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Latest Executive Roundtables

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Sales and Marketing

1rem

A panel of experts examines how stakeholders can support strategies that improve patient outcomes, while also navigating various distribution models.

Asembia 2025: Rethinking the Drug Distribution Process

IQVIA’s Doug Long offers a lay of the land, including trends and developing issues that are shaping the current market. 

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Investment includes $1.5 billion in capital expenditures to expand manufacturing and $500 million dedicated to R&D focused on high-impact innovations.

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Maintaining Supply Chain Resiliency, No Matter the Circumstances

Marcel Botha, CEO of 10XBeta, identifies the most significant technological innovations in medical device manufacturing that will be crucial for future pandemic preparedness and supply chain stability.

Crucial Technological Advancements for Future Pandemic Preparedness and Supply Chain Stability

 video interview, Marcel Botha, CEO of 10XBeta, discusses shortcomings in the US' emergency plans for rapid innovation and manufacturing during public health crises, highlighting the need for innovation pipelines, supply chain resilience, and regulatory clarity. He also emphasizes the importance of localized manufacturing, public-private partnerships, and advanced manufacturing techniques like AI and additive manufacturing. Botha notes the success of the Spiro Wave project in reducing ventilator size and the need for better coordination and funding structures. He also advocates for a more agile and collaborative approach to ensure rapid response in future emergencies.

Pharmaceutical Executive: Looking forward, what are the most significant technological advancements or innovations in medical device manufacturing that will be crucial for future pandemic preparedness and supply chain stability?

There are lots of interesting things coming down the pipe. Obviously, it's been two years of AI mayhem. Going from machine learning and large data lakes, to actually have an understanding of where our data can actually have an impact on these LLMs and critical feedback loops with AI tools. AI will play a huge role in everything from biotech to drug development, both discovery orphan drug reuse or repurposement and medical device design.

When I look at AI as a tool to reinforce what we build, it plays a role from the very inception of an idea all the way to regulatory clearance and post market surveillance. It plays a role in how we think about risk and risk analysis, and how we analyze large swaths of data to make sure that we can be risk averse and have a clear understanding of the potential risks outside of just a human assessment. 

Does the machine mean that we don't have a human in the loop? Everything we do, a human has to look and scrutinize what comes out of the trained AI data, but it's advancing at a speed faster than we can learn. It's like when Bill Gates wrote a book, 

 in 1997, which was a comment about how fast we're going to be learning with the age of the internet. This was first.com, and I would say that we are definitely at an age where AI has surpassed the speed of thought.

What is interesting is what type of innovation, opportunities, challenges, objections, or enlightenment we're going to want to get from this. AI is also affecting how we design physical product, how we scrutinize physical product, how we do critical, finite element, and flow analysis. Any tool that we use today can be augmented with an AI layer. Some of them are extremely clunky. I think we're in AI version 1.0 right now, and so I'm very excited to see where we are a couple of generations later, and where it becomes an extension of our intellectual capacity to contribute to the sector.

Anything with data or any physical device that is connected has a data stream, and you can build intelligence layers on top of any data stream. Real time monitoring brings the ability to move a lot of health care to the home, then intelligent systems like AI and ML, that can be run on top of those data streams from those devices in real time, bring the ability to reframe the quality of care. I see a future where we have better care at home than we have in the hospital today. That's an interesting future where we move from a reactive medical industry to a proactive medical industry.

Lastly, we see definitely opportunities for improvement. Beyond AI, there's many other opportunities that we can look up. I've touched on additive manufacturing to death, but we have advanced manufacturing, robotic manufacturing, new chemistries, new polymers, and organic chemistry. 

All of these things will affect how we make products in the future, and all of this is driven by new technologies that are coming on board. We're also seeing a lot of flexible manufacturing approaches. A lot of the sort of collaborative yet competitive, network manufacturing approach that I spoke about in terms of building a resilient ecosystem, from having the tools that can manage the supply chain risk and supply chain response and manufacturing response at a higher than single entity level.

When we have tools, especially with public private partnerships that can assess industry-level flexibility, response, and resource management—especially during disaster relief times—we have an extremely valuable network effect after of technologies and technology companies that can collaborate, compete, co-locate, depending on what season it is. Is it disaster season, or is it Christmas? We need to be able to have that agility, that's what's been missing from the US manufacturing ecosystem for the longest time.

Videos

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. 

Supply Chain

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