Automation in pharmacovigilance is nothing new. Drug companies have been using it for years to speed up the process of monitoring the safety of their products. But with the advent of new technologies, automation is becoming even more important in this field. Here’s how automation is helping drug companies save time and money.
As electronic health records become more prevalent, it is important to ensure that patient safety is not compromised. The World Health Organization has outlined some key risk management strategies for clinical research using electronic health records.
These include ensuring that data is complete and accurate, ensuring that patients are properly consented to, and ensuring that there is an audit trail of all data changes. By following these guidelines, we can help ensure that electronic health records are used safely and effectively.
What is Pharmacovigilance
Pharmacovigilance, or “pharmacovigilance research,” means taking a drug’s safety data as it is accumulated, and either giving the drug a clean bill of health or conducting additional studies to investigate potentially worrisome effects. From 2021 to 2028, the worldwide pharmacovigilance market is expected to grow at an 8.8% CAGR.
One of the primary reasons pharmacovigilance is important is that it provides guidance to the company on what adverse events to look into further. For example, suppose a new drug is approved for use in children and adolescents but toxicity data show that liver dysfunction occurs almost exclusively in adults. Does the company then shelve this drug? Probably not. It will investigate further just to make sure no infants are being harmed by its use.
The same applies to drugs in other therapeutic areas. For example, suppose a drug is safe and effective for treating asthma, but it only works for adults and children older than six. Based on this information alone, the company may decide that there is no reason to take any action.
However, if the company finds out that asthma is especially prevalent in adults 19 years and younger, it can investigate further to see if perhaps the young people have an underlying problem or condition that may have led them to develop asthma in the first place.
What are Pharmacovigilance Activities
In 2020, the global pharmacovigilance industry was estimated to be worth US$ 6.1 billion. Drug development is the process of developing novel medications and bringing them to market. Reviewing adverse events such as accidental deaths, but especially serious ones (i.e., death and near-death).
Monitoring safety at the time of sale, specifically for reports of serious AEs. This includes monitoring for signs that a product may have undesirable effects such as birth defects or impaired fertility and reproductive systems, fungal or bacterial infections, or cancer. Monitoring for signs of overtreatment means that physicians are prescribing products for uses outside of their approved indications.
Pharmaceutical companies go through many years of testing and approval before their product is available to the general public. During this time, new adverse events may be discovered that should have been flagged earlier on in development (such as in animal studies or during human clinical trials).
If they’re severe enough, they can sometimes require serious regulatory intervention such as drug recalls or an outright ban on the sale of a medicine. To prevent this from happening, researchers and scientists use a wide variety of safety tools during product development to identify potential hazards in advance.
What is the Role of Regulation in Pharmacovigilance
Healthcare professionals play a vital role in enhancing patient safety by reducing the incidence of adverse drug events. In the life sciences, an adverse drug reaction (ADR) is defined as an unwanted or harmful reaction that occurs after the administration of medication. The incidence of ADRs is increasing as the population ages and the use of medications increases.
Adverse drug reactions (ADRs) are a major patient safety concern. Drug safety is monitored through safety data from clinical trials and post-marketing surveillance. Despite these efforts, ADRs remain a significant cause of morbidity and mortality.
Pharmacovigilance activities can be regulated by the FDA. This means that they also take a very active role in monitoring every product that’s sold in the United States. If a problem occurs, they are required to immediately begin an investigation into the causes and how it should be resolved.
In most cases, this is left to drug developers themselves because they have a lot of expertise on the subject. However, if they fail to properly address a problem or if they misrepresent their information, it can result in serious regulatory penalties.
The Problem with Manual Processing
When you think of the word “manual,” you probably don’t think of efficiency. And when it comes to pharmacovigilance, it’s hard to argue that using a manual approach is efficient. After all, the whole reason for monitoring adverse events (AEs) is to spot potential problems that could affect the health or well-being of patients.
If monitoring takes too long or is done incorrectly, patients could be exposed to unnecessary risks. In extreme cases, adverse events could lead to drug recalls and serious financial consequences. Thankfully, automation is helping drug companies avoid these dangerous situations by taking human error and other factors out of the equation.
Signal detection is the process of identifying potential safety concerns from data collected during the development, marketing, and post-marketing of pharmaceutical products. Adverse events are any untoward medical occurrences that may present during treatment with a pharmaceutical product, regardless of whether they are considered causal.
Pharmaceutical companies are required to report all adverse events that come to their attention to regulatory authorities, who then assess the need for further investigation.
What is the Role of Automation in Pharmacovigilance
According to Grand View Research, the worldwide pharmacovigilance market would increase at a CAGR of 11.5% from 2021 to 2028.
Automation is essential in helping with the entire process of product development, from discovery to marketing, because it can speed up and improve every step. In addition to saving time and money, managers say that automation helps to keep their databases clean and accurate.
By doing more of the work on its own, automation ensures that there is less chance for human error or oversight in adverse event investigations. The result is a more reliable database of potential problems. If a change needs to be made, it happens quickly and efficiently because most of the work has already been done by automation tools.
It’s not just the actual data analysis that’s carried out by automation, but also the research and design processes. Drug development is more complex than it seems to be. Products being developed today have hundreds of different ingredients and combinations of ingredients in their formulations.
This makes it more important than ever that companies have a good idea of how those ingredients will interact with each other before they produce a final product. This can be hard to do perfectly, but after years of trial and error, a new medicine can be designed that incorporates all of the desired actions at once.
How Automation Helps with AE Monitoring
Automation has been around for years and is used for a lot of different things. But in pharmacovigilance, it is the latest and greatest innovation that has proven to be most effective. One of the reasons automation is able to do so well is because it’s designed to act like a human.
It helps drug companies review their databases and processes in real-time and makes use of sophisticated algorithms that are based on experience, research, and data collection. This type of automated approach can do what manual screening fails to do: eliminate much of the work that goes into monitoring adverse events. It can, for example, gauge the likelihood of an AES before it’s filed.
This means that, instead of having to wait for a reviewer to spot the potential problem and decide whether to file it, automation can do all the work on its own and report any problems that are discovered. The result is a faster and more efficient process. But once again, it’s not just about speed; there are important financial aspects.
With fewer mistakes, drug companies can reduce their spending on adverse event monitoring. As a result of automation, they can also cut out redundant monitoring and reduce the amount of time they spend on research and design processes. This direct savings has helped drug companies save money in other ways as well.
How Automation Helps with Product Development
The only thing that’s worse than an AE is a serious AE. There are many things that can lead to serious adverse events, including drug-drug interactions, allergic reactions, and viral infections. For each of these situations, drug companies need to know if the problem is occurring in their product and what steps they should take to stop it before it spreads to other patients.
That usually means conducting more research and developing new versions of the products to ensure safety (if at all possible). But with automation in place, drug companies can save significant time and money without sacrificing the quality of their products.
One way this happens is with drug development. Before a new product can be released, its efficacy and safety need to be tested. This process is costly, not just because of the money spent on studies but also because it involves extensive monitoring for potential problems. However, once an AE is discovered, there’s no time wasted re-testing a known risk.
Instead, the company can immediately seek out the manufacturer of the related medicine and ask if they have any reports on the issue or ways to prevent it from happening in the future. If they don’t have anything, they can immediately start looking into ways to produce a similar product without using that troublesome ingredient.
5 Benefits of Automation in Pharmacovigilance
No human errors: Most of the work in developing a new drug is done by machines and robots. This includes tasks such as performing huge amounts of data analysis, conducting research on other drugs and creating experimental models. All of this means that errors are less likely to occur when something goes wrong with a product.
This means fewer misdiagnoses or mistreatment due to an inaccurate database or incomplete information. Almost two-thirds of respondents from large pharmaceutical companies and nearly half of respondents from small and medium-sized enterprises (SMEs) said they used a standalone pharmacovigilance automation solution.
Most of the time, machine learning software is still incomplete. It doesn’t always have all the information necessary to perform certain tasks perfectly and will inevitably miss things that computers are able to see very clearly. However, computers are trained to recognize certain patterns and make their best guesses as to how they work.
These predictions can be used to further refine the training process so that future data is more likely to fit certain patterns. It also means that even if a problem doesn’t exist in the dataset being analyzed, the analysis of past data is still applicable.
2. No Human Input
Even though automated processes rely on data and information from humans, this does not mean that there is human input required for the entire process. Most businesses have protective measures in place to ensure that the most important processes are done by people who are well educated and trained in those specific areas of expertise. All other tasks could be performed by machines or robots without risk of error or oversight.
3. Increased Input
Since there are fewer errors, human intervention is also less necessary. This can be used to reduce the workload of employees that don’t require a large amount of extra work, or to allow for more time in other areas that need attention. A reduction in workload could also mean a reduction in turnover.
4. More Research
There should theoretically be no limit on the amount of research that can be done by a machine learning solution because it can do it all faster and cheaper than any human being could hope to do. This means that products should always get better and safer over time as new information comes to light and is incorporated into future models.
5 Challenges of Automating Pharmacovigilance
Only 6% of all respondents thought a lack of pharmacovigilance automation would be a “significant disadvantage” to their firm.
No matter how much you research artificial intelligence, it will never be able to make the right decision on its own. The data that these programs use must be real and should include accurate information about actual people and the problems they deal with. There is always a risk that this data may not be completely up-to-date, but if it’s properly maintained, that problem shouldn’t occur.
In order to ensure that the drug is being used only for the condition for which it was approved, there needs to be a “black box” functionality in place. That way, all of the inputs are closely monitored and any data can be analyzed by humans for safety purposes only.
3. Computational Complexity
Because the system is used to review the data and then reach a decision, it must be able to handle more information than a human can. This may mean that systems will have to become more sophisticated and complex in order to make decisions on their own.
Just because this information can be fully automated, doesn’t mean that it will be seen as legitimate by humans. It only works if it’s perceived as being legitimate and accurate by the people reviewing it. That’s why it’s important for developers to build machine learning on top of existing databases and incorporate them into a single platform that can optically analyze all of the data.
5. Social Acceptability
The general public needs to be able to trust that the information being reviewed is accurate and can be trusted to accurately assess risks by itself. If a problem does occur, people need to know that it’s due to a real problem with the product and not an isolated error.
Future of Automation in Pharmacovigilance
Since artificial intelligence is still fairly new, it’s difficult to determine how society will react if these technologies are fully incorporated into every industry. It’s possible that in the future pharmacovigilance may not be necessary at all.
However, that does not mean that companies should stop seeking new ways to improve the production and testing of drugs or to see how far artificial intelligence can go.
The global pharmacovigilance industry is anticipated to be worth $11.5 billion by the end of 2028. As the field continues to develop, there will be more opportunities for pharmacovigilance professionals to seek out positions in companies that use this technology.
In order to get ahead of the curve, they should learn all they can about machine learning and artificial intelligence in order to understand how it could work with their individual careers.
Pharmacovigilance professionals should continuously try to find ways to improve their own personal skills and work practices so that they are always a step ahead of the competition.