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Yosuke Ota, Yukihiro Itoh, Takashi Kurohara, Ritesh Singh, Elghareeb E. Elboray, Chenliang Hu, Farzad Zamani, Anirban Mukherjee, Yuri Takada, Yasunobu Yamashita, Mie Morita, Mano Horinaka, Yoshihiro Sowa, Mitsuharu Masuda, Toshiyuki Sakai, and Takayoshi Suzuki. ACS Medicinal Chemistry Letters 2022 13 (10), 1568-1573. DOI: 10.1021/acsmedchemlett.2c00126. Copyright © 2022 American Chemical Society., Anticancer drug delivery by small molecules offers a number of advantages over conventional macromolecular drug delivery systems. We previously developed phenylcyclopropylamine (PCPA)-drug conjugates (PDCs) as small-molecule-based drug delivery vehicles for targeting lysine-specific demethylase 1 (LSD1)-overexpressing cancers. In this study, we applied this PDC strategy to the HDAC-inhibitory anticancer agent vorinostat. Among three synthesized PCPA or arylcyclopropylamine (ACPA)-vorinostat conjugates 1, 9, and 32, conjugate 32 with a 4-oxybenzyl linker showed sufficient stability in buffer solutions, potent LSD1 inhibition, efficient LSD1-dependent vorinostat release, and potent and selective antiproliferative activity toward LSD1-expressing human breast cancer and small-cell lung cancer cell lines. These results indicate that the conjugate selectively releases vorinostat in cancer cells. A similar strategy may be applicable to other anticancer drugs.

Hepatic alveolar echinococcosis (HAE) and liver cancer had similarities in imaging results, clinical characteristics, and so on. And it is difficult for clinicians to distinguish them before operation. The aim of our study was to build a differential diagnosis nomogram based on platelet (PLT) score model and use internal validation to check the model. The predicting model was constructed by the retrospective database that included in 153 patients with HAE (66 cases) or liver cancer (87 cases), and all cases was confirmed by clinicopathology and collected from November 2011 to December 2018. Lasso regression analysis model was used to construct data dimensionality reduction, elements selection, and building prediction model based on the 9 PLT-based scores. A multi-factor regression analysis was performed to construct a simplified prediction model, and we added the selected PLT-based scores and relevant clinicopathologic features into the nomogram. Identification capability, calibration, and clinical serviceability of the simplified model were evaluated by the Harrell’s concordance index (C-index), calibration plot, receiver operating characteristic curve (ROC), and decision curve. An internal validation was also evaluated by the bootstrap resampling. The simplified model, including in 4 selected factors, was significantly associated with differential diagnosis of HAE and liver cancer. Predictors of the simplified diagnosis nomogram consisted of the API index, the FIB-4 index, fibro-quotent (FibroQ), and fibrosis index constructed by King’s College Hospital (King’s score). The model presented a perfect identification capability, with a high C-index of 0.929 (0.919 through internal validation), and good calibration. The area under the curve (AUC) values of this simplified prediction nomogram was 0.929, and the result of ROC indicated that this nomogram had a good predictive value. Decision curve analysis showed that our differential diagnosis nomogram had clinically identification capability. In conclusion, the differential diagnosis nomogram could be feasibly performed to verify the preoperative individualized diagnosis of HAE and liver cancer.