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Analysis Of Textual Data May Complement Traditional Pharmacovigilance

March 6, 2022 by Jose Rossello 2 Comments

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.
Pilipiec P, Liwicki M, Bota A. Using Machine Learning for Pharmacovigilance: A Systematic Review. Pharmaceutics. 2022;14(2). doi:10.3390/pharmaceutics14020266

Jose Rossello

Post-Randomization vs On Treatment Made All the Difference

September 15, 2020 by Jose Rossello 1 Comment

The Food and Drug Administration (FDA) recently requested withdraw of Belviq® and Belviq XR® (lorcaserin and extended-release lorcaserin) from the U.S. market.

When the FDA publishes a note in the New England Journal of Medicine, I pay attention to it. There are many lessons to learn, but also some questions to ask. On this occasion, the FDA explains why a product had to be withdrawn from the market, based, among other issues, on the detection of a safety signal for cancer. The signal appeared from a phase 4 study designed to address cardiovascular safety (MACE).

The following is a short timeline which can help us understand the course of events:

December 2009: the applicant submits marketing application for lorcaserin. The FDA did not approve it, in part due to non-clinical carcinogenicity studies revealing and increased incidence of tumors in rats exposed to the drug.
December 2011: the applicant submitted additional non-clinical and clinical data.
May 2012: FDA approval, with the condition of conducting a postmarketing study focusing on cardiovascular safety.
2014 to 2018: CAMELLIA-TIMI 61 trial was conducted.

Subsequently, the FDA performed a safety analysis of the study and identified a potential signal of increased cancers and cancer-related mortality:

“In contrast to the published study, when assessing cancer incidence, the FDA considered all postrandomization adverse events, not just ‘on treatment’ events.”

Demographic and clinical variables were balanced between the treatment groups at baseline and during the course of the study.

This was a long-latency safety signal, in which cancer numbers were elevated for lorcaserin for all latency periods beyond 180 days. The number of new cancer cases was similar in the two treatment groups for the first 180 days.

Benefit-Risk Evaluation

The FDA weighed the drug’s benefits against the excess cancer risk. The difficulty of mitigating that risk, and the uncertain clinical benefit led them to conclude that the benefits do not outweigh the risks.

To provide a little more context, following are some highlights from the U.S. label and from the trial:

Lorcaserin FDA Label

In the U.S. package insert there is no mention of cancer on the Adverse Reactions section. However, in section 13: Nonclinical Toxicology, Carcinogenesis subsection, it is stated:

Increase in mammary adenocarcinoma in female rats
Increase in mammary fibroadenoma in female rats at all doses
In male rats, treatment-related neoplastic changes in different organs

The CAMELLIA-TIMI 61 Trial

Title: A Study to Evaluate the Effect of Long-term Treatment With BELVIQ (Lorcaserin HCl) on the Incidence of Major Adverse Cardiovascular Events and Conversion to Type 2 Diabetes Mellitus in Obese and Overweight Subjects With Cardiovascular Disease or Multiple Cardiovascular Risk Factors (CAMELLIA-TIMI).

Arm/Group description: Participants received lorcaserin HCL 10 mg or lorcaserin HCL placebo-matching , tablets, orally, twice daily for up to 52 months.

Adverse Events Time Frame: From baseline up to 30 days after last dose of study drug (approximately 56 months). This constitutes the “On-Treatment plus 30 days analysis set”.

Study-Specific Events: Malignant neoplasms (with the exception of basal cell and squamous cell carcinomas of the skin), among others, were considered study-specific events, per protocol.

Comments

The following are my personal, independent thoughts.

Even though there were non-clinical results possibly indicating a higher risk of neoplasms, the end result was very difficult (if not impossible) to predict. The phase 4 study was designed with the specific purpose of responding to the cardiovascular safety question. In spite of that, cancer-related adverse events were given special attention in the protocol, but this was not enough for them to detect a safety concern. Twelve-thousand patients were studied, however the 95% CIs for the rate ratios were still not clearly statistically significant.

Should all studies analyzing products with a potential risk of causing cancer, allow for sufficient follow-up time as to reduce attrition risk?

Could an analysis of FAERS data have revealed that signal earlier?

Treatment and placebo groups were well balanced in terms of demographic characteristics and some clinically and epidemiologically relevant risk factors, which is expected.

But, what about the analysis of potential differences between patients who acquired cancer and those who didn’t? This is not going to change the outcome, obviously. But machine learning techniques applied to these safety data could help understand the differences between the 2 groups (cancer vs no cancer), both in treatment and placebo arms. The knowledge gained as a result could open our minds to a whole world of possibilities.

Jose Rossello

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

April 6, 2018 by Jose Rossello 3 Comments

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.

Jose Rossello