10 Ways To Speed Up CMC In Early-Stage Drug Product Development

By Fahimeh Mirakhori, M.Sc., Ph.D.

Technology adoption is a critical driver of success for early-stage drug development, enabling faster, more efficient and scalable processes. The rapid development of COVID mRNA vaccines exemplifies how platform technologies, innovative regulatory strategies, and adaptive manufacturing approaches can accelerate drug product development.

Moderna and BioNTech leveraged pre-existing mRNA platform technologies to quickly pivot and scale up production, demonstrating the power of modular and flexible bioprocessing systems.¹ These successes marked a blueprint for overcoming CMC challenges in cell and gene therapy (CGT) — a field with complex manufacturing, stringent regulatory requirements, and individualized treatments.

mRNA vaccines benefited from modular manufacturing platforms and real-time regulatory engagement.² These same principles can be applied to CGT, where personalized and decentralized production models, scalable automation, and quality-by-design (QbD) frameworks are essential for efficient development, enhanced risk management, and regulatory compliance. However, the inherent complexity of living cell-based products, vector-based gene therapies, and highly sensitive analytical methods necessitate robust change management strategies to ensure compliance, scalability, and product quality.

Strategies To Accelerate CMC And Enable Early Technology Adoption

1. Use Process models to build platform flexibility and scalability

For small biotech companies, adaptable platforms are essential for fostering innovation, efficient change management, and regulatory compliance. Modular and automated end-to-end platforms enable seamless integration of new technologies, reducing process variability and accelerating development timelines.^3,4 Digitalization plays a pivotal role in modern bioprocessing by optimizing workflows, enhancing real-time monitoring, and improving scalability. Implementing these flexible platforms allows companies to navigate manufacturing complexities while ensuring reproducibility and regulatory alignment. Process models — mathematical and digital representatives — enable strategic decision-making based on process knowledge and support the adoption of automated technologies.^4-6 In cell therapy manufacturing, these models can significantly improve process optimization, quality control, and regulatory compliance while mitigating supply chain risks.⁶ Leveraging process models early in development can support QbD initiatives, align with industry 4.0 principles, enhance efficiency, and minimize errors to streamline CMC and biomanufacturing processes to meet evolving industry standards.  

2. Assess organizational readiness and risks

Effective technology adoption necessitates a comprehensive assessment of key risks, including manufacturing complexity, technology transfer challenges, and regulatory compliance. For both autologous and allogeneic workflows, companies must address vector supply limitations and ensure automation is scalable.⁵ Additionally, technology transfer barriers such as raw material variability, process reproducibility, and site harmonization require proactive risk management strategies to prevent inefficiencies.

Early regulatory engagement is essential for navigating emerging CMC challenges, including potency assays, product comparability, and process standardization. A structured, risk-based approach enables companies to identify bottlenecks, optimize resource allocation, and implement contingency plans before transitioning to full-scale manufacturing. By integrating strategic risk mitigation and regulatory foresight, biotech firms can streamline technology implementation, enhance process reliability, and accelerate commercialization efforts while maintaining compliance with evolving industry standards.

3. Enhance QC workflow through cross-functional training

Effective technology adoption requires assessing manufacturing complexity, technology transfer hurdles, and regulatory compliance. The adoption of AI, automation, and digitalized quality control (QC) systems demands cross-functional expertise spanning bioinformatics, data science, regulatory compliance, and bioprocess engineering.^4,6 Companies should implement structured training programs covering GMP manufacturing, process analytics, and regulatory affairs to bridge knowledge gaps.

AI-driven predictive modeling can enhance process optimization, while adaptive learning models strengthen real-time quality control. Training modules should address evolving CMC trends, process analytical technology (PAT)-enabled monitoring, and real-time release testing (RTRT), ensuring personnel are well-prepared to manage risks associated with process variability and regulatory scrutiny.⁷

4. Reinforce continuous monitoring and adaptation

CMC strategies should be regularly assessed and updated based on real-time data, regulatory updates and changes, and technological advancements. Implementing PAT facilitates QbD implementation by real-time analytics for in-process monitoring and advanced control strategies. AI-enabled QC systems enhance batch consistency and deviation detection, reducing the risk of process failures. Digital twins and in-silico models further optimize workflows, supporting continuous improvement and scalability.

A proactive approach to monitoring ensures product robustness, regulatory compliance, and long-term sustainability in CGT manufacturing.⁸ The integration of PAT technologies, such as multi-attribute chromatography (MAM) and automated sampling, enables real-time release testing and enhances regulatory compliance. Additionally, data automation, visualization, and machine learning improve process insights and support data-driven decisions in biopharmaceutical development.⁹

5. Leverage AI-driven process models into CMC strategies

AI-driven automation, cloud-based data sharing, and real-time release strategies are transforming CMC processes and regulatory confidence. However, technology adoption faces cost constraints, expertise gaps, and regulatory uncertainty. Digitalization success requires integrating electronic batch records and AI-powered decision support systems for seamless manufacturing execution.

Advanced in-line process control using AI-based predictive analytics ensures process consistency, while real-time PAT enables continuous monitoring of critical quality attributes (CQAs).¹⁰ Applying Lean Six Sigma methodologies such as DMAIC (define-measure-analyze-improve-control) methodology and value-stream mapping can optimize these transitions, reducing inefficiencies and improving cross-functional alignment.

Lean Six Sigma, which integrates waste reduction (Lean) with variation control (Six Sigma), traditionally relies on human expertise and structured frameworks like DMAIC.¹¹ AI’s integration into process improvement challenges traditional Lean Six Sigma roles, replacing manual methods like Five Whys with AI-driven insights. This shift raises concerns about the future of human expertise, leading to resistance from professionals accustomed to conventional methodologies.

AI-driven automation may reduce employee engagement, as perceived loss of process ownership can increase resistance and hinder long-term performance gains.¹² Organizations must prioritize change management and workforce engagement to integrate AI into process improvement. Leaders should ensure employees are trained to collaborate with AI while understanding its capabilities and limitations. Improvement specialists must develop AI literacy, as these skills are not traditionally included in Lean Six Sigma training. Executives play a key role in building trust in AI-driven decisions, positioning AI as a tool to enhance human expertise rather than replace it.¹²

6. Include advanced manufacturing process controls with Lean Six Sigma

Implementing process automation through PAT, RTRT, and QbD principles ensures consistent product quality and faster market access. However, adoption hurdles such as process variability and workforce adaptation require structured change management. Lean Six Sigma tools, including Kaizen and value stream mapping, can optimize in-line and at-line PAT tools, such as Raman spectroscopy, mass spectrometry, and AI-driven image analysis, for real-time quality assurance. However, AI is transforming process improvement by automating tasks, accelerating decisions, and increasing efficiency.

RTRT strategies eliminate long QC lead times by integrating AI-driven in-process controls, improving batch release efficiency. A QbD-based control strategy defines design space parameters for vector production, transduction efficiency, and final cell product characterization, ensuring product consistency and regulatory compliance while mitigating resistance to new methodologies.

7. Raise regulatory awareness and early engagement

A proactive, iterative, and collaborative regulatory strategy is essential for accelerating approvals and ensuring smooth technology adoption. The mRNA vaccine development experience highlights the value of early engagement with global regulatory agencies, such as the U.S. FDA’s Emerging Technology Program, EMA’s PRIME, and MHRA’s ILAP. However, regulatory uncertainty often leads to internal hesitancy in adopting new technologies.

Stakeholder alignment across CMC, regulatory, manufacturing, and leadership teams is critical to overcoming resistance. Establishing a clear communication strategy, including rolling submissions, real-time scientific advice meetings, and adaptive clinical trial approaches, ensures regulatory alignment. Simultaneously, this approach fosters internal confidence in new digital and AI-driven CMC tools. Measuring success through technology adoption KPIs can help track progress, demonstrating efficiency gains and regulatory compliance improvements.^11,13,14 Sustained investment in national regulatory systems and global harmonization is crucial for ensuring equitable and effective responses to public health emergencies.

Achieving global regulatory harmonization remains challenging, particularly in low- and middle-income countries, where resource constraints hinder implementation efforts. The pandemic’s lessons provide key insights for enhancing regulatory processes in routine and crisis situations.   

8. Align stakeholders for scalability: decentralized and personalized manufacturing platforms

Autologous CGT therapies, such as CAR T cells, require decentralized and point-of-care manufacturing solutions, but standardizing workflows and securing stakeholder buy-in challenges remain.^15,16 Enabling point-of-care production can reduce manufacturing costs. However, decentralizing also raises concerns about ensuring consistent product quality across multiple institutions. Ensuring batch-to-batch reproducibility in decentralized workflows necessitates standardized training and alignment between manufacturing, regulatory, and supply chain teams. Effective decentralized manufacturing relies on stakeholder coordination, integrated technologies, and enhanced information tracking systems for successful implementation.

AuCT-Sim is a multiscale logistics simulation framework designed to model and optimize autologous cell therapies’ manufacturing facilities and supply chains. It integrates novel supply chain system modeling algorithms, methods, and tools, including both single-facility and system-wide network models.¹⁷ This and similar decision-support tools enable stakeholders to assess various manufacturing scenarios, improve processes, manage inventory, and mitigate supply disruptions, ultimately enhancing patient access to these therapies.

However, AuCT is not an AI tool for quality management systems. Lean Six Sigma frameworks help address logistical challenges by reducing variability, streamlining blockchain and cloud-based tracking systems for chain-of-identity (COI) and chain-of-custody (COC), and enhancing AI-driven logistics platforms for personalized supply chains.  

9. Leverage technology platforms and continuous improvement

Regulators increasingly support platform technologies that streamline CGT approvals by enabling multiple products to be developed under a unified regulatory framework. However, successful implementation requires efficient process optimization and continuous assessment. Lean Six Sigma tools like DMAIC and KPIs offer a measurable impact to support these efforts.

Utilizing pre-validated viral vector and gene-editing platforms reduces development timelines. Establishing master CMC dossiers simplifies regulatory submissions for platform-based products while applying Kaizen methodologies ensures iterative improvement and technology scalability. Expedited approval programs, such as Fast Track, RMAT, and Breakthrough Therapy designation, minimize regulatory burden while ensuring safety and efficacy.

Regulatory agencies are increasingly supporting innovative technology adoption in CMC, emphasizing risk-based oversight, data integrity, and product consistency. Early-stage drug developers can navigate regulatory expectations by engaging with agencies through programs like the FDA’s Emerging Technology Program and EMA’s PRIME.^11,13,14 Best practices include proactive communication, structured regulatory science initiatives, and utilizing rolling submissions for accelerated approvals. Real-world successes, such as mRNA vaccine approvals and AI-driven PAT adoption, show how early regulatory engagement and platform-based strategies can streamline CMC integration. These approaches reduce approval timelines and enhance product quality.

10. Map a balanced path forward

The rapid advancement of AI, automation, and digitalization in biopharmaceutical manufacturing presents both opportunities and challenges for the life science industry. While platform flexibility, scalable technologies, and regulatory engagement are essential, overcoming adoption resistance and addressing economic implications remain critical. Resistance to technology adoption often stems from economic and process complexity barriers. The pharmaceutical industry must balance innovation with economic stability, as AI-driven automation streamlines drug development but also introduces workforce disruptions, regulatory complexity, and pricing disparities.

To maintain global leadership, the U.S. must invest in domestic biomanufacturing, modernize regulatory pathways, and develop AI-ready talent. Companies can accelerate CMC adoption and regulatory approvals by engaging early with agencies and leveraging emerging technology programs. Real-world successes, such as mRNA vaccines and decentralized CGT manufacturing, demonstrate how integrating advanced process controls, real-time PAT, and AI-driven quality systems can improve product consistency, compliance, and scalability while ensuring economic viability. Moreover, sustainable biotech innovation requires a holistic approach that combines technology-driven efficiency, regulatory foresight, and operational agility. By embracing digital transformation, optimizing workflows, and fostering cross-functional collaboration, industry leaders can drive a resilient, technology-enabled future for drug development while ensuring equitable patient access.

References:

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About The Author:

Fahimeh Mirakhori, M.Sc., Ph.D. is a consultant who addresses scientific, technical, and regulatory challenges in cell and gene therapy, genome editing, regenerative medicine, and biologics product development. Her areas of expertise include autologous and allogeneic engineered cell therapeutics (CAR-T, CAR-NK, iPSCs), viral vectors (AAV, LVV), regulatory CMC, as well as process and analytical development. She earned her Ph.D. from the University of Tehran and completed her postdoctoral fellowship at Johns Hopkins University School of Medicine. She has held diverse roles in the industry, including at AstraZeneca, acquiring broad experience across various biotechnology modalities. She is also an adjunct professor at the University of Maryland.