Many of you may have read the LinkedIn posting announcing my new role at Fresh Gravity. I’m sure that more than a few readers are interested to learn why I accepted this role less than 6 months after announcing my retirement from AstraZeneca. I’ll use this article to share a snippet of my journey since retiring and what I hope to achieve in this new and exciting role.  

Firstly, after retirement, I travelled to Las Vegas, Hawaii, and New Zealand with my wife. This was a wonderful trip, but of course all good things come to an end, and I found myself in a cold, grey UK in the middle of December with wet and muddy footpaths that made it challenging to enjoy the countryside walks.  

Looking for something interesting to research, I scanned LinkedIn and technical resources. I became fascinated with the rapid pace of change relating to Large Language Models and was convinced that these introduced a truly significant change in Information Technology and perhaps society. Three things intrigued me at the time:  

1. What is the underlying mathematics underpinning LLMs? 
2. Why do LLMs hallucinate? 
3. Can LLMs, Knowledge Graphs, and Ontologies, supported by a strong backbone of master and reference data, be leveraged to improve the capabilities of an LLM? 

I found some excellent Google courses introducing LLMs and Transformers, but I wanted to understand more about the foundations. Lacking a formal background in Machine Learning and with plenty of time on my hands, I decided to invest some time in understanding the foundations.  

I found a wealth of recommendations for free learning materials on LinkedIn and progressed my learning of the foundations using the following resources.  

1. Linear Algebra (3Blue1Brown) – I have studied Linear Algebra before (>40 years ago), but the visualisations really bring the subject to life.  
2. Multivariate Calculus (Khan Academy) – Again, this is a topic I’ve studied and enjoyed before. Loved the material, though it was a bit stressful doing tests on this topic for the first time in 40 years. A key tip is that you need to practice calculus to avoid silly mistakes with minus signs.  
3. Statistics and Probability (StatQuest with Josh Starmer) – Another brilliant resource that also delves into Machine Learning and Large Language Models. I give it a triple BAM! 
4. Stanford Introduction to Machine Learning with Andrew Ng – I found this tremendously valuable as it emphasized the maths and statistics foundations of ML. Absolute highlight for me was solving the normal equation (X = (ATA)-1ATb) by hand. I’d never do it again (2 pages of matrix calculations are not to be taken lightly), but I learned a great deal! 

I skimmed several other resources related to Deep Learning and Transformers, but at that point my attention was drawn to some fascinating papers by Juan Sequeda and others, including Knowledge Graphs as a source of trust for LLM-powered enterprise question answering. This and similar papers have now become a new area for my research.  

What did I learn from all this?  

• Clearly, I need continued intellectual input as I move into my later years.  
• The capabilities of LLMs are absolutely astounding, but it’s not genuine intelligence; just applied mathematics and statistics. (In my view the capabilities are more to do with Linguistics and the remarkable properties of human language. Hallucinations are nothing unexpected, it’s just showing the limits of these techniques.)  
• My gut feeling about LLMs, Knowledge Graphs, and Ontologies supported with assured master and reference data seems to be true.

The latter brings me to the conclusion that the future of Enterprise Information Architecture with Life Sciences companies lies in seamlessly connecting assured master and reference data with ontologies and knowledge graphs, while leveraging them effectively in LLMs. 

Why Fresh Gravity?  

I have worked with Fresh Gravity and with Ajit Kumbhare in the past and have always found them to be innovative, focused and successful at implementing and integrating the range of technologies and capabilities that interest me. They also have a strong presence in the Life Sciences industry, which is my primary area of expertise and interest. 

Additionally, Fresh Gravity shares my view that the future for the Life Sciences industry should be to combine assured master and reference data, knowledge graphs, ontologies and Large Language Models (LLMs) to support scientific and enterprise questions. 

With that background, I jumped at Ajit’s offer to join Fresh Gravity as a Strategy and Solutions Leader for Life Sciences. Thank you for giving me this exciting new opportunity.  

For those who are concerned about my wife, this opportunity comes with sufficient flexibility to allow us to continue travelling and enjoying long-distance walks (we start the Southwest Coastal path in early May). 

The 3 areas I hope to develop in this new role are: 

1. Support Fresh Gravity’s clients by applying my 33 years of experience in the Life Science industry. I hope to achieve this by defining common patterns for strategic implementation and integration of assured master and reference data.  
2. Introduce new IT and data governance processes to support this vision of connected data. This may need new or upgraded tools and processes to support end-to-end use of data.  
3. Clearly define the role of an Enterprise Information Architect, a role that is poorly defined in most organisations, but in my opinion, is crucial for the future of the Life Sciences industry. 

Stay tuned for more blogs diving into each of these topics.  

The post Navigating the Next Frontier: An Enterprise Information Architect’s Ongoing Journey in Life Sciences appeared first on Fresh Gravity.

Pharmaceutical companies are at a significant crossroads in terms of how they engage with customers, innovate, and adopt digital capabilities. On one hand, there is an increasing demand for personalization of drugs, while on the other hand, global inflation and uncertain market trends are causing challenges for the industry. These factors, in addition to a lower number of clinical trials during the pandemic, caused a downturn in 2023.  

2024 has seen a revival for pharmaceutical companies, driven by increased consumer spending and stabilization of inflation in the US and EU regions. Clinical trials have also resumed as companies strive to develop and launch safer and more effective drugs while containing their R&D and manufacturing costs. We believe personalized drugs and patient care plans, technology adoption by pharmaceutical companies, improved quality and access to drugs, and mergers and acquisitions are the significant trends that will affect the pharmaceutical industry in 2024 and beyond. 

Personalized Drugs and Patient Care Plans 

As the industry evolves, a combination of new therapeutic developments and changing healthcare policies is reshaping its landscape. The future of this industry will not only be focused on developing new drugs but also on providing more holistic and focused patient care. Personalized patient care helps healthcare providers make accurate diagnosis and tailor a suitable treatment plan for patients resulting in more effective recovery and results. This can also reduce healthcare costs by avoiding unnecessary procedures and medications that may not be effective for an individual patient. This trend is expected to continue this year and beyond. Areas such as weight management/obesity, autoimmune diseases, oncology, and diabetes are expected to grow. In addition to treating diabetes, the inclusion of GLP-1 drugs for treating obesity and weight management issues is expected to be a significant growth driver and the demand for such drugs for weight loss has never been higher. Companies have also started to look for personalization opportunities in this domain.  For example, Eli Lilly, which manufactures Tirzepetide – an advanced GLP-1 drug, has started their distribution network along with personalized patient coaching. Innovations such as cell therapy and precision medicines will also play an important role. 

Technology Adoption 

Digital transformation and data can help pharma companies to increase their productivity and operational efficiency. Developing and establishing a secure digital core that includes a modern data foundation, flexible AI architecture, and smart business applications are key to driving growth and innovation. These capabilities allow companies to collect and integrate data across various platforms and locations. The data can then be leveraged to provide key business insights to decision-makers and managers. AI and ML can be of great use to generate intelligent business reports to reflect key business KPI’s and interpret market trends which in turn helps optimize business processes and maintain organizational agility. AI can also be adopted to deliver personalized patient care plans and has the potential to revolutionize disease detection and prevention. By analyzing large data sets, AI algorithms can detect subtle trends, patterns, and risk factors which can potentially contribute to certain diseases and epidemics. As per recent reports, Lilly is collaborating with OpenAI to discover novel medicines for treating drug-resistant bacterial infections. Such innovations will continue to revolutionize the way pharmaceutical companies operate. 

In the coming years, the ability to leverage AI and ML (Machine Learning) technologies will remain a critical differentiating factor which will provide pharmaceutical companies a key lever for gaining strategic advantage in the market. Such technologies can potentially be adopted across the drug development cycle, from the discovery of candidate molecules to streamlining clinical trials, resulting in faster time to market, while helping to reduce development, manufacturing, and logistical costs. Pharmaceutical companies also generate a huge amount of data from various sources. Managing such a huge volume of data efficiently while maintaining various regulatory compliances like IDMP, HIPAA, and GDPR is an ever-increasing challenge. Technology can play a key role in effectively managing this data in a secure and regulated manner while ensuring adequate monitoring and governance. There is also a need to integrate with third-party data providers to validate, enrich, and cleanse the data pharmaceutical companies are accumulating. Companies realize the value of maintaining high-quality data to drive their futuristic digital initiatives. Hence, as companies look to embrace newer technologies, a strong focus will remain on traditional areas such as master data management, data governance, and data quality. Senior industry leaders, such as Vas Narasimhan, CEO of Novartis, continue to emphasize the need for having access to high-quality, trusted data and the importance it has in the success of any digital initiative.  We expect companies to continue to invest in consolidating and streamlining their data assets, and this trend will only get stronger as more companies strive to make their business customer-focused, and data-driven. 

Improved Quality and Access 

Name-brand drugs are often expensive due to high initial investments in R&D and hence over a period of time, customers tend to move to cheaper options. Such migrations increase patient’s access to drugs while increasing pressure on pharma companies to provide high-quality generics at competitive prices. In addition to this, there are several existing and proposed policies focused on drug price reform and control. These factors mean that making high-quality drugs available at reasonable prices is still a challenge. The entry of Chinese pharmaceutical companies in Europe is also contributing to the evolving dynamics and pricing of pharmaceuticals. Per the European Parliament, the EU is 85-90% dependent on the Chinese market for all ingredients and 33% dependent on it for active ingredients. This poses critical challenges in terms of access and quality control, hence forcing policymakers to reevaluate their strategy to counter over-dependence on a single source.   

As per the 2023 report published by the WHO, 2021 saw a new high in global spending on healthcare. This reached US$ 9.8 trillion or 10.3% of global gross domestic product (GDP). Hence, debates around drug affordability, accessibility, and the strategies of payers in managing healthcare costs continue to remain a challenge for all parties concerned. Changes in policies and formulary decisions are also evident. Such adjustments are typically aimed at mitigating costs while avoiding drug overuse/abuse, indicating a greater level of scrutiny for access to critical and high-cost medications. The emphasis on policies to optimize access will continue to compete with efforts to ensure drugs are medically necessary. 

Mergers and Acquisitions  

Big Pharma will continue to focus on consolidating its market share and product pipelines through M&A. One such example is Pfizer’s acquisition of Seagen in a deal worth $43 billion. However, due to pushback from the United States Federal Trade Commission (FTC) on M&A, and high debt leverage, such large acquisitions will remain at moderate levels. At the same time, greater activity can be expected in smaller-sized acquisitions. Per a report published by S&P Global, the pharmaceutical industry will see robust growth through 2027.  

To conclude, the pharmaceutical industry will continue to see sturdy growth through 2024 and beyond.  Innovation and technology adoption will play a crucial role in this growth story while personalized health plans and policies to optimize access to high quality drugs will be significant contributing factors. M&A will continue to play a role but at relatively moderate levels; large acquisitions may be few and far between. However, as discussed above, there are some challenges and pitfalls that the industry needs to remain conscious of to ensure sustained growth and development.  

How can Fresh Gravity help?  

At Fresh Gravity, our team of domain experts and technology consultants have extensive experience working with some of the biggest pharmaceutical companies. We strive to enable and empower our customers by providing business-focused solutions on master data management, data quality, and governance. We have experts with hands-on experience working with and building cutting-edge solutions using AI/ML tools and various data platforms. We have also developed several in-house solutions to help our customers in areas such as clinical study protocol digitization, clinical study automation, IDMP compliance, and clinical data repositories. To know more about our offerings, please reach out to us at info@freshgravity.com

The post Pharma Industry Trends in 2024 & Beyond appeared first on Fresh Gravity.

In the highly regulated sectors of pharmaceuticals, biotechnology, and medical devices, adherence to Good Practice (GxP) guidelines is paramount. These guidelines ensure that the products are safe, achieve their intended purpose, and adhere to quality processes throughout their lifecycle. A crucial component of GxP compliance is the implementation and maintenance of validated data systems. These systems not only safeguard the integrity of data but also ensure that every piece of information is retrievable as well as accurately recorded and processed thus, supporting the overarching goal of protecting Patient health. 

Understanding GxP and Its Importance 

GxP encompasses a variety of “good practices,” including Good Laboratory Practice (GLP), Good Clinical Practice (GCP), and Good Manufacturing Practice (GMP), among others. While each set of practices addresses different aspects of product development and manufacturing, they all share a common thread: rigorous data management. GxP guidelines are designed to ensure that data, whether related to product development, manufacturing, or distribution, is handled with the utmost integrity, traceability, and security. 

The Role of Validated Data Systems in GxP Compliance 

A validated data system is meticulously tested and proven to consistently produce accurate, reliable results. Under GxP guidelines, such validation is not a one-time activity but an ongoing process of checks and balances. This ensures that the system remains compliant, even as software updates and technological advancements occur. The key aspects of validated data systems include:

• Data Integrity and Traceability

Every piece of data must be attributable, legible, contemporaneous, original, and accurate (ALCOA). Validated data systems ensure that data is recorded precisely at the time of the activity and is traceable back to its source, ensuring transparency and reliability.

• Data Security

Protecting sensitive data from unauthorized access is a critical component of GxP compliance. Validated systems employ robust security measures, including access controls and encryption, to safeguard data throughout its lifecycle.

• Data Backup and Retrieval

In the event of a system failure or data corruption, it is essential for all data to be recovered and easily retrievable for audit purposes. Validated systems are equipped with efficient backup and recovery procedures to mitigate data loss risks.

• Change Control and Documentation

GxP guidelines emphasize the importance of thorough documentation and controlled procedures for any changes made to the system. This includes software updates, changes in data handling processes, and modifications to security protocols. 

Navigating the Validation Process 

Validating a data system under GxP guidelines involves the following key steps: 

• Planning: Defining the scope and objectives of the validation process, including risk assessments to identify and mitigate potential data integrity issues. 
• Specification: Detailing the functional and operational requirements of the system to meet GxP standards. 

• Testing: Conducting rigorous testing to ensure the system meets all specified requirements. This includes both dynamic testing (testing the system under simulated real-world conditions) and static testing (reviewing the system’s code, design, and documentation). 
• Implementation: Carefully deploying the validated system while ensuring that all GxP requirements continue to be met. 
• Maintenance and Review: Regularly reviewing and maintaining the system to ensure ongoing compliance with GxP standards. 

Fresh Gravity’s Approach towards GxP Validation 

Fresh Gravity approaches GxP (Good Practices) validation with a meticulous and forward-thinking strategy,
understanding its crucial role in the life sciences and pharmaceutical industries where compliance, data integrity, and patient safety are paramount.
Fresh Gravity integrates the latest technological advancements with a deep commitment to maintaining regulatory standards, ensuring that systems and processes not only meet but exceed the stringent requirements set forth by global regulatory bodies. By leveraging a combination of automation, digital innovation, and expert insights, Fresh Gravity streamlines the validation process, making it more efficient and less prone to human error.
This approach facilitates faster time-to-market for essential medical products while establishing a robust foundation for compliance that can adapt to the evolving landscape of GxP requirements. Fresh Gravity’s dedication to incorporating best practices in technology and process management transforms GxP validation from a mandatory regulatory hurdle into a strategic advantage, enhancing quality, reliability, and trust in the products and services. 

The Bottom Line 

In the world of pharmaceuticals, biotech, and medical devices, where the stakes are incredibly high, investing in and maintaining validated data systems isn’t merely a regulatory requirement but a cornerstone of quality and trust. By ensuring compliance with GxP guidelines, companies not only safeguard their reputation and financial well-being but, more importantly, ensure the safety and efficacy of products that impact millions of lives around the world.  

The post The Importance of Validated Data Systems under GxP Guidelines  appeared first on Fresh Gravity.

Last month I lost a friend of mine to ovarian cancer. While I hadn’t been in touch with her in the past few years, I did often think of her and always thought she was going to do amazing things in life. So safe to say, this news came as a shock to me. Later, I learned that there is no cure for ovarian cancer and that there are ongoing studies to find a cure but that this may take years. A primary cause of this delay, I’ve come to understand, is the amount of time a clinical trial process takes.

This market gap presents an opportunity for companies to use technology and innovation to develop solutions to accelerate the clinical trial process. Hence, I was looking forward to speaking with Kedar Deshpande (KD) who is the Senior Director for Clinical Data and Analytics at Fresh Gravity and has been working in this space for over 17 years. In this piece, we learn about Kedar’s perspective on how innovation and technology can help significantly fast-track the clinical trial process.

MK: For the benefit of anyone reading this blog, can you explain what CSAT (Clinical Study Automation Tool) is? 

KD: CSAT or the Clinical Study Build Automation Tool is an end-to-end platform built to design and develop all the components of a clinical study build process in a single click.

Essentially, CSAT is a one-stop solution for all things related to a clinical study including the clinical protocol document. CSAT uses innovation as well as leverages the latest technologies to make the clinical study build process faster, and more efficient while ensuring improved quality.

This tool utilizes Natural Language Processing (NLP) and Machine Learning (ML) to extract the contents of the protocol and auto-predict, recommend, and generate the set of eCRFs (electronic case report form), edit checks for the study protocol, along with generating an EDC (Electronic Data Capture) importable file. The tool also auto-generates the SDTM (Study Data Tabulation Model) mapping and SDTM executable program to ensure the data transformation is complete.

MK: What, according to you, is the need for it?

KD: The current way of designing a clinical study is manual, skill-dependent, and resource-intensive.

According to clinicaltrials.gov data, there are 40K+ new clinical studies conducted each year and for every trial 14 weeks are spent just on the study build for EDC. The manual effort for each study build could amount to 700-man hours, not to mention the steep costs.

Fresh Gravity’s CSAT can help reduce the effort by 80% thereby saving around 500-man hours per study. If we were calculating for a sample of 300 trials, 150k hours would be saved which could also translate into saving 1 million dollars, thereby increasing productivity and profitability.

MK: Who can benefit from this tool and how? 

KD: In my opinion, all CROs (Clinical Research Organizations), Pharmaceutical companies, FSP partners, and Bio-Tech companies stand to gain from the CSAT tool. In the long run, it will also be beneficial to patients.

The benefits of the tool are two-fold:

• Faster go-live, which means earlier First Patient In (FPI) and faster availability of the SDTM datasets for analysis
• Niche skilled people can utilize their time more efficiently by focusing on the complicated things to be handled in the process of clinical trial

MK: What has been the most exciting part about working on this solution?

KD: What really drives me and is also the most exciting part for me is helping make newer medicines and drugs available to humankind at a faster pace. By bringing in innovative technology, we are helping bring new drugs to market faster.

MK: What are some of the things you are looking forward to in 2023 in terms of how CSAT could evolve/be a game changer?

KD: I believe that CSAT is going to be a game changer and will bring disruptive changes to the industry by drastically reducing the time it takes for some of the key processes in a clinical trial. I am looking forward to seeing the clinical industry adopt this kind of automation much like other industries have been doing for over a decade and are reaping the benefits.

MK: How can Fresh Gravity help in bringing about this change?

KD: By partnering with an increasing number of pharma companies, together we can revolutionize the way clinical trials are conducted and significantly reduce the go-to-market time and cost of drugs, which eventually will benefit patients in need of these medicines.

To learn more about CSAT, please write to Kedar Deshpande at kedar.deshpande@freshgravity.com or click here to read more.

The post How to Automate Your Clinical Study Build appeared first on Fresh Gravity.

IDMP Compliance – An opportunity for building Enterprise Data Assets

Biotech, pharmaceutical, and medical device companies need to conform to a plethora of guidelines and regulations, and often, find themselves responding to regulatory needs in more of a reactive vs. proactive manner. The Identification of Medicinal Products (IDMP) is one such regulation that requires all pharmaceutical companies that market their products globally, to ensure that all product data submissions follow a uniform data structure with universal standards and vocabulary. The European Medicines Agency (EMA) is pioneering the implementation and enforcement of IDMP standards using its SPOR (Substance, Product, Organization, and Referential) framework. By early 2023, pharmaceutical companies are expected to begin submitting their product data to the EMA using IDMP-compliant web-based forms.

For an organization to be IDMP compliant, which means adhering to the structures and data attributes as outlined by guidance that the EMA has provided, there are data points required from a variety of different systems and product owners as outlined in the diagram below:

Figure 1: IDMP compliance necessitates the consolidation of data from multiple systems

For a pharmaceutical company to comply with the IDMP standards, it is essential that business and IT stakeholders across R&D, Clinical, Supply Chain, Marketing, and Regulatory work together as a single cross-functional team and find ways to relate cross-functional data. This cross-functional data often does not conform to an enterprise-wide agreed-upon structure or definition and there is a lack of a common technical and business definition of the data. This leads to more efforts on manual curation to ensure that the most accurate and up-to-date data is submitted to regulatory authorities.

Master Data Management (MDM) solutions, by their very function, are well positioned to enable companies to bring together data from across multiple data sources, enforce standardization, and facilitate the creation of an acceptable enterprise-wide “golden record.” Data quality, governance, and management are the bedrock of an MDM solution and are also critical for the success of an IDMP initiative. Data governance puts into place controls and processes to drive better data quality and facilitate a more comprehensive understanding of data across the enterprise. It gives end-to-end visibility to all the data citizens, which includes data stewards, line-of-business users, data governance, and compliance teams. Data stewards can effortlessly locate data, understand its lineage and ownership and obtain comprehensive technical and business context for each data set.

Fresh Gravity’s Molecule to Market Solution

Fresh Gravity’s MDM-driven solution for IDMP compliance, Molecule to Market,

• Brings together data from multiple functions and systems thus allowing organizations to gain a much broader view of their interconnected data
• Lays the groundwork for breaking data silos between distinct functions of the pharma company (R&D, Regulatory, Manufacturing, Supply Chain, etc.)
• Allows the creation of a single version of truth for product data as it moves through the drug development lifecycle. Each stage in the lifecycle adds more details to the description of a medicinal product, and this can be effectively captured in an MDM system. Therefore, this solution is an opportunity to enable pharmaceutical companies to:
• Build a repository of Enterprise-wide Data Assets
• Identify opportunities for and improve Data Quality and Data Governance practices
• Create an Enterprise Architecture view for data across the Drug Development Value Chain and
• Improve Operational Efficiencies

More importantly, the MDM solution generates and traces the journey of a substance from drug discovery to commercialization and regulatory compliance and provides an enterprise-wide view of the data needed across the entire value chain.

Specifically, this solution brings to the table:

• An IDMP-compliant data model with “Medicinal Product” as the anchor entity, thereby ensuring that product data stays at the center of the relationships
• Integration with the EMA reference data service (SPOR RMS) for reference data standardization

Figure 2: Molecule to Market Solution in Action

The working structure of the Molecule to Market solution

Data is ingested through the following data sources:

• Internal Cross-Functional Data
• Enrichment Sources: Additional enrichment sources such as Citeline, DrugDev, and DrugBank can be integrated with the Molecule to Market solution as per the business use case and requirements to add more details to data internal to pharma companies.
• Reference data and/or taxonomies may exist in different systems within the organization. These can be brought into the Molecule to Market solution to standardize the list of values utilized across the enterprise. Fresh Gravity has also developed a utility that uses API calls to populate all the reference data to mirror what is in EMAs SPOR Referential Management Service (RMS).
• Unstructured Data: There is IDMP-related data in unstructured formats across the organization in documents like SmPC, Module 3, eCTD, or the labeling documents. Fresh Gravity has built a Natural Language Processing engine to add an unsupervised learning method where relevant information can be extracted from unstructured data. Extracting data relevant to IDMP from unstructured data sources works as an accelerator that pushes data down into the MDM system.

Once data is brought into the MDM system, typical MDM functions take over, creating an entity/profile and building relationships between different entities. Thereafter, cleansing and, transformation rules are applied to the data, and match rules are applied to find potential matches within the data set. Data Stewards are then tasked with actioning merges to de-duplicate and manage the data from the UI.  Once mastered, de-duplicated, and enriched data is ready, it can be sent to downstream data warehouses from where this data can be consumed by RIM systems for submission to the regulatory authorities.

Conclusion

As IDMP and similar directives include a variety of interactions with various health authorities over a span of time, Fresh Gravity recommends that pharmaceutical organizations opt for an iterative MDM solution. Fresh Gravity’s IDMP approach aims to build a robust MDM foundation that will assist in ongoing data quality maintenance and drive compliance and operational efficiencies in various areas for years to come.

For a demo of Fresh Gravity’s solution, or more information and questions, please write to neha.inamdar@freshgravity.com.

For more detailed information about this solution, please refer to the datasheet here.

The post Master Data-Driven IDMP Compliance: Fresh Gravity’s Approach appeared first on Fresh Gravity.

What is IDMP?

If you work in the Life Sciences industry, the term “IDMP” would be familiar to you.  IDMP stands for Identification of Medicinal Products which is a set of standards used to globally standardize data and structures to define and uniquely identify medicinal products. IDMP comprises of a set of five standards developed by ISO, or the International Organization for Standardization.

The five ISO IDMP standards, all of which deal with unique identification and exchange of information for medicinal products, are:

• ISO 11615: Standards relating to Medicinal Product
• ISO 11616: Standards relating to Pharmaceutical Product
• ISO 11238: Standards relating to Substances.
• ISO 11239: Standards relating to pharmaceutical dose forms, units of presentation, routes of administration, and packaging items related to medicinal products.
• ISO 11240: Standards relating to units of measurement.

The 5 ISO standards are illustrated and described in Figure 1:

Why IDMP?

The competitive, dynamic, and highly governed nature of the Life Sciences industry requires a continuous flow and exchange of data between regulatory authorities, pharmaceutical companies, and manufacturers, among other stakeholders. While there has always been a need for efficiently managing submissions and adverse events reporting, there lacked a single mechanism to reliably exchange accurate information between stakeholders. To address this need in the context of pharmacovigilance and improving adverse event reporting, IDMP was developed.

ISO IDMP provides a standard way for defining and storing medicinal product information, which will enable efficient reporting, tracking, faster decision-making, and response during adverse event reporting. At present IDMP focuses on standardization of data with a future goal of improving overall pharmacovigilance[1].

Currently, most of the medicinal product information is spread across fragmented systems within pharmaceutical organizations. Some of the systems are legacy systems and hold data in an unstructured format such as documents, pdfs, excel workbooks, and email messages. Standardizing this data in the IDMP format supports the regulatory submission processes within an organization as it not only maintains uniformity in managing data assets within an organization but also in exchanging data between the regulators (such as the EMA and the FDA) and Life Sciences organizations. The Medicinal Product data previously submitted to regulatory authorities can be re-used when submitting variations to authorized medicinal products. In case of additional requests from regulatory authorities for a submission or a query, the information will be readily accessible. For an Investigational Medicinal Product, as the clinical trial progresses, data generated through different business processes can be submitted periodically and standardization in IDMP format will help maintain transparency.

What has happened so far?

The European Medicines Agency (EMA), a regulatory agency of the European Union (EU) is pioneering the IDMP journey and has organized the implementation using its SPOR services. SPOR is a set of four data management services, namely:

• Substance Management Service (SMS)
• Product Management Service (PMS)
• Organization Management Service (OMS)
• Referential Management Service (RMS)

SPOR covers multiple master data domains of the medicinal products definition. IDMP is being implemented by EMA in phases with different timelines. RMS and OMS services were launched in 2017 and are currently used in submissions that need to be made to the EMA and other regulatory authorities within Europe. As per timelines published by EMA, the next in line is the implementation of the Product Management Service (PMS), expected to go live in Q2 2023. This means that all submissions for medicinal products will need to be made to the PMS service using web-based Digital Application Dataset Integration (DADI) forms (Latest Implementation Guidance IG v2.1 published here). Regulatory organizations, just like the EMA, will be holding the beacon for guidance on IDMP compliance for the industry.

The United States Food and Drug Administration (FDA) has not yet mandated the use of IDMP standards for the submission of data to the FDA. Nevertheless, preparation for EMAs SPOR services will also ensure readiness for FDAs’ future requirements.

Figure 2 is an illustrative depiction of the different approaches undertaken by the US FDA and EMA for defining IDMP ISO standards.

What next?

As IDMP timelines for EMA draw near, Life Sciences organizations are embarking on a digital transformation journey. At a strategy level, an organization can lay down a roadmap for IDMP and align it with its long-term goals to build Enterprise level Data Assets. IDMP compliance is to be viewed as an opportunity to break organizational data silos, improve overall organizational data quality and governance, and enhance operational efficiencies.

Organizations will have to be agile to implement IDMP. Life Sciences organizations and regulators will have to work in collaboration to fully leverage the adaptive, scalable, and nimble technologies available in the market, to achieve 100 percent IDMP compliance.

Did you know about our IDMP solution?

Fresh Gravity has built an MDM-driven solution to address IDMP Compliance needs. Read more about Fresh Gravity’s approach to this here.

For a demo of Fresh Gravity’s solution, or more information and questions, please write to neha.inamdar@freshgravity.com.

For more detailed information about this solution, please refer to the datasheet here.

[1] Pharmacovigilance is the science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other medicine/vaccine-related problem. [Source:who.int].

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