A heuristic approach to handling missing data in biologics manufacturing databases.


Type
Article
Change log
Authors
Mante, Jeanet 
Gangadharan, Nishanthi 
Sewell, David J 
Turner, Richard 
Field, Ray 
Abstract

The biologics sector has amassed a wealth of data in the past three decades, in line with the bioprocess development and manufacturing guidelines, and analysis of these data with precision is expected to reveal behavioural patterns in cell populations that can be used for making predictions on how future culture processes might behave. The historical bioprocessing data likely comprise experiments conducted using different cell lines, to produce different products and may be years apart; the situation causing inter-batch variability and missing data points to human- and instrument-associated technical oversights. These unavoidable complications necessitate the introduction of a pre-processing step prior to data mining. This study investigated the efficiency of mean imputation and multivariate regression for filling in the missing information in historical bio-manufacturing datasets, and evaluated their performance by symbolic regression models and Bayesian non-parametric models in subsequent data processing. Mean substitution was shown to be a simple and efficient imputation method for relatively smooth, non-dynamical datasets, and regression imputation was effective whilst maintaining the existing standard deviation and shape of the distribution in dynamical datasets with less than 30% missing data. The nature of the missing information, whether Missing Completely At Random, Missing At Random or Missing Not At Random, emerged as the key feature for selecting the imputation method.

Description
Keywords
Biologics manufacturing data, Data pre-processing, Imputation, Missing data, Parameter recurrence, Biological Products, Databases, Factual, Electronic Data Processing, Heuristics, Models, Theoretical
Journal Title
Bioprocess Biosyst Eng
Conference Name
Journal ISSN
1615-7591
1615-7605
Volume Title
42
Publisher
Springer Science and Business Media LLC
Sponsorship
Isaac Newton Trust (16.08(am))
Leverhulme Trust (ECF-2016-681)
Biotechnology and Biological Sciences Research Council (BB/K011138/1)
University of Cambridge – MedImmune Beacon Collaborative project The Leverhulme Trust (ECF-2016-681)