Why it's vital to minimise waste to make a more sustainable drug discovery industry a reality

Incorporating environmentally friendly practices has become everyone's responsibility. In recent years, policymakers and organisations have particularly intensified sustainability practices among pharma and biotech, the industries whose operations have significant environmental impacts.

While both industries are beginning to integrate sustainability practices, they still lag behind others.¹ This lag can be attributed to several factors, including the complexity of drug development or the need to adhere to regulatory standards, which prolong timelines and intensify resource consumption, ultimately impacting sustainability efforts.

Drug discovery and development are still frequently hindered by fragmented workflows, reliance on manual processes, and the existence of data silos. These issues make the process resource-intensive, consuming vast amounts of time, materials, human capital, and energy. According to industry estimates, approximately 85% of biomedical research is wasted due to these inefficiencies, highlighting the urgent need to implement more sustainable solutions².

The importance of design of experiments & multi-variate analysis in biopharma sustainability

Drug discovery and development frequently deal with multiple parameters, which increases the number of experiments required and contributes to sustainability issues. One tool that can enable greener practices during development within the biopharmaceutical industries is design of experiments (DoE), a statistical technique for optimising processes.³

This structured statistical method is used for unravelling the relationships between critical process parameters (CPPs) and their impact on critical quality attributes (CQAs). A well-implemented DoE allows for better control of the process, reducing the number of failed batches and, therefore, minimising waste. Furthermore, DoE can be used to optimise parameters while also minimising environmental impact. For example, running bioprocesses at lower temperatures reduces energy consumption, using smaller volumes decreases single-use plastics, and extending batch durations lowers water usage by reducing the need for system cleaning between runs.

Overall, DoE is a valuable approach to improving the quality and efficacy of the final product, streamlining the process, and reducing redundancy — and, consequently, waste. Therefore, it is important to provide scientists with the tools and platforms that support this efficient method for increasing sustainability in the pharmaceutical industry.

One possible way of enabling more sustainable pharma is by incorporating software solutions that integrate the aspects of DoE from design to execution. Digital tools, such as IDBS Polar, can help manage and analyse scientific processes with pre-built applications, including DoE, which enables real-time monitoring of multiple parameters, such as temperature, pH, and nutrient levels, providing insights and helping to prevent errors before they escalate. Moreover, integrating GxP standards within these workflows ensures adherence to industry standards, including documentation, data management, and reporting. As a result, companies can avoid delays or the need to rerun experiments, reducing potential errors, increasing efficiency, and supporting sustainability in the long term.

Another particularly helpful digital tool that can contribute to more sustainable bioprocesses is multivariate analysis (MVA). By identifying the relationships between process control parameters, MVA can provide a deeper understanding of interdependencies compared to traditional analytical methods. Implementing MVA as part of workflow digitalisation allows for more informed decision-making and early detection of potential issues, helping to avoid the loss of batches downstream.

The role of digitalisation

Digitalisation has become an integral part of the biopharmaceutical industry, aiding in the optimisation at every stage, from R&D to manufacturing.⁴ While digitalisation offers multiple advantages, two that stand out are maximising automation and enabling real-time monitoring.

Automation of workflows provides real value by reducing the need for manual sample handling and the errors that can arise from it. Digital tools enable researchers to design, run, and analyse experiments within a unified digital environment, making necessary adjustments in real-time. This helps avoid batch failures, ensuring a high-quality final product with an optimum yield.

The landscape of real-time monitoring is advancing rapidly by leveraging artificial intelligence (AI) and machine learning (ML) solutions. The integration of AI/ML capabilities within processes allows for quick and accurate analysis of scientific data, far beyond the capacities of manual monitoring.⁵ The rapid identification of patterns and abnormalities can accelerate the detection of potential problems, enabling proactive adjustments, reducing the number of failed batches and minimising the use of single-use plastics and water.

Collaboration and data sharing as a green initiative

Lifecycle management in biopharmaceutical development requires collaboration among multiple teams, from process and analytical development through quality assurance (QA) and quality control (QC) to manufacturing. Close collaboration between these teams is imperative for effective knowledge transfer.

In the traditional siloed approach, different teams or labs may unknowingly conduct similar or identical experiments, leading to a waste of resources, time, and effort. To address these inefficiencies, it is crucial to provide solutions that enable data accessibility so researchers can share findings and insights, building on existing knowledge and streamlining the discovery and development process. For example, cloud solutions enable researchers to access, analyse, and share data without working with fragmented datasets, leading to more meaningful and productive outcomes.

The advantages of comprehensive digital reporting extend beyond a company’s internal operations. On a broader scale, it facilitates collaboration with external partners, including academic institutions, outsourcing partners such as CMOs, CDMOs, or CROs and regulatory bodies. By enabling data sharing among various stakeholders, companies can streamline drug development and move therapeutic modalities through the pipeline more quickly. Additionally, operating within a GxP environment provides confidence when sharing information across organisations, ensuring that data integrity and compliance are maintained throughout the process.

In terms of regulatory bodies, digital solutions can facilitate standardised and structured data management. The Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) strongly encourage using data that meets FDA standards for sharing data types between computer systems during the drug development process. Implementing these standards early in the product development lifecycle ensures that data is structured and consistent with regulatory expectations. This proactive approach helps to minimise potential redundancies and inefficiencies, ultimately saving time and resources. By adhering to these FDA requirements from the outset, developers can streamline the approval process and enhance the overall quality and reliability of their submissions.⁶

Future outlook of biopharma sustainability

As the pharmaceutical industry continues to evolve, sustainability is becoming a backbone for designing a better future. The pursuit of greener solutions is driving the integration of digital workflows and other technological advancements. With the rapid expansion of generative AI, we foresee that digitalisation will continue to improve by integrating machine learning algorithms to optimise processes and reduce resource consumption.

Another trend we expect to see is the expansion of workflows beyond laboratory settings to integrate the entire supply chain. Enhanced digitalisation would enable greater traceability and control over the process, optimising biopharma activities for minimal waste and maximum production outcomes. This holistic approach, incorporating digital solutions, would enhance sustainability by producing more eco-friendly products that fulfill regulatory requirements.

Finally, this enhanced digitalisation, combined with automation and miniaturisation of experiments, would help introduce Lab 4.0 to the biopharmaceutical industry.⁷ In Lab 4.0, data-driven decisions, taken in real-time, would improve efficiency, accuracy, and sustainability.