Risk assessment for quality issue

Any medicinal product, be it a device, drug, or combination, may have a quality issue that is also interconnected to pharmacovigilance. The quality issue itself may or may not lead to an adverse event (AE), and the ones associated with AE will be tracked in the global safety database, at the ICSR level. However, certain quality issues may require aggregate analysis and market actions that can impact patient safety and have a public health impact. These quality issues will require medical assessment on how serious the risk is, which is performed by a medical safety physician from pharmacovigilance.

These are typically non-product-specific and are deviations in manufacturing, packaging, or distribution of the product. These deviations or non-compliance with health authority specifications or internal marketing authorization (MAH) specifications are analyzed, evaluated, and then a recommendation is made by the pharmacovigilance team on the outcome of this deviation and its impact on patient and public health.

The following are the criteria /source of information used to perform the risk assessment of the deviation/quality issue/non-compliance. Although these are different terminologies and can mean different things as per the MAH internal process, for the sake of this blog, we will use them interchangeably. 

1. Global Safety database:

Using a search strategy that can retrieve all cases about that particular quality issue, all ICSRs should be analyzed and evaluated.  The time period is from the date of manufacturing of the product/ but ideally, from the date of market launch of the product until the quality issue was identified. If the dates are not available or complicated to retrieve, 3 years can be an ideal period retrospectively from the date of identification of the quality issue.

Most important is to choose the correct search strategy to retrieve the correct cases, the search strategy should be broad enough, but to avoid noise, narrow to exclude non-relevant cases. If the quality issue is contamination, retrieve cases of all reported contamination, color change issues reported.

2. Other sources:

Any literature articles published and available publicly about the reported quality issue. The search period is the same as explained above.

Any previous similar quality issues that occurred with similar risk assessment reports should be analyzed. 

Request toxicology reports, stability testing results, and investigation analysis done on the affected batches vs the non-issue batches. 

3. Risk assessment:

Once all the data is available, as a safety physician, an assessment needs to happen regarding the impact of the error on public health. While assessing, consider the type of adverse events that can occur or have occurred. The seriousness, severity, treatability, reversibility, and preventability of these adverse events. The patient population affected, e.g. comorbidities, age (geriatric or pediatric). 

4. Conclusion 

A conclusion should be provided regarding the mitigation of this risk, market recall being the most conservative for serious public health impact, sending a direct healthcare professional communication, change in the label, education material update etc.

Written by:

Dr.Shraddha Bhange.

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 References:

ICH guideline Q9 (R1) on quality risk management 

FDA – Recalls, Corrections, and Removals (21 CFR Part 7) 

Session 1- Complaints Investigation & Review- Jaidev Rajpal + Avinash Joshi 

Post-Authorization Safety Studies (NI-PASS)

There are two different studies that a Health authority may request an MAH (Marketing Authorisation Holder) to conduct after authorisation. One with the key objective of safety and another for efficacy, although it's always that we will get both outputs, and safety and efficacy go hand in hand.

So the post-authorisation efficacy study and post-authorisation safety studies (PASS). Both these studies can be interventional or non-interventional, and each MAH has its own processes to define and implement these regulations.

PASS falls under the realm of pharmacovigilance; therefore, we will discuss it further in this blog. 

PASS includes the following when non-interventional. Non-interventional studies wherein the product is prescribed and administered to patients as a part of routine clinical practice. 

PASS when interventional, they are our clinical trials. 

•  HCP surveys conducted for the effectiveness of any risk minimization measures implemented by MAH ( e.g., providing patient alert cards to each patient and explaining to them the risk it outlines
• Public registries that MAH has to enroll patients of approved therapeutic indications and characterise the known risk, or establish the safety profile
• Consortia (one or more MAH form the group to conduct a particular registry to gather data)
• Market surveys to characterise and analyse the product safety.

Post-authorization safety studies (PASSs) can be mandatory or voluntary.

• Mandatory PASSs are required by Health Authorities. For example, they may be needed when a medicine has been approved under exceptional circumstances or when the Pharmacovigilance Risk Assessment Committee (PRAC) asks the MAH to conduct PASS to confirm the safety profile, characterize the risk or measure the effectiveness of risk minimization measures.

• Voluntary PASSs are started by MAH themselves. These can include studies that the company chooses to do, or studies that are part of a risk management plan but not formally required by any Health authority.

Starting January 2025, all marketing authorization holders will need to use the IRIS platform to manage their post-authorization safety studies (PASSs) after the initial submission in the EU, as per EMA. PASS studies are also registered in the EU PAS register/now called as catalogue of real-world data studies, which is a European Union register electronically for post-authorization studies on the European network of centers for pharmacoepidemiology and pharmacovigilance (https://catalogues.ema.europa.eu/). A EU CT number is mandatory for all PASS registered by MAH. 

Table on the category of PASS:

Category	PASS	PAES
Category 1	Imposed (GVP Module V)	Imposed
Category 2	Imposed (Commitment in MA, exceptional approval)	Imposed
Category 3	Voluntary, Part of RMP commitment	Required/voluntary
Other		Voluntary

Every PASS study submitted as imposed PASS to EMA will require a Study protocol, major amendments to the protocol after the PRAC comments are received/ for nationally required PASS- assessment will be done by that national health authority. MAH will then need to submit interim study reports, final study reports to EMA, and also provide the summary and details of PASS in PV documents like PSUR, RMP in the respective sections. Any change in the safety profile of the product due to ongoing PASS based on interim results will then be submitted as part of an update to RMP, smpc, or based on the frequency and timing of PV documents. The timelines and requirements of PASS progress to EMA are specified in the study protocol approval and will be part of study planning.

EMA publishes the outcome of PASS in the EPAR (European public assessment report ) and on EU PAS register platform.

Key takeaway:

As PV  professional, you will be involved in PASS depending on your role in the organisation, but having a basic understanding helps. To know the definition of PASS, how they become part of RMPs, and PSURs. Knowing the concepts helps to review or take strategic decisions from PV for protocols and outcomes of studies and their impact on the safety of the product.

Written by:

Dr.Shraddha Bhange.

Connect with me Via comments below. (I do not respond to Facebook messages)

Support the cause of better rural education with me: ThinkSharp Foundation http://thinksharpfoundation.org/#home 

References:

1. Non-interventional imposed PASS are set in Articles 107n-q of Directive 2001/83/EC.

2.https://www.ema.europa.eu/en/human-regulatory-overview/post-authorisation/pharmacovigilance-post-authorisation/post-authorisation-safety-studies-pass#non-interventional-imposed-pass-questions-and-answers-6931

3. https://www.ema.europa.eu/en/documents/other/guidance-format-and-content-protocol-non-interventional-post-authorisation-safety-studies_en.pdf

4. guideline-good-pharmacovigilance-practices-gvp-module-viii-post-authorisation-safety-studies-rev-3_en.pdf

5. GVP V module and EMA website

Risk minimization measures and implementation

 Implementation of risk minimization measures 

In my earlier blog on Risk minimization measures (Apr 2026), we covered what risk is in PV and what routine and additional risk minimisation measures are. Today, we will see the implementation of these risk measures. Implementation drives the success of anything rather than only the idea itself.

Risk minimization measure:

To understand the implementation it's critical to understand the difference between messages and tools about RMM.

RMM messages	RMM tools
the key information (i.e. not the full wording) about the risk and the actions intended to be taken by the healthcare professional or the patient for minimising the risk	The tool by which the RMM messages are disseminated and adherence to the intended actions for risk minimisation is supported and/or controlled, belonging either to the category of routine or additional RMM tools.
Scientific content, but as per the targeted audience and platform e.g. HCP vs patients, Patients vs caregivers, and digital vs physical copies	Educational/Safety advice tools that include a patient alert card, an HCP checklist for the risk, HCP educational leaflet
No promotional language	Risk minimisation control tools that include HCP qualification, Hospital accreditation, traceability systems, patient certificates, and a systematic process for patient documentation in the product prescribing programs
The key messages should not be duplicative of Smpc or PIL . The main outcome we should achieve is that the educational advice to patients should be about the particular risk and its management.	To select the correct tool, the following criteria need to be considered: seriousness, severity of risk, patient target population, Healthcare professional target population for the RMM, product classification and details (dosing, administration, etc.), possible burden of the RMM on the healthcare system, and effectiveness of the RMM.

Once the RMM message is finalized, for the implementation the tool should be selected accordingly.

A. Develop the plan for implementation: Some questions/points that need to be answered are

•  Is the RMM implementated gloablly or locally?
• Who is the target audience?
• Ensure that while choosing the healthcare system and settings and the typical patient environments are considered.
•  RMM message should be considered for adequacy, comprehensibility of language, and usability as well as user-friendliness of the RMM material

B. Distribution plan:

• Who /Which functions will be leading the distribution globally and locally?
• Number or recipients as per the classification ( e.g. how many HCPs are subdivided into printed vs web form)
• Electronic and physical systems for the distribution (Webpage for HCP alert card distribution, patient enrollment database to monitor and archive tests)
• Map all the steps with start date and end date (if available)

It is critical to ensure the outcome expected from RMM and RMM tools is well thought out for patient safety and they should be supportive for achieving it rather than causing a burden of PV and healthcare systems. RMM tools and RMM messages are key to reducing the burden of risk by helping with prevention and early diagnosis and treatment.

Written by:

Dr.Shraddha Bhange.

Connect with me Via comments below. (I do not respond to Facebook messages)

Support the cause of better rural education with me: ThinkSharp Foundation http://thinksharpfoundation.org/#home

References:

GVP Module Risk Minimisation Measures

Artificial Intelligence in Pharmacovigilance (AI in PV)

We all keep hearing about Artificial Intelligence everywhere, and it's no surprise that it has also consumed the Pharmacovigilance industry. While I am no AI expert, I tried to talk about a few things that, as a safety physician found fascinating, we have achieved with the use of AI, and a few things that I am eagerly awaiting to be implemented in PV.

The technological revolution that helped us achieve tremendous transformation, some examples below, not just in PV but in the healthcare industry, as noted below. 

1. Identification of disease has really become faster, cost-efficient, and more cost-effective. 

2. Predicting the disease progression and at-risk population has also become easier with the help of technology, which allows patients to know the early signs, symptoms, and Healthcare providers to have tech-assisted platforms to then also utilize faster diagnosis.

3. We are using diagnostic algorithms, telemedicine, virtual patient monitoring, and robotic surgeries. 

4. AI-powered tools assist in drug discovery, automation in manufacturing and distribution, virtual and decentralized trials, and large data volumes are managed by machine learning.

5. Data collection has become easier due to wearable devices.

In the pharmacovigilance world, we have also made some good strides with respect to embracing automation, and I have noted some well-known examples below.

1. ICSR: Automation has been implemented for duplicate check during case intake and triage, making it less time-consuming and cost-efficient not to manually check during intake if it's an initial case or follow-up.

2. Literature monitoring: An AI-powered literature database that allows for excluding and including articles based on the four minimum criteria and the parameters added at the product level (brand name, ingredient, MAH territory).

3. Signal detection: Many signal tools are available that allow to use of statistical models for signals and unified signal outcomes from various sources. 

4. RMP and Aggregate report management: 

Various tools and databases are available to track, schedule, and submit aggregate reports.

RMP tracking and RMP commitments are also tracked via tools that allow for ensuring compliance. 

There are multiple advantages of technology integration, and those we are already aware of; however, to ensure the summary below includes some most critical advantages. 

1. Cost efficiency - Intelligent and smart cost utilization. 

2. Speed and real-time analysis - continuous monitoring of data from diverse sources enables quick and timely identification of new signals and hence provides risk management.

3. Improvised and consistent quality: Automation and AI integration have led us to move from volume-based to value-based.

4. Increased efficiency - Automate labor-intensive tasks, allowing faster processing and analysis of large volumes of data. 

5. Scalability- Automation across global databases and platforms has led to facilitating signal detection for large databases. It has also allowed to use of AI/Automation to generate scientific outputs.

However, there are a lot of opportunities still existing in pharmacovigilance for not only embracing technology but integrating it for pioneering patient safety, and I have noted a few that I could pick from various sources.

1. Structured and regulated processes 

2. Time-driven assessments to be performed regarding the implementation of technology.

3. Patients' outcomes are monitored closely after the implementation of technology. 

4. Data privacy and confidentiality are unified to have simpler and effective implementation

5. Use of robust database, human scientific intelligence for validation, security, transparency, and validity. Dataset standardization with collaboration and communication.

6. Implementation of technology has to be in parallel to training human resources on technology, collaboration

7. Implement technologies that use cloud-based solutions, open-source platforms to improve reach, knowledge sharing, and collaboration.

Some of the examples about integrating technology are AI-driven case processing, allowing only relevant cases to come into the medical workflow, and Natural language processing (NLP) for literature mining, AI-driven signal detection for classification of signal, and addressing the impact of signal.

Many avenues exist for integration of AI in technology, and if we are open-minded to accept them, we will be able to achieve the patient safety outcome we all work for i.e. safe and effective drugs in the market.

Written by:

Written by:

Dr.Shraddha Bhange.

Connect with me Via comments below. (I do not respond to Facebook messages)

Support the cause of better rural education with me: ThinkSharp Foundation http://thinksharpfoundation.org/#home 

 References:

VigiAccess: Promoting public access to VigiBase - PubMed

https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/guideline-good-pharmacovigilance-practices-gvp-module-vi-addendum-i-duplicate-management-suspected-adverse-reaction-reports_en.pdf

(PDF) Artificial Intelligence in Pharmacovigilance: a regulatory perspective on explainability

Artificial Intelligence and Machine Learning (AI/ML) for Biological and Other Products Regulated by CBER | FDA

Signal detection in rare diseases

What are the rare disease definitions?

No single global definition of rare diseases: 

Europe: Defined as incidence < 1 in 2,000 people. 

United States: Defined as incidence < 1 in 1,250 people.

Prevalence and scope: Affects 6–10% of the global population. encompasses around 6,000–8,000 disorders. Each year, 250–280 additional diseases are classified as rare diseases.

Impact on patients: They primarily affect children and significantly reduce quality of life and life expectancy. Typically chronic and progressive.

Challenges in diagnosis:

• Rarity of cases makes diagnosis difficult and delayed.
• Require multiple, often invasive and repetitive tests.
• Diagnosis may take up to 5 years from first symptoms.
• Involves multiple visits to different specialists.

Treatment gaps:

• Majority of rare diseases have no approved treatments.
• Available treatments are often very costly.
• Access to treatment remains limited for most patients.

  Improvements:

·       However, all is not black but more grey in rare diseases now.

·       Better diagnostics, affordable genetic testing reduce the time and accessibility of tests.

·       Better treatments that are affordable and accessible

·       Early diagnosis improved the treatment outcomes

 

Traditional Signal detection Vs Signal detection in rare disease 

Traditional signal detection encompasses sources such as interventional studies, ICSR, Literature, real-world data, and regulatory databases.

But when we are applying the same sources, methods, and processes for rare diseases that we apply for other diseases, we face challenges. This is due to rare diseases having fewer patients, data availability is lower, so statistical methods will offer false negatives, and fewer clinical studies are conducted. The disease itself has its own limitations; genetic pathways are not identified, and the complete pathophysiology of the disease is not well recognised or well known. There is heterogeneity for disease and a lack of baseline data for standardised diagnostics, risks, and treatments.

Critical elements to consider for Signal detection in rare diseases

The potential safety concerns identified from the animal studies or pharmacological properties are monitored closely.

Focus on immunogenicity testing for therapeutic proteins, gene therapies, or cellular products. Additionally, expanding the safety database to include class drug effects, usage of patient records, market research surveys, and collaborative registries.

Utilization of technology, such as machine learning algorithms, for identifying patterns and determining the cause of adverse events. Other methods to utilize real-world evidence should be explored.

Involving multiple datasets like proteomics by leveraging biomarkers, genomics to see how genetic variations affect drug efficacy, utilization, and then AE profile, and transcriptomics to understand drug toxicity levels.

Regulatory Intelligence:

FDA: Potential safety concerns identified from animal studies or pharmacologic properties require proactive monitoring and mitigation strategies. This may include immunogenicity testing for therapeutic proteins gene therapies, or cellular products.

EMA : Database to retrieve actual patient medical records, collaborative registries to identify any drug event pair.

Initiative on rare and undiagnosed disease (IRUD) Japan: Comprehensive diagnostic system encompassing clinical information and other genetic analysis via data sharing. Promote pathology research that may lead to the development of new treatments and therapies. A database that allows a data sharing system.

Written by:

Dr.Shraddha Bhange.

Connect with me Via comments below. (I do not respond to Facebook messages)

Support the cause of better rural education with me: ThinkSharp Foundation http://thinksharpfoundation.org/#home

References :

Segura-Bedmar, I., Camino-Perdones, D. & Guerrero-Aspizua, S. Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts. BMC Bioinformatics 23, 263 (2022). https://doi.org/10.1186/s12859-022-04810-y

Schaefer, J., Lehne, M., Schepers, J. et al. The use of machine learning in rare diseases: a scoping review. Orphanet J Rare Dis 15, 145 (2020). https://doi.org/10.1186/s13023-020-01424-6

Banerjee J, Taroni JN, Allaway RJ, Prasad DV, Guinney J, Greene C. Machine learning in rare disease. Nat Methods. 2023 Jun;20(6):803-814. doi: 10.1038/s41592-023-01886-z. Epub 2023 May 29. PMID: 37248386.

Monaco L, Zanello G, Baynam G, Jonker AH, Julkowska D, Hartman AL, O'Connor D, Wang CM, Wong-Rieger D, Pearce DA. Research on rare diseases: ten years of progress and challenges at IRDiRC. Nat Rev Drug Discov. 2022 May;21(5):319-320. doi: 10.1038/d41573-022-00019-z. PMID: 35079160; PMCID: PMC7613920.

Austin CP, et al. Future of rare diseases research 2017-2027: an IRDiRC perspective. Clin Transl Sci. 2018;11:21–27. doi: 10.1111/cts.12500.

Lochmüller H, et al. The International Rare Diseases Research Consortium: policies and guidelines to maximize impact. Eur J Hum Genet. 2017;25:1293–1302. doi: 10.1038/s41431-017-0008-z