Effective visualization of integrated knowledge and data to enable informed decisions in drug development and translational medicine

Abstract

Integrative understanding of preclinical and clinical data is imperative to enable informed decisions and reduce the attrition rate during drug development. The volume and variety of data generated during drug development have increased tremendously. A new information model and visualization tool was developed to effectively utilize all available data and current knowledge. The Knowledge Plot integrates preclinical, clinical, efficacy and safety data by adding two concepts: knowledge from the different disciplines and protein binding.Internal and public available data were gathered and processed to allow flexible and interactive visualizations. The exposure was expressed as the unbound concentration of the compound and the treatment effect was normalized and scaled by including expert opinion on what a biologically meaningful treatment effect would be.The Knowledge Plot has been applied both retrospectively and prospectively in project teams in a number of different therapeutic areas, resulting in closer collaboration between multiple disciplines discussing both preclinical and clinical data. The Plot allows head to head comparisons of compounds and was used to support Candidate Drug selections and differentiation from comparators and competitors, back translation of clinical data, understanding the predictability of preclinical models and assays, reviewing drift in primary endpoints over the years, and evaluate or benchmark compounds in due diligence comparing multiple attributes.The Knowledge Plot concept allows flexible integration and visualization of relevant data for interpretation in order to enable scientific and informed decision-making in various stages of drug development. The concept can be used for communication, decision-making, knowledge management, and as a forward and back translational tool, that will result in an improved understanding of the competitive edge for a particular project or disease area portfolio. In addition, it also builds up a knowledge and translational continuum, which in turn will reduce the attrition rate and costs of clinical development by identifying poor candidates early.

Figure 1

Schematic overview of the knowledge plot data warehouse including the different sources. Data can be integrated for different purposes, i.e. validation of platforms, overview of project data, decision-making, due diligence, benchmarking, and predictability of preclinical models and back translation of clinical data.

Figure 2

Traditional visualizations compared to the knowledge plot. The left part demonstrate a mixture of scatter, bar and line plots combined with tables. The right part illustrates the Knowledge Plot concentration-response profiles by using Treatment Effect Index on the vertical axis and the unbound concentration on the horizontal axis. Safety endpoints are colored by red and efficacy endpoints are blue to visualize the therapeutic window, which is the difference in exposure between the blue efficacy endpoint and the red safety endpoint on the horizontal 100-line. In addition, one point estimate representing a Ki value and a concentration range (no observed adverse effect level (NOAEL) and the lowest observed adverse effect level (LOAEL)) are shown in the lower part of the graph.

Figure 3

Information model. This conceptual information model illustrates all the key information entities (boxes) and relationships (lines) that are needed to fully utilize the Knowledge Plot. The relationships are of two types; one-to-one or one-to-many, where 'many’ is illustrated with filled anchor points. The relationship names are to be read from the entity with the anchor point.

Figure 4

Relationship in the knowledge plot between drug candidate and internal or external comparators with focus on preclinical efficacy and safety: comparison of efficacy and safety data for compound A and B. Both compounds have similar efficacy profiles (blue). The safety profile for compound A is different from compound B, where compound A moves towards the Meaningful Effect i.e. is expected to demonstrate safety issues. The yellow curves describe target engagement for monkey (circles) and human (triangles) and will support the holistic understanding of the relationship between efficacy and safety vs level of target engagement (similar for rat (not shown), monkey and human). The black triangles in the lower part are human Ki-values for the different receptor subtypes derived from an in vitro assay. Note that there are two efficacy profiles for each compound, which corresponds to two different ways to assess efficacy. The concept of the Knowledge Plot is demonstrated here and all details of the biomarker, study information etc. are available in the data warehouse c.f. Appendix. Legends for species: rat (star), monkey (circle), human (triangle).

Figure 5

Relationship in the knowledge plot between drug candidate and internal or external comparators with focus on adverse event in single and multiple dose studies: comparison of adverse event frequencies (red triangles) between compound A and B, including data from both single and multiple ascending dose studies. The adverse event profiles are similar regardless of treatment duration time for the two compounds, respectively. However, there is a difference between compound A and B, where the former demonstrate a less favorable safety profile. By including target engagement c.f. Figure 4, there is a possibility to illustrate target related safety. Legends for species: rat (star), monkey (circle), human (triangle).

Figure 6

Napiergram for compound A and B. The unbound exposure ranges versus different preclinical and clinical endpoints or biomarkers using the same data as in Figure 4. The Knowledge Plot concept has been applied to Compound B, where the exposure range above the Meaningful Effect level is shown by change in color (from blue to green).

Figure 7

Performance of ADAS-Cog test in different subpopulations of alzheimer’s disease visualized in the knowledge plot. The Plot demonstrates that there are different responsiveness in ADAS-Cog in different subpopulations of Alzheimer’s Disease, treated with Donepezil for 3 months. Data are coming from clinical trials reported in the literature [12-16] where the Meaningful Effect is defined as a change of 2 scores from placebo in a three-month time-scale. The ADAS-Cog endpoint is the clinical outcome used in Alzheimer’s Disease trials.