What's the single most formidable hurdle for a promising biotech breakthrough? It's crafting a robust CMC (Chemistry, Manufacturing, and Controls) strategy. These are inherently complex projects, fraught with significant risks from the outset, especially during the challenging transition from late discovery to clinical trials, demanding meticulous planning and execution to navigate successfully.

This critical juncture, often termed the "valley of death" in biologics development, frequently sees projects facing prolonged delays or even termination. Financial pressures and the need to rapidly generate clinical data and evolve research-stage processes into manufacturable technologies underscore the unique hurdles biotech companies face in bridging the gap to clinical development and market readiness.

This concept is discussed in greater detail with Matthias Müllner in an episode of the Smart Biotech Scientist Podcast, hosted by David Brühlmann, founder of Brühlmann Consulting.

Key Challenges in Bioprocess Development and Manufacturing

Biotech scientists and companies face distinct hurdles when advancing products through bioprocess development and manufacturing. A fundamental challenge lies in the different skill set and mindset required compared to academic research.

While research excels in discovery and scientific understanding, bringing a product to clinics and the market from a technical or manufacturing perspective demands different capabilities and know-how. This specialized knowledge is often not readily available. Bridging this gap and accepting the need to expand one's network to include external expertise is a significant challenge.

The transition from lab discovery to clinical studies and large-scale manufacturing is a particularly critical period. Whether funded or seeking funding, companies face financial pressure to rapidly generate clinical data. They often begin with processes developed in a research or lab setting, needing to evolve these into manufacturable technologies suitable for clinical development. 

This process frequently reveals a lack of knowledge, expertise, infrastructure, and technology for robust process development and the creation of comprehensive CMC (Chemistry, Manufacturing, and Controls) strategies. Furthermore, identifying, auditing, and securing the right contract manufacturers (CMOs/CDMOs) to fit the entire CMC package is a significant hurdle.

Confronting the "Valley of Death"

The "valley of death" in biologics development refers to the challenging transition period from late discovery or preclinical testing to clinical testing. Many projects face prolonged delays or even termination in this phase for several reasons:

• Financial reasons: Process development, manufacturing, and bringing a process to a manufacturable state are costly, requiring significant funding.
• Preclinical data deficiencies: Insufficient preclinical data or the lack of an appropriate animal model can impede progress.
• Process development and CMC-related issues: These include developing a process that isn't robust enough, leading to potential GMP batch failures, or insufficient process understanding. Producing a product that lacks sufficient safety can also prevent clinical trial applications from proceeding.

This phase represents a critical juncture in a novel biotherapeutic's life cycle, demanding comprehensive solutions, with the CMC component often being a central issue.

The Imperative of a Solid CMC Strategy

A solid CMC strategy or roadmap is crucial, yet it's often overlooked. Many companies fail to fully appreciate the significant problems that can arise from a poorly designed CMC approach. It's essential to adopt a step-by-step, risk-based, phase-appropriate approach while always keeping the end game in mind. This involves understanding the target for commercialization and the market to be covered from the outset.

The CMC strategy must integrate aspects of regulatory, quality, and manufacturing process development. While not every detail needs to be established immediately, maintaining the "bigger picture" allows for streamlined activities. 

Strategic, risk-based decisions can then be made on where to accelerate, or, importantly, where not to cut corners, as such compromises could lead to project termination later—for example, if a process is found to be commercially unviable due to excessive costs even after positive clinical data. This upfront assessment, leveraging available knowledge, should precede hands-on development activities.

Many developers have really strong focus on science, on discovery, but they don't have the infrastructure and capabilities to develop processes. They don't have to know how to develop CMC strategies and those kind of things

Key Factors and Potential Pitfalls in Crafting a Robust CMC Strategy

Developing a robust CMC strategy requires foresight and careful execution. 

Key factors include:

• Early Initiation: It's essential to start crafting the CMC strategy as early as possible. Just as companies analyze market size for investors, they should scientifically assess their trajectory. The sooner the CMC strategy is developed, the better.
• Defining Requirements: Once the direction is clear, prioritize initial steps, including identifying specific quality and regulatory requirements, such as pharmacopoeial specifications.
• Living Document: The CMC strategy should be a living document that is regularly reassessed to ensure continued alignment and allow for necessary readjustments.

A significant pitfall, and an area where cutting corners is ill-advised, is cell banking. For most viral vector-based products, establishing a master cell bank (MCB) and working cell banks (WCBs), often with a two- or three-tier approach, is fundamental. Neglecting this early consideration, or realizing later that the MCB cannot support the market, can lead to costly re-manufacturing and comparability problems. 

The upfront investment in manufacturing sufficient MCB vials and thoroughly qualifying, testing, and characterizing cell banks or virus stocks can prevent substantial future difficulties and delays. Many complicated projects stem from a lack of complete understanding of these critical requirements.

Resources and Support Systems for Biotech Startups

Smaller companies and startups often struggle with managing CMC complexities due to limited internal knowledge and experience. While the long-term aim is to build internal CMC capacity, this is frequently not feasible in the short term. 

Fortunately, several support systems can help smooth this process:

• Leverage Networks: Tap into people within one's network who can provide technical or strategic CMC know-how.
• Engage Dedicated CMC Consultants: Bringing in dedicated CMC consultants early on can aid in defining robust strategies and identifying suitable manufacturers. These consultants act in the company's interest, rather than relying solely on a manufacturer's perspective.
• Strategic Use of CDMOs/CMOs: While contract development and manufacturing organizations provide services, finding the right one can be challenging. Having internal or dedicated external experts overseeing these relationships is often more efficient.
• Phased Capacity Building: The most efficient approach involves initially securing external capacity and then gradually building internal capacity by hiring personnel with CMC knowledge and developing in-house process development capabilities. This internal strength is a key factor investors and pharma companies assess during acquisition or investment considerations.
• Regulatory Body Interaction: For specific regulatory or quality-related CMC questions, seeking scientific advice from regulatory bodies early on is a clever strategy.

This approach parallels how companies utilize Contract Research Organizations (CROs) for clinical development expertise, underscoring the similar strategic importance of external CMC support.

The Advantages of Quality by Design (QbD) in Bioprocessing

Quality by Design (QbD) is a critical element gaining traction in biopharmaceutical development, despite the industry's historical conservatism in fully leveraging its potential. QbD's core principle is to design quality into processes from the outset, rather than solely relying on testing quality at the end. The ultimate goal is to produce high-quality, safe products consistently.

Achieving this requires a deep understanding of the process. Developers must understand which product parameters impact quality and safety (Critical Quality Attributes, or CQAs) and which process parameters affect these CQAs (Critical Process Parameters, or CPPs). Once this understanding is established, a design space can be defined around these parameters. Operating within this design space ensures product quality is maintained.

QbD principles, introduced in the 1990s and integrated into ICH regulations since 2010, are fundamentally about enhancing process understanding and control. Adopting this mindset early in the process development facilitates the creation of phase-appropriate, robust manufacturing processes. Knowing the critical parameters allows for focused development, avoiding broad "shotgun approaches" to experimentation that yield little understanding. 

While a complete QbD approach may not be necessary from the outset, embracing its advantages from the beginning can add significant value. This builds a strong foundation for ongoing process development throughout the clinical program. Companies delaying QbD implementation often find early process development data unusable, necessitating costly and time-consuming re-experimentation, because initial experiments, lacking proper Design of Experiments (DOEs), weren't designed to generate the required understanding.

Building process understanding is key, and developing the right data sets from the beginning is vital to avoid unnecessary rework.

QbD Drawbacks and Risk Mitigation Best Practices

While QbD offers considerable benefits, its implementation has specific considerations and demands careful planning:

• Resource Investment: Gaining the necessary process understanding through QbD requires a significant investment of time and resources. This includes defining a quality target profile, identifying CQAs, and conducting risk assessments of existing processes to evaluate the impact of individual parameters. This can be extensive, and the requisite know-how and data sets are often unavailable early on, leading some companies to defer or avoid QbD.
• Design and Interpretation Complexity: If incorrectly executed, QbD can lead to wasted effort. Flawed planning, inadequate DOE design, or misinterpretations can misdirect development. Expertise in statistics for DOE design is crucial; relying on inadequate tools or methods can result in erroneous conclusions.

Given the inherent risks in the entire development cycle, effective risk mitigation is paramount:

• Minimum Regulatory and Quality Requirements: Always prioritize the minimum regulatory and quality requirements. When uncertainty arises regarding quality or regulatory aspects during process development, seeking direct support from regulatory agencies or engaging consultants is advisable.
• Patient Safety First: The overarching goal is to develop a safe product for clinical testing. Decisions must be continually challenged to ensure a comprehensive understanding and knowledge base, guaranteeing clinical batches' safety. This includes establishing all required specifications, even if proprietary ones need to be built initially.
• Phase-Appropriate Risk Assessment: Adopt a step-by-step, risk-based approach to assessment. Early-stage risks primarily involve ensuring the GMP batch doesn't fail and that the product is safe for clinical testing. A non-robust manufacturing process can lead to failed clinical trials or material that isn't comparable to preclinical studies, making it difficult to secure further funding.

Integrated Approaches: Expertise, Technology, and Digitalization

To simplify the journey from scientific breakthroughs to clinical development, an integrated approach combining bioprocessing expertise with state-of-the-art technology, digitalization, and machine learning proves highly effective. This fosters significant efficiency and a deeper understanding of the process.

This strategy stems from over a decade of experience in vaccine development, where common difficulties highlighted the need for more efficient process development in viral vector-based medicines. Recognizing the need for companies to generate clinical data rapidly without unlimited time and resources, the focus shifts to maximizing the efficiency of available time and resources by leveraging prior knowledge.

This involves pre-developing unit operations or entire process streams for various viral vectors to generate a foundational understanding of the process. This applies to upstream (e.g., seed trains, production phases) and downstream (e.g., purification technologies) processes. During the development of these unit operations, extensive data is generated and used for data modeling, specifically hybrid modeling. These hybrid models combine mechanistic models and machine learning to create a virtual model, or digital twin, of these process steps and unit operations.

These predefined building blocks and models serve as a powerful decision support tool for designing process development experiments for customers correctly from the first time. Instead of relying on extensive lab experimentation that may not yield a critical understanding of the process, this tool allows for the selection of potential essential parameters of the process before entering the lab. By analyzing different models, unit operations, and prior knowledge, parameters most likely to have an effect are identified. 

DOEs are then explicitly designed around these selected parameters, significantly reducing the time spent on experiments that don't increase process understanding. This approach has generated proof-of-concept data for a viral vector-based vaccine, demonstrating a 30% to 40% reduction in experimental resources.

Once customer-specific data from these process experiments is obtained, it's used to build a customer-specific hybrid model or digital twin. This bespoke model then further defines the development strategy.

Quality by design, when you look at the principles of IT and as the name says, it's really about designing quality into processes rather than testing it into. And at the end of the day, what you want to achieve is producing high quality, safe products consistently. And how can you do that is by process understanding. You need to understand what parameters affect my quality and my safety.

Vision and Future Outlook for Bioprocess Innovation

The viral vector space is characterized by continuous innovation, creating a dynamic tension with regulatory requirements and the need for rapid process development. 

This interplay is managed through a dual approach:

1. Advanced Development: Continuously leveraging prior knowledge to enhance efficiency in process development.
2. New Technology Evaluation: Actively assessing and implementing new technologies, whether developed internally or sourced externally. However, "new" doesn't always equate to "better." A careful understanding of whether an innovation truly adds value is crucial, especially given that regulatory pathways are often conservative, and novelties are not always immediately embraced. A compromise must be struck between product optimization and regulatory acceptance.

Significant future potential lies in the early adoption of machine learning, digitalization, and data modeling. While digital twins are well-established in commercial manufacturing, bringing these opportunities into process development is less common but can be a game-changer.

Over the next five to ten years, the CMC sector, traditionally slower to innovate, needs a paradigm change. There's a growing need for greater openness to testing new technologies and fostering more creative approaches. The vision is to integrate new technologies and innovations, making them available to customers who can benefit without needing to become experts in their implementation. 

A key area to watch is how in-silico data generated from process development digital models and digital twins will be used to support initial trial applications, and how regulators will react to this.

Final Remarks:

Effective project management in bioprocess development and scale-up is critical for bringing groundbreaking therapies to market. It necessitates meticulous upfront planning, strategic resource allocation, and a relentless focus on scalability.

The most important takeaway is that, while the industry is well-positioned to drive innovation and increase process development efficiency, significant progress remains to be made. It demands a fundamental mindset shift: CMC should not be underestimated or ignored from the beginning. Instead, it must be proactively managed, ideally through a step-by-step, phase-appropriate approach.

About Matthias Müllner

Matthias Müllner is Co-Founder and CEO of bespark*bio, bringing over a decade of expertise in biopharmaceutical development and manufacturing. Previously, he led the Technical Operations Department at Themis Bioscience, successfully transitioning 6+ projects from pre-clinical to clinical development before the company's acquisition by MSD.

bespark*bio specializes in process development services for viral vector-based medicines including vaccines, immuno-oncology, and gene & cell therapies. The company combines advanced bioprocessing expertise with digitalization and machine learning, using Quality by Design principles to reduce development timelines by up to 50%.

Connect with Matthias Müllner on LinkedIn.

David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.

He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.  

Hear It From The Horse’s Mouth

Want to listen to the full interview? Go to Smart Biotech Scientist Podcast. 

Want to hear more? Do visit the podcast page and check out other episodes. 
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