RESEA RC H POSTER PRESENTATI ON DESIGN © 2015 www.PosterPresentations.com Introduction Materials and Methods Non-linear power laws demonstrating predictable distributions over multiple orders of magnitude have been known since the late 19 th century. One of the earliest examples is “Zipf’s Law” for the distribution of words in a language. Power laws are commonly observed in many disciplines including physics (distribution of lunar crater sizes), biology (allometric scaling), medicine (heart rate vs time), and the social sciences (Pareto income distributions). Power Law Example: Zipf’s Law for Word Frequency (Montemurro MA. 2001) Power Law Example: US City Population size (Newman MEJ.2006) Recent literature suggests that empirical power laws also occur with adverse event data (Chen, et al. 2015; JM Bryan, 2015). All evaluated AEPs demonstrated similar non-linear power law behavior. Specifically, despite the variation in mechanisms of action, dose, or route of deliver (e.g., Intravascular, Subcutaneous, Oral, Intravitreal, etc.) all AEPs demonstrated similar visual and numeric behavior with proportionality constants ranging between 0.03 and 0.08, and non-linear decay constants ranging between approximately - 0.5 and -0.7. Visually, all AEPs could be superimposed on the same graph with one equation fitting all molecules with proportionality constant ≈ 0.06 and decay constant ≈ -0.7 (see Composite AE Profile Graph). AE Profile Power Law Example with an Inflammatory Molecule AE Profile Parameters for Evaluated Molecules Results Conclusions This investigation observed empirical statistical power law behavior in AEPs across all evaluated therapeutic domains and molecules. Similar to empirical power law behavior in other areas of science (e.g., “Zipf’s Law”), this finding suggests an underlying pattern and predictability to the accumulation of adverse events from clinical research and pharmacovigilance, which may be useful in our efforts to streamline safety monitoring throughout a product’s lifecycle. References 1) Brown JH, et al. The fractal nature of nature: power laws, ecological complexity and biodiversity. Phil. Trans. R. Soc. Lond. B (2002) 357, 619–626. 2) Chen X, et al. Systematic Analysis of the Associations between Adverse Drug Reactions and Pathways. BioMed Research International Vole 2015, Article ID 670949, 12 pages http://dx.doi.org/10.1155/2015/670949. 3) Jonathon Bryan. Measuring the Relationship between Innovative Drugs and AE_2015. The Brookings Institution.http://www.slideshare.net/JonathanBryan5/mea suring-the-relationship-between-innovative-drugs-and- ae2015-50168899. 4) Montemurro MA. Beyond the Zipf –Mandelbrot law in quantitative linguistics. Physica A 300 (2001) 567–578. 5) Newman MEJ. Power laws, Pareto distributions and Zipf’s law . arXiv:cond-mat/0412004v3 [cond-mat.stat- mech] 29 May 2006. Acknowledgement(s) Barbara Tong, PhD Global Biostatistics | Roche/Genentech Gregory Bell MD VP PDS | Roche/Genentech For this project, the author extracted adverse event report listings in August 2015 from large, de-identified sponsor internal post-marketing safety datasets for four marketed products in the therapeutic areas of anti-infection, CNS thrombolysis, oncology and inflammation. The number of adverse events ranged between approximately 20,000 to 250,000 per molecule from multiple sources (spontaneous, non-interventional programs, and clinical trials as well as follow up cases) and over variable lifecycle times (5 years to 30+ years). All adverse events were coded using the Medical Dictionary for Regulatory Activities (MedDRA) and analyzed at the preferred term (PT) level. PTs for each product were then summarized and ranked in decreasing order of frequency of occurrence within the dataset, to create the respective AEP. This data was then transformed to a log-log scale in order to estimate the power law scaling (α) and non-linear decay constants (β) for each respective molecule. The general power function shown below was fit using the SAS-JMP 11.1.1 statistical software. () = − (1) An example of the overall AEP and power law fit are shown in the AE Profile Graph for the Inflammatory Indication molecule. Finally, the AEPs for all evaluated molecules were plotted together on the same graph to visually determine if there was similar power-law behavior across molecules. The resulting parameters for the evaluated molecules are shown in the corresponding table. Based on the evaluation of the AEPs for molecules from this Sponsor safety database, these results support the findings in recent publications that adverse event profiles demonstrate empirical power law behavior. In addition, a surprising finding is that this behavior appears to be robust across therapeutic class, historical period (e.g., data from 1970s, 80s, 90s, to 2015), mechanism of action, or delivery mechanism. The relationship is such that all evaluated molecule AEPs appear to follow one overall power law, as shown below. Composite AE Profile Power Law for all Evaluated Molecules Implications for Future Research: The observations from this investigation are based on a relatively large data sample from a single sponsor. These results suggest power law behavior may be a ubiquitous feature of AE profiles. However, this remains a hypothesis to be tested. One potential way to do this would be to conduct a similar experiment using a much larger safety database from a Regulatory Body (egg, FDA’s Adverse Event Reporting System) across many more molecules and diverse indications. Discussion Shaun Comfort, MD, MBA Associate Director of Risk Management & Sr.SSL, Product Development Safety Science-IIDO | Roche/Genentech Evidence for Empirical Power Law Scaling in Adverse Event Profiles The goal of this work was to evaluate the claims in recent publications, that non-linear power law relationships can be observed in post marketing adverse event profile (AEP) data (Chen, et al. 2015; JM Bryan, 2015). Overall Objective # Indication Route Events Alpha Beta 1 Oncology IV 268,512 0.047 -0.57 2 CNS IV 40,382 0.086 -0.75 3 Inflammatory SQ 124,576 0.031 -0.46 4 Anti-Infective PO 46464 0.058 -0.66 5 Ophthalmology ITV 19363 0.089 -0.83 Combined Data 499,297 0.058 -0.66 Disclosure(s) Author(s) of this presentation have the following to disclose concerning possible financial or personal relationships with commercial entities that may have an interest in the subject matter of this presentation: • Shaun Comfort MD, MBA – Roche/Genentech Employee