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Andrographis for COVID-19: real-time meta analysis of 7 studies

@CovidAnalysis, February 2024, Version 8V8
 
0 0.5 1 1.5+ All studies 27% 7 1,245 Improvement, Studies, Patients Relative Risk Hospitalization 50% 1 146 Progression 21% 4 970 Recovery 30% 4 520 Viral clearance 28% 4 390 RCTs 41% 5 580 Early 40% 4 450 Late 10% 3 795 Andrographis for COVID-19 c19early.org February 2024 Favorsandrographis Favorscontrol
Abstract
Statistically significant lower risk is seen for recovery. 2 studies from 2 independent teams in 2 countries show statistically significant improvements.
Meta analysis using the most serious outcome reported shows 27% [-8‑50%] lower risk, without reaching statistical significance. Results are similar for Randomized Controlled Trials. Early treatment is more effective than late treatment.
1 RCT with 3,060 patients has not reported results (1.5 years late).
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 are more effective.
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence Andrographis p=0.11 Acetaminophen p=0.00000029 2020 2021 2022 2023 2024 Effective Harmful c19early.org February 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
Highlights
Andrographis reduces risk for COVID-19 with high confidence for recovery, low confidence for pooled analysis, and very low confidence for viral clearance.
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 66 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Wanaratna (DB RCT) 86% 0.14 [0.01-2.61] progression 0/29 3/28 Improvement, RR [CI] Treatment Control APFaVi Siripong.. (DB RCT) 50% 0.50 [0.05-5.39] hosp. 1/73 2/73 Kanokkan.. (DB RCT) 51% 0.49 [0.15-1.58] progression 4/83 8/82 Prasoppokak.. (RCT) 37% 0.63 [0.45-0.87] no recov. 43 (n) 39 (n) Tau​2 = 0.00, I​2 = 0.0%, p = 0.0014 Early treatment 40% 0.60 [0.44-0.82] 5/228 13/222 40% lower risk Zhang (RCT) 92% 0.08 [0.01-0.76] severe case 0/65 6/65 Improvement, RR [CI] Treatment Control Tanwettiyanont -26% 1.26 [0.74-2.17] progression 37/351 22/254 Prempree 19% 0.81 [0.48-1.38] viral+ 13/30 16/30 OT​1 Tau​2 = 0.16, I​2 = 55.5%, p = 0.75 Late treatment 10% 0.90 [0.47-1.72] 50/446 44/349 10% lower risk All studies 27% 0.73 [0.50-1.08] 55/674 57/571 27% lower risk 7 andrographis COVID-19 studies c19early.org February 2024 Tau​2 = 0.08, I​2 = 38.0%, p = 0.11 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment Favors andrographis Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Wanaratna (DB RCT) 86% progression Improvement Relative Risk [CI] APFaVi Siripon.. (DB RCT) 50% hospitalization Kanokka.. (DB RCT) 51% progression Prasoppoka.. (RCT) 37% recovery Tau​2 = 0.00, I​2 = 0.0%, p = 0.0014 Early treatment 40% 40% lower risk Zhang (RCT) 92% severe case Tanwettiyanont -26% progression Prempree 19% viral- OT​1 Tau​2 = 0.16, I​2 = 55.5%, p = 0.75 Late treatment 10% 10% lower risk All studies 27% 27% lower risk 7 andrographis C19 studies c19early.org February 2024 Tau​2 = 0.08, I​2 = 38.0%, p = 0.11 Protocol pre-specified/rotate for details1 OT: comparison with other treatment Favors andrographis Favors control
B
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C
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D
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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. 0.6% of 6,642 proposed treatments show efficacy c19early.org. D. Timeline of results in andrographis studies.
SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological issues Scardua-Silva, Yang, cardiovascular complications Eberhardt, organ failure, and death. Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factors Note A, Malone, Murigneux, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 6,000 compounds may reduce COVID-19 risk c19early.org (B), either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
We analyze all significant controlled studies of andrographis 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.
2 In Silico studies support the efficacy of andrographis Dassanayake, Rajagopal.
3 In Vitro studies support the efficacy of andrographis Mohd Abd Razak, Pu, Siridechakorn.
An In Vivo animal study supports the efficacy of andrographis Pu.
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 1 summarizes the results for all stages combined, after exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, and 7 show forest plots for random effects meta-analysis of all studies with pooled effects, hospitalization, progression, recovery, and viral clearance.
Table 1. Random effects meta-analysis for all stages combined, after exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01.
Improvement Studies Patients Authors
All studies27% [-8‑50%]7 1,245 62
Randomized Controlled TrialsRCTs41% [20‑57%]
***
5 580 45
Recovery30% [5‑49%]
*
4 520 38
Viral28% [-13‑55%]4 390 38
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
All studies40% [18‑56%]
**
10% [-72‑53%]
Randomized Controlled TrialsRCTs40% [18‑56%]
**
92% [24‑99%]
*
Recovery24% [-3‑44%]48% [15‑68%]
**
Viral14% [-45‑49%]40% [-3‑65%]
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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 hospitalization.
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Figure 5. Random effects meta-analysis for progression.
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Figure 6. Random effects meta-analysis for recovery.
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Figure 7. Random effects meta-analysis for viral clearance.
Figure 8 shows a comparison of results for RCTs and non-RCT studies. Figure 9 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. 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 66 treatments we have analyzed, 63% 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).
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, 44 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 44 treatments with statistically significant efficacy/harm, 28 have been confirmed in RCTs, with a mean delay of 5.8 months. When considering only low cost treatments, 23 have been confirmed with a delay of 7.0 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 8. Results for RCTs and non-RCT studies.
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Figure 9. 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.
1 andrographis RCT has not reported results Numthavaj. The trial reports report an estimated total of 3,060 patients. The result is delayed over 1.5 years.
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 10 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 66 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 10. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 66 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 11. 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, 44 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 85% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.5 months. When restricting to RCTs only, 46% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.1 months.
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Figure 11. 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 andrographis, 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 12 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 12. 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. Andrographis for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 andrographis 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 andrographis 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.
1 of the 7 studies compare against other treatments, which may reduce the effect seen. Currently all studies are peer-reviewed.
Intharuksa et al. present a review covering andrographis for COVID-19.
Statistically significant lower risk is seen for recovery. 2 studies from 2 independent teams in 2 countries show statistically significant improvements. Meta analysis using the most serious outcome reported shows 27% [-8‑50%] lower risk, without reaching statistical significance. Results are similar for Randomized Controlled Trials. Early treatment is more effective than late treatment.
0 0.5 1 1.5 2+ Progression 51% Improvement Relative Risk Recovery 8% Andrographis  Kanokkangsadal et al.  EARLY TREATMENT  DB RCT Is early treatment with andrographis beneficial for COVID-19? Double-blind RCT 165 patients in Thailand (July - September 2021) Lower progression with andrographis (not stat. sig., p=0.25) c19early.org Kanokkangsadal et al., Research in Pha.., Nov 2023 Favors andrographis Favors control
Kanokkangsadal: RCT 165 low-risk mild COVID-19 patients in Thailand receiving either 180mg/day of Andrographis paniculata extract or placebo for 5 days. No significant difference was found between groups for disease progression, though A. paniculata showed lower progression. Most symptoms improved similarly between groups, though A. paniculata provided faster relief for headaches and loss of smell. All patients recovered with 14 days. The main side effect was mild diarrhea.
Numthavaj: Estimated 3,060 patient andrographis early treatment RCT with results not reported over 1.5 years after estimated completion.
0 0.5 1 1.5 2+ Recovery, all symptoms.. 37% Improvement Relative Risk Recovery, day 7, fever 18% Recovery, day 7, cough 46% Recovery, day 7, myalgia 55% Recovery, day 7, headache 40% Recovery, day 7, sore throat 30% Recovery, day 7, rhinitis 42% Recovery, all symp.. (b) 45% Recovery, day 14, fever 55% Recovery, day 14, cough 48% Recovery, day 14, headache 68% Recovery, day 14, sore thr.. 9% Recovery, day 14, rhinitis 9% Andrographis  Prasoppokakorn et al.  EARLY TREATMENT  RCT Is early treatment with andrographis beneficial for COVID-19? RCT 82 patients in Thailand (October 2021 - February 2022) Improved recovery with andrographis (p=0.005) c19early.org Prasoppokakorn et al., OBM Integrative.., Feb 2024 Favors andrographis Favors control
Prasoppokakorn: Randomized controlled trial of 82 mild COVID-19 outpatients showing significantly greater reduction in cough and lower inflammatory markers at day 7. Symptomatic improvement was significant at day 7 when combining all symptoms reported, but not for other symptoms individually. There was no progression to severe pneumonia in either group.
0 0.5 1 1.5 2+ Viral clearance 19% Improvement Relative Risk Time to viral- 31% no CI Andrographis  Prempree et al.  LATE TREATMENT Is late treatment with andrographis beneficial for COVID-19? Retrospective 60 patients in Thailand Study compares with favipiravir, results vs. placebo may differ No significant difference in viral clearance c19early.org Prempree et al., OSIR, December 2022 Favors andrographis Favors favipiravir
Prempree: Retrospective 120 patients in Thailand, showing improved viral clearance with andrographis compared with favipiravir.
0 0.5 1 1.5 2+ Hospitalization, day 28 50% Improvement Relative Risk Hospitalization, day 14 50% Oxygen therapy 86% Progression 49% WHO scale ≥2 37% WHO scale ≥1 4% CT severity 33% Ct improvement, E gene -5% Ct improvement, ORF -13% Andrographis  APFaVi  EARLY TREATMENT  DB RCT Is early treatment with andrographis beneficial for COVID-19? Double-blind RCT 146 patients in Thailand (June - September 2021) Lower need for oxygen therapy (p=0.24) and improved recovery (p=0.28), not sig. c19early.org Siripongboonsitti et al., Phytomedicine, Aug 2023 Favors andrographis Favors control
Siripongboonsitti: RCT 146 mild/moderate COVID-19 patients in Thailand, showing no significant difference in clinical outcomes. There were very few serious outcomes.
0 0.5 1 1.5 2+ Progression -26% Improvement Relative Risk Andrographis  Tanwettiyanont et al.  LATE TREATMENT Is late treatment with andrographis beneficial for COVID-19? Retrospective 605 patients in Thailand (March 2020 - August 2021) Higher progression with andrographis (not stat. sig., p=0.4) c19early.org Tanwettiyanont et al., Frontiers in Me.., Aug 2022 Favors andrographis Favors control
Tanwettiyanont: Retrospective 605 hospitalized patients in Thailand, showing higher progression with andrographis, without statistical significance.
0 0.5 1 1.5 2+ Progression 86% Improvement Relative Risk Viral clearance 40% Andrographis  Wanaratna et al.  EARLY TREATMENT  DB RCT Is early treatment with andrographis beneficial for COVID-19? Double-blind RCT 57 patients in Thailand (December 2020 - March 2021) Lower progression (p=0.11) and improved viral clearance (p=0.11), not sig. c19early.org Wanaratna et al., Archives of Internal.., Jul 2021 Favors andrographis Favors control
Wanaratna: RCT 63 mild COVID-19 patients showing lower progression and improved viral clearance with andrographis, without statistical significance.
0 0.5 1 1.5 2+ Severe case 92% Improvement Relative Risk Recovery 48% Recovery, fever 40% Recovery, cough 61% Viral clearance 53% Andrographis  Zhang et al.  LATE TREATMENT  RCT Is late treatment with andrographis beneficial for COVID-19? RCT 130 patients in China (January - February 2020) Lower severe cases (p=0.028) and improved recovery (p=0.008) c19early.org Zhang et al., Phytotherapy Research, May 2021 Favors andrographis Favors control
Zhang (B): RCT 130 hospitalized COVID-19 patients in China, showing lower progression and improved recovery with Xiyanping injection (9-dehydro-17-hydro-andrographolide and sodium 9-dehydro-17-hydro-andrographolide-19-yl sulfate, which are derived from andrographis).
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 andrographis 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 andrographis 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 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 test 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 little or no room for an effective treatment to do better, however faster recovery is valuable. 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. Reported confidence intervals and p-values were used when available, using adjusted values 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 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 (3.11.7) with scipy (1.12.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.1), and plotly (5.19.0).
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. 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 (4.1.2) using the metafor (3.0-2) and rms (6.2-0) 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 0.8.0 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 effective McLean, Treanor.
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 the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/apmeta.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.
Kanokkangsadal, 11/23/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Thailand, peer-reviewed, 9 authors, study period July 2021 - September 2021, trial TCTR20210809004. risk of progression, 50.6% lower, RR 0.49, p = 0.25, treatment 4 of 83 (4.8%), control 8 of 82 (9.8%), NNT 20.
risk of no recovery, 8.4% lower, RR 0.92, p = 0.33, treatment 64 of 83 (77.1%), control 69 of 82 (84.1%), NNT 14, total recovery, day 5.
Numthavaj, 5/30/2022, Single Blind Randomized Controlled Trial, Thailand, trial NCT05019326 (history). Estimated 3,060 patient RCT with results unknown and over 1.5 years late.
Prasoppokakorn, 2/2/2024, Randomized Controlled Trial, Thailand, peer-reviewed, 7 authors, study period October 2021 - February 2022, trial TCTR20210906002. risk of no recovery, 37.5% lower, RR 0.63, p = 0.005, treatment 43, control 39, all symptoms combined.
risk of no recovery, 17.5% lower, RR 0.82, p = 0.62, treatment 10 of 43 (23.3%), control 11 of 39 (28.2%), NNT 20, day 7, fever.
risk of no recovery, 46.4% lower, RR 0.54, p = 0.03, treatment 13 of 43 (30.2%), control 22 of 39 (56.4%), NNT 3.8, day 7, cough.
risk of no recovery, 54.7% lower, RR 0.45, p = 0.60, treatment 1 of 43 (2.3%), control 2 of 39 (5.1%), NNT 36, day 7, myalgia.
risk of no recovery, 39.5% lower, RR 0.60, p = 0.66, treatment 2 of 43 (4.7%), control 3 of 39 (7.7%), NNT 33, day 7, headache.
risk of no recovery, 30.2% lower, RR 0.70, p = 0.34, treatment 10 of 43 (23.3%), control 13 of 39 (33.3%), NNT 9.9, day 7, sore throat.
risk of no recovery, 42.3% lower, RR 0.58, p = 0.29, treatment 7 of 43 (16.3%), control 11 of 39 (28.2%), NNT 8.4, day 7, rhinitis.
risk of no recovery, 45.0% lower, RR 0.55, p = 0.18, treatment 43, control 39, all symptoms combined.
risk of no recovery, 54.7% lower, RR 0.45, p = 0.60, treatment 1 of 43 (2.3%), control 2 of 39 (5.1%), NNT 36, day 14, fever.
risk of no recovery, 48.2% lower, RR 0.52, p = 0.34, treatment 4 of 43 (9.3%), control 7 of 39 (17.9%), NNT 12, day 14, cough.
risk of no recovery, 67.8% lower, RR 0.32, p = 0.48, treatment 0 of 43 (0.0%), control 1 of 39 (2.6%), NNT 39, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 14, headache.
risk of no recovery, 9.3% lower, RR 0.91, p = 1.00, treatment 1 of 43 (2.3%), control 1 of 39 (2.6%), NNT 419, day 14, sore throat.
risk of no recovery, 9.3% lower, RR 0.91, p = 1.00, treatment 1 of 43 (2.3%), control 1 of 39 (2.6%), NNT 419, day 14, rhinitis.
Siripongboonsitti, 8/12/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Thailand, peer-reviewed, 10 authors, study period 11 June, 2021 - 15 September, 2021, APFaVi trial. risk of hospitalization, 50.0% lower, RR 0.50, p = 1.00, treatment 1 of 73 (1.4%), control 2 of 73 (2.7%), NNT 73, day 28.
risk of hospitalization, 50.0% lower, RR 0.50, p = 1.00, treatment 1 of 73 (1.4%), control 2 of 73 (2.7%), NNT 73, day 14.
risk of oxygen therapy, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 73 (0.0%), control 3 of 73 (4.1%), NNT 24, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of progression, 49.3% lower, RR 0.51, p = 1.00, treatment 1 of 71 (1.4%), control 2 of 72 (2.8%), NNT 73, day 4.
WHO scale ≥2, 36.6% lower, RR 0.63, p = 0.28, treatment 10 of 71 (14.1%), control 16 of 72 (22.2%), NNT 12, day 14.
WHO scale ≥1, 3.9% lower, RR 0.96, p = 0.69, treatment 54 of 71 (76.1%), control 57 of 72 (79.2%), NNT 32, day 14.
CT severity, 33.3% lower, RR 0.67, p = 0.24, treatment 71, control 72, day 5.
relative Ct improvement, 5.0% worse, RR 1.05, p = 0.46, treatment 71, control 72, E gene, day 5.
relative Ct improvement, 13.3% worse, RR 1.13, p = 0.34, treatment 71, control 72, ORF, day 5.
Wanaratna, 7/11/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Thailand, peer-reviewed, 7 authors, study period December 2020 - March 2021, trial TCTR20210708001. risk of progression, 85.9% lower, RR 0.14, p = 0.11, treatment 0 of 29 (0.0%), control 3 of 28 (10.7%), NNT 9.3, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of no viral clearance, 39.7% lower, RR 0.60, p = 0.11, treatment 10 of 29 (34.5%), control 16 of 28 (57.1%), NNT 4.4, day 5.
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.
Prempree, 12/31/2022, retrospective, Thailand, peer-reviewed, 9 authors, this trial compares with another treatment - results may be better when compared to placebo. risk of no viral clearance, 18.8% lower, RR 0.81, p = 0.61, treatment 13 of 30 (43.3%), control 16 of 30 (53.3%), NNT 10, day 14.
Tanwettiyanont, 8/10/2022, retrospective, Thailand, peer-reviewed, mean age 35.4, 8 authors, study period 1 March, 2020 - 31 August, 2021. risk of progression, 26.0% higher, HR 1.26, p = 0.40, treatment 37 of 351 (10.5%), control 22 of 254 (8.7%), Cox proportional hazards.
Zhang (B), 5/12/2021, Randomized Controlled Trial, China, peer-reviewed, mean age 46.3, 12 authors, study period 27 January, 2020 - 20 February, 2020, trial NCT04295551 (history). risk of severe case, 92.3% lower, RR 0.08, p = 0.03, treatment 0 of 65 (0.0%), control 6 of 65 (9.2%), NNT 11, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of no recovery, 48.2% lower, HR 0.52, p = 0.008, treatment 65, control 65, inverted to make HR<1 favor treatment.
risk of no recovery, 40.1% lower, HR 0.60, p = 0.07, treatment 65, control 65, inverted to make HR<1 favor treatment, fever.
risk of no recovery, 60.9% lower, HR 0.39, p = 0.001, treatment 65, control 65, inverted to make HR<1 favor treatment, cough.
risk of no viral clearance, 53.5% lower, HR 0.47, p < 0.001, treatment 65, control 65, inverted to make HR<1 favor treatment.
Please send us corrections, updates, or comments. c19early involves the extraction of 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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