Boosting Biotech Efficiency: Key Metrics and Strategies for Success

Boosting Biotech Efficiency: Key Metrics and Strategies for Success

This paper presents a framework for continuous process improvement, leveraging key metrics such as cycle time (Ct), space-time yield (STY), and throughput to ultimately reduce manufacturing costs. We outline actionable pathways for process enhancements implemented at industrial scale, using approaches such as downscaling, process debottlenecking, and fine-tuning of downstream processing. Our biotechnology contract development and manufacturing organization (CDMO) maintains a competitive edge in this rapidly evolving industry by enhancing performance metrics for the benefit of our customers – without compromising on quality.

Operational Excellence (OpEx) encompasses a holistic approach to improving quality, compliance, efficiency, and customer satisfaction. Quantifying OpEx relies on a set of key performance indicators (KPIs) that can cover several aspects of successful operations. E.g. compliance metrics evaluate adherence to regulatory standards through indicators such as the number of inspections or audit findings¹. Quality metrics such as deviations or batch rejections are critical in ensuring product safety. Efficiency metrics focus on overall manufacturing process productivity and cost-effectiveness, with measures directly leading to cost reduction playing a central role. Operational efficiency in biotechnology refers to the ability to maximize output while minimizing input, whether in terms of time, cost, or resources, without compromising product quality or regulatory compliance. Unlike other industries, biotechnology operations are inherently complex due to the biological nature of the processes involved. Variability in microbial behavior, sensitivity to environmental conditions, and stringent regulatory requirements all contribute to the challenge of maintaining consistent and efficient production. To effectively measure and improve operational efficiency, we focus on a set of important KPIs. Cycle time (Ct) is defined as the time between the start of one batch and the start of the next and directly impacts labor, energy and depreciation costs. Reducing Ct can lead to faster production rate and increased capacity but may also introduce trade-offs such as higher energy consumption or increased labor intensity. Space time yield (STY), a critical metric of the upstream process part (USP), evaluates the amount of product generated per unit volume per unit time. Improvements in STY can significantly enhance overall productivity but must be balanced against operational constraints and seen holistically considering downstream processing (DSP) capacity and set up. Other important metrics include throughput, which gauges the volume of product produced over a given period, and DSP yield, which reflects the proportion of product that meets product specification without requiring rework. In addition to these metrics, efficient integration of laboratory capabilities and problem-solving skills, and operational competence is becoming increasingly vital. Together, these capabilities and metrics form the foundation for robust operations and drive value across the product lifecycle.

Through illustrative examples, we demonstrate how continuous process improvements can be effectively tailored to diverse biotechnology processes utilizing various microorganisms across different scales and campaign durations. By leveraging our R&D capabilities, diverse bioprocess optimization strategies, and strong operational expertise, we helped customers unlock hidden value across their product pipelines. During the following model commercial campaigns, we applied several optimization strategies that can be categorized into two key areas:

(i) bioprocess downscaling and bench-scale experiments to enhance space-time yield (STY) and/or product purity, and (ii) debottlenecking and fine-tuning of downstream processing at scale, ultimately leading to reduced product unit costs.

Figure 1 highlights improvements achieved during the manufacturing campaign of a large molecule with application as an active nutraceutical ingredient. Experiencing reduced process performance when scaling up the process from lab to commercial scale, we conducted small-scale experiments to optimize culture medium composition and feeding strategy. Semi-throughput screening (STS) for the impact of various medium components and fractional factorial design of experiments (DoE), followed by statistical analysis, enabled us to define and fine-tune the parameters with significant effect on STY. These upstream optimizations, combined with more efficient DSP unit operations, significantly improved overall process productivity as measured by the overall yield of the pure product. Key insight – medium composition, and nutrient availability can vary due to sterilization conditions and/or nutrient gradients, influencing cellular responses. Unlike traditional trial-and-error methods, which often result in inconsistent and temporary gains, DoE-driven lab-scale experiments offer fast, reliable, and economically low-risk solutions that can be seamlessly integrated into ongoing production campaigns.

Another Design of Experiments (DoE) approach was employed to optimize the production process of an enzyme used as a feed additive. This method revealed a significant relationship between the substrate-specific uptake rate (qS) and enzyme activity. Implementing an optimized induction-phase feeding profile, along with adjustments to aeration and pressure at scale to meet increased oxygen demand, led to enhanced STY and reduced Ct during a large-scale manufacturing campaign (Figure 1). Key insight – it is important to choose relevant physiological parameters for optimization to correctly interpret experimental results as well as to leverage technical parameters of large bioreactors.