7 AI Tools Used for Automation in Pharmacovigilance

Dear Reader, As you're likely aware, artificial intelligence is rapidly gaining prominence across various industries, and the pharmaceutical sector is no exception. With the advent of Pharma 4.0, a revolutionary era has dawned, aimed at streamlining processes and enhancing efficiency within the industry. AI-powered tools have emerged as invaluable resources for pharmaceutical companies and regulatory agencies. However, navigating the array of available AI tools and understanding their specific applications can take time and effort.

To alleviate any confusion, I've crafted a comprehensive blog exploring 7 AI tools in pharmacovigilance and their benefits and limitations. Keep reading to delve deeper into the world of AI-driven pharmacovigilance. 

7 AI Tools Used in Pharmacovigilance

Here’s a list of 7 revolutionary AI tools currently transforming pharmacovigilance:

1. FDA's Sentinel Initiative

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The Sentinel System uses automated algorithms to analyse large healthcare databases and identify potential safety signals associated with drugs and other medical products. This system has a suite of tools and methods along with AI to proactively monitor the safety of medical products using real-world data from EHR (Electronic Health Records), insurance claims and more such resources. Besides the FDA, this system is accessible to collaborators from the healthcare system, academic institutions, and private sector organisations. 

Benefits

Along with real-time continuous monitoring of the medical products for quick detection of potential safety issues, Sentinel offers other benefits like: 

• Large Data Pool: Sentinel provides a comprehensive product safety analysis since the data is gathered from EHRs, Insurance claims, and other sources. 
• Supports Regulatory Decisions: Its robust data analytics aid in the approval and post-market evaluation of the pharma products. 
• Transparency: Since the data is shared with the public, it supports 100% transparency, which helps to build trust in the regulatory process. 

Click here to learn about Sentinel from the Epidemiologist at Sentinel - Danijela Stojanovic, PharmD, PhD: FDA's Sentinel Initiative - Pharmacovigilance 2020

Limitations

Because the Sentinel system gathers the data from the EHRs and insurance claims, incomplete or inaccurate data produces misleading results. More limitations are discussed below: 

• Data Privacy: Safeguarding sensitive patient data is of the utmost importance in complying with privacy regulations, thereby limiting data accessibility. 
• Biasness: The accuracy of the findings depends on the data collection methods implemented. When the data has inherent biases, the consistency and comparability of the analyses are affected.

2. VigiLanz

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The Care Management Software by Inovalon, formerly VigiLanz, is a SaaS-based clinical surveillance and patient safety solution that transforms complex patient data into meaningful and actionable real-time alerts. This software proves beneficial to clinicians in identifying opportunities to avoid or minimise harm, improve safety, and provide the highest quality healthcare. It implements NLP(natural language processing) and ML (machine learning) to identify potential ADRs and safety issues in electronic health records. 

Benefits

In addition to automating routine tasks like data collection, reporting, and timely compliance monitoring, the software offers other benefits such as: 

• Infection Surveillance: The system monitors infection rates and trends, allowing the control teams to identify outbreaks and implement appropriate measures. 
• Antimicrobial Stewardship: It supports the antimicrobial stewardship program by tracking antibiotic use and resistance patterns, thereby assisting in reducing the risk of antibiotic resistance. 
• Performance Benchmarking: Inovalon offers tools to healthcare organisations to compare their performance with the industry standards and those of their competitors. 

Click here to learn about cutting-edge AI/ML applications and real-world examples showcasing the impact of data in action by the Senior Software Architect at Inovalon- Charles Yee: https://www.youtube.com/watch?v=PvHcdLU23X0&ab_channel=Inovalon.

Limitations

One of the significant limitations is the ongoing subscription fees for smaller healthcare organisations having limited budgets, and some more as here: 

• Training cost: besides the Subscription fees, the other expenses like initial setup, customisation, and staff training incur additional costs. 
• Learning Curve: The healthcare staff needs time to adapt well to the system and become proficient. 
• Dependence on Vendor: Like every other software that needs timely updation, the system must also be maintained and updated. If the vendor does not provide timely support, it may hamper the process. 

3. Linguamatics' I2E

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Linguamatics’ I2E has successfully monitored COVID-19 vaccine safety! This unique blend of NLP and NMT technologies has unparalleled knowledge of the language, culture, and regulatory standards for scaling adverse event reports. Linguamatics translate is a secured and compliant neuro machine translation solution for efficiently analysing and processing pharmacovigilance data in various languages, encouraging cross-border collaboration and information-sharing among healthcare professionals, regulators, and pharmaceutical companies. Together with Linguamatics NLP, it can quickly identify and extract relevant information from large volumes of multilingual adverse event reports, enabling timely detection of potential safety concerns. 

Benefits

Linguamatics efficiently extracts valuable information from unstructured resources, including medical literature, clinical trial reports, social media, and EHRs, and more such benefits are as discussed here: 

• Signal Prioritization: The software allows you to prioritise the safety signals to assess the severity and frequency of the adverse events for faster risk management. 
• Target Identification: You can identify and validate potential drug targets by extracting and analysing relevant biological and chemical data. 
• Interdisciplinary Collaboration: It supports collaboration across different departments and disciplines by offering a common data platform. 

Check out the experience shared by Jonathan Hartmann, the clinical informationist at Georgetown University Medical Center, here: My experience of Linguamatics: Jonathan Hartmann, Georgetown University Medical Center

Limitations

Along with the expensive initial setup and regular maintenance, other limitations are as follows: 

• Normalisation of data: Since data is collected from various sources, this data may require to be normalised and cleaned before being utilised by the software, adding extra workload. 
• Algorithm Bias: Since Linguamatics uses NLP algorithms, any training data biases directly affect the fairness of the results. 

4. TCS ADD 

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TCS ADD™ has helped to accelerate the decision-making process by offering access to real-time data. With TCS ADD, you can now register your drug in no time with its adaptive trial design. It is a one-stop solution for sites, patients, and regulators. TCS ADD has intriguing solutions such as Metadata Repository, Data Management, Analytics and Insights, Connected Clinical Trials, Safety, and Regulatory. 

Benefits

TCS ADD offers advanced analytics to gain market insights and identify trends and emerging opportunities, and more such benefits are as follows: 

• Accelerates from drug discovery to Market: TCS ADD helps bring new drugs to market while ensuring compliance with regulatory standards. 
• Facilitates collaboration: This platform brings stakeholders, researchers, clinicians, and regulatory experts together to stay updated with the information, fostering collaborations. 
• Patient Engagement Tools: Patient engagement tools allow better monitoring and improve patient outcomes. 

Watch Chris Lee, Merck's VP of Global Regulatory Affairs and Quality Management, explain how Merck transformed its Regulatory Operations using the TCS ADD Regulatory solution. TCS ADD™ Regulatory: Future-mapped Platform to Improve Efficiencies

Limitations

Since there is no standardisation in the data formats and protocols across different systems, it can reduce the efficiency in data sharing and analysis; mentioned below are more such limitations: 

• Compatibility with Other Systems: Ensuring seamless interoperability with other systems and platforms used within the organisation can be challenging, potentially leading to data silos or integration gaps.
• Data Integration Issues: Disparate data sources with varying quality and formats may pose challenges in data integration, potentially impacting the accuracy and consistency of insights generated.

5. ArisGlobal's LifeSphere MultiVigilance

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It is a platform that employs machine learning models to analyse large data sets of adverse events, recognise patterns, and provide insights to support professionals in making informed decisions. LifeSphere offers Safety, Regulatory, Quality, Medical Affairs, and Data and Analytics solutions to accelerate your R&D lifecycle.

Benefits

With LifeSphere’s Multivigilance tool, resource allocation can be well-optimised for clinical trials and other projects. Read further to learn more benefits: 

• IP Protection: Supports the management and protection of intellectual property (IP) by maintaining secure records of research data and development processes.
• Patent Strategy: Assists in developing and managing patent strategies to safeguard innovations and maximise commercial potential.

Click here to watch a short video about the LifeSphere Safety Platform: LifeSphere Safety Platform

Limitations

During the process of migration, there is a potential risk of data loss or corruption, affecting the historical data’s integrity; here are some more limitations: 

• Advanced Analytics: Some advanced analytical features might require additional tools or integrations, adding complexity and cost.
• Long Payback Period: The payback period for the initial investment might be extended, especially for smaller organisations with limited budgets.

6. Saama Technologies' Life Science Analytics Cloud (LSAC)

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LSAC leverages AI and machine learning algorithms to analyse diverse data sources, like electronic health records, social media, and clinical trial data, to identify safety signals and trends. Its AI and advanced analytics automate the key clinical development processes, which it achieves in two ways: through a strong portfolio of SaaS-based solutions and custom solutions and services. Typically, AI models require years and millions of data points to be trained accurately. Saama brings you 10+ years of AI research with 90+ trained over 300 million data points. It offers solutions like Study Design and Startup, Study Conduct, Biometrics, Post Approval, and Digital Transformation.

Benefits

Saama Technologies helps recruit patients by identifying and targeting suitable patient populations using AI-driven insights. Check out its other benefits below: 

• Site Selection and Management: Optimizes site selection and management by analysing previous performance and predicting the best sites for new trials.
• Personalised Medicine: Supports the development of personalised medicine by identifying patient subgroups that may benefit from specific treatments.
• Contingency Planning: Supports the development of robust contingency plans by simulating different scenarios and outcomes.

Click on this video to learn the new features brought to you by Saama Technologies: LSAC 4.0 — AI-Powered Clinical Analytics Platform | Saama Technologies

Limitations

Similar to other AI tools, some AI algorithms it uses may function as “black boxes”, making it difficult for users to understand how specific conclusions/predictions are derived. Check its other limitations below: 

• Latency Issues: High data processing demands can sometimes lead to latency and slower response times, impacting real-time decision-making.
• Information Overload: Decision-makers may face information overload due to the volume of data and insights generated, making it difficult to focus on critical findings.

7. Oracle’s Life Sciences Products

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Oracle brings Life Science Products designed to help organisations unify their data from preclinical planning, clinical trial conduct, streamlined and automated safety management, market access and brand strategy, and post-launch performance activities while strengthening business operations.

Its innovative solutions like Clinical Research, Safety and Pharmacovigilance, and Real World Evidence assist organisations in Pharmacovigilance. 

Benefits

Oracle’s Life Sciences Solutions offer comprehensive audit trails to track changes and ensure accountability, along with other benefits like: 

• Holistic Risk View: Data is integrated from multiple sources to provide a comprehensive view of drug safety, aiding in risk assessment and management.
• Intuitive Design: Its User-friendly interface makes it easy for decision-makers to navigate and access the necessary information.
• Robust Security: It ensures the security of sensitive data, complying with data protection regulations like GDPR and HIPAA.

Click on this video to learn about Oracle’s solutions: Oracle Health Sciences–Pharmacovigilance

Limitations

Integrating Oracle Solutions with the existing system can be a herculean task and may lead to an increase in maintenance and future upgrade costs along with other limitations: 

• ROI: Given the potentially long return on investment, conducting a thorough cost-benefit analysis is essential to justify the investment in AI tools. This process can be challenging due to the high costs and complexity involved.

Conclusion

As we have reached the end of the blog, I want to underscore the transformation AI can bring about in pharmacovigilance. AI tools can detect adverse events and help you with data integration, signal detection, drug-drug interaction, predictive analysis, and more. 

Besides the FDA’s Sentinel System, other AI tools include VigiLanz, IBM Watson, Trifacta, Ariadne Genomics MedScan, Aris Global’s LifeSphere MultiVigilance and many more.  

These tools help save the professional manual hours by automating the tasks. Thus saving time for making critical, informed decisions. 

FAQs

1. How to use AI in Pharma?

AI is used in pharma for data integration, detecting patterns and adverse events, generating automatic reports, controlling quality, and studying drug-drug interactions. 

2. What is the use of AI in Pharma and Healthcare?

AI is helpful for Data Mining and Literature review, case report generation, and sorting and assessing large chunks of data sets. 

3. What are the commonly used AI tools in pharmacovigilance?

Pharmacovigilance's commonly used AI tools are VigiLanz, ArisGlobal’s LifeSphere MultiVigilance, TCS ADD, Linguamatics, and Oracle’s Solutions.