The Rising Tide of Biopharma Manufacturing Costs

The biopharmaceutical industry is facing a rising tide of manufacturing costs, particularly for complex therapies. Can you elaborate on the specific factors contributing to this trend? Are there any underlying issues in today’s current manufacturing landscape that digitalization can address?

I think we're moving to an era where the companies have to make products in low volumes, but with a high mix of different drugs, to treat all the different patients and all the different disease areas that are that are currently, you know, where there's currently a market. And, you know, 20 3040 years ago, the model in the pharmaceutical industry was high volume production of a low mix of different drugs in one factory, right, so a factory might be producing just one drug for heart disease, but we'll be producing in an extremely high volumes. And that high volume low mix meant that you had economies of scale, and you could use big production vessels, you know, big purification vessels, your there's one methodology, once people were trained up, that became very proficient in making that product. And you just had efficiency from that kind of simplicity and centralization. Now, you have a situation where things are constantly changing, your drugs are coming into the factories quicker, the factory has to adapt, they hardly have time to optimize one process where they have set workmen and other people are just about trained up on one process when they have to be trained up on another. And just when you think you have all the technology that you need to make all the drugs in the company's portfolio, a new modality comes along, like mRNA came along, during COVID, where companies had to kind of get ready to produce those products at scale.

So, I think this is constantly moving target of new technology coming in, in terms of manufacturing. And, you know, that challenging getting to economies of scale. So, I think the use of digitization has huge potential to help companies deal with that mix of, of, of modalities and products that they're making, and all of the recipes and all of the standard operating procedures that workers have to follow, they can be digitized. And so, move away from paper, and maybe kind of human memory or skills that are required to do specific tasks, that becomes untenable if there's so many different tasks to do. So, if you can use digitized standard operating procedures, digitize recipes, digitize methods, and then almost guide the worker through the execution of those methods that will reduce the time it takes to train people, and where they're maybe not very proficient, and also reduce the amount of errors which are is resulting in very costly waste in the farm industry. And so, you get execution of all this big mix of complex processes at lower cost and lower waste and greater sustainability.

I think, as we touched on earlier, you can also use advanced analytics to give cues to the people working on these processes or working on the supply chain on how they might make things better, because very often those you know, those improvements were made using tacit knowledge over time experts kind of figured out empirically how they might improve processes. But now, an algorithm may be able to suggest a way to improve productivity in a way that a human might not think of or might take longer to come up with that idea, so you get to those higher productivity approaches faster.