CMAUP database update 2024: extended functional and association information of useful plants for biomedical research.

Knowledge of the collective activities of individual plants together with the derived clinical effects and targeted disease associations is useful for plant-based biomedical research. To provide the information in complement to the established databases, we introduced a major update of CMAUP database, previously featured in NAR. This update includes (i) human transcriptomic changes overlapping with 1152 targets of 5765 individual plants, covering 74 diseases from 20 027 patient samples; (ii) clinical information for 185 individual plants in 691 clinical trials; (iii) drug development information for 4694 drug-producing plants with metabolites developed into approved or clinical trial drugs; (iv) plant and human disease associations (428 737 associations by target, 220 935 reversion of transcriptomic changes, 764 and 154121 associations by clinical trials of individual plants and plant ingredients); (v) the location of individual plants in the phylogenetic tree for navigating taxonomic neighbors, (vi) DNA barcodes of 3949 plants, (vii) predicted human oral bioavailability of plant ingredients by the established SwissADME and HobPre algorithm, (viii) 21-107% increase of CMAUP data over the previous version to cover 60 222 chemical ingredients, 7865 plants, 758 targets, 1399 diseases, 238 KEGG human pathways, 3013 gene ontologies and 1203 disease ontologies. CMAUP update version is freely accessible at https://bidd.group/CMAUP/index.html.

NPASS database update 2023: quantitative natural product activity and species source database for biomedical research.

Quantitative activity and species source data of natural products (NPs) are important for drug discovery, medicinal plant research, and microbial investigations. Activity values of NPs against specific targets are useful for discovering targeted therapeutic agents and investigating the mechanism of medicinal plants. Composition/concentration values of NPs in individual species facilitate the assessments and investigations of the therapeutic quality of herbs and phenotypes of microbes. Here, we describe an update of the NPASS natural product activity and species source database previously featured in NAR. This update includes: (i) new data of ∼95 000 records of the composition/concentration values of ∼1 490 NPs/NP clusters in ∼390 species, (ii) extended data of activity values of ∼43 200 NPs against ∼7 700 targets (∼40% and ∼32% increase, respectively), (iii) extended data of ∼31 600 species sources of ∼94 400 NPs (∼26% and ∼32% increase, respectively), (iv) new species types of ∼440 co-cultured microbes and ∼420 engineered microbes, (v) new data of ∼66 600 NPs without experimental activity values but with estimated activity profiles from the established chemical similarity tool Chemical Checker, (vi) new data of the computed drug-likeness properties and the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties for all NPs. NPASS update version is freely accessible at http://bidd.group/NPASS.

Chinese Medicine Meets Conventional Medicine in Targeting COVID-19 Pathophysiology, Complications and Comorbidities.

OBJECTIVE: To investigate how the National Health Commission of China (NHCC)-recommended Chinese medicines (CMs) modulate the major maladjustments of coronavirus disease 2019 (COVID-19), particularly the clinically observed complications and comorbidities. METHODS: By focusing on the potent targets in common with the conventional medicines, we investigated the mechanisms of 11 NHCC-recommended CMs in the modulation of the major COVID-19 pathophysiology (hyperinflammations, viral replication), complications (pain, headache) and comorbidities (hypertension, obesity, diabetes). The constituent herbs of these CMs and their chemical ingredients were from the Traditional Chinese Medicine Information Database. The experimentally-determined targets and the activity values of the chemical ingredients of these CMs were from the Natural Product Activity and Species Source Database. The approved and clinical trial drugs against these targets were searched from the Therapeutic Target Database and DrugBank Database. Pathways of the targets was obtained from Kyoto Encyclopedia of Genes and Genomes and additional literature search. RESULTS: Overall, 9 CMs modulated 6 targets discovered by the COVID-19 target discovery studies, 8 and 11 CMs modulated 8 and 6 targets of the approved or clinical trial drugs for the treatment of the major COVID-19 complications and comorbidities, respectively. CONCLUSION: The coordinated actions of each NHCC-recommended CM against a few targets of the major COVID-19 pathophysiology, complications and comorbidities, partly have common mechanisms with the conventional medicines.

CMAUP: a database of collective molecular activities of useful plants.

The beneficial effects of functionally useful plants (e.g. medicinal and food plants) arise from the multi-target activities of multiple ingredients of these plants. The knowledge of the collective molecular activities of these plants facilitates mechanistic studies and expanded applications. A number of databases provide information about the effects and targets of various plants and ingredients. More comprehensive information is needed for broader classes of plants and for the landscapes of individual plant's multiple targets, collective activities and regulated biological pathways, processes and diseases. We therefore developed a new database, Collective Molecular Activities of Useful Plants (CMAUP), to provide the collective landscapes of multiple targets (ChEMBL target classes) and activity levels (in 2D target-ingredient heatmap), and regulated gene ontologies (GO categories), biological pathways (KEGG categories) and diseases (ICD blocks) for 5645 plants (2567 medicinal, 170 food, 1567 edible, 3 agricultural and 119 garden plants) collected from or traditionally used in 153 countries and regions. These landscapes were derived from 47 645 plant ingredients active against 646 targets in 234 KEGG pathways associated with 2473 gene ontologies and 656 diseases. CMAUP (http://bidd2.nus.edu.sg/CMAUP/) is freely accessible and searchable by keywords, plant usage classes, species families, targets, KEGG pathways, gene ontologies, diseases (ICD code) and geographical locations.

Discovery of indolylpiperazinylpyrimidines with dual-target profiles at adenosine A2A and dopamine D2 receptors for Parkinson's disease treatment.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by progressive loss of dopaminergic neurons in the substantia nigra of the human brain, leading to depletion of dopamine production. Dopamine replacement therapy remains the mainstay for attenuation of PD symptoms. Nonetheless, the potential benefit of current pharmacotherapies is mostly limited by adverse side effects, such as drug-induced dyskinesia, motor fluctuations and psychosis. Non-dopaminergic receptors, such as human A2A adenosine receptors, have emerged as important therapeutic targets in potentiating therapeutic effects and reducing the unwanted side effects. In this study, new chemical entities targeting both human A2A adenosine receptor and dopamine D2 receptor were designed and evaluated. Two computational methods, namely support vector machine (SVM) models and Tanimoto similarity-based clustering analysis, were integrated for the identification of compounds containing indole-piperazine-pyrimidine (IPP) scaffold. Subsequent synthesis and testing resulted in compounds 5 and 6, which acted as human A2A adenosine receptor binders in the radioligand competition assay (Ki = 8.7-11.2 µM) as well as human dopamine D2 receptor binders in the artificial cell membrane assay (EC50 = 22.5-40.2 µM). Moreover, compound 5 showed improvement in movement and mitigation of the loss of dopaminergic neurons in Drosophila models of PD. Furthermore, in vitro toxicity studies on compounds 5 and 6 did not reveal any mutagenicity (up to 100 µM), hepatotoxicity (up to 30 µM) or cardiotoxicity (up to 30 µM).

Discovery of novel dual VEGFR2 and Src inhibitors using a multistep virtual screening approach.

AIM: Simultaneous inhibition of VEGFR2 and Src may enhance the efficacy of VEGFR2-targeted cancer therapeutics. Hence, development of dual inhibitors on VEGFR2 and Src can be a useful strategy for such treatments. MATERIALS & METHODS: A multistep virtual screening protocol, comprising ligand-based support vector machines method, drug-likeness rules filter and structure-based molecular docking, was developed and employed to identify dual inhibitors of VEGFR2 and Src from a large commercial chemical library. Kinase inhibitory assays and cell viability assays were then used for experimental validation. RESULTS: A set of compounds belonging to six different molecular scaffolds was identified and sent for biological evaluation. Compound 3c belonging to the 2-amino-3-cyanopyridine scaffold exhibited good antiproliferative effect and dual-target activities against VEGFR2 and Src. CONCLUSION: This study demonstrated the ability of the multistep virtual screening approach to identify novel multitarget agents.

A preclinical evaluation of a novel multikinase inhibitor, SKLB-329, as a therapeutic agent against hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a serious life-threatening malignant disease of liver. Molecular targeted therapies are considered a promising strategy for the treatment of HCC. Sorafenib is the first, and so far the only targeted drug approved by the US Food and Drug Administration (FDA) for clinical therapy of HCC. Despite being effective in some HCC patients, some demerits of sorafenib in the treatment of HCC, such as modest survival benefits, and drug resistance, have also been reported, which highlights the unmet medical need among patients with HCC. Here, we report a novel multikinase inhibitor discovered by us, SKLB-329, which potently inhibits angiogenesis-related kinases including VEGFR1/2/3, and FGFR2, and the Src kinase. SKLB-329 significantly inhibited endothelial cell growth, migration, invasion and tube formation. It showed potent anti-angiogenic activity in a transgenic zebrafish model. Moreover, SKLB-329 could efficiently restrain the proliferation of HCC cells through down-regulation of Src-mediated FAK and Stat3 activity. In vivo, oral administration of SKLB-329 considerably suppressed the tumor growth in HCC xenograft models (HepG2 and SMMC7721) in a dose-dependent manner. In all of the in vitro and in vivo assays of this investigation, sorafenib was used as a positive control, and in most assays SKLB-329 exhibited a higher potency compared with the positive control. In addition, SKLB-329 also bears favorable pharmacokinetic properties. Collectively, the results of preclinical studies presented here demonstrate that SKLB-329 is a promising drug candidate for HCC treatment.

What does it take to synergistically combine sub-potent natural products into drug-level potent combinations?

There have been renewed interests in natural products as drug discovery sources. In particular, natural product combinations have been extensively studied, clinically tested, and widely used in traditional, folk and alternative medicines. But opinions about their therapeutic efficacies vary from placebo to synergistic effects. The important questions are whether synergistic effects can sufficiently elevate therapeutic potencies to drug levels, and by what mechanisms and at what odds such combinations can be assembled. We studied these questions by analyzing literature-reported cell-based potencies of 190 approved anticancer and antimicrobial drugs, 1378 anticancer and antimicrobial natural products, 99 natural product extracts, 124 synergistic natural product combinations, and 122 molecular interaction profiles of the 19 natural product combinations with collective potency enhanced to drug level or by >10-fold. Most of the evaluated natural products and combinations are sub-potent to drugs. Sub-potent natural products can be assembled into combinations of drug level potency at low probabilities by distinguished multi-target modes modulating primary targets, their regulators and effectors, and intracellular bioavailability of the active natural products.

Drug discovery prospect from untapped species: indications from approved natural product drugs.

Due to extensive bioprospecting efforts of the past and technology factors, there have been questions about drug discovery prospect from untapped species. We analyzed recent trends of approved drugs derived from previously untapped species, which show no sign of untapped drug-productive species being near extinction and suggest high probability of deriving new drugs from new species in existing drug-productive species families and clusters. Case histories of recently approved drugs reveal useful strategies for deriving new drugs from the scaffolds and pharmacophores of the natural product leads of these untapped species. New technologies such as cryptic gene-cluster exploration may generate novel natural products with highly anticipated potential impact on drug discovery.

Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries.

Various in vitro and in-silico methods have been used for drug genotoxicity tests, which show limited genotoxicity (GT+) and non-genotoxicity (GT-) identification rates. New methods and combinatorial approaches have been explored for enhanced collective identification capability. The rates of in-silco methods may be further improved by significantly diversified training data enriched by the large number of recently reported GT+ and GT- compounds, but a major concern is the increased noise levels arising from high false-positive rates of in vitro data. In this work, we evaluated the effect of training data size and noise level on the performance of support vector machines (SVM) method known to tolerate high noise levels in training data. Two SVMs of different diversity/noise levels were developed and tested. H-SVM trained by higher diversity higher noise data (GT+ in any in vivo or in vitro test) outperforms L-SVM trained by lower noise lower diversity data (GT+ in in vivo or Ames test only). H-SVM trained by 4,763 GT+ compounds reported before 2008 and 8,232 GT- compounds excluding clinical trial drugs correctly identified 81.6% of the 38 GT+ compounds reported since 2008, predicted 83.1% of the 2,008 clinical trial drugs as GT-, and 23.96% of 168 K MDDR and 27.23% of 17.86M PubChem compounds as GT+. These are comparable to the 43.1-51.9% GT+ and 75-93% GT- rates of existing in-silico methods, 58.8% GT+ and 79% GT- rates of Ames method, and the estimated percentages of 23% in vivo and 31-33% in vitro GT+ compounds in the "universe of chemicals". There is a substantial level of agreement between H-SVM and L-SVM predicted GT+ and GT- MDDR compounds and the prediction from TOPKAT. SVM showed good potential in identifying GT+ compounds from large compound libraries based on higher diversity and higher noise training data.

In-silico approaches to multi-target drug discovery : computer aided multi-target drug design, multi-target virtual screening.

Multi-target drugs against selective multiple targets improve therapeutic efficacy, safety and resistance profiles by collective regulations of a primary therapeutic target together with compensatory elements and resistance activities. Efforts have been made to employ in-silico methods for facilitating the search and design of selective multi-target agents. These methods have shown promising potential in facilitating drug discovery directed at selective multiple targets.

Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties.

Multi-herb prescriptions of traditional Chinese medicine (TCM) often include special herb-pairs for mutual enhancement, assistance, and restraint. These TCM herb-pairs have been assembled and interpreted based on traditionally defined herbal properties (TCM-HPs) without knowledge of mechanism of their assumed synergy. While these mechanisms are yet to be determined, properties of TCM herb-pairs can be investigated to determine if they exhibit features consistent with their claimed unique synergistic combinations. We analyzed distribution patterns of TCM-HPs of TCM herb-pairs to detect signs indicative of possible synergy and used artificial intelligence (AI) methods to examine whether combination of their TCM-HPs are distinguishable from those of non-TCM herb-pairs assembled by random combinations and by modification of known TCM herb-pairs. Patterns of the majority of 394 known TCM herb-pairs were found to exhibit signs of herb-pair correlation. Three AI systems, trained and tested by using 394 TCM herb-pairs and 2470 non-TCM herb-pairs, correctly classified 72.1-87.9% of TCM herb-pairs and 91.6-97.6% of the non-TCM herb-pairs. The best AI system predicted 96.3% of the 27 known non-TCM herb-pairs and 99.7% of the other 1,065,100 possible herb-pairs as non-TCM herb-pairs. Our studies suggest that TCM-HPs of known TCM herb-pairs contain features distinguishable from those of non-TCM herb-pairs consistent with their claimed synergistic or modulating combinations.

Drug bioactivation, covalent binding to target proteins and toxicity relevance.

A number of therapeutic drugs with different structures and mechanisms of action have been reported to undergo metabolic activation by Phase I or Phase II drug-metabolizing enzymes. The bioactivation gives rise to reactive metabolites/intermediates, which readily confer covalent binding to various target proteins by nucleophilic substitution and/or Schiff's base mechanism. These drugs include analgesics (e.g., acetaminophen), antibacterial agents (e.g., sulfonamides and macrolide antibiotics), anticancer drugs (e.g., irinotecan), antiepileptic drugs (e.g., carbamazepine), anti-HIV agents (e.g., ritonavir), antipsychotics (e.g., clozapine), cardiovascular drugs (e.g., procainamide and hydralazine), immunosupressants (e.g., cyclosporine A), inhalational anesthetics (e.g., halothane), nonsteroidal anti-inflammatory drugs (NSAIDSs) (e.g., diclofenac), and steroids and their receptor modulators (e.g., estrogens and tamoxifen). Some herbal and dietary constituents are also bioactivated to reactive metabolites capable of binding covalently and inactivating cytochrome P450s (CYPs). A number of important target proteins of drugs have been identified by mass spectrometric techniques and proteomic approaches. The covalent binding and formation of drug-protein adducts are generally considered to be related to drug toxicity, and selective protein covalent binding by drug metabolites may lead to selective organ toxicity. However, the mechanisms involved in the protein adduct-induced toxicity are largely undefined, although it has been suggested that drug-protein adducts may cause toxicity either through impairing physiological functions of the modified proteins or through immune-mediated mechanisms. In addition, mechanism-based inhibition of CYPs may result in toxic drug-drug interactions. The clinical consequences of drug bioactivation and covalent binding to proteins are unpredictable, depending on many factors that are associated with the administered drugs and patients. Further studies using proteomic and genomic approaches with high throughput capacity are needed to identify the protein targets of reactive drug metabolites, and to elucidate the structure-activity relationships of drug's covalent binding to proteins and their clinical outcomes.