Comparison of controlled drugs and new psychoactive substances (NPS) regulations in East and Southeast Asia.

New psychoactive substances (NPS) are substances of abuse that easily evade existing controlled drug regulations. This study conducted a systematic review on controlled drug regulations and analyzed the numbers of new psychoactive substances (NPS) reported in six East and Southeast Asian countries in comparison to US and UK from 2009 to 2020. Generally, more NPS were reported in the US (551) and UK (400), compared to Japan (379), China (221), Singapore (142), South Korea (99), Malaysia (41), and Taiwan (35). Legislative mechanisms including the specific listing of individual substances, generic control of a family of substances, analogue control of similar substances, temporary bans of new substances were evaluated. In this review, countries that have adopted a combination of legislative mechanisms were able to identify higher numbers of NPS for regulatory control, such as the US, UK, Japan, Singapore, and South Korea. These findings can provide references to countries like Malaysia and Taiwan, to strengthen NPS-related regulations nationally. Countries in the East and Southeast Asian region should be encouraged to collaborate more closely and to implement additional legislative approaches most relevant to the regional NPS trends to bridge the regulatory gap and to prevent the spread of emerging NPS.

Taiwan Controlled Substances Database.

New psychoactive substances (NPS) have increasingly been illegally synthesized and used around the world in recent years. Due to the large volume and the variety of NPS, most do not have sufficient information about their addictive potential and harmful effects to human subjects. This makes it difficult to evaluate these potential substances of abuse. This study aims to build a database based on Taiwan's controlled substances, to provide quick structural and pharmacological feedback. Taiwan Controlled Substances Database (TCSD) includes the collection of controlled substances, relevant experimental and structural information, as well as computational features such as molecular fingerprints and descriptors. Two types of structural search were added: substructure search and topological fingerprint similarity search. A web framework was used to enhance accessibility and usability (https://cs2search.cmdm.tw).

Human Breathomics Database.

Breathomics is a special branch of metabolomics that quantifies volatile organic compounds (VOCs) from collected exhaled breath samples. Understanding how breath molecules are related to diseases, mechanisms and pathways identified from experimental analytical measurements is challenging due to the lack of an organized resource describing breath molecules, related references and biomedical information embedded in the literature. To provide breath VOCs, related references and biomedical information, we aim to organize a database composed of manually curated information and automatically extracted biomedical information. First, VOCs-related disease information was manually organized from 207 literature linked to 99 VOCs and known Medical Subject Headings (MeSH) terms. Then an automated text mining algorithm was used to extract biomedical information from this literature. In the end, the manually curated information and auto-extracted biomedical information was combined to form a breath molecule database-the Human Breathomics Database (HBDB). We first manually curated and organized disease information including MeSH term from 207 literatures associated with 99 VOCs. Then, an automatic pipeline of text mining approach was used to collect 2766 literatures and extract biomedical information from breath researches. We combined curated information with automatically extracted biomedical information to assemble a breath molecule database, the HBDB. The HBDB is a database that includes references, VOCs and diseases associated with human breathomics. Most of these VOCs were detected in human breath samples or exhaled breath condensate samples. So far, the database contains a total of 913 VOCs in relation to human exhaled breath researches reported in 2766 publications. The HBDB is the most comprehensive HBDB of VOCs in human exhaled breath to date. It is a useful and organized resource for researchers and clinicians to identify and further investigate potential biomarkers from the breath of patients. Database URL: https://hbdb.cmdm.tw.

Mouse transient receptor potential channel type 6 selectively regulates agonist-induced platelet function.

While changes in intracellular calcium levels is a central step in platelet activation and thrombus formation, the contribution and mechanism of receptor-operated calcium entry (ROCE) via transient receptor potential channels (TRPCs) in platelets remains poorly defined. In previous studies, we have shown that TRPC6 regulates hemostasis and thrombosis, in mice. In the present studies, we employed a knockout mouse model system to characterize the role of TRPC6 in ROCE and platelet activation. It was observed that the TRPC6 deletion (Trpc6 -/- ) platelets displayed impaired elevation of intracellular calcium, i.e., defective ROCE. Moreover, these platelets also exhibited defects in a host of functional responses, namely aggregation, granule secretion, and integrin αIIbß3. Interestingly, the aforementioned defects were specific to the thromboxane receptor (TPR), as no impaired responses were observed in response to ADP or the thrombin receptor-activating peptide 4 (TRAP4). The defect in ROCE in the Trpc6 -/- was also observed with 1-oleoyl-2-acetyl-sn-glycerol (OAG). Finally, our studies also revealed that TRPC6 regulates clot retraction. Taken together, our findings demonstrate that TRPC6 directly regulates TPR-dependent ROCE and platelet function. Thus, TRPC6 may serve as a novel target for the therapeutic management of thrombotic diseases.

Investigation of a Thromboxane A2 Receptor-Based Vaccine for Managing Thrombogenesis.

BACKGROUND: Despite the well-established role for the thromboxane A2 receptor (TPR) in the development of thrombotic disorders, none of the antagonists developed to date has been approved for clinical use. To this end, we have previously shown that an antibody targeted against TPR's ligand-binding domain inhibits platelet activation and thrombus formation, without exerting any effects on hemostasis. Thus, the goal of the present studies is to design a novel TPR-based vaccine, demonstrate its ability to trigger an immune response, and characterize its antiplatelet and antithrombotic activity. METHODS AND RESULTS: We used a mouse keyhole limpet hemocyanin/peptide-based vaccination approach rationalized over the TPR ligand-binding domain (ie, the C-terminus of the second extracellular loop). The biological activity of this vaccine was assessed in the context of platelets and thrombotic diseases, and using a host of in vitro and in vivo platelet function experiments. Our results revealed that the TPR C-terminus of the second extracellular loop vaccine, in mice: (1) triggered an immune response, which resulted in the development of a C-terminus of the second extracellular loop antibody; (2) did not affect expression of major platelet integrins (eg, glycoprotein IIb-IIIa); (3) selectively inhibited TPR-mediated platelet aggregation, platelet-leukocyte aggregation, integrin glycoprotein IIb-IIIa activation, as well as dense and α granule release; (4) significantly prolonged thrombus formation; and (5) did so without impairing physiological hemostasis. CONCLUSIONS: Collectively, our findings shed light on TPR's structural biological features, and demonstrate that the C-terminus of the second extracellular loop domain may define a new therapeutic target and a TPR vaccine-based approach that should have therapeutic applications.

An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming algorithm.

The identification of chemical structures in natural product mixtures is an important task in drug discovery but is still a challenging problem, as structural elucidation is a time-consuming process and is limited by the available mass spectra of known natural products. Computer-aided structure elucidation (CASE) strategies seek to automatically propose a list of possible chemical structures in mixtures by utilizing chromatographic and spectroscopic methods. However, current CASE tools still cannot automatically solve structures for experienced natural product chemists. Here, we formulated the structural elucidation of natural products in a mixture as a computational problem by extending a list of scaffolds using a weighted side chain list after analyzing a collection of 243,130 natural products and designed an efficient algorithm to precisely identify the chemical structures. The complexity of such a problem is NP-complete. A dynamic programming (DP) algorithm can solve this NP-complete problem in pseudo-polynomial time after converting floating point molecular weights into integers. However, the running time of the DP algorithm degrades exponentially as the precision of the mass spectrometry experiment grows. To ideally solve in polynomial time, we proposed a novel iterative DP algorithm that can quickly recognize the chemical structures of natural products. By utilizing this algorithm to elucidate the structures of four natural products that were experimentally and structurally determined, the algorithm can search the exact solutions, and the time performance was shown to be in polynomial time for average cases. The proposed method improved the speed of the structural elucidation of natural products and helped broaden the spectrum of available compounds that could be applied as new drug candidates. A web service built for structural elucidation studies is freely accessible via the following link ( http://csccp.cmdm.tw/ ).

NP-StructurePredictor: Prediction of Unknown Natural Products in Plant Mixtures.

Identification of the individual chemical constituents of a mixture, especially solutions extracted from medicinal plants, is a time-consuming task. The identification results are often limited by challenges such as the development of separation methods and the availability of known reference standards. A novel structure elucidation system, NP-StructurePredictor, is presented and used to accelerate the process of identifying chemical structures in a mixture based on a branch and bound algorithm combined with a large collection of natural product databases. NP-StructurePredictor requires only targeted molecular weights calculated from a list of m/z values from liquid chromatography-mass spectrometry (LC-MS) experiments as input information to predict the chemical structures of individual components matching the weights in a mixture. NP-StructurePredictor also provides the predicted structures with statistically calculated probabilities so that the most likely chemical structures of the natural products and their analogs can be proposed accordingly. Four data sets consisting of different Chinese herbs with mixtures containing known compounds were selected for validation studies, and all their components were correctly identified and highly predicted using NP-StructurePredictor. NP-StructurePredictor demonstrated its applicability for predicting the chemical structures of novel compounds by returning highly accurate results from four different validation case studies.

G.A.M.E.: GPU-accelerated mixture elucidator.

GPU acceleration is useful in solving complex chemical information problems. Identifying unknown structures from the mass spectra of natural product mixtures has been a desirable yet unresolved issue in metabolomics. However, this elucidation process has been hampered by complex experimental data and the inability of instruments to completely separate different compounds. Fortunately, with current high-resolution mass spectrometry, one feasible strategy is to define this problem as extending a scaffold database with sidechains of different probabilities to match the high-resolution mass obtained from a high-resolution mass spectrum. By introducing a dynamic programming (DP) algorithm, it is possible to solve this NP-complete problem in pseudo-polynomial time. However, the running time of the DP algorithm grows by orders of magnitude as the number of mass decimal digits increases, thus limiting the boost in structural prediction capabilities. By harnessing the heavily parallel architecture of modern GPUs, we designed a "compute unified device architecture" (CUDA)-based GPU-accelerated mixture elucidator (G.A.M.E.) that considerably improves the performance of the DP, allowing up to five decimal digits for input mass data. As exemplified by four testing datasets with verified constitutions from natural products, G.A.M.E. allows for efficient and automatic structural elucidation of unknown mixtures for practical procedures. Graphical abstract .

Web Server for Peak Detection, Baseline Correction, and Alignment in Two-Dimensional Gas Chromatography Mass Spectrometry-Based Metabolomics Data.

Two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS) is superior for chromatographic separation and provides great sensitivity for complex biological fluid analysis in metabolomics. However, GC×GC/TOF-MS data processing is currently limited to vendor software and typically requires several preprocessing steps. In this work, we implement a web-based platform, which we call GC2MS, to facilitate the application of recent advances in GC×GC/TOF-MS, especially for metabolomics studies. The core processing workflow of GC2MS consists of blob/peak detection, baseline correction, and blob alignment. GC2MS treats GC×GC/TOF-MS data as pictures and clusters the pixels as blobs according to the brightness of each pixel to generate a blob table. GC2MS then aligns the blobs of two GC×GC/TOF-MS data sets according to their distance and similarity. The blob distance and similarity are the Euclidean distance of the first and second retention times of two blobs and the Pearson's correlation coefficient of the two mass spectra, respectively. GC2MS also directly corrects the raw data baseline. The analytical performance of GC2MS was evaluated using GC×GC/TOF-MS data sets of Angelica sinensis compounds acquired under different experimental conditions and of human plasma samples. The results show that GC2MS is an easy-to-use tool for detecting peaks and correcting baselines, and GC2MS is able to align GC×GC/TOF-MS data sets acquired under different experimental conditions. GC2MS is freely accessible at http://gc2ms.web.cmdm.tw .

Mutagenicity in a Molecule: Identification of Core Structural Features of Mutagenicity Using a Scaffold Analysis.

With advances in the development and application of Ames mutagenicity in silico prediction tools, the International Conference on Harmonisation (ICH) has amended its M7 guideline to reflect the use of such prediction models for the detection of mutagenic activity in early drug safety evaluation processes. Since current Ames mutagenicity prediction tools only focus on functional group alerts or side chain modifications of an analog series, these tools are unable to identify mutagenicity derived from core structures or specific scaffolds of a compound. In this study, a large collection of 6512 compounds are used to perform scaffold tree analysis. By relating different scaffolds on constructed scaffold trees with Ames mutagenicity, four major and one minor novel mutagenic groups of scaffold are identified. The recognized mutagenic groups of scaffold can serve as a guide for medicinal chemists to prevent the development of potentially mutagenic therapeutic agents in early drug design or development phases, by modifying the core structures of mutagenic compounds to form non-mutagenic compounds. In addition, five series of substructures are provided as recommendations, for direct modification of potentially mutagenic scaffolds to decrease associated mutagenic activities.

Synthesis and inhibitory effects of novel pyrimido-pyrrolo-quinoxalinedione analogues targeting nucleoproteins of influenza A virus H1N1.

The influenza nucleoprotein (NP) is a single-strand RNA-binding protein and the core of the influenza ribonucleoprotein (RNP) particle that serves many critical functions for influenza replication. NP has been considered as a promising anti-influenza target. A new class of anti-influenza compounds, nucleozin and analogues were reported recently in several laboratories to inhibit the synthesis of influenza macromolecules and prevent the cytoplasmic trafficking of the influenza RNP. In this study, pyrimido-pyrrolo-quinoxalinedione (PPQ) analogues as a new class of novel anti-influenza agents are reported. Compound PPQ-581 was identified as a potential anti-influenza lead with EC50 value of 1 µM for preventing virus-induced cytopathic effects. PPQ produces similar anti-influenza effects as nucleozin does in influenza-infected cells. Treatment with PPQ at the beginning of H1N1 infection inhibited viral protein synthesis, while treatment at later times blocked the RNP nuclear export and the appearance of cytoplasmic RNP aggregation. PPQ resistant H1N1 (WSN) viruses were isolated and found to have a NPS377G mutation. Recombinant WSN carrying the S377G NP is resistant to PPQ in anti-influenza and RNA polymerase assays. The WSN virus with the NPS377G mutation also is devoid of the PPQ-mediated RNP nuclear retention and cytoplasmic aggregation. The NPS377G expressing WSN virus is not resistant to the reported NP inhibitors nucleozin. Similarly, the nucleozin resistant WSN viruses are not resistant to PPQ, suggesting that PPQ targets a different site from the nucleozin-binding site. Our results also suggest that NP can be targeted through various binding sites to interrupt the crucial RNP trafficking, resulting in influenza replication inhibition.

Rule-Based Prediction Models of Cytochrome P450 Inhibition.

Hepatotoxicity, drug-induced liver injury, and competitive Cytochrome P-450 (CYP) isozyme binding are serious problems associated with drug use. It would be favorable to avoid or to understand potential CYP inhibition at the developmental stages. However, current in silico CYP prediction models or available public prediction servers can provide only yes/no classification results for just one or a few CYP enzymes. In this study, we utilized a rule-based C5.0 algorithm with different descriptors, including PaDEL, Mold(2), and PubChem fingerprints, to construct rule-based inhibition prediction models for five major CYP enzymes-CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4-that account for 90% of drug oxidation or hydrolysis. We also developed a rational sampling algorithm for the selection of compounds in the training data set, to enhance the performance of these CYP prediction models. The optimized models include several improved features. First, the final models significantly outperformed all of the currently available models. Second, the final models can also be used for rapid virtual screening of a large set of compounds due to their ruleset-based nature. Moreover, such rule-based prediction models can provide rulesets for structural features related to the five major CYP enzymes. The five most significant rules for CYP inhibition were identified for each CYP enzymes and discussed. An example was chosen for each of the five CYP enzymes to demonstrate how rule-based models can be used to gain insights into structural features that correspond with CYP inhibitions. A newer version of the freely accessible CYP prediction server, CypRules, is presented here as a result of the aforementioned improvements.

The regulator of G-protein signaling 18 regulates platelet aggregation, hemostasis and thrombosis.

Regulators of G protein signaling (RGS) proteins are known to interact with and negatively regulate/turn-off G protein activation. RGS18 is identified as an R4 subfamily member of this family with specific expression in hematopoietic progenitors, myeloerythroid cells, megakaryocytes and platelets. Studies focused on understanding its function in platelet biology have been limited, in part, due to lack of pharmacological inhibitors. Thus, the present study investigated the function of RGS18 in platelets, using the RGS18 knockout mouse model (RGS18(-/-)). We identified phenotypic differences between RGS18(-/-) and wild-type (WT) mice, and show that RGS18 plays a significant role in hemostasis and thrombosis. Hence, RGS18 deficiency markedly shortened bleeding as well as occlusion times (in vivo). Furthermore, RGS18(-/-) platelets displayed hyper-responsiveness with regards to agonist induced aggregation (in vitro). This gain of function phenotype may serve as the mechanism or explain, at least in part, the enhanced hemostasis and thrombosis phenotype observed in the RGS18 deletion mice. Collectively, our findings provide valuable insight and highlight a critical and direct role for RGS18 in modulating platelet function.

CypRules: a rule-based P450 inhibition prediction server.

UNLABELLED: Cytochrome P450 (CYPs) are the major enzymes involved in drug metabolism and bioactivation. Inhibition models were constructed for five of the most popular enzymes from the CYP superfamily in human liver. The five enzymes chosen for this study, namely CYP1A2, CYP2D6, CYP2C19, CYP2C9 and CYP3A4, account for 90% of the xenobiotic and drug metabolism in human body. CYP enzymes can be inhibited or induced by various drugs or chemical compounds. In this work, a rule-based CYP inhibition prediction online server, CypRules, was created based on predictive models generated by the rule-based C5.0 algorithm. CypRules can predict and provide structural rulesets for CYP inhibition for each compound uploaded to the server. Capable of fast execution performance, it can be used for virtual high-throughput screening (VHTS) of a large set of testing compounds. AVAILABILITY AND IMPLEMENTATION: CypRules is freely accessible at http://cyprules.cmdm.tw/ and models, descriptor and program files for all compounds are publically available at http://cyprules.cmdm.tw/sources/sources.rar.

Rule-based classification models of molecular autofluorescence.

Fluorescence-based detection has been commonly used in high-throughput screening (HTS) assays. Autofluorescent compounds, which can emit light in the absence of artificial fluorescent markers, often interfere with the detection of fluorophores and result in false positive signals in these assays. This interference presents a major issue in fluorescence-based screening techniques. In an effort to reduce the time and cost that will be spent on prescreening of autofluorescent compounds, in silico autofluorescence prediction models were developed for selected fluorescence-based assays in this study. Five prediction models were developed based on the respective fluorophores used in these HTS assays, which absorb and emit light at specific wavelengths (excitation/emission): Alexa Fluor 350 (A350) (340 nm/450 nm), 7-amino-4-trifluoromethyl-coumarin (AFC) (405 nm/520 nm), Alexa Fluor 488 (A488) (480 nm/540 nm), Rhodamine (547 nm/598 nm), and Texas Red (547 nm/618 nm). The C5.0 rule-based classification algorithm and PubChem 2D chemical structure fingerprints were used to develop prediction models. To optimize the accuracies of these prediction models despite the highly imbalanced ratio of fluorescent versus nonfluorescent compounds presented in the collected data sets, oversampling and undersampling strategies were applied. The average final accuracy achieved for the training set was 97%, and that for the testing set was 92%. In addition, five external data sets were used to further validate the models. Ultimately, 14 representative structural features (or rules) were determined to efficiently predict autofluorescence in data sets containing both fluorescent and nonfluorescent compounds. Several cases were illustrated in this study to demonstrate the applicability of these rules.

Characterization of a novel function-blocking antibody targeted against the platelet P2Y1 receptor.

OBJECTIVE: Platelet hyperactivity is associated with vascular disease and contributes to the genesis of thrombotic disorders. ADP plays an important role in platelet activation and activates platelets through 2 G-protein-coupled receptors, the Gq-coupled P2Y1 receptor (P2Y1R), and the Gi-coupled P2Y12 receptor. Although the involvement of the P2Y1R in thrombogenesis is well established, there are no antagonists that are currently available for clinical use. APPROACH AND RESULTS: Our goal is to determine whether a novel antibody targeting the ligand-binding domain, ie, second extracellular loop (EL2) of the P2Y1R (EL2Ab) could inhibit platelet function and protect against thrombogenesis. Our results revealed that the EL2Ab does indeed inhibit ADP-induced platelet aggregation, in a dose-dependent manner. Furthermore, EL2Ab was found to inhibit integrin GPIIb-IIIa activation, dense and α granule secretion, and phosphatidylserine exposure. These inhibitory effects translated into protection against thrombus formation, as evident by a prolonged time for occlusion in a FeCl3-induced thrombosis model, but this was accompanied by a prolonged tail bleeding time. We also observed a dose-dependent displacement of the radiolabeled P2Y1R antagonist [(3)H]MRS2500 from its ligand-binding site by EL2Ab. CONCLUSIONS: Collectively, our findings demonstrate that EL2Ab binds to and exhibits P2Y1R-dependent function-blocking activity in the context of platelets. These results add further evidence for a role of the P2Y1R in thrombosis and validate the concept that targeting it is a relevant alternative or complement to current antiplatelet strategies.

The antidepressant 5-HT2A receptor antagonists pizotifen and cyproheptadine inhibit serotonin-enhanced platelet function.

There is considerable interest in defining new agents or targets for antithrombotic purposes. The 5-HT2A receptor is a G-protein coupled receptor (GPCR) expressed on many cell types, and a known therapeutic target for many disease states. This serotonin receptor is also known to regulate platelet function. Thus, in our FDA-approved drug repurposing efforts, we investigated the antiplatelet activity of cyproheptadine and pizotifen, two antidepressant 5-HT2A Receptor antagonists. Our results revealed that cyproheptadine and pizotifen reversed serotonin-enhanced ADP-induced platelet aggregation in vitro and ex vivo. And the inhibitory effects of these two agents were found to be similar to that of EMD 281014, a 5-HT2A Receptor antagonist under development. In separate experiments, our studies revealed that these 5-HT2A receptor antagonists have the capacity to reduce serotonin-enhanced ADP-induced elevation in intracellular calcium levels and tyrosine phosphorylation. Using flow cytometry, we also observed that cyproheptadine, pizotifen, and EMD 281014 inhibited serotonin-enhanced ADP-induced phosphatidylserine (PS) exposure, P-selectin expression, and glycoprotein IIb-IIIa activation. Furthermore, using a carotid artery thrombosis model, these agents prolonged the time for thrombotic occlusion in mice in vivo. Finally, the tail-bleeding time was investigated to assess the effect of cyproheptadine and pizotifen on hemostasis. Our findings indicated prolonged bleeding time in both cyproheptadine- and pizotifen-treated mice. Notably, the increases in occlusion and bleeding times associated with these two agents were comparable to that of EMD 281014, and to clopidogrel, a commonly used antiplatelet drug, again, in a fashion comparable to clopidogrel and EMD 281014. Collectively, our data indicate that the antidepressant 5-HT2A antagonists, cyproheptadine and pizotifen do exert antiplatelet and thromboprotective effects, but similar to clopidogrel and EMD 281014, their use may interfere with normal hemostasis.