Treatment Emergent AEs (TEAEs), On-Study AEs, On-Treatment AEs, Non-TEAEs

During the clinical trial, the adverse events (AEs) are collected from the signing of the informed consent to the last dose of the study drug plus some follow-time. For statistical analyses of adverse event data, the treatment-emergent AEs (TEAEs) are usually defined. AEs with an onset date at or after the first dose of the study drug will be defined as TEAEs. AEs with an onset date prior to the first dose of the study drug will then be defined as Non-TEAEs. 

For example, the TEAE can be defined as:

"TEAEs are defined as events that start within the day of the first dose of trial treatment until 28 days after the last dose of treatment" in an SAP for an EMD Serono study.

"Treatment-emergent AEs (TEAEs) are defined as AEs that are not present at baseline or represent an exacerbation of a preexisting condition during the treatment period. Therefore, referencing the protocol, TEAEs will be defined programmatically as any AE record with a start date/time on or after the first study treatment administration (greater than or equal to study day 1), inclusive to the end of the study (specifically the EOS visit or ET visit)." in an SAP for a Regeneron's study

The TEAEs can be further defined based on the comparison of the AE onset date with a cut-off date where the cut-off date may be the last dose date or 28 days after the last dose date. 

In FDA's clinical review document for AstraZeneca's asthma drug, the on-study AE and on-treatment AE were defined:

• On-study AE: events with onset between the first-day dosing and the scheduled follow-up visit. 
• On-treatment AE: events with onset between the first day of treatment and the scheduled end of treatment (EOT) or investigational product discontinuation (IPD) visit. 
• Post-treatment AE: events with onset after the on-treatment period defined above

 On-study AEs include all TEAEs - all AEs recorded on or after the first dose date. 

On-treatment AEs are a subset of all TEAEs or on-study AEs. The AEs with an onset date after the cut-off date will be excluded from on-treatment AEs. 

There are some clinical trials with on-treatment AEs defined as the same as traditional TEAEs (i.e., any AEs with an onset date on or after the first dose of the study drug regardless of the cut-off date).

In a recent workshop "Advancing Premarket Safety Analysis" organized by FDA and Duke Margolis Center for Health Policy, the on-study and on-treatment AEs were specifically discussed. Here are the presentation slides for this topic:

The concept of on-treatment AEs has already been implemented in some clinical trials. For example, in a GSK-sponsored trial "A Phase 3a, Repeat Dose, Open-label, Long-term Safety Study of Mepolizumab in Asthmatic Subjects", the primary outcome measure is "Number of Participants With Any On-treatment Adverse Event (AE) or On-treatment Serious AE (SAE)" where On-treatment AEs and on-treatment SAEs are the events occurring on/after the first dose of open-label mepolizumab date and before/on last dose+28 days.

In a BMS trial "A randomized, open-label, phase 3 study of  BMS-936558 vs. Everolimus in Subjects with advanced or metastatic clear-cell renal cell carcinoma who have received prior anti-angiogenic therapy", the on-treatment AEs were defined as the following with a cut-off date of 100 days of the last dose of study treatment.

"On-treatment AEs will be defined as AEs with an onset date-time on or after the DateTime of the first dose of study treatment (or with an onset date on or after the day of first dose of study treatment if time is not collected or is missing). For subjects who are off study treatment, AEs will be counted as on-treatment if event occurred within 100 days of the last dose of study treatment. No “subtracting rule” will be applied when an AE occurs both pre-treatment and post-treatment with the same preferred term and grade."

Defining on-treatment AEs will require specifying a cut-off date and the cut-off date may be different depending on the potential impact of the study drug after the drug discontinuation and the half-life of the investigational products. 

Defining on-treatment AEs may be necessary for studies with the treatment policy estimand where the efficacy data and AE/SAEs are continued to be collected after the study participants have discontinued the study treatment. 

Previous discussions: 

• Adverse events (AE),treatment-emergent adverse events (TEAE), and adverse drug reaction (ADR)
• Adverse Event Collection: When to Start and When to Stop?

Rolling Review, Real time oncology review (RTOR), and Split real time application review (STAR) Program

For New Drug Application (NDA) and Biological License Application (BLA), the usual process is to submit the entire package with different modules at the same time. The submission package will include the quality, CMC, non-clinical study reports, and clinical study reports,... However, there are processes by which the sponsor can submit the submission package piece by piece: rolling review, real-time oncology review, and split real-time application review (STAR) program.

Rolling Review

Rolling review was one of the benefits for drug products with Fast Track Designation. According to FDA's website "Fast Track" 

A drug that receives Fast Track designation is eligible for some or all of the following:

More frequent meetings with the  FDA to discuss the drug's development plan and ensure the collection of appropriate data needed to support drug approval

More frequent written communication from FDA about such things as the design of the proposed clinical trials and use of biomarkers

Eligibility for Accelerated Approval and Priority Review, if relevant criteria are met

Rolling Review, which means that a drug company can submit completed sections of its Biologic License Application (BLA) or New Drug Application (NDA) for review by FDA, rather than waiting until every section of the NDA is completed before the entire application can be reviewed. BLA or NDA review usually does not begin until the drug company has submitted the entire application to the FDA

Fast Track Designation was one of the expedited programs described in FDA's guidance "Expedited Programs for Serious Conditions – Drugs and Biologics". Other expedited programs are breakthrough therapy designation, accelerated approval, and priority review designation. In FDA's guidance, the Fast Track Designation contains the benefit of submission of portions of an application (Rolling Review): 

Here are rolling review examples: earlier this month, Iveric Bio Announces Submission of First Part of NDA for Rolling Review of Avacincaptad Pegol for the Treatment of Geographic Atrophy; in September, 2022, Vertex and CRISPR Therapeutics Announce Global exa-cel Regulatory Submissions for Sickle Cell Disease and Beta Thalassemia

Real-Time Oncology Review (RTOR) Program

For oncology products, FDA's The Oncology Center of Excellence has a program called "real-time oncology review (RTOR)". RTOR facilitates earlier submission of topline efficacy and safety results, prior to the submission of the complete application, to support an earlier start to the FDA’s evaluation of the application. FDA's website "Real-Time Oncology Review" described the details about RTOR program. 

Some companies have utilized this program in hope of expediting their submission/review process. 

In a press release "SpringWorks Therapeutics Announces Data from Phase 3 DeFi Trial Evaluating Nirogacestat in Adult Patients with Progressing Desmoid Tumors at the European Society for Medical Oncology (ESMO) Congress 2022", it stated that their Nirogacestat for R/R desmoid tumors will be filed through real-time oncology review (RTOR) program.

Nirogacestat has received Orphan Drug Designation from the U.S. Food and Drug Administration (FDA) for the treatment of desmoid tumors and from the European Commission for the treatment of soft tissue sarcoma. The FDA also granted Fast Track and Breakthrough Therapy Designations for the treatment of adult patients with progressive, unresectable, recurrent or refractory desmoid tumors or deep fibromatosis. SpringWorks plans to submit a New Drug Application (NDA) to the FDA in the second half of 2022, which will be submitted for review under the FDA’s Real-Time Oncology Review (RTOR) program.

Amgen's Sotorasib was approved by FDA for the first and only targeted treatment for patients with KRAS G12C-mutated locally advanced or metastatic non-small cell lung cancer. The Sotorasib's BLA submission was through RTOR:

"In the U.S., LUMAKRAS was reviewed by the FDA under its Real-Time Oncology Review (RTOR), a pilot program that aims to explore a more efficient review process that ensures safe and effective treatments are made available to patients as early as possible."

 An article in Life Science Leader magazine "FDA's RTOR Program: Draft Guidance & Insights" provides a good summary of the RTOR program.

Split Real-Time Application Review (STAR)

STAR program builds off the Oncology Center of Excellence’s Real-Time Oncology Review (RTOR) program, In the newly passed PDUFA VII for the years 2023 through 2027, a new program called Split real-time application review (STAR) was proposed. According to PDUFA reauthorization performance goal and procedures fiscal years 2023 through 2027, the STAR program was described as the following: 

D. SPLIT REAL TIME APPLICATION REVIEW (STAR) PILOT PROGRAM

FDA will establish a STAR pilot program, which has the goal of shortening the time from the date of complete submission to the action date, in order to allow earlier patient access to therapies that address an unmet medical need. The STAR pilot program will apply to efficacy supplements across all therapeutic areas and review disciplines that meet specific criteria. Accepted STAR applications will be submitted in a “split” fashion, specifically in two parts (with the components submitted approximately 2 months apart).

1. Scope: The STAR program will seek to expedite patient access to novel uses for existing therapies by supporting initiation of review earlier than would otherwise occur and therefore allowing earlier approval for qualified efficacy supplements. This program will apply across all therapeutic areas and review disciplines for applications that meet specific criteria. An application will be considered eligible for STAR if each of the following criteria are met: a. Clinical evidence from adequate and well-controlled investigation(s) indicates that the drug may demonstrate substantial improvement on a clinically relevant endpoint(s) over available therapies. Breakthrough Therapy Designation (BTD) or Regenerative Medicine Advanced Therapy Designation (RMAT) is not required, but above criteria must be met. b. The application is for a drug intended to treat a serious condition with an unmet medical need. c. No aspect of the submission is likely to require a longer review time (e.g., requirement for new REMS, etc.). d. There is no chemistry, manufacturing, or control information that would require a foreign manufacturing site inspection (i.e., domestic site inspections may be allowed if it does not affect the expedited timeframe).

FDA's website "Split Real-Time Application Review (STAR)" described how the STAR program should be operated.

Multiple Endpoints in Clinical Trials (Final) - FDA Guidance for Industry

Today, the FDA finalized its guidance for industry "Multiple Endpoints in Clinical Trials". The draft version of this guidance was issued in 2017. The guidance is intended to help sponsors better understand FDA's current thinking about the issues related to the multiple endpoints and multiplicity issues for multiple endpoints, and different approaches in handling multiplicity issues. The guidance also discussed composite endpoints and multi-component endpoints. 

Typically, an adequate and well-controlled study will include only one primary efficacy endpoint and then multiple secondary efficacy endpoints, and additional exploratory endpoints. Exploratory endpoints are those endpoints for research purposes or for new hypotheses generation and not for the purpose of the product label. Primary and secondary efficacy endpoints can potentially be included on the product label. However, the testing hierarchy and sound approach for multiplicity adjustment must be pre-specified. The Fixed-Sequence Method in the appendix of this guidance seems to be commonly used. With Fixed-Sequence Method, the secondary efficacy endpoints will be tested only if the primary efficacy endpoint is statistically significant. The secondary efficacy endpoints are ranked or ordered based on the importance of the endpoints and the likelihood of getting statistically significant results. The next secondary efficacy endpoint will be tested only if the previous secondary endpoint is statistically significant. The testing hierarchy will stop once the hypothesis test for one of the secondary endpoints is not statistically significant.  

If the sponsor wants to include secondary endpoints in the product label, multiplicity adjustment for secondary endpoints must be included in the statistical analysis plan. 

In the section discussing the co-primary endpoints, "When Demonstration of Treatment Effects on Two or More Distinct Endpoints Is Recommended to Establish Clinical Benefit (Co-Primary Endpoints)", the examples of clinical trials with co-primary endpoints included in the draft version of this guidance were removed from the final guidance. For example, the draft guidance mentioned the following example and the final guidance did not:

Presumably, this is due to the revised FDA guidance "Early Alzheimer's Disease: Developing Drugs for Treatment" and the availability of the integrated scale - Clinical Dementia Rating Sum of Boxes (CDR-SB) Score:

"An integrated scale that adequately and meaningfully assesses both daily function and cognitive effects in early AD patients is acceptable as a single primary efficacy outcome measure. " 

"Common Statistical Methods for Addressing Multiple Endpoint-Related Multiplicity Problems" was included in the body of the guidance in the draft guidance and is now moved to the Appendix: Statistical Methods. The list of methods remains the same and includes the Bonferroni method; the Holm procedure; the Hochberg procedure; prospective alpha allocation scheme; the fixed-sequence method; resampling-based, multiple-testing procedures; gatekeeping testing strategies; and graphical approaches based on sequentially rejective tests. 

As a regulatory agency, FDA is conservative and tries to avoid false conclusions. Without adequate adjustment for multiplicity, the alpha level (type I error rate) can be inflated, and statistically, significant differences may be wrongly declared for an ineffective drug. In the summary of this guidance, FDA concludes: 

Previous Posts:

• Weighted Bonferroni Method (or partition of alpha) in Clinical Trials with Multiple Endpoints
• Co-primary endpoints and multiple primary endpoints

Risk difference and confidence interval for Analyses of AEs and Clinical Laboratory Data

Several years ago, I posted an article "Should hypothesis tests be performed and p-values be provided for safety variables in efficacy evaluation clinical trials?". There are some new development on this topic. 

Recently, FDA in collaboration with the Duke-Margolis Center for Health Policy hosted a one-day virtual meeting focused on advancing pre-market safety analytics. At this workshop, it was revealed that FDA Biomedical Informatics and Regulatory Review Science (BIRRS) Team was working on a document called "Standard Safety Tables and Figures: Integrated Guide". The document is currently posted on regulations.gov for public comments. The integrated guide proposed the mockup shells how the safety data analyses (adverse events and clinical laboratory data) should be displayed. Throughout all the proposed shells, we can see that a column for "'Risk Difference (%) (95% CI)" are included. Here are a couple of examples. 

If this integrated guide become official and is implemented, the future analyses for safety data (adverse events and clinical laboratory parameters) will be shifted from the pure summary statistics to summary statistics + point estimate and 95% confidence interval for risk differences. p-values and hypothesis testing should not be provided. 

Risk difference and its 95% confidence interval are provided for the descriptive purpose, not for inferential purpose. As stated in the integrated guide "These safety analyses are exploratory in nature and confidence intervals (CIs) for the risk difference presented here are not adjusted for multiplicity."

In AE tables, the sort order will be by the risk difference (from the highest to the lowest). In this way, the reviewers can easily identify the AEs with largest risk difference between two treatment groups. 

There are several ways in calculating the confidence interval for risk difference. The commonly used approach is Wilson score method - a method of estimating the population probability from a sample probability when the probability follows the binomial distribution.

There seems to be some differences between the regulatory requirement and the requirement by the medical journals. We continue to see the requests from journals like New England Journal of Medicine for providing the p-values for AE summary tables. In our published article, "Inhaled Treprostinil in Pulmonary Hypertension Due to Interstitial Lung Disease", we had to provide the p values for AEs and other safety endpoints for treatment group comparison per NEJM's editor's request. 

PDUFA, GDUFA, BSUFA, and MDUFA - FDA's User Fee Programs

For professionals who are working in drug development areas, the term PDUFA should be a very familiar term. As a matter of fact, FDA's action date or decision date to approve a new drug application (NDA) or biological license application (BLA) is called 'PDUFA date'. There are several trackers to track FDA's calendar for NDA/BLA approvals based on the PSUFA dates. 

PDUFA stands for 'Prescription Drug User Fee Amendments' and it is a program allowing FDA to collect the application fees from the sponsor. The PDUFA was created by Congress in 1992 and authorizes FDA to collect fees from companies that produce certain human drug and biological products. Since the passage of PDUFA, user fees have played an important role in expediting the drug approval process. In "Regulatory Education for Industry (REdI) Annual Conference 2022", Dr. Kevin Bugin from FDA presented "PDUFA Overview and Reauthorization".

"Timely review of the safety and effectiveness of the new drug applications and biologics license applications is essential to FDA mission to protect and promote public health. PDUFA is essential to these efforts. In fact, before PSDUFA is enacted in 1992, American's access to innovative new medicines lacked behind other countries. FDA’s premarket review process was unstaffed and unpredictable, and frankly slow. Agency lacks sufficient staff to perform a timely review and lacks in procedures and standards to ensure a rigorous, consistent, and predictable process.

So to tackle these challenges Congress passed PDUFA and this authorized FDA to collect industry user fees to hire additional staff and upgrade its information technology systems, processes, and so on, in return, it committed the agency to timelines for the application review process for new drugs without compromising its high standards for new drug safety efficacy and quality and, over the years as the PDUFA program has been reauthorized now six times going into its seventh time here hopefully there have been additional enhancements."

PDUFA must be reauthorized or renewed by congress every five years. Since its inception in 1992, it has been amended six times and we are now waiting for congress to authorize the 7th amendment (PDUFA VII). Notice that each PDUFA amendment may have its own name associated with it. 

PDUFA I - Original 

PDUFA II - FDAMA (Food and Drug Administration Modernization Act) 

PDUFA III - Public Health Security and Bioterrorism Preparedness and Response Act of 2002

PDUFA IV - FDAAA (Food and Drug Administration Amendments Act of 2007)

PDUFA V - FDASIA (Food and Drug Administration Safety and Innovation Act)

PDUFA VI - FDARA (FDA Reauthorization Act of 2017)

There are several sister programs designed with the same purpose as PDUFA, but for different approval pathways: GDUFA (Generic Drug User Fee Amendment), BSUFA (Biosimilar User Fee Amendment), and MDUFA (Medical Device User Fee Amendment). Recently, PDUFA VII was authorized by congress, and so were these sister programs. 

Along with the approval of PDUFA, GDUFA, BSUFA, and MDUFA, the user fee rates for various types of applications are updated and released by the FDA:

PDUFA: Prescription Drug User Fee Amendments

Prescription Drug User Fee Rates for Fiscal Year 2023

GDUFA: Generic Drug User Fee Amendment

Generic Drug User Fee Rates for Fiscal Year 2023

BSUFA: Biosimilar User Fee Amendment

Biosimilar User Fee Rates for Fiscal Year 2023

MDUFA: Medical Device User Fee Amendment

Medical Device User Fee Rates for Fiscal Year 2023

For companies that are well-funded and have steady revenues, these user fees are affordable. However, these user fees could be a burden for small companies. 

For drug development in diseases with orphan drug designation, the application fees for NDAs or BLAs are waived - this is an approach to encourage the companies to develop the drugs in orphan diseases. 

FDA's guidance "User Fee Waivers, Reductions,and Refunds for Drug andBiological Products" listed the situations where the user fees can be waived.  

FMQ (FDA Medical Query) and SMQ (Standardized MedDRA Query)

For clinical trials, the safety analyses are mainly based on the analyses of the adverse events including serious adverse events. The adverse events are recorded in CRFs/eCRFs with investigators' verbatim terms (text field). Before the adverse event data can be summarized and analyzed, the verbatim terms need to be coded and standardized. The common practice is to code the adverse events based on the MedDRA dictionary and the coded terms are then summarized and analyzed by system organ class and preferred term. For a while, this approach seems to work very well. However, there are issues with this approach, mainly because different preferred terms may point to the same disease/condition. 

Last Wednesday, The FDA in collaboration with the Duke-Margolis Center for Health Policy hosted a one-day virtual meeting focused on advancing premarket safety analytics. In the morning session, FDA officers discussed the FMQ (FDA Medical Query). In the afternoon session, FDA discussed the standardized presentation of the safety data including the tables and figures for adverse event data and laboratory data. 

The FDA Medical Queries (FMQs) is a standardized approach to group preferred terms. Recognizing the limitation of the current analysis of adverse event data by preferred term, using FMQs can consolidate a medical condition with scattered preferred terms and be more likely to identify any signal of safety issues. The rationales for FDA's efforts in developing various FMQs are described in the slide below: 

FMQs were defined as the following: 

FMQs can be used to identify the safety signals that may be missed by using the conventional preferred term approach. When FMQs are adopted by the regulatory agency and the industry, FMQs (grouped term information) can be included in the ADVERSE REACTIONS Section of the Prescribing Information (or product label). The slide below illustrates a fictitious example of using FMQ term in the ADVERSE REACTIONS section of the product label. The AE table in the product label will include the mixture of the FMQ term (grouped term) and the MedDRA preferred terms. 

A real example of FMQs in the product label can be the drug called Injectafer. The tables for adverse reactions included the mixture of the preferred terms and the grouped terms (based on FDA's FMQs). 

FMQ is exactly the same concept as the SMQ (standardized MedDRA query). The SMQ was defined as the following: 

With each version of the MedDRA dictionary, a set of available SMQs will be included. Included is also the document "Introductory Guide for Standardised MedDRAQueries (SMQs)". 

Both FMQ and SMQs included narrow terms and broad terms. However, the narrow terms are more commonly used in practice. 

One natural question is: what is the difference between FMQ and SMQ? why can't we just use the SMQ? Here are a couple of slides indicating the differences between FMQ and SMQ. There are almost equal numbers of FMQs (104 FMQs for now) to SMQs (110 SMQs).

It is true that SMQs have primarily been used in pharmacovigilance, not in premarket safety assessment. I have previously written a couple of articles about the SMQs: 

• Adverse Event of Special Interest (AESI), Standardized MedDRA Query (SMQs), Customer Queries (CQs), and SAS Programming
• Standardized MedDRA Queries (SMQs) and Standardized Drug Groupings (SDGs)

One drawback of FMQ is that it is developed by the US FDA and its use and application may be limited for market authorization applications in countries outside the US. 

One thing for sure is that we will hear the term FMQ more often in the future and may see the request from FDA to present the grouped terms according to FMQs in the summary and analysis tables for AEs. 

Further reading:

Biomedical Informatics and Safety Analytics in the Office of New Drugs, CDER/FDA – A 2021 Update

Communicating with FDA: Type A, B, C, D meetings, and INTERACT meeting,

For any drug development program, the early and sometimes frequent communications are critical. However, the formal communications between the sponsor and the FDA are a cumbersome process. The sponsor representatives can not directly reach out to FDA reviewers (such as medical reviewer, statistical reviewer, clinical pharmacology reviewer, CMC reviewer,...). On the sponsor side, the communications with FDA is always through the regulatory affairs group. On the FDA side, the communication with the sponsor is through the regulatory project manager (RPM) - each review division at FDA has its own RPM. Direct communications between the sponsor representatives and the FDA reviewers/officers are not good practice and can cause the trouble down the road. We all knew how badly the situation was with Biogen's Aduhelm approval where Biogen executives met with FDA officials outside the normal communication channel. See "FDA chief asks for independent investigation into approval of Biogen's Alzheimer's drug Aduhelm".

Best Communications Practices with FDA is as the following:  

The FDA RPM: The review division regulatory project manager (RPM) is the primary point of contact for communications between IND sponsors and FDA during the life cycle of drug development, and has comprehensive knowledge of the drug and its regulatory history. The RPM is also the primary contact for facilitating the timely resolution of technical, scientific, and regulatory questions, conflicts, or communication challenges between the sponsor and the review team. If sponsors encounter challenges in obtaining timely feedback to inquiries to the review division RPM, they should contact the RPM’s next level supervisor for timely resolution of the issue. 

Communications with FDA are usually through the formal meetings. There are different type of meetings for different purpose. The processes for requesting the formal meetings are described in FDA's guidance below:

• Formal Meetings Between the FDA and Sponsors or Applicants of PDUFA Products Guidance for Industry
• Best Practices for Communication Between IND Sponsors and FDA During Drug Development Guidance for Industry and Review Staff Good Review Practice

In Regulatory Education for Industry (REdI) Annual Conference 2022 - Day 1, Dr Kevin Bugin gave a PSUFA overview where additional type of meetings with FDA were discussed and FDA officer, Jeannie Roule presented "Guidance for Industry: Formal Meetings Between the FDA and Sponsors or Applicants of PDUFA Products".

Different type of Meetings with FDA and the Comparisons:

The Prescription Drug User Fee Act (PDUFA) was created by Congress in 1992 and authorizes FDA to collect fees from companies that produce certain human drug and biological products. Since the passage of PDUFA, user fees have played an important role in expediting the drug approval process. PDUFA needs to be reauthorized by Congress every five years. PDUFA VII for fiscal years 2023 through 2027 is being discussed by Congress and is expected to be reauthorized before the end of the current fiscal year. 

To-be-reauthorized PDUFA VII will create some additional meetings with FDA, specifically, Type D meeting. Once the PDUFA is reauthorized by Congress, the sponsor can request for Type D meeting. 

According to PDUFA REAUTHORIZATION PERFORMANCE GOALS AND PROCEDURES FISCAL YEARS 2023 THROUGH 2027, the Type D meeting is described as the following: 

FDA also had a meeting called INTERACT. INTERACT stands for INitial Targeted Engagement for Regulatory Advice on CBER/CDER ProducTs. It is like the pre-pre-IND meeting and is especially useful in CAR-T, Gene therapy, xenotransplantation development programs. 

Story of BrainStorm's Stem Cell Treatment for ALS - Criticality of the Statistical Analyses

This past week, the biotech company BrainStorm announced the decision to submit a BLA to the FDA for NurOwn® (a stem cell treatment) for the treatment of ALS (Amyotrophic Lateral Sclerosis). The news stirred quite some discussions. The decision to submit the BLA is driven by the reanalysis or the corrected analysis of the previously announced negative results from their pivotal study. In their news announcement, they stated the following: 

New clinical analyses strengthen the conclusions from NurOwn's® Phase 3 clinical trial

A correction was made to the Muscle and Nerve publication from December 2021 describing the results of NurOwn's® Phase 3 clinical trial in ALS following new clinical analyses which strengthen the Company's original conclusions from the trial. The correction results in a statistically significant treatment difference (p=0.050) of more than 2 points for an important secondary endpoint, average change from baseline in ALSFRS-R, in the pre-specified efficacy subgroup of participants with a baseline score of at least 35. Analyses reported in the original publication utilized an efficacy model that unintentionally deviated from the trial's pre-specified statistical analysis plan by erroneously incorporating interaction terms between the subgroup and treatment. The newly published results, which includes supporting information to the publication, employ the efficacy model as pre-specified in the trial's statistical analysis plan, correcting the analyses. The correction also relates to the other subgroup analyses published for this endpoint, demonstrating that all subgroups with ALSFRS-R baseline scores of at least 26 to 35 showed a statistically significant benefit following treatment with NurOwn® (p≤0.050) on this secondary endpoint.

The reanalysis (or as they called it 'correction') was only on the pre-specified subgroup analyses for the secondary endpoint of ALSFRS-R total score (as highlighted in yellow below from the original publication). 

An erratum was issued to present the 'corrected' results for this endpoint: 

The original publication reported results for ALSFRS-R total score subgroup endpoint using a model that unintentionally deviated from the pre-specified statistical analysis plan by erroneously incorporating interaction terms between the subgroup and treatment. The error was made by the CRO who performed the statistical analyses. Applying the correct statistical model for that outcome resulted in the average difference between NurOwn- and placebo-treated patients going from 2.01 points to 2.09 points, but importantly this difference became statistically significant with a P-value of 0.05 (from a p-value of 0.20 in the original analysis). 

While the trial did not reach statistical significance on the primary or secondary endpoints, the company believes these corrected analyses support the conclusion that NurOwn has a positive treatment effect for patients with ALS. 

A year and a half ago, FDA put out a statement (unusual) to advise the BrainStorm not to file the BLA based on the announced results after unblinding of their phase 3 study. FDA specifically stated the following: 

With the recent completion of a randomized phase 3 controlled clinical trial comparing NurOwn to placebo, it has become clear that data do not support the proposed clinical benefit of this therapy. Data indicated that none of the primary or secondary endpoints were met in the group of patients who were randomized. For the main (primary) endpoint, 27.7% of people given the placebo were scored as responding compared to 32.6% of people given NurOwn. The 4.9% absolute difference in responders was not at all statistically significant, and the small difference between the two groups was most likely due to chance. In addition, there was a modest excess in deaths in those treated with NurOwn, the significance of which is unclear at this time. If BrainStorm plans further studies of NurOwn to determine if the product can provide clinical benefit to individuals with ALS, FDA will continue to provide advice to the company on their development program.

Now, A year after FDA slammed on the breaks, BrainStorm is hitting the gas with updated data, approval plans, we will see how the FDA will react to BrainStorm's plan and if FDA will accept the BLA filing by BrainStorm. 

No matter what the fate is for BrainStorm's BLA, one thing is clear: the statistical analyses are critical to the clinical trials and to the overall drug development. It is so important to avoid errors/mistakes in the statistical analyses. This important point has been discussed in previous posts such as "Statistician's nightmare - mistakes in statistical analyses of clinical trials" and "Futility Analysis and Conditional Power When Two Phase 3 Studies are Simultaneously Conducted" where the inappropriate method for futility analysis was implemented. 

It is surprising that the p-value and the statistical significance are still playing a critical role in regulatory decision-making after all of these discussions about retiring statistical significance and p-value. 

Mediation analysis and SAS CAUSALMED procedure

In a recent publication (Benza et al "Contemporary Risk Scores Predict Clinical Worsening in Pulmonary Arterial Hypertension - An Analysis of FREEDOM-EV"), we conducted an analysis called 'Mediation analysis'. In the statistical analysis section, the 'mediation analysis' was stated as the following: 

"To determine whether the change in Week 12 REVEAL Lite 2 risk score ‘mediated’ the treatment effect in delaying clinical worsening, we used SAS (v14.3) CAUSALMED procedure which operationalizes the work of Valeri and VanderWeele.

This analysis attempts to determine what fraction of the total treatment effect appears to be attributable to the treatment effect on the REVEAL Lite 2 score. The analysis was adjusted for baseline REVEAL Lite 2 score; we did the analysis both with and without assuming that there is a treatment and mediator (REVEAL Lite 2 score) interaction on the outcome model (clinical worsening). The definition ‘net clinical benefit’ has been previously proposed as the achievement of all three French non-invasive low risk factors without a clinical worsening event; we retrospectively used the present database to model the performance of this definition."

According to Wikipedia, the mediation model and mediation analysis are defined as the following: 

In statistics, a mediation model seeks to identify and explain the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third hypothetical variable, known as a mediator variable (also a mediating variable, intermediary variable, or intervening variable). Rather than a direct causal relationship between the independent variable and the dependent variable, a mediation model proposes that the independent variable influences the mediator variable, which in turn influences the dependent variable. Thus, the mediator variable serves to clarify the nature of the relationship between the independent and dependent variables.

Mediation analyses are employed to understand a known relationship by exploring the underlying mechanism or process by which one variable influences another variable through a mediator variable. In particular, mediation analysis can contribute to better understanding the relationship between an independent variable and a dependent variable when these variables do not have an obvious direct connection.

A mediator variable can either account for all or some of the observed relationship between two variables. 

Full Mediation

Maximum evidence for mediation, also called full mediation, would occur if the inclusion of the mediation variable drops the relationship between the independent variable and dependent variable to zero. In other words, the effect of the independent variable on the dependent variable is all through the mediator variable. 

Partial mediation

Partial mediation maintains that the mediating variable accounts for some, but not all, of the relationship between the independent variable and dependent variable. Partial mediation implies that there is no only a significant relationship between the mediator and the dependent variable, but also some direct relationship between the independent and dependent variable - the line from independent variable to dependent variable is solid and c is not equal to zero. 

 

The mediation analysis has been used in the data analysis for observational data, clinical trial data, survey data, and epidemiology study data. 

In an article by Eyre et al "Effect of Covid-19 Vaccination on Transmission of Alpha and Delta Variants", the mediation analysis was used to assess whether the effect of the vaccination status of the index patient was explained by Ct values at diagnosis.  Ct values are cycle-threshold values (indicative of viral load21) in the index patient.

In an article by Reaven et al "Intensive Glucose Control in Patients with Type 2 Diabetes — 15-Year Follow-up", mediation analyses were performed:

In prespecified mediation analyses, Cox proportional-hazards models were used to examine the effects of the glycated hemoglobin level on the primary cardiovascular disease outcome and on the observed treatment effects. Specifically, the log-linear association of the cumulative glycated hemoglobin level (modeled as a time-varying covariate) with the primary cardiovascular disease outcome was assessed during the period of separation of the glycated hemoglobin curves and after convergence. Models examined the effect of treatment group (intensive therapy or standard therapy) on the primary outcome in an unadjusted analysis (model 1) or while accounting for baseline, most recent, or cumulative mean glycated hemoglobin level (models 2, 3, and 4, respectively).

A paper by Vo et al summarized "the conduct and reporting of mediation analysis in recently published randomized controlled trials: results from a methodological systematic review"

Mediation analysis can be performed using SAS procedure CAUSALMED. CAUSALMED procedure was developed for estimating causal mediation effects from observational data, but can definitely be used for estimating mediation effects from the randomized controlled clinical trial data. Please see the references below:

• SAS documentation about CAUSALMED procedure including the application examples
• Yung et al Causal Mediation Analysis with the CAUSALMED Procedure
• Yung Introducing the CAUSALMED procedure for causal mediation analysis - Youtube video 
• Youtube video: Modeling causal effect using SAS/Stat

Mediation analysis can be performed using other software. This is very well summarized in a paper by Valente et al "Causal Mediation Programs in R, Mplus, SAS, SPSS, and Stata".

Handling of values below or above a threshold (Below the Low Limit of Quantification or Above the Upper Limit of Quantification)?

In clinical trials, the samples are often collected and sent to the central laboratory or specialty laboratory for measuring certain parameters (drug concentrations, metabolite concentrations, biomarkers,...). It is not uncommon that the results may be reported as "<xxx" or ">xxx" indicating that the measurement is below or above a threshold, outside the range, or the quality control curves. We call them below the low limit of quantification or above the upper limit of quantification.

How to handle them in the data set and in the analyses?

In the data set, the laboratory results should be reported as it is in character variable. The data listings should use the character variable so that the signs of '<' or '>' will be kept and displayed.

For the purpose of the statistical summaries and analyses, a separate numeric variable should be derived and appropriate rules will be applied to these values below the low limit of quantification or above the upper limit of quantification.

 
Most of the discussions were about the handling of the values below the low limit of quantification (BLQs). See a previous post "BLQs (below limit of quantification) and LLOQ (Lower Limit of Quantification): how to handle them in analyses?" and the researchgate.net discussion board "How should one treat data with <LOQ values during statistical analysis?".

The options for handling the BLQs are:

• Treat BLQs as missing
• Treat BLQs as 0
• Treat BLQs as 1/2 of the LLQ (lower limit of qualification). For example, if the result was reported as "<10" µg, take 5 µg as the measure - this approach is pretty common in handling pharmacokinetic concentration data. 
•  Simply remove the sign of  '<' and take the face value (i.e. LLQ value). For example, if the result was reported as "<10" µg, take 10 µg as the measure. 
• More complicated methods using statistical (regression, maximum likelihood,...) approaches 

There are fewer discussions about handling the values above the upper limit of quantification (ULQ). Usually, these values above the upper threshold will be handled by:

• Treat values above ULQ as missing
• Simply remove the sign of  '>' and take the face value (i.e. ULQ value). For example, if the result was reported as ">100" mg, take 100 mg as the numeric value

In an SAP developed by Astellas, the simple rule was specified for handling the values below or above a threshold:

"For continuous variables that are recorded as “< X” or “> X”, the value of “X” will be used in the calculation of summary statistics. The original values will be used for the listings."

In an SAP developed by Galapagos for their phase 3 study of GLPG1690 in subjects with idiopathic pulmonary fibrosis, the following rules were proposed to handle values below or above a threshold. Their approach of adding or deducting a small number from the face value is unconventional.

7.3. Handling of Values Below (or Above) a Threshold 

Values below (above) the detection limit will be imputed by the value one unit smaller or larger than the detection limit itself. In listings, the original value will be presented. Example: if the database contains the value “<0.04”, then for the descriptive statistics the value “0.03” will be used. The value “>1000” will be imputed by “1001”. 

Placebo effect and its impact on the overall treatment effect

RCTs (randomized, controlled clinical trials) are still the golden standard in clinical research. In RCTs, the most common control group is the Placebo. According to Wikipedia, a placebo is a sham substance or treatment which is designed to have no known therapeutic value. Common placebos include inert tablets (like sugar pills), inert injections (like saline), sham surgery, and other procedures. In order to maintain the blinding (masking), the placebo group may include additional excipients similar to the experimental drug so that the placebo group will have the same characteristics as the experimental drug in shape, size, color, texture, weight, taste, smell,......

Placebo is assumed to have no therapeutic effect or detrimental effect. However, the assumption may not be true especially when the composition of the placebo is in consideration. 

In previous article "Placebo Effect, Honest Placebo, Open-label Placebo", we discussed the placebo effect in diseases in the CNS and psychological area or in diseases with subjective symptom measures. In the post "Placebo effect and the choice of placebo", we discussed the composition of the placebo and some 'placebo' may actually have therapeutic effect. For example, in clinical trials to test the therapeutic effects of IGIV, the  low concentration of albumin may be selected as the placebo control - the low concentration of albumin may actually have the therapeutic effect. In both of these cases, the placebo effect or potential therapeutic effect from the 'placebo' can cause the unexpected higher response rate in Placebo arm, therefore decrease the difference between the experimental drug and the placebo groups, result in the failed trials. 

On the flip side, the placebo can have detrimental effect. There are quite some recent discussions about the placebo having the detrimental effect - consequently, the overall treatment effect observed in the clinical trials may not be due to the therapeutic effect of the experimental drug, but due to the detrimental effect of the placebo group. In other words, the overall treatment effect can be  overestimated due to the detrimental effect of the placebo.

Here are some articles discussing the potential detrimental effects of the placebo in clinical trials to study the effects of fish oil in the prevention of the cardiovascular events. The REDUCT-IT trial was published in NEJM and was the pivotal trial resulting in the FDA and EMA's approval. According to the study protocol, "the matching placebo capsule is filled with light liquid paraffin and contains 0 mg of AMR101 (icosapent ethyl [ethyl-EPA])." The detrimental effect of the placebo may come from the paraffin. 

• Effects of Randomized Treatment With Icosapent Ethyl and a Mineral Oil Comparator on Interleukin-1β, Interleukin-6, C-Reactive Protein, Oxidized Low-Density Lipoprotein Cholesterol, Homocysteine, Lipoprotein(a), and Lipoprotein-Associated Phospholipase A2: A REDUCE-IT Biomarker Substudy
• Amarin stands behind Vascepa after new analysis raises questions about its efficacy
• New analysis of Amarin’s Vascepa resurrects big questions about effectiveness
• New Biomarker Data Add to Concerns Over REDUCE-IT Trial
• Mineral oil: safety and use as placebo in REDUCE-IT and other clinical studies

When we conduct the placebo-controlled clinical trials, the composition of the placebo needs to be carefully considered and the potential therapeutic effect from the placebo needs to be minimized. 

CRO, ARO, and VRO - All types of clinical research organizations

CRO (Contract Research Organization or Clinical Research Organization)

A CRO is a company that provides clinical trial management services for the pharmaceutical, biotech, and medical device industries.

Although there are different types of CROs and diverse levels of specialization (distinct therapeutic areas for instance), typical CRO services include regulatory affairs, site selection and activation, recruitment support, clinical monitoring, data management, trial logistics, pharmacovigilance, biostatistics, medical writing, and project management, among others. Given that the clinical trials are going global, the large CROs usually have foot prints in all of these countries that are the usual spots for doing clinical trials.

In a clinical trial, CROs are hired by sponsors to perform a set of tasks, taking various technical and administrative responsibilities on the sponsor’s behalf.

The main role of the CRO is to plan, coordinate, execute, and supervise the processes involved in the development of a clinical trial, being a central contact point between the sponsor and other trial actors (e.g. ethics committees, regulatory agencies, vendors, and hospitals).

CROs are key players in clinical research, since they have the knowledge and the capabilities needed for the proper development of a clinical study. They help sponsors by reducing their workload, while ensuring trial quality and compliance with national and international standards.

At the same time, many CROs supply innovative technological tools to increase efficiency in the study processes, which translates into cost reductions.

Without doubt, CROs play a crucial role in the success of a clinical trial. Sponsors should carefully assess the particular needs of their projects, and look for the CRO that best meets their technical requirements and budget.

The website https://www.proclinical.com/blogs/2022-3/top-10-cros-to-watch-in-2022 listed the top ten CROs in 2022 which are:

• PPD (now part of ThermoFisher)
• KCR
• ICON (it just acquired PRA International)
• IQVIA (I still like their previous name Quintiles)
• Parexel
• Medpace
• Syneos Health
• Labcorp (it just acquired Covance)
• CTI
• PSI

I would also add WCG as one of the top CROs.

ARO (Academic Research Organization)

An ARO is an academic or non-profit organization that provides support mainly to the academic principal investigators in the form of research services: biopharmaceutical development, biologic assay development, commercialization, preclinical research, clinical research, clinical trials management, and pharmacovigilance.

Comparing to the CRO, AROs are non-profit; are usually cheaper, are usually strictly related to their main center; are mainly focused on studies oriented to improve the standard clinical practice, thus they are majorly involved in observational studies, phase IV studies, pragmatic clinical trials,...

ARO can also be good to take on the role of the central reader and clinical event adjudications. 

This book chapter discussed the role of ARO in clinical research:

Chapter 3. The Role of Academic Research Organizations in Clinical Research

The most prominent ARO is perhaps the Duke Clinical Research Institute (DCRI). 

VRO (Virtual Research Organization)

With the recent rapid development in conducting the virtual clinical trials or decentralized clinical trials, the new term VRO is appearing. VRO is specialized in helping the sponsors to do virtual clinical trials. A paper by Hong et al "Virtual Research Organization: Nature and Forms" discussed the roles of the VROs. 

An example of a VRO is ObvioHealth - they claim to be the VRO who are specialized in doing decentralized clinical trials. 

Adverse event / serious adverse event data entry when death event occurs

If the death event is an efficacy endpoint in the study, should the death event still be reported as AE/SAE? 

If the death (or mortality) is an efficacy endpoint, the death event will ordinarily not be reported as AE/SAE. Only in some special situation, for example, a death due to anaphylactic reaction, car accident,... that are not related to the underlying disease, will the death be reported as AE/SAE. 

FDA's guidance Safety Reporting Requirements for INDs and BA/BE Studies clearly stated this: 

Generally, study endpoints refer to outcomes that sponsors are measuring to evaluate efficacy. For trials designed to evaluate the effect of a drug on disease-related mortality or major morbidity, endpoint information should be collected, tracked, and monitored, usually by a Data Monitoring Committee (DMC), during the course of the study. The protocol would prespecify a monitoring plan for determining whether subjects receiving the drug treatment are at higher risk for the outcome (e.g., all-cause mortality), and such results would be reported according to the protocol. The study endpoints must be reported to FDA by the sponsor according to the protocol, and ordinarily would not be reported as IND safety reports, except when there is evidence suggesting a causal relationship between the drug and the event (21 CFR 312.32(c)(5)). For example, a death ordinarily would not be reported as an individual case in an expedited report from a trial designed to compare all-cause mortality in subjects receiving either drug treatment or a placebo. On the other hand, in the same trial with an all-cause mortality endpoint, if the death occurred as a result of an anaphylactic reaction that coincided with initial exposure to the drug, or as a result of fatal hepatic necrosis, the death must be reported as an individual case in an IND safety report because there would then be evidence suggesting a causal relationship between the drug and the event (21 CFR 312.32(c)(5)).  

In our clinical trial "Dinutuximab and Irinotecan Versus Irinotecan to Treat Subjects With Relapsed or Refractory Small Cell Lung Cancer", the overall survival (time to death) was the primary efficacy endpoint. The death event would not be reported as AE/SAE and not recorded on AE/SAE case report form. It would be odd and inappropriate to record the AEs like "Death due to disease progression', 'death due to small cell lung cancer', 'death due to the underlying disease'. With Death being an efficacy endpoint, the death information should be recorded on separate case report form - Death Details form according to CDASH. 

Should death be reported as an event or an outcome of an AE/SAE?

Usually, when death event occurred, there were the adverse events leading to the death. The adverse event leading to the death will need to be reported as series adverse event with fatal outcome. The death is the outcome of a SAE and is not entered as an adverse event on its own. 

There are situations that the death is instantaneous or within very short period (for example one hour) of onset of symptoms or an unobserved cessation of life that cannot be attributed to a specific AE term. The death in this situation will be reported as Sudden Death or Sudden Death NOS on the AE case report form. 

If there are multiple ongoing AEs at the time of trial participant's death, should all these ongoing AEs be considered as SAEs with fatal outcome? 

This issue was discussed in a previous post "Recording the outcome for AE/SAE when multiple events contribute to Death". It is preferred in my opinion that a single AE/SAE should be identified as the primary cause for the death. This AE/SAE will have the fatal outcome recorded on the AE case report form. Other ongoing AEs at the time of death will have the AE outcome recorded as 'not recovered/not resolved"

How to fill out other fields on AE forms when death event occurs? 

As discussed above, for AE outcome, 'Fatal' should be selected for the AE/SAE that directly or primarily contribute to the study participant's death. 

For other ongoing AEs at the time of death, the AE outcome 'Not recovered or Not Resolved' should be selected.  

'Action Taken with Study Treatment' is another field on AE form and it is difficult to decide which choice should be selected when a death event occurs. 

At the time of death, there are two appropriate choices for 'Action Taken with Study Treatment": Dose Not Changed or Drug Withdrawn. Which choice to select depends on the sequence of the events: the last dose date/time in relevance to the death date/time. Assuming a study treatment with QD dose frequency, if the last dose date is the same as the death date or if the last dose date is one day prior to the death date, it is reasonable to assume that there is no dose withdrawn. Therefore, it is appropriate to select 'Dose Not Changed' for 'Action Taken with Study Treatment' field. On the other hand, if the last dose date is two days or earlier than the death date, it is appropriate to select 'Dose Withdrawn' for 'Action Taken with Study Treatment' field.