Chu Qin

Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery

Knowledge and investigation of therapeutic targets (responsible for drug efficacy) and the targeted drugs facilitate target and drug discovery and validation. Therapeutic Target Database (TTD, http://bidd.nus.edu.sg/group/ttd/ttd.asp) has been developed to provide comprehensive information about efficacy targets and the corresponding approved, clinical trial and investigative drugs. Since its last update, major improvements and updates have been made to TTD. In addition to the significant increase of data content (from 1894 targets and 5028 drugs to 2025 targets and 17 816 drugs), we added target validation information (drug potency against target, effect against disease models and effect of target knockout, knockdown or genetic variations) for 932 targets, and 841 quantitative structure activity relationship models for active compounds of 228 chemical types against 121 targets. Moreover, we added the data from our previous drug studies including 3681 multi-target agents against 108 target pairs, 116 drug combinations with their synergistic, additive, antagonistic, potentiative or reductive mechanisms, 1427 natural product-derived approved, clinical trial and pre-clinical drugs and cross-links to the clinical trial information page in the ClinicalTrials.gov database for 770 clinical trial drugs. These updates are useful for facilitating target discovery and validation, drug lead discovery and optimization, and the development of multi-target drugs and drug combinations.

Clustered patterns of species origins of nature-derived drugs and clues for future bioprospecting.

Many drugs are nature derived. Low drug productivity has renewed interest in natural products as drug-discovery sources. Nature-derived drugs are composed of dozens of molecular scaffolds generated by specific secondary-metabolite gene clusters in selected species. It can be hypothesized that drug-like structures probably are distributed in selective groups of species. We compared the species origins of 939 approved and 369 clinical-trial drugs with those of 119 preclinical drugs and 19,721 bioactive natural products. In contrast to the scattered distribution of bioactive natural products, these drugs are clustered into 144 of the 6,763 known species families in nature, with 80% of the approved drugs and 67% of the clinical-trial drugs concentrated in 17 and 30 drug-prolific families, respectively. Four lines of evidence from historical drug data, 13,548 marine natural products, 767 medicinal plants, and 19,721 bioactive natural products suggest that drugs are derived mostly from preexisting drug-productive families. Drug-productive clusters expand slowly by conventional technologies. The lack of drugs outside drug-productive families is not necessarily the result of under-exploration or late exploration by conventional technologies. New technologies that explore cryptic gene clusters, pathways, interspecies crosstalk, and high-throughput fermentation enable the discovery of novel natural products. The potential impact of these technologies on drug productivity and on the distribution patterns of drug-productive families is yet to be revealed.

Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information

Extensive drug discovery efforts have yielded many approved and candidate drugs targeting various targets in different biological pathways. Several freely accessible databases provide the drug, target and drug-targeted pathway information for facilitating drug discovery efforts, but there is an insufficient coverage of the clinical trial drugs and the drug-targeted pathways. Here, we describe an update of the Therapeutic Target Database (TTD) previously featured in NAR. The updated contents include: (i) significantly increased coverage of the clinical trial targets and drugs (1.6 and 2.3 times of the previous release, respectively), (ii) cross-links of most TTD target and drug entries to the corresponding pathway entries of KEGG, MetaCyc/BioCyc, NetPath, PANTHER pathway, Pathway Interaction Database (PID), PathWhiz, Reactome and WikiPathways, (iii) the convenient access of the multiple targets and drugs cross-linked to each of these pathway entries and (iv) the recently emerged approved and investigative drugs. This update makes TTD a more useful resource to complement other databases for facilitating the drug discovery efforts. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp.

Therapeutic target database update 2014: a resource for targeted therapeutics

Here we describe an update of the Therapeutic Target Database (http://bidd.nus.edu.sg/group/ttd/ttd.asp) for better serving the bench-to-clinic communities and for enabling more convenient data access, processing and exchange. Extensive efforts from the research, industry, clinical, regulatory and management communities have been collectively directed at the discovery, investigation, application, monitoring and management of targeted therapeutics. Increasing efforts have been directed at the development of stratified and personalized medicines. These efforts may be facilitated by the knowledge of the efficacy targets and biomarkers of targeted therapeutics. Therefore, we added search tools for using the International Classification of Disease ICD-10-CM and ICD-9-CM codes to retrieve the target, biomarker and drug information (currently enabling the search of almost 900 targets, 1800 biomarkers and 6000 drugs related to 900 disease conditions). We added information of almost 1800 biomarkers for 300 disease conditions and 200 drug scaffolds for 700 drugs. We significantly expanded Therapeutic Target Database data contents to cover >2300 targets (388 successful and 461 clinical trial targets), 20 600 drugs (2003 approved and 3147 clinical trial drugs), 20 000 multitarget agents against almost 400 target-pairs and the activity data of 1400 agents against 300 cell lines.

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.

SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.

Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.

Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools☆

In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanisms, are well suited for predicting ADME and ADME regulatory properties. Recent efforts have been directed at the broadening of application scopes and the improvement of predictive performance with particular focuses on the coverage of ADME properties, and exploration of more diversified training data, appropriate molecular features, and consensus modeling. Moreover, several online machine learning ADME prediction servers have emerged. Here we review these progresses and discuss the performances, application prospects and challenges of exploring machine learning methods as useful tools in predicting ADME and ADME regulatory properties.

Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries.

Selective multi-target serotonin reuptake inhibitors enhance antidepressant efficacy. Their discovery can be facilitated by multiple methods, including in silico ones. In this study, we developed and tested an in silico method, combinatorial support vector machines (COMBI-SVMs), for virtual screening (VS) multi-target serotonin reuptake inhibitors of seven target pairs (serotonin transporter paired with noradrenaline transporter, H(3) receptor, 5-HT(1A) receptor, 5-HT(1B) receptor, 5-HT(2C) receptor, melanocortin 4 receptor and neurokinin 1 receptor respectively) from large compound libraries. COMBI-SVMs trained with 917-1951 individual target inhibitors correctly identified 22-83.3% (majority >31.1%) of the 6-216 dual inhibitors collected from literature as independent testing sets. COMBI-SVMs showed moderate to good target selectivity in misclassifying as dual inhibitors 2.2-29.8% (majority <15.4%) of the individual target inhibitors of the same target pair and 0.58-7.1% of the other 6 targets outside the target pair. COMBI-SVMs showed low dual inhibitor false hit rates (0.006-0.056%, 0.042-0.21%, 0.2-4%) in screening 17 million PubChem compounds, 168,000 MDDR compounds, and 7-8181 MDDR compounds similar to the dual inhibitors. Compared with similarity searching, k-NN and PNN methods, COMBI-SVM produced comparable dual inhibitor yields, similar target selectivity, and lower false hit rate in screening 168,000 MDDR compounds. The annotated classes of many COMBI-SVMs identified MDDR virtual hits correlate with the reported effects of their predicted targets. COMBI-SVM is potentially useful for searching selective multi-target agents without explicit knowledge of these agents.

Nature's contribution to today's pharmacopeia.

979 20S proteasome, mTOR, dipeptidyl peptidase 4, hepatitis C virus NS3/4A protease, and CFTR and CaCC channel, respectively). The 23.8% rate for these post-NP era NME leads is comparable to the rate of previously undrugged targets addressed by synthetic drugs (17.9% rate)4. Thus, NPs and NP derivatives may still offer promise for drugs addressing new targets. Existing NP drug scaffolds appear to be productive templates for deriving new drugs with 16/21 (76.2%) NME leads derived from 8 preexisting NP drug scaffold groups, including G protein–coupled receptor (GPCR)-binding peptide hormones (4 leads), macrolides (3 leads), nucleotides/nucleosides (3 leads), cephalosporins (2 leads), progestogens (1 lead), statins (1 lead), taxanes (1 lead) and xanthines (1 lead). The high percentage of new drugs derived from preexisting drug scaffolds is consistent with the report that drug-like bioactive compounds of specific target classes cluster in specific regions of chemical space1. The leads of four of the five drugs outside preexisting drug scaffold groups (carfilzomib, telaprevir, dabigatran and romidepsin) are peptides (epoxyketone oligopeptide, NS5A-5B substrate peptide, thrombin-interacting fragment of fibrinogen and depsipeptide cyclic structure, respectively). These further show the usefulness of peptides in deriving target-selective drugs against such difficult target classes as proteases5 and transferases6. Also consistent with reports that most nature-derived drugs are from preexisting drug-productive species families and clusters3, we found 18/21 (85.7%) NME leads are from preexisting drug-productive species families and 2/21 (9.5%) leads are from previously unexplored families in preexisting drug-productive clusters. Although their development started in the post-NP era, 16/21 (76.2%) leads have been initially discovered in the NP era by low-throughput screening (LTS) (8 leads), exploration of known target-binders (ETB), such as hormones/factors and ligands/ substrates (5 leads), focused library screening (FLT) of selected structural, target or Nature’s contribution to today’s pharmacopeia

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.