Leap Forward in Automation Anticipated for Small-Molecule Drug Product Manufacturing

Automation has been recognized for over a century to enable consistent, efficient manufacturing. In the past few decades, even the conservative pharmaceutical industry has begun to deploy automation solutions with the goal of increasing quality and reducing cost (1). The rate of adoption is steadily increasing as not only equipment innovations, but advances in digital technologies, have led to significant improvements in efficiency, reliability, and data analytics (2,3). While many of the earliest efforts at automation of pharmaceutical manufacturing were applied to oral solid dose (OSD) production, OSD manufacturers have been slower to pursue automation solutions than biologics producers. That trend seems to be changing (4).

What are the drivers for adoption of automation?

There are many drivers for the more widespread automation of OSD manufacturing operations, including increased drug substance and formulation complexity and pressure to reduce time and cost. Automation offers the benefits of speed, precision, consistency, and reduced manual interventions.

While time-saving and cost pressures are key motivators in adopting automation, these factors only scratch the surface of the technology’s potential, says John Nadzam, global manager, OSD equipment and novel technology in the pharma services business of Thermo Fisher Scientific. “The ability to automate processes acts as a force multiplier for efficiency, consistency, and quality in drug manufacturing. As modern pharmaceutical operations face increasing complexity, smaller batch sizes, and demands for faster scale-up from development to commercial production, automation helps reconcile these competing needs,” he observes.

From a quality perspective, automation supports right-first-time execution and data integrity by minimizing manual data entry and transcription, Nadzam explains. Automated data capture also enables real-time process understanding and tighter control, helping reduce deviations, improve compliance, and support ongoing process verification.

Automation also helps manufacturers address expanding workforce challenges. “As the industry faces workforce shortages and skills gaps, automation amplifies limited resources, allowing scientists and operators to focus on critical process understanding rather than repetitive tasks,” Nazdam notes.

Why is automation still underutilized?

While automation in small-molecule drug product manufacturing has moved well beyond the pilot phase and is now a strategic imperative, it remains underutilized and fragmented in traditional batch manufacturing.

Many operations use automation primarily for recipe management; proportional, integral, derivative loop control; and basic process data acquisition, according to Nadzam. He observes that use cases do persist across unit operations in areas like granulation, compression, and coating, but most do not currently benefit from comprehensive data integration. “In many instances, we still see reliance on paper records, manual signatures, and standalone data systems. These factors limit the ability to derive cross-unit insights or perform predictive analytics. The contrasts between biologics production and continuous manufacturing are stark compared to small-molecule batch production, which, in comparison, has been slower to modernize,” he states.

What are the benefits beyond incremental improvement?

Automation delivers precision and consistency much faster than human-driven systems. “When it comes to throughput and uptime, real-time data acquisition through automated processes enables closed-loop adjustments, reducing out-of-spec material and increasing equipment availability,” says Doug Hausner, senior R&D staff scientist in the pharma services business of Thermo Fisher. As an example, he highlights the integration of tablet press feedback with in-line hardness and weight testers to minimize rejection and downtime.

Many unit operations involved in small-molecule drug manufacturing also require tight control of critical process parameters that impact product quality attributes, and automation provides a means for improving performance by enabling real-time monitoring and adjustment of process performance (5–7).

Electronic batch records, meanwhile, increase data security and integrity by eliminating transcription errors and streamlining review by exception. “These efforts ultimately support attributable, legible and intelligent, contemporaneous, original, accurate principles and audit readiness,” Hausner states. They also support greater process understanding. Digital connection of multiple unit operations, he observes, allows teams to correlate process data, such as granule particle size distribution, tablet hardness, and coating uniformity, to reveal root causes of variability.

Automated systems with built-in analytics can also identify early signs of drift, allowing for proactive interventions (e.g., predictive maintenance and process optimization to improve quality), Hausner notes. They can also operate continuously and with minimal downtime, enabling faster cycle times, and higher output based on high-quality operational data. In tableting and encapsulation, for example, robotics can maintain consistent production rates while reducing manual handling.

Other benefits of automation include reduced waste generation, process deviations, and batch failures, all of which contribute to higher costs (6–8). The specific ways in which processes are enhanced vary for each unique unit operation, but overall, they add up to better process control and product quality. As a result, automation is becoming a necessity for small-molecule drug manufacturers seeking to establish and maintain competitive advantage.

Combination of coordinated hardware and software solutions

Effective automation in small-molecule drug product manufacturing is not achieved with a single technology. Both hardware and software work in concert to achieve enhanced process performance (2).

With respect to hardware, robotic solutions play an increasing role in small-molecule drug product manufacturing (9). Examples include robotic presses and feeders used in tableting and encapsulation processes, real-time monitoring tools such as those based on near-infrared (NIR) and Raman spectroscopy across many different unit operations, and autonomous mobile robots and robotic picking systems used in warehousing and packaging.

Software solutions today are equally important, as they enable integration of disparate automated unit operations with data flowing between them and supporting a holistic approach to small-molecule drug product manufacturing. As such, software advances are equally important to the expanding adoption of automation solutions.

Nadzam highlights the growing role of artificial intelligence (AI) and machine learning (ML) in improving the effectiveness of automation systems for small-molecule drug product manufacturing operations. “Not only can these tools forecast formulation challenges ahead of time, but they also enable innovators to tackle issues around solubility, permeability, bioavailability, optimal first-in-human dosing, packaging selection, investigational new drug data generation and scale-up planning—insights that can ultimately accelerate progress from development to commercialization,” Nadzam contends.

Required investments present challenges

Although the benefits of automating small-molecule drug manufacturing are increasingly clear, there are still several challenges the industry must overcome to see widespread adoption. This complex set of challenges must be navigated with care and foresight.

The two most significant challenges, according to Hausner, continue to be the high upfront investment required and the ongoing need for robust skills development. “Configuring automation requires both capital investment and technical expertise, which is why it is often outsourced due to internal resource constraints. However, the reality is that outsourcing can also increase project timelines and costs,” he observes. The need for significant upfront capital investment can also be a barrier to adoption (10), particularly for small-volume products, such as those targeting rare diseases with limited patient populations and personalized therapies.

Moving to automated small-molecule drug product manufacturing also requires a shift in perspective and often training of operators in new skill areas beyond operation of the new systems, such as in data analysis and troubleshooting. That requires effective and appropriate change management, which can be difficult to achieve but is crucial to success, Hausner says.

“Operators may initially view automation as restrictive or complex. Demonstrating how automation relieves them of repetitive documentation, enhances process visibility, and improves quality of life on the floor is key to success,” he notes.

Another challenge highlighted by Hausner is data standardization and contextualization. “Even with open protocols like open platform communications unified architecture, integrating legacy systems across vendors and facilities remains complex. Without consistent data structures, the promise of AI/ML-driven optimization remains difficult to realize,” he explains.

Regulatory expectations for validation and assurance of data security, integrity, and traceability can also create challenges as targets continue to evolve.

A defined roadmap and comprehensive strategy support success

The key to successful automation of small-molecule drug product manufacturing is to pursue the right strategy that leverages the organization’s broad expertise and process knowledge. “Automation is not simply a technical upgrade—it’s a strategic transformation,” insists Hausner. “To realize its full potential, organizations must adopt a holistic approach that blends technology, process design, and cultural readiness,” he says.

Companies should first, adds Hausner, establish cross-functional teams involving quality, information technology, regulatory, and operations personnel to ensure alignment and data integrity. Development of digital roadmaps that provide these teams with articulate short- and long-term goals for connectivity, data standards, and system interoperability will help keep manufacturing operations on track, he says.

In-depth process understanding is also essential, as it supports the use of a quality-by-design approach to process development, which in turns leads to robust, scalable processes. In addition, because human oversight remains essential for automation to be successful, developing internal automation expertise and building or training a core team capable of vendor management, configuration, and troubleshooting are essential, Hausner observes.

Drug developers should, continues Hausner, follow an approach that begins with smaller pilot projects and systems, with the ability to scale up to future needs. As an example, he suggests starting with focused automation projects such as process analytical technology (PAT) integration on a single-unit operation to demonstrate value, and then expand incrementally. Hausner notesthat working with contract development and manufacturing organizations that have these key processes in place can help make it easier for pharmaceutical and biotech organizations to adopt automation, reduce bottlenecks across the drug development continuum, and ultimately increase efficiency in bringing critical medications to patients.

How are barriers to adoption lowered?

Although technology is not the only crucial element to successful automation of small-molecule drug manufacturing, it is an essential component, and effective, advanced solutions are needed to ensure the maximum benefits can be realized by implementing automation.

Innovations in both equipment and software enabling smarter systems, seamless integration, and real-time responsiveness are not only improving performance, but also making implementation more accessible across scales and product types.

Several converging trends are lowering barriers to adoption of automation solutions for small-molecule drug product manufacturing operations, says Nazdam. They include a growing availability of open vendor architects as equipment suppliers increasingly support standardized communication interfaces, enabling interoperability; advanced AI/ML tools capable of predicting formulation behaviors and, therefore, addressing key challenges throughout small-molecule drug development; cloud-based manufacturing execution systems that improve scalability and enable enterprise-wide visibility; and digital twins and simulation tools that allow for virtual commissioning, predictive tuning, and training without disrupting operations.

What is the role of AI going forward?

Ongoing innovations in technology that enable better integration of automation systems, wider applications for real-time monitoring, and greater use of AI/ML are expected to not only drive greater adoption in small-molecule drug product manufacturing but increase their performance and the benefits they provide.

A broadening array of advanced PAT tools will enable more effective real-time monitoring for better process control and improved quality while supporting higher productivity through reduction in the need for traditional batch testing. Wider implementation of PAT-driven feedback control will also, notes Hausner, enable greater integration between operations, even in traditional batch environments.

Broader adoption of AI/ML is also anticipated by Hausner as these tools become more accessible. In the short term, they will support optimization of small-molecule drug product manufacturing across all unit operations by enabling real-time adjustments and predictive maintenance. Longer-term, the integration of AI-driven decision support, digital twins, and autonomous optimization loops will redefine how pharmaceutical facilities are operated, according to Hausner. “These systems will use live data streams to continually refine process parameters and minimize human intervention while maintaining compliance through automated documentation,” he notes.

“The ultimate vision,” concludes Hausner, “is a self-regulating, fully digital plant where real-time data drives dynamic control, lifecycle management, and continuous improvement.”

References

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About the author

Cynthia A. Challener, PhD, is a contributing editor to Pharmaceutical Technology®.

Article details

Pharmaceutical Technology®
Vol. 49, No. 9
November/December 2025
Pages: 18–21

Citation

When referring to this article, please cite it as Challener, C.A. Leap Forward in Automation Anticipated for Small-Molecule Drug Product Manufacturing. Pharmaceutical Technology 2025 49 (9).