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Chemical Process R&D

Home > Solutions > Chemical Process R&D
A Consistent and Reliable Approach to Chemical Process R&D

In Chemical Process R&D, multidisciplinary teams collaborate to develop practical chemical syntheses to drug substances and drug intermediates. These teams also work to produce drug active for use in preclinical and clinical evaluations, while concurrently developing practical, efficient, scalable, environmentally responsible and economically viable avenues to drug active for implementation in manufacturing.
 
The main role of Chemical Process R&D is to supply drug substance, also called active pharmaceutical ingredients (API), for use in preclinical and clinical evaluations.  Its secondary role is to design practical, efficient, scalable, environmentally responsible and economically viable avenues to drug substances for implementation in manufacturing.  In these roles, Chemical Process R&D exploits the latest chemical technologies and synthetic methods by investing considerable resources into understanding the underlying reaction mechanisms of chemical processes, characterize these processes, and evaluating the hazard operability of the processes.
 
Inference for QbD provides Chemical Process R&D teams with the following benefits (categorized by team role):

Chemical Process Scientists and Engineers
  • enjoy a consistent approach to identification of chemical process parameters that are critical to product quality using the standardized and validated Inference for QbD function library
  • employ a reliable approach to identifications, visualization, documentation and verification of chemical process design space using the Inference for QbD template library
  • gain valuable chemical process understanding from low-value material and process attributes data using machine learning methods
  • save time by automating repetitive QbD tasks, e.g. creating standard chemical analyses reports and capturing them in an electronic lab notebook
  • save time by rapidly prototyping, testing and deploying new data-analysis methods for QbD applications
  • shorten turn-around time for delivery of custom chemical process reports by enabling the scientists to perform data pipelining and documentation directly in Inference Word
  • easily find and reuse prior QbD information from a global searchable database of QbD chemical process methods for assembly into SOPs

Chemical Process Managers

  • deploy a consistent approach to implementing QbD goals across Chemical Process R&D
  • ensure consistent, accurate and traceable chemical process QbD records and reports
  • shorten time-to-decision and improve efficiency by providing streamlined, regulatory compliant chemical process workflows
  • improve Chemical Process R&D’s capability to share and collaborate on QbD projects
  • leverage and exploit an underutilized asset: QbD information from prior chemical process projects 
  • collect a broadly accessible, enterprise wide chemical process R&D knowledge base for training new employees and initiating future QbD projects
  • improve decision quality and reduce time-to-decision by supplying facts-based predictive modeling of chemical process methods to entire R&D team
  • shorten QbD training by using templates and leveraging familiarity with Microsoft Office

Adjoining Development Functions  

  • clearly communicate QbD project status, uncertainties and development strategy to senior stakeholders and the entire development team
  • enable adjoining development functions involved in QbD project to have real-time access to relevant chemical process R&D information
  • support regulatory by maintaining QbD study data in a searchable 21 CFR Part 11 compliant centralized repository with full audit trails
  • enable regulatory and CMC project teams to easily assemble standardized QbD results for CMC filing
  • support process engineering during scale-up and validation by enabling direct access to chemical process QbD information
  • support IT by providing: a single platform that spans the full development organization; a standards-based architecture that is scalable to thousands of users; and a set of core applications that requires minimal training and support built on familiar Microsoft Office
 
 
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Screencasts
  Overview of Inference for Office
  Perform Data Analysis Using an Inference Template
  Build and Deploy an Inference Template
Screenshots
Representation of process control sensors and process signature for batches
Critical process parameters measured by impact and partial dependence plots of batch success prediction
Probability of FAIL outcome vs. batch age and probability of predicted FAIL outcome vs. batch age
 
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