Here’s what we know about the role of laboratory software in drug discovery

Drug discovery research increasingly relies on laboratory informatics to manage vast amounts of data. Laboratory information management systems (LIMS) and electronic laboratory notebooks (ELNs) enhance efficiency, data integrity, and collaboration. Additionally, AI-driven analytics offer new ways to interpret experimental results. However, implementing these technologies presents challenges, such as cost, scalability, and user adoption.

The role of LIMS and ELNs in drug discovery

LIMS and ELNs are informatics tools that enable today’s drug discovery laboratories to handle large amounts of experimental data efficiently. LIMS facilitate sample tracking, metadata management, and standardization of experimental protocols (1). In drug discovery, LIMS streamline data collection and organization from high throughput screening assays, analytical instruments, and experimental procedures, making it easier for researchers to compare results and maintain consistency. “LIMS allow scientists to standardize their processes and more easily collaborate with each other in real time to reduce duplication of effort,” said Eric Borries, a manager at Clarkston Consulting, a firm offering laboratory informatics support services such as assistance with LIMS and ELN implementation.

ELNs provide a digital alternative to traditional lab notebooks, allowing researchers to document experiments, record observations, and analyze results in a searchable and shareable format (1). “Using these technologies makes analyzing large amounts of research data faster and more reliable. One way to accomplish this is with interfacing instruments. LIMS/ELNs enable data movement from the instrument to the LIMS and/or ELNs seamlessly and without the risk of transcription error,” said Tina Yauger, a consultant at Clarkston Consulting. Unlike paper-based systems, ELNs allow real-time collaboration, regulatory compliance, and streamlined data access.

Managing high volume data

Modern laboratory informatics systems have transformed the way researchers collect, analyze, and share data. High throughput screening, automation, and multiomics approaches have resulted in an explosion of experimental data (2).

The Cancer Genome Atlas, for example, has generated in excess of 2.5 petabytes of data, with researchers fully expecting these volumes to increase as new technologies continue to emerge (3). Yet many research institutions still rely on outdated data management practices, reducing efficiency, and creating reproducibility challenges. Inadequate data management can result in researchers needing to repeat experiments, waste resources, and experience delays in drug discovery pipelines.

In addition to LIMS and ELNs, AI-driven analytics tools help researchers navigate this data surge. Machine learning algorithms detect patterns, predict compound efficacy, and optimize assay conditions (4). AI-powered image analysis programs, for instance, enhance high-content screening workflows by rapidly identifying promising drug candidates (4). For example, Recursion Pharmaceuticals utilizes AI-driven pattern recognition to analyze high-resolution photos from cell biology experiments, integrating this information into their data models (5). Similarly, predictive modeling tools can improve decision-making by identifying the most viable compounds, reducing reliance on costly and time-consuming experimental iterations (6). As an example, Insilico Medicine’s AI platform uses predictive modeling to assess biological and chemical interactions to predict drug effectiveness (7).

Challenges and solutions in informatics implementation

Despite their advantages, laboratory informatics tools present adoption challenges. Budget constraints often limit access to sophisticated LIMS and ELN solutions, and many institutions struggle with the cost of software licensing, infrastructure upgrades, and ongoing maintenance. However, leveraging cloud-based or open source solutions substantially reduces upfront costs, minimizes infrastructure expenses, and simplifies workflows, ultimately saving resources.

High turnover in research institutions complicates informatics implementation. Training new researchers on complex informatics systems requires time and resources. Without standardized data structures, experimental records may become fragmented or lost when personnel leave (8). To address this, institutions should design user-friendly interfaces and implement thorough onboarding for new users.

Staffing shortages in clinical and research laboratories further exacerbate these challenges. An American Society of Microbiology Workforce Survey revealed that 80 percent of participants had difficulty filling open medical laboratory technologist positions, leading to delays in research and increased workloads for existing staff (9). Automated data management solutions, such as LIMS and ELNs, alleviate this burden by reducing manual data entry tasks and enabling researchers to focus on higher-value activities. “However, implementing these systems is not a simple undertaking. Labs need to consider the impact of an increased turnover rate and ensure the system can be easily adopted by new staff,” Borries added.

Overcoming software adoption barriers

Researchers may hesitate to adopt new laboratory software due to usability concerns, workflow disruption, and the perceived complexity of implementation (10). Previous negative experiences with rigid or cumbersome software can create further resistance to change (10). To overcome these challenges, institutions should involve researchers early in the process of software selection, system design and customization. By doing so, labs can ensure that LIMS and ELNs meet their needs without imposing unnecessary constraints on experimental workflows.

Expert guidance from external consultants can also facilitate software adoption. Informatics specialists can assist labs with vendor selection, system customization, and staff training, smoothing the learning curve associated with new software. Their expertise ensures that the chosen system aligns with research objectives, minimizing disruptions and maximizing long-term benefits.

Balancing flexibility with standardization is also key to successful adoption of LIMS and ELNs. While researchers value the ability to customize workflows, excessive rigidity in system design can hinder creativity and discourage adoption. “If an ELN isn’t properly designed for a drug discovery laboratory, researchers can struggle with too much system rigidity, making it more of a hindrance than a valuable resource,” said Yauger. “To achieve successful system adoption, a good system design and focus on user needs during implementation is necessary to see the benefits in the laboratory.”

Future trends in laboratory informatics

Emerging technologies continue to expand the potential of laboratory informatics. Digital twins, virtual models of laboratory processes, are gaining traction as a means of simulating experiments and optimizing workflows in real time (11). “This technology allows research teams across organizations to collaborate using a single standardized model for hypothesis testing and experiment simulation. Outcomes from the experiments are available much faster since there is no wait for the physical experiments to be completed and the increased speed and efficiency can lead to shortened discovery timelines,” Borries explained. These models allow researchers to predict outcomes, refine protocols, and reduce reliance on physical experiments.

Lab 4.0, inspired by Industry 4.0, envisions research laboratories integrating automation, robotics, and Internet of Things (IoT) connectivity to streamline operations. Yauger explained that “all of these tools can enable breakthroughs in drug discovery,” with cloud computing allowing remote data access and AI assistance in early detection through predictive algorithms. IoT centralizes laboratory components, while robotics and automation reduce reliance on technicians, enabling more time for research. Yauger also noted that “LIMS are now expanding into the realm of voice commands, mixed reality headsets, and the ability to search by an image,” which enhances efficiency in identifying chemical compounds.

Despite implementation challenges, strategic planning and expert support can optimize LIMS/ELN adoption. Borries highlighted that external consultants “offer an unbiased approach to selecting the system that is best suited for the organization” by leveraging their industry experience and vendor knowledge. Borries added that “it’s important to consider a business’s unique needs first and foremost” to ensure a sustainable solution, emphasizing that a trusted advisor can help navigate the complexities of life sciences and align systems with research objectives.

“LIMS systems have evolved and grown exponentially,” Yauger pointed out, “expanding beyond sample management to interface with instruments and other systems”. This interoperability enhances data integrity, fosters collaboration, and improves research efficiency, making laboratory software solutions crucial in transforming the future of drug discovery.

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