Pharma R&D Today
Ideas and Insight supporting all stages of Drug Discovery & Development
Data, Data and More Data
Posted on October 23rd, 2015 by Thibault Geoui in Chemistry
Read an excerpt from the Elsevier whitepaper, Big Data, Wider Mindset to discover how systems biology and systems chemistry enable a holistic view of a biological system that is more than it’s sum of molecular components.
Driving the interest in expanding the exploratory space of medicinal chemistry to include a comprehensive perspective of all factors that influence the therapeutic window of a drug is the recognition that current attrition rates in pharmaceutical development are not sustainable (an estimated 80-90%; see 4, 22), and likely result from the disconnect in scope between how drugs are developed and how they are ultimately used. Every step of the drug discovery process has hurdles and entails risks. Minimizing these by narrowing this disconnect means developing drugs within the context of complex biological systems and this conversely means informing drug discovery, design and optimization with as much knowledge as possible about the various interaction dimensions that result from compounds exerting their effect on networks of biological molecules in a physiologically heterogeneous environment.
Network- or systems-based approaches can support a more comprehensive conceptual framework for drug development but they rely heavily on large amounts of high-quality data. Mechanistic models are informative only if constructed based on solid, empirically determined parameters, which must be extracted from the literature or defined. The model from Bianconi et al. (13) included 45 parameters for which they established values based on available literature. Furthermore, mechanistic models must be tested experimentally, improved based on results, and then tested again. Similarly data-intensive was the work by Liu et al. (16) who used over 1.5 Gb of raw data to construct and validate their gene module via WCNA, and by Yabuuchi et al. (17) who used over 15,000 pairs of kinases and kinase inhibitors to construct their virtual screening model and validate it with EGFR and CDK2.
Looking into the future, as the search for novel pharmacotherapies taps into this expanded exploratory space, the magnitude and diversity of data needed to elucidate interactions in complex biological systems, understand how those interactions contribute to health or disease, and ultimately identify potentially effective therapies will grow. Effective use of data-driven methodologies will require drug developers themselves to adapt to a new reality—one that calls for better management and use of information across a range of scientific domains. Dr. Scott Lusher examines data challenges in different scientific disciplines to apply lessons learned. He explains that as more data inform the drug design cycle, data evaluation and decision-making will take longer. In addition to new ways of managing, sharing and visualizing more complex data, he explains that all data will also need to be openly available at all times to team members of a drug development program, and traceable to their source. This will demand new strategies to incentivize members to enter their data into a shared knowledge management platform and to control data accuracy. The nature of project meetings will also need to change , he continues. Solid evaluation and decision-making will require the input of everyone in the team, including those generating the data. Interpretation of the data generated by team members should be done as a group, with everyone looking at the same, most up-to-date data . Truly understanding generated data and making meaningful connections between data points will prove to be the most fruitful analysis strategy, but that will require the time and flexibility to explore different ideas and to make mistakes.
With all its challenges, Dr. Köppen sees the incorporation of systems-based approaches to drug development as inevitable. Quite honestly , he says, I see no way to escape this paradigm shift. The fact is, only drugs that provide a real therapeutic benefit will pay off research and development investment, and it is clear that the single-target approach no longer meets that demand. In response to the question of what it will take for systems biology to be used routinely in drug development, he says, It will take one research division head to have the foresight to make an investment along this line; it will take a lot of work to validate techniques and models; and it will take time for this field, which is in its infancy, to find strong footing . However, exploring these unchartered waters may also usher in a new era in pharmaceutical innovation.
Read the entire whitepaper here.
1. U.S. Food and Drug Administration.
www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm (Accessed 7 May 2015).
2. Lee, J.A. and Berg, E.L. (2013) Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches. J Biomol Screen 18, 1143-1155.
3. LaMattina, J. (2015)
www.forbes.com/sites/johnlamattina/2015/01/07/fda-approvals-1996-vs-2014-the-two-most-prolific-years-but-stark-differences (Accessed 6 June 2015).
4. Hay, M., Thomas, D.W., Craighead, J.L., Economides, C. and Rosenthal, J. (2014) Clinical development success rates for investigational drugs. Nature Biotechnol 32, 40-51.
5. U.S. Food and Drug Administration.
6. DiMasi, J.A., Hansen, R.W., Grabowski, H.G. and Lasagna, L. (1991) Cost of innovation in the pharmaceutical industry. J Health Economics 10, 107-142.
www.statista.com/statistics/265090/us-pharmaceutical-industry-spending-on-research-and-development (Accessed 8 May 2015).
8. Booth, B. (2012)
www.forbes.com/sites/brucebooth/2012/06/07/cancer-drug-targets-the-march-of-the-lemmings (Accessed March 24, 2015).
9. Swinney, D.C. and Anthony, J. How were new medicines discovered? Nature Rev Drug Discov 10, 507-519.
10. Mestres, J., Gregori-Puigjané, E., Valverde, S. and Solé, R.V. (2009) The topology of drug-target interaction networks: implicit dependence on drug properties and target families.
11. Choi, H., Sheng, J., Gao, D., Li, F., Durrans, A., Ryu, S., Lee, S.B., Narula, N., Rafiii, S., Elemento, O., Altorki, N.K., Wong, S.T.C. And Mittal, V. (2015)
Transcriptome analysis of individual stromal cell populations identifies stoma-tumor crosstalk in mouse lung cancer model. Cell Reports 10, 1187-1201.
12. Werner, H.M.J., Mills, G.B. and Ram, P.T. (2014) Cancer systems biology: a peek into the future of patient care? Nature Rev Clin Oncol 11, 167-176.
13. Bianconi, F., Baldelli, E., Ludivini, V., Crinò, L., Flacco, A. and Valigi, P. (2012)
Computational model of EGFR and iGF1R pathways in lung cancer: a systems biology approach to translational oncology. Biotechnology Advances 30, 142-153.
14. Cupples, L.A., Heeren, T., Schatzkin, A. and Colton, T. (1984) Multiple testing of hypotheses in comparing two groups. Ann Intern Med 100, 122-129.
15. Bin, Z. and Horvath, S. (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4 (Article 17).
16. Liu, R., Cheng, Y., Yu, J., Lv, Q-L. and Zhou, H.-H. (2015)
Identification and validation of gene module associated with lung cancer through coexpression network analysis. Gene 563, 56-62.
17. Yabuuchi, H., Nijima, S., Takematsu, H., Ida, T., Hirokawa, T., Hara, T., Ogawa, T., Minowa, Y., Tsujimoto, G. and Okuno, Y. (2011)
Analysis of multiple compound protein interactions reveals novel bioactive molecules. Mol Syst Biol 7, 472.
18. Berg, E.L., Hsu, Y.-C. and Lee, J.A. (2014) Consideration of the cellular microenvironment: physiologically relevant co-culture systems in drug discovery. Adv Drug Delivery Rev 67-70, 190-204.
19. Yong, X., Wang, P., Jiang, T., Yu, W., Shang, Y., Zhang, P. and Li, Q. (2014) Fibroblasts weaken the anti-tumor effect of gefitinib on co-cultured non-small cell lung cancer cells. Chin Med J 127, 2091-2096.
20. Straussman, R., Morikawa, T., Shee, K., Barzily-Rokni, M., QIan. Z.R., Du, J., Davis, A., Mongare., M.M., Gould, J., Frederick, D.T., Cooper, Z.A., Chapman, P.B., Solit, D.B., Ribas, A., Lo, R.S., Flaherty, K.T., Ogino, S., Wargo, J.A. and Golub, T.R. Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion. Nature 487, 505-509.
21. Lee, J. and Bogyo, M. (2013) Target deconvolution techniques in modern phenotypic profiling. Curr Opin Chem Biol 17, 118-126.
22. DiMasi, J.A., Grabowski, H.G. and Hansen, R.W. (2014) Innovation in the pharmaceutical industry: new estimates of R&D costs. Boston: Tufts Center for the Study of Drug Development.
R&D Solutions for Pharma & Life SciencesWe're happy to discuss your needs and show you how Elsevier's Solution can help.
- Broadening the Field of Research While Staying Focused
- Predicting Adverse Event Risks and Gaining Insights from Disparate Data
- Avoiding the Risks of Being Wrong in Drug Discovery
- Introducing e-Therapeutics: An In Silico Drug Discovery Lab
- How Are You Handling Big Data & Innovation?