Drug–target interaction prediction by integrating multiview network data

Drug–target interaction (DTI) prediction is a challenging step in further drug repositioning, drug discovery and drug design. The advent of high-throughput technologies brings convenience to the development of DTI prediction methods. With the generation of a high number of data sets, many mathematical models and computational algorithms have been developed to identify the potential drug–target pairs. However, most existing methods are proposed based on the single view data. By integrating the drug and target data from different views, we aim to get more stable and accurate prediction results.In this paper, a multiview DTI prediction method based on clustering is proposed. We first introduce a model for single view drug–target data. The model is formulated as an optimization problem, which aims to identify the clusters in both drug similarity network and target protein similarity network, and at the same time make the clusters with more known DTIs be connected together. Then the model is extended to multiview network data by maximizing the consistency of the clusters in each view. An approximation method is proposed to solve the optimization problem. We apply the proposed algorithms to two views of data. Comparisons with some existing algorithms show that the multiview DTI prediction algorithm can produce more accurate predictions. For the considered data set, we finally predict 54 possible DTIs. From the similarity analysis of the drugs/targets, enrichment analysis of DTIs and genes in each cluster, it is shown that the predicted DTIs have a high possibility to be true.     

The Current Directions of Searching for Antiparasitic Drugs

Parasitic diseases are still a huge problem for mankind. They are becoming the main cause of chronic diseases in the world. Migration of the population, pollution of the natural environment, and climate changes cause the rapid spread of diseases. Additionally, a growing resistance of parasites to drugs is observed. Many research groups are looking for effective antiparasitic drugs with low side effects. In this work, we present the current trends in the search for antiparasitic drugs. We report known drugs used in other disease entities with proven antiparasitic activity and research on new chemical structures that may be potential drugs in parasitic diseases. The described investigations of antiparasitic compounds can be helpful for further drug development.     

Synthesis and Characterization of Novel Amphiphilic Polymers as Drug Delivery Nano Carriers

Polymer nano-particles have been widely investigated in the last decade due to a variety of potential applications. In particular, polymers which can self assemble into micellar nano-particles can be effectively used as vehicles for drug delivery. Considerable efforts are underway to develop better drug delivery nano carriers for high drug loading capacity for a wide variety of bioactive compounds. In this study, several new polymers were synthesized in bulk (solventless condition) by a chemo-enzymatic methodology using Candida antarctica lipase B (Novozyme 435) and molecular sieves (MS). The synthesized polymers demonstrated high drug loading capacity and the potential to encapsulate drugs which are poorly soluble in aqueous solvents.     

Novel PEGylated Amphiphilic Copolymers as Nanocarriers for Drug Delivery: Synthesis,Characterization and Curcumin Encapsulation

Amphiphilic polymers can self assemble into micellar nano-particles and can be effectively used as nano carriers for drug delivery. A number of macromolecular delivery systems are under investigation to improve the efficacy of prospective drugs. In this study, seven new co-polymers were synthesized under mild reaction conditions in bulk (without solvent) by chemoenzymatic approach using Candida antarctica lipase (Novozyme 435) and molecular sieves, subsequently these polymers were treated with different long chain bromoalkanes and acid chlorides for attachment of the lipophilic moieties to the backbone polymer via an ether or an ester linkage, respectively in order to make them amphiphilic. These synthesized nano-particles demonstrated high drug loading capacity and have the potential to encapsulate hydrophobic drugs.     

Molecular Imprinting of Cyclodextrin Supramolecular Hydrogels Improves Drug Loading and Delivery

Cyclodextrin‐based controlled delivery materials have previously been developed for controlled release of different therapeutic drugs. In this study, a supramolecular hydrogel made from cyclodextrin‐based macromonomers is subjected to molecular imprinting to investigate the impact on release kinetics and drug loading, when compared with non‐imprinted, or alternately imprinted hydrogels. Mild synthesis conditions are used to molecularly imprint three antibiotics—novobiocin, rifampicin, and vancomycin—and to test two different hydrogel chemistries. The release profile and drug loading of the molecularly imprinted hydrogels are characterized using ultraviolet spectroscopy over a period of 35 days and compared to non‐imprinted, and alternately imprinted hydrogels. While only modest differences are observed in the release rate of the antibiotics tested, a substantial difference is observed in the total drug‐loading amount possible for hydrogels releasing drugs which has been templated by those drugs. Hydrogels releasing drugs which are templated by other drugs do not show improved release or loading. Analysis by FTIR does not show substantial incorporation of drug into the polymer. Lastly, bioactivity assays confirmed long‐term stability and release of incorporated antibiotics.     

壳聚糖/乙酰半胱氨酸纳米粒子的性质及体外释药性

制备了一种基于壳聚糖/乙酰半胱氨酸偶合物(CS-NAC)的新型巯基纳米粒子并进行了结构表征, 同时对纳米粒子的黏附性、溶胀性和药物释放进行了测试. 结果表明, 纳米粒子具有较小的粒径(140~210 nm)和正的表面电位(19.5~31.7 mV), 胰岛素的载药量达到13%~42%. 这些性质随着巯基含量的变化而变化. 与壳聚糖纳米粒子相比, 巯基壳聚糖纳米粒子表现出了更强更快的黏附性质. 体外释放研究结果表明, 巯基壳聚糖纳米粒子的胰岛素释放具有pH响应性. 在pH=6.8时, 15 min即能释放58.6 %的胰岛素; 而在pH=5.4时, 24 h内仅有不到40%的胰岛素被释放. 因此, CS-NAC纳米粒子用于胰岛素的黏膜给药体系具有很好的应用前景.     

TEMPO-Oxidized Cellulose Beads as Potential pH-Responsive Carriers for Site-Specific Drug Delivery in the Gastrointestinal Tract

The development of controlled drug delivery systems based on bio-renewable materials is an emerging strategy. In this work, a controlled drug delivery system based on mesoporous oxidized cellulose beads (OCBs) was successfully developed by a facile and green method. The introduction of the carboxyl groups mediated by the TEMPO(2,2,6,6-tetramethylpiperidine-1-oxyradical)/NaClO/NaClO₂ system presents the pH-responsive ability to cellulose beads, which can retain the drug in beads at pH = 1.2 and release at pH = 7.0. The release rate can be controlled by simply adjusting the degree of oxidation to achieve drug release at different locations and periods. A higher degree of oxidation corresponds to a faster release rate, which is attributed to a higher degree of re-swelling and higher hydrophilicity of OCBs. The zero-order release kinetics of the model drugs from the OCBs suggested a constant drug release rate, which is conducive to maintaining blood drug concentration, reducing side effects and administration frequency. At the same time, the effects of different model drugs and different drug-loading solvents on the release behavior and the physical state of the drugs loaded in the beads were studied. In summary, the pH-responsive oxidized cellulose beads with good biocompatibility, low cost, and adjustable release rate have shown great potential in the field of controlled drug release.     

Control of release kinetics of macromolecules from polymers

Internal and external means for controlling the release rates of large molecules, such as proteins, from ethylene—vinyl acetate copolymer matrices are presented. Internal approaches include alteration of the polymer—drug design, such as changing drug loading and particle size, coating the matrix, or altering matrix geometry. Kinetic and microstructural analyses are discussed. Applications of these polymeric systems, for instance, in delivery of insulin for diabetes, improved immunization procedures, and in developing bioassays for informational macromolecules are considered. In addition, a new approach for externally controlling release rates of drugs using magnetism has been developed. Until now, drug delivery systems were capable of delivering drugs at either constant or decreasing rates. We sought a system that permitted delivery of increased doses on demand, and achieved this by incorporating magnetic particles and drugs into polymeric matrices. Drug release rates can then be increased by an appropriate application of an external magnetic field. Over a five-day period, the magnetic field was applied ten times and drug release rates increased by up to 100% each time. Initial results indicate that this system does not cause tissue damage.     

Impact of microgel morphology on functionalized microgel-drug interactions

The interactions of a range of water-soluble drugs of different charges and hydrophobicities with carboxylic acid-functionalized poly(N-isopropylacrylamide)-based microgels containing different functional group distributions are investigated to determine the impact of drug properties and microgel morphologies on drug uptake and release. The radial distribution of carboxylic acid functional groups in the microgel and the hydrophobicities of the cationic drugs both strongly affect drug partitioning between the solution and microgel phases. Microgels with surface-localized functional group distributions bind less cationic drug than bulk-functionalized microgels, likely due to the formation of a locally collapsed "skin layer" at the acid-base drug binding sites at the microgel surface. In this way, cationic drugs induce a local phase transition that can be used to regulate small molecule diffusion in and out of the gel. As the drug hydrophobicity is increased, the skin layer becomes more condensed and less drug uptake is achieved. In the case of anionic or neutral drugs, high drug uptakes are achieved independent of the functional group distribution within the microgel. High drug uptake is also observed when nonfunctionalized poly(N-isopropylacrylamide) microgels are used as the uptake matrix, suggesting the importance of hydrophobic partitioning in regulating drug-microgel interactions.     

毛细管电泳药物筛选方法与技术

毛细管电泳（CE）在新药研发领域显示着重要的应用前景。CE使用水溶液介质作为实验体系,保证了药物筛选在类似于生命介质的环境中进行,优于其他传统体外仪器筛选方法。除了维持被筛选分子和作用对象的生物活性外,CE筛选过程着重突出配体与受体之间的相互作用。毛细管电泳药物筛选瞄准与药理学理论相关的重要参数,如结合常数K_b 、结合速率常数K_on 和解离速率常数K_off ,有利于模拟并预测机体内靶标与药物之间的相互作用过程。该文回顾了毛细管电泳进行药物筛选的历史,评述了毛细管电泳药物筛选方法所依据的理论和相对成熟的各种常用方法,并抽取了部分典型实例以及相关技术进行说明,对以亲和毛细管电泳、动力学毛细管电泳为手段的药物筛选方法进行了介绍,包括分子和细胞层次的药物筛选,以及针对不同类型的候选药物的研究工作都有提及。毛细管电泳与多种技术的联用,包括与质谱以及化学发光等联用发挥了更大的效能。联用方法还应用于中药有效成分的筛选。毛细管电泳在DNA编码化合物库筛选中将有良好应用前景。馏分收集的发展为筛选药物提供了广阔前景,它配合指数富集配体系统进化技术为毛细管电泳药物筛选提供了更多可能。总之,毛细管电泳多样可选的药物筛选方法和技术将为新概念的药物筛选与药物评价提供有力支撑。     

In Silico Drug Repurposing of FDA-Approved Drugs Highlighting Promacta as a Potential Inhibitor of H7N9 Influenza Virus

Influenza virus infections continue to be a significant and recurrent public health problem. Although vaccine efficacy varies, regular immunisation is the most effective method for suppressing the influenza virus. Antiviral drugs are available for influenza, although two of the four FDA-approved antiviral treatments have resulted in significant drug resistance. Therefore, new treatments are being sought to reduce the burden of flu-related illness. The time-consuming development of treatments for new and re-emerging diseases such as influenza and the high failure rate are increasing concerns. In this context, we used an in silico-based drug repurposing method to repurpose FDA-approved drugs as potential therapies against the H7N9 virus. To find potential inhibitors, a total of 2568 drugs were screened. Promacta, tucatinib, and lurasidone were identified as promising hits in the DrugBank database. According to the calculations of MM-GBSA, tucatinib (−54.11 kcal/mol) and Promacta (−56.20 kcal/mol) occupied the active site of neuraminidase with a higher binding affinity than the standard drug peramivir (−49.09 kcal/mol). Molecular dynamics (MD) simulation studies showed that the C-α atom backbones of the complexes of tucatinib and Promacta neuraminidase were stable throughout the simulation period. According to ADME analysis, the hit compounds have a high gastrointestinal absorption (GI) and do not exhibit properties that allow them to cross the blood–brain barrier (BBB). According to the in silico toxicity prediction, Promacta is not cardiotoxic, while lurasidone and tucatinib show only weak inhibition. Therefore, we propose to test these compounds experimentally against the influenza H7N9 virus. The investigation and validation of these potential H7N9 inhibitors would be beneficial in order to bring these compounds into clinical settings.     

Revolutionizing Cancer Treatment: Biopolymer-Based Aerogels as Smart Platforms for Targeted Drug Delivery

Cancer stands as a leading cause of global mortality, with chemotherapy being a pivotal treatment approach, either alone or in conjunction with other therapies. The primary goal of these therapies is to inhibit the growth of cancer cells specifically, while minimizing harm to healthy dividing cells. Conventional treatments, often causing patient discomfort due to side effects, have led researchers to explore innovative, targeted cancer cell therapies. Thus, biopolymer-based aerogels emerge as innovative platforms, showcasing unique properties that respond intelligently to diverse stimuli. This responsiveness enables precise control over the release of anticancer drugs, enhancing therapeutic outcomes. The significance of these aerogels lies in their ability to offer targeted drug delivery with increased efficacy, biocompatibility, and a high drug payload. In this comprehensive review, the author discuss the role of biopolymer-based aerogels as an emerging functionalized platforms in anticancer drug delivery. The review addresses the unique properties of biopolymer-based aerogels showing their smart behavior in responding to different stimuli including temperature, pH, magnetic and redox potential to control anticancer drug release. Finally, the review discusses the application of different biopolymer-based aerogel in delivering different anticancer drugs and also discusses the potential of these platforms in gene delivery applications.     

DR2DI: a powerful computational tool for predicting novel drug-disease associations

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.     

New analysis of the optimization of electromagnetic shielding properties using conducting polymers and a multi‐objective approach

Recently, an approach was proposed to optimize multi‐layer shields of polyaniline–polyurethane (PAni/PU) conducting composites in the microwave band. Though by this method shields for different applications can be obtained which are light‐weight and offer a low percolation threshold, the full potential of the design process could not be tapped since the underlying optimization problem includes only one objective. In this work we go one step beyond and re‐formulate the design problem as a multi‐objective optimization problem (MOP). To be more precise, we involve simultaneously the shielding efficiency as well as the weight and the cost of the material—i.e. all the requirements for modern shielding materials—within the optimization process. After having stated the model we present two possible ways to approximate the solution set—the so‐called Pareto set—and address the related and important decision‐making problem. All steps are demonstrated on a particular three‐layered composite in order to show the applicability of the novel approach. Copyright © 2008 John Wiley & Sons, Ltd.     

Structural characterization of micelles formed of cholesteryl-functionalized cyclodextrins

Amphiphilic cholesteryl 2,6-di-O-methyl-β-cyclodextrins (chol-DIMEB) can self-aggregate into spherical micelles of noteworthy potential for drug delivery. All-atom molecular dynamics simulations of chol-DIMEB micelles consisting of 3-24 monomers have been performed in aqueous solution. chol-DIMEB exhibits a pronounced tendency to self-assemble into core-shell structures. van der Waals interactions within the cholesteryl nucleus constitute the main driving force responsible for the formation of the micelle. The calculated radii of the hydrophobic core and of the hydrophilic shell for the micellar structure formed by 24 monomers agree well with the experiment. The cyclodextrin moieties are found to be exposed toward the aqueous medium and possess the appropriate flexibility to capture drugs in an effective fashion. Analysis of the solvent accessible surface area and hydration number indicates that the micelles are highly hydrosoluble species and can, therefore, enhance significantly the aqueous solubility of lipophilic drugs. In addition, the spatial structure of the micelles is suggestive of multiple potential drug binding sites. The present contribution unveils how micelles endowed with specific characteristics can form, while opening exciting perspectives for the design of novel micellar nanoparticles envisioned to be drug carriers of high potential.     

pH‐Responsive Drug‐Delivery Systems

In many biomedical applications, drugs need to be delivered in response to the pH value in the body. In fact, it is desirable if the drugs can be administered in a controlled manner that precisely matches physiological needs at targeted sites and at predetermined release rates for predefined periods of time. Different organs, tissues, and cellular compartments have different pH values, which makes the pH value a suitable stimulus for controlled drug release. pH‐Responsive drug‐delivery systems have attracted more and more interest as “smart” drug‐delivery systems for overcoming the shortcomings of conventional drug formulations because they are able to deliver drugs in a controlled manner at a specific site and time, which results in high therapeutic efficacy. This focus review is not intended to offer a comprehensive review on the research devoted to pH‐responsive drug‐delivery systems; instead, it presents some recent progress obtained for pH‐responsive drug‐delivery systems and future perspectives. There are a large number of publications available on this topic, but only a selection of examples will be discussed.     

Two Possible Strategies for Drug Modification of Gemcitabine and Future Contributions to Personalized Medicine

Drug repurposing is an emerging strategy, which uses already approved drugs for new medical indications. One such drug is gemcitabine, an anticancer drug that only works at high doses since a portion is deactivated in the serum, which causes toxicity. In this review, two methods were discussed that could improve the anticancer effect of gemcitabine. The first is a chemical modification by conjugation with cell-penetrating peptides, namely penetratin, pVEC, and different kinds of CPP6, which mostly all showed an increased anticancer effect. The other method is combining gemcitabine with repurposed drugs, namely itraconazole, which also showed great cancer cell inhibition growth. Besides these two strategies, physiologically based pharmacokinetic models (PBPK models) are also the key for predicting drug distribution based on physiological data, which is very important for personalized medicine, so that the correct drug and dosage regimen can be administered according to each patient’s physiology. Taking all of this into consideration, it is believed that gemcitabine can be repurposed to have better anticancer effects.     

The effect of phenyl substituents on the release rates of esterase-sensitive coumarin-based prodrugs

A coumarin-based prodrug system has been recently developed in our laboratory for the preparation of esterase-sensitive prodrugs of amines, peptides, and peptidomimetics. The drug release rates from this prodrug system were found to be dependent on the structural features of the drug moiety. In certain cases, the release can be undesirably slow for drugs that are secondary amines with relatively high pKa's. Aimed at finding ways to manipulate the release rates to suit the need of different drugs, we have examined the effect of the phenyl ring substitutions on the release kinetics of such prodrugs and found that appropriately positioned alkyl substituents on the phenyl ring could help to facilitate the release by as much as 16-fold. Therefore, introduction of alkyl substituents on the phenyl ring should allow us to manipulate the release rates and, therefore, time profiles for different drugs.     

Classification of highly unbalanced CYP450 data of drugs using cost sensitive machine learning techniques

In this paper, we study the classifications of unbalanced data sets of drugs. As an example we chose a data set of 2D6 inhibitors of cytochrome P450. The human cytochrome P450 2D6 isoform plays a key role in the metabolism of many drugs in the preclinical drug discovery process. We have collected a data set from annotated public data and calculated physicochemical properties with chemoinformatics methods. On top of this data, we have built classifiers based on machine learning methods. Data sets with different class distributions lead to the effect that conventional machine learning methods are biased toward the larger class. To overcome this problem and to obtain sensitive but also accurate classifiers we combine machine learning and feature selection methods with techniques addressing the problem of unbalanced classification, such as oversampling and threshold moving. We have used our own implementation of a support vector machine algorithm as well as the maximum entropy method. Our feature selection is based on the unsupervised McCabe method. The classification results from our test set are compared structurally with compounds from the training set. We show that the applied algorithms enable the effective high throughput in silico classification of potential drug candidates.     

色谱技术在药物-血浆蛋白相互作用研究中的应用进展

小分子药物进入人体血液循环系统后与人血清白蛋白(HSA)、α1-酸性糖蛋白(AGP)等血浆蛋白存在广泛的相互作用,这些相互作用深刻影响药物在体内的分布及其与靶标蛋白的结合,进而影响药物效应的发挥.深入探究药物与血浆蛋白间的相互作用对于候选药物的成药性优化、新药研发、联合用药的风险评控等意义重大.而发展高效、灵敏、准确的...