Because undesirable pharmacokinetics and toxicity of candidate compounds are the main reasons for the failure of drug development, it has been widely recognized that absorption, distribution, metabolism, excretion and toxicity (ADMET) should be evaluated as early as possible. In silico ADMET evaluation models have been developed as an additional tool to assist medicinal chemists in the design and optimization of leads. Here, we announced the release of ADMETlab 2.0, a completely redesigned version of the widely used AMDETlab web server for the predictions of pharmacokinetics and toxicity properties of chemicals, of which the supported ADMET-related endpoints are approximately twice the number of the endpoints in the previous version, including 17 physicochemical properties, 13 medicinal chemistry properties, 23 ADME properties, 27 toxicity endpoints and 8 toxicophore rules (751 substructures). A multi-task graph attention framework was employed to develop the robust and accurate models in ADMETlab 2.0. The batch computation module was provided in response to numerous requests from users, and the representation of the results was further optimized. The ADMETlab 2.0 server is freely available, without registration, at https://admetmesh.scbdd.com/.

Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at http://admet.scbdd.com/.Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0283-x) contains supplementary material, which is available to authorized users.

Surface functionalization of liposomes can play a key role in overcoming the current limitations of nanocarriers to treat solid tumors, i.e., biological barriers and physiological factors. The phospholipid vesicles (liposomes) containing anticancer agents produce fewer side effects than non-liposomal anticancer formulations, and can effectively target the solid tumors. This article reviews information about the strategies for targeting of liposomes to solid tumors along with the possible targets in cancer cells, i.e., extracellular and intracellular targets and targets in tumor microenvironment or vasculature. Targeting ligands for functionalization of liposomes with relevant surface engineering techniques have been described. Stimuli strategies for enhanced delivery of anticancer agents at requisite location using stimuli-responsive functionalized liposomes have been discussed. Recent approaches for enhanced delivery of anticancer agents at tumor site with relevant surface functionalization techniques have been reviewed. Finally, current challenges of functionalized liposomes and future perspective of smart functionalized liposomes have been discussed.

Aptamers are short DNA/RNA oligonucleotides capable of binding to target molecules with high affinity and specificity. The process of selecting an aptamer is called Systematic Evolution of Ligands by Exponential Enrichment (SELEX). Thanks to the inherit merits, aptamers have been used in a wide range of applications, including disease diagnosis, targeted delivery agents and therapeutic uses. To date, great achievements regarding the selection, modifications and application of aptamers have been made. However, few aptamer-based products have already successfully entered into clinical and industrial use. Besides, it is still a challenge to obtain aptamers with high affinity in a more efficient way. Thus, it is important to comprehensively review the current shortage and achievement of aptamer-related technology. In this review, we first present the limitations and notable advances of aptamer selection. Then, we compare the different methods used in the kinetic characterization of aptamers. We also discuss the impetus and developments of the clinical application of aptamers.

Antibody–drug conjugates (ADCs) comprised of a desirable monoclonal antibody, an active cytotoxic drug and an appropriate linker are considered to be an innovative therapeutic approach for targeted treatment of various types of tumors and cancers, enhancing the therapeutic parameter of the cytotoxic drug and reducing the possibility of systemic cytotoxicity. An appropriate linker between the antibody and the cytotoxic drug provides a specific bridge, and thus helps the antibody to selectively deliver the cytotoxic drug to tumor cells and accurately releases the cytotoxic drug at tumor sites. In addition to conjugation, the linkers maintain ADCs’ stability during the preparation and storage stages of the ADCs and during the systemic circulation period. The design of linkers for ADCs is a challenge in terms of extracellular stability and intracellular release, and intracellular circumstances, such as the acid environment, the reducing environment and cathepsin, are considered as the catalysts to activate the triggers for initiating the cleavage of ADCs. This review discusses the linkers used in the clinical and marketing stages for ADCs and details the fracture modes of the linkers for the further development of ADCs.

Autophagy dysfunction is a common feature in neurodegenerative disorders characterized by accumulation of toxic protein aggregates. Increasing evidence has demonstrated that activation of TFEB (transcription factor EB), a master regulator of autophagy and lysosomal biogenesis, can ameliorate neurotoxicity and rescue neurodegeneration in animal models. Currently known TFEB activators are mainly inhibitors of MTOR (mechanistic target of rapamycin [serine/threonine kinase]), which, as a master regulator of cell growth and metabolism, is involved in a wide range of biological functions. Thus, the identification of TFEB modulators acting without inhibiting the MTOR pathway would be preferred and probably less deleterious to cells. In this study, a synthesized curcumin derivative termed C1 is identified as a novel MTOR-independent activator of TFEB. Compound C1 specifically binds to TFEB at the N terminus and promotes TFEB nuclear translocation without inhibiting MTOR activity. By activating TFEB, C1 enhances autophagy and lysosome biogenesis in vitro and in vivo. Collectively, compound C1 is an orally effective activator of TFEB and is a potential therapeutic agent for the treatment of neurodegenerative diseases.

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

Systems pharmacology is an emerging field that integrates systems biology and pharmacology to advance the process of drug discovery, development and the understanding of therapeutic mechanisms. The aim of the present work is to highlight the role that the systems pharmacology plays across the traditional herbal medicines discipline, which is exemplified by a case study of botanical drugs applied in the treatment of depression. First, based on critically examined pharmacology and clinical knowledge, we propose a large-scale statistical analysis to evaluate the efficiency of herbs used in traditional medicines. Second, we focus on the exploration of the active ingredients and targets by carrying out complex structure-, omics- and network-based systematic investigations. Third, specific informatics methods are developed to infer drug-disease connections, with purpose to understand how drugs work on the specific targets and pathways. Finally, we propose a new systems pharmacology method, which is further applied to an integrated platform (Herbal medicine Systems Pharmacology) of blended herbal medicine and omics data sets, allowing for the systematization of current and traditional knowledge of herbal medicines and, importantly, for the application of this emerging body of knowledge to the development of new drugs for complex human diseases.