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Phthalocyanine for COVID-19: real-time meta analysis of 4 studies

@CovidAnalysis, December 2023
https://c19early.org/ptmeta.html
 
0 0.5 1 1.5+ All studies 38% 4 5,245 Improvement, Studies, Patients Relative Risk Mortality 85% 1 41 ICU admission 92% 1 41 Hospitalization 54% 1 41 Cases 54% 1 5,040 Viral clearance 51% 1 30 RCTs 38% 3 205 Prophylaxis 54% 1 5,040 Early 29% 1 134 Late 52% 2 71 Phthalocyanine for COVID-19 c19early.org December 2023 Favorsphthalocyanine Favorscontrol
Statistically significant lower risk is seen for ICU admission, hospitalization, recovery, and viral clearance. 3 studies from 3 independent teams (all from the same country) show statistically significant improvements.
Meta analysis using the most serious outcome reported shows 38% [20‑51%] lower risk. Results are similar for Randomized Controlled Trials.
No treatment or intervention is 100% effective. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective.
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence Phthalocyanine p=0.00019 Acetaminophen p=0.00000045 2020 2021 2022 2023 Effective Harmful c19early.org December 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
Highlights
Phthalocyanine reduces risk for COVID-19 with very high confidence for pooled analysis, low confidence for ICU admission, hospitalization, recovery, cases, and viral clearance, and very low confidence for mortality.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 62 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Poleti (DB RCT) 29% 0.71 [0.53-0.96] no recov. 29/59 52/75 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.024 Early treatment 29% 0.71 [0.53-0.96] 29/59 52/75 29% lower risk da Silva.. (DB RCT) 85% 0.15 [0.01-2.66] death 0/20 3/21 Improvement, RR [CI] Treatment Control Colado .. (DB RCT) 51% 0.49 [0.29-0.83] viral load 15 (n) 15 (n) Tau​2 = 0.00, I​2 = 0.0%, p = 0.0043 Late treatment 52% 0.48 [0.29-0.79] 0/35 3/36 52% lower risk Brito-Reia 54% 0.46 [0.20-1.08] cases 6/1,153 44/3,887 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.073 Prophylaxis 54% 0.46 [0.20-1.08] 6/1,153 44/3,887 54% lower risk All studies 38% 0.62 [0.49-0.80] 35/1,247 99/3,998 38% lower risk 4 phthalocyanine COVID-19 studies c19early.org December 2023 Tau​2 = 0.00, I​2 = 0.0%, p = 0.00019 Effect extraction pre-specified(most serious outcome, see appendix) Favors phthalocyanine Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Poleti (DB RCT) 29% recovery Improvement Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 0.024 Early treatment 29% 29% lower risk da Silv.. (DB RCT) 85% death Colado .. (DB RCT) 51% viral- Tau​2 = 0.00, I​2 = 0.0%, p = 0.0043 Late treatment 52% 52% lower risk Brito-Reia 54% case Tau​2 = 0.00, I​2 = 0.0%, p = 0.073 Prophylaxis 54% 54% lower risk All studies 38% 38% lower risk 4 phthalocyanine C19 studies c19early.org December 2023 Tau​2 = 0.00, I​2 = 0.0%, p = 0.00019 Effect extraction pre-specifiedRotate device for details Favors phthalocyanine Favors control
B
0 0.25 0.5 0.75 1 1.25 1.5+ All studies Late treatment Early treatment Prophylaxis Efficacy in COVID-19 phthalocyanine studies (pooled effects) Favors phthalocyanine Favors control c19early.org December 2023
C
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D
-100% -50% 0% 50% 100% Timeline of COVID-19 phthalocyanine studies (pooled effects) 2020 2021 2022 2023 Favorsphthalocyanine Favorscontrol c19early.org December 2023 December 2021: efficacy (pooled outcomes) June 2023: efficacy (RCT pooled)
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. 0.7% of 5,900 proposed treatments show efficacy c19early.org. D. Timeline of results in phthalocyanine studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and pooled outcomes in RCTs. Efficacy based on RCTs only was delayed by 18.5 months, compared to using all studies.
We analyze all significant studies concerning the use of phthalocyanine 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, studies within each treatment stage, individual outcomes, and Randomized Controlled Trials (RCTs).
Figure 2 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 2. Treatment stages.
Table 1 summarizes the results for all stages combined and after exclusions. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, and 9 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ICU admission, hospitalization, recovery, cases, and viral clearance.
Table 1. Random effects meta-analysis for all stages combined and after exclusions. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01.
Improvement Studies Patients Authors
All studies38% [20‑51%]
***
4 5,245 47
Randomized Controlled TrialsRCTs38% [14‑55%]
**
3 205 40
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01.
Early treatment Late treatment Prophylaxis
All studies29% [4‑47%]
*
52% [21‑71%]
**
54% [-8‑80%]
Randomized Controlled TrialsRCTs29% [4‑47%]
*
52% [21‑71%]
**
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ICU admission.
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Figure 6. Random effects meta-analysis for hospitalization.
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Figure 7. Random effects meta-analysis for recovery.
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Figure 8. Random effects meta-analysis for cases.
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Figure 9. Random effects meta-analysis for viral clearance.
Figure 10 shows a comparison of results for RCTs and non-RCT studies. Figure 11 and 12 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. RCT results are included in Table 1 and Table 2.
Bias in clinical research may be defined as something that tends to make conclusions differ systematically from the truth. RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases Jadad, and analysis of double-blind RCTs has identified extreme levels of bias Gøtzsche. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 62 treatments we have analyzed, 64% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments (they may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration).
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. RCTs for phthalocyanine are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments, and may be greater when the risk of a serious outcome is overstated. This bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. Concato find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. Lee shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see Deaton, Nichol.
Currently, 41 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 41 treatments with statistically significant efficacy/harm, 25 have been confirmed in RCTs, with a mean delay of 5.5 months. For the 16 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 13 are all consistent with the overall results (benefit or harm), with 10 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.
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Figure 10. Results for RCTs and non-RCT studies.
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Figure 11. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 12. Random effects meta-analysis for RCT mortality results.
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 hours McLean, Treanor. Baloxavir studies for influenza also show that treatment delay is critical — Ikematsu report an 86% reduction in cases for post-exposure prophylaxis, Hayden 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 report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases Ikematsu
<24 hours-33 hours symptoms Hayden
24-48 hours-13 hours symptoms Hayden
Inpatients-2.5 hours to improvement Kumar
Figure 13 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 62 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 13. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 62 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 (as in López-Medina).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics Faria, Karita, Nonaka, Zavascki. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective Peacock, Willett.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 14. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
Currently, 41 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 89% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months. When restricting to RCTs only, 52% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months.
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Figure 14. The time when studies showed that treatments were effective, defined as statistically significant improvement of ≥10% from ≥3 studies. Pooled results typically show efficacy earlier than specific outcome results. Results from all studies often shows efficacy much earlier than when restricting to RCTs. Results reflect conditions as used in trials to date, these depend on the population treated, treatment delay, and treatment regimen.
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. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
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.
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 results Boulware, Meeus, Meneguesso. For phthalocyanine, there is currently not enough data to evaluate publication bias with high confidence.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 15 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 15. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Phthalocyanine for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 phthalocyanine 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 phthalocyanine 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 by 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 affiliated with special interests 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 alone Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu. 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, vaccine, 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.
Studies to date show that phthalocyanine is an effective treatment for COVID-19. Statistically significant lower risk is seen for ICU admission, hospitalization, recovery, and viral clearance. 3 studies from 3 independent teams (all from the same country) show statistically significant improvements. Meta analysis using the most serious outcome reported shows 38% [20‑51%] lower risk. Results are similar for Randomized Controlled Trials.
0 0.5 1 1.5 2+ Case 54% Improvement Relative Risk Phthalocyanine  Brito-Reia et al.  Prophylaxis Does phthalocyanine reduce COVID-19 infections? Prospective study of 5,040 patients in Brazil Fewer cases with phthalocyanine (not stat. sig., p=0.076) c19early.org Brito-Reia et al., German Medical Scie.., Nov 2021 Favors phthalocyanine Favors control
Brito-Reia: Comparison of two similar communities in Brazil, with one using a phthalocyanine derivative mouthwash, suggesting efficacy of the treatment in lowering COVID-19 cases. There was 54% lower risk of confirmed cases during the intervention in the treatment community, compared with 15% higher and 8% lower risk before and after the intervention. Gargle/rinse with 5mL of mouthwash containing phthalocyanine derivative for 1 minute, 3 to 5 times per day.
0 0.5 1 1.5 2+ Ct improvement, day 3 51% Improvement Relative Risk Ct improvement, day 3 (b) 38% Ct improvement, day 1 72% Ct improvement, day 1 (b) 68% Phthalocyanine  Colado Simão et al.  LATE TREATMENT  DB RCT Is late treatment with phthalocyanine beneficial for COVID-19? Double-blind RCT 75 patients in Brazil (November 2020 - February 2021) Improved viral clearance with phthalocyanine (p=0.0075) c19early.org Colado Simão et al., German Medical Sc.., Jun 2023 Favors phthalocyanine Favors control
Colado Simão: RCT 75 patients in Brazil, showing significantly lower viral load with phthalocyanine mouthwash and nasal spray. The combination was more effective than mouthwash alone.
0 0.5 1 1.5 2+ Mortality 85% Improvement Relative Risk ICU admission 92% Discharge 54% Phthalocyanine  da Silva Santos et al.  LATE TREATMENT  DB RCT Is late treatment with phthalocyanine beneficial for COVID-19? Double-blind RCT 41 patients in Brazil (August - November 2020) Lower ICU admission (p=0.021) and hospitalization (p=0.031) c19early.org da Silva Santos et al., Scientific Rep.., Oct 2021 Favors phthalocyanine Favors control
da Silva Santos: RCT 41 patients in Brazil, 20 treated with a phthalocyanine derivative mouthwash, showing shorter hosptalization and lower ICU admission with treatment. One minute gargling/rinsing 5 times per day.
0 0.5 1 1.5 2+ Recovery, day 7 29% Improvement Relative Risk Recovery, day 3 22% Recovery, day 7, dyspnea 46% Recovery, day 3, dyspnea 32% Phthalocyanine  Poleti et al.  EARLY TREATMENT  DB RCT Is early treatment with phthalocyanine beneficial for COVID-19? Double-blind RCT 134 patients in Brazil (November - November 2020) Improved recovery with phthalocyanine (p=0.021) c19early.org Poleti et al., J. Evidence-Based Denta.., Dec 2021 Favors phthalocyanine Favors control
Poleti: RCT 500 patients in Brazil, showing improved recovery with a phthalocyanine derivative mouthwash and toothpaste. Toothbrushing for 2 minutes, three times per day, and gargling/rising (5ml) for one minute, three times a day, for 7 days.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were phthalocyanine, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of phthalocyanine for COVID-19 that report a comparison with a control group are included in the main analysis. This is a living analysis and is updated regularly.
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 both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). 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 outcome is considered more important than PCR testing status. 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 no room for an effective treatment to do better). 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 computed the relative risk when possible, or converted to a relative risk according to Zhang. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome 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 1 Sweeting. 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 (3.11.6) with scipy (1.11.3), pythonmeta (1.26), numpy (1.26.1), statsmodels (0.14.0), and plotly (5.17.0).
Forest plots are computed using PythonMeta Deng with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome. Grobid 0.8.0 is used to parse PDF documents.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
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 effective McLean, Treanor.
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/ptmeta.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.
Poleti, 12/8/2021, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 10 authors, study period 6 November, 2020 - 19 November, 2020, trial RBR-8x8g36. risk of no recovery, 29.1% lower, RR 0.71, p = 0.02, treatment 29 of 59 (49.2%), control 52 of 75 (69.3%), NNT 5.0, day 7.
risk of no recovery, 22.1% lower, RR 0.78, p = 0.02, treatment 38 of 59 (64.4%), control 62 of 75 (82.7%), NNT 5.5, day 3.
risk of no recovery, 45.5% lower, RR 0.54, p = 0.04, treatment 12 of 59 (20.3%), control 28 of 75 (37.3%), NNT 5.9, day 7, dyspnea.
risk of no recovery, 32.5% lower, RR 0.68, p = 0.11, treatment 17 of 59 (28.8%), control 32 of 75 (42.7%), NNT 7.2, day 3, dyspnea.
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.
Colado Simão, 6/23/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, peer-reviewed, 13 authors, study period 1 November, 2020 - 1 February, 2021, average treatment delay 5.4 days. relative Ct improvement, 50.7% better, RR 0.49, p = 0.008, treatment mean 11.21 (±4.35) n=15, control mean 5.53 (±6.28) n=15, mouthwash and nasal spray, day 3.
relative Ct improvement, 38.3% better, RR 0.62, p = 0.08, treatment mean 8.96 (±4.01) n=16, control mean 5.53 (±6.28) n=15, mouthwash only, day 3.
relative Ct improvement, 71.7% better, RR 0.28, p = 0.06, treatment mean 5.48 (±5.33) n=15, control mean 1.55 (±5.54) n=15, mouthwash and nasal spray, day 1.
relative Ct improvement, 68.4% better, RR 0.32, p = 0.08, treatment mean 4.91 (±4.89) n=16, control mean 1.55 (±5.54) n=15, mouthwash only, day 1.
da Silva Santos, 10/7/2021, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 17 authors, study period 10 August, 2020 - 4 November, 2020. risk of death, 85.4% lower, RR 0.15, p = 0.23, treatment 0 of 20 (0.0%), control 3 of 21 (14.3%), NNT 7.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of ICU admission, 92.1% lower, RR 0.08, p = 0.02, treatment 0 of 20 (0.0%), control 6 of 21 (28.6%), NNT 3.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
discharge, 53.7% lower, HR 0.46, p = 0.03, treatment 20, control 21, inverted to make HR<1 favor treatment, Cox proportional hazards.
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.
Brito-Reia, 11/15/2021, prospective, Brazil, peer-reviewed, 7 authors, trial RBR-6c9xnw3. risk of case, 54.0% lower, RR 0.46, p = 0.08, treatment 6 of 1,153 (0.5%), control 44 of 3,887 (1.1%), NNT 164.
Please send us corrections, updates, or comments. c19early involves the extraction of over 100,000 datapoints from thousands of papers. Community updates help ensure high accuracy. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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