Breaking Barriers in bioanalytical automation
Challenges, milestones, and future pathways
Tom Zhang, Chief Scientist, Worldwide Clinical Trials
Bioanalytical automation has made remarkable progress over the past two decades, yet adoption across bioanalytical laboratories remains limited. In this interview, I will share a comprehensive perspective on the evolution of automation in bioanalysis - tracing key technological milestones and innovations that have shaped the field. I will also discuss the practical challenges that often slow adoption, including system integration, cost–benefit considerations, and the need for specialized expertise. Drawing on real-world case studies, I will highlight successful implementations and provide insights into strategies that can help laboratories overcome barriers and fully realise the potential of automation in bioanalytical science.
1. Dr. Zhang, you have witnessed the evolution of bioanalytical automation over the past two decades. How would you describe the most transformative milestones that have significantly changed the landscape of bioanalysis during this period?
Over the past two decades, the most transformative milestones in bioanalytical automation have redefined how quality, speed, and reliability are achieved in regulated science.
The first major inflection point was the adoption of robotic liquid handling systems. Transitioning from manual pipetting to programmable platforms introduced a level of reproducibility and scalability that had never been possible, enabling high-throughput operations without compromising precision.
A second breakthrough came with the automation of sample preparation integrated into plate-based immunoassay platforms. What once relied heavily on the skill and consistency of individual operators became standardized, driving both efficiency and robustness across complex workflows.
Equally pivotal was the development of digital audit and record systems. By embedding ALCOA+ principles directly into routine operations, these systems elevated data integrity and regulatory compliance from add-ons to intrinsic features of the bioanalytical process.
2. When examining the trajectory of automation in bioanalytical science, which innovations do you believe have had the greatest impact on laboratory efficiency and data reliability, and why?
The first innovation came with robotic liquid handling, which replaced manual pipetting with programmable systems. This shift removed a significant source of variability and enabled reproducibility and throughput far beyond what manual workflows could achieve.
The second advance was the automation of sample preparation integrated with plate-based immunoassays. Multi-step processes that once depended on individual operator skill became standardized, ensuring consistent quality across large study volumes and redefining the scientist’s role from manual execution to designing and overseeing workflows.
Blurb: Automation has redefined how quality, timelines, and compliance are achieved in regulated bioanalytical science.
3. Adoption of automation remains uneven across laboratories worldwide. From your perspective, what are the primary systemic barriers preventing widespread integration, despite the demonstrated technological advantages?
Cost is the most immediate hurdle — automation requires substantial upfront investment in instruments and software, while the return is often realised over many years. Smaller labs or those operating on tight margins struggle to justify that outlay.
A second barrier is integration. Automation doesn’t work in isolation — it must fit into existing LIMS, quality systems, and regulatory frameworks. Many labs underestimate the complexity of harmonising new automated workflows with legacy processes, which leads to delays or partial adoption.
Equally important is expertise. Automated systems demand new skill sets — engineers who can program, validate, and maintain them. Many labs lack this capability and find retraining or hiring a challenge.
Finally, there’s a cultural barrier. Bioanalysis has historically relied on skilled bench scientists, and shifting to a model where the scientist supervises systems rather than performing every step can meet resistance.
4. System integration is often cited as a complex hurdle. Could you elaborate on the most pressing integration challenges laboratories face when adopting bioanalytical automation systems, and how these might be addressed?
A major hurdle is hardware integration. Laboratories often rely on multiple automation instruments, each with its own interface and workflow. Getting these systems to communicate and function as a cohesive unit can be a significant challenge.
Another difficulty is regulatory readiness. Many commercial automation platforms do not include features such as secure audit trails, electronic signatures, or full traceability. To bring them up to GLP or GCP standards, labs are forced to invest in customization, which drives up cost and adds a heavy validation burden.
Overcoming these issues requires both technical solutions — such as open standards and modular designs—and organizational commitment to building the expertise needed to integrate and validate complex automated systems.
5. In terms of financial considerations, what framework or models do you suggest laboratories use to accurately assess the cost– benefit equation of automation investments?
ROI.
6. Specialised expertise is another recurring concern. What kind of workforce training, skill development, and cross-disciplinary collaboration are necessary to ensure automation is successfully implemented and sustained in bioanalytical labs?
Technical training: scientists and technicians need to go beyond basic operation of automated platforms— they must also be able to troubleshoot issues and program workflows. This calls for a solid understanding of both the automation systems themselves and the underlying bioanalytical science.
Cultural training and role evolution: As manual bench work gives way to system oversight, workflow design, and data interpretation, training should help staff adapt to this new role. The emphasis should be on how automation enhances their contribution rather than replaces it.
7. Based on your experience, what distinguishes laboratories that have successfully adopted automation from those that continue to struggle with partial or failed implementations?
Successful adoption depends on whether automation can consistently meet assay requirements, generate data that comply with GCLP standards, and deliver a measurable gain in efficiency. Just as important is its ability to meaningfully reduce manual workload, freeing scientists to focus on less repetitive tasks. Labs that align these elements with their workflows tend to integrate automation smoothly, while those that overlook them often face stalled or incomplete implementations.
8. You’ve emphasized real-world case studies in your work. Could you share an example of a project where automation not only enhanced efficiency but also contributed to improved scientific or clinical outcomes?
A notable example was the adoption of automated liquid handling and sample preparation in a Phase III pharmacokinetic study involving thousands of clinical samples. Before automation, the workflow depended on manual pipetting, creating significant efficiency bottlenecks. With integrated robotic systems, each unit processed up to eight times more samples per day compared to manual methods.
The real impact, however, came from gains in precision and accuracy. Automation removed operator-dependent variability, strengthening the reliability of exposure–response modeling. At the same time, the run failure rate dropped sharply, reducing the need for investigations and repeat assays.
In this case, automation went well beyond improving throughput — it elevated data quality to a level that directly shaped clinical outcomes. That combination of efficiency and scientific rigor represents the true value of automation.
9. How do you see the role of bioanalytical automation evolving in the context of increasing complexity in drug development, particularly with biologics and cell and gene therapies?
Bioanalytical assays are becoming increasingly complex in order to meet specific scientific and regulatory purposes. As complexity grows, so does analyst-to-analyst variability, which can negatively impact assay sensitivity, reproducibility, and overall consistency. Automation addresses this challenge by standardising workflows and reducing human error, thereby improving both reliability and throughput.
However, emerging drug modalities often demand new analytical platforms. These platforms are not always readily compatible with existing automation systems, whether due to hardware limitations or lack of software integration. This mismatch constrains the application of automation in certain cutting-edge assays and highlights the need for more flexible, modular automation solutions.
Looking forward, the future of bioanalytical automation will depend on bridging this gap—developing adaptable systems that can evolve in parallel with new therapeutic modalities, ensuring that innovation in drug development is matched by innovation in laboratory technology.
10. To what extent do you believe artificial intelligence and machine learning will shape the next generation of bioanalytical automation, and what limitations should scientists be cautious of?
I’m still learning how AI can support bioanalysis. At this moment, I do not have much experience here.
11. Collaboration between vendors, regulators, and laboratory scientists is essential. How do you envision these stakeholders working together more effectively to accelerate the adoption of automation?
To collaborate more effectively, each stakeholder must address a specific responsibility.
Vendors should commit to open technical details and long-term support to prevent laboratories from being locked into fragile, proprietary systems.
Regulators can accelerate adoption by providing clear expectations for bioanalytical automation — spanning system validation through application — and by embracing a risk-based mindset that emphasizes proactive risk identification and mitigation, enabling innovation without compromising compliance.
Laboratories and automation users must actively participate in pre-competitive consortia to establish common data standards, share validation practices, and deliver practical feedback to both vendors and regulators.
12. What regulatory considerations or compliance frameworks do laboratories need to be mindful of when introducing automated bioanalytical platforms into their workflows?
From a regulatory standpoint, compliance with 21 CFR Part 11 and EU Annex 11 is fundamental, as both define the requirements for electronic records, electronic signatures, and system security. Equally important is GAMP 5, which establishes a structured, risk-based approach for computer system validation. Building on this, regulators are now promoting the Computer Software Assurance framework, which directs validation resources toward functions that pose the greatest risk to data integrity and patient safety, rather than applying uniform testing across all features. Compliance also extends beyond initial validation. Laboratories must maintain robust audit trails, role-based access controls, cybersecurity protections, disaster recovery plans, and periodic system reviews.
13. Looking ahead, what strategic recommendations would you give laboratory leaders who are planning to future-proof their infrastructure with scalable and flexible automation solutions?
Laboratories should begin by designing modularity and scalability. Over-customized systems often lock workflows into rigid formats that quickly become obsolete. Modular platforms with open interfaces, by contrast, allow infrastructure to expand or pivot as new modalities and assay formats are introduced.
Equally critical is building a robust digital backbone. Automation delivers value only if the underlying data infrastructure supports it. Leaders should ensure seamless LIMS/ELN integration, enforce standardized metadata, and adopt API-driven connectivity. This digital foundation enables instruments, analytics, and compliance systems to work as one coherent ecosystem.
Finally, leaders must invest in people as deliberately as in platforms. Scalable automation requires cross-trained teams: scientists who understand coding, engineers who appreciate assay biology, and QA staff fluent in digital validation. Continuous training and clear career paths for “automation scientists” are essential to sustain momentum and protect investments.
14. Finally, if you could redefine the roadmap for bioanalytical automation adoption over the next decade, what priorities would you place at the forefront to ensure both scientific progress and broad accessibility?
Data integrity: Build automation around ALCOA+ and Part 11/Annex 11 from the start. Immutable audit trails, role-based access, synchronized time-stamps, and automated QC at data entry make integrity a default, not an afterthought.
Flexible system: Adopt flexible, “LEGO-style” systems with swappable decks and configurable modules. This keeps platforms relevant as assays evolve, avoiding costly re-platforming and preserving long-term value.
Accessible automation: Shared, managed hubs—whether CRO-hosted or consortium-based—can deliver automation as a service. This lowers the barrier for smaller labs and expands access to compliant, high-quality bioanalysis.
Pre-validated automation methods and datasets: Starter kits with validated methods, plate maps, QC rules, and reference datasets cut onboarding time and reduce variability. Standardization speeds adoption while ensuring comparability across sites.
Cybersecurity: Protect connected platforms with hardened endpoints, segmented networks, and tested backup/restore systems. Regular incident drills ensure rapid recovery, safeguarding both compliance and sponsor trust.
Author Bio
Tom Zhang, PhD, is Chief Scientist at Worldwide Clinical Trials, dedicated to biologics bioanalysis. He previously held senior leadership roles as Senior Director at WuXi AppTec and Laboratory Director at Frontage Laboratories. Dr. Zhang earned his doctorate in Bioengineering from Penn State University and has been recognized with multiple honors, including Penn State’s OEA Award (2023), the 40 Under 40 Award (2021), and a National Scholarship from China’s Ministry of Education (2009–2014). He has also contributed to academia and industry as a member of Penn State’s Biomedical Engineering Industrial and Professional Advisory Council since 2020.
