In today’s dynamic landscape, biopharmaceutical R&D organizations face mounting pressure to enhance productivity amid increasing complexity, rising costs, and heightened expectations for breakthrough treatments. The preclinical phase now encompasses sophisticated assays, intricate “omics” datasets, and advanced humanized models for candidate drug evaluation. Clinical trials have evolved to incorporate innovative design approaches, precision medicine strategies, and an expanded array of endpoints.1
To navigate these challenges, pharmaceutical executives are embracing cutting-edge digital and analytics (DnA) solutions to transform R&D processes and achieve greater efficiency, including accelerated discovery of novel molecular entities and therapeutic pathways. Industry leaders are already harvesting the benefits of enhanced DnA capabilities, leveraging in silico modeling to expedite molecule identification, AI-driven solutions for streamlined clinical trials (encompassing site selection, participant enrollment, and trial supervision), and automated generative AI (gen AI) for superior documentation, reducing submission cycles. Gen AI solutions alone are expected to generate up to $53 billion in annual value across the R&D spectrum.
However, despite these technological strides and the fact that US Food and Drug Administration approvals for innovative therapeutics reached near-record levels in three decades,2 the sector’s R&D productivity remains stagnant. Development cycles continue to span over ten years, average R&D investment per asset exceeds $2 billion, and only 13 percent of candidates entering Phase 1 trials successfully reach market launch. Several factors contribute to the limited DnA adoption success in pharmaceuticals compared to other sectors. Common challenges include adherence to outdated processes that hinder standardization, insufficient change management, and technology implementation without clear value propositions. Further complications arise from conducting isolated departmental transformations while depending on rigid systems compromised by poor-quality, fragmented data.
These obstacles can be surmounted through the implementation of a sophisticated, contemporary R&D technology infrastructure—a comprehensive IT framework that facilitates insights, streamlines workflows, and enables efficient data management throughout all stages of discovery, research, and clinical development. In this analysis—which explores the sixth component (of eight) in our comprehensive framework for sustained R&D excellence outlined in our landmark RewiR&D publication—”Making more medicines that matter”—we outline the framework for a state-of-the-art technology stack tailored for biopharma R&D organizations and examine its crucial role in enabling next-generation (next-gen) data, analytics, and technological capabilities.
Core layers of a modern R&D tech stack
A well-integrated, modern technology stack enables seamless research and clinical data management and analysis. Currently, numerous R&D organizations operate with decentralized technology infrastructures, comprising standalone core systems and applications linked through direct interfaces. While this decentralized approach might offer initial implementation advantages, it presents scalability challenges as organizational needs expand.
A contemporary technology stack can be conceptualized and structured into four interconnected layers (Exhibit 1). This architectural approach enables organizations to develop and implement innovative workflows without being constrained by time-intensive legacy system updates, promotes data reusability and AI workflow integration, and enhances interoperability through optimized data and service management across R&D functions. This framework allows organizations to maintain agility and rapid technological advancement while maximizing existing resource utilization.
The infrastructure layer serves as the foundation, delivering cloud-based capabilities that ensure robust performance, scalability, and security across the entire ecosystem. The data layer orchestrates the integration, curation, and accessibility of clinical and operational information, following FAIR principles (findable, accessible, interoperable, and reusable). The application layer encompasses essential clinical systems, including electronic data capture (EDC) and clinical-data management systems (CDMSs), alongside specialized tools like electronic lab notebooks.
These applications incorporate integrated workflows specifically designed to address clinical development requirements. The analytics layer crowns the technology stack, featuring both fundamental and advanced capabilities (including AI and gen AI) for data analysis and visualization, enabling real-time insights and informed decision-making throughout the R&D journey.
Unlocking value from a rewired tech stack
A modern R&D technology infrastructure can enhance pharmaceutical R&D performance primarily by strengthening IT infrastructure, optimizing quality and compliance processes, and ultimately improving R&D productivity (Exhibit 2). Below, we explore how the technology stack contributes to these performance enhancements.
Cycle time acceleration.
A cutting-edge R&D technology stack, encompassing analytics, application, and data layers, serves as a catalyst in expediting every stage from initial drug discovery through market introduction. Within the analytics layer, artificial intelligence and machine learning (AI/ML) technologies enable accurate prediction of drug-target interactions, enhance trial optimization, and facilitate improved decision-making processes, particularly in experiment prioritization and addressing trial-related challenges. A prime example is AstraZeneca’s sophisticated drug design platform, which leverages AI to enhance the design workflow. By seamlessly combining molecule conceptualization, activity forecasting, and synthesis prediction capabilities, AstraZeneca has successfully reduced the required design-make-test-analyze (DMTA) cycles, thereby accelerating the discovery process. AI-driven predictive models deliver instantaneous insights across the entire asset portfolio, enabling swift decision-making during trials, including strategic choices like establishing “rescue sites” to prevent potential delays.
Within the application layer, where operational and patient information converge, organizations have successfully implemented generative AI at scale, revolutionizing the creation of clinical-study reports and regulatory dossier submissions with unprecedented efficiency. These advanced systems contribute to cycle time reduction through comprehensive automation. Specifically, processes related to study initiation, clinical-trial management systems (CTMSs), EDC, and CDMSs benefit from AI-driven streamlining and automation. When built upon a modern data infrastructure, CDMS and EDC applications significantly compress the timeline between last patient, last visit (LPLV) and database lock, facilitating expedited decision-making and submission processes.
Cost reduction
Technology plays a pivotal role in managing substantial R&D expenses. The seamless integration of analytics, application, and data layers enhances workflow efficiency and maximizes productivity. AI integration and in silico modeling enable strategic experiment prioritization, reducing costly laboratory procedures. Automated laboratory workflows, supported by advanced laboratory information management systems and electronic lab notebooks, optimize data handling and minimize manual interventions. Trial management automation through CTMSs and EDC systems facilitates optimal site selection and patient recruitment, minimizing delays and trial failures. The infrastructure layer contributes additional cost benefits through scalable cloud resources, eliminating the need for costly on-premises hardware installations.
Probability of success
Leading pharmaceutical companies are witnessing improved success rates through the integration of sophisticated analytics tools. During the research phase, biotechnology companies, exemplified by Recursion’s Operating System, utilize AI-powered hypotheses to facilitate millions of weekly experiments through combined in silico modeling and automated wet-lab processes. In clinical development, researchers harness AI to analyze integrated real-world and clinical-trial data, enhancing trial design and patient recruitment strategies. Additionally, generative AI technology is revolutionizing regulatory submission quality, increasing the likelihood of successful first-cycle approvals.
Central to these advancements is the contemporary technology stack architecture functioning across four essential layers. The seamless integration between data and infrastructure layers facilitates comprehensive analysis of diverse datasets, encompassing historical documentation, real-world evidence (RWE), scientific publications, and genome-wide association research. These analytical insights empower researchers to enhance predictive capabilities, optimize experimental approaches, and make data-driven decisions regarding target selection, ultimately enhancing success probability.
Innovation
Innovation remains fundamental in addressing unmet medical requirements and revitalizing biopharma portfolios. The technology stack’s layered architecture plays a decisive role in this transformation. The analytics layer drives discovery through biological and disease simulation, molecular target identification, and the design of promising drug candidates that advance from laboratory testing to clinical trials. The data layer expertly manages, integrates, and delivers diverse information—including omics data and RWE—to continuously enhance and refine predictive models.
Several pharmaceutical companies are exploring innovative data-sharing methodologies while safeguarding intellectual property. Federated learning emerges as a promising approach, enabling organizations to contribute research findings while accessing enhanced discovery algorithms. Through a “sanitization” process, as implemented within the European Union’s MELLODDY (machine learning ledger orchestration for drug discovery) consortium, proprietary molecular and compound data remains protected.
Future readiness
Modernizing the R&D technology stack enhances pharmaceutical IT infrastructure adaptability, enabling swift and flexible responses to evolving requirements, such as regulatory data localization mandates. This modernization facilitates rapid adoption of innovations, from decentralized clinical-trial technologies during the COVID-19 pandemic to potential quantum computing applications. Streamlined technology stacks minimize technical debt by eliminating complex point-to-point integrations that typically require API replacement.
Through software consolidation and cloud migration, organizations can reduce legacy technology dependence, redirecting IT resources toward advanced automation and generative AI innovation. Cloud infrastructure provides the necessary scalability for investing in strategic tools, including AI-driven decision-making systems and automated workflows. Based on experience, these transformations can liberate up to 30 percent of R&D IT expenditure, accelerating digital and AI initiatives while enabling comprehensive digital transformation.
Quality and compliance
In the pharmaceutical sector’s stringent regulatory environment, maintaining data integrity and compliance is crucial. The application and data layers foster transparency and adherence to regulations throughout research and development activities. During research phases, standardized datasets enable seamless team collaboration while preserving data accuracy. Throughout development stages, electronic clinical-outcome assessment systems and EDC platforms streamline data collection and report generation, ensuring alignment with global regulatory requirements. Advanced AI solutions can identify compliance concerns early, enabling teams to implement preemptive measures. Furthermore, analytics-driven real-time insights facilitate early detection and resolution of trial issues, leading to superior quality submissions and expedited regulatory approvals.
Designing the modern R&D tech stack
While each biopharma organization must customize its technology stack according to specific requirements, several universal principles distinguish modern tech stack architectures from traditional designs (Exhibit 3).
- A consolidated analytics platform can streamline and accelerate the scaling of dispersed AI initiatives, facilitating standardized AI implementations and component reusability.
- Contemporary SaaS platforms with real-time tracking capabilities can supersede multiple custom dashboards, enhancing user experience in applications like clinical trial management systems and redirecting resources from maintenance to innovation.
- Modular SaaS solutions can replace fragmented custom applications, delivering intuitive, standardized platforms with automated workflows and real-time collaboration for laboratory operations, clinical processes, safety protocols, and regulatory compliance.
- A unified API-based data exchange system can substitute fragile point-to-point connections, integrating various tech stack layers, use cases, systems, and data sources.
- Digital data flow driven by metadata can replace manual document-centric transcription, enabling automation in processes like case report form generation and clinical study documentation.
- Automated real-time systems for data cleaning, query handling, and transformation can substitute manual data management, reducing cycle times and enhancing data quality.
- Reusable data products that systematically support multiple AI and machine learning models can replace manual data engineering for secondary usage, improving efficiency in processes like trial site selection.
A contemporary pharmaceutical R&D tech stack should incorporate a modular, adaptable architecture supporting SaaS platforms for core functionalities, cloud-based data management solutions, and sophisticated DnA for distinctive insights. This modularity enables pharmaceutical companies to integrate emerging technologies seamlessly. Additionally, robust cybersecurity measures, including end-to-end encryption and role-based access controls, should be embedded across all layers to protect sensitive information and ensure compliance with regulations such as HIPAA in the United States and GDPR in the European Union. The modern architecture simplifies the overall tech stack by standardizing common platforms and minimizing point solutions, integrations, and temporary workarounds.
Analytics layer
This layer functions as the system’s cognitive center, leveraging artificial intelligence and machine learning capabilities to generate comprehensive insights into pharmaceutical efficacy, patient response patterns, and clinical trial optimization strategies. Through well-designed APIs, research teams can readily access and utilize pre-built AI models and sophisticated data analysis tools, enabling rapid deployment of solutions across diverse research initiatives. This architecture supports, for instance, instantaneous processing of data streams from wearable monitoring devices during clinical trials, enabling swift protocol modifications based on real-time insights. The contemporary design incorporates intuitive dashboards for standardized reporting and analytics, enabling R&D teams to concentrate their efforts on developing distinctive analytical approaches and advanced insights that differentiate their research portfolio.
Application layer
Within the application layer, core R&D functionalities are primarily delivered through SaaS platform providers, encompassing laboratory information management systems (LIMSs) for research activities and CTMS software for development phases. R&D organizations should prioritize selecting an integrated, ready-to-deploy core platform requiring minimal customization and configuration—an approach that can substantially reduce operational costs and enhance efficiency.
Supplementary capabilities can be incorporated through additional SaaS vendors to address strategically critical functions or bridge capability gaps. Following core platform implementation, API integration ensures fluid connectivity between essential systems such as electronic laboratory notebooks and LIMSs in research, or CTMSs and EDC in development, while facilitating seamless interaction with other tech stack layers and external partners like clinical research organizations, enabling uninterrupted data flow throughout R&D operations.
Data layer
The data layer consolidates R&D information through cloud-based infrastructures, such as comprehensive data lakes or distributed data mesh architectures, enabling efficient handling of complex genomic and molecular data sets alongside other large-scale information repositories. Cloud platforms ensure universal accessibility for diverse teams while supporting AI-driven drug development insights, all within regulatory compliance frameworks. API implementations facilitate the integration of varied data sets across organizational divisions, optimizing data accessibility and reducing analytical timeframes.
Infrastructure layer
The infrastructure layer provides the foundational support for all research activities. A hybrid cloud architecture optimizes both security measures and scalability requirements. This hybrid strategy ensures sensitive information remains protected in private cloud environments while allowing less critical workloads to operate in public cloud spaces. Furthermore, the infrastructure layer incorporates “infrastructure as code” principles, automating complex processes such as molecular simulation calculations and drug interaction analyses, thereby ensuring consistent and repeatable research workflows.
Implementing the next-gen R&D tech stack
Establishing a modern R&D technology stack demands a strategic methodology extending beyond mere tool adoption. The process initiates with comprehensive evaluation of business requirements and existing technological capabilities to establish a clear future-state vision. This approach encompasses capability assessment, gap identification, and modernization prioritization. Organizations should benchmark against industry leaders while developing a customized North Star vision aligned with their specific requirements.
To construct an advanced R&D tech stack, pharmaceutical leaders must address four essential considerations relative to their strategic objectives:
- Scope of modernization. Organizations must first establish the boundaries of their modernization initiatives by evaluating which technological stack components require enhancement, based on current capabilities and identified deficiencies. Leadership should determine whether to concentrate on clinical development specifically or encompass broader R&D operations, ensuring alignment with data management strategies and value creation objectives. They must also pinpoint areas for competitive differentiation, particularly in AI and analytics implementation—this analysis could reveal high-impact opportunities such as AI-driven therapeutic indication discovery or predictive modeling for patient retention.
- Modernization archetype. Decision-makers should evaluate which technological architecture best supports their organizational objectives: platform-centric, best-of-breed, or hybrid approaches. Platform-based solutions utilize pre-configured offerings from select vendors, enhanced with custom elements. Best-of-breed architectures involve tailoring various vendors’ solutions for specific applications. Hybrid frameworks combine platform solutions with specialized best-of-breed tools in strategic areas for competitive advantage.
- Vendor combinations. The selection of optimal vendor partnerships to fulfill R&D requirements represents a crucial strategic decision. This choice must emphasize seamless integration across technological layers, optimize the balance between user experience and solution quality, and address vendor dependency concerns.
- Ownership and collaboration. Effective application modernization necessitates harmonious cooperation between business and IT divisions, where business objectives guide strategic direction while IT teams orchestrate implementation. IT should function as a strategic catalyst, converting broad business objectives into precise technical specifications and implementable use cases. R&D leadership and IT executives must maintain close coordination to ensure smooth organizational adoption and integration of modernized systems.
