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Considering lifestyle and environment to maximize the economic value of R&D

Posted on November 10th, 2017 by in Pharma R&D


With an interest in systems approaches, I am always on the look-out for interesting papers in this area. A recent paper in Drug Discovery Today caught my eye for several reasons (1). The first was the extent of the problem with non-alcoholic liver disease. It is estimated that 25 million people will be diagnosed in the USA by 2025 (2), with treatment costs (transplant) rising to approach US $20 billion (3). This is yet another significant healthcare problem developing in society, consequently developing an unmet medical and economic need. The second aspect was that the developments in systems biology appear to have made significant advances in the diagnosis, monitoring and potential treatments for the condition.

The Drug Discovery Today paper outlined differential modelling approaches that developed from “omics” and PK/PD model data, predictive mass-balance approaches and real-world approaches that include proteomics, transcriptomics, metabolomics and fluxomics (metabolic rate) data. Some success has already been seen with these types of approaches with the identification of plasma biomarkers of disease in diabetes and other metabolic conditions. The potential power of these approaches could be revolutionary in the impact on R&D.

What initially sparked my interest was the bold statement that a systems approach could “improve the economics” of treatment. This is the focus that we all need to bring into R&D. The article falls short of providing direct evidence to the fact, and some implicit assumptions regarding improved productivity and reduced timelines may create value at a project or micro level. The challenge is to bring these economic improvements at a systems level, impacting the whole. Many developments over the last few years have supported decision-making on projects and yet still the industry is challenged with issues regarding overall productivity and medicines’ affordability.

The other aspect of my interest was the concept of modelling real-world conditions for testing ideas for investment in a “whole-body” or “whole-disease” paradigm. The use of modelling scenarios in this regard will be transformational. However, I suggest we need not only to focus on genes, metabolism and pathways. For a whole person or disease model, there also needs to be consideration of lifestyle choices and individual behaviors. How we make lifestyle choices on what we eat and how we spend our time in physical activity or exercise has an impact clinically (4).

Without integrating the medical/biological and social/psychological aspects of disease, the models will never truly be integrative or represent the whole person or disease. Taking this approach further would also take account of environmental factors, including diet, environmental stressors and toxins. This would also allow an approach to model the effects of existing medications and co-morbidities. This approach would certainly increase the complexity of interactions and be a challenge to develop. However, an integrative approach is needed to truly model the whole biological system as it adapts to different situations.

Perhaps it is time to think differently about our biological systems and include socio-environmental aspects that impact the whole. The 2017 Nobel Economics Prize was awarded to Richard Thaler for his work in behavioral economics—perhaps pharma R&D can learn from the nudge approach? To improve the economics of drug discovery, development and healthcare, we need to remember that our biological systems don’t work in isolation. The nature or nurture debate has moved on from being one or the other; it is both.

Systems modelling approaches that can take both aspects of biology and lifestyle will provide the greatest insights and provide ways to realize scientific and economic value.

  1. Bosley et al., (2017), Drug Discovery Today 22: 1532-1538.
  2. Anon (2013). Non-alcoholic steatohepatitis forecast to 2022. Report Linker
  3. Habka et al., (2015), PLoS One 10: e0131764
  4. Romero-Gomez et al., (2017), J. Hepatology 67: 829-846.

All opinions shared in this post are the author’s own.

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