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A Guide to Digitizing Biopharma


Historically, the biopharma space has always lagged behind other industries when it comes to digitizing processes. But over the last few years, digitization has accelerated. This is partly because companies have been desperately searching for new ways to improve the currently inefficient drug discovery process and increase the use for valuable datasets. Other contributing factors include the growing availability of biodata and advanced analytical approaches, such as machine learning.

Therefore, particularly since the pandemic, digitization has encouraged organizations to revise their business models and approach modern drug discovery in alternative ways. This is in the hope that the implementation of new technologies will help to save time and money, as well as obtain a wealth of data, in turn increasing the efficiency of several R&D stages.

For more information about how digitization is helping biopharma companies improve the way they collect, analyze and store data for R&D, check out the Digital Transformation in Biopharma report. It includes perspectives from organizations who are currently on their digital journey and provides insight into some of the key challenges they have faced. Download it here:

How can digitization improve biopharma?
The advantages of digitization for biopharma companies are discussed, including how digital tools were involved at each stage of COVID vaccination programs.

AI in biopharma
The applications of AI in biopharma are explained, including drug target identification, drug repurposing, biomarker development, analysis of literature and biomedical imaging.

Automation in biopharma
The ways in which automation has increased laboratory efficiency are discussed, along with the challenges faced.

Digital health technologies in biopharma
A summary of how digital health technologies are improving aspects of clinical trials, including patient recruitment and data collection.

Challenges facing digital transformation biopharma
The many challenges facing biopharma companies on their digitization journey are explained and tips for adopting digital technologies are summarised.

How can digitization improve biopharma?

It is recognized that pharma R&D productivity is decreasing. Over the last three decades, the cost of drug R&D has increased by ten-fold. For example, the launch of a new cancer drug has doubled over the last 20 years. The amount of money spent to develop any individual drug depends largely on what costs were necessary to gain regulatory approval, ranging between $10 million to $2 billion. These average expenditures are so high due to the high fact that 90% of drugs that start being tested on humans do not reach the market, mainly because they have not been proven safe and effective.

Lack of Productivity in Biopharma R&D

Nevertheless, new technologies and innovations are starting to enable biopharma companies to improve drug discovery and development processes. While there is still a long way to go, recent developments in the industry indicate that it may be beneficial to invest in becoming a ‘digital pharma player’. Essentially, companies that put effort and time into digital R&D, including implementing digital structures, processes and mindsets, will enable them to become adapted and thrive within the rapidly-changing healthcare landscape.

Advantages of digitization for a biopharma company

Historically, biopharma companies have been cautious to apply digital technologies to R&D processes, but as the industry continues to face mounting challenges, digitization has the potential to help researchers overcome certain obstacles.

Avoid lagging behind

Currently, the biopharma industry is thought to be in an ‘early mature’ phase of utilizing digital technologies in R&D. Therefore, pharma companies are at risk of being left behind by digital leaders. No matter how fast or efficient product development is, if ancient tools, such as paper notebooks, are being used to record data biopharma companies will never be able to keep up. Not being able to achieve efficient data demands will ultimately lengthen the time to drug approval and distribution. This gap will continue to grow if traditional means are continually used.

It is better to be ahead of the game, as opposed to trying to catch up with trends. In fact, it is likely that in less than 15 years’ time, digital transformation will no longer be a ‘hot topic’, but instead will be a thing of the past. Biopharma companies should try to avoid lagging behind at all costs. Although digital transformation in biopharma is not yet recognized as standard procedure, the COVID pandemic has begun to accelerate the adoption of digital ecosystems within the industry.

Improved efficiency

Advanced technologies can be harnessed to dramatically improve the efficiency of current R&D processes. Data-driven techniques reduce the time taken for procedures and decrease the chance of human errors. In the long term, this will inevitably enhance cost efficiencies too. Companies who embrace digitalization are likely to significantly increase the visibility into their supply chain operations, leading to faster decision-making and allowing for more adaptive processes.

For example, if companies such as AstraZeneca and Pfizer had not already been harnessing digital technologies prior to the pandemic breakout, the development and marketing of a COVID vaccine would not have been nearly as rapid. Digital solutions were used to support functions across the whole vaccine deployment lifecycle, from planning and management, to supply and distribution, program delivery and post vaccination. Ultimately, if these organizations had been stuck with ineffective and outdated tools, a great deal more patients would have suffered globally.

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Meeting the FAIR data principles

The implementation of the FAIR data principles has been a popular topic recently. These are guidelines for data management and stewardship, specifying that data should be findable, accessible and interoperable in order to be successfully re-used. The principles were published in 2016 as a way to help companies cope with the huge scale and complexity of data now being generated. Compliance with FAIR makes it easier to search for data and transfer information between external and internal systems. This not only reduces the cost of research, but also allows investigators to spend more time on conducting research rather than handling data. For more information about the FAIR principles and moving towards a more data centric future, check out A Guide to a FAIR Data Future in Biopharma

Driving competition

Intensifying competition is driving digital transformation because companies that choose to invest in digitization are likely to have a competitive advantage over those that don’t. Traditionally, drug R&D data tends to be kept privately within biopharma organizations. However, technology companies, such as Apple, are now collecting large amounts of health data from apps and fitness devices. Furthermore, Google and Amazon are also pushing into healthcare by attempting to use their huge computing power and expertise in data analytics to disrupt the industry, cut costs and make new discoveries. This will put pressure on the rest of the biopharma industry, to decide whether to directly compete with these giant technology companies or to find ways to collaborate with these emerging players.

AI in biopharma

Artificial intelligence (AI) is already proving useful in a number of tasks, such as natural language processing, image recognition and self-driving cars. But it also presents a great promise to transform the biopharma industry, particularly by helping to develop new drugs more efficiently and effectively.

Due to ongoing technological advances, huge amounts of biological and medical data, both structured and unstructured, are being made available at rapid speeds. Therefore, it is unsurprising that the interest in AI-driven solutions for early-stage drug discovery is growing steadily among biopharma leaders. In fact, the AI-based drug market is set to reach $10 billion by 2024 and nearly 80% of executives, managers and professionals in biopharma companies state that their firms plan to use, or are using, AI approaches to improve R&D performance.

The use of AI and machine learning is helping the biopharma industry to cope with such a vast amount of data being generated quicker than it can be analyzed by humans alone. Machine learning was first coined by Arthur Samuel in 1959 as “the field of study that gives computers the ability to learn without being explicitly programmed”. Although machine learning is not a new field of research, the last decade has been particularly exciting progress in relation to R&D. There have now been several efforts to combine machine learning with every phase of the drug discovery and development pipeline as the tools can be used to sift through and decode data from large amounts of scientific literature. This increase in the searchability and accuracy of information has the potential to accelerate project timelines by months.

Recently, there has been a wave of new R&D collaborations between key biopharma players and AI-driven companies, using a variety of different approaches for different applications.

Image credit: Outsourcing Pharma

Using AI in biopharma

Drug target identification using AI

Genentech, a member of the Roche Group, announced a research partnership with GNS Healthcare, a leading precision medicine company. This relationship was formed to identify and validate novel cancer drug targets using a machine learning and simulation AI platform. The Reverse Engineering and Forward Simulation (REFS) technology turns large and diverse data streams into mechanistic computer models that reveal novel pathways, new targets and diagnostic markers. This was the first commercially available tool that automated the transformation of diverse biomedical data into computer models representative of individual patients.

Another example is the collaboration between GSK and an AI-driven company called Insilico Medicine, to enhance the identification of novel biological targets and pathways of interest for drug development and ageing research. The data-driven technologies are used to conduct several processes including data mining, hypothesis generation and lead compound identification. Together, these enable an improvement of the overall output of health information, especially as larger amounts of data become available for analysis.

Drug repurposing using AI

Repurposing previously known drugs towards new therapeutic areas is a desired strategy for many biopharma companies because it presents less risk of unexpected toxicity or side effects in human trials, often resulting in less R&D spend. AI models capture a lot of data about a drug in question and so can be highly valuable for drug repurposing.

Astellas Pharma, a Japanese pharma company, formed a collaboration with the data intelligence organization called NuMedii in 2016. This partnership was to identify new indications for a number of compounds and conduct drug repurposing projects using machine learning. NuMedii used neural network-based algorithms to find drug candidates that could be used for other medication indications using hundreds of millions of human, biological, pharmacological and clinical data points.

Biomarker development using AI

Biomarkers are important for medical diagnostics and are used in drug development programs. Berg Health is a company that applies AI-driven modelling for biomarker development. Genome, proteome, metabolome and lipidome data is fed into AI algorithms to unravel complex biological networks that play a role in diseases. This deep-learning multi-omic approach allows the in-depth screening of biomarkers from a huge amount of patient data.

Berg Health has partnered with Sanofi Pasteur, a leader in the vaccination industry, so the company can use the bAIsics tool to identify molecular signatures and potential biomarkers for assessing the immunological response to the influenza vaccine. Berg Health has also announced its collaboration with AstraZeneca to focus on novel approaches for treating Parkinson’s disease and other neurological disorders. The AI-driven company will use its drug discovery platform to explore a selection of chemical fragments to find promising drug candidates.

AstraZeneca announced a deal with Benevolent AI in 2019. This long-term collaboration will use machine learning and AI to discover potential new drugs for chronic kidney disease and idiopathic pulmonary fibrosis. Benevolent AI is a company that uses a target identification platform and biomedical knowledge graph to extrapolate previously unknown connections between the scientific data. Together, these companies will interpret the results to understand the underlying mechanisms of the two complex diseases to identify potential drug targets more quickly.

Analyzing literature using AI

Text mining employs many computational technologies to find novel outcomes within unstructured biomedical publications. These tools help to maximize discovery and unlock information from huge volumes of text. AI is commonly used for reading, clustering and interpreting large volumes of textual data. This is particularly useful in the biopharma industry, due to the growing number of research publications. Therefore, today it is unrealistic that a researcher would be able to sift through all the data they require on a daily basis.

BenchSci is a company that empowers scientists with advanced data searching abilities using AI. It has developed an AI-Assisted Reagent Selection application, which helps to promote improved productivity and return on investment for biopharma companies. It has been applied to decode over 11.3 million scientific articles, 18.1 million experimental data points and data from over 32.2 million products by more than 550 vendors. This provides scientists with the best information in shorter periods of time, increasing overall workflow productivity.

To read an exclusive interview with Cassandra Mangroo, the Vice President of Science and Science Team Leader at BenchSci, download the Digital Transformation in Biopharma report:

Bio-Modelling Systems is a French company that is developing a Computer-Assisted Deductive Integration (CADI) drug discovery platform that is based on models that generate hypotheses from scientific, medical and health data. The technology allows for data mining, organization and structuring, alongside a representation and visualization tool. The company has had numerous projects with pharma companies.

Biomedical image analysis using AI

There are a huge variety of imaging modalities, from digital pathology to CT scanning. Recent advances in AI are helping to identify, classify and quantify patterns within medical images. In particular, radiology and radiotherapy are anticipated to harness AI-based techniques more and more as imaging technology advances. Google Health has partnered with the Mayo Clinic to develop an AI system that can support physicians and improve the efficiency of radiotherapy. Together, the organizations will work on an algorithm that can help to distinguish healthy tissue from tumours. They will then work out how this technology could be deployed effectively in the real world.

By adopting these advanced technologies, companies can massively reduce unnecessary reagent spend, whilst also accelerating research and increase the reproducibility of their results. In turn, this will improve the quality of the overall R&D workflow.

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Challenges facing AI in biopharma

Although AI can be used to address major drug development obstacles, adopting AI to transform the biopharma industry is also linked to a variety of challenges. Most of these issues are due to a lack of relevant expertise and understanding, in turn holding back progress within companies.

Quality of data

Data can sometimes be described as ‘garbage’, meaning that it can be poorly labelled, inaccurate or reflects underlying human prejudices. But high-quality data is crucial for building AI models, which can be extremely time-consuming. In fact, it is estimated that over 80% of data scientists’ time is spent on data preparation tasks, such as cleaning, transforming and homogenising the information from multiple sources in suitable formats. This is because information is often stored in multiple locations and the amount of data relevant to a given drug may be limited or hard to access.

Caution for adoption

Biopharma has been one of the most cautious industries in terms of adopting AI. This is probably due to the current unclear real-world benefits that the technology brings to healthcare. Although the efficacy of AI at many stages of R&D has been evidenced, there is often no impact on the cost or overall probability of success. Therefore, the biopharma industry has an innate resistance to change. This is especially true when few stakeholders have the relevant expertise in AI to fully appreciate the potential of its application in drug development. Moreover, collaboration and interaction between cross-functional teams is essential for data-driven success, which doesn’t come naturally to many biopharma organizations.

Methodological challenges

One problem is the lack of explainable AI methods, or many of the existing ones failing to satisfy the Ethics and Guidelines for Trustworthy AI laid out by the European Commission in 2018. Explainable AI refers to transparent solutions of AI-based methods and techniques being understood by human experts. This is contrary to the concept of the ‘black box’ in machine learning. Effort needs to be invested into the development of various AI explainable methods to allow scientists to understand how an algorithm arrives at a certain prediction. This will help to determine causal reasoning, rather than associative inference, to enable these methods to be more widely adopted in pharma R&D and surpass the capabilities of human experts in these domains.

Overcoming these challenges and successfully combining AI with relevant data, and cross-functional domain expertise will significantly benefit the biopharma industry. To lower barriers to adoption, pharma companies should improve the collection and storage of internal data and ensure data is shared across divisions. Furthermore, it is important that AI companies secure enough biopharma expertise and cater to their needs, to ensure that the partnerships are truly mutually beneficial.

Automation in biopharma

Since its introduction, automation has revolutionized the laboratory environment. Today, robotics is rapidly changing the way that drugs are being discovered. The applications of automating the drug discovery process include computational molecular design, high throughput analysis and robotic synthesis. Essentially, advanced technologies increase a laboratory’s ability to reliably perform large volumes of repetitive tasks and help to alleviate some of the pressures on scientists today, in turn increasing workflow efficiency.

Benefits of labratory automation

  • Reduced costs: Automation enables large-scale testing and reduces the associated utility and equipment costs – perhaps most significant is the decreased need for manual labour.
  • Reduced human error: Automation eliminates the risk of human errors that arise from repetitive tasks and manual recording.
  • Unified devices: Automation facilitates opportunities to connect laboratory processes, which are often left isolated. Remote controlling of devices can also enable experiments to be conducted anytime, from anywhere.
  • Maximise data generated: Automation enables easier collection of data and can provide extra information that may otherwise be missed. This data can then be streamed onto an online platform and used to improve experimental decision making.
  • Increased speed: Automation allows workflows to be carried out much faster than they would be manually, enabling more rapid results.
  • Increased flexibility: Automated machines can be customised to meet each individual laboratory’s needs so that they can save space and, in the long run, evolve accordingly.
  • Increased time and resources: Eliminating time-consuming and tedious processes can save scientists huge amounts of time, meaning that their skills can be used for more specialist tasks.

Arctoris is an Oxford-based technology company that operates a fully automated drug discovery platform. The organization was established in 2016 as a start-up, when at the time, researchers were struggling to implement remote work in their laboratories. Since then, Arctoris has grown into a global company that delivers integrated drug discovery projects with partners across three continents. This has been accelerated by the desire to fully automate the entire laboratory workflow and conduct remote research during the COVID pandemic, in order to keep workers safe. Recently, its new and expanded next-generation platform was unveiled, called Ulysses.

Martin-Immanuel Bittner, CEO and co-founder of Arctoris, has explained: “Five years ago, when we founded Arctoris, automating entire discovery functions was considered impossible, and often unnecessary, by many. However, we have witnessed a radical change in perception during these past years, with many industry leaders recognizing how important data quality is for R&D success. One of the main drivers for this mindset change has been the growing role that artificial intelligence and machine learning are playing in our industry. Every day, we see more and more companies and researchers trying to harness AI for different tasks in drug discovery—target validation, information synthesis, de novo drug design, etc.”

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Challenges with laboratory automation

Although automation in the laboratory is linked to numerous benefits, it also faces some pressing challenges. The transition into automation is not as simple as replacing humans with machines, and many considerations need to take place before jumping into implementing these new technologies.

Skill changes and loss

Scientists that monitor the software need to be trained on how to use it. These new skills may be completely different to those that were used for previous manual tasks, sometimes creating a huge hurdle for companies. For example, laboratory technicians may be accustomed to handling specimens, rather than technology. In this case, a ‘learning curve’ takes place. This is the time it takes for scientists to become comfortable with the new technology, before it can become fully optimized.As these machines inevitably advance, there is a need for further training to learn how to work newer models. Additionally, as updated technology is adopted, old technology is often discarded, along with a portion of the skills and knowledge gain by scientists about that system.

High short-term cost

Large upfront investment is required for accommodating the project, system installation and new hardware. Some facilities may not be able to afford these high costs. Therefore, it is important to clearly demonstrate the possible return on investment, alongside a reliable financial plan.

Infrastructure requirements

The need for space and the correct infrastructure is a major issue for implementing automation. Trying to accommodate new equipment into a pre-existing facility can be challenging, especially if the space was not purpose built or there are service obligations. Adopting automation when moving into an open space is often easiest, but is rarely feasible.


The convergences of smart workflow and machine learning has both a professional and social impact. The main ethical issue surrounding automation is probably the reduced need for humans in the laboratory, and the subsequent loss of employed technicians. Legislation is soon expected to include autonomous tools, which may lead to more acceptance of the new technologies due to a balance between increased efficiency and social impact.

Digital health technologies in biopharma

The concept of a “digital clinical trial” is gaining traction among the biopharma industry and involves leveraging digital technologies to improve participant access, optimize engagement and enhance measurements. Overall, digitization offers the opportunity to significantly improve the efficiency of the process in numerous ways.

The US National Institutes of Health (NIH) and the National Science Foundation (NSF) held a workshop in 2019, bringing together experts in clinical trials, digital technology and data analytics to discuss strategies that could be implemented to overcome certain challenges within the sector.

Digital health technologies offer user-friendly measurement of participant health markers, such as physical activity, sleep, heart rate, medication adherence and respiration patterns. However, many of these systems have not been specifically designed for research use, but instead are part of the booming wellness industry. Regardless, they still provide the scientific community with new tools, which could greatly enhance the clinical trial process.

Patient recruitment and retention

Digital health technologies enable access for potential participants, regardless of their location. This reduces the cost and effort that patients typically have to endure while taking part in an in-person clinical trial. Remote monitoring is also often more efficient and allows multiple opportunities for interactive patient management or assessment. This results in more intensive work being done with smaller budgets and less resources. Therefore, these technologies have the potential to reduce the burden on not only the participants, making it more likely that they agree to take part and remain in the trial, but also the research team. This also increases the outreach to previously underrepresented ethnic or age groups, as this method helps to break down barriers, such as mistrust and fear.

Pfizer launched a web-based clinical trial outreach platform, called Pfizer Link, in 2012. It is an opt-in Clinical Trials Alumni Portal that provides participants with valuable information and resources to help manage their conditions. It also informs them about future research opportunities. More recently, Science 37 developed a Network Oriented Research Assistant (NORA) platform, which is a system that allows for real-time video chat, electronic data collection and electronic consent. The clinical study for AOBiome Therapeutics was conducted entirely through a smartphone app and was concluded to be the first completed fully remote clinical trial.

Dr Noah Craft, the study’s principal investigator, explained: “The completion of the AOBiome study is a great demonstration of efficacy and the ability to scale and run entire trials remotely through Science 37’s Metasite model without the need for brick-and-mortar facilities. We believe that at least half of trials today can be done [virtually] with the right expertise and logistics behind them.” 

Data collection

Data can take one of many forms, such as demographic, clinical or activity data, or patient recorded outcomes, images, electronic medical records and biological samples. The advantage of using digital tools is that they can continuously collect data and improve clinicians understanding of infrequent events or highly specific conditions. Nevertheless, more work is needed to ensure that digital technology always meet standards for reliability and validity, as well as ensuring privacy and safety measures against data security breaches are set in place.

eClinicalHealth  launched the first end-to-end clinical research platform that is purpose-built for virtual patient studies called Clinpal. Patients are able to log in from anywhere, on any device. The particular advantage of this system is that research teams are able to harness data and analytics across the entire duration of the trial. Sanofi, a global biopharma company and a technology organization called eClinicalHealth announced their VERKKO Phase 4 Trial for diabetes in 2015. This was an investigation of a wireless cloud-connected glucometer involving 60 participants, all recruited through Facebook. The glucometer was 3G-enabled, meaning that glucose measurements were automatically transmitted from the device into the Clinpal system, where they were available to view in real-time. Furthermore, patients signed their consent forms electronically and study materials were delivered directly to the patients houses so that none of the participants needed to visit the study site once.

Challenges facing digital transformation biopharma

It is recognized that digital transformation is one of the greatest and most complex challenges facing companies today. Organizations are required to take a journey, from non-digital to partially digital to fully digital. This will not only impact the technology systems, but also the entire business strategy, and it is crucial that leaders at all levels of the company completely understand the benefits of the transition. Moreover, the attitudes of colleagues, who may be asked to change their skillsets or adjust their role slightly in the future, need to be onboard with the transformation. Therefore, it is unsurprising that there are multiple challenges facing the adoption of digital technologies within biopharma.

Data quality

Maintaining data quality when it is derived from a variety of public sources can be difficult. It is important to try to maintain the provenance of the data itself and apply some degree of quality metrics, for example, following the FAIR data principles. Also, integrating different data types generated from different sources can be challenging for many companies. Interoperability can significantly improve the data integration process, for example between biological data and real-world data. This is increasingly being done with the help of digital technologies.

Data silos

A data silo is a situation whereby only one group in an organization can gain access to a dataset. Although they are common, they are often the source of huge inefficiencies in any department or company. Data silos can arise by many situations, such as employees keeping data from each other for social reasons, hierarchal separation by many layers of management and by technological factors. Nevertheless, they hinder collaboration and limit pharma companies from embracing the full potential of digital transformation.

In order to generate value from vast amounts of data, organizations must first gain access to it, which requires navigating across hundreds of data silos. This can be challenging due to the variation in data standards and the wide range of data types. Many data experts believe that opportunities are missed when biopharma companies silo their data, rather than making it easily available for further exploration. The isolation of data severely impacts the way it can be used and limits the possible collaboration across the industry. Breaking silos down requires cultural change first, to encourage the concept of social working environments and the maintenance of transparent data-sharing practices.

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Resistance to change

Humans like routine. Therefore, experiencing digital transformation can make some employees feel threatened and reluctant to discard processes that they have built and learned. However, push back from colleagues makes it difficult to progress in a digital transition.

Concerns cannot be completely removed, but leaders being consistent and transparent throughout the journey is crucial, alongside employees constantly being engaged. Perhaps the most effective way of doing this is providing constant reminders that the digital transformation will not reduce the company headcount, but instead will work in harmony with existing employees. Pistoia Alliance recently launched a series of programs to help organizations collaborate on advancing the role of digital technology with R&D and healthcare. They are a not-for-profit group of life science companies, vendors, publishers and academic groups that work together to lower barriers to innovation in biopharma.


Digitizing pharma R&D is costly and can be extremely daunting, especially if there is no set-in-stone date for when the return on investment will materialise. However, it is important to constantly re-visit the budget and start small with manageable goals. Companies will also need to invest in people as it is crucial to acquire the right talent. Training a workforce to be digitally literate and building the skills needed for innovation is key. The right leadership is also required to provide support and the ability to reach out to industry peers for valuable insight. Collaborating with digital partners will almost certainly make the transformation easier and can help create a clear digital strategy to reassure employees.


A clear focus on cybersecurity is needed to avoid exposure to emerging threats. Patient data that is found outside of the biopharma company, as well as data generated by the organization, can present issues of secondary use and consent. Also, data being leaked out of systems is always a concern, as it can completely damage a company’s reputation. Big data analytics software can use advanced encryption algorithms and pseudo-anonymisation of personal data. Good data governance practises and security on the network level should be used to prevent such avoidable problems. Unfortunately, for the majority of digital transformation activities, cybersecurity remains an afterthought. But it is essential to intertwine it as part of the initial management plan so that it can be embedded across the entire organisation.

Adoption of digital transformation in biopharma

The success of digital transformation in biopharma is largely going to be determined by the persistence and willingness of companies. The process needs to be an ongoing evolution of testing, implementation, integration and refinement of new technologies and business models. Today, many biopharma companies are at a difficult crossroads between choosing whether to break the mould and follow the new era of digital technologies, or whether to remain stationary, without embracing change, and risk becoming less competitive in the long-term.

The transition can seem daunting, but with the vision of future improvement and innovation, it should be recognized that the benefits of digitization are achievable. It is important to acknowledge that every biopharma digital player is at a different stage of their transformation, and it is vital that lessons learned are shared to help the industry as a whole drive towards a new digital era.

To read an exclusive interview with Cindy Novak, Systems Manager at Bristol Myers Squibb, download the Digital Transformation in Biopharma report. She shares her lessons learned from working within organizations at different stages of their digital journey and discusses how adoption of digitization is being addressed by biopharma today:

Resources for digitizing biopharma

Digital Transformation in Biopharma – A Review

Driving FAIR in Biopharma

Considerations for starting a FAIR data library

Accelerating Research in the Cloud

Don’t forget to follow Front Line Genomics for more information about how genomics is being used to benefit patients.



Image credit: BioSpace

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