Signal Management

Written by Caffeinate

on 5 September 2021

Signal Management

A drug undergoes a variety of clinical trials before it is introduced in the market for public use. However, the clinical trials conducted have some limitations including time constraint, number and type of patients enrolled. The experimental setup is difficult to match ‘real world’ scenario which is more complex and unpredictable with multiple factors at play. As a result, the possibility of an adverse reaction to a drug, when it is introduced to the general public, can never be ruled out. Hence, it is important to stay vigilant, detect any adverse reaction to the drug timely, quickly analyze and assess it in order to improve the product and prevent such reactions occurring in future. This is where signal management and general pharmacovigilance are essential.

Pharmacovigilance is the science concerned with the detection, evaluation, analysis and prevention of adverse effects that may occur after administration of a drug that has been licensed for use. The goal of pharmacovigilance is to ensure medicine safety by making sure that the potential benefits of the medicine outweigh the potential risks throughout the product’s lifecycle.

Signal Management is a crucial part of Pharmacovigilance that is as challenging as it is important. The signal management process is a ‘set of activities performed to determine whether, based on an examination of individual case safety reports, aggregated data from active surveillance systems or studies, literature information or other data sources, there are new risks associated with a medicinal product or whether risks have changed’.

The definition of a signal as provided by the Council for International Organizations of Medical Sciences (CIOMS) 8 Working Group:

Information that arises from one or multiple sources (including observations and experiments), which suggests a new potentially causal association, or a new aspect of a known association, between an intervention and an event or set of related events, either adverse or beneficial, that is judged to be of sufficient likelihood to justify verificatory action.

A Signal can be either described as “Qualitative” i.e., characterized by being based on spontaneously reported data or “Quantitative” i.e., based on data mining, epidemiologic data, or trial data. Mere detection of a signal is not enough and requires further investigations to find out whether the novel problem is in any way associated to the drug and is of sufficient severity to require caution thus alerting the public and the medical community a change in, for instance, product labelling, or in rare cases suspend the ongoing trials or withdraw the product from the market.

2. Sources of Signal Detection

Signals can arise from a variety of resources such as a review of a clinical trial or spontaneous case reports, data obtained from active surveillance being conducted, or from literature reviews. To assess whether the signal poses true risks related to the medicine, further analysis is required. Based on the analysis, an appropriate course of action can be taken which may include amendment of product information, initiation of referral, urgent safety restrictions, post-authorization safety studies, or possibly, no action needed besides routine pharmacovigilance. However,  among normally many potential signals identified, only a few will require regulatory actions such as updates in the product information.

Below is a brief account of safety signal sources:

2.1 Spontaneous Reporting

According to ICH, spontaneous reporting can be defined as:

An unsolicited communication by a healthcare professional or consumer to a company, regulatory authority or other organization (e.g. WHO, Regional Centre, Poison Control Centre) that describes one or more adverse drug reactions in a patient who was given one or more medicinal products and that does not derive from a study or any organized data collection scheme(ICH 2003).

Spontaneous reporting is the most common source of safety signal in marketed products.

2.2 Active Monitoring Systems

An example of active monitoring systems is FDA’s FARES program or EMAs EVDAS program which are medical product safety surveillance systems where reported adverse events are collected in large databases, which to some extent are accessible for the industry and the general public.

2.3 Interventional and Non-Interventional Studies

Human volunteers take part in clinical studies that are aimed at adding to the existing medical knowledge. These studies can be categorized into Clinical Trials (Interventional Studies in which the participants receive a medicinal product that are in accordance with the designed research plan. Observational Studies (Non-Interventional studies in which health outcomes are assessed in a particular group of individuals who have not been assigned to a specific intervention) are important additions to safety information about a product as it is less costly and can provide information about large section of a population without directly interfering in their daily lives and treatments.

2.4 Non-Clinical Studies

These studies involve in vivo or in vitro experimentations in which test articles are examined in test systems under laboratory conditions to determine their safety. This term does not comprise of studies that are conducted by utilizing humans as subjects or field trials that are conducted in animals.

2.5 Systematic Reviews

Systematic reviews, as the name implies, typically involve a detailed and comprehensive plan and search strategy derived a priori, with the goal of reducing bias by identifying, appraising, and synthesizing all relevant studies on a particular topic. Often, systematic reviews include a meta-analysis component which involves using statistical techniques to synthesize the data from several studies into a single quantitative estimate or summary effect size (Petticrew & Roberts, 2006).

Systematic Reviews unlike conventional narrative reviews provides the reader with the clinical significance of the data and measures the strength of the relationship between two variables to establish causality.

2.6 Meta-Analysis

This type of statistical analysis combines the results of a number of scientific studies. This technique can come in handy when there are multiple scientific studies, each addressing the same question but with each study reporting measurements that pose some degree of error thus helping the scientists in assessing them to derive conclusions by using a quantitative, formal, epidemiological model.

3. Signal Assessment in Pharmacovigilance Investigation

3.1 Signal Characteristics

Drug safety scientists look for characteristics of a potentially important signal during the signal assessment process. These characteristics have been categorized into 3 groups which are the strength of evidence, public health impact, and the novelty of the drug. This rationale for categorization has been provided in the following text.

3.1.1 Characteristics related to Strength of Evidence
  • Source of Evidence: Signals arising from a variety of sources can help in strengthening the evidence in support of signals. For every signal, the class of data source providing the evidence suggesting co-relation between the drug and event should be recorded i.e. spontaneous case reports, observational studies, interventional studies, and pre-clinical studies.
  • Mechanistic Plausibility: The presence of a mechanism through which the candidate cause could result in the specific biological effects is an important factor that supports the association. The mechanism can either be hypothetical or an established mechanism.
  • Presence of Disproportionate Reporting: A rise in the frequency of reported cases related to a specific drug compared to the general reporting frequency can be a potential signal. “If the lower boundary of a 95% confidence interval of a Proportional Reporting Ratio (PRR) was equal or greater than one or the value of Empirical Bayes Geometric Mean (EBGM) was equal or greater than one, the signals can be classified as disproportional.” (Meyboom RHB, 2002)
  • Positive dechallenge and rechallenge: This factor is very important in establishing the association between individually reported cases. A dechallenge was considered present if at least one spontaneous report was present that suggested the disappearance of the adverse effects after the drug was withdrawn. A positive rechallenge was noted if the signal’s assessment included at least one report of the instance where the event reappeared after restarting the use of the drug.
  • Possible Class Effect: Any prior knowledge of a drug from the same class causing the same unwanted effect might hint towards the reliability of the signal. A possible class effect could be reflected in a signal if during signal working it was found that the same suspected event was reported before from the same class.
3.1.2 Criteria Related to Public Health Importance
  • The seriousness of the Event: Serious events take precedence over the non-serious ones and are addressed on a priority basis. Any medical event that is fatal, life-threatening, requires hospitalization, or prolong existing hospitalization, can cause disability or birth defect are considered serious.
3.1.3 Criterion Related to Drug Novelty
  • Age of a Drug: It is more likely for risks to be observed in newer drugs. The age of a drug can be calculated from the day marked with first authorization to the date when the PRAC recommendation was made. Based on age, the drugs can be classified into these groups 0 to 5; 5 to 10; 10 to 15; greater than equal to 15.

 The whole signal management process can be summed up into the following steps:

  1. Signal Detection: The signal generated through a source is detected by the pharmacovigilance (PV) professionals.
  2. Signal Validation: The data is analyzed to see if the source contains considerable evidence to warrant further examination. A combination of statistical and analytical techniques is employed to justify causality.
  3. Signal Confirmation: A causal association between the drug and event is assumed.
  4. Signal Prioritization: The signals are prioritized based on public health impact. The signals that comprise life-threatening situations are dealt with first.
  5. Signal Assessment: The objective is to further analyze the significance and risk factor of the validated signal.
  6. Recommendations: Appropriate action as recommended by the regulatory authorities is taken.
  7. Exchange of Information and Implementation: Seeing the severity of the situation, the results are communicated to the appropriate regulatory authorities.

4. Course of Action and Recommendations

4.1 By European Medicines Agency (EMA)

It is the responsibility of the Pharmacovigilance Risk Assessment Committee (PRAC) to prioritize and assess signals and layout recommendations on the medicines that have been authorized in European Union. This constitutes all of the national and central authorized medicines.

The recommendations should include one or a combination of the following conclusions:

  • No further action or evaluation is needed at present time.
  • Additional information is required, including:
    • Monitorization of any relevant information that comes to light in the future.
    • Further analysis in Eudra Vigilance or any other sources of data.
    • More data from the market authorization holder in the coming periodic safety update report (PSUR) or submit a PSUR on an ad-hoc basis.
  • Regulatory action required, such as:
    • Updating of product information (summary of characteristics of product and leaflet that comes with the package) or incorporation of a risk management plan by introducing a variation.
    • A referral procedure.
    • Restrictions for safety purposes.

Since January 2015, EMA publishes recommendations for updating product information which can be used by marketing authorization holders to update their product information. The aim is to make it certain that consistent, concise, and clear information is readily available to the patients in all states that are members of the European Union.

4.2 By Food and Drug Administration (FDA)

In March 2005, a guidance document for Industry titled “Good Pharmacovigilance Practices and Pharmacoepidemiologic Assessment” was issued by the Center for Drug Evaluation & Research and Center for Biologics Evaluation and Research (FDA). The document gives a brief account of the FDA’s point of view on the subject while reflecting on the ongoing industrial practices. The document was revised and reissued later on in May of the same year. The recommended procedure as mentioned in the document is as follows.

  1. Identification and Description of Safety Signals:
  • The document stipulates that complete information related to the case should be procured by initial and follow-up contacts. The health care professionals dealing first hand with the matter should be aptly trained. If a case is reported by the consumer, a health care practitioner should be contacted to validate the claim. The most vigilance should be shown towards the serious adverse effects especially those that are previously unknown.
  • After the initial post-marketing spontaneous case report is found, the sponsor’s database, FDA Adverse Event Reporting System database, already existing literature, and other databases should be further examined. Cases regarding the subject should be evaluated and follow-up should be sought where required if possible. Any data that supports or rejects the correlation between the drug and effect is significant. Although FDA agrees that there is no agreed-upon causality classification, they have not ruled out possibilities. Cases with commonalities should be kept under surveillance and not ruled out.
  • After going through a series of reviews, the cases that warrant further investigation should be summarized in tabular form to highlight clinical characteristics.
  • Though FDA recommends the usage of data mining techniques but doesn’t render it integral to signal identification or evaluation.
  • Guidance is provided for the signals that should be evaluated further. These signals may include:
    • Novel unlabeled serious adverse effects.
    • The apparent increase in severity of a labeled event.
    • The surfacing of adverse effects that are extremely rare among the general population.
    • New drug-drug, drug-food, or drug-dietary supplement interactions.
    • Identification of previously unrecognized at-risk population.
    • Confusion about a product.
    • Concerns about product usage.
    • Concerns that current risk management is not adequate.
    • Or “other” substantial signals.
  • The FDA recommends that the drug sponsor should calculate crude adverse effects reporting rates using the number of the cases that signal has been reported in the United States of America (USA) as the numerator and an estimate USA patient exposure as the denominator. Depending upon feasibility plots of reporting rates over a period of time or versus products belonging to the same class or versus drugs of the similar class or estimated background rate for the particular event in the general population can come in handy. However, these figures should be only used for exploratory purposes or as the basis for formulating a hypothesis. They note that reporting rate and incident rate are not the same. In practical applications, the use of these figures comes with challenges. The numerator is unreliable as there is always under-reporting to an unknown degree. The denominator is worse because of the difficulty in finding out how many patients took the drug after filling the prescription and the duration of the usage. These factors may render the ratio meaningless. A high reporting rate may suggest that the signal is real but a lower rate does not guarantee absolution to the drug.

The follow-up actions after an adverse reaction is detected may include:

  1. Label change for drugs that have been already marketed. (i.e. inclusion of a warning), drafting of a communication plan for public and health care professionals.
  2. More study and consultation.
  3. Stop or change the course of studies to enhance patient protection, amendments in the brochure, and informed consent from the user.
  4. Notifying the event to agency via phone, fax, or letter.
  5. Follow-up actions later on.

5. References

  1. Meyboom RHB, Lindquist M, Egberts ACG, Edwards IR. Signal selection and follow‐up in pharmacovigilance. Drug Saf Int J Med Toxicol Drug Exp. 2002;25(6):459‐465. [PubMed] [Google Scholar]
  2. Waller PC, Lee EH. Responding to drug safety issues. Pharmacoepidemiol Drug Saf. 1999;8(7):535‐552. [PubMed] [Google Scholar]
  3. Bate A, Lindquist M, Orre R, Edwards IR, Meyboom RH. Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs. Eur J Clin Pharmacol. 2002 Oct;58(7):483-90. doi: 10.1007/s00228-002-0484-z. Epub 2002 Sep 3. PMID: 12389072.
  4. Good Pharmacovigilance Practices and Pharmacoepidemiologic Assessment (2005), US Department of Health and Human Services, Retrieved: 16th February, 2021.
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