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Is the complexity of medical product (medicines and medical devices) regulation impacting innovation in the US? If so, how? Here, this question is investigated as follows: Various novel proxy metrics of regulation (FDA-issued guidelines) and innovation (corresponding FDA-registrations) from 1976-2020 are used to determine interdependence, a concept relying on strong correlation and reciprocal causality (estimated via variable lag transfer entropy and wavelet coherence). Based on this interdependence, a mapping of regulation onto innovation is conducted and finds that regulation seems to accelerate then supports innovation until on or around 2015; at which time, an inverted U-curve emerged. If empirically evidentiary, an important innovation-regulation nexus in the US has been reached; and, as such, stakeholders should (re)consider the complexity of the regulatory landscape to enhance US medical product innovation. Study limitations, extensions, and further thoughts complete this investigation., Comment: 53 pages (from Title Page to References), 6 Tables, 9 Figures. Pharm Res (2023)

Introduction: Since inception, the United States (US) Food and Drug Administration (FDA) has kept a robust record of regulated medical devices (MDs). Based on these data, can we gain insight into the innovation dynamics of the industry, including the potential for industrial transformation? Areas Covered: Using Premarket Notifications (PMNs) and Approvals (PMAs) data, it is shown that from 1976 to 2020 the total composite (PMN + PMA) metric follows a single secular period: 20.5 years (applications peak-to-peak: 1992-2012; trough: 2002) and 26.5 years (registrations peak to peak: 1992 to 2019; trough: 2003), with a peak to trough relative percentage difference of 24% and 28%, respectively. Importantly, PMNs and PMAs independently present as an inverse structure. Expert Opinion: The evidence suggests: MD innovation is driven by a singular secular Kutnets-like cyclic phenomenon (independent of economic crises) derived from a fundamental shift from simple (PMNs) to complex (PMAs) MDs. Portentously, while the COVID-19 crisis may not affect the overriding dynamic, the anticipated yet significant (~25%) MD innovation drop may be potentially attenuated with attentive measures by MD stakeholders. Limitations of this approach and further thoughts complete this perspective.

Background: The US Food and Drug Administration (FDA) regulates medical devices (MD), which are predicated on a concoction of economic and policy forces (e.g., supply/demand, crises, patents). Assuming that the number of FDA MD (Premarketing Notifications (PMN), Approvals (PMAs), and their sum) Applications behaves similarly to those of other econometrics, this work explores the hypothesis of the existence (and, if so, the length scale(s)) of economic cycles (periodicities). Methods: Beyond summary statistics, the monthly (May, 1976 to December, 2020) number of observed FDA MD Applications are investigated via an assortment of time series techniques (including: Discrete Wavelet Transform, Running Moving Average Filter (RMAF), Complete Ensemble Empirical Mode with Adaptive Noise decomposition (CEEMDAN), and Seasonal Trend Loess (STL) decomposition) to exhaustively search and characterize such periodicities. Results: The data were found to be non-normal, non-stationary (fractional order of integration < 1), non-linear, and strongly persistent (Hurst > 0.5). Importantly, periodicities exist and follow seasonal, 1 year short-term, 5-6 year (Juglar), and a single 24-year medium-term (Kuznets) period (when considering the total number of MD Applications). Economic crises (e.g., COVID-19) do not seem to affect the evolution of the periodicities. Conclusions: This work concludes that (1) PMA and PMN data may be viewed as a proxy measure of the MD industry; (2) periodicities exists in the data with time lengths associated with seasonal/1-year, Juglar and Kuznets affects; (4) these metrics do not seem affected by specific crises (such as COVID-19) (similarly with other econometrics used in periodicity assessments); (5) PMNs and PMAs evolve inversely and suggest a structural industrial transformation; (6) Total MDs are predicted to continue their decline into the mid-2020s prior to recovery., Comment: 5 figures, 4 tables. Ther Innov Regul Sci (2021)

Objective: An understanding of when one or more external factors may influence the evolution of innovation tracking indices (such as US patent and trademark applications (PTA)) is an important aspect of examining economic progress/regress. Using exploratory statistics, the analysis uses a novel tool to leverage the long-range dependency (LRD) intrinsic to PTA to resolve when such factor(s) may have caused significant disruptions in the evolution of the indices, and thus give insight into substantive economic growth dynamics. Approach: This paper explores the use of the Chronological Hurst Exponent (CHE) to explore the LRD using overlapping time windows to quantify long-memory dynamics in the monthly PTA time-series spanning 1977 to 2016. Results/Discussion: The CHE is found to increase in a clear S-curve pattern, achieving persistence (H~1) from non-persistence (H~0.5). For patents, the inflection occurred over a span of 10 years (1980-1990), while it was much sharper (3 years) for trademarks (1977-1980). Conclusions/Originality/Value: This analysis suggests (in part) that the rapid augmentation in R&D expenditure and the introduction of the various patent directed policy acts (e.g., Bayh-Dole, Stevenson-Wydler) are the key impetuses behind persistency, latent in PTA. The post-1990s exogenic factors seem to be simply maintaining the high degree and consistency of the persistency metric. These findings suggest investigators should consider latent persistency when using these data and the CHE may be an important tool to investigate the impact of substantive exogenous variables on growth dynamics., Comment: 3 Figures; 2 Tables

It is challenging to elucidate the effects of changes in external influences (such as economic or policy) on the rate of US drug approvals. Here, a novel approach, termed the Chronological Hurst Exponent (CHE), is proposed, which hypothesizes that changes in the long-range memory latent within the dynamics of time series data may be temporally associated with changes in such influences. Using the monthly number the FDA Center for Drug Evaluation and Research (CDER) approvals from 1939 to 2019 as the data source, it is demonstrated that the CHE has a distinct S-shaped structure demarcated by an 8-year (1939-1947) Stagnation Period, a 27-year (1947-1974) Emergent (time-varying Period, and a 45-year (1974-2019) Saturation Period. Further, dominant periodicities (resolved via wavelet analyses) are identified during the most recent 45-year CHE Saturation Period at 17, 8 and 4 years; thus, US drug approvals have been following a Juglar-Kuznet mid-term cycle with Kitchin-like bursts. As discussed, this work suggests that (1) changes in extrinsic factors (e.g., of economic and/or policy origin ) during the Emergent Period may have led to persistent growth in US drug approvals enjoyed since 1974, (2) the CHE may be a valued method to explore influences on time series data, and (3) innovation-related economic cycles exist (as viewed via the proxy metric of US drug approvals)., Comment: Ther Innov Regul Sci (2021)

Through time series analysis, this paper empirically explores, confirms and extends the trademark/patent inter-relationship as proposed in the normative intellectual-property (IP)-oriented Innovation Agenda view of the science and technology (S&T) firm. Beyond simple correlation, it is shown that trademark-filing (Trademarks) and patent-application counts (Patents) have similar (if not, identical) structural attributes (including similar distribution characteristics and seasonal variation, cross-wavelet synchronicity/coherency (short-term cross-periodicity) and structural breaks) and are cointegrated (integration order of 1) over a period of approximately 40 years (given the monthly observations). The existence of cointegration strongly suggests a "long-run" equilibrium between the two indices; that is, there is (are) exogenous force(s) restraining the two indices from diverging from one another. Structural breakpoints in the chrono-dynamics of the indices supports the existence of potentially similar exogeneous forces(s), as the break dates are simultaneous/near-simultaneous (Trademarks: 1987, 1993, 1999, 2005, 2011; Patents: 1988, 1994, 2000, and 2011). A discussion of potential triggers (affecting both time series) causing these breaks, and the concept of equilibrium in the context of these proxy measures are presented. The cointegration order and structural co-movements resemble other macro-economic variables, stoking the opportunity of using econometrics approaches to further analyze these data. As a corollary, this work further supports the inclusion of trademark analysis in innovation studies. Lastly, the data and corresponding analysis tools (R program) are presented as Supplementary Materials for reproducibility and convenience to conduct future work for interested readers.