The Impact of US Medical Product Regulatory Complexity on Innovation: Preliminary Evidence of Interdependence, Early Acceleration, and Subsequent Inversion.

PURPOSE: Is the complexity of medical product (medicines and medical devices) regulation impacting innovation in the US? If so, how? METHODS: 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). RESULTS: Based on the observed 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. CONCLUSIONS: 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.

Why did the number of US FDA medical device guidelines begin to rise in the mid-2010s? A perspective.

AREAS COVERED: An initial investigation of US medical device guidelines is presented, with the aid of those of medicines as qualitative comparator. Since the first recorded FDA medical device guideline (February 1975) until the mid-2010s, the number of medical device guidelines has been basically stable, then rapidly rose. EXPERT OPINION: The rise of the COVID-19 pandemic and digital health technologies explains 50% of the upward momentum in guidelines since the mid-2010s. Concomitantly, medical device and medicinal guidelines became moderately correlated. This perspective posits that this trend will continue irrespective of the ebbing pandemic as it is embedded in the concept of 'innovation saltus' - i.e. discrete periods of elevated innovation. A key aspiration of this work is to inspire additional research into this interesting area of regulatory science; namely, examination of guidelines (as proxy measures of regulations) and their influence on innovation.

Singular secular Kuznets-like period realized amid industrial transformation in US FDA medical devices: a perspective on growth from 1976 to 2020.

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: 1992-2019; 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 that MD innovation is driven by a singular secular Kuznets-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.

Seasonal and Secular Periodicities Identified in the Dynamics of US FDA Medical Devices (1976-2020): Portends Intrinsic Industrial Transformation and Independence of Certain Crises.

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), under primarily two administrative circuits: premarketing notifications (PMN) and Approvals (PMAs). This work considers the dynamics of FDA PMNs and PMAs applications as an proxy metric for the evolution of the MD industry, and specifically seeks to test the existence [and, if so, identify the length scale(s)] of economic/business cycles. 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, complete ensemble empirical mode with adaptive noise decomposition, and Seasonal Trend Loess decomposition) to exhaustively seek and find such periodicities. This work finds that from 1976 to 2020, the dynamics of MD applications are (1) non-normal, non-stationary (fractional order of integration < 1), non-linear, and strongly persistent (Hurst > 0.5); (2) regular (non-variance), with latent periodicities following seasonal, 1-year (short-term), 5-6 year (Juglar; mid-term), and a single 24-year (Kuznets; medium-term) period (when considering the total number of MD applications); (3) evolving independently of any specific exogenous factor (such as the COVID-19 crisis); (4) comprised of two inversely opposing processes (PMNs and PMAs) suggesting an intrinsic structural industrial transformation occurring within the MD industry; and, (6) predicted to continue its decline (as a totality) into the mid-2020s until recovery. Ramifications of these findings are discussed.

US FDA Drug Approvals are Persistent and Polycyclic: Insights into Economic Cycles, Innovation Dynamics, and National Policy.

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 FDA's 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 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).

Has the COVID-19 Crisis Affected the Growth of United States Food and Drug Administration Drug Approvals? The Answer is Not Yet! A Time Series (Forecasting) Study.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-19; HCoV-19; COVID-19) has affected all daily activities. Has it also affected the number of United States (FDA) drug approvals over time? The short answer from empirical time series forecasting is not yet. Care should be taken as the crisis continues through maintaining the scientific, economic, political, and social supportive structures to sustain momentum. This conclusion is based on analyzing the results of (non-overlapping) forecasting routines (viz., complex exponential smoothing, auto-regressive fractionally integrated moving average, extreme learning machine, and multi-layer perceptron) performed on longitudinal (1939-present) FDA (CDER) drug approvals taking into regard pre- and extant-COVID-19 eras. This is an initial study and there are caveats with the approach, and as such, all data and programs are provided to support replication of the results and furthering of the investigation.

Investigating Rates of Food and Drug Administration Approvals and Guidances in Drug Development: A Structural Breakpoint/Cointegration Timeseries Analysis.

BACKGROUND: The number of original and supplemental ANDAs, BLAs, NDAs, and Biosimilars FDA drug/biologic approvals (Approvals) has risen dramatically in the recent years, incidentally, so has the number of issued FDA guidances (Guidances). It is hypothesized that if the structures of the two timeseries are similar and/or concomitantly co-evolving, then there is a relationship between the two variables that may be worthy of further investigation. METHODS: Structural breakpoint (SBP) and cointegration (CI) analyses are used to provide insights into the relatedness of the two timeseries (Approvals, Guidances). Various descriptive statistics (e.g., nonparametric correlation testing, decomposition, unit testing, stationarity, and maximum order of integration) were also performed to better understand the nature of the timeseries understudy. RESULTS: Structural breaks were identified with the following dates: Approvals (1983, 1989, 1996, 2004, and 2012) and Guidances (1995 and 2012). Approvals and Guidances were (medium) correlative, nonstationary, and cointegrated with a maximum order of integration of one (I(1)). Descriptive statistical markers suggest additional similarities (e.g., seasonal variation) between the two timeseries. CONCLUSIONS: To the author's knowledge, this is the first work to empirically investigate Guidances and their relationship with Approvals. The similarity in the structure of the timeseries (e.g., seasonal variation, SBPs and CI) suggests a deeper relationship between Guidances and Approvals, including the existence of a "long-run" equilibrium (wherein one or more exogenous factors restrain the divergence) between the two variables. This work offers an exciting opportunity for further research into the processes influencing the rates of Approvals and Guidances. A discussion on the limitations of the approach is also presented.

Pulmonary Arterial Hypertension: A Case Study in FDA Expedited Program Designations.

BACKGROUND: FDA expedited program designations (EPDs) are intended to facilitate drug development for serious conditions with an unmet medical need. There are over 10 FDA-approved therapies for the rare disease pulmonary arterial hypertension (PAH). This work investigates the landscape of EPDs in the context of FDA-approved PAH therapies in order to inform on future drug development. METHODS: The publicly available FDA Action Package (AP) was manually culled for information related to EPDs for 10 FDA-approved treatments for PAH. Documentation supporting the EPD request and/or its review (including potential rejection) was not found during the data cull. RESULTS: This investigation finds that (1) only ambrisentan received the Fast Track Designation; (2) no Breakthrough Designations were elucidated; (3) bosentan and treprostinil received Accelerated Approval Designations, and (4) ambrisentan, sildenafil, riociguat, epoprostenol, iloprost, and treprostinil received Priority Review Designations. All therapies (except sildenafil) received an Orphan Drug Designation. CONCLUSION: Based on these results, it is recommended that drug developers be encouraged to revisit traditional endpoint measures, explore novel biological mechanisms, and/or effectively differentiate in other dimensions (eg, safety). Developers should also consider engaging the FDA early in development (ideally prior to first-in-human) to agree on the kind and amount of data to meet the statutory bar with the intention of increasing the probability of securing a Fast Track or Breakthrough Designation.

Electron transfer in ferredoxin: are tunneling pathways evolutionarily conserved?

A theoretical study of electron transfer (ET) pathways in a recently crystallized Clostridium acidurici ferredoxin is reported. The electronic structure of the protein complex is treated at the semiempirical extended Hückel level, and the tunneling pathways are calculated with the rigorous quantum mechanical method of tunneling currents. The model predicts two pathways between the two [4Fe-4S] cubanes: a strong one running directly from Cys(14) to Cys(43) and a weaker one from Cys(14) via Ile(23) to Cys(18), whereas other amino acids do not play a significant role in the electron tunneling. The cysteine ligands conduct almost all of the current when Ile(23) is mutated to valine in silico, so that there is no appreciable change in the ET rate. The calculated value of the transfer matrix element is consistent with the experimentally determined rate of transfer. Results of the sequence analysis performed on this ferredoxin reveal that Ile(23) is a highly variable amino acid compared with the cubane-ligating cysteine amino acids, even though Ile(23) lies directly between the donor and acceptor complexes. We further argue that the homologous proteins with a [3Fe-4S] cofactor, which does not have one of the four cysteine ligands, use the same tunneling pathways as those in this ferredoxin, on the basis of the high homology as well as the absolute conservation of Cys(14) and Cys(43) which serve as the main tunneling conduit. Our results explain why mutation of amino acids around and between the donor and acceptor cubane clusters, including that of Ile(23), does not appreciably affect the rate of transfer and add support to the proposal that there exist evolutionarily conserved electron tunneling pathways in biological ET reactions.