Three-dimensional quantitative structure activity relationship (QSAR) of cytotoxic to the positive control doxorubicin (IC50 = μM) as reference drug. . All compounds were built using the Discovery Studio software. A structure–activity relationship based on a homology model of a . The IC50 (50 % inhibitory concentration) module of this software cal- .. Lineweaver–Burk plot for methotrexate and monastrol inhibition of LdPTR1. Circles. Cell specific cytotoxicity and structure-activity relationship of lipophilic Cytarabine/analogs & derivatives*; Cytarabine/chemistry; Cytarabine/ pharmacology.
In this context FB-QSAR proves to be a promising strategy for fragment library design and in fragment-to-lead identification endeavours. The training set needs to be superimposed aligned by either experimental data e. It uses computed potentials, e.
It examined the steric fields shape of the molecule and the electrostatic fields  which were correlated by means of partial least squares regression PLS. The created data space is then usually reduced by a following feature extraction see also dimensionality reduction.
The following learning method can be any of the already mentioned machine learning methods, e. A label or response is assigned to each set corresponding to the activity of the molecule, which is assumed to be determined by at least one instance in the set i. This approach is different from the 3D-QSAR approach in that the descriptors are computed from scalar quantities e. An example of this approach is the QSARs developed for olefin polymerization by half sandwich compounds.
Data mining approach[ edit ] Computer SAR models typically calculate a relatively large number of features.
Because those lack structural interpretation ability, the preprocessing steps face a feature selection problem i. Feature selection can be accomplished by visual inspection qualitative selection by a human ; by data mining; or by molecule mining. A typical data mining based prediction uses e. Molecule mining approaches, a special case of structured data mining approaches, apply a similarity matrix based prediction or an automatic fragmentation scheme into molecular substructures.
Furthermore, there exist also approaches using maximum common subgraph searches or graph kernels. 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.
- Login using
- Discovery of a FLT3 inhibitor LDD1937 as an anti-leukemic agent for acute myeloid leukemia
- Quantitative structure–activity relationship
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.
Quantitative structure–activity relationship - Wikipedia
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.
At day 21 after drug administration, mice were sacrificed, and the tumor weights measured B. Based on the pharmacokinetic profile of LDD Figure 5an in vivo xenograft study was performed.
Inhibition of Mnk enhances apoptotic activity of cytarabine in acute myeloid leukemia cells
The tumor volume and weight were dramatically suppressed by LDD Figure 6 indicating the potential of LDD as an antileukemic agent. The combination effect of a cytotoxic drug and targeted agent should be evaluated because antagonism may exist between the two drugs.
The combination index, CI, was measured using the principle based on Chou et al. As mechanisms of the anti-leukemic effects, apoptotic cell death Figure 2 and cell cycle arrest Figure 3 by the LDD treatment was investigated in this study.
Other potential mechanisms such as differentiation and cellular senescence were also explored. Differentiation of leukemic cells was evaluated with Wright-Giemsa staining.
However, differentiated cells were not observed in LDDtreated cells data not shown. The pharmacokinetic properties of LDD were investigated Table 3. Based on the low F value 1. The pharmacokinetic profile of the metabolite LDD was measured and is shown in Table 3. These favorable pharmacokinetic properties may contribute to the effective anti-tumor activity in vivo. Its indication is newly diagnosed AML that is FLT3 positive, in combination with standard cytarabine and daunorubicin induction and cytarabine consolidation.
Monotherapy of midostaurin for induction therapy is not an approved indication. The oral route of administration of 50 mg twice daily with food is recommended. In terms of clinical application, pulmonary toxicity and interaction with CYP3A4 inhibitor and inducer are major disadvantages of this drug. Midostaurin is a derivative of staurosporine, a pan-kinase inhibitor. Improvement of the kinase selectivity, overcoming adverse effects especially pulmonary toxicity, and the removal of the drug interaction mediated by CYP3A4 will result in a better drug than that of midostaurin.
Many approaches for developing novel therapies for AML are ongoing, such as antibodies against CD33, epigenetic targets, and T cell immunotherapy [ 27 ]. FLT3 targeting is still a promising approach to overcome the treatment failure of AML despite the insufficient clinical results from recent trials. Experiences from FLT3 inhibitor clinical trials have accumulated, and the follow-up analysis of the clinical data suggests that more effective FLT inhibitors are still required [ 28 ].
Here, we presented the LDD compound which has great potency in vitro and in vivo for antileukemic activity. The IC50 was calculated with nonlinear regression using Prism version 5. Cells were treated with each compound alone and a combination of two compounds.