvement), and treatment started within 5 days of
the onset of symptoms. If studies contain a mix of early treatment and late
treatment patients, we consider the treatment time of patients contributing
most to the events (for example, consider a study where most patients are
treated early but late treatment patients are included, and all mortality
events were observed with late treatment patients).
We note that a shorter time may be preferable. Antivirals are typically only
considered effective when used within a shorter timeframe, for example 0-36 or
0-48 hours for oseltamivir, with longer delays not being effective35,36.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
A summary of study results is below. Please submit
updates and corrections at https://c19early.org/phxmeta.html.
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Balmforth, 7/25/2022, Double Blind Randomized Controlled Trial, placebo-controlled, India, peer-reviewed, 15 authors, study period April 2021 - July 2021, trial CTRI/2021/04/032989. | risk of symptomatic case, 47.4% lower, RR 0.53, p < 0.001, treatment 57 of 275 (20.7%), control 112 of 281 (39.9%), NNT 5.2, odds ratio converted to relative risk. |
| risk of IgG positive, 62.7% lower, RR 0.37, p < 0.001, treatment 36 of 275 (13.1%), control 97 of 281 (34.5%), NNT 4.7, adjusted per study, odds ratio converted to relative risk, multivariable. |
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