SAR Approach Research Articles

Discovery of highly potent and selective D4 ligands by interactive SAR study

A series of thienylmethylphenylpiperazins was synthesized and tested for affinity towards the five subtypes of dopaminergic receptors. Compound 5f showed more than 1000 folds selectivity to D4 receptors; analogue 5e showed the highest affinity to D4 receptors with Ki 3.9nM. An interactive SAR approach was adopted and lead to compound 14a with Ki (D4) as low as 0.03nM. Molecular docking studies showed a potential, first to report arene cation interaction between the D4 unique residue Arg-186 and the ligands’ arene moiety, explaining the importance of having a strong negative electrostatic potential at this area of the compound structure.

Design and synthesis of thiophene dihydroisoquinolines as novel BACE1 inhibitors

Utilizing a structure based design approach, combined with extensive medicinal chemistry execution, highly selective, potent and novel BACE1 inhibitor 8 (BACE1 Alpha assay IC50=8nM) was made from a weak μM potency hit in an extremely efficient way. The detailed SAR and general design approaches will be discussed.

Polarimetric SAR remote sensing of the 2011 Tohoku earthquake using ALOS/PALSAR

The 2011 magnitude 9.0 Tohoku earthquake caused catastrophic damage to coastal areas in northeastern Japan. It is quite difficult to obtain an immediate assessment of large-scale natural disasters by ground survey. Microwave remote sensing by airborne or spaceborne sensors is a suitable tool for monitoring near-real-time damage over large areas. However, it is not always possible to acquire datasets with sufficient spatial and temporal resolutions for the precise detection of large areas of damage; therefore, this study focuses on the application of fully polarimetric scattering observations for deduction of information about damaged areas. Earthquake-induced changes in the radar backscattering mechanism are investigated by polarimetric scattering mechanism indicators. In addition, a change detection technique using the Expectation–Maximization (EM) based thresholding approach is applied to various polarimetric features. Furthermore, a new polarimetric information fusion method based on the contextual Markov Random Field (MRF) is proposed. The mapping accuracy for tsunami-swept urban areas can be improved by about 50% with significant reduction of false- and missed-alarm rates compared with the single-polarization SAR approach.

Integrated in silico approaches for the prediction of Ames test mutagenicity

The bacterial reverse mutation assay (Ames test) is a biological assay used to assess the mutagenic potential of chemical compounds. In this paper approaches for the development of an in silico mutagenicity screening tool are described. Three individual in silico models, which cover both structure activity relationship methods (SARs) and quantitative structure activity relationship methods (QSARs), were built using three different modelling techniques: (1) an in-house alert model: which uses SAR approach where alerts are generated based on experts judgements; (2) a kNN approach (k-Nearest Neighbours), which is a QSAR model where a prediction is given based on outcomes of its k chemical neighbours; (3) a naive Bayesian model (NB), which is another QSAR model, where a prediction is derived using a Bayesian formula through preselected identified informative chemical features (e.g., physico-chemical, structural descriptors). These in silico models, were compared against two well-known alert models (DEREK and ToxTree) and also against three different consensus approaches (Categorical Bayesian Integration Approach (CBI), Partial Least Squares Discriminate Analysis (PLS-DA) and simple majority vote approach). By applying these integration methods on the validation sets it was shown that both integration models (PLS-DA and CBI) achieved better performance than any of the individual models or consensus obtained by simple majority rule. In conclusion, the recommendation of this paper is that when obtaining consensus predictions for Ames mutagenicity, approaches like PLS-DA or CBI should be the first choice for the integration as compared to a simple majority vote approach.

INTEGRATION OF QSAR AND SAR METHODS FOR THE MECHANISTIC INTERPRETATION OF PREDICTIVE MODELS FOR CARCINOGENICITY

The knowledge-based Toxtree expert system (SAR approach) was integrated with the statistically based counter propagation artificial neural network (CP ANN) model (QSAR approach) to contribute to a better mechanistic understanding of a carcinogenicity model for non-congeneric chemicals using Dragon descriptors and carcinogenic potency for rats as a response. The transparency of the CP ANN algorithm was demonstrated using intrinsic mapping technique specifically Kohonen maps. Chemical structures were represented by Dragon descriptors that express the structural and electronic features of molecules such as their shape and electronic surrounding related to reactivity of molecules. It was illustrated how the descriptors are correlated with particular structural alerts (SAs) for carcinogenicity with recognized mechanistic link to carcinogenic activity. Moreover, the Kohonen mapping technique enables one to examine the separation of carcinogens and non-carcinogens (for rats) within a family of chemicals with a particular SA for carcinogenicity. The mechanistic interpretation of models is important for the evaluation of safety of chemicals.

Compressive sensing SAR range compression with chirp scaling principle

Compressive sensing (CS) techniques offer a framework for the detection and allocation of sparse signal with a reduced number of measurements. This paper proposes a novel SAR range compression, namely compressive sensing with chirp scaling (CS-CS), achieving the same range resolution as conventional SAR approach, while using fewer range samplings. In order to realize accurate range cell migration correction (RCMC), chirp scaling principle is used to construct reference matrix for compressive sensing recovery. Additionally, error diagrams are designed for measurement of the performance of CS-CS, and some experiments of using real data are performed to deal with the errors caused by three conditions: SNR, sparsity and sampling.

Design, Synthesis, and Qualitative Structure–Activity Evaluations of Novel β-Secretase Inhibitors as Potential Alzheimer’s Drug Leads

We have identified highly selective imidazopyridines armed with benzimidazol and/or arylimidazole as potent β-secretase inhibitors. The most effective and selective analogues demonstrated low nanomolar potency for the BACE1 enzyme as measured by FRET and cell-based (ELISA) assays and exhibited comparable affinity (KI) and high ligand efficiency (LE). In addition, these motifs were highly selective (>200) against the structurally related aspartyl protease BACE2. Our design strategy followed a traditional SAR approach and was supported by molecular modeling studies based on the previously reported hydroxyethylene transition state inhibitor derived from isophthalic acid I. Of the most potent compounds, 34 displayed an IC50 for BACE1 of 18 nM and exhibited cellular activity with an EC50 of 37 nM in the cell-based ELISA assay, as well as high affinity (KI=17 nM) and ligand efficiency (LE=1.7 kJ/mol). Compound 34 was found to be 204-fold more selective for BACE1 compared to the closely related aspartyl protease BACE2.

Abstract C70: Antitumor activity of CUDC-907, a single small molecule inhibitor that targets both PI3K and HDAC, in hematologic cancer models.

Abstract In order to achieve cancer signaling network disruption and overcome primary and/or secondary resistance to standard of care agents in hematologic cancers, we have developed a novel class of dual HDAC and PI3K inhibitors by integrating a HDAC inhibitory functionality into a pharmacophore of PI3K inhibitors. Utilizing structure-based design and extensive SAR approach, CUDC-907 has been selected as a clinical candidate that inhibits not only PI3K directly but also other essential signaling pathways indirectly through epigenetic regulation following HDAC inhibition. Enzymatic assays indicate that CUDC-907 potently inhibits Class I PI3K subtypes as well as Class I and II HDAC subtypes. In anti-proliferation assays, CUDC-907 displays potent inhibition of proliferation of a variety of hematologic cancer cells (mean IC50 = 27nM in 23 cell lines). In mechanism studies, CUDC-907 potently and durably inhibits HDAC and PI3K, as indicated by increase of acetylated histone H3, inhibition of phospho-AKT and phospho-STAT3, decrease of anti-apoptotic molecules (BCL-2, survivin, etc.), and induction of P21, acetylated P53 and cleaved caspase3. CUDC-907 is orally bioavailable in animals, and displays a favorable PK profile in tumor-bearing animals. PD studies in xenograft models exhibit that CUDC-907 dose-dependently inhibits both targets, decreases tumor cell proliferation, and induces apoptosis in hematologic tumor xenografts, correlating well with its drug exposure in tumors. In efficacy studies, CUDC-907 inhibits growth of subcutaneously implanted tumor xenografts of hematologic cancers in a dose-dependent manner. Furthermore, in a Daudi NHL model, CUDC-907 is more efficacious than either a single PI3K or HDAC inhibitor reference compound or a combination of the two single agents at maximally tolerated doses. CUDC-907 also displays a favorable safety profiles. Taken together, CUDC-907 may offer greater therapeutic benefits through broad network disruption in hematologic cancer therapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr C70.

Using Local Models to Improve (Q)SAR Predictivity.

We present a novel (Q)SAR approach that detects groups of structures for local (Q)SAR modeling. The algorithm combines clustering and classification or regression for making predictions on chemical structure data. A clustering procedure producing clusters with shared structural scaffolds is applied as a preprocessing step, before a (local) model is learned for each relevant cluster. Instead of using only one global model (classical approach), we use weighted local models for predictions of query compounds dependent on cluster memberships. The approach is evaluated and compared against standard statistical (Q)SAR algorithms on various datasets. The results show that in many cases the application of local models significantly improves the predictive power of the derived (Q)SAR models compared to the classical approach, to models that are induced by a fingerprint-based or a hierarchical clustering approach and to locally weighted learning.

Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 2. α-β unsaturated aliphatic aldehydes

The OECD QSAR Application Toolbox versions 1.1.01 and 1.1.02 and Toxtree version 1.60, which were developed for facilitating the practical use of (Q)SAR approaches by regulators, include a mechanistic SAR model for predicting the mutagenicity of α-β unsaturated aliphatic aldehydes. The aim of this study was to estimate the interest and limitations of this model. First, the model was re-computed to check its transparency and to verify its statistical validity. Then, the model implemented in the two software tools was tested on 34 chemicals not previously used for its design and for which experimental mutagenic activity data were available in the literature. A critical analysis of the results was performed and the practical interest of the model was discussed.

Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 1. Aromatic amines

The Ames Salmonella typhimurium mutagenicity assay is a short-term bacterial reverse mutation test that was designed to detect mutagens. For several decades, it has been used in research laboratories and by regulatory agencies throughout the world for the detection and characterization of potential mutagens among natural products and man-made chemicals. Faced with the ever-growing number of chemicals available on the market, congeneric and non-congeneric (Q)SAR models have been designed from Ames test results obtained on specific S. typhimurium strains such as TA 100 or TA 98. Such models have great potential for a quick and cheap identification and classification of large numbers of potential chemical mutagens. The OECD QSAR Application Toolbox and Toxtree, which were developed for facilitating the practical use of (Q)SAR approaches in regulatory contexts, include two mechanistic SAR models for predicting the mutagenicity of aromatic amines and α-β unsaturated aliphatic aldehydes. The aim of this study was to estimate the interest and limitations of the former model. The model was first re-computed to check its transparency and to verify its statistical validity. Then, it was tested on about 150 chemicals not previously used for the design of the model but belonging to its domain of application. A critical analysis of the results was performed and proposals were made for increasing the model performances.

Pharmacoinformatic Approaches to Design Natural Product Type Ligands of ABC-Transporters

ABC-transporter have been recognized as being responsible for multiple drug resistance in tumor therapy, for decreased brain uptake and low oral bioavailability of drug candidates, and for drug-drug interactions and drug induced cholestasis. P-glycoprotein (ABCB1), the paradigm protein in the field, is mainly effluxing natural product toxins and shows very broad substrate specificity. Within this article we will highlight SAR and QSAR approaches for designing natural product type inhibitors of ABCB1 and related proteins as well as in silico strategies to predict ABCB1 substrates and inhibitors in order to design out undesirable drug/protein interaction.

Identification of very potent inhibitor of human aminopeptidase N (CD13)

In this Letter we describe broad comparision studies toward rat, pig, and human aminopeptidase N (CD13) orthologs using phosphinate inhibitors related in structure to hydroxamic acids. This SAR approach yielded a very potent inhibitor of human aminopeptidase N: α 1-amino-3-phenylpropyl(α 2-hydroxy-3-phenylpropyl)phosphinic acid with an IC 50 = 60 nM.

Di-substituted pyridinyl aminohydantoins as potent and highly selective human β-secretase (BACE1) inhibitors

The identification of highly selective small molecule di-substituted pyridinyl aminohydantoins as β-secretase inhibitors is reported. The more potent and selective analogs demonstrate low nanomolar potency for the BACE1 enzyme as measured in a FRET assay, and exhibit comparable activity in a cell-based (ELISA) assay. In addition, these pyridine-aminohydantoins are highly selectivity (>500×) against the other structurally related aspartyl proteases BACE2, cathepsin D, pepsin and renin. Our design strategy followed a traditional SAR approach and was supported by molecular modeling studies based on the previously reported aminohydantoin 3a. We have taken advantage of the amino acid difference between the BACE1 and BACE2 at the S2′ pocket (BACE1 Pro 70 changed to BACE2 Lys 86) to build ligands with >500-fold selectivity against BACE2. The addition of large substituents on the targeted ligand at the vicinity of this aberration has generated a steric conflict between the ligand and these two proteins, thus impacting the ligand’s affinity and selectivity. These ligands have also shown an exceptional selectivity against cathepsin D (>5000-fold) as well as the other aspartyl proteases mentioned. One of the more potent compounds ( S)- 39 displayed an IC 50 value for BACE1 of 10 nM, and exhibited cellular activity with an EC 50 value of 130 nM in the ELISA assay.

On the number of EINECS compounds that can be covered by (Q)SAR models for acute toxicity

The new EU legislation for managing chemicals called REACH aims to fill in gaps in toxicity information that exist for the chemicals listed on the European Inventory of Existing Chemical Substances (EINECS). REACH advocates the use of alternatives to animal experimentation including, amongst others, (quantitative) structure–activity relationship models [(Q)SARs] to help fill in the toxicity data gaps. The aim of the present study was to provide a science-based estimate of the number of EINECS compounds that can be covered by (Q)SAR models for acute toxicity. Using the ECOSAR software, 54% of the 100 196 EINECS chemicals were classified into 49 classes that can be potentially covered by (Q)SAR models. The largest proportion of the classified compounds (40% of the EINECS list) falls into the classes of non-polar and polar narcotics. Compounds that were not classified include, for example, fish oils, botanical and animal extracts, and crude oil distillates. With rapid improvements in analytical tools, the number of EINECS compounds for which toxicity evaluations may be based on (Q)SAR approaches may be extended by further developing the method recently developed for the safety assessment of natural flavor complexes used as ingredients in food. This method is based on identification of the individual components in a mixture, and judgment of the safety of these identified individual compounds using toxicity information on structurally similar congeners in the respective classes. Such (Q)SAR approaches may be applied to an additional 2938 EINECS compounds, representing botanical and animal extracts, leading to a total estimate of 57% of the EINECS compounds for which (Q)SAR-based approaches may assist in their safety assessment. It is concluded that, despite the fact that individual (Q)SARs may often each cover only a limited number, i.e. less than 1%, of the EINECS compounds, the potential for applying (Q)SAR approaches for safety assessment of EINECS compounds may prove to be significant.

Dopamine/serotonin receptor ligands. Part 17: A cross-target SAR approach: Affinities of azecine-styled ligands for 5-HT 2A versus D 1 and D 2 receptors

Dibenzo- and benzindolo-azecines represent a novel class of high-affinity dopamine receptor antagonists. To further characterize these drugs as potential neuroleptics, we selected a set of azecine derivatives and ring expanded homologues and we measured their antagonist activity at the 5-HT 2A receptor in the porcine coronary artery. SARs found for the 5-HT 2A receptor resemble those for the D 1 but not the D 2 receptor. The protein–ligand interactions were discussed with respect to the different binding pockets.

Global (Q)SARs for skin sensitisation–assessment against OECD principles‖

As part of a EuropeanChemicals Bureau contract relating to the evaluation of (Q)SARs for toxicological endpoints of regulatory importance, we have reviewed and analysed (Q)SARs for skin sensitisation. Here we consider some recently published global (Q)SAR approaches against the OECD principles and present re-analysis of the data. Our analyses indicate that “statistical” (Q)SARs which aim to be global in their applicability tend to be insufficiently robust mechanistically, leading to an unacceptably high failure rate. Our conclusions are that, for skin sensitisation, the mechanistic chemistry is very important and consequently the best non-animal approach currently applicable to predict skin sensitisation potential is with the help of an expert system. This would assign compounds into mechanistic applicability domains and apply mechanism-based (Q)SARs specific for those domains and, very importantly, recognise when a compound is outside its range of competence. In such situations, it would call for human expert input supported by experimental chemistry studies as necessary. ‖Presented at the 12th International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology (QSAR2006), 8–12 May 2006, Lyon, France.

Use of computer-assisted prediction of toxic effects of chemical substances

The current revision of the European policy for the evaluation of chemicals (REACH) has lead to a controversy with regard to the need of additional animal safety testing. To avoid increases in animal testing but also to save time and resources, alternative in silico or in vitro tests for the assessment of toxic effects of chemicals are advocated. The draft of the original document issued in 29th October 2003 by the European Commission foresees the use of alternative methods but does not give further specification on which methods should be used. Computer-assisted prediction models, so-called predictive tools, besides in vitro models, will likely play an essential role in the proposed repertoire of “alternative methods”. The current discussion has urged the Advisory Committee of the German Toxicology Society to present its position on the use of predictive tools in toxicology. Acceptable prediction models already exist for those toxicological endpoints which are based on well-understood mechanism, such as mutagenicity and skin sensitization, whereas mechanistically more complex endpoints such as acute, chronic or organ toxicities currently cannot be satisfactorily predicted. A potential strategy to assess such complex toxicities will lie in their dissection into models for the different steps or pathways leading to the final endpoint. Integration of these models should result in a higher predictivity. Despite these limitations, computer-assisted prediction tools already today play a complementary role for the assessment of chemicals for which no data is available or for which toxicological testing is impractical due to the lack of availability of sufficient compounds for testing. Furthermore, predictive tools offer support in the screening and the subsequent prioritization of compound for further toxicological testing, as expected within the scope of the European REACH program. This program will also lead to the collection of high-quality data which will broaden the database for further (Q)SAR approaches and will in turn increase the predictivity of predictive tools.

The Use of in vitro Data in Risk Assessment

Describing the toxicological profile of a substance is the first step required for risk assessment. Although a wide range of in vitro methods are widely used to characterise toxicological properties including toxicokinetics, regulatory acceptance is mainly confined to in vitro tests which investigate genotoxic end-points. In vitro tests have been proposed for the endpoints acute toxicity, repeated dose toxicity and toxicity to reproduction which encompass the minimum requirements in the OECD SIDS programme. However, until now, limitations of the proposed tests preclude their application in a regulatory framework. Presently, in vitro tests play a major role in obtaining information on mechanism of toxicity with the perspective to be able to identify pathways of toxic responses by applying toxicogenomics techniques. Physiologically based toxicokinetic modelling is using data from in vitro studies to build up the model for a specific compound. Information from both areas is incorporated into the risk assessment to derive compound-specific safety factors, which account for species differences and for the variability among the human population, including possible sensitive subpopulations. Future developments to further enhance the use of in vitro methods in regulatory toxicology include the development of (Q)SAR approaches supplemented by mechanisms of toxicity, which can be addressed by developing methods of molecular toxicology.

Design and synthesis of aryl diphenolic azoles as potent and selective estrogen receptor-beta ligands.

New diphenolic azoles as highly selective estrogen receptor-beta agonists are reported. The more potent and selective analogues of these series have comparable binding affinities for ERbeta as the natural ligand 17beta-estradiol but are >100-fold selective over ERalpha. Our design strategy not only followed a traditional SAR approach but also was supported by X-ray structures of ERbeta cocrystallized with various ligands as well as molecular modeling studies. These strategies enabled us to take advantage of a single conservative residue substitution in the ligand-binding pocket, ERalpha Met(421) --> ERbeta Ile(373), to optimize ERbeta selectivity. The 7-position-substituted benzoxazoles (Table 5) were the most selective ligands of both azole series, with ERB-041 (117) being >200-fold selective for ERbeta. The majority of ERbeta selective agonists tested that were at least approximately 50-fold selective displayed a consistent in vivo profile: they were inactive in several models of classic estrogen action (uterotrophic, osteopenia, and vasomotor instability models) and yet were active in the HLA-B27 transgenic rat model of inflammatory bowel disease. These data suggest that ERbeta-selective agonists are devoid of classic estrogenic effects and may offer a novel therapy to treat certain inflammatory conditions.