Vincent Buchheit, Nicolas Frey
Hoffmann La Roche
Objective: The objective of this poster is to describe the current role of the Clinical Pharmacology Data Scientist (CPDS) within Roche pRED and to illustrate how this role enables efficient quantitative clinical pharmacology activities.
Methods: The Clinical Pharmacometrics (CPM) group at Roche pRED consists of Pharmacometrician (PHME) and CPDS. One of the main objectives of each CPM group, within the pharmaceutical industry, is to provide impactful modeling and simulation (M&S) inputs to clinical project [1] [2] and the PHME main accountabilities are to define the M&S strategy, perform the work and communicate the results. The CPDS main accountabilities are to provide ready to use modeling input files from different sources, conduct data exploration, integrate important information into graphics easy to read, use previously developed models to identify and fix (if possible) data inconsistencies [3] [4], perform simulations, produce outputs for reports, etc.
Results: Over the past few years, we have established a group of 9 CPDS, which supports M&S activities at the study, project and disease levels. Based on our experience, a CPDS free up time of a PHME around 30 to 40% allowing for more M&S activities to be conducted. Therefore, our recommendation to maximize the efficiency of a CPM group, is to have a ratio of at least one 1 CPDS for 2.5 PHME. With usually a Master degree in computational science and engineering, a CPDS brings flexibility and efficiency in the analysis process, increase the quality of the deliverables and also ensure full traceability and reproducibility. The most significant improvement is in the reduction of the time between clinical database lock and dataset ready for analysis which is usually around 70%. The efficiency of a CPDS is further increased when prior models have been developed and can then be used by the CPDS to further assess the data quality and initiate data interpretation. Some examples of deliverables and associated impacts will be shown.
Conclusions: In order to provide impactful inputs to clinical project, Pharmacometricians have to deliver high quality works on time. Performing effectively requires many skills that not all Pharmacometricians have. It is therefore essential to work in collaboration with data scientist to maximize quality and efficiency of the m&s analysis.
References:
[1] FDA, March 2004: Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. www.fda.gov/oc/initiatives/criticalpath/whitepaper.html
[2] Aarons, L. et al., Role of modelling and simulation in Phase I drug development.
[3] Buchheit, V. et al., Efficient quality review for modeling input dataset, PAGE 20 (2011) Abstr 2041 [www.page-meeting.org/?abstract=2041]
[4] Buchheit, V. and Frey, N., Data quality impacts on modeling results, PAGE 22 (2013) Abstr 2749 [http://www.page-meeting.org/default.asp?abstract=2749]
Reference: PAGE 25 (2016) Abstr 5884 [www.page-meeting.org/?abstract=5884]
Poster: Methodology - Other topics