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Articles

In an ideal world, an anti-counterfeit solution would provide protection throughout the supply chain, allow for easy product identification by physicians, pharmacists, and patients, be easily implemented without ongoing costs-and improve brand image and marketability while it's at it. Yet most current anti-counterfeiting measures involve packaging technologies such as holograms, inks, bar codes and radio frequency ID (RFID) that, although useful, cannot ensure the integrity of the pharmaceutical supply chain, because drugs do not remain in their original packaging. Legitimate repackaging regularly occurs in the pharmacy and elsewhere, and authentic packaging-recycled or stolen-can contain adulterated, counterfeited drugs.

More Than Just a Pretty Color

As skepticism surrounding AI fades, pharma industry execs are ready to embrace the technology.

AI in Pharma: Where, When, and How Executives are Considering it

Ann Beliën, PhD, founder and CEO of Rejuvenate Biomed, discusses how AI is being used to find new ways to treat age-related conditions.

Drilling in on Disease Hallmarks: New Tricks to Solve Age-Old Problems

Exploring those areas and functions where the application of generative artificial intelligence—through use cases borne out in practice—are demonstrating tangible value and driving life sciences innovation to new heights. 

The Leap Forward: Practical Gains for Pharma AI Use 

Pleio’s chief experience officer discusses why these platforms are being embraced by the industry.

The Rise of Digital Platforms: Q&A with Abby Reynolds

Pharma looks to take advantage of expanded access programs across Asia-Pacific region. 

Passage to Asia: Early Access for  Innovative Therapies 

Femasys Femaseed Technology

PE: The FDA first gave clearance to Femaseed last September. For our audience, can you briefly explain how this technology works, and any enhancements that you see being made to it as time goes on?

Femaseed was purposely designed to enhance the normal transport of sperm that's required in order for conception to occur. We place sperm directly into the fallopian tube that is the site of fertilization. Our technology purposely positions balloon technology at the opening of the tube, so it's a much safer way to deliver it to the tube. The goal is to enhance something that's naturally going to occur, so we eliminate the production of embryos or retrieval of eggs. None of that occurs with our technology. It's simply placement of the sperm directly in the fallopian tube. As far as enhancements, we've been developing this product for some time, and we have thoughtfully enhanced it along the development process. 

We feel it's very important to deliver the entire specimen, so we redesigned the product during the development process to allow for filling of the device by the tip of the catheter, so that we would allow for almost all of the specimen to be delivered to the patient. In other technologies, that’s not the case. It gets filled and then you lose sample within the constructs of the catheter. We wanted to ensure that everything goes where it's supposed to be delivered as intended.

PE: Can you identify some of the main areas of unmet need within the reproductive health space and how current research efforts are working to fill those gaps?

When I founded the company, I did not want us to be a single product company. So, we've taken initiatives and we've looked at those major areas of gaps, one of which is permanent birth control. We see a lot of developments and innovations along the way, as far as temporary birth control or reversible, but at the end, when women have to make a choice, or families have to make a choice of so what they're going to do to control that risk, it’s often for a very long period of time. Most women only want a certain number of children. We felt permanent birth control was a really strong focal point, and where we've seen such a lack of innovation. That’s one of the reasons we've advanced a product called Femblock to address that need. On the other end of the spectrum, we saw great need in infertility. We've come a long way over the last few decades, but where I've seen a lot of the attention is innovation around the most expensive end of infertility care, which is in vitro fertilization. Most women can’t afford or don't want to emotionally go through what's required with IVF treatment. We felt there was a huge gap in infertility treatment on the front end that could be accessible and affordable for patients alike. We also see cancer detection tools that are very lacking from a diagnostic standpoint. So we chose to also advance technology there as well.

Videos

In an interview with Pharm Exec Associate Editor Don Tracy, Kathy Lee-Sepsick, Founder, CEO, Femasys, offers a description of how Femaseed works to support women who struggle with infertility. 

Video Series

Femasys Founder Discusses Femaseed Technology Designed to Support Infertility

View on AI from the FDA

PE: Today, you were featured in a keynote interview titled “The AI view from the FDA.” For our audience, can you briefly explain what the discussion entailed and any questions that stood out to you?

One of the points that came up that I thought was interesting was FDA’s experience with submissions that include AI and machine learning components. What I described was that since 2016, we've received over 300 submissions with AI and machine learning components across different stages of drug development, with the vast majority being in clinical research. The other topic that came up was FDA’s role in enabling this technology across the spectrum. We also discussed the fact that a lot of AI is being used in drug discovery, whether you're predicting a molecule that could be a target for a clinical trial or the folding of a protein. This is generally outside of what FDA would look at, because eventually these therapeutics would enter through clinical testing, which is where the FDA would look at this data.

The third topic that I thought was really interesting was related to how the FDA looks at the validity of these models when they're used in drug development and what areas we focus on. The way that we look at these models is that we want to make sure they are credible and trustworthy for the specific context of use. So, we would be asking questions about the data that was used to train or develop these models. We want to make sure that the data is fit for use, it's robust to be able to develop good machine learning or AI models, and we would look at the performance of these models to make sure that they are doing what they're intended to do.

PE: Recently, you led the development of a discussion paper titled 'Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products.’ Can you discuss the key takeaways of the paper?

The discussion paper was published in May 2023, and it was actually accompanied by another discussion paper that focused on the use of AI in drug manufacturing. For these discussion papers, we wanted to describe the field where AI is used in the development of drugs. What are the key considerations when you're using these tools? Again, looking at issues such as AI governance, accountability, trustworthiness, transparency, issues related to the quality of the data, and then issues related to model performance. The papers went out in May, and we received over 800 comments from 65 different organizations. We actually analyzed all of these comments, and we intend to use all of that information. We received comments from pharmaceutical companies, smaller biotech companies, and academia. We plan to use that information to inform a guidance that we are developing this year, hopefully published by the end of this year.

PE: Artificial intelligence is a fairly new concept when it comes to pharma. As it evolves, how do you see the FDA’s policy for its use in drug development changing?

For the FDA, our approach is always risk-based for drug approvals. We also do our best to be responsive to new emerging technologies. This is not just for AI if you think about the use of real-world data, or the use of digital health technologies in clinical research. In the context of a clinical trial, these are all emerging technologies that are continuously evolving. We plan to grow with all of these emerging tools as they become available. Like I said, we’ve received over 300 submissions with AI and machine learning components. Depending on the specific context of the risk associated with using that model, how much am I relying on information or data from that model to make a regulatory decision? All of these would drive the type of information that we would ask the sponsor to provide us with.

In an interview with Pharm Exec Associate Editor Don Tracy at Financial Times’ US Pharma and Biotech Summit, Tala Fakhouri, Associate Director for Policy Analysis, FDA, discusses the use of artificial intelligence and machine learning in drug development. 

Latest Conference Coverage

US Pharma and Biotech Summit 2024: Artificial Intelligence and Machine Learning Through the Eyes of the FDA

Stevanato Group Artificial Intelligence 

PE: Pharmaceutical companies have been increasingly relying on artificial intelligence to increase production volume for biologics. Can you briefly explain this process, and what makes it so effective?

When you look at the production of biologics and specifically in our field of inspecting glass containers, utilization of AI is allowing the company to focus more on the quality of their products, also getting a higher quality product out for patients. This is because AI is used in specific tools to help identify the most critical areas of the application with that system. Therefore, it is absolutely possible to discriminate much better from what is a true defect and what is not. It avoids costly re-inspection and more in general with the data collection, it's also possible to improve on that. 

PE: How exactly does AI contribute when it comes to keeping supply chains organized and how does it help to prevent potential disruptions?

This isn't in our field of application, as we don't work much with supply chains in terms of application AI. However, even if you still compare it to being able to minimize the waste and unusable products, it will facilitate the supply chain allocating some of the needs that are in that field. 

PE: In its current state, do you believe that there’s potential for generative AI to make substantial mistakes regarding the development of biologics?

We work i a very controlled environment that is FDA approved. The use of generative AI is not yet implementable in the area where we are. Basically, the utilization of the AI that we have is used before the inspection. What this means is that while in the future, there might be some development in this area but right now, it is something that is used upstream from where we come into play with the AI. 

PE: As artificial intelligence continues to develop, where do you believe improvements can be made regarding production volume in the next decade?

We are at the beginning of utilization for AI in many fields. I'll give you a brief explanation of how it is done for us. We use AI to really zoom in and rebuild the tool that is then inserted inside of our equipment that is AI generated, and that tool is frozen. The complexity of that tool is limited, because when we inspect containers at the rate of 600 per minute, we are talking milliseconds of time to do the elaboration. Where the technology is going to help us is in developing more powerful miniaturized computers that will allow more complex models to be used. Therefore, AI will become more effective in the future. 

In an interview with Pharm Exec Associate Editor Don Tracy, Alessandro Zannini, Global After-Sales Manager, Stevanato Group discusses the benefits of generative artificial intelligence. 

The Support of Artificial Intelligence in Biologics Production Volume: A Conversation with Alessandro Zannini, Global After-Sales Manager, Stevanato Group 

A conversation with Dr. Jo Varshney, CEO & Founder of VeriSIMLife

Using AI to Solve Drug Shortage Issues

What’s causing drug shortages right now?

So, the causes for drug shortage are several, one of them being high demand for a certain type of drug, which means that the manufacturing and the QA folks have to increase the production and it's often not that straightforward. The second is increase of generics. So, when a drug gets the pants is expiring, you get generics in the market, but which are much cheaper than the brand. But also, that means the incentives to manufacture generics is lacking. Because the margins profit margins on these generics is slim. So, it really comes down to only one or two manufacturing plants generating those generics. And one key example is in the cancer drugs. So unfortunately, given there's no commercial incentive to pharma to cream to generate a lot of generics, the patients suffer. And lastly, it's really the way the supply and demand work within the drug development cycle and what happens in the distribution of the drugs.

Is there a ‘light at the end of the tunnel’ to these shortages or will they continue?

Unfortunately, I don't think there's going to be a light at the end of the tunnel. And in fact, it's going to have more and more significant repercussions as we get more drugs off banned. There's more demand for certain types of drugs and having production challenges and quality control challenges to manufacture drugs. I just don't see that this issue is going to be resolved anytime soon.

How can AI/Machine Learning help solve drug shortage issues?

Definitely, I'm biased since we are an AI company. But I do believe that AI is not going to solve every problem in healthcare. But the challenges that are we seeing in drug shortage such as what do we do to create financial incentives for companies so that they help in manufacturing production, this is where AI can help improve the manufacturing efficiency. And there are several ways that certain companies are tackling this challenge. The other is the parent challenges, right, so each drug comes with the parent lifespan. So, if AI can help increase that lifespan, so for example, our technology helps improve the formulation or a change of route of administration. And those all elements can improve the patent extension, and that that can help resolve some of these drug shortages challenges. And the third is increasing the number of drugs coming to the market so that the pharma companies don't have to rely on one or two mega blockbusters, since you probably are aware of the R&D to create the drug to the market is very expensive and very long. So, if there's any way to cut down the time, and improve the odds of getting drugs, more drugs into the market, it's a win-win for everybody. That means when the generics come into the market, the companies are not feeling financially strapped to not do nothing about it. In fact, there are other drugs in the pipeline or in the market, that they can generate revenue. And the and also enable and help manufacturing plants and do up those, you know, increase the production of those generics. So, I think those are all the big places, AI can be very helpful.

AI isn’t the only component needed to solve this problem, so what else will it take from industry?

This is a pretty complicated question and a very, I will be giving a very complicated answer. The there's a lot of things need to change to really resolve drug shortage. One is specific reforms that need to happen in the drug development from how the incentives are tied to a drug to how what manufacturing plants need to generate or produce that number of drugs to getting aligned with the government agencies to enable How do we create a win-win solution. So, the economic reforms are needed to you know, be it could be specific funding from the government to enable production There's mass production of these generics or increasing the manufacturing plants and helping boost up production of drugs that are much needed by the patients. And lastly, I think having more drugs to the market is a big incentive to help reduce the shortage.

With drugs like Ozempic and Wegovy significantly increasing in popularity amongst patients just looking for that quick weight loss fix, instead of its intended use, how can the industry avoid shortages for therapies like these blockbuster drugs?

Yeah, this is a very important conversation in the society we need to have. I think it's not just for drugs like Ozempic, but also for mental health drugs, there's been an insane amount of abuse in a way to drugs because somebody comes and says, like, hey, you know, I have read game challenges. So, if they get prescribed drugs, psychosom piglet without knowing, I mean, they know the side effects. Every drug comes with the side effects, but they don't really know the implications of being on a drug for so long. And those are real challenges that as a society, first, we need to address that, do you really need? And how do you qualify for a drug like that, and then really, taking steps backwards, like type two diabetes, there's a lot of nutritional and weight gain challenges. So, the big four, first couple of things you need to do is really get on a regime of diet that works in favor of reducing the insulin resistance that your body is generating, versus getting a drug, which is sort of like a Band-Aid, to a larger problem. So, I think having that clarity and a clear checklist of what group of patients shouldn't be given a certain type of drug is a very much needed conversation. And unfortunately, you know, the incentives for pharma companies to do so it's really nothing because you know, if you have lots of patients on the drugs, it's a huge quarterly gain as you can see in the market. So, it is it is a tough problem, but I think it needs to start from the patients and the doctors to really have an open conversation whether or not you need drugs to come back for weight loss.

In this video interview with Pharm Exec Associate Editor Miranda Schmalfuhs, Dr. Jo Varshney, CEO & Founder of VeriSIMLife, discusses how artificial intelligence and machine learning can assist in the drug shortage problem. 

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