Cytiva

Nicolas Pivet

The future is digital. We’ve heard it, but what, exactly, does it mean for biomanufacturing? How does our industry transition from a heavily manual plant to a digitalized one?  The short answer:  gradually. The good news is, we’ve already started. The biopharmaceutical industry has been compelled to advance, in part, by the rapid pace of drug development amid soaring demand for therapeutics, particularly monoclonal antibodies and entirely new classes of biologic drugs. Bioprocessing 4.0 has arrived, but only in pockets.

The classification of 4.0 involves the digital transformation of biopharmaceutical manufacturing. It spans the entire product lifecycle, leveraging the Internet of Things (IoT) for connected manufacturing systems, automation, streamlined processes, and real-time data management. However, there remains a pressing challenge to ensure universal recognition of the need for a substantial shift towards digitalizing development and manufacturing processes. Various challenges plague the industry, including the growth, complexity and uncertainty of the drug pipeline. As some move to manufacture smaller batch sizes for personalized medicine, the necessity for increased agility in manufacturing, with the concept of a reconfigurable plant, becomes apparent. Despite our gains, the sector's digital evolution varies based on organizational size, scale, and maturity.

Innovative technologies such as data lakes, digital twin technology, virtual and mixed reality (VR and MR) or Advanced Process Control (APC) address these challenges by placing data at the core. Digital twins (accurate simulations of real-world processes) enhance operational efficiency and predictability of batch processes, while smart hardware optimizes overall equipment effectiveness. Despite the potential benefits of digitalization, the biopharma industry lags in digital maturity, hovering at a Level 2 out of 5 on the Digital Plant Maturity Model (DPMM). Resistance to digital practices stems from cybersecurity concerns, general change management, or the cost of implementation. The COVID-19 pandemic prompted a shift towards digital possibilities out of necessity, with companies connecting equipment to networks for remote monitoring.

So how do we evolve to a standardized digital industry when adoption varies so greatly? We start by meeting organizations where they are to drive quantifiable improvements. Incorporating digital solutions needs to be customizable for the diverse landscape of automation needs. Addressing the manpower and skillset disparities across smaller biotech firms and larger pharmaceutical companies is also needed. As digital capability and usage matures, companies will need expertise in areas that they may not be able to provide in-house. Talent recruiting and retention is a real challenge, and smaller firms can benefit from contract partners and the technical expertise of their suppliers.

Prioritizing areas for transformation include manufacturing execution systems (MES) or simpler electronic Batch Records (eBR), operational technology (OT) for analytics and predictive models and Process Analytical Technology (PAT). By implementing real-time monitoring of critical process parameters (CPP) and performance attributes (CQA), to analyse and control manufacturing processes, PAT drives improvement of the quality and consistency of pharmaceutical products while enhancing efficiency and reducing production costs.

We see the digital transformation of biomanufacturing for fast, flexible and reliable operations, happening in four areas:

In Silico Process Development: leveraging software modelling tools and services for rapid and robust Process Development (PD). The goal should be to accelerate, standardize and de-risk PD as well as ease transfer from emerging biotech companies to CMOs. Ambitious goals shall be set on quality (ppk >>1), speed (PD cycle in months) and cost (reduced by a factor of four).

Prescriptive Batch:  digitally enhancing consumables and process data to feed advanced process control models and drive predictable outcomes for the drug batch. Cell culture media, resins lots, bags etc. come with eCOA (Certificate of Analysis) and genealogy, in an electronic format. This combined with process information can enable fast, high-confidence release of on-spec products. Ambitious goal shall be to reduce batch loss from 12% in average to low single digits, and release in one day.

Effective Equipment (OEE): making equipment “smart” for Overall Equipment Effectiveness (maximum uptime and throughput). Connectivity platforms and MR tools enable for fast equipment set-up, reconfiguration and revalidation as well as predictive maintenance. Ambitious goal shall be to increase asset utilization in biopharma from 70% to >90%.

Flexible Plant: the idea here is to drive fast scale out, tech transfer and plant reconfiguration for flexible manufacturing. This can be enabled through automation, workflow execution support (with eSOPs, enabled by MR), operator onboarding (enabled by VR training) and recipe management (“digital tech transfer”). Ambitious goal shall be to reduce product change-over and transfer time/cost by 10.

The industry's future success hinges on its openness to digital solutions, fostering collaboration across the value chain to realize the full potential of automation and digital transformation in delivering therapeutics efficiently and cost-effectively.