Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets

The re-emergence of natural products for drug discovery in the genomics era

Natural products have been a rich source of compounds for drug discovery. However, their use has diminished in the past two decades, in part because of technical barriers to screening natural products in high-throughput assays against molecular targets. Here, we review strategies for natural product screening that harness the recent technical advances that have reduced these barriers. We also assess the use of genomic and metabolomic approaches to augment traditional methods of studying natural products, and highlight recent examples of natural products in antimicrobial drug discovery and as inhibitors of protein-protein interactions. The growing appreciation of functional assays and phenotypic screens may further contribute to a revival of interest in natural products for drug discovery

Structural similarity assessment for drug sensitivity prediction in cancer

<p>Abstract</p> <p>Background</p> <p>The ability to predict drug sensitivity in cancer is one of the exciting promises of pharmacogenomic research. Several groups have demonstrated the ability to predict drug sensitivity by integrating chemo-sensitivity data and associated gene expression measurements from large anti-cancer drug screens such as NCI-60. The general approach is based on comparing gene expression measurements from sensitive and resistant cancer cell lines and deriving drug sensitivity profiles consisting of lists of genes whose expression is predictive of response to a drug. Importantly, it has been shown that such profiles are generic and can be applied to cancer cell lines that are not part of the anti-cancer screen. However, one limitation is that the profiles can not be generated for untested drugs (i.e., drugs that are not part of an anti-cancer drug screen). In this work, we propose using an existing drug sensitivity profile for drug A as a substitute for an untested drug B given high structural similarities between drugs A and B.</p> <p>Results</p> <p>We first show that structural similarity between pairs of compounds in the NCI-60 dataset highly correlates with the similarity between their activities across the cancer cell lines. This result shows that structurally similar drugs can be expected to have a similar effect on cancer cell lines. We next set out to test our hypothesis that we can use existing drug sensitivity profiles as substitute profiles for untested drugs. In a cross-validation experiment, we found that the use of substitute profiles is possible without a significant loss of prediction accuracy if the substitute profile was generated from a compound with high structural similarity to the untested compound.</p> <p>Conclusion</p> <p>Anti-cancer drug screens are a valuable resource for generating omics-based drug sensitivity profiles. We show that it is possible to extend the usefulness of existing screens to untested drugs by deriving substitute sensitivity profiles from structurally similar drugs part of the screen.</p

Digitizing mass spectrometry data to explore the chemical diversity and distribution of marine cyanobacteria and algae.

Natural product screening programs have uncovered molecules from diverse natural sources with various biological activities and unique structures. However, much is yet underexplored and additional information is hidden in these exceptional collections. We applied untargeted mass spectrometry approaches to capture the chemical space and dispersal patterns of metabolites from an in-house library of marine cyanobacterial and algal collections. Remarkably, 86% of the metabolomics signals detected were not found in other available datasets of similar nature, supporting the hypothesis that marine cyanobacteria and algae possess distinctive metabolomes. The data were plotted onto a world map representing eight major sampling sites, and revealed potential geographic locations with high chemical diversity. We demonstrate the use of these inventories as a tool to explore the diversity and distribution of natural products. Finally, we utilized this tool to guide the isolation of a new cyclic lipopeptide, yuvalamide A, from a marine cyanobacterium

Metabolomics-guided isolation of anti-trypanosomal metabolites from the endophytic fungus Lasiodiplodia theobromae

Fungal endophytes offer diverse and unique secondary metabolites, making these organisms potential sources of promising drug leads. The application of high-resolution-liquid chromatography mass spectrometry and nuclear magnetic resonance-based metabolomics to fungal endophytes is practical in terms of dereplication studies and the mining of bioactive compounds. In this paper, we report the application of metabolomics in parallel with anti-trypanosomal assays to determine the ideal conditions for the medium-scale fermentation of the endophyte Lasiodiplodia theobromae. The (1)H NMR comparison between the active versus inactive fractions identified several unique chemical fingerprints belonging to the active fractions. Furthermore, by integrating high-resolution-liquid chromatography mass spectrometry data with multivariate data analysis, such as orthogonal partial least squares-discriminant analysis (OPLS-DA) and the bioactivity results of the fractions of L. theobromae, the anti-trypanosomal agents were easily discerned. With available databases such as Antibase and Dictionary of Natural Products coupled to MZmine through in-house algorithms optimized in our laboratory, the predicted metabolites were readily identified prior to isolation. Fractionation was performed on the active fractions and three known compounds were isolated, namely, cladospirone B, desmethyl-lasiodiplodin, and R-(-)-mellein. Cladospirone B and desmethyl-lasiodiplodin were among the predicted compounds generated by the OPLS-DA S-plot, and these compounds exhibited good activity against Trypanosoma brucei brucei with minimum inhibitory concentrations of 17.8 µM and 22.5 µM, respectively

Public data and open source tools for multi-assay genomic investigation of disease

Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods

Disain ja modelleerimine HIV-1 pöördtranskriptaasi ja Malaaria ravimite väljatöötamise varajases faasis

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKäesolev uurimus keskendub kahele ohtlikule infektsioonhaigusele: inimese immuunpuudulikkuse viirus tüüp 1 (HIV-1) ja malaaria. Uue ravimi väljatöötamine algusest lõppuni on aega nõudev ning kulukas protsess, mis jaotatakse viieks etapiks: baas uurimistöö, põhi sihtmärgi ja baas ühendi(te) leidmine, eelkliiniline arendus, kliiniline arendus ja vajalike dokumentide esitamine ravimiametisse. Antud väitekirjas keskendutakse kahele esimesele etappidele, mida tuntakse ka varajase ravimiarenduse faasina. HIV-1 uurimisel oli kaks põhisuunda. Esmalt tuginedes eelnevalt tehtud virtuaalsõelumise tulemustele teostati uudsete s-triasiini derivaatide avastamine, disainimine, ja süntees, mille tulemused valideeriti eksperimentaalselt ning analüüsiti valk-ligand interaktsioonimudelite abil. Kõige tõhusam HIV-1 mitte-nukleosiidne pöördtranskriptaasi inhibiitor oli madala molekulmassiga, heade ligandi efektiivsust näitavate parameetritega, ja madala toksilisusega, võimaldades edasist modifitseerimist ja arendamist. Tehtud aktiivse keemilise struktuuri avastus motiveeris HIV-1 inhibiitorite keemilise struktuuriruumi laiemat uurimist, et kindlaks teha kas uudsed s-triasiinid moodustavad ka unikaalsed keemiliste ühendite grupi HIV-1 mitte-nukleosiidsete pöördtranskriptaasi inhibiitorite maastikul. Selle läbiviimiseks koostati, korrastati ja kureeriti ChEMBL-i andmebaasist saadud andmetest fokusseeritud andmeseeriad HIV-1 mitte-nukleosiidne ja nukelosiidsete pöördtranskriptaasi inhibiitorite jaoks, kuhu lisati ka avastatud s-triasiini derivaadid. Andmeseeriate struktuuride analüüs hierarhilise klassifitseerimise meetodil grupeeris ühendid keemiliste struktuuritüüpide (nn. vanematüüp) järgi. Selgus, et avastatud s-triasiinid moodustasid eraldiseisva struktuuritüübi grupi. Leitud struktuuritüüpe analüüsiti, lisades juurde ka vastavad mõõdetud seondumise afiinsuse tasakaalukonstandid (Ki). Selle analüüsi käigus toodi välja struktuurifragmendid, mis omavad olulist rolli afiinsuse ning stabiilsuse seisukohast. Lisaks võimaldasid struktuurselt mitmekesised ja unikaalsed HIV-1 mitte-nukleosiidne ja nukelosiidsete pöördtranskriptaasi inhibiitorite andmeseeriad esmakordselt arendada kirjeldavaid kvantitatiivsete struktuur-aktiivsus sõltuvuste prognoosmudeleid, mida on võimalik kasutada järgnevas uurimustöös uute aktiivsete keemiliste ühendite avastamisel. Selleks et leida uudseid malaaria ravimikanditaate koostati ja kureeriti süsteemselt andmebaas eksperimentaalsete anti-Plasmodium andmetega kasutades nii asutusesisesed, kui ka ChEMBL-i andmebaasis olevad andmed. Saadud andmete ulatusliku kureerimise, filtreerimise ning ühendamise tulemusena saadi kolmkümmend modelleeritavat andmeseeriat, millele koostati klassifitseerimise mudelid, eesmärgiga eristada aktiivsed ja mitteaktiivsed ühendid. Nendest seitsmeteistkümnele andmeseeriale saadi ennustusvõimelised nn. üksmeele (inglise keeles consensus) mudelid. Loodud mudelitega teostati ennustusi asutusesiseselt olemasolevatele curcuminoidide seerjale ning nende analoogidele, millest parima ennustusvõimega ühenditele teostati eksperimentaalne valideerimine in vitro katsetega, kus aktiivseks osutusid seitseteist ühendit, mida saab edasistes uuringutes täpsemini uurida. Samuti tehti kindaks, et arvutuslikult tuvastatud mitteaktiivsed ühendid jäid mitteaktiivseks ka eksperimentaalse valideerimise käigus, mis näitas süsteemselt kureeritud ja koostatud andmeseeriate ning prognoosmudelite jätkusuutlikust.Current thesis focused on study of two highly prevalent infections affecting many regions in the world: alaria and human immunodeficiency virus 1 (HIV-1). Developing a new drug from scratch is time consuming and costly process. This could be divided into five stages: basic research, lead target and lead compound(s) discovery, preclinical development, clinical development and filing to drug administration agency. Present thesis focused on basic research and lead compound discovery stages, i.e. to the early drug discovery. For the HIV-1, the focus was two-fold. First, based on the earlier multi-objective in silico screening, novel s-triazine derivatives were designed, discovered, synthesized, and findings where supported by the modelling tasks and validated with biological evaluation. The most potent compound is with small molecular size, potent ligand efficiencies, and measured low toxicity permitting further exploration and modifications. Second, the discovered new bioactive s-triazines motivated to analyse the chemical landscape of HIV-1 RT inhibitors. For this the dataset was systematically created and curated for HIV-1 NNRT (non-nucleoside reverse transcriptase) and NRT (nucleoside reverse transcriptase) inhibitors based on data from ChEMBL database. The hierarchical classification of scaffold structures of curated datasets revealed common chemical parent types for the compounds, hierarchy in chemical structures and showed that discovered s-triazines formed a separate structural parent type group. Each group of compounds related to the parent type was analysed and examined together with corresponding binding affinity equilibrium constants (Ki). The structural fragments affecting the potency and stability of compounds were highlighted. The structurally diverse datasets for the HIV-1 NNRTIs and NRTIs with binding affinity equilibrium constants allowed development of novel descriptive and predictive QSAR models for log Ki, that in future will help in design of new compounds. In order to discover new promising antimalarial compounds, the experimental anti-Plasmodium data was gathered and systematically curated from in-house experimental studies and expanded with data from ChEMBL database. Extracted data was carefully extensively curated, fused, filtered, and grouped into thirty data sets for the modelling. The consensus models for each dataset for the classification of active/inactive compounds were established and seventeen models with promising prediction ability were used in consensus predictions and in identifying the series of curcuminoids and their structural analogues as potential inhibitors for the malaria. The selection of compounds was experimentally validated, i.e. tested in vitro, revealing seventeen potentially active compounds for further testing and modifications. The validation showed that computationally predicted inactive compounds were also inactive in experiment, being additional proof for the quality of data curation and dataset assembly process forming the ground for the modelling task

Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation

BACKGROUND: During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue. METHODS: We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations. RESULTS: We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data. CONCLUSIONS: We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation