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inCyght Data Science for Stage 1 Process Validation


  • Data science and statistical services for stage 1 pharmaceutical process validation
  • Fully aligned with the FDA’s 2011 process validation guidance for industry
  • Leverage process development and piloting data to demonstrate stage of control


  • Experienced pharma data science team to adress validation challenges
  • Scientific reporting of process design rationales
  • Increase process understanding and achieve a state of control
Exputec Data Science Framework

Exputec Data Science Framework

Process validation is defined by the U.S. food and drug administration as “…the collection and evaluation of data, from the process design stage through commercial production, which establishes scientific evidence that a process is capable of consistently delivering quality product”. Data science supports process validation by revealing insights on parameter interactions and process variation, addressing following targets which are defined in the regulatory guideline:

  • Understand the sources of variation
  • Detect the presence and degree of variation
  • Understand the impact of variation on the process and ultimately on product attributes
  • Control the variation in a manner commensurate with the risk it represents to the process and product”

For process stage 1 validation, regulatory guidelines recommend to establish knowledge to understand the sources of variation, detect the presence and degree of variation and understand the impact of variation on the process and ultimately on product attributes [1]. This can be achieved through sound process development and data science methods, aiming at the extraction of knowledge from process development-, scale-up, piloting and manufacturing datasets.

Exputec, a leading data science company for the pharma industry, is providing advanced data science services supporting stage 1 process validation. Exputec biopharma data scientists and biopharma engineering experts structure and analyze data from process development- and scale-up runs using the combination of information mining, multivariate data analysis tools and biopharma/ regulatory expertise. This can be achieved by applying a data science framework on recorded process development and manufacturing data.

Benefits of applying a data science approach for process validation stage 1 are:

  • Challenge and advance process design rationales
  • Gain insights on the presence and degree of process variation
  • Gain insights on impact of variation on the process and product attributes
  • Gain insights on possible scale-up effects

Clear deliverables support the regulatory filing of a pharmaceutical product:

  • Scientific report, discussing and interpreting the developed insights and process design rationales
  • Aligned process development and scale-up data
  • Multivariate models describing the process variance and possible impact on product quality attributes
[1] – http://www.fda.gov/downloads/Drugs/…/Guidances/UCM070336.pdf