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Introduction: The access gap for novel pharmaceuticals between China and the developed countries is a major public health issue in China. It is crucial to understand the determinants of this gap to ensure timely access to new drugs and enhance patient health. Methods: We included all new drugs approved by the US Food and Drug Administration (FDA) between 2012 and 2019, and collected their approval timings in China. Major factors of interest comprised orphan designation and expedited review pathways granted by the FDA, along with the proportion of Asian subjects in the pivotal trial supporting the FDA approval and whether the trial included study sites in China. The elapsed time from the FDA approval to the market authorization in China constituted the time-to-event outcome, and Cox proportional-hazards regression was used for multivariate analysis. Results: A total of 327 new drugs were approved by the FDA between 2012 and 2019, among which 41.3% were found to be authorized in China as of 1 November 2023. The median lag time for the mutually approved drugs was 3.5 years. The Cox model found that orphan drugs had lower likelihood of being approved in China (HR = 0.59, 95% CI 0.39-0.89; p = 0.011), while the FDA's Breakthrough-Therapy drugs (HR = 2.33, 95% CI 1.39-3.89; p = 0.001) and Fast-Track drugs (HR = 1.58, 95% CI 1.05-2.38; p = 0.028) had shorter lag times. In the pivotal trials that supported the FDA approvals, a higher proportion of Asian subjects was associated with faster drug entry into the Chinese market (HR = 1.02, 95% CI 1.01-1.03; p < 0.001), and the inclusion of study sites in China mainland was likewise conducive to reducing the drug lag (HR = 5.30, 95% CI 3.20-8.77; p < 0.001). After the trials with China-based sites supported the FDA approvals, 77.8% of the trials also supported the subsequent approvals in China. Discussion: China's involvement in global drug co-development can streamline clinical development, by reducing repeated trials solely in the Chinese population. This is primarily due to the openness of the Chinese drug agency towards overseas clinical data and is a positive sign that encourages global drug developers to include Chinese patients in their development plans as early as possible.
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A stochastic model was developed for simultaneous growth of low numbers of Listeria monocytogenes and populations of lactic acid bacteria from the aroma producing cultures applied in cottage cheese. During more than two years, different batches of cottage cheese with aroma culture were analysed for pH, lactic acid concentration and initial concentration of lactic acid bacteria. These data and bootstrap sampling were used to represent product variability in the stochastic model. Lag time data were estimated from observed growth data (lactic acid bacteria) and from literature on L. monocytogenes single cells. These lag time data were expressed as relative lag times and included in growth models. A stochastic model was developed from an existing deterministic growth model including the effect of five environmental factors and inter-bacterial interaction [Østergaard, N.B, Eklöw, A and Dalgaard, P. 2014. Modelling the effect of lactic acid bacteria from starter- and aroma culture on growth of Listeria monocytogenes in cottage cheese. International Journal of Food Microbiology. 188, 15-25]. Growth of L. monocytogenes single cells, using lag time distributions corresponding to three different stress levels, was simulated. The simulated growth was subsequently compared to growth of low concentrations (0.4-1.0 CFU/g) of L. monocytogenes in cottage cheese, exposed to similar stresses, and in general a good agreement was observed. In addition, growth simulations were performed using population relative lag time distributions for L. monocytogenes as reported in literature. Comparably good predictions were obtained as for the simulations performed using lag time data for individual cells of L. monocytogenes. Therefore, when lag time data for individual cells are not available, it was suggested that relative lag time distributions for L. monocytogenes can be used as a qualified default assumption when simulating growth of low concentrations of L. monocytogenes.