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  1. R Validation for Life Sciences 29th July 2010 Charles Roosen (Mango Solutions) - Introduction Andrew Ellis (ETH Zurich) - Desktop Publishing with Sweave Dominik Locher (THETA AG) - Professional Reporting with RExcel Sebastian Pérez Saaibi (ETH Zurich) - R Generator Tool for Google Motion Charts Validating R for Regulated Purposes

  2. A quick introduction • Ian Francis • Started as Analytical Chemist with GlaxoSmithkline • IT Compliance and Validation for 12 years • Living and working in Basel for last 2 years • Fortunate to have the opportunity to work with Tony Rossini and Mark Schwartz updating the “R: Regulatory Compliance and Validation Issues A Guidance Document for the Use of R in Regulated Clinical Trial Environments” (R-FDA) for the R Foundation

  3. Why am I here? • In the past some people have thought R cannot be used for regulated purposes • Just last month, Aug-2010….LinkedIn forum • I do not see similar questions about SAS… • Why is this…? • Regulatory requirements for “validation” cannot be met (too much testing)? • Open Source Model?

  4. What is Validation? • Different meaning depending on who you ask • Here’s what the FDA state… • Validation - Establishing documented evidence which provides a high degree of assurance that a specific process will consistently produce a product meeting its predetermined specifications and quality attributes. FDA GLOSSARY OF COMPUTERIZED SYSTEM AND SOFTWARE DEVELOPMENT TERMINOLOGY

  5. verification validation testing qualification What is Validation? • Let us also say what validation is NOT • testing...OR... verification...OR...qualification • It is all these things and more…. • So what exactly do these terms mean?!? • …and how do they fit together?

  6. Training How do these terms relate? validation • testing • verification • qualification Computerized SYSTEM validation qualification phase qualification phase qualification phase phase activities phase activities verification Procedures verification activities verification activities Processes Software testing Code Review

  7. Where can we get help? Lots of places… AND MORE!!

  8. What do these guidances say? • Do they talk about statistical analysis software? • NO • Do they talk about any specific type of software? • NO • So what DO they describe? • A generic methodology that can be applied to a wide range of software and hardware solutions. • Any specific approach should be based on minimising risk to products and consumers.

  9. What are the basics of all these methodologies? Plan RISK MANAGEMENT Report Specify Verify Build Disclaimer: This is not intended to imply or endorse a waterfall lifecycle!!

  10. What does this mean in practice? Define Risk Process Deliverables (e.g. specifications, protocols, reports) Environments (e.g. dev/test/prod) and how are they controlled Responsibilities (e.g. IT, QA, Users) Governance (e.g. change control, configuration management) Suppliers (e.g. assessment of software development) Plan RISK MANAGEMENT Report Assess Risks Requirements – What we want from a solution Design – How we will achieve it Specification – Define customisation Specify Verify Control Risks Install the software - Installation Qualification Configure where required Create new code to fill gaps - Code review Build Verify high risk areas • Verify the solution, e.g. • Functional testing • Boundary Testing • Business Process Testing

  11. R Validation Example • A pharmaceutical company based in Basel, using R for statistical modelling and clinical trial analysis, asked for a validation strategy for R… • Defined a Validation Plan – based on the approach outlined earlier • Include a risk assessment documenting that Part 11 controls do not apply • Describe the server environment/windows desktop that R will be installed to ands how that area is “validated”; describe how the IT group will be involved, e.g. by doing installs, controlling software (SCM), backup/restore • Include a supplier assessment (a regulatory expectation) based on R-FDA document Plan • Define a set of user requirements • Included non-functional requirements, such as security and availability • Mapped requirements to functions based on package licenses (not all R packages are freely available (e.g. ~850 out of ~1200) • e.g. UR: the system can perform ANOVA • DS: Packages dae, GAD, granova, maanova, TANOVA Specify

  12. R Validation Example Build • Review of existing qualification for infrastructure • Installation to controlled test and production environments • List the packages to be installed; Steps for installing packages, can refer to “R Installation and Administration” document • Use built-in R verification routines (make check) • Define test strategy based on package risk • Risk dimensions include; • Frequency of use, complexity of function, what the output will be used for, and risk of input error • Functional testing only of high risk packages • Why? • Balance between the need to test and the time / resource available • Cannot “test in” quality; more testing does not increase quality of code • Certain level of testing already performed by developers Verify

  13. Summary

  14. So can we use R for regulated work? • Of course...the FDA use it themselves! • Open Source does not mean uncontrolled • Quote... “At Novartis, we've got the open source version set up with appropriate processes and guidances for usage for health authority work. It's not a problem, just a matter of getting the details right….the critical problem is the packages, ascertaining and accepting risks inherent in that code. And more critically, knowing that it's a whole process you are validating, not a piece of software. So it's just a matter of getting the risks and components of what you are doing identified and put together. (no software can be validated unless it "is" the process -- qualified, yes, but despite … claims by commercial companies looking for a cheap slogan, "we have validated software!", they can't deliver it in the regulatory sense).” Anthony Rossini, LinkedIn response, 02-Sep-2010

  15. What does it all mean…? Extra work and piles of paper?! ?! It doesn’t have to be that way. • Use risk analysis to identify what needs documentation and testing; • Use supplier documentation to justify decisions (R-FDA) and avoid repeating work (e.g. testing); • Use built-in R tools to qualify deployment; • Be pragmatic, butdocument your decisions.

  16. Thank you for your time Ian Francis  Life Sciences IT Compliance and Validation BIOP AG ian.francis@biop.ch Statistics | Programming | Medical Writing | Programme Management | Clinical Data Solutions