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Analysis Of Textual Data May Complement Traditional Pharmacovigilance

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According to a well-written systematic review on the application of natural language processing (NLP), Pilipiec et al.,​1​ concluded that the analysis of data based on texts highlighting adverse events may constitute an improvement in current pharmacovigilance analysis and related data-gathering.

The increasing amount of user-generated content on the Internet is becoming a potential source of pharmacovigilance data which, with the advent of text mining techniques and artificial intelligence, has resulted in powerful algorithms and methods for NLP.

The aim of this study was to review the existing evidence on, and the effectiveness of NLP to understand user-generated content for pharmacovigilance.

From 5318 initially selected records, the authors chose and read the 16 publications considered relevant for the systematic review. The authors highlight several important findings from their study:

  • Promising potential for the application of natural language processing for pharmacovigilance purposes
  • Many of the identified adverse drug reactions, or ADRs, were consistent with those found in the package insert. However, there were some correctly identified new, previously unknown ADRs
  • The application of computational linguistics may be useful for pharmacovigilance, as a complementary tool to retrieve ADRs shown on user-generated content
  1. 1.
    Pilipiec P, Liwicki M, Bota A. Using Machine Learning for Pharmacovigilance: A Systematic Review. Pharmaceutics. 2022;14(2). doi:10.3390/pharmaceutics14020266

The Pharmacovigilance of the Future: Prospective, Proactive, and Predictive

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Peter J Pitts, President of the Center for Medicine in the Public Interest, and Hervé Le Louet, President of CIOMS, have just published an intellectually-stimulating essay on the future of pharmacovigilance entitled “Advancing Drug Safety Through Prospective Pharmacovigilance“. The complete reference of the article is: Pitts PJ, Le Louet H. Ther Innov Regul Sci 2018; https://doi.org/10.1177/2168479018766887.

First, the authors point out that we are entering a new era in drug development. To support that statement, they refer to how the FDA is transforming its way of thinking. On the FDA guidelines on collaborative approach for drug development for pediatric rare diseases, the agency proposes new design types for rare diseases, utilizing the example of Gaucher disease. The proposed study design features include: double-blind, controlled, randomized, multi-center, multi-arm, multi-company noninferiority or superiority trial to evaluate the efficacy and safety of product A, B, C…

Other innovative approaches found in the FDA guideline are those related to the use of modeling and simulation to optimize pediatric studies, as for example to predict the effect of a drug in children based on previously known performance in adults, particularly to inform the dosing rationale.

Small frequency of the disease or the outcome under study should never be an excuse for the weaknesses of a study design. As we were taught when studying Epidemiology, if you don’t have enough cases in your center, then you should try a multi-center study. Now, the next frontier is, not only multi-center studies, but multi-company studies.

The pharmacovigilance paradigm is changing and evolving very fast, keeping up with all the new developments in artificial intelligence (AI), the analysis of real world data to obtain real world evidence, and the multiple, really diverse sources of safety information that are available today. According to the authors:

Artificial intelligence will facilitate what the pharmacovigilance ecosystem lacks today – coordinated and efficient systems for developing actionable evidence on safety and effectiveness

The field of artificial intelligence is evolving so rapidly, that I’m convinced we will pretty soon face the paradox of needing AI help for human intelligence to understand what AI is delivering.

To me, the most important point of this paper relies on the subtle comparison between what I would call the ‘old’ pharmacovigilance, which is reactive and non-anticipatory, and the ‘new’ pharmacovigilance, which is proactive in continuously evaluating the benefit-risk profile of a product, elaborating predictive models giving place to predictive pharmacovigilance.

I cannot finish my review without mentioning the most interesting and intriguing section of the paper “Inventing the Pharmacovigilance Future“. In this section, the authors present brilliant ideas they very probably can help to put into practice. I would like to highlight their suggestion of “an international effort under the tripartite chairmanship of the WHO, the ICH, and the CIOMS, to investigate, debate and develop prototype programs for drugs approved via expedited review pathways, based on more sensitive premarket metrics of risk pontential”. And the last, and most intriguing of the concepts presented in this paper, is the Real World Pharmacovigilance Score (RWPS), a baseline prediction of likely adverse events based on projected volume and specific clinical use. Many questions I have about RWPS are not responded in the paper: how is it calculated, do you have any example of application in ‘real world’? I wish they will publish a paper on this matter.

I recommend you to read the essay, eye opening and intellectually challenging.

Deep Learning, Machine Learning, and Artificial Intelligence – What are the Differences?

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In this video, Bernardo F. Nunes explains how these 3 concepts (artificial intelligence, machine learning, and deep learning) do not represent the same thing:

Bernardo is breaking down the 3 concepts for us, in a very easy and understandable way.

Artificial intelligence exists when a machine has cognitive capabilities, such as problem solving and learning. It’s normally associated to a human benchmark, as, for example reasoning, speech, and vision.

In the video, he differentiates 3 levels of A.I.:

  1. Narrow A.I., when a machine is better than us in a specific task (we are here now)
  2. General A.I., when a machine is like us in any intellectual task
  3. Strong A.I, when a machine is better than us in many tasks

One of the favorites early developments in A.I. is the perceptron (1957). It was a single layer of artificial neural networks designed for image recognition. They are called neural networks because the first practitioners on A.I. thought that these interconnected nodes looked like the human neural system, which has neural networks. These are the natural neural networks. The perceptron is a rudimentary version of an artificial neural network.

Machine learning, appeared on the 1980s, when a body of researchers worked on what is called supervised learning. Algorithms are trained with datasets based on past examples, in a model in which the trained algorithm is applied to a new dataset for classification purposes. Widely used for business purposes.

Deep learning makes use of deep neural networks. Shallow neural nets have only one hidden layer between the input and the output. However, deep neural nets have 2 or more hidden layers between the input and the output. It’s the responsible for the advancement in image recognition. If you can represent an image numerically, then you can process it with deep learning.

Bernardo’s conclusion is that deep learning, machine learning, and artificial intelligence are not 3 different things. They simply are subsamples of each other: deep learning belongs to machine learning, and machine learning belongs to artificial intelligence.