手性药物的酶催化不对称定向合成

手性药物目前已成为国际上新药研究的热点。手性药物及其中间体的酶催化定向合成与传统的有机合成反应相比,所需条件温和,无需强酸强碱、高温高压等极端条件,可大大降低对设备的要求,同时具备反应速度快、立体选择性高等优势,而且对环境污染小,属于绿色合成技术,因而成为制备手性化合物的最有前景的方法之一。本文主要介绍了手性药物的酶催化不对称定向合成的方式、催化机理及应用,并引入了一些最新进展情况。     

Mesoporous materials for drug delivery

Research on mesoporous materials for biomedical purposes has experienced an outstanding increase during recent years. Since 2001, when MCM-41 was first proposed as drug-delivery system, silica-based materials, such as SBA-15 or MCM-48, and some metal-organic frameworks have been discussed as drug carriers and controlled-release systems. Mesoporous materials are intended for both systemic-delivery systems and implantable local-delivery devices. The latter application provides very promising possibilities in the field of bone-tissue repair because of the excellent behavior of these materials as bioceramics. This Minireview deals with the advances in this field by the control of the textural parameters, surface functionalization, and the synthesis of sophisticated stimuli-response systems.     

Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity

Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation system learning model to prioritize candidate cancer drugs. We designed a drug–drug pathway functional similarity by integrating multiple genetic and epigenetic alterations such as gene expression, copy number variation (CNV), and DNA methylation. When compared with other similarities, such as SMILES chemical structures and drug targets based on the protein–protein interaction network, our approach provided better interpretable models capturing drug response mechanisms. Furthermore, our approach can achieve comparable accuracy when evaluated with other learning models based on large public datasets (CCLE and GDSC). A case study about the Erlotinib and OSI-906 (Linsitinib) indicated that they have a synergistic effect to reduce the growth rate of tumors, which is an alternative targeted therapy option for patients. Taken together, our computational method characterized drug response from the viewpoint of a multi-omics pathway and systematically predicted candidate cancer drugs with similar therapeutic effects.     

Micro- and Nanocapsules Based on Artificial Peptides

The encapsulation of active ingredients into solid capsules from biodegradable materials has received significant attention over the last decades. In this short review, we focus on the formation of micro- and nano-sized capsules and emulsions based on artificial peptides as a fully degradable material. It deals with various approaches for the preparation of peptide-based capsules as well as with their crucial properties such as size and stability. We categorize all preparation procedures into three basic approaches: self-assembly, polymerization and crosslinking, and layer-by-layer technology. This article is meant to offer a short overview over all successful methods suitable for obtaining access to these very promising carrier systems.     

Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index

This research aims to compare the performance of ARIMA as a linear model with that of the combination of ARIMA and GARCH family models to forecast S&P500 log returns in order to construct algorithmic investment strategies on this index. We used the data collected from Yahoo Finance with daily frequency for the period from 1 January 2000 to 31 December 2019. By using a rolling window approach, we compared ARIMA with the hybrid models to examine whether hybrid ARIMA-SGARCH and ARIMA-EGARCH can really reflect the specific time-series characteristics and have better predictive power than the simple ARIMA model. In order to assess the precision and quality of these models in forecasting, we compared their equity lines, their forecasting error metrics (MAE, MAPE, RMSE, MAPE), and their performance metrics (annualized return compounded, annualized standard deviation, maximum drawdown, information ratio, and adjusted information ratio). The main contribution of this research is to show that the hybrid models outperform ARIMA and the benchmark (Buy&Hold strategy on S&P500 index) over the long term. These results are not sensitive to varying window sizes, the type of distribution, and the type of the GARCH model.     

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors’ decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. We propose a new stock index forecasting model based on time series decomposition and a hybrid model. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the stock index into a series of Intrinsic Mode Functions (IMFs) with different feature scales and trend term. The Augmented Dickey Fuller (ADF) method judges the stability of each IMFs and trend term. The Autoregressive Moving Average (ARMA) model is used on stationary time series, and a Long Short-Term Memory (LSTM) model extracts abstract features of unstable time series. The predicted results of each time sequence are reconstructed to obtain the final predicted value. Experiments are conducted on four stock index time series, and the results show that the prediction of the proposed model is closer to the real value than that of seven reference models, and has a good quantitative investment reference value.     

Rational Design and Synthesis of HSF1-PROTACs for Anticancer Drug Development

PROTACs employ the proteosome-mediated proteolysis via E3 ligase and recruit the natural protein degradation machinery to selectively degrade the cancerous proteins. Herein, we have designed and synthesized heterobifunctional small molecules that consist of different linkers tethering KRIBB11, a HSF1 inhibitor, with pomalidomide, a commonly used E3 ligase ligand for anticancer drug development.     

Quantification of Venetoclax for Therapeutic Drug Monitoring in Chinese Acute Myeloid Leukemia Patients by a Validated UPLC-MS/MS Method

Venetoclax has emerged as a breakthrough for treatment of leukemia with a wide interindividual variability in pharmacokinetics. Herein, a rapid, sensitive, and reliable UPLC-MS/MS method for quantification of venetoclax in plasma was developed and validated. The method was operated in the multiple-reaction monitoring (MRM) mode to detect venetoclax at m/z transition 868.5 > 321.0 and IS at 875.5 > 321.0, respectively. Protein precipitation prior to injection into the LC-MS/MS and the analyte was separated on an ACQUITY UPLC BEH C18 column by gradient elution with acetonitrile and 0.1% formic acid in water. Linear calibration curves were obtained in the range of 25–8000 ng/mL. The specificity, recovery, matrix effect, and stability also met the acceptance criteria of FDA guidance. The method was successfully applied to analyze plasma in acute myeloid leukemia (AML) patients. The peak plasma concentration (C_max) of venetoclax in Chinese AML patient was 2966.0 ± 1595.0 ng/mL while the trough concentration (C_min) was 1018.0 ± 729.4 ng/mL. Additionally, C_max and C_min showed a positive correlation with AST levels. Furthermore, C_max was significantly higher in the older patients. The present method can be applied for TDM of venetoclax in treatment of hematological cancers.     

CO2-switchable vesicles-network structure transition and drug release property

A series of amphiphilic triblock polymers based on poly(ethylene glycol) (PEG) and two symmetrical poly(2-(dimethylamino)ethyl methacrylate) (PDMAEMA) blocks was synthesized via the Atom Transfer Radical Polymerization (ATRP) method. Conductivity, pH, and viscosity tests demonstrated the CO₂-switchability jointly; Cryogenic transmission electron microscopy (Cryo-TEM), Dynamic light scattering (DLS) revealed the self-assembly morphology transformation from unilamellar vesicle to network structure when bubbling CO₂. These changes were all attributed to the protonation of tertiary amine groups in PDMAEMA blocks and the mechanism was proved by ^?H NMR. The vesicles have a relatively low release rate of drug; once stimulated by CO₂, the release rate will be accelerated. The polymeric vesicle has the possibility to find potential applications in drug delivery and release domains.     

Dextramabs: A Novel Format of Antibody-Drug Conjugates Featuring a Multivalent Polysaccharide Scaffold

Antibody-drug conjugates (ADCs) are multicomponent biomolecules that have emerged as a powerful tool for targeted tumor therapy. Combining specific binding of an immunoglobulin with toxic properties of a payload, they however often suffer from poor hydrophilicity when loaded with a high amount of toxins. To address these issues simultaneously, we developed dextramabs, a novel class of hybrid antibody-drug conjugates. In these architectures, the therapeutic antibody trastuzumab is equipped with a multivalent dextran polysaccharide that enables efficient loading with a potent toxin in a controllable fashion. Our modular chemoenzymatic approach provides an access to synthetic dextramabs bearing monomethyl auristatin as releasable cytotoxic cargo. They possess high drug-to-antibody ratios, remarkable hydrophilicity, and high toxicity in vitro.