A Study to Investigate the Safety, Tolerability, Pharmacokinetics and Pharmacodynamics of Single and Multiple Doses of RO7049389 in Healthy Volunteers and Chronic Hepatitis B Virus (HBV) Infected Participants: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for A Study to Investigate the Safety, Tolerability, Pharmacokinetics and Pharmacodynamics of Single and Multiple Doses of RO7049389 in Healthy Volunteers and Chronic Hepatitis B Virus (HBV) Infected Participants (NCT02952924), a randomized phase 1 study. The primary outcome is Part 1: Percentage of Participants With Adverse Events.

2. Clinical Question

3. Trial Design

NCT02952924 is a completed phase 1, multicenter study of RO7049389. The registry lists randomized allocation, double masking, and a parallel model, but those labels need to be read alongside the three-part design: the first two parts were randomized, blinded, and placebo-controlled; Part 3 was non-randomized, non-controlled, and open-label. The listed enrollment is 192.

Part 1 evaluated single-ascending doses in healthy volunteers, including a food-effect component, and multiple-ascending doses, including a midazolam component. Cohort doses and regimens could be informed by available safety and pharmacokinetic data from previously dosed participants. Part 2 was a proof-of-mechanism study in chronic hepatitis B participants, with RO7049389 or matching placebo administered for 28 days.

Part 3 enrolled chronic hepatitis B participants in three cohorts. Cohort A received RO7049389 on top of nucleos(t)ide therapy for 48 weeks. Cohort B received RO7049389 for four weeks, followed by RO7049389 with nucleos(t)ide therapy for 44 weeks. Cohort C received RO7049389, nucleos(t)ide therapy, and Pegylated-Interferon for 48 weeks. These cohort regimens differ, and there was no randomized control group in Part 3.

The primary endpoint concerns adverse events in Part 1, with follow-up extending up to Day 29, Day 44, or Day 42 depending on the component. The Part 3 secondary adverse-event endpoint covers 72 weeks. The original statistical plan, including any sample-size justification, is not reported in the registry record.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Only the first 6 of 22 groups are shown.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: The safety analysis population included all participants who received at least one dose of study drug, whether prematurely withdrawn from the study or not.

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

Only the first 6 of 23 groups are shown.

6. Primary Endpoint

The registry lists the primary outcome as Part 1: Percentage of Participants With Adverse Events, measured over Up to Day 29 (Part 1a), Day 44 (Part 1b), Day 42 (Part 1c).

The primary endpoint is the percentage of participants with adverse events in Part 1. It is a binary participant-level outcome: each participant is counted according to whether an adverse event was reported during the applicable follow-up, rather than according to the total number of events experienced. The safety analysis population included participants who received at least one dose of study drug, including those who withdrew early.

The registry specifies different observation windows for the Part 1 components: up to Day 29 for Part 1a, Day 44 for Part 1b, and Day 42 for Part 1c. Those differences matter when comparing percentages, because the opportunity to observe an event is not identical across components. A participant-level percentage also does not describe the event’s severity, duration, recurrence, or relationship to treatment.

General statistical context: a binary safety endpoint is useful in early-phase research because it gives a direct description of how many participants experienced at least one event. It can help identify patterns that warrant further investigation. However, the percentage alone does not show whether an event was caused by the study drug, nor does it measure efficacy. Interpretation also depends on the number of participants in each cohort and the uncertainty around each estimate.

The primary endpoint is a safety measure, not a test of whether RO7049389 improves hepatitis B outcomes. The registry record does not report a primary efficacy endpoint for the result.

7. Results Posted in the Registry

• Part 1c: Multiple Ascending Dose (MAD) Placebo: 60.0% (6/10) versus 27.3% (3/11) on control; risk difference 32.7 percentage points (95% CI -7.5 to 72.9), risk ratio 2.20 (0.74–6.54); unadjusted two-proportion z-test p = 0.130.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Part 3: Percentage of Participants With AEs

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Part 3: Cohort B: 90.0% (9/10) versus 68.8% (22/32) on control; risk difference 21.3 percentage points (95% CI -3.3 to 45.8), risk ratio 1.31 (0.96–1.79); unadjusted two-proportion z-test p = 0.182.
  • Part 3: Cohort C: 100.0% (30/30) versus 68.8% (22/32) on control; risk difference 31.2 percentage points (95% CI 15.2 to 47.3), risk ratio 1.45 (1.15–1.84); unadjusted two-proportion z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Part 3: Cohort B and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically higher for Part 3: Cohort C than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The registry lists secondary outcomes covering pharmacokinetic measures, including food effects on RO7049389 exposure and measures involving midazolam, as well as a Part 3 measure of the percentage of participants with adverse events. These outcomes address different questions from the primary Part 1 safety endpoint. Pharmacokinetic measures describe drug exposure or related properties; the Part 3 adverse-event measure continues the safety description over a longer follow-up period.

For Part 3, the posted adverse-event percentages are 68.8% in Cohort A, 90.0% in Cohort B, and 100.0% in Cohort C. These results provide a descriptive picture of event occurrence in the three open-label regimens, but they do not establish which treatment component accounts for any difference. The cohort regimens and designs differ, and the registry record does not report random allocation or a controlled comparison for Part 3.

The secondary findings complement the primary safety result by extending observation to chronic hepatitis B participants and a longer treatment period. They do not convert the primary endpoint into an efficacy result. The primary measure is the percentage with adverse events in Part 1; the Part 3 measure is also about adverse events, while pharmacokinetic outcomes concern exposure rather than clinical benefit.

Unadjusted secondary comparisons carry less weight because multiple outcomes and comparisons create more opportunities for findings to arise by chance. The registry record does not report a multiplicity adjustment or a hierarchy identifying which secondary outcomes should be treated as confirmatory. Specific numerical results for the listed pharmacokinetic secondary outcomes are not reported in the registry record. Consequently, those outcomes cannot be used to strengthen or qualify the adverse-event comparisons. The Part 3 comparisons should remain descriptive and exploratory, particularly given their non-randomized, non-controlled design.

Secondary outcomes registered for this trial, for reference (only those above were recalculated):

• Part 1b: Food Effect on Cmax of RO7049389
• Part 1b: Food Effect on AUCinf of RO7049389
• Part 1c: Cmax of Midazolam
• Part 1c: AUCinf of Midazolam
• Part 1c: Tmax of RO7049389
• Part 1c: Cmax of RO7049389
• Part 1c: AUC0-12hr of RO7049389
• Part 1c: CLr of RO7049389

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is binary at the participant level: a participant either had an adverse event during the specified observation window or did not. A percentage describes the proportion of the group with at least one event. It is not the percentage of all recorded events, and it does not distinguish one event from multiple events in the same participant. The registry identifies the primary measure as a percentage of participants and specifies a safety analysis population of participants who received at least one dose.

What does a risk difference describe?

General statistical explanation: a risk difference subtracts the event proportion in one group from the event proportion in another. A value of zero corresponds to equal proportions; a positive or negative value indicates the direction of the difference according to the groups’ ordering. The Part 1 posted comparison is reported as a risk difference of 0.327. The Part 3 comparisons are 0.212 for Cohort B versus Cohort A and 0.312 for Cohort C versus Cohort A. These estimates are unadjusted comparisons, and they do not account for the differing design and treatment context of the cohorts.

General statistical explanation: a confidence interval expresses a range of estimates compatible with the observed data under the method used. Wider intervals indicate less precision. When an interval for a risk difference includes zero, the data are compatible with no difference as well as with differences in either direction. The Part 1 comparison interval and the Part 3 Cohort B interval include zero; the Cohort C interval does not. These interval statements describe statistical uncertainty, not clinical importance or causality.

How should the p-values be read?

A p-value describes how unusual data at least as inconsistent with a specified null model would be if that model were true. It is not the probability that a treatment works, the probability that the null model is true, or a measure of the size of an effect. The posted Part 1 comparison has an unadjusted p-value of 0.13. The Part 3 Cohort B comparison has an unadjusted p-value of 0.182, and the Cohort C comparison has p < 0.001. These are unadjusted results. The registry record does not report a multiplicity adjustment or a sponsor-reported analysis for these comparisons.

The comparison groups also matter. The Part 1 comparison is between SAD placebo and MAD placebo groups; neither group is a RO7049389 treatment cohort. The Part 3 comparisons use Cohort A as the reference for Cohorts B and C. Since Part 3 was non-randomized, non-controlled, and open-label, differences can reflect cohort composition, the regimens, or other factors, as well as any treatment-related differences. The comparison estimates should not be interpreted as causal effects.

What can an arm percentage tell us?

Arm percentages provide a descriptive view of observed event occurrence. The Part 1 values vary across cohorts, but group sizes are small. General statistical context: estimates from small groups tend to be unstable because a small change in the number of participants with an event can produce a noticeable change in the percentage. The registry does not report confidence intervals for the individual arm percentages in the listed primary results. The arm percentages alone therefore do not quantify their precision.

The adverse-event endpoint is also limited in what it captures. A participant-level indicator does not show event counts, timing, duration, seriousness, or whether investigators considered an event related to study treatment. Separate serious adverse-event data are posted by cohort, but those counts are not interchangeable with the primary endpoint’s any-adverse-event measure.

The registry record lists 44 outcome measures and reports no analyses in its analysis count. A complete statistical analysis plan, including model specifications, handling of missing data, multiplicity control, and any prespecified subgroup or interim analyses, is not reported in the registry record. No assumptions about those methods should be inferred from the posted unadjusted comparisons. The calculations used for planning illustrations are separate from the trial’s original design and do not describe the study’s observed power.

11. Confidence Intervals and Precision

Precision is limited by the small cohort sizes and, for the reported comparisons, by broad confidence intervals. For the Part 1 placebo-group comparison, the risk difference is 0.327, and the 95% confidence interval runs from -0.075 to 0.729. Because this interval includes zero and spans a range of effects in either direction, the estimate does not precisely distinguish a difference from no difference. The unadjusted p-value is 0.13.

For Part 3 Cohort B versus Cohort A, the risk difference is 0.212, with a 95% confidence interval from -0.033 to 0.458. That interval also includes zero. For Cohort C versus Cohort A, the risk difference is 0.312, with a 95% confidence interval from 0.152 to 0.473; this interval does not include zero. The corresponding posted p-value is p < 0.001. These intervals describe uncertainty around unadjusted comparisons and do not resolve the non-randomized design limitations.

General statistical context: a confidence interval is not a guarantee that the true effect lies within its endpoints. Its width reflects the data and the method used, and wider intervals indicate greater uncertainty. Here, the individual primary arm percentages do not have confidence intervals reported in the registry record. Their small cohort sizes make it especially important not to interpret the point estimates as precise estimates of underlying event rates.

Precision is not the same as importance. Even a comparatively narrow interval would not establish whether events were caused by RO7049389, and a small p-value would not overcome the lack of randomization in Part 3. The registry record does not report a prespecified threshold for a meaningful difference in adverse-event percentages.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The sample-size illustrations are planning scenarios, not descriptions of the trial’s enrollment targets. The figures are participants per arm, not total events. They use a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is set to the stated fraction of the observed effect. The trial’s original sample-size assumptions and justification are not reported in the registry record.

The 80% power column gives the illustrative number of participants per arm for a design that would have an 80% chance of detecting the specified effect under the stated assumptions. The 90% power column gives the corresponding number per arm for a 90% chance. These are planning probabilities conditional on the assumptions and effect size; they are not the chance that a result is true.

Rows for planning for 100%, 75%, or 50% of the observed effect show how the assumed effect size changes the illustration. Smaller effects require more participants to detect under the same assumptions. For example, the primary comparison’s planning figures increase from 35 participants per arm for 80% power at 100% of the observed effect to 133 participants per arm at 50% of the observed effect. The corresponding figures for 90% power are 47 and 178. These figures do not imply that the study was designed with those group sizes or assumptions.

This is not observed power. The illustrations are not a retrospective measure of whether the trial succeeded, and they do not compensate for the small cohorts, non-randomized Part 3 design, or uncertainty in the observed effects. They are best read as demonstrations of how the required planning size changes when the target effect or desired power changes.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.273; treatment proportion p₁ = 0.600; average p̄ = 0.436.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.7014 = 1.3746.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6621 = 0.5572.
4. n = (1.3746 + 0.5572)² / (0.3273)² = 34.8, rounded up to 35 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
p0 <- 0.272727272727273                          # control proportion
p1 <- 0.6                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(2952924)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 35 (80% power), 47 (90% power)
 75% of observed effect: n per arm = 62 (80% power), 82 (90% power)
 50% of observed effect: n per arm = 133 (80% power), 178 (90% power)
power.prop.test: 34.8 
Simulated power at n = 35 per arm: 0.790 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Multiplicity, Missing Data and Analysis Populations

The registry lists many outcome measures, including the primary Part 1 adverse-event outcome, secondary outcomes, and multiple treatment cohorts. When many outcomes or comparisons are examined, the chance of obtaining at least one apparently unusual result can increase. General statistical context: multiplicity procedures can control or otherwise address this issue, but the choice of procedure depends on the study’s prespecified analysis plan and inferential goals.

The registry record does not report whether multiplicity was addressed, whether outcomes were prioritized in a formal testing hierarchy, or whether any secondary comparisons were intended to be confirmatory. The posted comparison p-values are unadjusted. They should therefore be interpreted cautiously, especially when read alongside results from other outcomes or cohorts. The record also does not report a complete statistical analysis plan, interim analyses, or a sponsor-reported analysis for the displayed comparisons.

The primary safety population is defined as participants who received at least one dose of study drug, whether or not they withdrew early. That definition clarifies who is included in the stated safety population, but it does not explain how incomplete follow-up or missing endpoint information was handled. Missing-data methods and the amount of missing data are not reported in the registry record.

Analysis populations for each secondary comparison are not fully described in the available record. Nor is it reported whether the listed analyses followed an intention-to-treat, per-protocol, or other approach. For Part 3, the open-label, non-randomized cohorts further limit what can be inferred from unadjusted comparisons. These reporting gaps do not invalidate the descriptive percentages, but they prevent a full assessment of the inferential plan and the robustness of the results.

15. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Deaths

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: liver function test increased (16.7%), lymphadenitis (10.0%), cellulitis (10.0%), malignant melanoma (10.0%), hypersensitivity (3.3%).

The serious adverse-event record is separate from the primary endpoint, which counts participants with any adverse event. In the listed Part 1 placebo and RO7049389 cohorts, serious adverse events were recorded in zero participants. In Part 2, the placebo group had one serious adverse event, while the listed Part 2 RO7049389 cohorts had zero. In Part 3, Cohort A had one, Cohort B had one, and Cohort C had two. One death was recorded in Part 3 Cohort B; no deaths were recorded in the other listed groups.

These are small groups, and the counts should be read as descriptive observations rather than stable estimates of risk. The record also lists serious adverse-event terms, including liver function test increased at 16.7%, lymphadenitis at 10.0%, cellulitis at 10.0%, and malignant melanoma at 10.0%. The registry record does not provide enough context in these figures alone to determine causation or compare risk across regimens. Serious events, any events, and deaths are distinct safety summaries and should not be conflated.

16. What the Result Does, and Does Not, Mean

The posted results describe the proportion of participants with adverse events across the study’s Part 1 dose cohorts and placebo groups, and across Part 3 chronic hepatitis B cohorts. They show variation in the observed percentages, with small group sizes in Part 1 and distinct treatment regimens in Part 3. The confidence intervals for the displayed comparisons communicate substantial uncertainty in some estimates, while the Part 3 Cohort C comparison interval does not include zero.

These findings do not establish that RO7049389 caused or prevented the reported events. The primary comparison is between two placebo groups, not between study drug and placebo. Part 3 was non-randomized, non-controlled, and open-label, and its cohorts received different regimens. Differences between those cohorts cannot be attributed to RO7049389 alone. The participant-level any-event measure also does not convey severity, duration, event count, or attribution.

The results do not show whether RO7049389 improves hepatitis B outcomes. The primary endpoint is a safety measure, and the secondary endpoint results also concern adverse events. Although the study included pharmacokinetic and pharmacodynamic aims, specific results for those measures are not reported in the registry record. An adverse-event percentage is not a measure of antiviral efficacy.

General statistical context: early-phase studies often provide descriptive information and can inform further research questions, but small cohorts generally yield uncertain estimates. Statistical significance, where reported, does not establish clinical importance or causation. The results should therefore be understood as observations from this study’s particular populations, follow-up periods, and regimens, not as a definitive estimate of safety or efficacy in broader clinical use.

17. Why This Trial Matters Statistically

This study is statistically informative because it spans several distinct research settings within one phase 1 record: single- and multiple-dose evaluation in healthy volunteers, a proof-of-mechanism component in chronic hepatitis B, and longer open-label combination regimens. That breadth makes it important to keep each part’s population, follow-up, comparator, and endpoint separate when reading the results.

The primary outcome is a participant-level safety percentage, while the separate serious adverse-event record reports serious-event counts. Recognizing the difference prevents a common interpretive error: treating any adverse event and serious adverse events as if they were the same endpoint. The study also shows why a percentage without a confidence interval may appear more definitive than the underlying small cohort can support.

Part 3 illustrates another key principle: numerical differences between cohorts are not necessarily treatment effects. Without randomization or a control group, and with different combination regimens, the Cohort A, B, and C percentages cannot isolate the impact of one component. The unadjusted comparisons add a numerical contrast but do not remove those design limitations.

Finally, the planning illustrations demonstrate how assumed effect size and desired power influence planning numbers. They are separate from the trial’s original design, which is not reported in the registry record. Together, these features make the record useful for understanding uncertainty, comparator choice, multiplicity, and the limits of descriptive safety findings in early-phase research.

18. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.
• Participant counts were reconstructed from posted percentages and arm sizes, so small rounding differences are possible.

The primary endpoint covers Part 1, while the study as a whole includes three parts with different populations, designs, treatments, and follow-up periods. The Part 1 cohorts are small, and the registry does not report confidence intervals for the individual arm percentages. Consequently, the arm-level estimates should not be treated as precise measures of event rates.

The posted Part 1 comparison is between SAD placebo and MAD placebo. It does not directly compare RO7049389 with placebo. Part 3 adds a different limitation: it was non-randomized, non-controlled, and open-label. Its cohorts received different combination regimens, so the observed percentages cannot isolate the effects of RO7049389 or support a controlled comparison among regimens.

The endpoint records whether a participant had any adverse event, not the number or severity of events or whether they were considered related to study treatment. Follow-up also differs across Part 1 components, and the Part 3 adverse-event measure covers 72 weeks. Differences in observation duration can affect the opportunity for events to be reported.

The registry record does not report the original sample-size justification, full statistical analysis plan, missing-data procedures, multiplicity adjustment, or specific pharmacokinetic results. It also does not provide a complete account of analysis populations for the secondary comparisons. These gaps limit assessment of prespecification and robustness. The planning illustrations are not a substitute for the original design assumptions, and the posted adverse-event results do not establish efficacy or causation.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: A Study to Investigate the Safety, Tolerability, Pharmacokinetics and Pharmacodynamics of Single and Multiple Doses of RO7049389 in Healthy Volunteers and Chronic Hepatitis B Virus (HBV) Infected Participants — NCT02952924.
• Registry record last updated 2024-10-31; status last verified 2024-08.
• Results first posted 2024-10-31.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

22. Record Summary

NCT02952924 is a completed phase 1, multicenter study of RO7049389 with listed enrollment of 192. It included randomized, blinded, placebo-controlled single- and multiple-dose components, followed by a non-randomized, non-controlled, open-label component in chronic hepatitis B participants.

The primary endpoint was the percentage of participants with adverse events in Part 1, assessed up to Day 29, Day 44, or Day 42 according to the study component. The Part 1 cohorts show varied observed percentages, and the posted Part 1 comparison is between placebo groups. Part 3 reports adverse-event percentages for three different combination regimens. Because Part 3 was not randomized or controlled, those comparisons are descriptive rather than causal.

Small cohorts, uncertainty around comparisons, differing regimens, and unreported analysis details limit interpretation. The record describes safety observations; it does not establish efficacy.