Tuesday, May 26, 2020

Binding Antibodies, nonneutralizing antibodies (n-NAbs), and Neutralizing Antibodies (NAbs)

Last Monday, Moderna announced the positive results from its phase I clinical trial for its mRNA vaccine against Covid-19. The study was listed under clinicaltrials.gov as "Phase I, Open-Label, Dose-Ranging Study of the Safety and Immunogenicity of 2019-nCoV Vaccine (mRNA-1273) in Healthy Adults". This phase I study contains 9 cohorts with 3 different vaccine doses (25 mcg, 100 mcg, and 250 mcg) in three different age groups (18-55, 56-70, and 71 or above years of age). The announced results were from three dose groups in 18-55 years of age. The phase I study was the first step to test the safety and the immunogenicity (i.e. vaccine's ability to trigger an immune response in the body) of the vaccine, but the released preliminary results caused a strong reaction from the wall street.

The immunogenicity data indicated that the binding antibodies were detected in all 45 subjects in three dose groups in 18-55 years of age and the neutralizing antibodies were detected in all 8 subjects in the first four subjects in 25 mcg and 100 mcg groups. It is worth discussing what the difference is between the binding antibodies and the neutralizing antibodies.
Moderna Announces Positive Interim Phase 1 Data for its mRNA Vaccine (mRNA-1273) Against Novel Coronavirus
"Immunogenicity data are currently available for the 25 µg and 100 µg dose level (ages 18-55) after two doses (day 43) and at the 250 µg level (ages 18-55) after one dose (day 29). Dose dependent increases in immunogenicity were seen across the three dose levels, and between prime and boost within the 25 µg and 100 µg dose levels. All participants ages 18-55 (n=15 per cohort) across all three dose levels seroconverted by day 15 after a single dose. At day 43, two weeks following the second dose, at the 25 µg dose level (n=15), levels of binding antibodies were at the levels seen in convalescent sera (blood samples from people who have recovered from COVID-19) tested in the same assay. At day 43, at the 100 µg dose level (n=10), levels of binding antibodies significantly exceeded the levels seen in convalescent sera. Samples are not yet available for remaining participants.
At this time, neutralizing antibody data are available only for the first four participants in each of the 25 µg and 100 µg dose level cohorts. Consistent with the binding antibody data, mRNA-1273 vaccination elicited neutralizing antibodies in all eight of these participants, as measured by plaque reduction neutralization (PRNT) assays against live SARS-CoV-2. The levels of neutralizing antibodies at day 43 were at or above levels generally seen in convalescent sera."
Moderna reports positive data on early-stage coronavirus vaccine trial, shares surge
"Moderna’s closely watched early-stage human trial for a coronavirus vaccine produced Covid-19 antibodies in all 45 participants, the biotech company announced Monday, sending the company’s shares surging nearly 20%."
"The vaccine also produced neutralizing antibodies against Covid-19 in at least eight participants, the company said. Experts have said neutralizing antibodies appear to be important in acquiring protection."
Vaccines are examples of antigens in an immunogenic form, which are intentionally administered to a recipient (usually healthy volunteers) to induce the memory function of adaptive immune system toward the antigens of the pathogen invading that recipient. In Covid-19 situation, vaccines are developed against the SARS-Cov-2 (the pathogen (the virus) that caused Covid-19. In phase I studies, besides the safety, we want to see if administering the vaccines can generate the antibodies (first binding antibodies, then neutralizing antibodies). Phase I study can fail if there is a safety issue or if no or not enough antibodies are generated in recipients. 

Binding Antibodies: 
Not all antibodies that bind a pathogenic particle are neutralizing. Binding antibodies, or non-neutralizing antibodies (n-NAb), bind specifically to the pathogen (SARS-Cov-2 virus), but do not interfere with their infectivity. that might be because they do not bind to the right region. Non-neutralizing antibodies can be important to flag the particle for immune cells, signaling that it has been targeted, after which the particle is processed and consequently destroyed by recruited immune cells.

Binding antibodies are produced at high levels throughout the life of an infected individual but are characterized by their inability to prevent viral infection. Even though they do not have impact on virus' infectivity, binding antibodies are useful as diagnostic indicators of whether an individual is infected or not.

Neutralizing Antibodies (NAb):
Neutralizing antibodies on the other hand can neutralize the biological effects of the antigen without a need for immune cells. In some cases, non-neutralizing antibodies or insufficient amounts of neutralizing antibodies binding to virus particles can be utilized by some virus species to facilitate uptake into their host cells.

Binding antibodies and neutralizing antibodies are similarly discussed in therapeutic protein products or biological products including monoclonal antibody (mAb) drug. In vaccine studies, immunogenicity is a good thing and is what we want to see. In biological products (protein therapy or mAb), the immunogenicity is a thing we try to avoid - developing the anti-drug antibody will have impact on the efficacy and safety of a biological product or mAb.

Binding antibodies and neutralizing antibodies are measured with different assays (i.e., binding antibody assay and neutralization assay). According to FDA guidance "Immunogenicity Testing of Therapeutic Protein Products — Developing and Validating Assays for Anti-Drug Antibody (ADA) Detection"
"Screening assays, also known as binding antibody assays, are used to detect antibodies that bind to the therapeutic protein product. The specificity of ADA for the therapeutic protein product is usually established by competition with a therapeutic protein in a confirmatory assay. ADAs are characterized further using titration and neutralization assays. Titration assays characterize the magnitude of the ADA response. It is important to characterize this magnitude with titration assays because the impact of ADA on pharmacokinetics, pharmacodynamics, safety, and efficacy may correlate with ADA titer and persistence rather than incidence (Cohen and Rivera 2010). Neutralizing antibodies (NAbs) refer to those ADA with the ability to interfere with interactions between the therapeutic protein product and its target. Neutralization assays assess ADA for neutralizing activity. It is important to characterize neutralizing activity of ADA because the impact of ADA on pharmacokinetics, pharmacodynamics, safety, and efficacy may correlate with NAb activity rather than ADA incidence"

Sunday, May 24, 2020

Pre, During, and Post Pandemic: what are the cut points for defining pandemic start/end?


The Covid-19 pandemic will have a lasting impact on clinical trials. The way how the clinical trials are conducted will forever be changed. For ongoing clinical trials that are impacted by the Covid-19, the regulatory agencies have rushed to publish the guidance/guidelines. Many countries have published their own guidance/guidelines for clinical trials in the middle of Covid-19 pandemic. The most significant ones are those by US FDA and EMA: 



    The impact of Covid-19 on the ongoing clinical trials is multi-front. I listened to a webinar organized by DIA "Study and Data Integrity Considerations for Clinical Trials Impacted by COVID-19" and the potential impacts of Covid-19 on clinical trials were listed as the following: 




     The impact of Covid-19 on clinical trials is beyond what the table above can summarize. For example, the psychological impact could change the behavior of subjects who participate in the clinical trials - patients may be less compliant with the study protocol and more difficult to retain in the study - healthy volunteers may be less willing to participate in clinical trials. 

    One thing for sure is that the Covid-19 will have impact on the study quality, efficacy evaluation, and safety evaluation of the ongoing clinical trials. How to assess the impact is not an easy task. For the statistical analysis plan (SAP), additional sections will need to be included to describe the impact of the Covid-19 on study quality, efficacy, and safety evaluations.  

    EMA also issued guidance (Points to consider) for methodological aspects / statistical analyses.
    Points to consider on implications of Coronavirus disease (COVID-19) on methodological aspects of ongoing clinical trials
    The Points to consider suggested:

    "Sponsors are advised to contemplate an analysis of the accumulating trial data in order to evaluate the implications on recruitment, loss of patients during the trial, ability to record data and ability to interpret the treatment effect in light of the pre-, during and post-pandemic measures phases. "
    The suggested approach seems to be reasonable. The subjects in the whole study can be grouped into three categories: subjects who were enrolled into the study and completed the study prior to the pandemic start; subjects who were enrolled into the study and ongoing in the study during the pandemic; and subjects who are enrolled into the study after the pandemic. Statistical comparisons can be performed to compare the differences between these groups regarding the subject characteristics, compliance, efficacy, and safety endpoints. The findings from these comparisons may trigger further actions on how the final analyses should be performed. 

    The analysis by pre, during, and post pandemic has been discussed in several webinars - it seems to be an approach that people will adopt in handling the statistical analyses for studies that are impacted by Covid-19 pandemic - until the following question is being asked:
    How to define the cut points for deciding pre, during, and post pandemic? 
    We know that the Covid-19 outbreak started in Wuhan, China, and then spread to other countries (China -> Neighboring countries of China -> European Countries -> United States -> all over the world). The exact date of the Covid-19 outbreak is still in debate and may not be known forever. 

    For clinical trials involving multiple countries, are we going to define a cut point for a pandemic start for each country?   

    Officially, WHO declared the Covid-19 as a pandemic on March 11, 2020. But we know that by then, Covid-19 had already spread in so many countries.


    If the pandemic start is difficult to define, the cut point for the pandemic end will be even more challenging. Covid-19 may stay with us for a long time (until there is an effective and safe vaccine available). 

    Thursday, May 07, 2020

    Human Challenge Study Design for Covid-19 Vaccine Clinical Trials?

    The world is desperate to develop vaccines for Covid-19. Pharmaceutical and biotechnology companies are racing to start the clinical trials for Covid-19 vaccines. With the traditional phased approach for drug development (no exception for vaccine development), after the early phase clinical trials, a pivotal phase III study with thousands of volunteer subjects will need to be conducted to demonstrate the efficacy and safety of the vaccine. It is a very lengthy process that may not be successful. With the Covid-19 pandemic and the urgent situation, an alternative approach is desired to expedite the Covid-19 vaccine development. This is why we see the discussions about 'human challenge studies' heating up recently.

    The Human Challenge Studies may be also called "controlled human infection (CHI) studies". In human challenge studies, volunteer subjects were intentionally infected with the virus or other pathogen. It is supposed to shorten the time of the vaccine development program so that we know the results earlier if a vaccine works or not.   
    When I see the discussions about the human challenge studies, I immediately ask myself: is it ethical to intentionally infect people just to speed up the vaccine development program? Some infamous studies came to my mind: Guatemala syphilis experiments, Holmesburg Prison, Tuskegee syphilis experiment even though the ethical issues with these studies were mainly lack of informed consent. 

    However, human challenge studies were not new and had been used in other vaccine developments. Here are some examples: 
    Human challenge studies were also mentioned in FDA guidance for industry:
    "To date, prospectively designed studies to evaluate the effectiveness of influenza vaccines have not identified a specific HI antibody titer associated with protection against culture-confirmed influenza illness. Some studies of influenza infection, including human challenge studies following vaccination, have suggested that HI antibody titers ranging from 1:15 to 1:65 may be associated with protection from illness in 50% of subjects and that protection from illness is increased with higher titers. Evaluations of seroconversion and GMT have been used as measures of vaccine activity." 
    "B. Human Challenge Studies
    In some situations, it may be possible to conduct challenge studies in human subjects during early development or in lieu of clinical trials in an endemic area. Such studies may be conducted to demonstrate “proof of concept” of the vaccine antigen early in clinical development (e.g., Plasmodium falciparum sporozoite challenge of malaria-naïve U.S. volunteers previously administered a candidate malaria vaccine). Human challenge studies may also be conducted to demonstrate the efficacy of the vaccine. For example, in 1993 and 1998, the Agency convened the Vaccines and Related Biologics Products Advisory Committee meetings to consider whether data from human challenge studies in U.S. subjects could be sufficient to demonstrate efficacy of a cholera vaccine in travelers to endemic areas, who are at high risk for contracting the disease. In 1998, the committee agreed that human challenge studies could suffice to demonstrate efficacy of a cholera vaccine provided that studies were adequate and well-controlled and conducted under the provisions of GCP (Ref. 13). Of note, use of challenge studies to demonstrate efficacy does not preclude the requirement for adequate safety data. As human challenge studies may present unique considerations, we recommend that the sponsor discuss its development plan with CBER prior to initiation of such studies for either proof of concept or vaccine efficacy."
    What does a human challenge study for Covid-19 vaccine look like? 

    Here are the excerpts from the article "A Challenge to Accept The FDA should allow testing Covid-19 vaccines through deliberate human infection" to describe what the conventional phase III trial and the human challenge trial look like:
    "In Phase III trials, thousands of volunteers get the vaccine or a placebo, are checked for immediate adverse reactions to the vaccine, and then are tracked for a few months as they go about daily life, to ascertain the vaccine’s efficacy. Once enough trial participants have been infected, if a large enough difference in infection rates exists between the vaccinated and control groups, then the vaccine can enter general use. This comparison, the Times notes, is especially difficult to make once the spread of a disease has slowed: “The scientists [making the vaccine] would declare victory,” the Times says, “if as many as a dozen participants who are given a placebo become sick with Covid-19 compared with only one or two who receive the inoculation.” But if too few test subjects get infected to make a valid comparison, the researchers will simply “have to try again elsewhere,” repeating the time-consuming process."
     "In human-challenge trials, volunteers are given either a candidate vaccine or a placebo and then deliberately infected with the disease that the vaccine should prevent, and closely monitored, often in medical isolation. Such trials have been conducted in exigent circumstances for several diseases, including cholera, malaria, typhoid, and dengue fever."
    In another article "Human challenge trials,” where healthy volunteers would be exposed to Covid-19, explained", the human challenge study for Covid-19 vaccine was described as the following:
    A human challenge trial replaces that process with one in which all of the trial volunteers are exposed to the virus that causes Covid-19 after being dosed with a vaccine or placebo. That way, you get a much clearer sense of how the vaccine performs when actually confronted with the disease it’s intended to guard against.
    "To allow for a high portion to get exposed, the standard trial would have to take at least several months," Eyal, the bioethicist who co-authored the Journal of Infectious Diseases article calling for human challenge trials, told me. "By contrast, in a coronavirus challenge ... the success or failure of the vaccine to protect against the disease becomes apparent much faster."
    What’s more, a human challenge trial could be conducted with fewer people than a standard Phase III trial. A Covid-19 vaccine being developed at Oxford, for instance, aims to have 5,000 volunteers for Phase III. A human challenge trial, Eyal argues, could happen with only about 100 people. The total number of participants needed would likely be higher than that, since (among other reasons) multiple vaccine candidates need to be tested, but the total would be much lower compared with a conventional study.
    I drafted two diagrams to illustrate what the conventional design and the human challenge study design look like (for illustration only, SARs-Cov-2 is the virus that causes Covid-19).



    Will the human challenge study be feasible for Covid-19 vaccine studies? 

    There are people who push for conducting human challenge study to speed up the vaccine development - the argument is to sacrifice the few for the many.


    However, the human challenge studies that had been done so far were for those pathogens that the resulted diseases were either not severe enough (for example influenza) or could be treated or cured (for example, cholera).

    For Covid-19, the human challenge study design is not feasible for the following reasons:
    Safety: currently there is no effective treatment for Covid-19. For healthy volunteers who are inoculated with SARs-Cov-2, they could end up in ICU or death.

    • Ethical issue: even though the subjects sign the informed consent to participate in the human challenge study voluntarily, inoculating the subjects with the deadly and extremely contagious virus of SARs-Cov-2  
    • Uncertainties about SARs-Cov-2: for human challenge studies, extensive modeling needs to be performed to understand the characteristics of the virus and appropriate virus load (sort of dose level) to be used for inoculation. 
    • Timeline: it is arguable if it is true that using human challenge design will actually speed up the vaccine development. Covid-19 is new and a lot of unknowns need to be learned. Significant leading time may be needed to do the modeling to decide what the appropriate virus load for inoculation before the study can be initiated. 
    • Recruitment: it is still uncertain whether there will be difficulties in recruiting the volunteers to participate in the human challenge study knowing that the volunteers will be infected with the potentially deadly virus that has no effective treatment so far. There is a website "one day sooner" that tracks the volunteers who are willing to participate in human challenge study for Covid-19. At the time I check, 14183 volunteers from 102 countries have signed up. 
    • Generalization: if the human challenge study is conducted in volunteers who are young adults, the results may not be generalized to the vulnerable groups (elderly, with underlying diseases).  
    In a digital event organized by STATNEWS.com, Tal Zaks, chief medical officer, Moderna answered the question about the human challenge study design. He argued against the use of human challenge design in Covid-19 vaccine development for issues in practicality, timeline (may not save time), and ethical (justification the risk for the benefit we gain).

    Sunday, May 03, 2020

    Interpreting Hazard Ratio: Can we say "percent reduction in risk"?

    For time to event variable, the most commonly used statistics is hazard ratio. Hazard ratio is the ratio of hazards and equals to the hazard rate in the treatment group ÷ the hazard rate in the control group. Hazard rate represents the instantaneous event rate, which means the probability that an individual would experience an event at a particular given point in time after the intervention. While the hazard rate is associated with the event rate or median survival time, the hazard rate itself does not have a lot of meaning in interpreting the clinical trial results (see a previous post "Some Explanations about Survival Analysis or Time to Event Analysis"). However, we use hazard ratio (HR) to measure the treatment effect (i.e., the effect of an intervention on an outcome of interest over time comparing to the control group). Hazard ratio is reported most commonly in time-to-event analysis or survival analysis (i.e. when we are interested in knowing how long it takes for a particular event/outcome to occur). Hazard ratio can be obtained and calculated from the Cox regression - or Cox proportional hazard regression model.

    The event outcome could be an adverse/negative outcome, for example, progression-free survival (PFS) and overall survival (OS) in oncology studies or a positive outcome, for example, time to cure/discharge/conceive/heal/virus not detectable. The very publicized trial by NIAID "A Multicenter, Adaptive, Randomized Blinded Controlled Trial of the Safety and Efficacy of Investigational Therapeutics for the Treatment of COVID-19 in Hospitalized Adults" demonstrated the benefit of Remdesivir in treatment of Covid-19 through the primary efficacy endpoint of 'time to recovery' where the event of recovery is a positive outcome defined as "the first day on which the subject satisfies one of the following three categories from the ordinal scale: 1) Hospitalized, not requiring supplemental oxygen - no longer requires ongoing medical care; 2) Not hospitalized, limitation on activities and/or requiring home oxygen; 3) Not hospitalized, no limitations on activities."

    How to interpret the hazard ratio in lay language? 

    According to ScienceDirect.com, The hazard ratio is the ratio of (chance of an event occurring in the treatment arm)/(chance of an event occurring in the control arm). The HR has also been defined as, the ratio of (risk of outcome in one group)/(risk of outcome in another group), occurring at a given interval of time.

    While these interpretations of the hazard ratio may be clear to the statisticians, they are not straight forward to the public and non-statisticians. 

    As described in an article by Sashegyi and Ferry "On the Interpretation of the Hazard Ratio and Communication of Survival Benefit", people would like to use an interpretation of hazard ratio in lay language. 

    For a study with time to event variable where the event is a negative outcome, a hazard ratio < 1 is desirable for a successful trial. The hazard ratio will be interpreted as "percent reduction in risk". The hazard ratio is converted into "percent reduction in risk" using: 

         (1 − HR) ×100%

    We could find many examples where that hazard ratio (and its 95% confidence interval) was used in the scientific papers, but "percent of reduction in risk" was used in the press releases or public announcements. 

    In an article by de Bono et al (2020) "Olaparib for Metastatic Castration-Resistant Prostate Cancer", the result for PFS was presented as:
    In cohort A, imaging-based progression-free survival was significantly longer in the olaparib group than in the control group (median, 7.4 months vs. 3.6 months; hazard ratio for progression or death, 0.34; 95% confidence interval, 0.25 to 0.47; P less than 0.001); a significant benefit was also observed with respect to the confirmed objective response rate and the time to pain progression.
    In Sponsor's press release, "Submission based on PROfound, the first positive Phase III trial testinga targeted treatment in biomarker-selected prostate cancer patients"
    Results of the PROfound trial showed Lynparza significantly reduced the risk of disease progression or death by 66% based on a hazard ratio of 0.34 (p less than 0.0001) vs. abiraterone or enzalutamide in patients with BRCA1/2 or ATM-mutated mCRPC, the primary endpoint of the trial.
    In an article by Kato et al (2019) "Nivolumab versus chemotherapy in patients with advanced oesophageal squamous cell carcinoma refractory or intolerant to previous chemotherapy (ATTRACTION-3): a multicentre, randomised, open-label, phase 3 trial"
    overall survival was significantly improved in the nivolumab group compared with the chemotherapy group (median 10·9 months, 95% CI 9·2–13·3 vs 8·4 months, 7·2–9·9; hazard ratio for death 0·77, 95% CI 0·62–0·96; p=0·019).

    However, in the sponsor's press release, "% reduction in risk of death" was also added in addition to HR:
    Opdivo demonstrated a 23% reduction in the risk of death and 2.5-month improvement in median overall survival compared to chemotherapy.
    Opdivo demonstrated a statistically significant improvement over chemotherapy, with a 23% reduction in risk of death [Hazard Ratio (HR) 0.77; 95% Confidence Interval (CI): 0.62 to 0.96; p=0.019] and a 2.5-month improvement in median OS [10.9 months (95% CI: 9.2 to 13.3)] compared to patients treated with chemotherapy [8.4 months (95% CI: 7.2 to 9.9)]. The safety profile of Opdivo in this trial was consistent with previously reported studies in ESCC and other solid tumors.
    The press release by BMS: "Phase III CheckMate -067 Trial Demonstrates Superior Progression-Free Survival of Opdivo+Yervoy Regimen or Opdivo Monotherapy vs. Yervoy Monotherapy in Previously Untreated Patients with Advanced Melanoma":
    The Opdivo+Yervoy regimen demonstrated a 58% reduction in the risk of disease progression vs. Yervoy (hazard ratio: 0.42; 99.5% CI, 0.31 to 0.57; P less than 0.0001), while Opdivo monotherapy demonstrated a 43% risk reduction versus Yervoy monotherapy (hazard ratio: 0.57; 99.5% CI, 0.43 to 0.76; P less than 0.00001).

    "Medivation and Astellas Announce The Phase 3 PREVAIL Trial of Enzalutamide Meets Both Co-Primary Endpoints of Overall Survival and Radiographic Progression-Free Survival in Chemotherapy-Naïve Patients With Advanced Prostate Cancer":
    30% Reduction in the Risk of Death, Hazard Ratio=0.70 (p less than0.0001)

    81% Reduction in the Risk of Radiographic Progression or Death, Hazard Ratio=0.19 (p less than 0.0001)
    We can see from these examples that when an event is a negative outcome, it is pretty common to interpret the hazard ratio to "percent reduction in risk".

    Similarly, when an event is a positive outcome, a hazard ratio greater than 1 is desirable for a successful trial. We may interpret the hazard ratio as "increase the probability of ...by xx%" or "increase the chance of [event] by xx%".

     xx% =  ( HR - 1) ×100%

    In an article by Statnews "Data for Gilead’s potential coronavirus therapy are coming soon. Here’s what you need to know", it says the following about the hazard ratio:
    If people who take the placebo show clinical improvement after 16 days, remdesivir would have to track at 13 days to demonstrate superiority with statistical significance, Raffat said. This would be described in what researchers call a “hazard ratio.” The magic number would be 1.2, meaning that patients do 20% better on remdesivir than placebo.
    If the median time to event can be calculated, it is also straight forward to list the median time to event. It is easy for people to compare the median time to event between treatment groups to determine which group performs better. For example, if the overall survival (OS) is the endpoint and if at least 50% patients die in both treatment groups, the median survival times can be calculated using the Kaplan-Meier approach and the group with longer median survival time performs better. 

    The issue with this approach is that a lot of clinical trials with time to event variable as efficacy endpoint, the median time to event may not be calculable because of the low frequency of the events. 

    Wednesday, April 22, 2020

    For multi-center, open label clinical trials, can individual investigation sites disclose results from their sites while study is ongoing?

    The golden standard for clinical trials is RCT (randomized, control trials) including three key components: randomization, including a control group (preferably concurrent control), and blinding (or concealment of the treatment assignment). The blinding can minimize the biases (conscious or unconscious) introduced into the study by investigators, patients, and the study team.

    However, in some situations, blinding is not possible or is very difficult to implement. A randomized, open-label study may be more appropriate. In the randomized, open-label study, while patients are randomized assigned to receive the experimental new drug or control, the investigator, patients, (in some cases the sponsor study team) are aware of the treatment assignment. 

    In an early posting "Some Blinding Techniques in Clinical Trial", some blinding techniques can be applicable even for the randomized, open-label study. On the conservative side, the sponsor can follow the same procedures in handling the randomization, open-label study as the double-blind study - i.e., establishing a firewall to keep the sponsor study team or anyone with the capability to do the aggregate analyses and decision making from accessing the data during the study. 

    With a randomized open-label study, both site investigators and patients participating in the study know which treatment group the patient is receiving - knowing the treatment assignment itself may cause biases in efficacy and safety assessments, in concomitant medication selection, in subsequent therapy selection,...

    In the last several days, we see an extreme case (presumably due to the special situation in fighting the Covid-19 pandemic). In multi-center, randomized, open-label studies for remdesivir, while the studies are ongoing, the individual sites start to disclose the results / experiences from patients in their perspective sites. 

    "The University of Chicago Medicine recruited 125 people with Covid-19 into Gilead’s two Phase 3 clinical trials. Of those people, 113 had severe disease. All the patients have been treated with daily infusions of remdesivir."
    "The best news is that most of our patients have already been discharged, which is great. We’ve only had two patients perish,” said Kathleen Mullane, the University of Chicago infectious disease specialist overseeing the remdesivir studies for the hospital.
     Komo News (04/21/2020) Experimental anti-viral drug Remdesivir used to treat COVID-19 patients
    Dr. Vinay Malhotra said at his hospital, 40 patients have been treated with the anti-viral drug.
    According to Dr. Malhotra, 32 patients were in severe condition. 8 were in moderate condition. Of the 40 patents treated, Malhotra said 17 of them have already been discharged.
    “Everyone is talking about the discharge of these patients. Everyone is talking about how good the effect is in three days,” Malhotra said. "We were fortunate to be chosen for this trial. And that has helped our population tremendously."
    Both the University of Chicago Hospital in Illinois and the Multicare Institute for Research and Innovation in Tacoma, Washington are investigation sites participating in two pivotal clinical trials sponsored by Gilead:
    Both studies (one for severe Covid-19 and one for moderate Covid-19 patients) were designed as randomized, open-label studies with one group receiving Remdesivir (on top of standard of care) and one group receiving standard of care only. According to clinicaltrials.gov, there are 179 and 178 investigation sites for these two studies.

    If all sites do the same as the University of Chicago hospital and Multicare Institute for Research and Innovation to disclose their experiences, we could see many reports: some may be good, some may not be very good. The sites with good results may be eager to get their information out; the sites with results not very good may keep silent.

    In both of these reports, there was no mention of how many patients in the control group (receiving standard of care) - maybe these patients in the control group are doing pretty well too (unless there are selection biases in determining which patients are allocated to receiving remdesivir or not receiving remdesivir (standard of care)). 

    It is not clear if there is an agreement between the sponsor (Gilead) and the investigation sites about disclosing the data while the study is ongoing. Disclosing the data for individual sites while the study is still ongoing should certainly be discouraged or prohibited even in the public health emergency situation. The better way will be that the sponsor designs the study as group sequential design and perform the interim analyses using the data from all sites - if the results are indeed great, the pre-specified boundaries may be crossed and the sponsor may be able to stop the study for efficacy. In this way, the study results may be disclosed earlier while the study integrity is maintained. 

    There are already reports that the motive and wrongdoing should be investigated: 

    There are also reports that criticize the study design (no adequate control group and no blinding) for Gilead's designed studies: 

    Thursday, April 09, 2020

    Gilead's Remdesivir Clinical Trials - What Do Drastic Design Changes Mean?

    In the midst of the Covid-19 pandemic, people are rushing to find effective treatments for Covid-19 infected patients. Among the drugs being investigated is remdesivir, an experimental antiviral made by the US drug company Gilead Sciences. It has been characterized as one of the most promising by health authorities, including WHO officials—though that optimism is inspired only by anecdotal information.

    There are four randomized, controlled trials with remdesivir that may have the results available in very near term. Two studies conducted in China should have the results available very imminent (before the end of April). Two studies conducted by Gilead itself should have results sometime in May, 2020. 

    We noticed that for two pivotal studies run by Gilead, the study design was significantly modified. According to the clinicaltrials.gov, the major changes are listed below: sample sizes are increased; the primary efficacy endpoint is revised, the treatment arms are tweaked.

    See the article "Gilead supersizes remdesivir trials, changes primary endpoint".

     Protocol Title
    Previous Version (before April 6, 2020)
    Revised Version (after April 6, 2020)
    400 Subjects

    Two Strata and Three arms: Remdesivir for 5 days versus Standard of care; Remdesivir for 10 days versus Standard of care


    2400 Subjects

    Four Strata and Five arms:
    Remdesivir for 5 days (Not Mechanically Ventilated) versus Standard of care;
    Remdesivir for 10 days (Not Mechanically Ventilated) versus Standard of care
    Remdesivir, 5 or 10 Days (Extension) versus Standard care
    Remdesivir 10 days (Mechanically Ventilated) versus Standard of care
    Proportion of Participants With Normalization of Fever and Oxygen Saturation Through Day 14

    This is a composite outcome measure. Criteria for fever normalization: Temperature < 36.6 °C armpit, < 37.2 °C oral, or < 37.8 °C rectal sustained for at least 24 hours and criteria for oxygen normalization: peripheral capillary oxygen saturation (Sp02) > 94% sustained for at least 24 hours.

    The Odds of Ratio for Improvement on a 7-point Ordinal Scale on Day 14.
    The odds ratio represents the odds of improvement in the ordinal scale between the treatment groups. The ordinal scale is an assessment of the clinical status at a given day. Each day, the worst score from the previous day will be recorded. The scale is as follows: 1. Death 2. Hospitalized, on invasive mechanical ventilation or Extracorporeal Membrane Oxygenation (ECMO) 3. Hospitalized, on non-invasive ventilation or high flow oxygen devices 4. Hospitalized, requiring low flow supplemental oxygen 5. Hospitalized, not requiring supplemental oxygen - requiring ongoing medical care (coronavirus (COVID-19) related or otherwise) 6. Hospitalized, not requiring supplemental oxygen - no longer required ongoing medical care (other than per protocol Remdesivir administration 7. Not hospitalized.

    600 Subjects

    Three arms: Remdesivir for 5 days
    Remdesivir for 10 days
    Standard of care

    1600 Subjects

    Added a fourth arm:
    Experimental: Part B: Extension Treatment, Remdesivir 5 or 10 days
    Participants will receive continued standard of care therapy together with RDV 200 mg on Day 1 followed by RDV 100 mg on Days 2, 3, 4, 5, 6, 7, 8, 9, and 10.
    Proportion of Participants Discharged by Day 14
    The Odds of Ratio for Improvement on a 7-point Ordinal Scale on Day 11.
    The odds ratio represents the odds of improvement in the ordinal scale between the treatment groups. The ordinal scale is an assessment of the clinical status at a given day. Each day, the worst score from the previous day will be recorded. The scale is as follows: 1. Death 2. Hospitalized, on invasive mechanical ventilation or Extracorporeal Membrane Oxygenation (ECMO) 3. Hospitalized, on non-invasive ventilation or high flow oxygen devices 4. Hospitalized, requiring low flow supplemental oxygen 5. Hospitalized, not requiring supplemental oxygen - requiring ongoing medical care (coronavirus (COVID-19) related or otherwise) 6. Hospitalized, not requiring supplemental oxygen - no longer required ongoing medical care (other than per protocol Remdesivir administration 7. Not hospitalized.

    It is not clear what these protocol changes are based on: the external data from other studies or the cumulative data to date from these two ongoing studies? The fact is that these two Gilead-run studies are randomized, open-label – unless they treated them as the blinded studies, the Sponsor would be able to see the cumulative data and monitor the treatment effects along the way. 

    A dramatic increase in sample size seems to suggest that the treatment effect is much smaller and the previous study design may be underpowered.

    It may also indicate that two randomized, double-blinded Remdesivir studies in China will most likely be underpowered as well (308 subjects for study in mild-moderate patients and 453 subjects for study in severe patients). Unless there is a magic or luck, these two studies from China may not be able to reach the statistical significance because of the smaller sample size. We hope that there will be at least some trends - at a time of no effective treatment for Covid-19 infection, a study with a trend will be encouraging news. 

    For the primary efficacy endpoint, odds ratio should be more sensitive to detect the treatment difference for the ordinal data - ordinal logistic regression will need to be used to calculate the odds ratios. 

    Monday, March 30, 2020

    Gliead asked FDA to rescind the orphan drug designation for coronavirus treatment remdesivir it just received 48 hours earlier - Why?

    Last Monday, Gilead received the orphan drug designation from US FDA for remdesivir to treat Covid-19. Two days later on Wednesday, we saw the news that Gilead asked FDA to rescind its orphan drug designation - it was really an unusual step taken by a company in 37-year history since Orphan Drug Act was enacted in 1983.

    A drug with orphan designation will give many benefits to the drug developer.
    • Exemption from PDUFA (Prescription Drug User Fee Act) marketing application fee (FY 2020: $2.94+ million)
    • 7 years of exclusivity in the U.S.
    • Tax credits related to clinical trial expenses
    • Orphan products grants program
    • Submission of a pediatric assessment per PREA is not required
    • FDA assistance in clinical trial design
    After Gilead received the orphan drug designation, it immediately drew a lot of critics. The company was criticized by lawmakers and patient advocates after receiving the orphan designation for the experimental antiviral drug, saying it was taking advantage of the rapidly accelerating health crisis.
    "It's embarrassing to take something that's potentially the most widespread disease in the history of the pharmaceutical industry and claim it's a rare disease."
    “This is an unconscionable abuse of a programme designed to incentivise research and development of treatments for rare diseases.”
    “Calling COVID-19 a rare disease mocks people’s suffering and exploits a loophole in the law to profiteer off a deadly pandemic,”
    Gilead had maintained that at the time that it had filed for the orphan designation, the coronavirus outbreak was nowhere near the level of severity it is at now. It also contended that it had sought the orphan status to expedite approval for the drug, in particular for a required paediatric study plan.

    According to the Orphan Drug Act approved in 1983, the orphan drug designation was for drug development in rare disease or condition where the rare disease or condition means: 
    (A) affects less than 200,000 persons in the United States, or
    (B) affects more than 200,000 in the United States and for which there is no reasonable expectation that the cost of developing and making available in the United States a drug for such disease or condition will recovered from sales in the United States of such drug. Determinations under the preceding sentence with respect to any drug shall be made on the basis of the facts and circumstances as of the date the request for designation of the drug under this subsection is made.
    Determining orphan drug designation based on the # of patients is usually good for ultra-rare diseases and for diseases with genetic disorders. However, it can be problematic when it comes to diseases with a dynamic feature in the number count (the Covid-19 pandemic is a great example):
    • Defining and counting rare diseases is not straightforward.
    • Difficulties in obtaining definitive diagnoses contribute
    • Limitations in systems for reporting and tracking orphan disease diagnoses.
    • Countries have adopted different definitions of a rare disease
    • Researchers are continuously identifying new diseases or disease variants.
    The epidemiology of rare diseases—including the determination of prevalence (the number of people affected at any one time), incidence (the number of new cases in a given year), and patterns of disease (e.g., age distribution) in the population—is inexact.

    A rare disease may become a prevalent disease
    • Some conditions that initially are classified as rare eventually outgrow that categorization. Covid-19 infection is a good example of this. Even though the # of confirmed cases is still below the 200,000 mark, very soon, the threshold of 200,000 cased will be crossed. Another example is HIV/AIDS. When AIDS emerged in the United States, it fit the legislative definition of a rare disease—affecting fewer than 200,000 individuals. As the infection spread, as diagnostic capabilities and data collection systems improved, and as researchers developed effective treatments that reduced mortality without curing the disease, the total number of individuals with AIDS grew to nearly 470,000 by 2007 and the number of individuals with HIV infection exceeded 1.1 million (CDC, 2009c). 
    • If effective but not curative treatment can turn a rare disease into a common one,
    A prevalent disease may become a rare disease
    Effective prevention can, conversely, turn a common condition into a rare disease. This is the case with many once common childhood infections such as mumps and measles. Public health officials are concerned, however, that factors such as the development of drug-resistant infectious agents and the opposition of some parents to childhood vaccinations could reverse the situation for some now rare diseases. The former concern—drug resistance—is partly a significant scientific challenge (i.e., developing new anti-infectives) and partly a public health and clinical practice challenge (i.e., discouraging overuse of antibiotics). Preventing negative health consequences from anti-vaccination sentiment involves public health expertise, social science research, clinician communication skills, and public policy responses.
    • Post-polio syndrome
    • Precision Medicine – genetic biomarkers further divide the disease into subgroups
    For the Covid-19 situation, if we purely base the orphan designation on the # of patients, it will meet the criteria in the beginning, then not meet the criteria, and meet the criteria again. In any way, obtaining an orphan drug designation for remdesivir in treating Covid-19 patients is indeed unethical.