Biomedical subjects

John W Davies

Publications and source records attributed to John W Davies.

14 recordsLinked to original sources

PubMed2006

Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors.

Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities. In practice, effectively choosing a similarity metric is case dependent. This work compares both 2D and 3D chemical descriptors as tools for predicting the biological targets of ligand probes, on the basis of their similarity to reference molecules in a 46,000 compound, biologically annotated chemical database. Overall, we found that the 2D methods employed here outperform the 3D (88% vs 67% success) in correct target prediction. However, the 3D descriptors proved superior in cases of probes with low structural similarity to other compounds in the database (singletons). Additionally, the 3D method (FEPOPS) shows promise for providing pharmacophoric alignment of the small molecules' chemical features consistent with those seen in experimental ligand/ receptor complexes. These results suggest that querying annotated chemical databases with a systematic combination of both 2D and 3D descriptors will prove more effective than employing single methods.

Adenosine Triphosphate↗

PubMed2006

Streamlining lead discovery by aligning in silico and high-throughput screening.

Lead discovery in the pharmaceutical environment is largely an industrial-scale process in which it is typical to screen 1-5 million compounds in a matter of weeks using High Throughput Screening (HTS). This process is a very costly endeavor. Typically a HTS campaign of 1 million compounds will cost anywhere from $500000 to $1000000. There is consequently a great deal of pressure to maximize the return on investment by finding fast and more effective ways to screen. A panacea that has emerged over the past few years to help address this issue is in silico screening. In silico screening is now incorporated in all areas of lead discovery; from target identification and library design, to hit analysis and compound profiling. However, as lead discovery has evolved over the past few years, so has the role of in silico screening.

Bayes Theorem↗

PubMed2004

A 3D similarity method for scaffold hopping from known drugs or natural ligands to new chemotypes.

A primary goal of 3D similarity searching is to find compounds with similar bioactivity to a reference ligand but with different chemotypes, i.e., "scaffold hopping". However, an adequate description of chemical structures in 3D conformational space is difficult due to the high-dimensionality of the problem. We present an automated method that simplifies flexible 3D chemical descriptions in which clustering techniques traditionally used in data mining are exploited to create "fuzzy" molecular representations called FEPOPS (feature point pharmacophores). The representations can be used for flexible 3D similarity searching given one or more active compounds without a priori knowledge of bioactive conformations or pharmacophores. We demonstrate that similarity searching with FEPOPS significantly enriches for actives taken from in-house high-throughput screening datasets and from MDDR activity classes COX-2, 5-HT3A, and HIV-RT, while also scaffold or ring-system hopping to new chemical frameworks. Further, inhibitors of target proteins (dopamine 2 and retinoic acid receptor) are recalled by FEPOPS by scaffold hopping from their associated endogenous ligands (dopamine and retinoic acid). Importantly, the method excels in comparison to commonly used 2D similarity methods (DAYLIGHT, MACCS, Pipeline Pilot fingerprints) and a commercial 3D method (Pharmacophore Distance Triplets) at finding novel scaffold classes given a single query molecule.

Cyclooxygenase 2↗

PubMed2004

Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results.

We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.

Algorithms↗

PubMed2004

Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results.

The technology underpinning high-throughput docking (HTD) has developed over the past few years to where it has become a vital tool in modern drug discovery. Although the performance of various docking algorithms is adequate, the ability to accurately and consistently rank compounds using a scoring function remains problematic. We show that by employing a simple machine learning method (naïve Bayes) it is possible to significantly overcome this deficiency. Compounds from the Available Chemical Directory (ACD), along with known active compounds, were docked into two protein targets using three software packages. In cases where HTD alone was able to show some enrichment, the application of naïve Bayes was able to improve upon the enrichment. The application of this methodology to enrich HTD results can be carried out without a priori knowledge of the activity of compounds and results in superior enrichment of known actives compared to the use of scoring methods alone.

Bayes Theorem↗

PubMed2004

Targeting angiogenesis with a conjugate of HPMA copolymer and TNP-470.

Angiogenesis is crucial for tumor growth. Angiogenesis inhibitors, such as O-(chloracetyl-carbamoyl) fumagillol (TNP-470), are thus emerging as a new class of anticancer drugs. In clinical trials, TNP-470 slowed tumor growth in patients with metastatic cancer. However, at higher doses necessary for tumor regression, many patients experienced neurotoxicity. We therefore synthesized and characterized a water-soluble conjugate of N-(2-hydroxypropyl)methacrylamide (HPMA) copolymer, Gly-Phe-Leu-Gly linker and TNP-470. This conjugate accumulated selectively in tumor vessels because of the enhanced permeability and retention (EPR) effect. HPMA copolymer-TNP-470 substantially enhanced and prolonged the activity of TNP-470 in vivo in tumor and hepatectomy models. Polymer conjugation prevented TNP-470 from crossing the blood-brain barrier (BBB) and decreased its accumulation in normal organs, thereby avoiding drug-related toxicities. Treatment with TNP-470 caused weight loss and neurotoxic effects in mice, whereas treatment with the conjugate did not. This new approach for targeting angiogenesis inhibitors specifically to the tumor vasculature may provide a new strategy for the rational design of cancer therapies.

Angiogenesis Inhibitors↗

PubMed2004

Enrichment of extremely noisy high-throughput screening data using a naïve Bayes classifier.

The noise level of a high-throughput screening (HTS) experiment depends on various factors such as the quality and robustness of the assay itself and the quality of the robotic platform. Screening of compound mixtures is noisier than screening single compounds per well. A classification model based on naïve Bayes (NB) may be used to enrich such data. The authors studied the ability of the NB classifier to prioritize noisy primary HTS data of compound mixtures (5 compounds/well) in 4 campaigns in which the percentage of noise presumed to be inactive compounds ranged between 81% and 91%. The top 10% of the compounds suggested by the classifier captured between 26% and 45% of the active compounds. These results are reasonable and useful, considering the poor quality of the training set and the short computing time that is needed to build and deploy the classifier.

Bayes Theorem↗

PubMed2003

Modification of 1-substituents in the 2-azabicyclo[2.1.1]hexane ring system; approaches to potential nicotinic acetylcholine receptor ligands from 2,4-methanoproline derivatives.

Successful nucleophilic substitution at a methylene attached to the bridgehead (1-) position of the 2-azabicyclo[2.1.1]hexane ring system opens the way to construction of novel derivatives having a wider range of functional groups attached to the 1-position via a methylene "spacer" (including the beta-amino acid homologue of 2,4-methanoproline) and provides access to epibatidine analogues containing heterocyclic substituents and also to further homologation at the 1-position. Displacements with loss of a nucleofuge (e.g., loss of mesylate anion from the 1-mesyloxymethyl derivative) require thermal activation but proceed without the rearrangement initially anticipated in such a strained bicyclic ring system. A novel tricyclic carbamate intermediate 17 has been isolated; nucleophilic attack on 17 leads directly to the isolation of N-deprotected substitution products (with concomitant decarboxylation).

Aza Compounds↗

PubMed2002

Nonpeptide bradykinin B2 receptor antagonists: conversion of rodent-selective bradyzide analogues into potent, orally-active human bradykinin B2 receptor antagonists.

The 1-(2-nitrophenyl)thiosemicarbazide (TSC) derivative, (S)-1-[4-(4-benzhydrylthiosemicarbazido)-3-nitrobenzenesulfonyl]pyrrolidine-2-carboxylic acid [2-[(2-dimethylaminoethyl)methylamino]ethyl]amide (bradyzide; (S)-4), was recently disclosed as a novel, potent, orally active nonpeptide bradykinin (BK) B2 receptor antagonist. The compound inhibited the specific binding of [3H]BK to NG108-15 cell membrane preparations (rodent neuroblastoma-glioma) expressing B2 receptors with a K(i) of 0.5 +/- 0.2 nM. Compound (S)-4 also demonstrated oral efficacy against Freund's complete adjuvant (FCA)-induced mechanical hyperalgesia in rats with an ED50 value of 0.84 micromol/kg. After we optimized the terminal binding determinants projecting from the TSC framework, we found that it was possible to replace the potentially toxicophoric nitro and divalent sulfur moieties with only a 15-fold loss in binding affinity ((S)-14a). However, bradyzide and its congeners were found to have much lower affinities for cloned human B2 receptors, expressed in Cos-7 cells. The hitherto synthesized TSC series was screened against the human B2 receptor, and the dibenzosuberane (DBS) pharmacophore emerged as the key structural requirement for potency. Incorporation of this group resulted in a series of derivatives ((S)-14d,e and 19b-d) with K(i) ranges of 10.7-176 nM in NG108-15 cells (expressing the rodent B2 receptor) and 0.79-253 nM in Cos-7 cells (expressing the human B2 receptor). There was no evidence of agonist activity with any of the nonpeptides in any of the cell lines tested. In vivo, oral administration of compound 19c reversed FCA-induced and turpentine-induced mechanical hyperalgesia in rodents with ED50 values of 0.027 and 0.32 micromol/kg, respectively. The selectivity profiles of compounds (S)-14f and (S)-14g were also assessed to determine the conformational and/or steric preferences of the double-ring arrangement. The affinity of (S)-14 g for the human B2 receptor suggested that it may be a hydrophobic interaction with the ethane bridge of the DBS moiety that accounts for the increased potency of compounds (S)-14d,e and 19b,c at this receptor, by favoring a binding mode inaccessible to the unsubstituted diphenylmethyl derivative, (S)-4.

Administration, Oral↗

PubMedDate not supplied

PDEPT: polymer-directed enzyme prodrug therapy. 2. HPMA copolymer-beta-lactamase and HPMA copolymer-C-Dox as a model combination.

Polymer-directed enzyme prodrug therapy (PDEPT) is a novel two-step antitumor approach that uses a combination of a polymeric prodrug and polymer-enzyme conjugate to generate a cytotoxic drug rapidly and selectively at the tumor site. Previously we have shown that N-(2-hydroxypropyl)methacrylamide (HPMA) copolymer-bound cathepsin B can release doxorubicin intratumorally from an HPMA copolymer conjugate PK1. Here we describe for the first time the synthesis and biological characterization of a PDEPT model combination that uses an HPMA-copolymer-methacryloyl-glycine-glycine-cephalosporin-doxorubicin (HPMA-co-MA-GG-C-Dox) as the macromolecular prodrug and an HPMA copolymer conjugate containing the nonmammalian enzyme beta-lactamase (HPMA-co-MA-GG-beta-L) as the activating component. HPMA-co-MA-GG-C-Dox had a molecular weight of approximately 31 600 Da and a C-Dox content of 5.85 wt %. Whereas free beta-L has a molecular weight of 45 kDa, the HPMA-co-MA-GG-beta-L conjugate had a molecular weight in the range of 75-150 kDa, and following purification no free enzyme was detectable. Against the cephalosporin C or HPMA-co-MA-GG-C-Dox substrates, the HPMA-co-MA-GG-beta-L conjugate retained 70% and 80% of its activity, respectively. In vivo (125)I-labeled HPMA-co-MA-GG-beta-L showed prolonged plasma concentration and greater tumor targeting than (125)I-labeled beta-L due to the enhanced permeability and retention (EPR) effect. Moreover, administration of HPMA-co-MA-GG-C-Dox iv to mice bearing sc B16F10 melanoma followed after 5 h by HPMA-co-MA-GG-beta-L led to release of free Dox. The PDEPT combination caused a significant decrease in tumor growth (T/C = 132%) whereas neither free Dox nor HPMA-co-MA-GG-C-Dox alone displayed activity. The PDEPT combination displayed no toxicity at the doses used, so further evaluation of this approach to establish the maximum tolerated dose (MTD) is recommended.

Animals↗

PubMedDate not supplied

Application of machine learning to improve the results of high-throughput docking against the HIV-1 protease.

We have previously reported that the application of a Laplacian-modified naive Bayesian (NB) classifier may be used to improve the ranking of known inhibitors from a random database of compounds after High-Throughput Docking (HTD). The method relies upon the frequency of substructural features among the active and inactive compounds from 2D fingerprint information of the compounds. Here we present an investigation of the role of extended connectivity fingerprints in training the NB classifier against HTD studies on the HIV-1 protease using three docking programs: Glide, FlexX, and GOLD. The results show that the performance of the NB classifier is due to the presence of a large number of features common to the set of known active compounds rather than a single structural or substructural scaffold. We demonstrate that the Laplacian-modified naive Bayesian classifier trained with data from high-throughput docking is superior at identifying active compounds from a target database in comparison to conventional two-dimensional substructure search methods alone.

Algorithms↗

PubMedDate not supplied

Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers.

High-throughput screening (HTS) plays a pivotal role in lead discovery for the pharmaceutical industry. In tandem, cheminformatics approaches are employed to increase the probability of the identification of novel biologically active compounds by mining the HTS data. HTS data is notoriously noisy, and therefore, the selection of the optimal data mining method is important for the success of such an analysis. Here, we describe a retrospective analysis of four HTS data sets using three mining approaches: Laplacian-modified naive Bayes, recursive partitioning, and support vector machine (SVM) classifiers with increasing stochastic noise in the form of false positives and false negatives. All three of the data mining methods at hand tolerated increasing levels of false positives even when the ratio of misclassified compounds to true active compounds was 5:1 in the training set. False negatives in the ratio of 1:1 were tolerated as well. SVM outperformed the other two methods in capturing active compounds and scaffolds in the top 1%. A Murcko scaffold analysis could explain the differences in enrichments among the four data sets. This study demonstrates that data mining methods can add a true value to the screen even when the data is contaminated with a high level of stochastic noise.

Journal Article↗

PubMedDate not supplied

Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases.

Target identification is a critical step following the discovery of small molecules that elicit a biological phenotype. The present work seeks to provide an in silico correlate of experimental target fishing technologies in order to rapidly fish out potential targets for compounds on the basis of chemical structure alone. A multiple-category Laplacian-modified naïve Bayesian model was trained on extended-connectivity fingerprints of compounds from 964 target classes in the WOMBAT (World Of Molecular BioAcTivity) chemogenomics database. The model was employed to predict the top three most likely protein targets for all MDDR (MDL Drug Database Report) database compounds. On average, the correct target was found 77% of the time for compounds from 10 MDDR activity classes with known targets. For MDDR compounds annotated with only therapeutic or generic activities such as "antineoplastic", "kinase inhibitor", or "anti-inflammatory", the model was able to systematically deconvolute the generic activities to specific targets associated with the therapeutic effect. Examples of successful deconvolution are given, demonstrating the usefulness of the tool for improving knowledge in chemogenomics databases and for predicting new targets for orphan compounds.

Bayes Theorem↗

PubMedDate not supplied

"Bayes affinity fingerprints" improve retrieval rates in virtual screening and define orthogonal bioactivity space: when are multitarget drugs a feasible concept?

Conventional similarity searching of molecules compares single (or multiple) active query structures to each other in a relative framework, by means of a structural descriptor and a similarity measure. While this often works well, depending on the target, we show here that retrieval rates can be improved considerably by incorporating an external framework describing ligand bioactivity space for comparisons ("Bayes affinity fingerprints"). Structures are described by Bayes scores for a ligand panel comprising about 1000 activity classes extracted from the WOMBAT database. The comparison of structures is performed via the Pearson correlation coefficient of activity classes, that is, the order in which two structures are similar to the panel activity classes. Compound retrieval on a recently published data set could be improved by as much as 24% relative (9% absolute). Knowledge about the shape of the "bioactive chemical universe" is thus beneficial to identifying similar bioactivities. Principal component analysis was employed to further analyze activity space with the objective to define orthogonal ligand bioactive chemical space, leading to nine major (roughly orthogonal) activity axes. Employing only those nine activity classes, retrieval rates are still comparable to original Bayes affinity fingerprints; thus, the concept of orthogonal bioactive ligand chemical space was validated as being an information-rich but low-dimensional representation of bioactivity space. Correlations between activity classes are a major determinant to gauge whether the desired multitarget activity of drugs is (on the basis of current knowledge) a feasible concept because it measures the extent to which activities can be optimized independently, or only by strongly influencing one another.

Algorithms↗