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inCyght Data Science for Stage 3 Process Validation


  • Data science and statistical services for stage 3 pharmaceutical process validation
  • Fully aligned with the FDA’s 2011 process validation guidance for industry
  • Leverage manufacturing data to report and demonstrate state of control during routine manufacturing


  • 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 3 validation, regulatory guidelines recommend to evaluate the design of the process to gain assurance during routine production that the process remains in a state of control during commercial manufacture [1]. Further it is stated that a system or systems for detecting unplanned departures from the process as designed is essential to accomplish this goal. This systems need to detect undesired variability potentially harmful in respect to the final product quality. The regulatory guidelines strongly recommend that a statistician or person with adequate training in statistical process control techniques develop the data collection plan and statistical methods and procedures used in measuring and evaluating process stability and process capability. Moreover, a sound process design and qualification, as described in stage 1 and 2, is the basis of anticipating meaningful counteractions and process improvements. Even sources of variation not previously detected can be characterized and root causes identified by means of statistical tools and process engineering knowledge.

Exputec, a leading data science company for the pharma industry, is providing advanced data science services supporting stage 3 process validation. Exputec biopharma data scientists and biopharma engineering experts establish a mechanistic and statistical monitoring concept using data from the process control system, batch records and analytical data. This framework enables to holistically evaluate the state of control of the process by means of statistical methods and mechanistic information mining routines. Based on the developed insights, counteractions are developed for continuous improvment of the manufacturing process.

Benefits of applying a data science approach for process validation stage 3 are:

  • Evaluate and anticipate process variation in the manufacturing scale
  • Detect unexpected variation and root causes
  • Develop multivariate control and risk mitigation strategies
  • Apply multivariate control strategies
[1] – http://www.fda.gov/downloads/Drugs/…/Guidances/UCM070336.pdf