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In Japan in October 2016, the Pharmaceuticals and Medical Devices Agency (PMDA) began to receive electronic data in new drug applications (NDAs). These electronic data are useful to conduct regulatory assessment of sponsors’ submissions and contribute to the PMDA's research. In this article, we summarize the number of submissions of quantitative modeling and simulation (M&S) documents in NDAs in Japan, and we describe our current thinking and activities about quantitative M&S in PMDA.

Tanzawaic acids have been isolated from Penicillium citrinum and their structures were elucidated by spectroscopic analysis. Their relative stereochemistries were also clarified by detailed analyses of 1H–1H coupling constants and NOE data. Tanzawaic acid B (GS-1302-1) significantly inhibits superoxide anion production in human neutrophils.

Tanzawaic Acids A, B, C, and D: Inhibitors of Superoxide Anion Production from Penicillium citrinum -[isolation and structure isolation of the title carboxylic acids (I) and (II)]. -(KURAMOTO, M.; YAMADA, K.; SHIKANO, M.; YAZAWA, K.; ARIMOTO, H.; OKAMURA, T.; UEMURA, D.; Chem. Lett. (1997) 9, 885-886; Dep. Chem., Fac. Sci., Shizuoka Univ., Oya, Shizuoka 422, Japan; EN)

We found that 12-O-tetradecanoylphorbol-13-acetate (TPA) promoted anchorage-independent growth but did not affect anchorage-dependent growth of MIA PaCa-2 human pancreatic carcinoma cells. TPA markedly activated mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase in an anchorage-independent manner. Two protein kinase C (PKC) isoforms, conventional PKC (cPKC) and novel PKC (nPKC), but not apical PKC, translocated from the cytosolic to the particulate fraction upon TPA treatment. To identify the PKC isoforms involved in the regulation of anchorage-independent growth, four PKC isoforms (alpha, delta, epsilon, and zeta) were forced to be expressed in MIA PaCa-2 cells with an adenovirus vector. Overexpression of nPKCdelta or nPKC epsilon activated MAPK and promoted anchorage-independent growth. Overexpression of cPKCalpha alone did not influence anchorage-independent growth but lowered the concentration of TPA that was required to enhance such growth. Expression of constitutively active MAPK kinase-1 (MEK1) also promoted anchorage-independent growth. Furthermore, PKC inhibitors or an MEK inhibitor completely suppressed both TPA-induced activation of MAPK and promotion of anchorage-independent growth, but a cPKC-selective inhibitor partially suppressed TPA-induced promotion of the growth. Based on these results, we suggest that MAPK activation, mediated by certain isoforms of PKC, plays a part in oncogenic growth of MIA PaCa-2 cells. In summary, our data indicated that specific inhibitors of the cPKC and nPKC signaling pathway might be selective anti-oncogenic growth agents for some types of human pancreatic cancer.

BackgroundThe need for a new style of clinical trials, called decentralized clinical trials (DCTs), has been increasing as they do not depend on physical visits to clinical sites. DCTs are expected to provide a new opportunity to patients who cannot participate in a clinical trial due to geographical and time limitations. For the adoption of DCTs, it is essential that medical devices with Internet of Medical Things (IoMT) and Internet of Health Things (IoHT) based technologies are developed and commercially adopted. In this study, we aimed to identify the regulatory considerations when IoMT/IoHT-based technologies are used in DCTs or products developed using DCTs.MethodTo understand the study and development field of IoMT/IoHT comprehensively and panoramically, relevant papers published in Web of Science were searched online. Subsequently, a citation network was obtained and characterized as a cluster using a text mining method to identify IoMT/IoHT-based technologies expected to be utilized in DCTs or products developed using DCTs.Result and DiscussionUpon analysis of the top 15 clusters and subsequent 51 sub-clusters, we identified the therapeutic areas (psychology, neurology) and IoMT/IoHT-based technologies (telemedicine, remote monitoring, and virtual reality) that are expected to be used in DCTs. We also identified several considerations based on the current regulatory guidance.ConclusionIoMT/IoHT-based technologies that are expected to be used or products developed using DCTs and key considerations made when they are used in DCTs were identified. The considerations could encourage conducting DCTs using IoMT/IoHT-based technologies.

Horizon scanning for innovative technologies that might be applied to medical products and requires new assessment approaches to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. The purpose of this study is to confirm that citation network analysis and text mining for bibliographic information analysis can be used for horizon scanning of the rapidly developing field of AI-based medical technologies and extract the latest research trend information from the field. We classified 119,553 publications obtained from SCI constructed with the keywords “conventional,” “machine-learning,” or “deep-learning" and grouped them into 36 clusters, which demonstrated the academic landscape of AI applications. We also confirmed that one or two close clusters included the key articles on AI-based medical image analysis, suggesting that clusters specific to the technology were appropriately formed. Significant research progress could be detected as a quick increase in constituent papers and the number of citations of hub papers in the cluster. Then we tracked recent research trends by re-analyzing “young” clusters based on the average publication year of the constituent papers of each cluster. The latest topics in AI-based medical technologies include electrocardiograms and electroencephalograms (ECG/EEG), human activity recognition, natural language processing of clinical records, and drug discovery. We could detect rapid increase in research activity of AI-based ECG/EEG a few years prior to the issuance of the draft guidance by US-FDA. Our study showed that a citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of rapidly developing AI-based medical technologies.