• Data science and statistical services for stage 2 pharmaceutical process validation
  • Fully aligned with the FDA’s 2011 process validation guidance for industry
  • Leverage piloting and PPQ run data to demonstrate reproducibility and state of control


  • Experienced pharma data science team to adress validation challanges
  • Scientific reporting PPQ runs
  • Increase and report process understanding to 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 2 validation, regulatory guidelines recommend to evaluate the design of the process to determine if the process is capable of reproducible commercial manufacturing [1]. The approach to this process performance qualification (PPQ) is requested to be based on sound science and the manufacturer’s overall level of product and process understanding using process data from all relevant scales and understand the effect of scale. However, it is not requested to conduct all PPQ runs on manufacturing scale if a valid down scale model is available which is confirmed by objective, statistical measures.

Exputec, a leading data science company for the pharma industry, is providing advanced data science services supporting stage 2 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 to consider the effect of scale und validate downscale models. 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 2 are:

  • Challenge and advance scale down models
  • Gain insights on the effect of scale
  • Gain insights on impact of variation on manufacturing scale
  • Gain insights on the reproducibility of PPQ runs

Clear deliverables support the regulatory filing of a pharmaceutical product:

  • Aligned process development and scale-up data
  • Scientific report, discussing and interpreting the developed insights in scale down models
  • Multivariate models describing the process variance on manufacturing scale and possible impact on product quality attributes
  • Scientific report on the reproducibility of PPQ runs

[1] – http://www.fda.gov/downloads/Drugs/…/Guidances/UCM070336.pdf