IDBS BlogPharma process development

IDBS Blog | 30th June 2023

A digital data backbone connects the pieces of pharma process development lifecycle management

Pharma process development

By Unjulie Bhanot, Head of BPLM Solutions, IDBS

To bring promising new therapies to the patients who need them sooner, an approach for bringing people, processes and data together across the full pharma process development lifecycle management is needed. This is the overarching message that Pietro Forgione, now General Manager at IDBS, shares in a recent BioPharm International article. Rooting scientific understanding and know-how into a data backbone that supports a therapeutic on its journey from research to manufacturing will alleviate many of the bottlenecks and quality risks triggered by manual tasks. This strategy, he says, produces a strong data foundation that unleashes the power of artificial intelligence (AI) and digital twins, offering the insights required to drive innovation, speed regulatory filing and technology transfer, and eventually cut the time needed to release high-quality medicines.

Credit for video below: Atos Group (2022). Digital twin for smart pharma. Available at: 

Lakes and layers are lacking in pharma process development

Today, the critical data captured during the pharma process development lifecycle still rely on paper-based systems and manual processes and the data often reside in disconnected silos. While many life science organizations have implemented some digital technologies, these disjointed tools with their isolated deployments are not working together in harmony. As a result, Forgione says that upwards of 20% of time1 is wasted on time-consuming data administration. For instance, an Analytical Development scientist could get a request from another team, such as from the Upstream Development or Downstream Processing team to execute an ELISA assay; if the request is not clear, this may require additional work to obtain the correct sample information, such as the cell line or product concentration. These details can greatly impact how the assay is carried out. Then comes the time spent writing in logbooks and getting ready to run the test. Once the assay is performed, data from the instrument may then be manually transferred for analysis. The time pressure is further increased when scientists need to ask when and where the results can be found. In this way, data management issues only worsen over time, especially if you’re also attempting to locate previous data, such as in response to regulatory authorities’ inquiries.

Additionally, due to the disparate data storage, anywhere from 10 to 20% of work needs to be repeated.2 To bring the separate silos together in one location, some companies have tried integrating their legacy systems through central repositories (data lakes) and enterprise data warehouses (integration layers). Unfortunately, this does not record how the various data sets are related, and the vital experimental context that can be used for process optimization is lost.

A better way to manage data

Forgione stresses that a transition from these legacy systems to cloud-based platforms can enable data integration and create a strong digital data backbone to connect all the data points collected through the pharma process development lifecycle management. This enables the industry to not only eliminate those disparate data silos but also unlock the power of AI/ML tools, which can provide invaluable insight. One example of where a data backbone offers significant benefit is the connection between process parameters and product quality. He writes: “A more intact digital data backbone leads to better predictive analytics in the manufacturing process, reducing the number of failed batches. Additionally, the increased quality of emerging data reduces the administrative burden on scientists and the need for excessive quality assurance.”

IDBS understands these challenges well. We offer a platform that puts a digital backbone and data integration at its forefront. IDBS Polar is a cloud-based platform that eliminates repetitive manual tasks, enabling efficient execution of BioPharma processes while curating the data needed to accelerate time to market by tackling the biggest challenges in process design, optimization, scale-up and technology transfer.

Now, let’s go back to our example of the Analytical Development scientist performing an ELISA assay. With IDBS Polar, all the information the scientist needs to perform the assay is readily available;  product concentration, specified process steps and parameters, requisite buffers etc., reducing manual intervention while enabling the scientist to capture the experimental data relevant to the assay itself. Digital workflows make it easy to plan and perform experiments, and automatically integrate the data into the Polar Data Backbone. IDBS Polar also allows for the bi-directional exchange of data between instruments and other software systems and the capturing of online and offline data; combined with experimental data, the scientist has all the relevant data available at their fingertips from which they can make data-driven (strategic) decisions. Additionally, with one centralized system, the requesting scientists can now find the experiments, review the data and determine conclusions themselves.

By standardizing how data is captured within the execution steps, and augmenting this with metadata, process and instrument data in the data backbone, scientists can spend more time focusing on science and leveraging this data within advanced analytics tools. Data science capabilities in the IDBS Polar platform enable organizations to integrate with next-generation data analytics tools such as AI/ML to explore the maximum potential of scientific and business processes.

Contextual and transferable data

A digital data backbone offers a repository to collect and connect metadata and critical experimental context in one single place, enabling organizations to understand the real value of their data and make data-driven decisions and process improvements – ultimately enabling the release of the therapeutic to patients faster. In fact, the International Council for Harmonisation (ICH) underlines the importance of data, insight and quality throughout the lifecycle of a drug. Regulatory scrutiny can be lessened by displaying a thorough understanding of the process. This opens up the possibility of further process optimization after approval, which can significantly increase yield while lowering costs and length of time to deliver medicines to patients.

Another area where a data backbone provides immense value to the pharma industry is technology transfer. Technology transfer from Process Development to Manufacturing involves transferring the process, methods and specifications required for developing the drug consistently and accurately. This is often a costly and -heavy process, subject to manual interventions, review stages and risks. Instead, capturing this information in a cloud-based digital platform, like IDBS Polar, at the process development stage, connects process parameters with the drug’s yield and quality. This allows for a more seamless transfer of this information in a contextualized, standardized and compliant manner, which also mitigates the risks associated with outsourcing to a CDMO partner.

Unlock the power of data in pharma process development

More streamlined data management gives pharma companies access to more advanced technology, says Forgione. For example, data science capabilities in the IDBS Polar platform accelerate pharma process development via advanced data analytics and visualization tools. Accessing product and process insights across the full drug lifecycle will be critical to accelerating time to market for life-saving therapeutics.

Additionally, digital twin technologies can leverage the data captured in the Polar Data Backbone to make the gyógyszerfejlesztés lifecycle more efficient by providing the ability to virtualize an entire lab experiment or manufacturing process, suggest optimal development conditions and predict potential safety issues.

Ultimately, Forgione writes that a digital data backbone for pharma process development lifecycle management will accelerate next-generation therapeutics, assuring quality and bringing life-saving products to commercial launch faster.


A szerzőről

Unjulie Bhanot

Unjulie Bhanot is the Head of BPLM (BioPharma Lifecycle Management) Solutions, and part of the IDBS Strategy team based in the UK. With over 10 years of experience in the Biopharma Informatics space, she now owns the strategy, development and delivery of IDBS Polar Solutions.

She joined IDBS in 2016 and spent over three years as part of the Global Professional Services and Solutions Consulting teams, where she was responsible for presenting business and technical value of IDBS Solutions to customers and deploying solutions using the IDBS product stack into BioPharma organizations Since 2017 she assumed a leading role in establishing the IDBS BioProcess Solution and closely continues her relationship with Bioprocess Development today as a core SME for Polar BioProcess.  

 Prior to joining IDBS, Unjulie worked as an R&D scientist at both Lonza Biologics and UCB and received a BSc in Biochemistry and MSc in Immunology from Imperial College London. 



  1. IDBS. (2021). Biopharma Development Data Management. Retrieved from []
  2. Morris, et al. (2005). Making the Most of Drug Development Data. Retrieved from []
  3. Forgione, P. (2022). Accelerating Time to Insight Across the Biopharma Lifecycle. BioPharm International. Retrieved from []


Further reading

Datasheet: Analytical Development

Why Polar? A day in the life of a scientist

Digital strategies to power next-generation BioPharma

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