The Value of Streamlining Manufacturing

Drug manufacturing makes up a significant part of what it costs to develop drugs. As expenditures for ingredients, power, equipment, rent and staff increase, companies are working hard to find ways to cut outlays through streamlining. It's not just about money, though – making manufacturing more efficient reduces waste, gets drugs to patients more quickly and combats drug shortages.

Adding automation and robotics

Manufacturing can involve hundreds of steps and many repetitive processes, and these need to be carried out efficiently, accurately and reliably to reduce error, improve quality, maintain batch consistency and keep patients safe. Using automation reduces the chance of mistakes and allows processes to be integrated. Automation supports the safety of manufacturing staff by tracking personal protective equipment (PPE) and monitoring climate. It also helps companies meet regulatory standards. [1]

Robotic process automation (RPA) is a key part of automation in pharmaceutical manufacturing. Robots can handle active pharmaceutical ingredients (APIs), including high potency APIs, protecting staff from harm. Using robotic technology also reduces the risk of microbial contamination by operators, particularly important for sterile products produced in isolated environments, and for therapeutics used in highly vulnerable patients. Robotic production lines can also check for inaccuracies and errors in filling and packaging, and make corrections or reject products. Some equipment can allow an operator to intervene robotically from outside an isolation unit to make a correction, change a filter, clean up broken glass or fix a jam without breaking the barrier. [2]

Process analytical technologies (PAT) can be integrated into production, reducing the need for manual sampling, freeing up the time required to send samples to external laboratories, cutting the number of rejected batches and allowing continuous improvements in processes. As well as in-line/on-line analytical instruments, PAT tools include multivariate data acquisition and analysis software, and knowledge management systems [3, 4]

Batch and continuous manufacturing

Traditionally, pharmaceutical manufacturing has been carried out in batches. The batch release quality assurance process involves testing and accurately documenting a batch of drugs, which is then certified. Because this focuses on accuracy, it can reduce errors, ensure smoother workflows and improve compliance with good manufacturing practice (GMP) and regulatory guidelines. However, the sops and starts between batches can cause delays.

• There are processes that can streamline batch release: [5]

• Standardising documentation to ensure consistency and clarity

• Automating data collection to minimise errors and save time

• Training staff and keeping them updated

• Carrying out internal audits to identify inefficiencies and gaps, and correct errors early

• Using digital tools to track the manufacturing process in real time

In continuous manufacturing, materials are constantly fed into the production process, and products are continuously removed. This means that production lines can run continually around the clock. By cutting the downtime between steps, continuous manufacturing can make the manufacturing process faster, more efficient and more cost-effective compared with batch manufacturing. Fewer manual steps are required, so the risk of error and contamination is reduced. The flexibility means that quantities of product can be adjusted by making changes to process runtime or ingredient flow rates, reducing the need to maintain high levels of inventory and therefore cutting storage needs. Continuous production lines are often smaller, so manufacturing plants can take up less space, or manufacturers can fit in more lines into the same area. [4, 6, 7]

Using continuous manufacturing can cut operating costs by half and waste by a third, reduce time from testing to release by two-thirds and lower manufacturing and testing cycle time by 80%. By reducing operator involvement and manual material handling, continuous manufacturing can also increase safety for operators, especially for processes that generate heat or require the use of volatile materials. [7]

Process modelling

As manufacturing processes become more complex, the need for optimisation and control increases. Using modelling to simulate the process helps manufacturers to refine process steps, improve cost effectiveness, optimise production scheduling and design more effective production lines and factories. [4]

Building AI into manufacturing

Artificial intelligence (AI), machine learning (ML) and deep learning are increasingly playing a powerful role in drug discovery and development[SE1] , from identifying drug targets to predicting efficacy and safety. AI can also be applied to enhance efficiency in pharmaceutical manufacturing. [8]

Ways that AI can help to streamline manufacturing:

• Artificial neural networks (ANNs) can optimise manufacturing processes through quality-by-design (QBD) [9]

• AI systems can support the manufacturing of optimised drug delivery nanosystems [8]

• AI can detect flaws, irregularities and deviations in manufacturing and packaging [10]

          o  For example, in oral medication manufacturing, internal tablet cracking can cause tablet fracturing, leading to batch failure. Deep learning convolutional neural networks can carry out rapid analysis of thousands of X-ray computed tomography (XRCT) images to detect internal cracks. AI can also be used to pick up issues with variations in tablet size and shape [11, 12]

• AI can predict product quality failures and production line faults, allowing early intervention, reducing downtime and cutting waste [11, 13, 14]

• By automating data entry and analysis, AI can improve documentation efficiency and reduce errors [15]

• AI can improve regulatory reporting and compliance tracking [11, 16]

• In continuous manufacturing, AI can track, analyse and improve steps during continuous manufacturing [6]

• AI and ML can optimise workflows, predict demand and help to manage inventory. [17]

Outsourcing manufacturing

In-house manufacturing isn’t always practical, particularly for smaller companies, or companies manufacturing complex biologics, high-potency cancer drugs, or smaller quantities of drugs for rare diseases. Working with a contract manufacturer means that drug developers can optimise resources and eliminate bottlenecks by accessing specialist expertise, cutting edge equipment, and enhanced capabilities. It also allows the use of flexible and short-run manufacturing solutions. [18, 19]

The value of streamlining manufacturing

As the drug market becomes more competitive, populations age and healthcare budgets are squeezed, keeping drug costs low is ever more important. By using the best options for production lines, modelling and planning manufacturing workflows, outsourcing where appropriate and exploring the use of AI and ML, manufacturers can be efficient and cost-effective, as well as getting drugs to patients and healthcare professionals in a timely way.

References

1.Miller, N.J. Automation of Pharmaceutical Industry: Processes that Can be Assigned to Robots and Neural Networks. 2024 23 July [cited 2024 23 July]; Available from: https://goodschecker.com/blog/automation-of-pharmaceutical-industry/.

2.Markarian, J., Advancing Robotic Automation, in PharmTech.com. 2024.

3.Haigney, S., Streamlining Bioprocesses with Automation, in BioPharm International. 2024.

4.5 Key Trends Driving the Pharmaceutical Manufacturing Industry. 2024  [cited 2024 15 May]; Available from: https://powdersystems.com/2024/05/5-key-trends-driving-the-pharmaceutical-manufacturing-industry/.

5.Moradi, E. Batch Release Simplified: Streamline Production Outcomes. 2024  [cited 2024 13 December]; Available from: https://pharmuni.com/2024/12/13/batch-release-simplified-streamline-production-outcomes/.

6.Anselmo, J., Embracing continuous manufacturing in the pharmaceutical industry, in ManufacturingDive. 2024.

7.Kaylor, A., Advancing continuous manufacturing with FDA support, in TechTarget. 2024.

8.Huanbutta, K., et al., Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. Eur J Pharm Sci, 2024. 203: p. 106938.

9.Simoes, M.F., et al., Artificial neural networks applied to quality-by-design: From formulation development to clinical outcome. Eur J Pharm Biopharm, 2020. 152: p. 282-295.

10.Saha, G., et al., Artificial Intelligence in Pharmaceutical Manufacturing: Enhancing Quality Control and Decision Making. Rivista Italiana di Filosofia Analitica Junior, 2023. 14(2): p. 116-126.

11.Ma, X., et al., Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability. J Pharm Sci, 2020. 109(4): p. 1547-1557.

12.Prajwala, N.B., Defect detection in pharma pills using image processing. International Journal of Engineering and Technology (UAE), 2018. 7(3): p. 102-106.

13.Vora, L.K., et al., Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics, 2023. 15(7).

14.Paul, D., et al., Artificial intelligence in drug discovery and development. Drug Discov Today, 2021. 26(1): p. 80-93.

15.Vaghela, M.C., et al., Leveraging AI and Machine Learning in Six-Sigma Documentation for Pharmaceutical Quality Assurance. Zhongguo Ying Yong Sheng Li Xue Za Zhi, 2024. 40: p. e20240005.

16.Ficzere, M., et al., Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision. Eur J Pharm Sci, 2023. 191: p. 106611.

17.Nitheezkant, R., et al., Predictive drug quality control using machine learning and big data. 2024 IEEE International Conference on Big Data and Smart Computing (BigComp), 2024: p. 381-382.

18.Wright, T., 2024: Trends Shaping the Future of Pharma, in Contract Pharma. 2024.

19.Holland, D. and K. Ackland. Life sciences A to Z – M is for Manufacturing: Is insourcing the new outsourcing? 2024  [cited 2024 6 June]; Available from: https://www.shlegal.com/news/life-sciences-a-to-z-m-is-for-manufacturing-is-insourcing-the-new-outsourcing.

About the Author:

Suzanne Elvidge

Based in the north of England, Suzanne Elvidge is a freelance medical writer with a 30-year experience in journalism, feature writing, publishing, communications and PR. She has written features and news for a range of publications, including BioPharma Dive, Pharmaceutical Journal, Nature Biotechnology, Nature BioPharma Dealmakers, Nature InsideView and other Nature publications, to name just a few. She has also written in-depth reports and ebooks on a range of industry and disease topics for FirstWord, PharmaSources, and FierceMarkets. Suzanne became a freelancer in 2006, and she writes about pharmaceuticals, consumer healthcare and medicine, and the healthcare, pharmaceutical and biotechnology industries, for industry, science, healthcare professional and patient audiences.