Manufacturing complex pharmaceutical products with new technologies
As pharmaceutical products become increasingly complex, companies can leverage new technologies in their manufacturing process to improve efficacy.
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- Scientific advancement and increasingly complex pharmaceutical products have reshaped the healthcare industry, providing novel tools and solutions for managing broad conditions, from acute viral infections to chronic diseases to oncology. As science evolves, developing increasingly complex pharmaceutical products, manufacturers and biopharmaceutical companies must evolve their strategies, tools, and procedures to keep up with the rate of scientific discovery.
Pharmaceutical Product Complexity Challenges
Despite the seemingly never-ending benefits of pharmaceutical development, complex medicines and products pose many challenges for manufacturers and pharmaceutical companies.
Cost
One of the chief challenges is production costs. Many industry leaders maintain that research and development (R&D) costs are increasing dramatically. For example, a 2022 Deloitte Insights report revealed that bringing a drug to market costs roughly $2 million.
However, the high costs of developing a product expand beyond the research and development process. Complex products are more labor intensive, require more precise steps, and result in greater manufacturing costs.
“Increasing complexity means that the drugs are typically more complex to manufacture. More complexity usually means more cost. Also, because [new drug innovations are] more complex, they tend to be less stable, so their shelf life tends to be lower. They have to be very carefully curated from beginning to end of the supply chain,” Barry Heavey, PhD, Accenture’s Life Sciences Supply Chain Lead, told PharmaNewsIntelligence in a recent interview, touching on Accenture's ongoing work to improve manufacturing processes for complex pharmaceutical products.
Smaller Drug Batches
Another challenge posed by more complex medications is that pharmaceutical manufacturers have had to move toward making smaller drug batches.
“We've been talking about personalized medicine for 30 years. It's almost an overutilized term, but it is happening now. [Companies are] making batches of a drug like CAR T-cells for literally one patient,” added Heavey.
An article in the Manufacturing Chemist notes that many advanced therapy medicinal products, including cell and gene therapies and bioengineered tissues, cannot be produced in huge batches because they are highly specialized.
Having to produce these more individualized and patient-specific therapies and medication shifts industry standards, which have previously used larger reactors or vessels to make sizeable batches of medication. Instead, increased complexity has driven companies toward developing smaller batched products.
Quality Assurance
Another challenge posed by increasingly complex pharmaceutical products that may be specialized for each patient is quality assurance.
“People forget that [companies] have to manufacture these drugs and to do quality control and quality assurance on those drugs,” Heavey continued. “That can be more onerous and more challenging when the drugs are more complex and take a lot of time and a lot of resources. That adds cost and complexity to the supply of these products.”
Addressing Challenges in Complex Drug Manufacturing
Despite these challenges, Heavey remains optimistic about the future of pharmaceutical manufacturing for complex biopharmaceutical products, emphasizing that existing knowledge and ongoing innovation will help address some of these challenges.
“The hope would be that — using a combination of scientific brilliance and advances in data gathering and data analytics — the process of understanding and optimizing the biology happening inside the manufacturing facility will be quicker.”
Using the new modalities to get to the appropriate molecule quicker also ensures that medications get to patients faster.
Robotics
One factor that may help ease the manufacturing burden of newer, more complex medications is using robotics in sample processing.
“Researchers can build robots in a factory that can be sent out to more hubs globally so that more patient samples can be processed in more locations. Then [they’re] not bottlenecked through a few human-based production facilities.”
He compared it to the strategies used in in vitro diagnostic testing. At one point, healthcare professionals had to take samples and send them to a distant, specialized lab, wait for the results to return, and so on. Today, many large healthcare facilities have in-house technology to process samples and provide results.
“That happens quite quickly in big health centers using advanced robots from diagnostic companies. That might happen in the cell and gene therapy space [down the line], where some aspect of the cell processing happens locally in the hospital or close to the hospital,” he noted.
Data Gathering
Beyond that, data gathering systems have increased dramatically, with an increasing ability to do more detailed off-line data gathering. For example, the advancement of high-performance liquid chromatography (HPLC) machines, mass spectrometry machines, and other devices have helped the detailed characterization of samples, allowing companies to accurately assess what is going on through the manufacturing process.
Another example is whole genome sequencing of cells, which is also typically done off-line.
“Companies can then use that data to help interpret your results. [They] can do detailed characterizations of the product and use that data to interpret your results or detailed testing of your raw materials to see if they're varying,” noted Heavey. “So the data gathering technology of lab instruments has developed massively in the last few years for off-line analysis of the process manufacturing process.”
In-Line Testing
Additionally, there are ways to test the process in-line. Companies can use sensors like RAMAN spectroscopy to examine what is happening in real time. That allows companies to pause the manufacturing process and make changes if something is incorrect.
“In-line data gathering is used for real-time understanding of the process because taking a sample and going away and doing off-line means there's a delay and [results may be slower].”
Advanced Analytics
“Then the third piece of that is advanced analytics modeling, multivariate statistical analysis, AI, and neural networks,” noted Heavey.
In-line data gathering, off-line data gathering, and advanced analytics modeling “create a kind of virtuous circle.” Gathering more data and feeding them into the models allows them to begin interpreting and identifying areas that need adjustment.
Case Study: Monoclonal Antibodies
To provide an example of the progress made in complex biopharmaceutical manufacturing, Heavey pointed to monoclonal antibodies.
“They were new in the 1990s, and at the time, the manufacturing processes were super inefficient,” he said.
Initially, companies manufacturing these products had to use large production facilities to develop very small quantities of these monoclonal antibodies. Heavey estimated that the production cost $10,000 per gram early in monoclonal antibody manufacturing.
“Now they've used a bunch of hardware-based technology and some scientific experimentation to boost the productivity of the manufacturing processes using science and engineering,” he expanded.
Today, instead of producing small quantities of expensive monoclonal antibodies, companies can make tens or hundreds of kilograms per batch at significantly lower costs.
As a result, these drugs have been able to reach more patients, providing wider spread benefits.
“Companies have invested in research of the biology of those cells to try and improve productivity, keep those cells alive longer, more productive, and more consistent in what they produce to speed up the end-to-end manufacturing process,” he explained. “Some of the same scientific ingenuity can be applied to new modalities such as viral vector production, which also require cells or stem cell production.”
Being able to understand and manipulate the biology of cells inside the manufacturing factory is critical. Unlike when monoclonal antibodies were first introduced, today, companies have more data and data analytics capabilities that allow them to conduct more efficient experiments and manufacturing runs, which helps them deconvolute the biology of cells faster, Heavey told PharmaNewsIntelligence.
Editor's Note: This article has been edited to make some corrections about the type of spectroscopy and adjust the comparisons made under the "Robotics" section.
