FDA Guidance Webinar Series

FDA's Guidance Webinar series aims to foster collaboration and transparency in the development of guidance documents through direct outreach to affected stakeholders. The webinar series are free to the public and the past webinars are achieved for free access. These webinars are presented by senior FDA officers and the author/coauthor of the issued FDA guidance.

FDA's Guidance Webinar series

Since 2011, the following webinars have been conducted:

• CDER Small Business Webinar on Stability Guidance for ANDAs, and draft Questions and Answers guidance – considerations – November 4, 2013
• FDA Webinar: 60-Day Draft GFI On Antibacterial Therapies For Patients With Unmet Medical Need For The Treatment Of Serious Bacterial Diseases – September 27, 2013
• FDA Webinar: Draft GFI On Expedited Programs For Serious Conditions Drugs And Biologics - August 5, 2013
• FDA Webinar: Webinar Draft GFI on Human Immunodeficiency Virus-1 Infection: Developing Antiretroviral Drugs for Treatment; Availability - July 25, 2013
• FDA Webinar: Webinar Draft GFI On Rheumatoid Arthritis - Developing Drug Products For Treatment; Availability – July 25, 2013
• FDA Webinar: Draft Guidance For Industry On Alzheimer’s Disease: Developing Drugs For The Treatment Of Early Stage Disease; Availability - March 28, 2013
• FDA Webinar: Webinar Draft GFI On Enrichment Strategies For Clinical Trials To Support Approval Of Human Drugs And Biological Products – March 25, 2013
• FDA Webinar: New Draft Guidance on "FDA Guidance for Industry Webinar on Draft Guidance Vaginal Microbicides: Development for the Prevention of HIV Infection" - January 22, 2013
• FDA Webinar: 60-Day Draft Guidance on Using an Endpoint to Support Accelerated Approval of Drugs to Treat Early-Stage Breast Cancer - June 28, 2012
• FDA Webinar: New Draft Guidance on Safety Data Collection - March 27, 2012
• Guidance Webinar: Oversight of Clinical Investigations--A Risk-Based Approach to Monitoring - October 24, 2011

Overrunning Issue In Adaptive Design Clinical Trials

Adaptive designs have been more and more commonly used from early phase clinical trials to late phase pivotal clinical trials and from the limited therapeutic areas (such as oncology) to broad therapeutic areas. While adaptive design can have different forms (group sequential design,  seamless phase II/III design, sample size re-estimation, futility design,…), it all requires formal interim analyses with results to be reviewed by the independent Data Monitoring Committee. The rule for adaptation is pre-specified and the decision for adaptation is based on the interim analysis results.

In the phased clinical trials, the period between the analysis of phase II data and recruitment of phase III patients, is called “white space”. During the “white space” period, the results from early phase studies are digested and the additional time is needed for the study start up including regulatory submissions. The common perception is that with adaptive design such as seamless phase II/III design, the ‘white space’ can be eliminated – that is why the term ‘seamless’ is used in the first place. However, with adaptive design, the ‘white space’ may be shortened, not be eliminated. In studies with adaptive designs, there is usually no break in patient enrollment between the phases or stages or no break while preparing for the interim analysis. This brings in another issue – overrunning issue.

During the period while waiting for the endpoint data available for interim analysis, If the treatment duration is too long, too many patients would be randomized during the transition period – so called ‘overrunning’, which could result in inefficiencies and losing the benefits of the adaptive design. 

Recruitment rate relative to treatment data availability is the critical factor for overrunning. According to a paper by Judith Quinlan and Michael Krams (Implementing adaptive designs: logistical and operational considerations, the ideal ratio of recruitment / treatment duration is 4.

“As a rule of thumb we propose to establish whether the overall recruitment duration is at least four times the observational period required before the primary endpoint reads out in any one patient.  For example, in a stroke trial where each patient is observed over a period of 3 months, an adaptive design could be considered if the trial is open for recruitment for 12 months or longer.  This view may need to be modified, should there be a good early predictor of final outcome, allowing for the deployment of a longitudinal model.  For example, in acute stroke it might be possible to use early measurements of the stroke scale to predict final outcome (Grieve and Krams, 2005).  Should the early observation be a good predictor of the final outcome, we may consider using it in our assessment of weighing recruitment speed versus time needed before endpoint readout.  To formally establish the “optimal” recruitment speed, we propose to conduct clinical trial simulations, mimicking the potential real life environment of the trial and exploring the impact of longitudinal models.”

The overrunning issue was not discussed in FDA’s guidance “Adaptive Design Clinical Trials for Drugs and Biologicals”, however, it is discussed in EMA guidance “Reflection Paper In Methodological Issues in Confirmatory Clinical Trials Planned With an Adaptive Design”. Section 4.1.3 of this guidance has specific discussions about the overrunning issue. 

"4.1.3 Overrunning
In many clinical trials the primary endpoint is not observed immediately for each patient (e.g. survival or time to event data). Furthermore, in trials with a complex organisational structure, additional patients are likely to be randomised or some even followed to the primary endpoint before the results of a pre-planned interim analysis are known. If a trial is to be terminated as a result of an interim analysis it is always important to carry out an additional analysis including all of these further patients that did not contribute to the interim analysis. It may be that when this analysis is carried out, the null hypothesis can no longer be rejected and apparently decision making may depend on whether or not these so called overrunning patients are included or excluded from the analysis. In such a situation, it is accepted regulatory practice to base decision making on the final results of the trial (not the interim analysis). This is also in accordance with the intention to treat principle that all randomised patients should be analysed. Obviously, overrunning patients need to be treated and observed according to the protocol and due attention should be given to this at the planning stage of the trial.

A full discussion of the results of a trial should be based on estimates of the treatment effect rather  than simply on P-values alone. If the estimate of the treatment effect including the overrunning patients is not very different from that excluding them, then a small increase in the P-value might not be regarded as a concern. An important reduction in the size of the point estimate might, in contrast, lead to reluctance to accept the overall result as “positive”, especially as, unless a trial is stopped very early, the proportion of overrunning patients will usually be sufficiently small such that the estimate of the treatment effect should not be substantially altered. In all cases, results including and excluding the overrunning patients should be presented and differences between these two analyses should be discussed.”

The overrunning issue is discussed in many adaptive clinical trial implementations. Here are some of them:

•  Introduction to Adaptive Designs by Werner Brannath
• Pitfalls in Clinical Trials Using Adaptive Design by Thomas Zwingers
• Monitoring Continuous Long-Term Outcomes in Adaptive Designs by Kirsten Wust & Meinhard Kieser
• On the efficiency of adaptive designs for flexible interim decisions in clinical trials by Werner Brannath et al

Archives of Webcast and Presentation Slides for Public Workshop on Complex Issues in Developing Drug and Biological Products for Rare Diseases

To meet the requirements by PDUFA V and FDASIA, FDA organized a public workshop on "Complex Issues in Developing Drug and Biological Products for Rare Diseases".

The webcast and presentation slides for this public workshop are accessible to the public for free.

To access the Webcase and presentation slides for this workshop, please follow the link below:

Complex Issues in Developing Drug and Biological Products for Rare Diseases

Free Webinar by FDA on the final guidance for industry Electronic Source Data in Clinical Investigations

On Wednesday, January 29, 2014, from 2:00PM - 3:00PM EST, FDA will present a webinar on the final guidance for industry Electronic Source Data in Clinical Investigations.

• FINAL GUIDANCE: Electronic Source Data In Clinical Investigations (Procedural) 
• Federal Registration FR NOA 

SUMMARY: The Food and Drug Administration (FDA) has announced the availability of a final guidance for industry titled “Electronic Source Data in Clinical Investigations.” This final guidance provides recommendations to sponsors, Contract Research Organizations (CROs), clinical investigators, and others involved in the capture, review, and retention of electronic source data in FDA-regulated clinical investigations. In an effort to streamline and modernize clinical investigations this guidance promotes capturing source data in electronic form, and it is intended to assist in ensuring the reliability, quality, integrity, and traceability of data from electronic source to electronic regulatory submission.

Guidance Webinar Online-Access Instructions: To access this webinar, follow the link provided below. Audio will broadcast from your computer speakers.

After following the link, enter as a guest and provide your FULL NAME and organization (i.e. "John Smith - FDA/CBER"). The host will then allow you to enter. If you experience technical difficulties email Jeffery.Rexrode@fda.hhs.gov for assistance. Closed captioning will be provided.
Questions/Comments can be submitted live via a Q/A chat window.

Webinar Access link: https://collaboration.fda.gov/guidancewebinars

SPEAKERS:
        Leonard V. Sacks, MD
        Associate Director
        Office of Medical Policy
        Center for Drug Evaluation and Research
        Food and Drug Administration

        Ron Fitzmartin, PhD, MBA
        Office of Strategic Programs
        Center for Drug Evaluation and Research
        Food and Drug Administration

        Jonathan S. Helfgott, MS
        Associate Director for Risk Science (Acting)
        Office of Scientific Investigations
        Center for Drug Evaluation and Research
        Food and Drug Administration

Artistic and Creative Way in Naming a New Drug

We all may have difficulties in remembering the drug names and wonder why many drug names are so awkward and difficult to read. For drug makers, finding a name is more art than science. For a new drug, the proprietary name or brand name needs to reflect certain features.

“Want to sound high-tech? Go for lots of Z's and X's, such as Xanax, Xalatan, Zyban and Zostrix.

Want to sound poetic? Try Lyrica, Truvada and Femara.

Want to suggest what it does? Flonase is an allergy medicine that aims to stop nasal flow. Lunesta, a sleeping drug, implies "luna," the Latin word for moon — a full night's sleep.

Then there's Viagra, the erectile-dysfunction drug made by Pfizer. It uses the prefix "vi" to suggest vigor and vitality. The word rhymes with Niagara, suggesting a mighty flow.”

On the other hand, the proprietary name for a new drug is closely regulated to avoid the similar names that may cause the medical errors. For example, in an article "This Is How Easy It Is to Pick Up the Wrong Prescription Drug", the similar drug names increases the chances for making mistakes in prescribing and in pharmacy. In US, FDA needs to approve the proprietary names of prescription drugs.

“New prescription drugs approved by FDA have both a scientific name, known as the generic (also called the established name), and a name given by the manufacturer, known as the proprietary name (also called the brand name or trade name).  Before a drug is approved by FDA, the Agency will carefully review the proposed proprietary name.  

It is important for safety reasons that the written proprietary name not look like that of another proprietary name nor sound like another proprietary name when spoken.  If there is similarity between the proprietary name of a new prescription drug and the proprietary name of an existing drug, a mix-up could occur in ordering and a patient could receive one drug instead of the other. FDA’s Division of Medication Error Prevention and Analysis is responsible for proprietary name review prior to approval in the Center for Drug Evaluations and Research.  If a company submits a name that is too similar to another name, FDA will require the company to select another name, for safety reasons, as part of the approval process. “

See the following links for more discussion:

• Drug name confusion: FDA regulations and pharma create bizarre new names
• How FDA review Proposed Drug Names?
• FDA guidance: Contents of a Complete Submission for the Evaluation of Proprietary Names
• PhRMA demands an overhaul of FDA's regs on drug names

Recently, the United Therapeutics is very creative in naming their new drug. They simply used their CEO’s name (backward) for their new drug in treating the pulmonary hypertension. The new drug name Orenitram is Martine Ro. backward. And that would be the name of Martine Rothblatt, United Therapeutics’ founder/CEO and one of the most captivating people in the biotechnology industry.

• This Drug Company CEO Just Named Her New Drug After Herself
• Drugmaker Names Pill After CEO Who Sought Daughter's Cure
• CEO of drug company in RTP names new drug after herself

Quantification of Risk – Breaking down adverse reaction into common and uncommon categories

When we conduct a clinical trial using an approved product, we may run into the requests for the list of common, very common adverse events. This may be especially true when the clinical trials are conducted in European countries. The requests may come from the IRB (institutional review board) or EC (ethics committee).

Breaking down the AEs into very common, common, uncommon, rare, and very rare categories is part of the requirements for SmPC (product label in EU countries). The requirements are included in various EC or EMA guidelines

A GUIDELINE ON SUMMARY OF PRODUCT CHARACTERISTICS (2005) stated:

Within each system organ class, the ADRs should be ranked under headings of frequency, most frequent reactions first, using the following convention:
 Very common (greater than and equal to 1/10); common (greater than and equal to 1/100 to less than 1/10); uncommon (greater than and equal to 1/1,000 to less than 1/100); rare  (greater than and equal to 1/10,000 to less than 1/1,000); very rare (less than and equal to 1/10,000), not known (cannot be estimated form the available data).

A GUIDELINE ON SUMMARY OF PRODUCT CHARACTERISTICS  (SmPC) 2009 stated:

Within each system organ class, the adverse reactions should be ranked under headings of frequency, most frequent reactions first. Within each frequency grouping, adverse reactions should be presented in the order of decreasing seriousness. The names used to describe each of the frequency groupings should follow standard terms established in each official language using the following convention: Very common (≥1/10); common (≥1/100 to <1/10); uncommon (≥1/1,000 to <1/100); rare (≥1/10,000 to <1/1,000); very rare (<1/10,000). 

An EMA presentation specifically discussed what the Section 4.8: Undesirable effects should include in terms of quantifying the adverse drug reactions.

There are plenty of examples for showing how the adverse drug reaction tables in Summary of Product Characteristics should be presented.

• Cimzia 200 mg solution for injection
• Avastin 25 mg/ml concentrate for solution for infusion.
• Example SmPC - European Medicines Agency - Europa

In the recent issued EMA guidance for Fibrin Sealant, it also requires to breakdown the AEs into the following categories:

Tabulated list of adverse reactions

The table presented below is according to the MedDRA system organ classification (SOC and Preferred 265 Term Level).  

Frequencies have been evaluated according to the following convention: Very common (greater than and equal to 1/10); common (greater than and equal to 1/100 to less than 1/10); uncommon (greater than and equal to 1/1,000 to less than 1/100); rare (greater than and equal to 1/10,000 to less than 1/1,000); very rare (less than 1/10,000), not known (cannot be estimated from the available data).

The source for the quantification of risk for adverse reactions is the CIOMS. For example, Benefit-Risk Balance for Marketed Drugs: Evaluating Safety Signals Report of CIOMS Working Group IV has a section for “quantification of risk”

5. Quantification of Risk Incidence of the reaction

To put the newly identified risk into perspective, it is important to quantify it in terms of incidence. Precise quantification will usually be difficult in the post-marketing environment, in which most new safety signals arise from spontaneous reporting, with its associated uncertainties as to numerators (reported cases) and denominators (patient exposures). However, risk can often be approximated in terms of magnitudes of 10, as suggested in the CIOMS III report: greater than 1% (common or frequent); greater than 1 per 1000 but less than 1 per cent (uncommon or infrequent); greater than 1 per 10,000 but less than 1 per 1000 (rare); less than 1 per 10,000 (very rare). 

When possible, attempts should be made to determine whether the incidence is affected by the existence of any apparent high-risk groups. These might be defined by, for example, dose or duration of treatment, use of other drugs (e.g., drug interactions), presence of other diseases (e.g., renal failure), or special populations defined by demographics or ethnicity. In principle, one of the most important functions of risk evaluation is to identify individual patients at increased risk of serious adverse reactions. Although some mechanisms are fairly well understood (enzyme inhibition processes; drug interactions), the pharmacological and biological basis of drug-induced diseases (e.g., role of pharmacogenetics) is relatively unexplored. Work in this area is needed and should be encouraged.

In US, when we prepare the summary tables for adverse events or adverse drug reactions, we typically don’t include a table to break down the frequency into very common, common, uncommon, rare and very rare categories. The drug label did not require to include the quantification of risk using these categories. However, for drug to be approved in European countries, it is a good idea to include a summary table with these quantification categories. 

Pharmacokinetic studies when endogenous compounds exist and pre-dose concentrations are not zero

This Monday, FDA issued a new guidance titled “ Bioequivalence Studies with Pharmacokinetic Endpoints for Drugs Submitted Under an ANDA”. While the guidance is more for bioequivalence studies for generic drugs, a paragraph on Endogenous Compounds caught my eyes:

E. Endogenous Compounds
 Endogenous compounds are drugs that are already present in the body either because the body produces them or they are present in the normal diet. Because these compounds are identical to the drug that is being administered, determining the amount of drug released from the dosage form and absorbed by each subject can be difficult. We recommend that applicants measure and approximate the baseline endogenous levels in blood (plasma) and subtract these levels from the total concentrations measured from each subject after the drug product has been administered. In this way, you can achieve an estimate of the actual drug availability from the drug product. Depending on whether the endogenous compound is naturally produced by the body or is present in the diet, the recommended approaches for determining BE differ as follows:  When the body produces the compound, we recommend that you measure multiple baseline concentrations in the time period before administration of the study drug and subtract the baseline in an appropriate manner consistent with the pharmacokinetic properties of the drug.
  When there is dietary intake of the compound, we recommend that you strictly
control the intake both before and during the study. Subjects should be housed at a
clinic before the study and served standardized meals containing an amount of the
compound similar to that in the meals to be served on the pharmacokinetic sampling day.
 For both of the approaches above, we recommend that you determine baseline concentrations for each dosing period that are period specific. If a baseline correction results in a negative plasma concentration value, the value should be set equal to 0 before calculating the baseline-corrected AUC. Pharmacokinetic and statistical analysis should be performed on both uncorrected and corrected data. Determination of BE should be based on the baseline-corrected data.

When we study the therapeutic proteins, we often need to deal with the endogenous concentration issue. Studies using human plasma derived products (proteins) will always involve in the endogenous concentration issue since these therapeutic proteins are naturally occurring substances and are already present in the body. The pharmacology studies for these therapeutic proteins need to consider both the endogenous (already in the body) and exogenous (through augmentation) concentrations. In a book “Clinical pharmacology of therapeutic proteins” by Dr Mahmood, three approaches are discussed to deal with this issue:

1. subtract the pre-dose concentration – baseline-corrected pharmacokinetic analysis
2. using the sum of exogenous and endogenous proteins following the administration of exogenous protein – uncorrected pharmacokinetic analysis;
3. the use of radio-labeled proteins to differentiate the exogenous proteins from the endogenous proteins.

For a bioequivalence study, it is easier to show the bioequivalence with approach #2 above.

When using baseline-corrected pharmacokinetic analyses, the accurate measure of the pre-dose concentration is important. If all possible, there should be multiple measures at pre-dose and then mean value of the pre-dose measuresments can be used as the baseline for correction.

In FDA’s Draft Guidance on Progesterone, it has the following comments regarding the baseline-correction.

 Please measure baseline progesterone levels at -1.0, -0.5, and 0 hours before dosing. The mean of the pre-dose progesterone levels should be used for the baseline adjustment of the post-dose levels. Baseline concentrations should be determined for each dosing period, and baseline corrections should be period specific. If a negative plasma concentration value results after baseline correction, this should be set to 0 prior to calculating the baseline-corrected AUC. Please analyze the data using both uncorrected and corrected data.

  

In a clinical pharmacology review document for a Factor XIII Concentrate, the sponsor presented the pharmacokinetic parameters based on baseline adjusted FXIII activity (Berichrom assay) and  also the pharmacokinetic parameters based on un-adjusted FXIII activity.

In summary, while both baseline-adjusted and unadjusted PK analyses are viable approaches in dealing with the existence of endogenous concentrations, the baseline-adjusted PK analyses are the safer approach to go. In this approach, the pre-dose concentration or average pre-dose concentration will be subtracted from all post-dose concentration measures before the PK parameters (for example AUC) are calculated.  

SAS Programming for PK Time-Concentration Curve Using SGPLOT

Comparing with other software/tools for generating the graphs, SAS/Graph may be inferior. Programming to generate graphs using SAS/Graph languages can be time consuming and the quality of the graphs is usually not so great and not in publication quality. It is also not easy to output the graphs in the commonly used format. With SAS/Graph, we usually create the graph files in .cgm and then use a MS Word macro to load the .cgm files into MS Word file.

Now SAS has offered two new ways to generate the graphs: SASODS Graphics and SAS Stat Graphics. I had previously discussed SAS ODS Graphicswith template using Proc Lifetest to generate the Kaplan-Meier curve.

The procedure SGPLOT in SAS ODS Graphics can be used to generate high quality graph. With SGPLOT available, SAS/Graph procedure GPLOT become obsolete. The programs below illustrate the use of SGPLOT to generate the time-concentration curve for PK concentration data.

The graph generated from the programs is directly outputted in a .pdf file.

data pconc;

  input drug $ time concentration;

datalines;

Test  0   0

Test  1  32

Test  3 100

Test  6 140

Test  12 70

Test  24 25

Test  48  5

Test  72  1

Ref   0   0

Ref   1  40

Ref   3 105

Ref   6 135

Ref  12  80

Ref  24  28

Ref  48   7

Ref  72   0

;

options orientation=landscape nodate;

ods graphics / reset width=9in noborder;

    *width option defines the size of the graph. Noborder option remove  

     the border around the figure;

ods pdf file = "c:\temp\MySGPLOT.pdf"

 pdftoc = 0

 startpage = no

 style = printer

 dpi = 250;   

ods pdf nobookmarkgen;

title1 j=c "FIGURE #.#" ;

title2 j=c "MEAN PLASMA CONCENTRATION VS. TIME CURVES" ;

title3 j=c "BY TREATMENT" ;

title4 j=C "POPULATION:  PK" ;

footnote1 j=l "%upcase(PROGRAM: xxx.sas)                                                                                                                 (&sysdate &systime)";

run ;

proc sgplot data=pconc ;

    series x = time y = concentration

         /group=drug lineattrs = (thickness = 1 pattern=solid) markers;

    xaxis label = 'Time Post Study Drug Administration (hours)' 

         grid values = (0 to 80 by 5);

    yaxis label = 'Mean Concentration' grid values = (0 to 160 by 40); 

    keylegend / location=inside position=topright;

run;

ods pdf close;

References: 

• PROC SGPLOT over PROC GPLOT
• Using SAS® ODS Graphics
• ODS Statistical Graphics
• PK Plot with SGPLOT

Causality Assessment, Causality Categories for Reporting Adverse Events or Adverse Reactions

In clinical trials, safety reporting is a critical issue. This includes the reporting of safety information (adverse events or adverse reactions) from the investigational sites to the sponsor and then from sponsor to the regulatory agencies (FDA, EU) and distribution of the safety information to other investigational sites and IRBs. There have been many regulatory guidelines regarding the reporting of the safety information. However, none of the guidelines give clear instruction on how to categorize the causality for adverse events or adverse reactions.

In recent FDA’s guidance “Safety Reporting Requirements for INDs and BA/BE Studies”, it reiterates the concept of “reasonable possibility” for causality assessment, but does not provide the causality categories.

“Suspected adverse reaction means any adverse event for which there is a reasonable possibility that the drug caused the adverse event. For the purposes of IND safety reporting, ‘reasonable possibility’ means there is evidence to suggest a causal relationship between the drug and the adverse event. A suspected adverse reaction implies a lesser degree of certainty about causality than adverse reaction, which means any adverse event caused by a drug. Suspected adverse reactions are the subset of all adverse events for which there is a reasonable possibility that the drug caused the event. Inherent in this definition, and in the requirement to report suspected adverse reactions, is the need for the sponsor to evaluate the available evidence and make a judgment about the likelihood that the drug actually caused the adverse event. We consider the application of the reasonable possibility causality standard to be consistent with the discussion about causality in the International Conference on Harmonization (ICH) E2A Guideline (“ICH E2A guidance”)."

In FDA’s guidance “Good Pharmacovigilance Practices and Pharmacoepidemiologic Assessment”, they cited WHO, the Uppsala Monitoring Center, 2000, Safety Monitoring of Medicinal Product.

 “FDA does not recommend any specific categorization of causality, but the categories probable, possible, or unlikely have been used previously. If a causality assessment is undertaken, FDA suggests that the causal categories be specified and described in sufficient detail to understand the underlying logic in the classification.”

The regulatory guidance leaves the sponsor to decide what to be considered as ‘reasonable possibility’. The sponsor can design the data collection form (SAE form or adverse event case report form) using various categories for causality assessment/reporting.

For data collection purpose, what categories should be collected for causality assessment?

In ICH E2A CLINICAL SAFETY DATA MANAGEMENT: DEFINITIONS AND STANDARDS FOR EXPEDITED REPORTING”, there is a paragraph regarding the causality assessment and causality categories.  

“Many terms and scales are in use to describe the degree of causality (attributability) between a medicinal product and an event, such as certainly, definitely, probably, possibly or likely related or not related. Phrases such as "plausible relationship," "suspected causality," or "causal relationship cannot be ruled out" are also invoked to describe cause and effect. However, there is currently no standard international nomenclature. The expression "reasonable causal relationship" is meant to convey in general that there are facts (evidence) or arguments to suggest a causal relationship. “

In FDA guidance for reviewer “Conducting a Clinical Safety Review of a New Product Application and Preparing a Report on the Review, the following categories are referenced:

Causality assessment by investigator (definitely; probably; possibly; unlikely related)

In ICH E2B “MAINTENANCE OF THE ICH GUIDELINE ON CLINICAL SAFETY DATA MANAGEMENT: DATA ELEMENTS FOR TRANSMISSION OF INDIVIDUAL CASE SAFETY REPORTS “, the following causality categories are referenced in the examples provided in the guidelines. It turns out that these categories seem to be the most commonly used in practice.

The CDISC, CDASH considered causality assessment (relationship to study treatment) as a sponsored defined field. However, it recommends the categories listed in ICH E2B example:

“Sponsored-defined terminology will be used to indicate the relationship between the AE and the study treatment (e.g. ICH E2B examples: Not Related, Unlikely Related, Possibly Related, Related). “

EU regulations regarding the safety report are generally consistent with the ICH guidance (specifically ICH EB2) and do not provide any specific guidance on specific causality categories to use.

EMA Guideline on Good Pharmacovigilance Practice (GPV) recommends the following:

Different methods may be applied for assessing the causal role of a medicinal product on the reported adverse event (e.g. WHO-UMC system for standardised case causality assessment). In this situation, the levels of causality, which correspond to a reasonable possibility of causal relationship, should be established in advance in order to determine when an adverse event is considered as an adverse reaction.

Some sponsors used the causality categories from WHO 2000 Safety Monitoring of Medicinal Product that used the following categories. Definitely Probably Possibly Unlikely Conditional Related Related Related Related. For example, A FDA communication with Cangene Corporation indicated the following AE causality categories are used: Definitely related, Probably related, Possibly related, Unlikely related, and Conditional. This is the same as the above-mentioned WHO-UMC system for standardised case causality assessment.

CAUSALITY CATEGORIES

The causality categories described by the Uppsala Monitoring Centre are as follows. However, the following categories may be modified in the practical use. 

1. Certain: a clinical event, including laboratory test abnormality, occurring in a plausible time relationship to drug administration, and which cannot be explained by concurrent disease or other drugs or chemicals. The response to withdrawal of the drug (dechallenge) should be clinically plausible. The event must be definitive pharmacologically or phenomenologically, using a satisfactory rechallenge procedure if necessary.
2. Probable/Likely: a clinical event, including laboratory test abnormality, with a reasonable time sequence to administration of the drug, unlikely to be attributed to concurrent disease or other drugs or chemicals, and which follows a clinically reasonable response on withdrawal (dechallenge). Rechallenge information is not required to fulfil this definition.
3. Possible: a clinical event, including laboratory test abnormality, with a reasonable time sequence to administrations of the drug, but which could also be explained by concurrent disease or other drugs or chemicals. Information on drug withdrawal may be lacking or unclear.
4. Unlikely: a clinical event, including laboratory test abnormality, with a temporal relationship to drug administration which makes a causal relationship improbable, and in which other drugs, chemicals or underlying disease provide plausible explanations.
5. Conditional/Unclassified: a clinical event, including laboratory test abnormality, reported as an adverse reaction, about which more data is essential for a proper assessment, or the additional data is under examination.
6. Unassessable/Unclassifiable: a report suggesting an adverse reaction which cannot be judged because information is insufficient or contradictory, and which cannot be supplemented or verified.

Some sponsors may use the following categories and definitions for AE causality. For example, an NDA review memo from FDA indicated that the sponsor used the following causality categories: probable, possible, unlikely and not assessable. The instructions for the AE causality assessment are explained below:

Relationship to Investigational ProductThe assessment of the relationship of an adverse event to the administration of study drug (none, unlikely (remote), possible, probable, not assessable) is a clinical decision based on all available information at the time of the completion of the case report form.
None – includes: (1) the existence of a clear alternative explanation (e.g. mechanical bleeding at surgical site); or (2) non-plausibility (e.g., the patient is struck by an automobile at least where there is no indication that the drug caused disorientation that may have led to the event; cancer developing a few days after drug administration).
Unlikely (remote) – a clinical event, including lab abnormality, with an improbable time sequence to drug administration and in which other drugs, chemicals or underlying disease provide plausible explanation.
Possible – a clinical event including lab abnormality, with a reasonable time sequence to administration of the drug, which could also be explained by concurrent disease* or other drugs or chemicals.  Information on drug withdrawal may be lacking or unclear.
Probable – a clinical event including lab abnormality, with a reasonable time sequence to administration of the drug, unlikely to be attributed to concurrent disease* or other drugs or chemicals, and which follows a clinically reasonable response on withdrawal (dechallenge).
Not assessable – a report of an AE which cannot be judged because information is insufficient or contradictory, and which cannot be supplemented or verified.

Table below summarized various causality categories:

ICH E2B	Not Related, Unlikely Related, Possibly Related, Related)
CDISC	Not Related, Unlikely Related, Possibly Related, Related).
WHO	Certain, Probable/Likely, Possible, Unlikely, Conditional/Unclassified
Other options	None, Unlikely, Possible, Probable, Not assessable

Due to the fact that there is no clear regulatory guidance on the use of the causality categories, different sponsors could use different causality categories. The consequence is that the so-called drug–related adverse events (or the new term adverse drug reactions) can not be directly compared across the studies by different sponsors. The biases can arise if we attempt to compare the results from different studies conducted by different sponsors where the different causality categories are used.