The journey of drug discovery is notoriously long; from initial discovery and development to clinical assessment and final approval, the process can take anywhere from 10 to 30 years and cost billions of dollars1, 2. To address the issues of time and cost, pharmaceutical companies rely on central laboratories (CLs) for consistency, organization, and efficiency during the clinical trial phase, which is essential for drug approval.
Central labs play a critical role in supporting clinical trials by handling critical services. Large pharmaceutical companies may operate their own CLs or subcontract these services to Contract Research Organizations (CROs) with established central lab networks. This approach provides cost benefits and a broader geographic reach, enabling companies to manage complex, multi-site trials more efficiently. By facilitating the clinical trial process, central labs are vital in bringing new drugs to market more quickly and affordably.
In recent years, central labs have adopted innovative strategies that aim to further accelerate drug development to continue addressing the stubborn issues of time and cost. In this blog, we will focus on three key approaches: automation, outsourcing, and artificial intelligence (AI). Keep an eye out for future blogs, in which we will discuss more approaches.
The Role of Central Labs in Drug Development
The aforementioned central labs are essential to the clinical trial process, helping pharmaceutical companies achieve the precision and consistency necessary to meet regulatory standards for new drug approvals. Acting as core facilities or networks of facilities, CLs are responsible for a range of critical services, ranging from kit production, specimen management, logistics, and trial data analysis. These tasks ensure uniformity across trial sites, allowing companies to conduct trials efficiently, even when they span multiple geographic locations.3
Central labs support the in-human phase of drug development, working to maintain quality control and reduce variability across clinical sites, which is crucial for accurate data collection and trial success. Large pharmaceutical companies sometimes operate their own central labs to maintain control over these processes. However, many companies choose to subcontract these services to CROs - if they do not have the resources to operate their own CLs - which offer extensive lab networks, specialized expertise, and established workflows, enhancing both speed and cost-effectiveness.4
In partnering with CROs, pharmaceutical companies can scale their operations and leverage the CROs' global infrastructure and technical knowledge. This connection allows central labs to manage large, multi-site trials more effectively, helping companies to navigate the logistical and regulatory demands of clinical trials.
The life science industry is no stranger to innovation. As the landscape of drug development becomes increasingly complex, central labs have adopted advanced strategies to further accelerate the clinical trial process. By focusing on automation, outsourcing, and artificial intelligence, central labs are playing a pivotal role in bringing new treatments to market faster, while maintaining high standards of accuracy and quality.
1. Automation in Central Labs
Automation has revolutionized laboratory work by reducing manual tasks and improving overall efficiency. Automating laboratory processes like pipetting, measurement, reagent preparation, sample tracking and processing, running assays, data handling, and quality control management allows lab professionals to focus on more value-added tasks, such as test validation and new assay development.2 These tasks require more of a human hand, and free up massive amounts of time previously used on menial, repetitive tasks. This has not only enhanced the quality of results but also significantly reduced turnaround times, making it easier for pharmaceutical developers to make timely decisions during drug trials.
The adoption of automation systems has led to improvements in labor use, quality, and safety, which has sparked interest across the industry. These systems reduce errors and provide greater consistency in testing and analysis, making the drug development process faster and more accurate. 2 In clinical trials, central labs that leverage automation can handle large-scale sample processing more efficiently, helping to accelerate the movement of potential treatments through the pipeline.
2. Outsourcing to Specialist Service Laboratories
Outsourcing is another strategy central labs use to reduce the time and cost of drug development. Central lab service providers commonly outsource activities to local and regional laboratories that provide specialized expertise, optimal geographic reach, reduce processing time constraints, and cost advantages.5 Such specialist facilities are at the forefront of implementing new technologies and processes, enabling pharmaceutical developers to benefit from the latest innovations without having to invest in them directly.
The geographical reach and expertise of central labs, often operated by CROs, enable pharmaceutical companies to streamline trial operations across multiple sites, resulting in greater efficiencies and accelerated drug development timelines.6 Central labs coordinate activities across trial locations, ensuring consistency. This coordination helps maintain high-quality, standardized data, which is essential for reliable trial outcomes.
Outsourcing specific trial components to local or regional specialist labs can further optimize the process. While the central lab oversees and manages the logistics of multi-site trials, specialist labs contribute local knowledge and specialized capabilities for certain trial components. This collaboration allows the central lab to coordinate complex, large-scale trials effectively, helping pharmaceutical companies to focus on core competencies while outsourcing resource-intensive tasks. Ultimately, this partnership structure enhances efficiency and shortens the timeline from drug discovery to market2.
Artificial Intelligence: The Future of Drug Development
Artificial intelligence (AI) is transforming drug development by enhancing efficiency, accuracy, and speed throughout the clinical trial process. In preclinical phases, AI accelerates drug discovery by aiding diagnostics, biomarker selection, drug design, and protocol development for clinical trials. By automating complex data analyses, AI enables pharmaceutical companies to make data-driven decisions more quickly and with higher precision, ultimately leading to a faster progression through the initial phases of drug development.7
For central labs, AI offers targeted advantages that streamline trial logistics and improve data integrity. AI-driven systems can monitor key elements like kit production, reagent supply, and equipment maintenance in real-time, reducing the risk of trial delays due to equipment malfunctions or supply shortages. 7 Additionally, AI can be used to oversee data tracking and integration across multiple trial sites, helping to ensure that sample data is accurately recorded, transmitted, and analyzed without manual errors.
Incorporating AI within a central lab’s quality management system allows for automated, real-time monitoring of clinical trial data, which enhances consistency and compliance. Through advanced analytics, AI can detect data anomalies early on, helping central labs manage large datasets and identify potential issues before they impact trial outcomes. This approach not only supports regulatory compliance but also allows for faster, more reliable trial results. 7
Ultimately, by leveraging AI, central labs can increase their capacity to manage complex multi-site trials while reducing turnaround times. This technological advancement ensures that central labs play an active role in accelerating the drug development process, supporting pharmaceutical companies in their mission to bring new treatments to market as efficiently as possible.
Conclusion
Drug development has historically been a lengthy, expensive, and complex process. However, by adopting strategies such as automation, outsourcing, and AI, central labs are helping to shorten the timeline from drug discovery to market. These innovations not only reduce the costs and time associated with aspects of drug development but also improve the accuracy and success rates of clinical trials.8 As the pharmaceutical industry continues to evolve, these strategies will play an increasingly critical role in bringing life-saving treatments to patients faster than ever before.
By leveraging the power of these strategies, central labs are not only improving their own operations but also playing a crucial role in transforming the future of drug discovery.
References
1. Ledley, Fred D. 30 years is too long to wait for new medicines. There are ways to speed up drug development. June 6, 2018.
2. BioPharma. Revolutionizing Drug Development: How AI is Reducing Time and Costs of Bringing New Treatments to Market. March 10, 2023.
3. Lindus Health. The Importance of Central Labs in Advancing Clinical Trials: A Comprehensive Guide.
4. Labcorp. Journey of a Sample through Our Central Lab. Sep 29, 2022.
5. Zaninotto, Martina & Plebani, Mario. (2010). The "hospital central laboratory": Automation, integration and clinical usefulness. Clinical chemistry and laboratory medicine : CCLM / FESCC. 48. 911-7. 10.1515/CCLM.2010.192.
6. Lucas, Nick. Contract Research Organizations: Key Partners in the Drug Development Journey. April 9, 2021.
7. Weissler EH, Naumann T, Andersson T, Ranganath R, Elemento O, Luo Y, Freitag DF, Benoit J, Hughes MC, Khan F, Slater P, Shameer K, Roe M, Hutchison E, Kollins SH, Broedl U, Meng Z, Wong JL, Curtis L, Huang E, Ghassemi M. The role of machine learning in clinical research: transforming the future of evidence generation. Trials. 2021 Aug 16;22(1):537. doi: 10.1186/s13063-021-05489-x. Erratum in: Trials. 2021 Sep 6;22(1):593. doi: 10.1186/s13063-021-05571-4. PMID: 34399832; PMCID: PMC8365941.
8. Kolluri S, Lin J, Liu R, Zhang Y, Zhang W. Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review. AAPS J. 2022 Jan 4;24(1):19. doi: 10.1208/s12248-021-00644-3. PMID: 34984579; PMCID: PMC8726514.
