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2018 1

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in vivo predictive dissolution 1

BCS subclassification 1

DNA methylation 1

GIS 1

ketoconazole 1

psoriasis 1

subclassification 1

supersaturation 1

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DNA methylation-based subclassification of psoriasis in the Chinese Han population

Fusheng Zhou, Changbing Shen, Yi-Hsiang Hsu, Jing Gao, Jinfa Dou, Randy Ko, Xiaodong Zheng, Liangdan Sun, Yong Cui, Xuejun Zhang

Frontiers of Medicine 2018, Volume 12, Issue 6, Pages 717-725 doi: 10.1007/s11684-017-0588-6

Abstract: Psoriasis (Ps) is an inflammatory skin disease caused by genetic and environmental factors. Previous studies on DNA methylation (DNAm) found genetic markers that are closely associated with Ps, and evidence has shown that DNAm mediates genetic risk in Ps. In this study, Consensus Clustering was used to analyze DNAm data, and 114 Ps patients were divided into three subclassifications. Investigation of the clinical characteristics and copy number variations (CNVs) of , , and in the three subclassifications revealed no significant differences in gender ratio and in Ps area and severity index (PASI) score. The proportion of late-onset (≥40 years) Ps patients was significantly higher in type I than in types II and III ( = 0.035). Type III contained the smallest proportion of smokers and the largest proportion of non-smoking Ps patients ( = 0.086). The CNVs of and showed no significant differences but the CNV of significantly differed among the three subclassifications ( = 0.044). This study is the first to profile Ps subclassifications based on DNAm data in the Chinese Han population. These results are useful in the treatment and management of Ps from the molecular and genetic perspectives.

Keywords： psoriasis DNA methylation subclassification

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Oral product input to the GI tract: GIS an oral product performance technology

Gordon L. Amidon, Yasuhiro Tsume

Frontiers of Chemical Science and Engineering 2017, Volume 11, Issue 4, Pages 516-520 doi: 10.1007/s11705-017-1658-7

Abstract: The patient receives a pharmaceutical product, not a drug. The pharmaceutical products are formulated with a drug, an active ingredient to produce the maximum therapeutic effect after oral absorption. Therefore, it is the product we must optimize for the patients. In order to assure the safety and efficacy of pharmaceutical products, we need an predictive tool for oral product performance in patients. Currently, we are a surprisingly long way from accomplishing that objective. If the 20th century was the ‘age of the drug’, i.e., the ‘magic bullet’, the 21st century must become the ‘age of the guided missile’, i.e., the delivery system, including the form of the active pharmaceutical ingredient (API) (‘drug’). The physical form of the drug and the delivery system must be optimized to maximize the therapeutic benefits of pharmaceutical products for humans. Oral immediate release (IR) dosage forms cannot be optimal for all drugs or likely even any drugs (APIs). Still, the formulation of pharmaceutical products has to be optimized for patients. But how do we optimize oral delivery of drugs? It is usually through ‘trial and error’, in humans! We need a better way to optimize the oral dosage forms. We have suggested to select different dissolution methodologies for this optimization based on BCS Subclasses. In this article, we present the predicted drug dissolution profile of ketoconazole as a model drug from our laboratory utilizing a gastrointestinal simulator (GIS), which is an adaptation of the ASD system. GIS consists of three chambers representing stomach, duodenum, and jejunum, to create the human gastrointestinal tract-like environment and enable the control the gastric emptying rate. This dissolution system allows the monitoring of the drug dissolution phenomena and the observation of the supersaturation and the precipitation of pharmaceutical products, which is useful information to predict dissolution of pharmaceutical products. This system can provide the actual input needed to accurately predict the input into the systemic circulation required by many of the absorption prediction packages available today.

Keywords： GIS in vivo predictive dissolution ketoconazole BCS subclassification supersaturation