Harnessing AI and unified data: the fast track to pharma R&D breakthroughs
This blog explains how pharmaceutical companies can overcome data fragmentation and accelerate drug discovery using integrated digital solutions that are designed for pharma R&D leaders and scientists.
Pharmaceutical scientists are heeding the call to create new types of drugs and therapies that promise incredible results. Gene and cell therapy, biologics and other pharma subsegments are changing what’s possible and giving hope that uncurable diseases can be fought and conquered.
Amid global health challenges, emerging pathogens and a growing burden of chronic and rare diseases, the need to accelerate drug development and discovery and bring effective therapies to patients faster is critical. This drive for new therapies is not just a scientific endeavor; it’s a humanitarian mission in which dedicated scientists work diligently to find treatments that will alleviate suffering, cure disease and save lives.
This means less time spent on data wrangling and more time dedicated to groundbreaking science.
- 50 days per year. Average amount of time scientists lose due to inefficient processes.1
- 10-20 percent. Average percentage of development work repeated due to data integrity and accessibility issues.2
Four critical challenges slowing pharma R&D innovation today
The journey from molecule to market is fraught with immense complexity, taking nearly a decade to complete with regulatory hurdles and a finite amount of time to achieve a return on investment.
Fragmented data
The increasing complexity of treatments, such as biologics and cell and gene therapies, demands more sophisticated data management and analytical capabilities than traditional systems can offer. Furthermore, collaboration becomes a significant challenge when data isn’t seamlessly integrated and accessible.
The costs of getting treatments to market
Biologics, as well as cell and gene therapies, remain costly and time-consuming to develop and manufacture due to the complexity of their design, the demanding bioengineering requirements involved, the complexity of the associated processes and their often highly personalized nature.
Managing the explosion of information
AI and machine learning (ML) is rapidly transforming every facet of drug discovery and must have quality data and accessibility for training models. The explosion of information accessibility leads to so much data generation that companies struggle with data overload and digitalization gaps.
Complex modern therapies
The large and complex molecular structures of biologics, as well as cell and gene therapies, present significant challenges while expanding the potential to treat diseases that are difficult or impossible to address with traditional small-molecule drugs. This complexity demands advanced analytical characterization, meticulous control of product stability and quality attributes, innovative delivery approaches and a deep understanding of the relationship between molecular structure, biological function, safety and efficacy.
Digitizing isn’t enough
Most pharma organizations have digitized their scientific records, but digitizing is not the same as structuring. The real shift is embedding scientific context directly into how work is performed, such as capturing intent, lineage and decisions at the source so that every experiment becomes a reusable, AI-ready asset rather than an isolated record. This is the foundation that makes AI reliable, accurate and relevant, automation scalable and portfolio decisions faster and more confident.
Navigating complexity with the pharma digital thread
Future-ready pharmaceutical organizations are moving beyond traditional approaches to drug discovery and development and taking steps to integrate data holistically, where the benefits of AI help navigate this complex landscape.
Siemens has taken steps to develop a drug discovery and development digital thread designed specifically to address the unique challenges, constraints and priorities of pharmaceutical industry stakeholders. This comprehensive molecule to market approach connects every stage of the pharmaceutical lifecycle, breaking down the data and information silos that hamper innovation and transforming the process into a seamless, intelligent approach to bringing drugs and therapies to patients.
Siemens’ Luma provides unified foundation for drug discovery
At the heart of this modernizing R&D is Siemens Luma, created to not just modernize R&D, but offer a new foundation: one that unifies data, adapts to evolving science and connects every team and tool across the full R&D lifecycle: Design-Make-Test-Decide.
Biologics, with their large and intricate molecular structures, cannot be developed and manufactured as simply as traditional small-molecule drugs. Their complex biological nature and sensitive production processes require advanced characterization, rigorous process understanding and thorough control at every stage to ensure consistency, reproducibility and regulatory compliance, making their journey to market longer and more expensive.
Legacy systems often fail to handle the complexity of modern biologic formats, leading to imprecise data representation and fragmented workflows as well as challenges with tracking molecular structures, often leading to operational inefficiencies, delays across research programs and costly errors.
Unifying science directly addresses data fragmentation, experimental design inefficiencies and analytical challenges that plague modern drug discovery. It reimagines R&D as a lab-in-a-loop — an agile, intelligent ecosystem that speeds up discovery and fosters an environment where scientists can make faster, more informed decisions.
Luma’s versatility extends its comprehensive capabilities to support small molecules and cell and gene therapies as well with an adaptable framework so, regardless of the modality, scientists have the tools to manage their data, design experiments and derive insights efficiently.
Future-proofing the pharma organization
When the data and insights generated in the early stages, via platforms like Luma, seamlessly flow into process development and manufacturing, critical information is preserved and leveraged downstream, simplifying handoffs, building traceable context, preventing costly errors and accelerating scale-up.
Furthermore, Siemens’ broader portfolio supports quality and compliance throughout the entire product lifecycle. By maintaining data integrity, ensuring traceability and adhering to strict regulatory requirements, Siemens helps pharmaceutical organizations:
- Navigate the complex landscape of approvals with confidence
- Drive continuous innovation through the strategic use of simulation and advanced analytics
- Optimize processes
- Predict outcomes
- Accelerate development cycles
Siemens’ solutions are designed to future-proof pharma and empower organizations with the digital technology to manage the voluminous amounts of data and use AI tools to speed up discovery and development. When pharma companies are equipped to remain at the forefront of medical innovation, they can develop the most advanced therapies while also improving existing essential treatments that remain indispensable to patient care.
Seizing the future of pharma
Key Takeaway: Pharma organizations that unify their data can reduce development time, minimize costly rework and bring life-saving therapies to patients faster.
The future of pharma, from small molecules to complex biologics and cell and gene therapies, hinges on integrated data and AI grounded in structured scientific work. Siemens’ Luma provides the digital thread to unify R&D. The benefits speak for themselves:
- Leveraging AI to accelerate discovery and development
- Guaranteeing every experiment can become reusable knowledge
- Ensuring precision at the bench
- Providing intelligence across the workflow
This continuity provides better opportunities to make better decisions sooner and bring lifesaving therapies to patients faster.
FAQs about AI and unifying data in pharma
1. How does data fragmentation specifically hinder pharmaceutical R&D, and how does a unified data approach address this?
Data fragmentation leads to inefficiencies, with scientists losing up to 50 days annually and 10-20% of development work having to be repeated.3 Critical information scattered across systems prevents a holistic view. Siemens Luma’s unified data approach integrates all R&D data into a single platform. This ensures every experiment, data point and decision is captured with scientific context, making it accessible, traceable and AI-ready, reducing rework and accelerating decisions.
2. What role does AI play in accelerating drug discovery and development within a unified data environment?
AI and machine learning’s effectiveness hinges on data quality and accessibility. A unified data environment provides AI models with rich, structured and comprehensive datasets for accurate training. This enables AI across R&D, from identifying drug candidates and predicting efficacy to optimizing experiments and analyzing complex biological data. Integrated data accelerates discovery, streamlines development and brings therapies to patients faster.
3. How does Siemens Luma support the development of complex modern therapies like biologics and cell and gene therapies?
Modern therapies like biologics and cell and gene therapies pose unique challenges due to their complex structures and sensitive production. Siemens Luma addresses this by providing a unified foundation that adapts to evolving science. It integrates data across the R&D lifecycle (Design-Make-Test-Decide), capturing critical information with precision and context. Luma’s adaptable framework supports various modalities, ensuring efficient data management and insights for all complex treatments.
- https://www.pharmamanufacturing.com/production/automation-control/article/11365550/processing-engineering-making-the-most-of-drug-development-data-pharmaceutical-manufacturing ↩︎
- https://www.pharmamanufacturing.com/production/automation-control/article/11365550/processing-engineering-making-the-most-of-drug-development-data-pharmaceutical-manufacturing ↩︎
- https://www.pharmamanufacturing.com/production/automation-control/article/11365550/processing-engineering-making-the-most-of-drug-development-data-pharmaceutical-manufacturing ↩︎
