Cardiac glycosides structure activity relationship of imatinib

cardiac glycosides structure activity relationship of imatinib

We built a structure-activity relationship (SAR) model for evaluating hepatotoxicity . Therapy with imatinib may lead to three forms of acute liver injury: . digitoxin and digoxin that belong to the class of cardiac glycosides. The mechanism whereby cardiac glycosides cause a positive inotropic effect and forth to explain the cardiac glycoside structure—activity relationships (SARs). 2. 14 β OH. 3. 17 β-unsaturated lactone ring. 4. Also, usually for maximum activity of cardiac glycoside structure the rings must comply with CATSC rule, that is.

Among computational models, quantitative structure-activity relationship QSAR and structure-activity relationship SAR are the most used ones. QSAR models quantitatively examine the toxicological activity of a compound starting from its chemical structure, on the principle that similar chemical substances should have similar biological behavior.

SAR focuses on the rule determining the relationship, as a classifier Pery et al. Considering the model structure, in silico models can be divided in two main groups: Besides software, other in silico models based on SAs have been recently described in the literature Egan et al. This model was built by developing automatically and manually-extracted SAs, which are chemical sub-structures linked to a particular activity or toxicity.

The use of human data for building the model means the information provided can be used without the need to extrapolate the results from different species, reducing the uncertainty linked to inter-species variability. Furthermore, this in silico model can be used as alternative to animal testing for screening purposes and will be implemented in the VEGA platform http: Materials and methods Hepatotoxicity data collection The first step was to collect data for modeling.

Few public datasets on DILI are available. We focused on the following data sources since they were easily detectable and downloadable from the web and they were reliable since already used by other authors Chen et al. The first was Fourches et al. These were extracted through a data mining approach based on a combination of lexical and linguistic methods and ontological rules in order to link substances to a series of liver diseases, searching the open literature.

This database contains data from in vitro and in vivo studies and follows a simple classification approach: More details can be found in Fourches et al. We selected only data referring to humans data and eliminated the rest. This contains unique pharmaceuticals, of which non-proprietary data have adverse drug reaction data for one or more of the 47 liver effects Coding Symbols for Thesaurus of Adverse Reaction COSTAR term endpoints Matthews et al. For each compound there is an overall activity category A for active, I for inactive and M for marginally active referring to five hepatic endpoints: Since only two compounds were labeled as M we eliminated them in order to reduce the uncertainty of the data set.

We merged the two data sets comparing the chemical structures of the compounds by using the software described in Floris et al. This tool uses multiple combinations of binary fingerprints and similarity metrics for computing the chemical similarity between compounds. In our combined dataset compounds from Fourches et al.

Among these we eliminated and excluded from further analysis those compounds with contrasting experimental values chemicals, After concordance analysis we obtained a unique list of compounds. The final data set was fairly balanced, with compounds labeled as hepatotoxic and non-hepatotoxic. We eliminated those compounds already present in the training or test set and we finally obtained a dataset of chemicals, 69 of which were labeled as hepatotoxic and 32 as non-hepatotoxic that we used for testing the performance of the model.

The complete list of compounds used in this work is provided in the supporting information Data Sheet 1. Manual extraction of SAs Unsupervised chemical similarity-based clustering To identify SAs for hepatotoxicity we created clusters of substances sharing similar chemical structure. This enabled us to hypothesize the presence of toxicity based on common structural features and to group all compounds with the same scaffold but different substituent groups.

This SI, described in Floris et al. For its calculation, a fingerprint and three molecular descriptors based on structural keys are combined with different weights of importance. Here we used an in-house software that employs the SI and can split the molecules of a given data set into chemical similarity-based clusters, in this way the similarity values between molecules inside a cluster is minimized and the similarity values between molecules of different clusters is maximized.

The clusters are further grouped into super-clusters, containing all clusters whose average similarity between their corresponding molecules is higher than a given threshold. This similarity algorithm relies on a K-means approach in the first stepwhere an iterative procedure is applied in order to build the most suitable clusters: In the second step, the algorithm exploits a hierarchical approach, where clusters are grouped on the basis of a given threshold, to support human expert reasoning i.

Cardiac Glycosides

We applied this clustering approach to the positive hepatotoxic compounds in the training set compounds in order to identify SAs only for positive substances.

The compounds were automatically divided into 78 clusters with average similarity ranging from 0. We checked each cluster and eliminated those with average similarity below 0.

cardiac glycosides structure activity relationship of imatinib

For each cluster we manually identified a common chemical structure. However, this last step was not possible for every single cluster since the chemicals in the cluster did not always share an unambiguous, unique chemical core.

In this case we disregarded the cluster. Click the following links to view the three dimensional structures of digitoxigenindigoxigeningitoxigeninstrophanthidin and bufalin. Let us discuss some of the important characteristics of each structural feature. The steroid nucleus has a unique set of fused ring system that makes the aglycone moiety structurally distinct from the other more common steroid ring systems. Such ring fusion give the aglycone nucleus of cardiac glycosides the characteristic 'U' shape as shown below.

To view the 3-dimensional structure of the aglycone moiety click on the figure.

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The steroid nucleus has hydroxyls at 3- and positions of which the sugar attachment uses the 3-OH group. Many genins have OH groups at and positions. These additional hydroxyl groups influence the partitioning of the cardiac glycosides into the aqueous media and greatly affect the duration of action.

Cardiac glycoside

The lactone moiety at C position is an important structural feature. The size and degree of unsaturation varies with the source of the glycoside. Normally plant sources provide a 5-membered unsaturated lactone while animal sources give a 6-membered unsaturated lactone. One to 4 sugars are found to be present in most cardiac glycosides attached to the 3b-OH group. These sugars predominantly exist in the cardiac glycosides in the b-conformation. The presence of acetyl group on the sugar affects the lipophilic character and the kinetics of the entire glycoside.

Because the order of sugars appears to have little to do with biological activity Nature has synthesized a repertoire of numerous cardiac glycosides with differing sugar skeleton but relatively few aglycone structures. Structure - Activity Relationships The sugar moiety appears to be important only for the partitioning and kinetics of action.

For example, elimination of the aglycone moiety eliminates the activity of alleviating symptoms associated with cardiac failure. The "backbone" U shape of the steroid nucleus appears to be very important. The 14b-OH groups is now believed to be dispensible. Lactones alone, when not attached to the steroid skeleton, are not active. Thus the activity rests in the steroid skeleton. The unsaturated lactone plays an important role in receptor binding.

Saturation of the lactone ring dramatically reduced the biological activity.

cardiac glycosides structure activity relationship of imatinib

The lactone ring is not absolutely required. The commercially available cardiac steroids differ markedly in their degree of absorption, half-life, and the time to maximal effect see table below.