Managing a Combination Product Project
Discover how we achived cost savings while ensuring compliance with regulatory standards when we supported a client in the development of a user-friendly injection device.
Good stories
When a pharmaceutical company faced critical challenges related to API product variation during production, they turned to our Research & Development team for assistance. Our team successfully optimized the company’s outdated pharmaceutical API manufacturing processes, resulting in improved efficiency, cost-effectiveness, and reduced variation.
THE CHALLENGE
Many pharmaceutical companies face significant hurdles when dealing with legacy processes that lack comprehensive development data. These challenges often include low process capability, high costs, and insufficient data for optimization.
In this case, a pharmaceutical company was facing withdrawal of their GMP license due to significant batch variability. Our Research & Development team was called in to solve this urgent problem.
OUR APPROACH
Our Research & Development team implemented a systematic approach to address the challenge of batch variation arising from an outdated pharmaceutical manufacturing process. Leveraging our scientific understanding, extensive regulatory experience, and employing LEAN tools, we developed a comprehensive strategy to tackle this issue. Here is a breakdown of the approach.
Our Research & Development team employs a systematic approach, utilizing detailed process maps, optimization scope evaluation, and statistical analysis to unravel vital insights. By capturing essential information at each step, including time, resources, raw material data, parameters, and yield variation, the team establishes a solid foundation for subsequent optimization efforts.
Through the evaluation of critical factors such as target product profiles, compliance requirements, and cost considerations, the team expertly identifies knowledge gaps and prioritizes optimization efforts. Join us as we explore these three pivotal steps in achieving process efficiency and optimization.
Emendo R&D began by creating detailed process maps that captured essential information for each process step, including time, resources, raw material data, parameter, and yield variation. These maps formed the basis for subsequent optimization efforts.
The team defined the scope of optimization by evaluating target product profiles, product specifications, historical process performance, and key drivers for optimization such as compliance, regulatory requirements, safety, scrap rates, cost, and lead time.
A comprehensive assessment of process parameters was performed to identify knowledge gaps and effectively prioritize optimization efforts with focus on variation and impact on critical quality attributes. The assessment was performed on-site with operators, process specialist, compliance team and regulatory to ensure that the evaluation was considering all aspects of the process from producibility to regulatory impact.
Our Research & Development team employs a meticulous approach, combining statistical analysis and root cause analysis to unravel the complexities of their manufacturing process. By delving into data-driven methodologies, the team retrofits a design space and gains a deeper understanding of the operations. Additionally, our Research & Development team implements the innovative Blackbird Smart Data Collection System, revolutionizing process parameter monitoring and knowledge enhancement.
With reduced reliance on additional test batches and small-scale Design of Experiments (DoEs), operations become more efficient. Furthermore, statistical evaluation and modeling play a pivotal role in defining the impact of process parameters, enabling informed decision-making for final process changes. Join us as we explore these next critical steps towards enhanced process insights and improved decision-making abilities.
Historical batch data was statistically analyzed along with root cause analysis for out-of-spec (OOS) and out-of-trend (OOT) observations. This data-driven approach retrofitted a design space and improved process understanding.
Our team implemented the Blackbird Smart Data Collection System to monitor and improve knowledge of process parameters, reducing the need for additional test batches and small-scale Design of Experiments (DoEs).
Statistical analysis and modeling helped define the impact of parameters on the process and guided decision-making on final process changes.
By implementing and automating key parameter monitoring through in-process controls, including pH, oxygen, metabolites, and biomass, our Research & Development team harnesses the power of simple measurements and cloud-based data analysis to significantly reduce process variability.
Thoroughly collecting process parameters during earlier stages increases the success rate of test runs and validation, ensuring accurate and reliable results. Finally, the project concludes with a revision of the control strategy, guaranteeing sustainable process control that adheres to regulatory standards. Regulatory files are updated to reflect any process changes implemented during optimization—an approach aligned with our customized QMS strategies for GxP-regulated environments. Join us as we explore these final crucial steps toward achieving heightened control, reliability, and adherence to global standards.
Appropriate in-process controls were implemented, automating the monitoring of key parameters such as pH, oxygen, metabolites and biomass through simple measurements and cloud-based data analysis. This significantly reduced process variability.
While test runs and validation were essential, thorough collection of process parameters earlier in the process significantly increased the success rate of these phases.
The project concluded with a revision of the control strategy to ensure sustainable process control. Regulatory files were updated to reflect any process changes made during optimization
The comprehensive optimization strategy of our R&D team yielded tangible results, including a near 100% reduction in API content variation. The batch scrap rate dropped from 40% to 10%.
In summary, our systematic approach solved critical production challenges, resulting in significant cost savings and improved efficiency. This highlights the importance of data-driven optimization in the pharmaceutical industry.
Keen to learn more?
If you’re facing challenges with legacy processes or inconsistent batch results, our R&D team can help streamline your operations. By leveraging data-driven strategies, cutting-edge technology, and our deep expertise in pharmaceutical consulting, we help you reduce variation, improve efficiency, and achieve cost savings.
Reach out to Britt Dam Raaby, Vice President at R&D, to learn how we can help you optimize your pharmaceutical manufacturing processes.
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HAVE A QUESTION
API process optimization involves improving the manufacturing methods used to produce Active Pharmaceutical Ingredients (APIs). It focuses on reducing batch variation, increasing process efficiency, and ensuring product quality by using data-driven strategies, advanced monitoring systems, and validated control methods.
Reducing batch variability is critical in pharmaceutical production because inconsistent product quality can lead to regulatory non-compliance, increased scrap rates, and higher production costs. Consistent API quality ensures product safety, efficacy, and adherence to Good Manufacturing Practice (GMP) standards.
Key steps include:
Smart data collection systems continuously monitor key process parameters like pH, oxygen, metabolites, and biomass in real time. This enables quicker detection of deviations, reduces reliance on manual sampling, and supports proactive decision-making for maintaining consistent product quality.
Statistical analysis helps identify variability sources, evaluate parameter impact, and support root cause analysis. By using historical batch data and modeling, pharmaceutical companies can retrofit design spaces, fine-tune process controls, and achieve more reliable and compliant manufacturing outcomes.
Optimizing legacy processes improves product quality, reduces waste, lowers production costs, and minimizes regulatory risks. It also enhances process scalability and ensures long-term sustainability by aligning manufacturing practices with modern standards and technologies.