Martin Brown, Group Leader – Hit ID & Profiling
Anne-Sophie Houvenaghel-Wesse, Research Leader – Hit ID & Profiling
Roger Clark, Director – Hit ID & Profiling
Over the past three decades, laboratory automation has undergone a remarkable transformation. The first generation of automated platforms emerged to address a specific challenge: the need to screen ever larger compound collections against biological targets at industrial scale. These early systems, built around robotic arms, liquid handlers, and plate readers, revolutionised high throughput screening by increasing throughput and reducing manual intervention. However, they were often purpose-built, highly specialised installations designed to execute a narrow set of predefined workflows.
As drug discovery evolved, so too did the demands placed on automation. The industry's focus shifted from simply generating more data to generating better data, faster, and with greater reproducibility. In response, automation platforms became increasingly sophisticated, integrating collaborative robotics, nano-litre liquid handling, environmentally controlled storage systems, advanced detection technologies, and powerful analytical software. Equally important was the emergence of interoperability standards such as SiLA2, enabling previously isolated instruments to communicate seamlessly within a connected ecosystem.
Today, automation is no longer viewed as a collection of individual instruments but as the digital backbone of the modern discovery laboratory. End-to-end workflows can now encompass assay setup, compound management, experimental execution, data acquisition, and analysis within a single coordinated environment. Real-time process monitoring, automated quality control, and comprehensive data traceability have dramatically improved reliability while reducing downtime and failed assay runs.
Perhaps the most significant shift has been the move from fixed, application-specific platforms to highly flexible automation environments. Whereas earlier systems were optimised for large-scale screening campaigns alone, modern platforms can be rapidly reconfigured to support diverse experimental approaches and evolving project needs. This adaptability is particularly valuable in contemporary drug discovery, where laboratories must balance industrial-scale screening with the rapid Design-Make-Test-Analyse (DMTA) cycles that drive lead optimisation. Automation has therefore evolved from a tool for increasing throughput into a strategic capability that accelerates decision-making, enhances scientific quality, and enables a more agile approach to discovery.
While automation continues to increase throughput and laboratory efficiency, its greatest value lies in the generation of high-quality, reproducible data. Standardised workflows minimise experimental variability and improve confidence in assay results, allowing researchers to identify subtle biological effects across large compound libraries.
The importance of robust data is growing as artificial intelligence and machine learning become increasingly embedded within drug discovery workflows. Predictive models and advanced analytics depend on consistent experimental datasets. Automated platforms provide the level of quality and reproducibility required to support confident data-driven decisions and maximise the value of AI-enabled research.
Increasingly, automation and AI operate as complementary technologies. Automation generates reliable datasets for model training and prediction, while AI helps identify patterns and optimise future experiments. As these technologies become more tightly integrated, automation platforms will be able to dynamically adapt workflows in response to emerging insights generated from live experimental data.
Drug discovery organisations face growing pressure to screen larger compound collections, evaluate more sophisticated biological models, and generate actionable insights more rapidly than ever before. Meeting these demands requires technologies that can simultaneously improve speed, scale, and scientific quality.
Recognising these challenges, IQVIA Laboratories continues to invest in advanced automation capabilities that accelerate project timelines while maintaining rigorous standards of reproducibility and data integrity. By integrating assay assembly, liquid handling, workflow management, and analytical technologies into connected platforms, automation helps compress the timeline from experiment to insight while creating the data-rich environments needed for AI-enabled discovery.
The industry's shift toward increasingly physiologically relevant models is transforming assay design. Cell-based, phenotypic, high-content imaging, and other advanced approaches provide deeper insights into disease biology, target engagement, efficacy, and safety. However, these methods often involve complex workflows, multiple process steps, extended incubation periods, and sophisticated analytical technologies.
Automation provides the precision, consistency, and scalability needed to execute these complex assays reliably. Standardised workflows reduce variability, improve reproducibility, and enable researchers to generate richer datasets from biologically relevant models without compromising throughput. This capability is becoming increasingly important as organisations adopt New Approach Methodologies (NAMs), advanced cellular systems, and higher-density analytical technologies.
Cellular assays have become central to modern drug discovery and play critical roles throughout the discovery cascade, from primary screening through target validation, biomarker identification, and early safety assessment. Regulatory and scientific trends are driving increased reliance on human-relevant models, placing greater demands on laboratory workflows.
These assays are ideally suited to automation because they require precise execution across multiple complex steps. Flexible automation platforms reduce manual intervention, increase consistency, and improve scalability, enabling laboratories to meet the growing demands of both modern drug discovery programmes and AI-driven data generation strategies.
The value of automation extends beyond instruments and robotics. By removing repetitive manual processes, automation enables scientists to devote more time to experimental design, data interpretation, and solving complex scientific problems.
At IQVIA Laboratories, automation is not viewed as a replacement for scientific expertise, but as a means of enhancing it. Providing researchers with efficient, reliable tools allows them to focus on generating deeper biological insights and delivering greater value to clients.
Although automation, AI, advanced cellular models, and high-content analytical technologies are often discussed separately, they share a common requirement: the ability to execute increasingly complex biological workflows reproducibly and at scale. Flexible automation provides the operational foundation needed to bring these technologies together effectively.
The future of drug discovery will depend on the integration of advanced automation, sophisticated human-relevant biological models, and AI-driven analytics. Organisations capable of generating high-quality, reproducible, and biologically relevant data rapidly and at scale will be best positioned to accelerate scientific decision-making and improve research outcomes.
Ultimately, laboratory automation is no longer simply a tool for efficiency. It is a strategic enabler of modern drug discovery, supporting better science, faster decisions, and the development of new therapies for patients. Through continued investment in automation and AI, IQVIA Laboratories is helping shape a smarter, faster, and more connected future for drug discovery.