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HSP Application note #35

Caco-2 Permeability and Solubility Parameter (HSP)


HSPiP Team Senior Developer, Dr. Hiroshi Yamamoto


I got the paper about Caco-2 cell monolayer apparent Permeability.
QSAR study of pharmacological permeabilities

They build QSAR model with CODESSA Pro as descriptors generator.
And almost all important descriptors are calculated by AM1 (Semi-empirical molecular orbital method).

I know almost all these QSAR analysis are based on this technology.
But it is very strange to me.
Why permeability can be explained by HOMO or LUMO?
So, I dare to explain permeability with HSP.

Hansen Solubility Parameters (HSP)

Hansen Solubility Parameters(HSP) were developed by Charles M. Hansen as a way of predicting if one material will dissolve in another and form a solution. They are based on the idea that "like dissolves like" where one molecule is defined as being 'like' another if it bonds to itself in a similar way.
Specifically, each molecule is given three Hansen parameters, each generally measured in MPa0.5:
dD:The energy from dispersion bonds between molecules
dP:The energy from dipolar intermolecular force between molecules
dH:The energy from hydrogen bonds between molecules.
These three parameters can be treated as Vector for a point in three dimensions also known as the Hansen space. The nearer two molecules HSP Vector are in this three dimensional space, the more likely they are to dissolve into each other.

What can perhaps be surprising is that one can assign HSP to so many different things. Gases like carbon dioxide, solids like carbon-60, sugar, and biological materials like human skin, depot fat, DNA, and even some proteins all have HSP. The list can be continued with drugs, polymers, plasticizers, and in fact any organic material and even many inorganic materials like salts. The only requirement for an experimental confirmation is that the material must behave differently in a sufficient number of test solvents upon contact.

Pirika JAVA Demo Applet calculate HSP. HSPLight is available here.
Please refer to e-Book of HSPiP if you want know more about HSP.
About the Power Tools that handle HSP more effectively.


CAS Name
57-50-1 Sucrose 67-56-1 Methyl alcohol 599-79-1 Sulfasalazine
129618-40-2 Nevirapine 52-53-9 Verapamil 103-90-2 Acetaminophen
3930-20-9 Sotalol 60142-96-3 Gabapentin 62571-86-2 Captopril
50-28-2 Estradiol 6452-71-7 Oxprenolol 51-34-3 Scopolamine
58-22-0 Testosterone 13655-52-2 Alprenolol 75847-73-3 Enalapril
50-24-8 Prednisolone 37350-58-6 Metoprolol 738-70-5 Trimethoprim
50-23-7 Hydrocortisone 23031-25-6 Terbutaline 60-80-0 Antipyrine
57-83-0 Progesterone 30516-87-1 Zidovudine 50-53-3 Chlorpromazine
50-22-6 Corticosterone 37517-30-9 Acebutolol 56-75-7 Chloramphenicol
39562-70-4 Nitrendipine 34841-39-9 Bupropion  15722-48-2 Olsalazine
72509-76-3 Felodipine 15687-27-1 Ibuprofen 70458-96-7 Norfloxacine
83-43-2 Methylprednisolone 22204-53-1 Naproxen 23214-92-8 Doxorubicin
50-02-2 Dexamethasone 6673-35-4 Practolol 81-81-2 Warfarin
33419-42-0 Etoposide 29122-68-7 Atenolol 126-07-8 Griseofulvin
466-06-8 Proscillaridin 637-07-0 Clofibrate 22071-15-4 Ketoprofen
114-07-8 Erythromycin 525-66-6 Propranolol 28797-61-7 Pirenzepine
59865-13-3 Cyclosporine 57-66-9 Probenecid 36322-90-4 Piroxicam
73384-59-5 Ceftriaxone 42200-33-9 Nadolol 15307-86-5 Diclofenac
54-31-9 Furosemide 42399-41-7 Diltiazem 57-41-0 Phenytoin
66357-35-5 Ranitidine 54910-89-3 Fluoxetine 79660-72-3 Fleroxacin
127779-20-8 Saquinavir 137-58-6 Lidocaine 58-93-5 Hydrochlorothiazide
59277-89-3 Acyclovir 51-43-4 Epinephrine 74103-06-3 Ketorolac
126222-34-2 Remikiren 77-10-1 Phencyclidine 91-64-5 Coumarin
59-05-2 Methotrexate 88495-63-0 Artesunate 5786-21-0 Clozapine
51481-61-9 Cimetidine 36894-69-6 Labetalol 4205-90-7 Clonidine
116644-53-2 Mibefradil 57-13-6 Urea 439-14-5 Diazepam
26839-75-8 Timolol 58-15-1 Aminopyrine 58-94-6 Chlorothiazide
13523-86-9 Pindolol 87-08-1 Penicillin V 298-46-4 Carbamazepine
71125-38-7 Meloxicam 5051-62-7 Guanabenz 81-07-2 Saccharin
56-54-2 Quinidine 26787-78-0 Amoxicillin
53-86-1 Indomethacin 54-11-5 Nicotine
58-08-2 Caffeine 28395-03-1 Bumetanide
19216-56-9 Prazosin 50-78-2 Acetylsalicylic acid
58-55-9 Theophyline 51-61-6 Dopamine
22916-47-8 Miconazole 69-72-7 Salicylic acid
25614-03-3 Bromocriptine 50-49-7 Imipramine
63590-64-7 Terazosine 148-82-3 Melphalan
43200-80-2 Zopiclone 50-47-5 Desipramine
65277-42-1 Ketoconazole 15676-16-1 Sulpiride
78755-81-4 Flumazenil 66-22-8 Uracil

At first, I compiled CAS number for these compounds, and get the Smiles notations.

Smiles(Simplified Molecular Input Line Entry Syntax)

SMILES is a string obtained by printing the symbol nodes encountered in a depth-first tree traversal of a chemical graph.
"Organic subset" of B, C, N, O, P, S, F, Cl, Br, and I, brackets can be omitted.
Branches are described with parentheses, as in CCC(=O)O for propionic acid
Double and triple bonds are represented by the symbols '=' and '#'
Ring closure labels are used to indicate connectivity between non-adjacent atoms in the SMILES

Pirika JAVA Demo Applet getting Smiles. Draw2Smiles is available here.
Now we have Power Tool "Draw 2 Smiles", GUI HTML5 software on HSPiP ver. 4.

Once I got the Smiles, HSPiP automatically break molecule into functional groups.

Y-MB Properties Estimation

Y-MB break Smiles into correspponding Functional Groups and Estimate various Properties. These estimation schemes are come from Pirika technologies.

Pirika JAVA Demo Applet calculate Properties. PirikaLight is available here.
Now we have Power Tool "Y-Predict", GUI HTML5 software on HSPiP ver. 4.


I analyzed logPapp(Coca-2) with Multiple Regression.

I can estimate logPapp(Coca-2) from only functional groups. (If this group contribution method did not converged, HOMO LUMO or other descriptions may be needed.)

And Molecular Volume, logP, logS did not have good correlation with logPapp(Caco-2).

I run the SOM analysis.

SOM: Self Organization Map Neural Network 

The 2D Map of "Smilar vector map to similar 2D position".

If we split dH to dHdo, dHac then HSP vector become 4 dimensions.
That is the very bad news for user. Because the Sphere and GUI can not expand to 4 dimensions.
So we start to develop SOM program to check vector similarity.

Pirika JAVA Demo Applet calculate SOM. SOMDemo is available here.

-4.0> blue area > -5.0. Average of blue area [19.1, 7.1, 7.3]
-6.0> orange area >-7.0. Average of Orange Area [19.5, 12.8, 13.8]

No. 10, Ranitidine
[17.8, 7.8, 7.8]
No. 22, Cimetidine
[17.8, 12.1, 6.1]

These two are in blue area, even though -5.0 > logPapp(Caco-2)
Maybe this come from (-NH)2C=N group.
This group should increase dP or dH.
So, I need define new functional group. If someone have HPLC data of these two compounds, please give me. I will determine HSP.

No. 37
[17.8, 8.9, 14.9]
No. 67
[20.9, 17.7, 14.1]
No. 70
[20.1, 14.4, 12.5]
No. 73
[19.5, 10.5, 14.2]

These four are very near to Orange area, even though -5.0 < logPapp(Caco-2)
I can say that these compounds are so complicated and estimated HSP is very bad.

Other case, if I have new molecule, I calculate SOM and find the winner, I can predict the range of logPapp(Caco-2).

It is not Quantitative but Qualitative. But sometime you can get much simple model and you can understand phenomena more easily.

Other topic of Bio Medical

GC data of class 1, class 2 solvents in Q3C:
HSP and Tamiflu: Solubility parameter of Tamiflu or other H1N1 antiviral compounds
HSP for Rabbit: How to design eau de Cologne for rabbit. what LD50(skin, rabbit) means.
HSP and logP: logP, logKow, it is just HSP volume.
HSP and Carcinogenicity: SOM(self organization map) analysis of Poly-chlorinated compounds
HSP and Endocrine Disruptor: categorize by SOM.
HSP and AntimicroBial. Sulfa Drugs and other kind of Drugs.
Gall stone solubilizer: How to dissolve Cholesterol base Gall Stone.
Caco-2 cell monolayer apparent Permeability:SOM analysis