Kinase compounds polypharmacology can it be studied using Mol Wt & Lipophilicity

I have came across a post in blog FBDD and also from the linkedIn groups about the effect of liphophilicity and Molecular weight in promiscuity. Peter have referred one very good paper from Michael Hahn and Andrew Leach of Molecular complexity and fragment-based drug discovery: ten years on .This review suggest the importance of liphophilicity, Molecular weight,positive charge , Heavy atoms count in promiscuity.According to the paper Molecular complexity is playing a crucial role in polypharmacology. 
My Question is here what do you mean by molecular complexity? 

From the paper they have mentioned the use of mol weight and heavy atom count to in molecular complexity.Several studies were made for example 75000 compounds from pfizer were tested against 220 assays ,they showed that promiscuity decrease with mol weight, to counter it Novartis did 160 HT assays were they found a postive effect of mol wt in promiscuity.They also found that compounds containing a carboxylic acid showed significant higher selectivity.Other than that sprinthorpe seminal work proposed that increased liphophilicity and presence of basic moiety is playing a important role in promiscuity. From the paper mentioned"People at Roche also mentioned presence of positively charged groups and increase promiscuity."

Based on leach paper i did some study of 72 known Kinase inhibitors which was assayed against 442 different kinases studied by Davis etal . The 72 different inhibitors are available at https://www.ebi.ac.uk/chembldb/target/inspect/CHEMBL1908385 .

I used AlogP for liphophilicty, mol weight, polar surface area and Heavy atoms count against the selectivity score of the compound ar 3 uM. The kinase selectivity score at 3uM is defined as the number of kinases the compound is bound at 3uM to the total number of kinase domain queried.The selectivity scores are being distributed between 0.2 nad 0.7  the The data is available in as additional file in  http://www.nature.com/nbt/journal/v29/n11/full/nbt.1990.html . They have observed "The lowest selectivity scores, and therefore the greatest selectivity, were observed for the MEK inhibitors AZD-6244/ARRY-886 and CI-1040, the MET inhibitor SGX-523, the CSF1R inhibitor GW-2580 and the ERBB2/EGFR inhibitor lapatinib (Tykerb)."

The figure below explains an interesting result. For liphophilicity there exist a negative correlation but for all other it was positve though the correlation was maximum for mol weight around 0.31 but PSA and heavy atoms count it was below 0.1. Both the figure displaying same type of distribution of compounds at 300nM and 3 uM in which lower selectivity observed for different classes of kinase inhibitors.

 

Most of the compounds studied was having a selectivity below 0.2 which indicates the selectivity of class I and class II. For class I and class II inhibitors they are not much a difference Class I was made for big gate keepers like phenyl alanine and class II made for small and medium gatekeepers.Figure below gives the distribution from selectivity scores of different classes of inhibitors from Davis paper.

I think more compounds needs to get explored for the study of kinase inhibitor selectivity and prosmiscuity. Does these signify that the kinases are selective for compounds? 
However i still have question what other property comes into molecular complexity when dealing with promiscuity?

PCA Plot

Continuing from the last post i have done some PCA anaylsis using 12 different physicochemical property descriptors including molecular refractivity, atom polarizabilities, bond polarizabilities, hydrogen bond donors and acceptors, petitjean number, topological polar surface area, number of rotatable bonds,liphophilicity XLogP, molecular weight, topological shape and geometrical shape. 

I used CDK for this developed by Rajarshi Guha and plotted the plot using ggplot2

Quite impressive that the compounds which have been screened was in the same area of the PknB inhibitors. Some outliers like Mitoxantrone,straurosporine are their which are scattered in plot . But majority of the predicted ones are in close association with the PknB inhibitors. 

Pharmacophore study for Mycobacterium tuberculosis Ser/thr kinase (PknB)

Pknb Study

This posting is to give you the idea about recent research i have done in Mtb PknB. Previously i have reported some of the predicted inhibitors. Now this time i am again updating some of the research done in identifying more inhibitors.I am here in India from June 21 to august 20 th 2012 under IUSSTF  science and technology scholarship at Birla Institute of technology hyderabad campus.

  

Previously some work had been done here on PknB inhibitors.(paper under publication),with collaboration with OSDD.They have identified some inhibitors under 1micro molar concentration . Well quite impressive!!
 But the problem with the PknB is that the inhibitors are not capable enough to inhibit the growth in cell line study.

Why is this so?

Is it due to the mycolic acids barrier which don't let compounds to enter the membrane.Not sure about this But more research needs to be done on the branch of drug permeation.

I set my target as PknB which i had been doing earlier and a recent paper by Lougheed team helped me to study the compounds well and also it's pharmacophores.Previously they also reported another group of inhibitors in a paper which was not so much potent as the current ones.But the study done by me helped me to identify the main pharmacophore behind these structures.

PknB a kinase so what it could be a typical kinase like pharmacophore. having hydrogen bond donor's and acceptors and hyrophobic moeity for example class I type inhibitors. Thanks to Jae hong shin  for good presentation on kinase inhibitors which helped me to study the inhibitors of PknB.

Kinase Inhibitors and its pharmacophoric positions.

               

From a review paper by Zhang etal Targeting cancer with small molecule kinase inhibitors  given in  the pic above we can see most of inhibitors and its scaffolds the basic pharmacophore looks like which is given below having 2 donors and one acceptors and big hydrophobic moiety.

Different types of Kinase pharmacophores.

List of published PknB Inhibitors.

The features of the kinase pharmacophore completely resemble inhibitors identified the Lougheed team. I collected  62 Inhibitors with IC 50 values and generated E-pharmacophore based on the compound VIII given in Figure below.E -Pharmacophores are based on the Energetic contribution of the group of atoms to the receptors .It is generated from the Glide XP descriptors. After docking the 62 compounds compound I showed the best docking score followed by compound VIII and then it was Mitoxantrone. I generated pharmacophores for three compounds.
 The pharmacophore  for compounds was resembling the typical kinase class-I type pharmacophore. I did some enrichment studies and BEDROC scores and AUC studies and found the pharmacophore for compound VIII was most appropiate for PknB till now from the dataset I collected. If anyone wants the dataset of 62 compounds email me at abseal@indiana.edu.For enrichment study 1000 decoys were collected from Schrodinger's website. The pharmacophore was having a high enrichment at 1% ,2%  and 5% of the database hits.
The paper by Salam etal on E-pharmacophore mention the choice of sites which have scores greater than -1 kcal/mol but i have a different scenario here . The ring aromatics score was less than -1 Kcal.mol and one extra donor was  having less than -1kcal/mol score. Still my selected pharmacohore was giving a very good enrichment results.
Not sure anywhere does selection of sites based on the energetic contirbution or not.But i selected the sites based on the structure of a typical kinase pharmacophore. The picture given below represents my pharmacophore .The previous work done not published didn't considered the extra donor D6 on the bottom right side. This donor has some special effect for most of the compounds when this donor site is present it brings down the IC 50 value below 0.1 micro molar which indicates that this is one of the important sites.

The docking pose of the compound is also given below for compound VIII along with the pharmacophore

I have done data fusion using the structure and ligand based methods  pharmacophore ,glide and rocs and  using sum score, sum rank and reciprocal rank . Well reciprocal rank is amazingly performing well giving me one of the best enrichment scores along with a BEDROC value of 0.875 and RIE 12.73.Also the AUC for 1% ,2% and 5% was 0.71,0.75 and 0.81. The next datafusion algorithm worked very well was sum score method with BEDROC of 0.785. Both the data fusion methods performance was better than usual Virtual screening methods.

I have done some screening using the datafusion methods using the Asinex screening library. Will post some materials in next post.

Happy reading my post.