Automation as a Strategic Enabler in Small Molecule Drug Development and Manufacturing

Key Takeaways
Automation in small molecule manufacturing improves quality, consistency, and cost efficiency by reducing manual intervention, variability, and batch failures while enabling tighter process control.
Robotics, process analytical technology (PAT), and digitalization accelerate development timelines, supporting high-throughput experimentation, real-time monitoring, and faster route and formulation optimization.
Artificial intelligence (AI) and machine learning (ML) enhance predictive capabilities, enabling trend analysis, early deviation detection, and more robust process control strategies.
Automation adoption is driven by increasing molecular complexity, workforce shortages, and regulatory expectations, particularly for highly potent APIs and small-volume, personalized medicines.
Successful automation requires a holistic, cross-functional strategy, integrating process design, data infrastructure, regulatory planning, and organizational readiness.

How Automation Improves Efficiency, Consistency, and Control

Efficient and cost-effective small molecule drug substance and drug product manufacturing require design and development of optimum processes that can be tightly controlled to ensure consistent, high-quality performance. Automation solutions are helping improve all these activities and are becoming essential as the complexity of small molecule active pharmaceutical ingredients (APIs) and formulation and drug delivery technologies continue to increase.^1–4

Automation reduces the need for manual interventions, thereby reducing the risk of human errors, contamination, and variability. When combined with real-time monitoring technologies, it also supports continuous adjustment of process conditions, ensuring that processes operate more consistently and provide better yields of higher-quality products on a consistent basis. Often, automated systems have faster cycle times and fewer process deviations and batch failures and result in less waste generation, leading to significant cost savings. Automation also frees operators from performing repetitive manual tasks, allowing them to focus on more value-adding activities. As a result, automated processes are more efficient and consistent and typically provide higher-quality products.

Digitalization, which occurs in conjunction with automation, also leads to increased data security and integrity, which supports regulatory compliance. Impacts of changes in process parameters for upstream unit operations on downstream process steps also can be better understood and controlled. The incorporation of artificial intelligence (AI) and machine learning (ML) algorithms into automated systems, meanwhile, enables predictive capabilities that can save additional time and money.

Importantly, automation can improve the efficiency and economics of not just small molecule API and drug product manufacturing but process development as well.⁴ It enables more rapid identification and optimization of drug candidates with greater safety and efficacy profiles, helping to bring better, more cost-effective medicines to patients more quicky.

Market and Operational Pressures Accelerating Automation Uptake

Bringing safe and effective new drugs to market is a costly and time-consuming endeavor. Drug companies are under tremendous pressure to accelerate timelines and reduce costs without sacrificing quality and safety and despite ever-increasing molecular, formulation, and delivery complexities, the shift to personalized medicines that require small-volume production, the shortage of skilled workers, and rising regulatory expectations and supply chain risks. Automation and digitalization of small molecule drug substance and drug product manufacturing have become important strategies for overcoming many of these challenges.^1,5

A few examples highlight how companies are leveraging automation in small molecule pharmaceutical manfuacturing.5 Janssen implemented a continuous process for production of an antiviral with a higher yield (+8%) and significantly lower production time (-40% a) compared with the original batch process. Verte, meanwhile, reduced setup time for a cystic fibrosis drug by 50%. Merck uses AI technology to detect early blend nonuniformity to reduce batch rejections by 30%, while robotic packaging lines installed by Roche have reduced labor costs by 25% and increased throughput by 15%. Eli Lilly used a digital twin model of a solid dose line and reduced the process development time by 30%. Takeda uses inline near-infrared and Raman analyses and a cloud-based multivariate model for real-time release of tablets.

Not surprisingly, the demand for automation systems for small molecule drug product manufacturing is increasingly at a healthy pace.⁶ The value of the small molecule drug product manufacturing automation market is projected to expand at a compound annual growth rate of 11.6% from $5.42 billion in 2024 to $11.68 billion by 2031.

Companies are also automating API synthesis processes. Novartis has installed technology for crystallization processes to improve yields and reduce solvent consumption.⁵ Both the safety and efficiency of highly potent API manufacturing are boosted with automation.⁶ Continuous processing in miniaturized flow reactors, meanwhile, supports process intensification and automation, providing more agile manufacturing, higher quality, and improved sustainability, combined with reduced footprints and cost — even for complex molecules.⁷

Automation in API Discovery Development and Manufacturing

Automation and digitalization have been impacting discovery of APIs and process development of drug substances. Examples include electronic lab notebooks, robotics for synthesis and analysis of compound libraries used in structure–activity relationship studies during the discovery phase, and statistical software and high-throughput technologies for design-of-experiment studies used during process optimizaiton.^2,4,8

Automation solutions are available that leverage miniaturization and parallelization to run processes from start to finish, including reagent addition and sampling and analysis, dramatically accelerating discovery and development efforts. Algorithms are also available for rapid, comprehensive analysis of literature data for identifying potential candidates, synthetic transformations of interest, and even raw material sourcing options.

Automated systems have also been developed that combine advanced algorithms and computational models to accelerate the selection of optimum synthetic routes, taking into consideration different intermediates and synthesis and purification strategies for each, as well as process conditions, thermodynamic and kinetic viability, environmental impacts, atom economy, and availability and cost of starting materials, among other factors.^4,8 High-throughput technologies are important not just for synthesizing and purifying large numbers of candidates but also for rapid analysis across early to late-stage development activities. Tools are even available for prediction of scalability challenges. Ultimately, automation can facilitate route selection and efficiency optimization, decreasing both development time and risk.

Real-time process monitoring using process analytical technologies (PAT) is another key enabler of accelerated process development.⁸ It provides immediate, in-process data for rapid evaluation of process performance. PAT in combination with automation solutions also makes flow chemistry (continuous processing) possible, which offers additional advantages with respect to time, cost, and quality and supports simpler scale-up from lab through commercial production. An increasingly wide array of sensor technologies and PAT tools are leading to growing use of real-time monitoring for pharmaceutical R&D.

Innovations are also being made in processing techniques leveraging automation and PAT. One example from National University of Singapore (NUS) researchers combines automated solid-phase synthesis and continuous flow (SPS-flow) for preparation of peptides and oligonucleotides.⁹ The ability to introduce modifications early in the process makes this technique suitable for rapid production of multiple derivatives during discovery and development.

Automation of Formulated Drug Production

Small molecule drug product manufacturing involves many different unit operations, depending on whether the product is an oral solid dosage form, a particulate product formulated as a suspension, an oral liquid solution, or a sterile injectable. Automation can be used for simpler tasks, such as batch recipe management, and more complex activities, such as in-line measurement of hardness and weight, to robotic tablet counting and bottle filling. Continuous processing solutions are also available, such as for tableting (from excipient and API loading and blending to tablet compression) and coating. Modern machine vision technology, meanwhile, supports inspection at high processing speeds, while robotic dispensing replaces repetitive, time-consuming tasks.

Building a Strategic Framework for Successful Automation

The key to successful automation of small molecule drug substance and drug product development and manufacturing is to select the most appropriate technologies for the given application and the expertise and knowledge of the organization.¹ Not just the technology must be considered, but also the design of the process and the cultural awareness throughout the company.

New solutions should be developed and implemented by a cross-functional team that includes representatives from plant operations management, process engineering, quality, regulatory, and IT, and only for processes for which the organization has deep process knowledge. The team needs to understand the problem, consider any necessary adjustments to process flows to ensure the maximum benefits of automation are realized, establish clear objectives, and prioritize the tasks that will provide the greatest rewards.^2,4

It is also essential to remember that automation requires specialized hardware and software that must work well together if optimal performance is to be realized.¹ Staying on top of advances in both types of technology is equally important, particularly if the solutions leverage AI and /or ML. Furthermore, in addition to deep process knowledge, expertise in the installation and operation of the systems is a must. Conducting a process audit is highly recommended to identify potential problem areas, as is partnering with an experienced automation integrators.² Ideally it is best to start with smaller, pilot projects and expand more broadly into large-scale systems once the solution has been demonstrated to be effective.^1,2

Last but not least, incorporation of appropriate analytic technologies and planning for effective data integration and sharing across unit operations and development phases are essential.⁴

Practical and Regulatory Challenges to Automation Adoption

The rate of adoption of automation solutions for small molecule API and drug product development and manufacturing has been slower than might be expected given the numerous benefits it offers. The key hindrances to implementing automation can be attributed to the relatively high upfront investment that must be made and the need for specialized skills that most companies that have not yet automated operations lack.^1–3 Many firms elect to outsource automation projects, but that can introduce additional costs and extend timelines. Once projects are completed, operators often must be trained not just in how to use automation systems but data analysis, maintenance, and other supporting activities.

The need to validate any new automation technologies introduced to GMP environments, whether high-throughput solutions for quality control laboratories, PAT and flow chemistry for API synthesis, or robotics for tableting and packaging, can be an additional challenge.⁵

The Next Phase of Automation and Digitalization

Despite these challenges, implementation of automation solutions in small molecule drug substance and drug product development and manufacturing is increasing as the benefits with respect to productivity, quality, and cost are clearly demonstrated by early adopters. Advances in automation and digitalization technologies are also helping overcome the challenges associated with automation. Improvements in PAT capacities, growing availability of software solutions that support standardized communication interfaces, and more effective digital twin and other simulation, cloud-based solutions for data sharing are just some examples.^1,2

More solutions leveraging AI/ML are also becoming available and expected to support accelerated discovery and process development, as well as better trending and predictive modeling for commercial processes.^1,4 Integration of these intelligent technologies into traditional workflows from the lab to the plant will further boost efficiencies and reduce time to market.²