Biomedical subjects

Tudor I Oprea

Publications and source records attributed to Tudor I Oprea.

At least 19 recordsLinked to original sources

PubMed2017

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Biological Ontologies↗

PubMed2006

Virtual and biomolecular screening converge on a selective agonist for GPR30.

Estrogen is a hormone critical in the development, normal physiology and pathophysiology of numerous human tissues. The effects of estrogen have traditionally been solely ascribed to estrogen receptor alpha (ERalpha) and more recently ERbeta, members of the soluble, nuclear ligand-activated family of transcription factors. We have recently shown that the seven-transmembrane G protein-coupled receptor GPR30 binds estrogen with high affinity and resides in the endoplasmic reticulum, where it activates multiple intracellular signaling pathways. To differentiate between the functions of ERalpha or ERbeta and GPR30, we used a combination of virtual and biomolecular screening to isolate compounds that selectively bind to GPR30. Here we describe the identification of the first GPR30-specific agonist, G-1 (1), capable of activating GPR30 in a complex environment of classical and new estrogen receptors. The development of compounds specific to estrogen receptor family members provides the opportunity to increase our understanding of these receptors and their contribution to estrogen biology.

Animals↗

PubMed2006

Oncology exploration: charting cancer medicinal chemistry space.

Approaches for the experimental determination of protein-ligand molecular interactions are reliant on the quality of the compounds being tested. The application of large, randomly designed combinatorial libraries has given way to the creation of more-focused 'drug-like' libraries. Prior to synthesis, we wish to screen the potential compounds to remove undesired chemical moieties and to be within a required range of physiochemical properties. We have used a principal-component analysis (PCA) computational approach to analyze the 3D descriptor space of active and non-active (hit-like) cancer medicinal chemistry compounds. We define hit-like those molecules passing the unmodified OpenEye FILTER program. Our analysis indicates that these compounds occupy quite different regions in space. Cancer-active compounds exist in a much greater volume of space than generic hit-like space and most of them fail the commonly applied filters for orally bioavailable drugs. This is of great significance when designing orally bioavailable cancer target drugs.

Antineoplastic Agents↗

PubMed2006

Chemical database preparation for compound acquisition or virtual screening.

Virtual and high-throughput screening are time-saving techniques that have been successfully applied to identify novel chemotypes in biologically active molecules. Both methods require the ability to aptly handle large numbers of chemicals prior to an experiment or acquisition. We describe a step-by-step preparation procedure for handling large collections of existing or virtual compounds prior to virtual screening or acquisition.

Algorithms↗

PubMed2005

Descriptor collision and confusion: toward the design of descriptors to mask chemical structures.

We examined "descriptor collision" for several chemical fingerprint systems (MDL 320, Daylight, SMDL), and for a 2D-based descriptor set. For large databases (ChemNavigator and WOMBAT), the smallest collision rate remains around 5%. We systematically increase the "descriptor collision" rate (here termed "descriptor confusion"), in order to design a set of "descriptors to mask chemical structures", DMCS. If effective, a DMCS system would not allow third parties to determine the original chemical structures used to derive the DMCS set (i.e., reverse engineering). Using SMDL keys, the "confusion" rate is increased to 45.6% by eliminating those keys that have a low frequency of occurrence in WOMBAT structures. We applied an automated PLS engine, WB-PLS [Olah et al., J. Comput. Aided Mol. Des., 18 (2004) 437], to 1277 series of structures from 948 targets in WOMBAT, in order to validate the biological relevance of the SMDL descriptors as a potential DMCS set. The "reduced set" of SMDL descriptors has a small loss of modeling power (around 20%) compared to the initial descriptor set, while the collision rate is significantly increased. These results indicate that the development of an effective DMCS is possible. If well documented, DMCS systems would encourage private sector data release (e.g., related to water solubility) and directly benefit public sector science.

Chemistry, Pharmaceutical↗

PubMed2005

Surrogate data--a secure way to share corporate data.

The privacy of chemical structure is of paramount importance for the industrial sector, in particular for the pharmaceutical industry. At the same time, companies handle large amounts of physico-chemical and biological data that could be shared in order to improve our molecular understanding of pharmacokinetic and toxicological properties, which could lead to improved predictivity and shorten the development time for drugs, in particular in the early phases of drug discovery. The current study provides some theoretical limits on the information required to produce reverse engineering of molecules from generated descriptors and demonstrates that the information content of molecules can be as low as less than one bit per atom. Thus theoretically just one descriptor can be used to completely disclose the molecular structure. Instead of sharing descriptors, we propose to share surrogate data. The sharing of surrogate data is nothing else but sharing of reliably predicted molecules. The use of surrogate data can provide the same information as the original set. We consider the practical application of this idea to predict lipophilicity of chemical compounds and we demonstrate that surrogate and real (original) data provides similar prediction ability. Thus, our proposed strategy makes it possible not only to share descriptors, but also complete collections of surrogate molecules without the danger of disclosing the underlying molecular structures.

Chemical Engineering↗

PubMed2005

Integration of virtual screening with high-throughput flow cytometry to identify novel small molecule formylpeptide receptor antagonists.

The formylpeptide receptor (FPR) family of G-protein-coupled receptors contributes to the localization and activation of tissue-damaging leukocytes at sites of chronic inflammation. We developed a FPR homology model and pharmacophore (based on the bovine rhodopsin crystal structure and known FPR ligands, respectively) for in silico screening of approximately 480,000 drug-like small molecules. A subset of 4324 compounds that matched the pharmacophore was then physically screened with the HyperCyt flow cytometry platform in high-throughput, no-wash assays that directly measure human FPR binding, with samples (each approximately 2500 cells in 2 microl) analyzed at 40/min. From 52 confirmed hits (1.2% hit rate), we identified 30 potential lead compounds (inhibition constant, Ki= 1-32 microM) representing nine distinct chemical families. Four compounds in one family were weak partial agonists. All others were antagonists. This virtual screening approach improved the physical screening hit rate by 12-fold (versus 0.1% hit-rate in a random compound collection), providing an efficient process for identifying small molecule antagonists.

Amino Acid Sequence↗

PubMed2005

Post-high-throughput screening analysis: an empirical compound prioritization scheme.

An empirical scheme to evaluate and prioritize screening hits from high-throughput screening (HTS) is proposed. Negative scores are given when chemotypes found in the HTS hits are present in annotated databases such as MDDR and WOMBAT or for testing positive in toxicity-related experiments reported in TOXNET. Positive scores were given for higher measured biological activities, for testing negative in toxicity-related literature, and for good overlap when profiled against drug-related properties. Particular emphasis is placed on estimating aqueous solubility to prioritize in vivo experiments. This empirical scheme is given as an illustration to assist the decision-making process in selecting chemotypes and individual compounds for further experimentation, when confronted with multiple hits from high-throughput experiments. The decision-making process is discussed for a set of G-protein coupled receptor antagonists and validated on a literature example for dihydrofolate reductase inhibition.

Automation↗

PubMed2005

High-throughput screening with HyperCyt flow cytometry to detect small molecule formylpeptide receptor ligands.

High-throughput flow cytometry (HTFC), enabled by faster automated sample processing, represents a promising high- content approach for compound library screening. HyperCyt is a recently developed automated HTFC analysis system by which cell samples are rapidly aspirated from microplate wells and delivered to the flow cytometer. The formylpeptide receptor (FPR) family of G protein-coupled receptors contributes to the localization and activation of tissue-damaging leukocytes at sites of chronic inflammation. Here, the authors describe development and application of an HTFC screening approach to detect potential anti-inflammatory compounds that block ligand binding to FPR. Using a homogeneous no-wash assay, samples were routinely processed at 1.5 s/well (approximately 2500 cells analyzed/sample), allowing a 96-well plate to be processed in less than 2.5 min. Assay sensitivity and accuracy were validated by detection of a previously documented active compound with relatively low FPR affinity (sulfinpyrazone, inhibition constant [K(i)]=14 microM) from among a collection of 880 compounds in the Prestwick Chemical Library. The HyperCyt system was therefore demonstrated to be a robust, sensitive, and highly quantitative method with which to screen lead compound libraries in a 96-well format.

Flow Cytometry↗

PubMed2004

Pursuing the leadlikeness concept in pharmaceutical research.

Lipinski and others, through concepts such as drug-likeness, re-focussed drug discovery back to the principles of medicinal chemistry in the high-throughput era as key to reducing attrition. More recently, the need to go further in defining what makes a good lead has been recognised with the concept of leadlikeness. Leadlikeness implies cut-off values in the physico-chemical profile of chemical libraries such that they have reduced complexity (e.g. MW below <400) and other more restricted properties. We examine these concepts in the context of Virtual (theoretically possible), Tangible (chemically feasible) and Real (physically available) worlds of molecules. In a thought experiment, we take the HTS concept to the extreme: screening an estimated 60 million 'Global Collection' on 5000 targets and realising that perhaps millions of drug candidates might be found that could not possibly be handled in reality. Sampling of the Virtual and Tangible worlds is therefore a necessity. We show that the world of Reals is significantly under-sampled as the MW of compounds increases. This supports the design and screening of 'reduced complexity' (leadlike) compound libraries, preferably with synthetic handles available for rapid chemical iteration and detected as interesting by careful screening or biophysical assays.

Carboxylic Acids↗

PubMed2004

Integrating virtual screening in lead discovery.

Target- and ligand-based virtual screening have emerged as resource-saving techniques that have been successfully applied to identify novel chemotypes in biologically active molecules. Eight confirmed virtual screening hits have recently been described and are discussed in this review, with focus on the workflow. These are then evaluated in the light of pharmacokinetics prediction (e.g. Caco-2 permeability, cytochrome P450 inhibition and hERG binding). We anticipate problems for five of these hits (e.g. cardiac toxicity), which warrant further experiments. Future challenges include dynamic tautomer/protonation treatment for both ligands and targets and improved pre- and post- virtual screening filters.

Animals↗

PubMed2004

Strategies for compound selection.

In-house pharmaceutical collections are no longer sufficient for sampling chemical spaces. As novel bioactive chemotypes are successfully identified by virtual and high -throughput screening, the ability to rapidly sift through large numbers of chemicals prior to acquisition or experiment is required. Strategies for compound selection include some of the following steps: 1.) database assembly ('in silico' inventory); 2a.) structural integrity verification (keep unique structures only); 2b.) limited exploration of alternative chemical representations for the uniques (stereoisomers, tautomers, ionization states); 3.) property and structural filtering (remove unwanted structures); 4.) 3D-structure generation (for virtual screening or 3D-based similarity); 5a.) clustering or statistical design for selection; 5b.) similarity-based selection (if bioactives are known); 5c.) receptor-based selection (if target binding site is known); 6.) add a random subset to the final list.

Cluster Analysis↗

PubMed2002

Chemical space navigation in lead discovery.

The number of new chemical entities has remained rather constant (averaging 37 per year) in the past decade, despite the multiple-fold increase in the number of compounds that are being made and tested. Chemical space requires novel methods that can handle the increasing number of potentially accessible molecules. Neighborhood behavior, as an approach to similarity, and chemical property space navigation are some of the recent advances that are discussed, in the context of lead discovery and appropriate pharmacokinetic properties.

Drug Design↗

PubMed2002

Current trends in lead discovery: are we looking for the appropriate properties?

The new drug discovery paradigm is based on high-throughput technologies, both with respect to synthesis and screening. The progression HTS hits --> lead series --> candidate drug --> marketed drug appears to indicate that the probability of reaching launched status is one in a million. This has shifted the focus from good quality candidate drugs to good quality leads. We examined the current trends in lead discovery by comparing MW (molecular weight), LogP (octanol/water partition coefficient, estimated by Kowwin) and LogSw (intrinsic water solubility, estimated by Wskowwin) for the following categories: 62 leads and 75 drugs; compounds in the development phase (I, II, III and launched), as indexed in MDDR; and compounds indexed in medicinal chemistry journals, categorized according to their biological activity. Comparing the distribution of the above properties, the 62 lead structures show the lowest median with respect to MW (smaller) and LogP (less hydrophobic), and the highest median with respect to LogSw (more soluble). By contrast, over 50% of the medicinal chemistry compounds with activities above 1 nanomolar have MW > 425, LogP > 4.25 and LogSw < -4.75, indicating that the reported active compounds are larger, more hydrophobic and lesssoluble when compared to time-tested quality leads. In the MDDR set, a progressive constraint to reduce MW and LogP, and to increase LogSw, can be observed when examining trends in the developmental sequence: phase I, II, III and launched drugs. These trends indicate that other properties besides binding affinity, e.g., solubility and hydrophobicity, need to be considered when choosing the appropriate leads.

Chemistry, Pharmaceutical↗

PubMedDate not supplied

An automated PLS search for biologically relevant QSAR descriptors.

An automated PLS engine, WB-PLS, was applied to 1632 QSAR series with at least 25 compounds per series extracted from WOMBAT (WOrld of Molecular BioAcTivity). WB-PLS extracts a single Y variable per series, as well as pre-computed X variables from a table. The table contained 2D descriptors, the drug-like MDL 320 keys as implemented in the Mesa A&C Fingerprint module, and in-house generated topological-pharmacophore SMARTS counts and fingerprints. Each descriptor type was treated as a block, with or without scaling. Cross-validation, variable importance on projections (VIP) above 0.8 and q2 > or = 0.3 were applied for model significance. Among cross-validation methods, leave-one-in-seven-out (CV7) is a better measure of model significance, compared to leave-one-out (measuring redundancy) and leave-half-out (too restrictive). SMARTS counts overlap with 2D descriptors (having a more quantitative nature), whereas MDL keys overlap with in-house fingerprints (both are more qualitative). The SMARTS counts is the most effective descriptor system, when compared to the other three. At the individual level, size-related descriptors and topological indices (in the 2D property space), and branched SMARTS, aromatic and ring atom types and halogens are found to be most relevant according to the VIP criterion.

Automation↗

PubMedDate not supplied

Pharmacokinetically based mapping device for chemical space navigation.

ChemGPS, the chemical global positioning system, is a tool that combines rules (equivalent to dimensions) and objects (chemical structures) to provide a consistent chemical space map (Oprea, T. I.; Gottfries, J. J. Comb. Chem. 2001, 3, 157-166.). Rules included, initially, general properties such as size, lipophilicity, and hydrogen bond capacity, while objects include "satellites", intentionally placed outside the druglike space, as well as "core" objects, mostly orally available drugs. ChemGPS molecules (objects) were used in conjunction with the VolSurf (http://www.moldiscovery.com) descriptors (rules), which are relevant for ADME (absorption, distribution, metabolism, and excretion) properties. The combination of ChemGPS and VolSurf, GPSVS, was investigated with respect to the biopharmaceutics classification system, which is recommended by the Food and Drug Administration (FDA) (http://www.fda.gov/cder/OPS/BCS_guidance.htm), in particular with respect to permeability and solubility. The first GPSVS principal component correlates, with no further training, to passive transcellular permeability, as illustrated for the Caco-2, ghost erythrocyte, and blood-brain barrier datasets, respectively. The second GPSVS principal component correlates, without prior training, to solubility, as shown for the octanol-water partition and intrinsic solubility datasets, respectively. Although derived from principal component analysis, the two property axes rotate and form an angle of approximately 43 degrees, thus being no longer orthogonal. GPSVS can be used to map the chemical space with respect to permeability and solubility, as recommended by FDA's biopharmaceutics classification system.

Journal Article↗

PubMedDate not supplied

MTD-PLS: A PLS-based variant of the MTD method. 2. Mapping ligand-receptor interactions. Enzymatic acetic acid esters hydrolysis.

The PLS variant of the MTD method (T. I. Oprea et al., SAR QSAR Environ. Res. 2001, 12, 75-92) was applied to a series of 25 acetylcholinesterase hydrolysis substrates. Statistically significant MTD-PLS models (q(2) between 0.7 and 0.8) are in agreement with previous MTD models, with the advantage that local contributions are understood beyond the occupancy/nonoccupancy interpretation in MTD. A "chemically intuitive" approach further forces MTD-PLS coefficients to assume only negative (or zero) values for fragmental volume descriptors and positive (or zero) values for fragmental hydrophobicity descriptors. This further separates the various kinds of local interactions at each vertex of the MTD hypermolecule, making this method suitable for medicinal chemistry synthesis planning.

Acetates↗

PubMedDate not supplied

MTD-PLS: a PLS variant of the minimal topologic difference method. III. Mapping interactions between estradiol derivatives and the alpha estrogenic receptor.

A homogeneous collection of 45 estrogen agonist derivatives with relative binding affinities measured to the estrogen receptor from Ratus norvegicus was used. The quantitative structure-activity relationships were derived using an improved minimal topologic difference (MTD) method in a partial least-squares (PLS) variant. The spatially assigned analysis of fragment properties can provide receptor site maps, within the limits of the existing series. A steric misfit was found for the steroidal position 2; benefic hydrophobic and van der Waals (enhanced by high polarizability) interactions were found for the 17alpha-CH=CH-X group. MTD-PLS mapping results are confirmed by the experimentally derived estradiol-estrogen receptor binding site contacts (based on X-ray crystallography). Our results suggest that this MTD-PLS method can yield useful results for interactions with receptors of unknown 3D structure and, generally, for the steric rigidity of receptor sites.

Algorithms↗