FormalPara Key Points

Digital transformation is an imperative for pharma companies of all sizes.

Chiesi developed a framework for research and development (R&D) digital transformation in a mid-sized company.

The framework is based on five pillars:

Strategic alignment and establishment of R&D Digital Coordination Team.

Identification of key focus areas across R&D value chain.

Definition of R&D digital roadmap.

Alignment on a common language, including R&D Data Model & Governance.

Monitoring and execution of digital priorities and KPIs.

A clear roadmap for digital transformation ensures most effective and efficient use of resources.

1 Introduction

The pharmaceutical industry has experienced a transformation in its traditional ways of working due to the breakthrough impact of new technologies such as artificial intelligence (AI), deep learning, cloud computing, generative AI, graph-based approaches, and internet of things (IoT), which offer a promising outlook for the future of health care and present opportunities for improved outcomes and optimized patient care [1,2,3].

Despite the many opportunities these new technologies present, the impact of digital transformation can be hampered by challenges characteristic of the pharmaceutical sector. As suggested by a recent study (currently only available as a non-peer reviewed preprint [4]), these range from the low probability of success associated with developing an innovative therapeutic solution, to the lengthy and resource-intensive research and development (R&D) process, to comprehensive regulatory compliance frameworks.

Nevertheless, there is a compelling need to deploy and invest more in digital technologies such as AI and machine learning, big data analytics, cloud computing, IoT, virtual and augmented reality, robotics and automation, digital twins solutions across the pharmaceutical industry, especially in R&D, to meet the growing demand for transformative solutions that can enhance the quality of life of patients worldwide.

Considering all the demands and existing challenges, pharmaceutical companies need to thoroughly assess their risk tolerance and investment capacity, along with their existing level of digital maturity, to establish a comprehensive long-term plan for digital strategy development. This evaluation is crucial before committing to the widespread adoption of digital technologies and the implementation of new processes [5].

In other words, it is necessary to consider that digital innovation represents an actual opportunity only if paired with a robust digital strategy and a clear digital vision across the organization.

Given this evidence, pharmaceutical companies should transition towards implementing comprehensive frameworks designed to tackle challenges associated with digitalization, including:

Focusing on value. While digital technologies offer numerous benefits and opportunities for innovation, it is important for organizations to approach their adoption with careful consideration and within a strategic management process. Simply embracing them without a clear understanding of how they align with business objectives or without assessing their potential impact on operations can lead to significant expenses without yielding desired outcomes.

Getting the right people on board. Digital transformation can have a direct influence on the workforce. Many factors might influence a company’s perceptions of the value and appeal of digitalization. A digital task force that is well-trained and focused can be a valuable option to tackle this challenge. This task force can work together to ensure a smooth-running culture and workplace throughout the digitalization process [6, 7].

Managing higher data complexity. Digitalization is based on the ability to collect, store, and analyze heterogeneous data sets that were generated by different sources. Extracting knowledge from such complex and numerous data requires some level of data standardization and harmonization, which can only be reached by introducing data management tools and policies aligned with the FAIR principles of data (Findability, Accessibility, Interoperability, and Reusability).

Embracing innovative thinking to uncover new opportunities through a broader lens. To successfully implement digital transformation in an organization, it is crucial to think beyond isolated digital projects or implementations. Transformation is about fundamentally rethinking and reshaping every aspect of company operations through a digital lens. This means taking a deep dive into all existing business processes and considering how they can be optimized, automated, or completely redesigned using digital technologies. It also requires buy-in and support from top management who understand the strategic importance of digital innovation and who are willing to allocate resources and drive organizational change accordingly [8].

This paper aims to achieve the following objectives:

  1. 1.

    Review the key aspects and challenges of implementing at scale digital technologies such as AI and machine learning, big data analytics, cloud computing, IoT, virtual and augmented reality, robotics and automation, digital twins in pharma, as well as implications for mid-sized pharma companies.

  2. 2.

    Describe the framework adopted by the mid-sized company Chiesi to successfully implement a digital transformation across the R&D value chain.

  3. 3.

    Share initial achievements and potential advantages of implementing similar frameworks in mid-sized pharma companies.

2 Background

In recent years, the pharmaceutical industry has seen an exciting growth in digital initiatives, including numerous examples of AI and other cutting-edge technologies applied to enhance drug development. As drug development is a long and expensive process with a low success rate, AI, machine learning (ML), and other digital technologies are attractive for their ability to automate, predict, and accelerate development efforts [9].

As described in [10], the applications of digital technologies to support drug design and development belong to four main areas:


Disease modeling. In this context, the application of ML techniques involves integrating information from diverse omics technologies, decoding disease mechanisms at the single-cell level, simulating the dynamic evolution of diseases, and categorizing patient subgroups based on dysregulated molecular pathways, as reported in studies [11,12,13].


Identification, prioritization, and validation of candidate therapeutic targets. Digital technologies and AI support companies in streamlining biomarkers and target discovery processes. As an example, text mining and natural language processing (NLP) have been used to identify relevant target-disease pairs from literature and to develop databases supporting target identification [14]. Also, enormous progress has recently been achieved in protein structure prediction with the development of AlphaFold 3.0 [15].


The design, synthesis, and optimization of drug candidates interacting with given targets. Digital methodologies contribute to the efficient design of drug molecules, synthesis planning, and optimization of their interactions with targeted biological entities [16,17,18,19,20,21,22].


Improved clinical development performance. Clinical development processes can be streamlined through various digital applications, such as predictive modeling, patient recruitment optimization, and data analysis. These digital technologies leverage AI to identify potential challenges, optimize trial designs, and improve decision making, ultimately improving effectiveness and accelerating timelines from development to clinical use [23,24,25]. Of remarkable importance is also the recent introduction of accurate digital endpoints accepted by regulatory agencies, such as Stride Velocity 95th Centile (SV95C) qualified by EMA as novel Digital Endpoint in Duchenne Muscular Dystrophy [26].

Alongside these examples that target specific vertical sectors of pharmaceutical R&D, such as drug discovery and clinical development, many other applications for digital technologies focus on introducing deeper, more radical changes in business models and processes, as well as in organizational culture and mindset. Among these solutions, those that focus on data FAIRification are especially notable, as they enable a shift in organizational mindsets towards data: from application-centric to data-centric. This leads to a transformation in the data management value chain and harmonizes the needs of multiple, diverse stakeholders through highly connected, analysis-ready datasets [27].

Several pharma companies are moving in this direction with compelling projects. For example, the Roche EDISON platform aims to enable prospective FAIRification of data when it enters the Roche environment. This involves harmonizing, automating, and integrating heterogeneous and complex processes across departments, with built-in data standards and quality checks at every stage. Although EDISON is currently scoped for clinical, non-Case Report Form (CRF) data, the platform is flexible and can scale to include many clinical and non-clinical data models [28].

It is important to note that despite the widespread adoption of digital applications in the pharmaceutical industry, which might suggest that pharmaceutical companies are in an 'early mature' phase of AI integration, the true impact of these applications remains uncertain and awaits conclusive evidence [29].

In fact, there are several instances where, in the face of large investments for digital initiatives, the effort fails to sufficiently contribute to R&D efficiency, effectiveness, or productivity [29].

Findings reported [30] show that new digital technologies are most impactful when R&D organizations are well tailored and integrated into the business. Adoption of new digital solutions most often leads to revenue growth in firms that pursue more specific digital strategies within R&D.

To fully benefit from digital technologies, including AI, pharmaceutical companies will need to establish digital strategies, develop related business cases, and provide dedicated budgets. However, implementing a digitally driven R&D strategy can be challenging, and pharma companies continue to be cautious with major digital investments [31].

For this reason, many companies, especially smaller ones without a robust R&D strategy or the technical and investment capabilities of larger firms, continue to explore how to best incorporate digital technologies into their businesses.

In the journey towards R&D digital transformation, pharmaceutical executives need to acknowledge and navigate key risks and challenges. These may include external risks beyond the company’s control, such as the dynamic regulatory environment or the competitive talent pool for individuals with expertise in digital processes. Internally, R&D leaders must also navigate the risk associated with making significant investments in digital transformation without the certainty of high returns. Other challenges relate to the costs of digital R&D, rigid organizational processes, change management, and the management of heterogeneous data [29].

3 Development of the P.O.L.A.R. Star Framework

Chiesi, a mid-sized pharmaceutical company, strategically embarked on a journey towards R&D digital transformation. Recognizing the critical role of a tailored framework in facilitating this transition, Chiesi developed the “P.O.L.A.R. (People coordination, Ownable focus areas, Long-term roadmap, common digital Alphabet, Reporting and monitoring) Star” framework, which constitutes the cornerstone on which collaboration can be enhanced, processes can be optimized, and the power of data can be harnessed to generate useful insights. P.O.L.A.R. Star is the result of a stepwise path kicked-off in early 2021. It involves all R&D business functions contributing in different ways to phases of the R&D innovation process. In this section, we provide a description of the methodology upon which P.O.L.A.R. Star was developed.

The structured approach consists of five steps—outlined in the following list—which function as foundational pillars of P.O.L.A.R. Star.


Step 1: Alignment with Company and R&D Strategy and Creation of the R&D Digital Coordination Team

The first step included conducting a comprehensive review and analysis of the company's overall strategy, as well as the specific objectives and strategy of the R&D organization. To support this, a dedicated and cross-functional team called the “R&D Digital Coordination Team” was created, which was responsible for coordinating and driving digital transformation efforts within R&D, under R&D leadership team steering. The team comprises representatives of all relevant R&D functions, who engage closely with the most pertinent departments beyond R&D (e.g., Global Information & Communication Technologies, Procurement, Business Development).


Step 2: Identification of Key Strategically Ownable Digital Focus Areas Across R&D Value Chain

The second important step was the identification of digital opportunities that align with the company's strategic goals and can enhance the effectiveness and efficiency of R&D processes, impacting R&D output as the final goal. The process included an analysis of external benchmarks, workshops, and co-creation activities. Then, the identified areas were grouped into a set of clusters prioritized through a cross-evaluation funnel built according to three main criteria: (i) impact on business; (ii) impact of digital tools and processes applied to the relevant area; and (iii) availability of or need for internal capabilities. An evaluation of digital therapeutics (DTx) was considered out of scope for this initiative, as it is managed separately within the company.


Step 3. Definition of the Digital Strategic Roadmap (until 2030)

This step contains two objectives: (i) plan for and kick-off relevant initiatives belonging to the identified key focus areas while ensuring continuity with relevant ongoing digital projects; and (ii) define a work plan to build a common language for fostering data interoperability and collaboration along the R&D digital value chain.


Step 4. Creation of a Common Language within The R&D Value Chain Through a Semantic Data Model, Identifying and Implementing Specific Use Cases

Semantic data models are well known for their capability to improve the value of data by providing a shared language between underlying systems and business users. This common language enables systems and users to exchange data without ambiguity in meaning. The Chiesi R&D Digital Coordination Team agreed on the need to create an ontology-based data model to enhance collaboration and better support data interoperability across the R&D value chain. Having run high-level comprehensive mapping of R&D data and systems to gain a first holistic perspective and considering the high variety and complexity of relevant data, they decided to adopt a stepwise approach to connect systems and data to the model. We are proceeding incrementally by identifying priorities based on both business requirements and technical feasibility, testing the most significant use cases to validate the ontology-based approach step-by-step and ensure adoption of the semantic platform.


Step 5. Implementation of a Monitoring Tool to Plan, Track, and Manage Ongoing Digital Initiatives

The last step was the definition of a set of key performance indicators (KPIs) and the adoption of data-driven analysis tools to monitor the progression of the ongoing digital transformation.

4 Advantages to R&D Digital Transformation

In the following sections, we provide a more detailed description of each of the above-mentioned framework pillars.

Moreover, we aim to emphasize the advantages identified in our approach to R&D digital transformation at Chiesi.

4.1 Alignment with Company and R&D strategy and creation of the R&D Digital Coordination Team

The overarching framework for defining opportunities, a high-level scope, and the mandate for R&D digital transformation was defined within the broader company and R&D strategic plan.

This starting point is considered of paramount importance to ensure alignment with company and R&D strategic objectives, and to ensure that patients, caregivers, and other stakeholders ultimately benefit from the transformation. This mindset helps to avoid investment in misaligned and resource-intensive R&D digital initiatives for the sake of digital hype.

One of the first steps in this process was the establishment of an R&D Digital Coordination Team. The team comprises representatives from various R&D functions with an aim to identify a single reference point for all the digital initiatives across the Chiesi R&D value chain. This ensures harmonization with other R&D functions rather than creating a new organizational silo.

It should be noted that the R&D Digital Coordination Team is not a dedicated digital working group. Following a thoughtful and precise strategic decision, it comprises digitally savvy individuals with specific operational or management roles, and who are therefore fully aware of business processes within the R&D value chain and any related issues and opportunities.

The team includes members from main R&D areas such as Clinical, CMC, Preclinical, Regulatory, Quality, Pharmacovigilance and Project & Portfolio Management and is supported by a third-party consultancy agency.

The three main objectives of the R&D Digital Coordination Team are to:

  1. 1.

    Build and refresh R&D digital strategy and data governance;

  2. 2.

    Set a relevant strategic roadmap and priorities; and

  3. 3.

    Enable new digital capabilities and skills in R&D.

To achieve these goals, the R&D Digital Coordination Team is continuously engaged in a series of initiatives to:

  • Enhance cross-functional collaboration on digital transformation among different functions and disciplines across the R&D value chain;

  • Promote the creation of a suitable mindset supportive of digital innovation and relevant change in ways of working across the company by interacting with leadership and all other functions and involved stakeholders;

  • Manage the identification of any missing technical expertise or assets within R&D departments, cover any short-term gaps, and satisfy longer-term needs for internal digital competences and new skills;

  • Gain access to external expertise and assets, including facilitating participation in consortia and networks focused on digital transformation and collaboration with third parties able to provide useful technologies; and

  • Identify and refresh long-term objectives in the digital area, then define the roadmap to achieve them.

4.2 Identification of Key Strategically Ownable Digital Focus Areas Across R&D Value Chain

This process, starting from an initial identification of >50 data sources, >25 methods and tools, >10 platforms, and >400 potential value-generating opportunities, allowed us to identify the most promising value generation areas along the R&D value chain. The main result has been the identification of six key focus areas to concentrate efforts on in the mid-term, as well as 10 satellite focus areas to complement these efforts in the longer-term (Fig. 1).

Fig. 1
figure 1

Key digital focus areas along R&D value chain. RWD real-world data

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The definition of priorities for the medium- and long-term, outlining the business areas where new technologies will add the most value throughout the development chain, is a fundamental step to enable focus and the efficient use of resources. This is especially important for a mid-sized company.

4.3 Definition of the Digital Strategic Roadmap (until 2030)

Having identified the main areas of action, all ongoing projects and new digital opportunities within those areas were mapped together with their own specific digital enablers (including tools, platforms, and methods). Any possible points of contact between projects were then investigated to find synergies and common processes, tools, and/or data sources to optimize the use of related resources.

The analysis resulted in the identification of three main clusters of data needed to support R&D digital transformation, which deserve dedicated attention, them being either proprietary or public, regulated, involved in good practice guidelines for pharmaceuticals framework (GxP):

  1. 1.

    GxP/Regulated proprietary data and documents;

  2. 2.

    Non-GxP/Non-regulated proprietary data; and

  3. 3.

    Publicly available data.

Another important result of the mapping exercise was the acknowledgement that, in order to ensure connection between the three data clusters and satisfy cross-functional data needs, it would be necessary to create a common, unique Chiesi digital R&D data model and improve data governance within the company (Fig. 2).

Fig. 2
figure 2

Main clusters of data across Chiesi R&D value chain. GxP good practices

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We believe that by identifying data clusters deserving of dedicated treatment, establishing a unique common data model to connect them, and enacting robust data governance practices, we form a foundation upon which diverse digital initiatives can build synergies and optimize data management.

4.4 Creation of a Common Language within the R&D Value Chain Through a Semantic Data Model, Identifying and Implementing Specific Use Cases

The semantic data model was identified as the most appropriate to describe and interrogate Chiesi R&D data. These models act as a semantic structure tying together the organization’s information and data, therefore improving the interoperability between machines and humans, and enabling access to the right data despite any physical boundaries of underlying data silos.

In the pharmaceutical sector, this could represent a fundamental competitive advantage. Enabling quick access and navigation of data generated across different areas of expertise, irrespective of possible underlying data silos, enables stronger and quicker interdepartmental communication and cooperation. In practice, this means efficiently answering business questions that require cross-functional data analysis, reducing redundancies, and supporting decision makers in taking informed action to improve business outcomes.

Ontologies are the most popular semantic data models, enabling the provision of data representations that are fully compliant with FAIR principles (Findable, Accessible, Interoperable and Reusable).

In light of the above considerations, the Chiesi R&D Digital Coordination Team agreed to create an ontology-based data model to enhance collaboration and better support interoperability of data originated within different areas of the R&D value chain.

The first step of this journey was the selection of the methodological approach used to support ontology development. After a careful analysis of the most popular approaches proposed in the scientific literature [32,33,34], the team chose the bottom-up iterative approach illustrated in Fig. 3.

Fig. 3
figure 3

Methodological approach adopted for the development of Chiesi Digital R&D ontology-based data model

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In alignment with this approach, the ontology has been built through the following phases:

  • Knowledge acquisition: primary terms belonging to each knowledge area along the R&D value chain were identified through dedicated meetings involving the main relevant R&D subject matter experts.

  • Conceptualization: the team reached an agreement on terms and definitions through a series of plenary team workshops. Afterwards, they identified main ontology concepts and enriched them by specifying the hierarchical relationships and associations between them.

  • Integration of existing ontologies: the ontology under development was integrated with public-domain ontologies and standard dictionaries to exploit previously established conceptualizations. Ontology mediation was performed following the ontology mapping approach that consists in a (declarative) specification of the semantic overlap between different entities of two ontologies [35].

  • Formalization: all gathered knowledge was then formalized in the W3C Web Ontology Language (OWL) with the support of Protégé, one of the most popular free, open-source ontology editors.

  • Evaluation, documentation and maintenance: the ontology was evaluated to assess the extent to which ontology specification requirements have been satisfied.

The most valuable result of these activities is the Chiesi Digital R&D OWL Ontology, which includes over 400 classes interconnected by nearly 500 relationships (Fig. 4).

Fig. 4
figure 4

Chiesi Digital R&D OWL Ontology

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The ontology includes concepts belonging to the R&D value chain in its entirety, semantically linked to the most well-known and widely accepted public ontologies in the biomedical domain (e.g., MeSH, ChEBI, NCIt, MedDRA, SNOMED CT). Cross-mapping with public ontologies is crucial for achieving a more connected, interoperable, and standardized information ecosystem, as well as enabling easier and faster updates to the overall ontology based on revisions to public ontologies.

The Chiesi Digital R&D OWL Ontology will facilitate data integration, promote semantic consistency, and support knowledge sharing across various domains and applications of the R&D value chain. Moreover, it will support the semantic analysis of data belonging to both Chiesi internal and external data sources, enabling the identification of actionable insights to drive business decisions.

To test the ability of the newly created Chiesi Digital R&D OWL Ontology to support data integration across the R&D value chain and to boost data-driven decision-making, the team decided to apply the ontology model to specific business cases. This approach follows the “lean startup” validated learning approach articulated by Ries [36], which is based on quickly testing hypotheses in the field.

The following process was followed to identify use cases that could help validate the ontology-based approach:

Step 1: Identify domain stakeholders’ main strategic interests. Here, they are represented by the key focus areas previously identified.

Step 2: Identify goals/tasks which must be achieved to satisfy the higher-level strategic interests. In our analysis, more than 40 use cases were initially identified by the full Chiesi R&D digital team and R&D subject matter experts in co-creation workshops.

Step 3: Define a priority list of the goals/tasks to be implemented first. Subject matter experts and data scientists worked together to select the first two use cases to be implemented according to two criteria: (1) business relevance and (2) technical feasibility (Fig. 5).

Fig. 5
figure 5

Use cases evaluation criteria. FAIR Findability, Accessibility, Interoperability, Reusability

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The evaluation criteria identified to prioritize use cases are the result of an attempt to balance long-term goals with quick-win use cases. While maintaining alignment with the long-term digital R&D strategy, the selected use cases can provide immediate and tangible results, boosting morale and engagement within the organization. This early success also helps to gain support from stakeholders and leadership, who may be more willing to invest in long-term goals once presented with tangible, short-term benefits. Moreover, implementing quick-win use cases allows organizations to test ideas and technologies on a smaller scale, minimizing potential risks and failures before committing to broader initiatives and larger related investments.

The use cases that have been selected will support the early stages of the R&D process (identifying potential correlated biomarkers between animal models and humans for a given disease) and pharmacovigilance (predicting possible side effects of drugs before they reach the market, starting from adverse events reporting). The implementation of the two use cases is ongoing with the support of cutting-edge semantic analytics technologies provided by a market leadership provider.

4.5 Implementation of a Monitoring Tool to Oversee and Control Planned and Ongoing Digital Initiatives

Recognizing the need for a monitoring tool, the R&D Digital Coordination Team deployed a Business Intelligence dashboard to oversee the progression of digital transformation and measure the value generated. The dashboard provides a series of business insights, including the number of ongoing digital initiatives per R&D digital strategic focus area. For each initiative, it offers a concise description of the main objectives, deliverables, timelines, and costs. This dashboard enables iterative monitoring of the R&D digital roadmap, thereby facilitating informed decision making on R&D digital project prioritization in the event of strategic adjustments

4.6 Discussion of Overall Process and Future Aims

In the context of pharma digital transformation, the literature presents a spectrum of frameworks. These range from comprehensive approaches to those focusing on either organizational cultural change, specific R&D value chain segments, or purely technical aspects [37,38,39,40]. The P.O.L.A.R. Star framework stands out as a comprehensive approach. It captures the extensive scope of holistic models while also addressing the strategic imperatives, stages, and technical considerations necessary for a detailed focus. Its adaptability positions the P.O.L.A.R. Star framework as a practical guide, adapt at navigating the theoretical breadth and the tangible demands of R&D in the pharmaceutical sector, thus ensuring a relevant and effective digital progression.

Although the Chiesi R&D digital transformation is ongoing and far from over, in summary we believe that the definition of a common framework to develop a unified R&D digital strategy enabled a solid foundation for digital transformation in R&D.

The implementation of the P.O.L.A.R. Star framework has already yielded tangible outcomes:

  • Enhanced communication effectiveness in sharing the R&D digital strategy and roadmap, facilitating closer collaboration with key stakeholders involved in the digital transformation process both internally (e.g., R&D Subject Matter Experts, Global Transformation Office, and Global Information & Communication Technologies) and externally (e.g., Consortia and 3rd party technology providers). This has also led to the establishment of a common data language across the R&D value chain.

  • Increased efficiency in the prioritization of digital initiatives targeting specific areas of the R&D value chain, accompanied by improved resource monitoring.

  • The nurture of an internal mindset that embraces innovation and encourages active participation in key external networks and consortia.

  • The opportunity of quickly deploying use cases of interest to evaluate the ontology-based approach.

These results represent a solid backbone, enabling us to drive digitalization synergistically and systematically across departments and functions of Chiesi R&D. In this, we can maximize the value of any data generated across the R&D value chain, regardless of whether the data originated in research, early development, late development, or product life-cycle management – including in real-world setting, which are essential for the discovery and development of new medical treatments by biopharmaceutical companies.

Future R&D digital transformation efforts at Chiesi will concentrate on enabling broader access to R&D data interrogation and analysis among digitally savvy individuals in the R&D organization with a deep knowledge of cross-functional internal processes. This will ensure the development of digital solutions that are not only technically sound, but also closely aligned with the organization’s goals, processes, and needs.

It will be similarly important to implement new use cases that validate and streamline the P.O.L.A.R. Star digitalization framework and R&D digital ontology to identify new and more innovative ways to bring value to health care.

As R&D digital transformation requires a significant commitment from the R&D Digital Coordination Team, especially in the first years, and a non-negligible investment of time and economic resources, it is important that the efforts generate a positive impact for the company. At Chiesi, the construction of P.O.L.A.R. Star and the definition of strategic directives for digital transformation will lead to a long-term, positive impact on the company in the following areas:

Investment optimization: Having a clear strategic plan centered on cross-functionally agreed upon focus areas will allow us to concentrate future efforts and resources on the most promising digital initiatives. This avoids the dispersion of capital to many initiatives that may not generate value. Moreover, the semantic-based technologies and approaches that foster knowledge sharing and data interoperability are scalable and reusable to use cases across organizational units, with minimal customization effort needed. This results in additional cost savings.

Reduced time: From an organizational point of view, the definition of a clear R&D digital strategy will enable teams to find synergies between projects, share insights, and accelerate new findings. On the technological side, the implementation of a common R&D data model across the R&D value chain will speed access to information, enable connections of data generated across different departments and by different systems, significantly reducing time wasted when processing and sharing data manually.

Improved quality: All pillars of the P.O.L.A.R. Star digitalization framework – from the dedicated team to the adoption of semantic technologies for data modelling – will improve ongoing internal processes and benefit R&D outcomes.

The P.O.L.A.R. Star framework has been designed to ensure its long-term sustainability and scalability. It is adaptable to companies of any size and provides a robust approach, which focuses on strategic drivers. The framework supports iterative updates of digital initiatives, helping to address risks arising from the rapidly changing technological environment. This ensures that digital transformation efforts are aligned with the company’s strategic goals and can adapt to future challenges.

5 Conclusion

Chiesi, a mid-sized global pharmaceutical company with headquarters in Italy, has defined and implemented a framework, P.O.L.A.R. Star, to support and drive digital transformation across its R&D value chain. In taking an agile approach, Chiesi was able to overcome traditional challenges that most companies face when undergoing digital transformation.

P.O.L.A.R. Star: A New Framework Developed and Applied by One Mid-Sized Pharmaceutical Company to Drive Digital Transformation in R&D (MP4 38858 kb)

This paper highlights the company’s valuable and successful experience thus far, already resulting in specific positive outcomes:

  • Deep links between the company and R&D strategy, the long-term digital strategy and a digital transformation roadmap;

  • Internal alignment on common focus across digital opportunities in R&D and clear communication with external stakeholders and partners, which are key areas of interest for Chiesi;

  • Harmonized digital efforts across the digital R&D value chain, coordinated by a dedicated cross-functional team, to avoid digital silos;

  • Holistic planning and tracking of digital transformation progress across R&D activities;

  • An R&D digital roadmap implemented through a stepwise approach and agile methodology, quickly tested via practical use cases; and

  • Cost and time efficiencies, achieved through synergies identified between initiatives that reduce redundancies in implementations.

The P.O.L.A.R. Star framework was designed and shaped based on the processes and needs of the Chiesi R&D organization. While fine-tuning will be required when applying it to other organizations, we firmly believe that the findings presented herein offer a valuable blueprint for other small and mid-sized pharmaceutical companies that are aiming to successfully and efficiently navigate their digital transformation journey. Our effort also constitutes the first real-world validation of a digital transformation framework worthy of future study in broader applications.

In conclusion, Chiesi R&D digital transformation is expected to deliver a long-term, positive impact on the company, fostering R&D output and innovation while aligning R&D digital strategy to the broader R&D and company strategies. This transformation is not merely a technological upgrade but a fundamental shift towards a data-driven and agile R&D ecosystem, which will empower Chiesi to swiftly adapt to future challenges and maintain a competitive edge in the pharmaceutical industry.