In silico prediction of chemical subcellular localization via multi-classification methods

Live-Cell Imaging of MicroRNA Expression via Photoinduced Electron Transfer Controlled by Catalytic Hairpin Assembly

MicroRNAs (miRNAs) serve as emerging biomarkers for a range of diseases, and their quantitative analysis draws increasing attention. Yet, current invasive methods limit continuous tracking within living cells. To overcome this, a nonenzymatic DNA-based nanoprobe is developed for dynamic, noninvasive miRNA tracking via live-cell imaging. This probe features a unique hairpin DNA structure with five guanines that act as internal quenchers, suppressing fluorescence from an attached fluorophore via photoinduced electron transfer. Target miRNA initiates toehold-mediated strand displacement, restoring, and amplifying the fluorescence signal. Additionally, by introducing a single mismatch to the hairpin DNA, the nanoprobe's sensitivity is significantly enhanced, lowering the detection limit to about 60 pM without compromising specificity. To optimize intracellular delivery for prolonged monitoring, the nanoprobe is encapsulated within multilamellar lipid nanovesicles, fluorescently labeled for dual-wavelength ratiometric analysis. The proposed nanoprobe demonstrates a significant advance in live-cell miRNA detection, promising enhanced in situ analysis for a better understanding of miRNAs' pathophysiological function.

Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition

Cytochrome P450 (CYP) is a family of enzymes that are responsible for about 75% of all metabolic reactions. Among them, CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 participate in the metabolism of most drugs and mediate many adverse drug reactions. Therefore, it is necessary to estimate the chemical inhibition of Cytochrome P450 enzymes in drug discovery and the food industry. In the past few decades, many computational models have been reported, and some provided good performance. However, there are still several issues that should be resolved for these models, such as single isoform, models with unbalanced performance, lack of structural characteristics analysis, and poor availability. In the present study, the deep learning models based on python using the Keras framework and TensorFlow were developed for the chemical inhibition of each CYP isoform. These models were established based on a large data set containing 85715 compounds extracted from the PubChem bioassay database. On external validation, the models provided good AUC values with 0.97, 0.94, 0.94, 0.96, and 0.94 for CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, respectively. The models can be freely accessed on the Web server named CYPi-DNNpredictor (cypi.sapredictor.cn), and the codes for the model were made open source in the Supporting Information. In addition, we also analyzed the structural characteristics of chemicals with CYP450 inhibition and detected the structural alerts (SAs), which should be responsible for the inhibition. The SAs were also made available online, named CYPi-SAdetector (cypisa.sapredictor.cn). The models can be used as a powerful tool for the prediction of CYP450 inhibitors, and the SAs should provide useful information for the mechanisms of Cytochrome P450 inhibition.

Chimeric Drug Design with a Noncharged Carrier for Mitochondrial Delivery

Recently, it was proposed that the thiophene ring is capable of promoting mitochondrial accumulation when linked to fluorescent markers. As a noncharged group, thiophene presents several advantages from a synthetic point of view, making it easier to incorporate such a side moiety into different molecules. Herein, we confirm the general applicability of the thiophene group as a mitochondrial carrier for drugs and fluorescent markers based on a new concept of nonprotonable, noncharged transporter. We implemented this concept in a medicinal chemistry application by developing an antitumor, metabolic chimeric drug based on the pyruvate dehydrogenase kinase (PDHK) inhibitor dichloroacetate (DCA). The promising features of the thiophene moiety as a noncharged carrier for targeting mitochondria may represent a starting point for the design of new metabolism-targeting drugs.

Insights into pesticide toxicity against aquatic organism: QSTR models on Daphnia Magna

The toxicities of agrochemicals to non-target aquatic organisms are key items in chemical ecological risk assessment. However, it is still an urgent need to develop new tools to assess the agrochemical aquatic toxicity efficiently and accurately. In this work, QSTR studies were performed on a data set containing 639 diverse pesticides with measured EC₅₀ toxicity against Daphnia magna, by using five machine learning methods combined with seven fingerprints and a set of molecular descriptors. The imbalance problem of the data set was successfully solved by clustering analysis. The top-10 QSTR models displayed greater predicative abilities than ECOSAR. The optimal model, Ext-SVM, showed the best performance in 10-fold cross validation (Q_high=0.807, Q_moderate=0.806, Q_low=0.755, Q_total=0.794), and also in the test set verification (Q_high=0.865, Q_moderate=0.783, Q_low=0.931, Q_total=0.848). The relevance of the key physical-chemical properties with the toxicity was also investigated, in which the MW, a_np, logP(o/w), GCUT_SLOGP_1, chilv and SMR_VSA7 values displayed positive correlation with Daphnia magna toxicity, whereas the logS and a_don showed negative correlation. The robust QSTR models provided efficient tools for assessing agrochemical aquatic toxicity, and the revealed different physical-chemical properties between the high and low toxic compounds might be useful in the discovery and design of low aquatic toxic pesticides.

Subcellular drug distribution: mechanisms and roles in drug efficacy, toxicity, resistance, and targeted delivery

After administration, drug molecules usually enter target cells to access their intracellular targets. In eukaryotic cells, these targets are often located in organelles, including the nucleus, endoplasmic reticulum, mitochondria, lysosomes, Golgi apparatus, and peroxisomes. Each organelle type possesses unique biological features. For example, mitochondria possess a negative transmembrane potential, while lysosomes have an intraluminal delta pH. Other properties are common to several organelle types, such as the presence of ATP-binding cassette (ABC) or solute carrier-type (SLC) transporters that sequester or pump out xenobiotic drugs. Studies on subcellular drug distribution are critical to understand the efficacy and toxicity of drugs along with the body's resistance to them and to potentially offer hints for targeted subcellular drug delivery. This review summarizes the results of studies from 1990 to 2017 that examined the subcellular distribution of small molecular drugs. We hope this review will aid in the understanding of drug distribution within cells.

In silico estimation of chemical aquatic toxicity on crustaceans using chemical category methods

With industrial development and eventual commercial use, environmental chemicals through accidental spills and effluents appear more frequently in aquatic ecosystems and may produce an enormous effect on water, soil, wildlife and human health. Therefore, aquatic toxicity becomes an increasingly important endpoint in the evaluation of the environmental impact of chemicals. In this study, based on ECOTOX database, a large data set containing 824 diverse compounds with experimental 48 h EC50 values on crustaceans was compiled. A series of in silico models were then developed using six machine learning methods combined with seven types of molecular fingerprints. Performance of these models was measured by an external validation set, involving 246 molecules. The best model proposed is MACCS fingerprint and SVM algorithm with high accuracy of 0.87 for external validation set. Additionally, we proposed five structural alerts identified by information gain and substructure frequency analysis for mechanistic interpretation. The models and structural alerts can provide critical information and useful tools for a priori evaluation of chemical aquatic toxicity in environmental hazard assessment.

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

Insights into the Molecular Basis of the Acute Contact Toxicity of Diverse Organic Chemicals in the Honey Bee

Use of chemical pollutants, including pesticides and other industrial chemicals, has resulted in significant risks to the whole ecosystem. Therefore, ecological risk assessment of chemicals is vital and necessary. Since the honey bee (Apis mellifera) is probably among the most exposed species to the polluting chemicals, we focused on the in silico estimation of honey bee toxicity (HBT) of chemicals and the analysis of the relevance of chemical HBT and several key physical-chemical properties and structural characteristics. A total of 40 classification models were developed by combination of five machine learning methods along with seven kinds of fingerprints and a set of molecular descriptors. After 5-fold cross validation and external validation, several models showed good predictive power. The relevance of 12 key physical-chemical properties and chemical HBT was also investigated. Five properties, including AlogP, logD, molecular weight (MW), molecular surface area (MSA), and the number of rotatable bonds (nRTB), indicated positive correlation coefficients with HBT, while molecular solubility (logS) and the number of hydrogen bond donors (nHBD) indicated negative correlation coefficients. Finally, seven privileged substructures responsible for chemical HBT were identified from KRFP and SubFP fingerprints. The results of this study should provide critical information and useful tools for chemical HBT estimation in environmental risk assessment.

In silico prediction of chronic toxicity with chemical category approaches

Chemical chronic toxicity, referring to the toxic effect of a chemical following long-term or repeated sub lethal exposures, is an important toxicological end point in drug design and environmental risk assessment.