Informatics
tribution of the data over time. The colouring is set to show where the maximum and minimum usages are occurring. The visualisation tool allows filtering by other
fields that are available in the SEND data. For example, filtering by study type and species to ascertain the relationship of the number of tests performed by location and instrument for each type of species and/or study type. Such opera- tional information is most useful for CROs which are doing high volume work and need to plan their equipment resources to align with incoming studies. Biopharmaceutical companies that are performing in-house studies would also benefit from metrics on their laboratories’ throughput. Companies that outsource most or all of their studies could gain insight that would help them in future CRO selection and study monitoring. In the past, data on laboratory metrics may only
have been available for individual studies. Now a data warehouse of SEND datasets opens up the possibility to gain an overall view of the data his- tory for many studies over time. This allows one to look for trends in types of analyses and where they are being carried out. This knowledge can be used to impact the cost and timeline for future study completion.
Example 2: Data mining In research it is useful to have the flexibility to examine the data in an open-ended fashion – searching for patterns and correlations. The ability to review any of the in-life, necropsy and histopathology observations across one or multiple studies is another benefit brought about by the standard representation of the data through SEND datasets (see Figure 2). In this visualisation, clinical pathology, organ
weights and micropathology data are available for filtering and exploring to search for meaningful correlations. Each section is coloured or separated by dose group. The controlled terminology standards being
developed for neoplasm and non-neoplasm allow cross-study comparison that was previously diffi- cult due to differing terms as well as different ways to combine base terminology with modifiers. The heat map is just one way to see the dimen-
sions represented in this graphic. Stacked bar graphs show the data separated horizontally or vertically which may be easier for some patholo- gists to see patterns that need further exploring. Scatter diagrams are often easier to see clusters of information from individual observations. Sunburst plots are another way of representing the incidence data as a series of concentric arcs, where
Figure 2 Visualisation of pathology data
Drug Discovery World Winter 2018/19
57
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68
