Ever since the intense scrutiny faced by organizations that developed COVID vaccines, pharmacovigilance has become a hotly discussed area of interest. The practice of pharmacovigilance basically aims to reduce the entry of drugs with adverse side effects into regular circulation. Artificial Intelligence (AI) has been permeating this field in recent years due to the immense potential for automated pharmaceutical discovery that it offers.
Pharmacovigilance is a non-negotiable activity in the pharmaceutical industry that helps weed out drugs that do not meet safety standards. The various benefits of automation that AI can inculcate into this practice can lead to faster and more accurate screening of drug data.
In this article, we will talk about pharmacovigilance and the various ways that the implementation of pharmacovigilance automation and AI-allied tools can boost the efficacy of this practice in general.
Pharmacovigilance is the highly essential science and practice of identifying, assessing, and preventing adverse effects of harmful drugs or any other drug-related problems. It is a crucial aspect of public health that helps ensure that drugs consumed by patients safety of medications used by patients. Pharmacovigilance collects, monitors, and evaluates data related to drug safety and aims to improve patient safety by identifying and minimizing potential harm.
Pharmacovigilance involves the systematic collection, analysis, and reporting of information about the safety of medicines, including prescription drugs, over-the-counter medications, and herbal supplements. Concerns regarding drug safety come to the fore as Adverse Events (AE) are reported, which are the occurrence of unfortunate consequences from the consumption of a specific drug. This information is gathered from various sources, such as healthcare professionals, patients, clinical trials, and scientific literature.
Source: BirlaSoft
Pharmacovigilance plays a vital role in protecting public health by ensuring that medicines are safe and effective for use. It helps to identify new and previously unknown adverse drug reactions, and the information generated from pharmacovigilance activities can inform regulatory authorities to make appropriate decisions regarding drug approval, labeling, and use.
The rising costs of traditional PV have been a significant concern for the industry for several years. These escalating costs have been driven by various factors, including increased data volume & regulatory requirements, the complexity of drug development, and the need for signal detection and analysis. Let's delve into how these rising costs are affecting pharmaceutical businesses and how many companies are turning to AI and automation to address this issue
1) Data Volume and Complexity: With the growth in the pharmaceutical industry and the number of drugs on the market, the volume and complexity of PV data have increased exponentially. Analyzing and managing vast datasets can strain resources and infrastructure.
2) Regulatory Demands: Regulatory bodies, such as the FDA and EMA, are demanding more comprehensive safety data and quicker reporting of adverse events. This necessitates hiring additional staff, implementing new technologies, and investing in training to meet these evolving compliance requirements.
3) Manual Data Processing: Traditional PV relies heavily on manual data entry and processing. This includes reviewing and recording adverse event reports from various sources, such as healthcare providers, patients, and regulatory authorities. The manual nature of these tasks can be time-consuming and error-prone, leading to increased operational costs.
A recent survey conducted by Deloitte among global biopharmaceutical companies indicates a growing trend in Pharmacovigilance (PV) practices. Companies are striving to enhance efficiency and reduce expenses. The study reveals that 90 percent of respondents aimed to reduce case processing costs as their primary goal.
There are several factors other than just AE that define the extent of harm caused by a pharmaceutical drug in official market-wide circulation. Some of these factors include:
These factors are extremely important and sensitive sources of data that require intense scrutiny since human lives are at stake. Only the most granular and in-depth analysis of drug effects can lead to ideal patient outcomes from future versions of a specific drug that has formerly been labeled as harmful and that is exactly where AI can help.
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Pharmacovigilance automation tools powered by AI have the potential to revolutionize the way adverse drug reactions (ADRs) are identified, reported, and managed. These tools use machine learning algorithms to analyze large amounts of data from multiple sources, including electronic health records, social media, and other forms of reliable and tangible real-world evidence.
One example of such a tool is the FDA's Sentinel System, which uses automated algorithms to analyze large healthcare databases in order to identify potential safety signals associated with drugs and other medical products. Another example is VigiLanz, a software platform that uses natural language processing and machine learning to identify potential ADRs and other safety issues in electronic health records.
As per a research survey conducted among US pharmaceutical companies by TransCelerate Biopharma Inc, a 45% improvement in compliance and an 80% improvement in efficiency was reported among a slew of other beneficial developments from the integration of AI and automation tools for pharmacovigilance.
Source: TransCelerate Biopharma Inc US Survey
Other pharmacovigilance automation tools powered by AI include IBM Watson for Drug Discovery, which uses machine learning and predictive analytics to accelerate drug discovery and development, and Trifacta, a data preparation and cleansing tool that can help identify and correct data quality issues in pharmacovigilance data sets.
FDA reporting of pharmaceuticals is generally carried out with the help of Adverse Event Reporting Systems (AERS) and with the integration of machine learning algorithms, the scope of drug data analysis can be scaled up with ease. AI/ML-driven solutions for AERS have helped develop clear explanations for timely FDA reporting of adverse events.
Back in 2020 when the pandemic arose, forcing drug safety departments to reassess the way they analyze data, several automation solutions were suggested by leading tech conglomerates. An important component that ensures timely reporting of adverse events is where consumers are empowered to report these events. And that is where Amazon's Interactive Voice Response (IVR) solution known as Amazon Connect came to the fore during the pandemic.
Throughout the pandemic, medical professionals were forced to provide quick solutions and this resulted in ill-conceived prescriptions being written out on the fly. Not all medications suited every case of COVID and the IVR-driven reporting of adverse events helped narrow down the most viable drugs that could hold off the fatal impact of the pandemic to a large extent.
Pharmaceutical companies are required to adhere to certain legal regulations imposed by the US Food and Drug Administration (FDA). These statutes are governed under the Individual Case Safety Reports (ICSR) shared among key stakeholders such as pharmaceutical companies and regulatory bodies subordinated by the FDA. However, the existing process involves a human pharmacovigilance professional manually assessing the validity of each case related to ADRs or events of serious reactions to specific drugs.
A breakthrough tooling strategy known as cognitive computing combines NLP and Deep Learning (DL) to automate these pharmacovigilance reporting workflows. This methodology also helps reduce the cost of case reporting and improves data quality by automating formerly resource-intensive activities such as pharmacovigilance analytics and benefit-risk assessment. AI-powered ICSR allows safety departments, and pharmacovigilance executives to ensure better reporting outcomes with highly relevant data at their fingertips.
Social media platforms and online forums have become places where individuals often share their healthcare experiences, including adverse drug reactions (ADRs). AI-powered sentiment analysis can sift through these vast amounts of unstructured text data to identify and categorize mentions of drug-related experiences. It can determine whether the sentiment expressed is positive, negative, or neutral and understand the context in which the drug is discussed.
The article, shared after the Pacific Symposium on Biocomputing, reveals how using AI to analyze five million posts can provide valuable insights into the effectiveness of antidepressants. The study also emphasizes the importance of social listening in understanding safe drug combinations and adverse reactions.
Integrating genomic data with pharmacovigilance efforts allows for a more personalized approach to drug safety. AI can analyze a patient's genetic profile to identify genetic markers associated with drug metabolism and adverse reactions. By considering genetic variations, healthcare providers can make more informed decisions about which medications are most suitable for individual patients, reducing the risk of adverse events.
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1) Data Quality and Quantity: High-quality data is the foundation of effective AI models. In pharmacovigilance, data comes from various sources such as electronic health records, patient reports, and clinical trials. Ensuring the accuracy, completeness, and consistency of this data can be challenging. Data may be incomplete, contain errors, or be subject to reporting bias, which can impact the reliability of AI predictions. Additionally, there may not always be sufficient data for rare adverse events or newly approved drugs, making it difficult to train AI models effectively.
2) Data Integration and Standardization: Pharmacovigilance data is often fragmented, residing in different databases and formats. Integrating and standardizing this data is a complex task. Diverse terminologies and coding standards can lead to interoperability issues, making it challenging to create a unified dataset for AI analysis. Harmonizing data from multiple sources is essential for meaningful insights but requires substantial effort and expertise.
3) Bias and Fairness: Bias in pharmacovigilance data can significantly affect the performance of AI models. Reporting bias, where certain groups or types of adverse events are underreported or overrepresented, can lead to skewed predictions. AI systems may inadvertently perpetuate or exacerbate existing biases in healthcare. Ensuring fairness and equity in AI models is not only an ethical concern but also critical for patient safety and regulatory compliance.
4) Regulatory Compliance: Pharmacovigilance is subject to stringent regulatory requirements, and AI systems used in this field must adhere to these regulations. Ensuring that AI models comply with guidelines set by regulatory agencies, such as the FDA or EMA, is a complex and resource-intensive process. Documentation, validation, and auditability of AI systems to meet these standards can pose significant challenges.
5) Interpretable Models: Transparency and interpretability are crucial in pharmacovigilance, where the consequences of AI-driven decisions can be life-critical. Many advanced AI algorithms, such as deep neural networks, are often considered as "black boxes," making it difficult to understand the reasoning behind their predictions. Developing AI models that are both accurate and interpretable is an ongoing challenge in pharmacovigilance.
7) Class Imbalance: Adverse events are relatively rare compared to the vast amount of data related to drug use. This class imbalance can lead to imbalanced model performance, with AI systems being better at predicting non-events but less effective at identifying rare adverse events. Addressing this challenge requires specialized techniques such as oversampling, undersampling, or the use of appropriate evaluation metrics to mitigate the impact of class imbalance.
8) Resource and Expertise Constraints: Implementing AI in pharmacovigilance demands substantial resources in terms of computational power, data infrastructure, and specialized personnel. Smaller pharmaceutical companies or healthcare organizations may face challenges in accessing these resources. Additionally, building and maintaining expertise in both AI and pharmacovigilance is essential, as the field is continually evolving, and keeping up with the latest developments can be demanding.
Despite the difficulties, the advantages of utilizing AI in pharmacovigilance outweigh the challenges. By working together, combining healthcare and AI expertise, and finding solutions for data issues and fairness concerns, we can make the most of AI's potential in improving drug safety monitoring, enhancing patient care, and meeting regulatory requirements.
The integration of AI technologies in pharmacovigilance offers significant advantages, including early detection of adverse drug reactions, improved data quality, and faster response times. By leveraging AI algorithms and machine learning techniques, pharmaceutical companies and regulatory agencies can better ensure the safety of patients and improve public health outcomes. While challenges remain, the potential benefits of AI in pharmacovigilance make it a promising area for continued research, and with Daffodil's AI Center of Excellence, you can enable an AI-powered pharmacovigilance solution that delivers. To start your journey with us, you can book a free consultation today.