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The integration of digital collaboration platforms into research and development (R&D) workflows is transforming life sciences. By automating data management and leveraging cloud computing’s scalability, companies can achieve groundbreaking advancements in predictive modeling and drug discovery, easing the process of bringing therapies to market. Such digital infrastructure not only fosters a collaborative global network, but also aids in regulatory compliance and traceability across all R&D processes. Below, we explore the multifaceted layers of digital-collaboration platforms (e.g., the LifeSphere platform by ArisGlobal, Benchling, and Synthace) that assist with evidence-based medical breakthroughs.
Support for Biopharmaceutical R&D
Developing a new drug requires a long lead time and significant financial investment, with little chance for success (1). To bring therapies to clinics faster and reduce prices for patients, the biopharmaceutical industry is increasing R&D efficiency by integrating new technologies such as digital applications, high-throughput technologies, and automated systems. Advanced data-management systems within those platforms automate the classification, quality control (QC), and processing of vast data sets. Such technologies significantly improve data integrity; ensure compliance with findable, accessible, interoperable, and reusable (FAIR) principles; and streamline regulatory compliance by providing audit trails. Cloud computing enables robust application of artificial intelligence (AI) and machine learning (ML) algorithms in digital-twins environments, which scientists implement for predictive modeling and simulation without physical infrastructures.
Digital-collaboration platforms promote intra- and interdepartmental collaboration. For example, electronic laboratory notebooks provide secure access to data across qualified users and departments. Clinical trial data can provide R&D and chemistry, manufacturing and controls (CMC) teams with drug performance information for diverse patient populations that can help identify a drug’s critical quality attributes (CQAs). Such data integration could advance precision medicine and improve patient outcomes (2).
Critical Capabilities of Digital Collaboration and Platforms
Scientific research, especially that which involves high-throughput systems, generates extensive data sets that require meticulous management. Digital platforms can automate data ingestion, classification, and QC processes. Collation, structuring, and standardization of data and metadata ensures that the platforms are ready for complex, real-time, automated analyses. Such platforms support data reproducibility, which is crucial wherever data integrity must be maintained for scientific validity and regulatory compliance. Reproducibility becomes especially relevant in laboratories that use different experimental conditions and in regions that use different measurement systems.
AI Integration with Cloud Computing: Cloud computing offers scalable solutions for the computational demands of life-science R&D. Examples include the development and deployment of complex algorithms for tasks such as genomic sequencing, biomarker identification, and image analysis. Researchers can integrate AI/ML algorithms into a cloud infrastructure to interpret large-scale biological data sets, informing hypotheses and shortening discovery cycles. Cloud-based technologies facilitate the storage, sharing, and collaboration of data sets in real time, irrespective of geography. They can not only improve efficiency, but also reduce costs associated with maintaining physical information technology (IT) infrastructures. For example, Google DeepMind’s AlphaFold software uses cloud-based technology for a computationally intensive protein folding model.
Decentralization and Remote Collaboration: Digital platforms facilitate collaboration across geographically dispersed R&D teams, enabling them to work together and share knowledge. By fostering collaboration, these platforms make databases location-agnostic, thereby helping scientists and researchers to share insights and data more effectively. Cloud-based data and processing pipelines support a collaborative approach and can be accessed by researchers around the globe, further democratizing research. For example, Amgen is transforming its drug-development process through centralized access to global data sets for data analytics (3). Such digital systems enable both synchronous and asynchronous collaboration across time zones, ensuring that all team members can contribute to a project effectively, regardless of location.
Predictive Modeling and Simulation: The use of digital platforms powered by AI/ML enables predictive modeling of biological processes and drug interactions. By leveraging diverse molecular and clinical data, researchers can predict outcomes accurately, identify potential drug candidates, and streamline clinical trials. Such predictive power requires advanced analytics, computational models, and AI/ML that can simulate complex biological systems and drug mechanisms. The information can be used to create digital twins, which are essential for evidence-based decision-making in drug development.
Real-World Evidence and Clinical Data: Digital platforms are crucial for collecting and analyzing the real-world evidence that complements clinical-trial data. Integration of such real-world data into the R&D process provides researchers with a comprehensive understanding of how drugs perform in diverse patient populations, thus improving patient outcomes and the development of precision-medicine approaches (2). Digital platforms that can handle large volumes of real-world data are becoming indispensable tools for life-science companies.
Regulatory Compliance and Provenance: Regulatory compliance is a critical aspect of life-science R&D. Digital platforms provide comprehensive provenance and audit trails for data and processes, which are essential for regulatory submissions. Traceability ensures that data have been handled per ALCOA principles, meaning they are attributable, legible, contemporaneous, original, and accurate (4). It also ensures that analyses have been conducted properly, and that supporting scientific evidence is robust and reliable. Automated data capture from a laboratory’s manufacturing equipment further reduces human intervention and improves data integrity and accuracy (5).
Evidence-Based Approach: Evidence-based R&D uses metrics and benchmarks to gauge efficiency. Digital platforms can provide analytics that measure R&D productivity, the rate of innovation, equipment use, and the effects of research outputs. Such metrics can help organizations make data-driven decisions to improve their R&D processes and align their strategies with industry best practices.
Digital Twins and Virtual-Reality (VR) Laboratories: Digital twins and enhanced VR technology are transforming R&D practices. Digital twins are sophisticated virtual models that reflect the real-time or predicted state of physical laboratories, offering dynamic simulations that evolve with incoming data. Such modeling and advanced analytics can help to predict the outcomes of experiments and processes.
Enhanced VR provides scientists with an immersive environment that enables them to interact intuitively with simulations. Within drug development, teams can conduct experiments in virtual spaces with unprecedented detail and interaction. They can manipulate molecular structures, witness the potential effects of pharmaceutical compounds on disease states, and gain insights that are inaccessible through traditional methods.
By combining digital twins with enhanced VR, scientists can further simulate and visualize complex biological interactions, enabling them to gain a deeper understanding of drug behaviors in virtual settings that closely mimic real-world conditions. This method offers a “sandbox” for safe experimentation, reducing the time and costs associated with physical laboratory work. The combination also offers an agile, cost-effective, and ethically responsible approach to R&D that accelerates innovation and enhances the precision of scientific discovery.
Figure 1: Modern digital-collaboration technologies provide many benefits that could improve drug development; AI = artificial intelligence, R&D = research and development.
Increased Productivity
Digital collaboration platforms streamline workflows and processes, enabling teams to work quickly and effectively together. Automation of routine tasks and real-time data sharing reduce the time spent on manual data entry and information transfer, leading to increased productivity and speeding completion of R&D projects. Such platforms also enable researchers from around the globe to collaborate in real time. Such connectivity fosters a dynamic exchange of ideas, expertise, and resources, driving innovation through collaboration. By leveraging digital tools, organizations can reduce the overhead costs associated with physical workspaces, travel, and traditional communication methods. Virtual collaboration also reduces the need for physical prototypes and trials by enabling digital simulations, further cutting down expenses.
Modern digital platforms often come with a suite of advanced tools that can analyze big data, manage complex algorithms, and perform simulations that are beyond the reach of traditional methods. Such access enables R&D teams to engage in high-level analysis and design, which is critical for research. The centralization of data within digital platforms ensures that teams can access the latest information, while maintaining data consistency and integrity. Effective data-management practices facilitate storage, retrieval, and interpretation of vast amounts of research data, thereby enhancing decision-making and strategic planning.
Challenges of Technology Integration
With those benefits come several problems and data-security concerns. Reliance on digital platforms raises concerns about data breaches, unauthorized access, and the potential for sensitive data to be compromised. It is crucial to ensure robust cybersecurity measures and compliance with data-protection regulations. Collaborating and sharing data through digital platforms also can increase the risk of intellectual property (IP) theft or misuse. It’s essential to establish clear data-governance and IP-protection strategies.
For real-time data collection, analysis, and use, internet network stability is critical to ensure uninterrupted operations. Similarly, network and data-management systems must have low error rates to prevent problems with data quality. Interoperability issues might arise when organizations integrate platforms or transition from legacy systems to new digital solutions. That can lead to challenges in data sharing and system compatibility. Data integrity also can be compromised when organizations rely on platforms run by external vendors — e.g., if a vendor experiences downtime or ceases operations.
As R&D activities become more data-intensive, managing the sheer volume and diversity of data can become difficult, requiring sophisticated data-management systems and skilled personnel. Differences in nomenclature and measurement systems across geographies and departments can lead to misalignment in data capture and interpretation.
In navigating international data-sovereignty laws — including stringent frameworks such as the General Data Protection Regulation (GDPR) in Europe — companies must reconcile the need for innovation with the complexities of varied and sometimes challenging legal requirements. The key challenge lies in managing the privacy, security, and IP rights associated with crucial R&D data. Excelling in this domain is both a legal and strategic imperative that can forge a competitive edge, safeguard IP, and solidify a company’s reputation as a leader in responsible data management.
Integration of digital-collaboration platforms has heralded a new era for biopharmaceutical research and development. Modern R&D necessitates a dynamic and interconnected environment where technologies and digital infrastructures converge to foster innovation. The physical and virtual realms of R&D are now intertwined. Virtual laboratories complement physical ones, and data flow freely among them, enabling a holistic and flexible approach to drug discovery.
Figure 2: Although digital collaboration platforms offer a number of benefits, it is also important to consider the challenges inherent to new technology integration.
The Future of R&D
Such platforms have augmented traditional R&D into a global, collaborative ecosystem that operates beyond the confines of geographical and disciplinary boundaries. Teams across continents can synchronize their efforts in real time by drawing on shared knowledge and resources. Such interconnectedness is palpable during daily R&D operations when the exchange of ideas and data is as fluid as the technologies that support it.
Acceleration of R&D through digital collaboration transforms the quality and nature of scientific inquiry. Enhanced R&D is characterized by a proactive, predictive, and patient-centric approach. With advanced tools, researchers can dive deeper into biological mysteries and emerge with new insights. Modern R&D enables researchers to use metrics and benchmarks that provide continuous feedback, ensuring that every step of development from hypothesis to market is optimized for success.
The future of R&D is one of seemingly limitless potential. By embracing opportunities presented by modern technologies, the biopharmaceutical industry is poised to deliver solutions to humanity’s most pressing health challenges with unprecedented precision and speed. Biopharmaceutical R&D has never been more vibrant and promising. It stands today at a new horizon of discovery and innovation.
References
1 DiMasi JA, Grabowski HG, Hansen RW. Innovation in the Pharmaceutical Industry: New Estimates of R&D Costs. J. Health Econ. 47, 2016: 20–33; https://doi.org/10.1016/j.jhealeco.2016.01.012.
2 Hulsen T, et al. From Big Data to Precision Medicine. Front. Med. 6, 2019: 34; https://doi.org/10.3389/fmed.2019.00034.
3 Case Study: Amgen Leads a Digital Transformation To “Accelerate the Speed to Market of New Therapies.” ZS Associates: Evanston, IL, 2024; https://www.zs.com/about/case-studies/amgen-leads-a-digital-transformation-to-accelerate-the-speed-to-market-of-new-therapies.
4 FDA Guidance: Data Integrity and Compliance with Drug CGMP. US Food and Drug Administration: Silver Spring, MD, 2018; https://www.fda.gov/media/119267/download.
5 Churchward D. Good Manufacturing Practice (GMP) Data Integrity: A New Look at an Old Topic, Part 2. Gov.UK: London, UK, 14 July 2015: https://mhrainspectorate.blog.gov.uk/2015/07/14/good-manufacturing-practice-gmp-data-integrity-a-new-look-at-an-old-topic-part-2.
Corresponding author Narendra Sharma is lead partner and chief executive officer of Trinity Bridge Professional Services; [email protected]. Siddhartha Jain is president of Shoolin Management Company in Cambridge, MA, USA; [email protected].
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