Toxicological testing is a high-stakes process that ensures the safety of large- and small-molecule therapies before they reach patients. However, managing the intricate schedules required for such testing has traditionally relied on manual methods prone to inefficiencies and delays. Even today, many organizations still rely on basic tools like Excel spreadsheets to track these critical processes. Enter artificial intelligence-powered scheduling platforms.  

Artificial intelligence should be designed to augment, not replace, human intelligence, intuition, or instincts. That said, artificial intelligence-powered platforms enable real-time adjustments, data-driven insights, and seamless team coordination. That means toxicology test results are generated faster, more reliably, and more accurately than ever. There are already companies using artificial intelligence for this critical testing, streamlining operations and improving testing for customers along the way. 

Traditional toxicology scheduling challenges 

Scheduling toxicological testing is an inherently complex task. It becomes even more challenging in large facilities where animal studies, technician shifts, and interdepartmental workflows must align. While spreadsheets are familiar to most scientists and are easy to create, manipulate, and replicate, they fall short when scheduling toxicology testing.  

 Manual scheduling methods are vulnerable to inefficiencies, particularly in testing room operations where precision and timing are critical. Schedules created by hand are time-consuming to develop and susceptible to errors—missteps that can disrupt workflows and compromise data quality. For technicians and staff, manual scheduling can mean starting the day without a clear sense of their to-dos, leading to bottlenecks and missed opportunities for skill development. 

Another drawback of manual methods is the lack of real-time data. Supervisors face an uphill battle in maintaining optimal efficiency and responding to unforeseen changes without real-time access to task completion times, adjustments, and technician availability. The absence of this data can also create costly delays and affect testing accuracy. As toxicological operations expand, the limitations of manual scheduling become more pronounced, clearly demonstrating a need for more sophisticated solutions. Once implemented, these platforms also allow supervisors to return to their roles leading teams to achieve business goals.

The benefits of aI-powered toxicology operations 

Moving from manual scheduling to an artificial intelligence-driven platform signals a commitment to efficiency and a higher quality of decision-making, interdepartmental coordination, and workforce management. The improvements that follow can impact operational success, compliance, and workforce satisfaction.  

Increased efficiency & throughput  

An artificial intelligence-driven scheduling system’s most immediate benefits are a notable increase in testing room utilization and technician productivity. “In 2020, my team and I built and implemented a proprietary artificial intelligence system called ‘Pyxis’ that drove an 18% improvement in room efficiency and a 23% increase in technicians’ daily tasks,” says Zoe Dai, Executive Director, Automation Department, at WuXi AppTec. These gains result from dynamic, real-time scheduling that continuously matches resources with specific needs, eliminating the delays and gaps common in manual scheduling. 

In traditional setups, reshuffling schedules to accommodate unexpected events—e.g., new study requirements or delays in supply—can take hours of manual effort and disrupt the workflow for multiple teams. However, artificial intelligence algorithms can respond to these shifts instantly, reorganizing schedules and redistributing tasks in minutes. This agility reduces downtime and optimizes schedules even under variable conditions. This allows the laboratory to handle more studies without compromising the quality or pace of work. 

Moreover, as the system tracks all operations in real-time, facility leaders can quickly identify patterns in task durations, bottlenecks, or resource usage. Consistently fine-tuning testing schedules based on these insights allows facilities to maintain high productivity even as demand fluctuates. 

Strategic decision-making 

Automating workflows is just one of the advantages of artificial intelligence-powered systems. The vast amounts of real-time data gathered on testing operations can be invaluable for building a granular, data-driven view of overall productivity, task durations, and potential issues. Facility managers can then reference interactive dashboards that monitor performance at a glance, diagnose operational challenges, and make informed adjustments. 

For example, analyzing time stamps and task completions allows managers to detect where specific procedures are causing delays and investigate their underlying causes. This data visibility allows for faster intervention, turning what might otherwise be reactive problem-solving into proactive, strategic adjustments. In toxicology testing, where precision and timing are crucial, having this insight into operations is invaluable for maintaining regulatory compliance, ensuring test quality, and optimizing resource usage. 

Data-driven decision-making also enhances quality control and compliance tracking. Each task is logged with precise details to support more accurate reporting and audits. This level of record-keeping assures clients and regulatory bodies that protocols are strictly followed. It also demonstrates the lab’s competitive edge regarding reliability and transparency. 

Improved cross-departmental collaboration 

Beyond the toxicology team, artificial intelligence-powered platforms promote efficient collaboration with other departments. In a typical toxicology study, multiple departments—e.g., pathology, quality assurance (QA), pharmacy, etc.—must coordinate their work, but manual scheduling methods can fragment communication and create operational silos. In these cases, the artificial intelligence platform can function as a centralized scheduling hub, where all departments can see the entire workflow and receive real-time updates on task progression and schedule changes.

For example, the QA team might receive a prompt when a testing room is ready for inspection, ensuring alignment between compliance checks and testing schedules. With everyone working from the same scheduling platform, transitions between tasks become seamless, and issues such as duplicate tasks or missed handoffs are minimized or eliminated. 

An artificial intelligence platform can also enable cross-departmental adjustments on short notice. For example, if the toxicology team encounters an unexpected obstacle, such as equipment failure or sample delays, the artificial intelligence automatically reschedules workflows to minimize disruption. This alignment creates a more integrated, agile operation that can keep pace with both study demands and regulatory requirements. 

Workforce management & employee growth  

An often-overlooked benefit of artificial intelligence scheduling is its impact on workforce satisfaction and growth. Toxicology testing demands a skilled and engaged workforce, yet manual scheduling systems rarely account for technician workloads and career development. The more robust artificial intelligence platforms can incorporate workforce management tools that optimize workload distribution and support employee growth and morale. 

“The Pyxis system analyzes each technician’s workload and task complexity,” Dai says. “Using this information, the platform then assigns tasks according to each technician’s skill level and availability while still protecting against burnout.” 

Dai explains how technical staff can use the platform to plan vacations and paid time off (PTO) with confidence, knowing they’ll have adequate coverage. The predictability this technology provides reduces attrition rates and fosters a more motivated, resilient workforce. 

Beyond optimizing workloads, robust artificial intelligence platforms pair experienced technicians with junior staff, enabling knowledge-sharing and skill development. The platforms can support performance reviews and alert managers when training opportunities arise by tracking technician skills, accomplishments, and growth milestones. This structured monitoring helps with career advancement and contributes to cultures of continuous improvement. Investing in workforce development can strengthen employee retention and help labs build capable, adaptable teams. 

Addressing concerns about artificial intelligence in toxicology  

The introduction of artificial intelligence into toxicological operations has spurred transformative changes, but it has also raised questions about the role of human judgment. To be sure, artificial intelligence technology is not designed to replace human judgment; instead, it serves as a powerful complement.  

While artificial intelligence excels at handling large volumes of data and automating repetitive tasks, humans bring essential critical thinking and flexibility. This human element remains indispensable in toxicology, where studies often require nuanced decisions based on complex variables. An artificial intelligence platform could conceivably optimize technician schedules and predict resource needs, while a human supervisor oversees the broader study design and makes judgment calls when unexpected challenges arise. This division of labor can enhance efficiency without compromising the integrity of the testing process. 

Moreover, the data insights provided by artificial intelligence can strengthen human decision-making. With access to accurate, real-time data, technicians and study directors are better equipped to identify issues early, make informed adjustments, and ensure compliance with regulatory standards. The combination of artificial intelligence’s precision and human intuition enables a more resilient, adaptable approach to toxicology that leverages the best of both worlds to maintain high standards of safety, quality, and ethical responsibility. 

As artificial intelligence capabilities evolve, its role in toxicological testing will expand far beyond scheduling. By integrating predictive analytics, artificial intelligence-driven systems will play an integral role in end-to-end operational optimization, streamlining processes from initial study setup to final analysis. artificial intelligence applications in pathology and data analysis are also increasing. For example, artificial intelligence -powered image recognition can assist pathologists by quickly and accurately identifying patterns in sample slides, reducing analysis time while maintaining precision.  

A final word 

Integrating artificial intelligence platforms into toxicology operations has led to remarkable advancements in the field. It has revolutionized scheduling, enhanced data-driven decision-making, and facilitated improved collaboration across departments. The potential for further improvements in efficiency, quality, and cross-functional collaboration through artificial intelligence-driven systems is vast.  

As the industry continues to adopt and refine these technologies, toxicological testing will become faster, more accurate, and more adaptable to changing demands. Embracing artificial intelligence technology is essential for forward-thinking drug sponsors and developers. It will pave the way for more efficient, precise, and innovative approaches to public health.

Bill Harrison is the Vice President and Head of the WuXiAppTec Toxicology business units based in Suzhou, Chengdu and Nantong, China. Bill is also the Global Medical Device General Manager for the site in Guoxiang, China, St. Paul, Minnesota and Atlanta, Georgia in the U.S. Prior to joining WuXi AppTec, he was the founder and CEO of VIDA Sciences, a company dedicated to serving CROs focusing on performance improvement. Bill holds a Bachelor of Science degree in Biomedical Engineering from the New Jersey Institute of Technology and has been an invited lecturer in North America, Japan, China, Korea and Australia.