European Journal of Pharmaceutical Sciences 52 (2014) 95–108

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European Journal of Pharmaceutical Sciences

journal homepage: www.elsevier .com/ locate/e jps

17b-carboxamide steroids – in vitro prediction of human skinpermeability and retention using PAMPA technique

0928-0987/$ - see front matter � 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.ejps.2013.10.017

⇑ Corresponding author. Tel.: +381 11 3951 335; fax: +381 11 3972 840.E-mail address: vladimir@pharmacy.bg.ac.rs (V. Dobricic).

Vladimir Dobricic a,⇑, Bojan Markovic a, Katarina Nikolic a, Vladimir Savic b, Sote Vladimirov a,Olivera Cudina a

a Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbiab Department of Organic Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia

a r t i c l e i n f o

Article history:Received 19 July 2013Received in revised form 30 September2013Accepted 23 October 2013Available online 12 November 2013

Keywords:Soft corticosteroidsPAMPAHuman skin permeability and retentionQuantitative structure–propertyrelationship

a b s t r a c t

In this paper, twenty-two 17b-carboxamide steroids were synthesized from five corticosteroids (hydro-cortisone, prednisolone, methylprednisolone, dexamethasone and betamethasone) in two steps. The firststep was periodic acid oxydation of these corticosteroids to corresponding cortienic acids and the secondstep was amidation of thus obtained cortienic acids with esterified L-amino acids. These compounds arepotential soft corticosteroids with local anti-inflammatory activity in the skin. Parallel artificial mem-brane permeability assay (PAMPA) was applied in order to predict permeability and retention of thesecompounds in human skin. Comparison of permeability and retention parameters between 17b-carbox-amide steroids and corresponding corticosteroids was performed. Compounds with significantly higherretention were identified and the derivative that does not have significantly higher permeability wasunderlined. Molecular structures of all compounds were optimized by use of Gaussian semiempirical/PM3 method. Geometrical, thermodynamic, physicochemical and electronical molecular parameters ofthe optimized structures were calculated and quantitative structure–property relationship (QSPR) anal-ysis was performed in order to explain permeability and retention of these compounds. ANN-, PLS- andMLR-QSPR models were created. Quality of these models was evaluated by commonly used statisticalparameters and the most reliable models were selected. Analyzing descriptors in the selected models,main molecular properties that influence permeability and retention in the PAMPA artificial membranewere identified. Based on these data, further structural modifications could be applied in order to increaseretention without significant increase of permeability, which can positively affect potential local anti-inflammatory activity of these compounds. Selected QSPR models could be used as in silico tool for pre-dicting human skin permeability and retention of novel 17b-carboxamide steroids without performingPAMPA experiments.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

Soft drugs are applied or administered at or near the site of ac-tion usually to produce local effect. Metabolical deactivation ofthese drugs prevents undesired pharmacological activity or toxic-ity. Presence of a metabolically sensitive moiety in the drug mole-cule enables its design and prediction of the major metabolicpathway. As a result, the formation of undesired toxic, active, orhigh-energy intermediates can be prevented. The soft nature of adrug is usually related to fast hydrolytic degradation. However, ifhydrolysis is too fast, poor activity may be obtained (Bodor andBuchwald, 2006). Therefore, a soft drug must be stable enough toreach the receptor sites at the target organ and to produce desiredeffect. By modifying the structure of a soft drug, it is possible to

change its pharmacokinetic profile, pharmacological activity andtoxicity.

An example of a soft corticosteroid is loteprednol etabonate, anester of cortienic acid (Bodor et al., 1992a,b; Druzgala et al.,1991a,b). Cortienic acid is the major corticosteroid metaboliteand it lacks glucocorticoid activity. Apart from esters, amides ofcortienic acids have been synthesized and tested for glucocorticoidor antiglucocorticoid activity (Formstecher et al., 1991; Manz et al.,1983, 1984; Rousseau et al., 1979). Several amides showed gluco-corticoid activity (inhibition of phytohaemagglutinin-mediatedproliferation of lymphocites) (Manz et al., 1983, 1984).

The parallel artificial membrane permeability assay (PAMPA) isa simple and rapid test intended to estimate passive transport per-meability (Kansy et al., 1998). In PAMPA, a 96-well filter plateimpregnated with a liquid artificial membrane is used to separatetwo compartments: donor compartment (contains a buffersolution of compounds to be tested) and acceptor compartment

96 V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108

(contains an initial fresh buffer solution) (Kansy et al., 1998; Otta-viani et al., 2006). Ottaviani et al. (2006) tested different combina-tions of silicone oil and isopropyl myristate (IPM) and concludedthat the artificial membrane composed of 30% IPM and 70% siliconeoil allows best prediction of human skin permeability. Apart fromthe combination of silicone oil and IPM, certramides (synthetic cer-amide analogs) could also be used for the simulation of barrierproperties of human skin (Sinkó et al., 2009, 2012).

The aim of this study was to synthesize, structurally character-ize twenty-two L-amino acid amides of cortienic acids obtainedfrom five corticosteroids (hydrocortisone, prednisolone, methyl-prednisolone, dexamethasone and betamethasone) and to predicttheir permeability through human skin and human skin retentionusing PAMPA technique. It is expected that these compounds aremetabolized in vivo to corresponding cortienic acid and L-aminoacid. Therefore, these derivatives are potential soft corticosteroidswith local anti-inflammatory activity in the skin. Geometrical,thermodynamic, physicochemical and electronical descriptors ofthese compounds will be calculated and quantitative structure–property relationship (QSPR) study will be performed in order toidentify the most important physicochemical properties that influ-ence permeability and retention in the artificial PAMPA membrane.These data could be used in further structural modifications of syn-thesized compounds in order to increase their retention withoutsignificant increase of permeability, which can positively affecttheir potential local anti-inflammatory activity.

2. Materials and methods

2.1. Chemicals

Hydrocortisone was purchased from Alfa Aesar (Karlsruhe, Ger-many). Prednisolone, methylprednisolone, dexamethasone, beta-methasone and methyl ester of L-alanine were purchased fromTokio Chemical Industry (Tokyo, Japan). Methyl ester of L-glycine,N-hydroxybenzotriazole (HOBt) and N,N-dimethylformamide(DMF) were purchased from Sigma Aldrich (Steinheim, Germany),whereas 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC),triethylamine (TEA), ethyl ester of L-glycine, ethyl ester of b-ala-nine and ethyl ester of L-phenylalanine were purchased from AcrosOrganics (Geel, Belgium). Tetrahydrofuran (THF), isopropyl myris-tate, IPM (>95%) and silicone oil (DC 200) were purchased fromFluka Chemie GmbH (Bruch, Switzerland), while NaH2PO4�H2Oand Na2HPO4 were from Merck (Darmstadt, Germany). Chloroformand methanol were purchased from JT Baker (Loughborough, UK).Silica gel for column chromatography was purchased from Merck(Darmstadt, Germany) and silica gel for preparative thin-layerchromatography was purchased from Sigma–Aldrich (Steinheim,Germany). Acetonitril Chromasolv HPLC purity (Sigma–AldrichChemie GmbH, Steinheim, Germany) and deionised water (TKAwater purification system, Niederelbert, Germany) were usedthroughout this study.

2.2. General procedure for the synthesis of cortienic acids

Cortienic acids were synthesized in high yields (90–97.5%) byperiodic acid oxydation of corticosteroids according to the litera-ture procedure (Bladh et al., 2010). The reaction scheme is pre-sented in Fig. 1.

2.3. General procedure for the synthesis of 17b�carboxamide steroids

Amides were synthesized according to the literature procedure(Anthes et al., 2009). Cortienic acids (0.14 mmol, 1 eq) were dis-solved in DMF (2 ml) at room temperature. Subsequently, corre-

sponding amino acid (0.17 mmol, 1.19 eq), EDC (0.21 mmol,1.5 eq), HOBt (0.21 mmol, 1.5 eq) and TEA (38.5 ll, 1.98 eq) wereadded. Reaction mixtures were stirred at room temperature over-night. The reaction mixtures were evaporated to dryness under re-duced pressure. The residues were dissolved in the mixture ofchloroform and methanol and purified by column chromatogra-phy. Mobile phases used for column chromatography purificationwere chloroform/methanol 99:1 (v/v) (A), chloroform/methanol98.5:1.5 (v/v) (B) and chloroform/methanol 98:2 (v/v) (C). The pur-ity of collected fractions was determined by TLC and HPLC. Unsat-isfactorily pure fractions were evaporated to dryness, dissolved inmethanol and repurified using preparative thin-layer chromatog-raphy. Mobile phases used for preparative thin-layer chromatogra-phy were chloroform/methanol/glacial acetic acid 95:5:1 (v/v/v)(D) and chloroform/methanol 95:5 (v/v) (E). Finally, purified prod-ucts were recrystallized in the mixture of water and methanol toobtain white or light-gray crystalline solids.

The synthesized compounds were structurally characterized bydeterming melting points and by spectroscopic methods (UV, IR,NMR, MS-TOF and MS/MS). Melting points were determined onBoetius PHMK 05 apparatus (Germany). UV spectra were recordedusing Evolution 300 UV–VIS spectrometer (Thermo Fisher Scien-tific, UK). IR spectra were recorded using FT-IR spectrometer Nico-let iS10 (Thermo Fisher Scientific, Madison, WI, USA). NMR spectrawere recorded on NMR BRUKER AVANCE III DMX 500 (Bruker Bio-spin GmbH, Rheinstetten, Germany). 13C NMR data are presentedin Supplementary Table 1. Exact masses were determined using li-quid chromatograph Agilent Technologies 1210 combined withmass detector Agilent 6210 Time-of-Flight (Agilent Technologies,Palo Alto, CA, USA) and LTQ Orbitrap XL FT Mass Spectrometer(Thermo Fisher Scientific, Bremen, Germany). MS/MS analyseswere performed using TSQ Quantum Access MAX triple quadrupolemass spectrometer (Thermo Fisher Scientific, San Jose, USA),equipped with heated electrospray ionization source (HESI).

2.4. PAMPA test

The membrane solution (30% isopropyl myristate–70% silicone)was prepared in n-hexane (35% v/v). Phosphate buffer (pH 5.5,ionic strength: 20 mM) was prepared by dissolving 2.6460 gNaH2PO4�H2O and 0.1177 g Na2HPO4 in water (1000 ml). Test solu-tions were prepared by dissolving about 1 mg of corticosteroidsand their 17b-carboxamide derivatives in DMSO (1.25 ml) in25 ml volumetric flasks and filling up to the mark with the phos-phate buffer. Acceptor solution was prepared as 5% DMSO solutionin the buffer.

The PAMPA test was performed according to the literature pro-cedure (Markovic et al., 2012; Ottaviani et al., 2006) in hydropho-bic PVDF 96-well filter plates (Multiscreen IP Filter Plate 0.45 lm)from Millipore (Bedford, MA, USA). Compounds were tested in trip-licates at iso-pH conditions. Each well of the donor plate wascoated with the membrane solution (17 ll). Next, in each well ofthe acceptor plate, acceptor solution (300 ll) was transferred andcovered by the donor plate to create a PAMPA sandwich. Finally,in each well of the donor plate test solutions (300 ll) were trans-ferred. The donor plate was covered to prevent evaporation fromdonor wells and the whole system was set up to interact withthe vibratory mixer. After 7 h, the PAMPA sandwich was disassem-bled. Concentrations of all tested compounds in each well, as wellas in starting solutions, were determined using LC-MS method.

2.4.1. LC-MS methodTSQ Quantum Access MAX triple quadrupole mass spectrome-

ter, equipped with heated electrospray ionization source (HESI),was utilized for mass spectrometric detection. The MS detectorwas set to detect and quantify corresponding ions (m/z) under

Fig. 1. Periodic acid oxidation of corticosteroids.

V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108 97

positive HESI mode for hydrocortisone, methylprednisolone, dexa-methasone and betamethasone or under negative HESI mode forother tested compounds. The chromatographic analysis was per-formed using Accela Thermo Scientific system consisted of AccelaPump and Autosampler. The mobile phase composition was aceto-nitrile: 0.1% formic acid (60: 40, v/v for determination of phenylal-anine derivatives and 50:50, v/v for determination of othercompounds). All runs were performed using Zorbax Eclipse XDB-C18 column (100 � 4.6 mm, 3.5 lm particle size), which was main-tained at 25 �C. The temperature of autosampler was 6 �C. Theinjection volume was 10 ll, while the flow rate of the mobile phasewas set at 500 ll/min. For data acquisition, Xcalibur 1.2 software(Thermo Fisher Scientific, San Jose, USA) was used.

2.4.2. Calculations of PAMPA parametersThe values of permeability coefficients-logPe, permeation

parameters-CA(t)/CD(0) and retention factors-R of tested com-pounds were calculated using Eqs. (1) and (2) (Avdeef, 2003; Otta-viani et al., 2006). R can be defined as the mole fraction retained inthe membrane and in the microplates (i.e., filters and platematerials):

R ¼ 1� CDðtÞCDð0Þ

� VA

VD

CAðtÞCDð0Þ

ð1Þ

log Pe ¼ log � 2:303VD

Aðt � tLAGÞVA

VA þ VD

� �log 1� VA þ VD

VDð1� RÞ

� �CAðtÞCDð0Þ

� �� �

ð2Þ

VA – the volume in the acceptor wells (ml); VD – the volume in thedonor wells (ml); A – the filtration area (cm2); t – the incubationtime (s); tLAG – the steady-state time (s); CD(t) – the concentrationof the compound in donor well at time t (lM); CA(t) – the concen-tration of the compound in acceptor well at time t (lM); CD(0) –the concentration of the compound in donor well at time 0 (lM).

Steady-state time (tLAG) which is needed to saturate the mem-branes in PAMPA is relatively short compared to the total perme-ation time (about 20 min with unstirred plates) (Avdeef et al.,2001).

2.5. Calculation of geometrical, thermodynamic, physicochemical andelectronical molecular parameters and statistical analysis

Geometry of all compounds was minimized by use of Gaussiansemiempirical/PM3 method, included in Chem3D Ultra 9.0.1 pro-

gram (CambridgeSoft Corporation, 2005). Geometrical, thermody-namic, physicochemical and electronical molecular parameters ofthe optimized molecular structures were calculated in Chem3D Ul-tra 9.0.1, MarvinSketch (ChemAxon, 2008) and Dragon software(Talete srl, 2010). Using Dragon, Chem3D Ultra 9.0.1 and Marvin-Sketch, more than 4500 molecular parameters were initially calcu-lated. Molecular parameters calculated in MarvinSketch and Chem3D Ultra 9.0.1 are presented in Table 1.

Quantum chemically based reactivity molecular parameters,such as chemical potential (l), electronegativity (v), hardness(g), global softness (S) and electrophilicity index (x) were calcu-lated from HOMO and LUMO energies (Filipic et al., 2013; Iczkow-ski and Margrave, 1961; Parr and Yang, 1989).

Subsequently, intercorrelation between calculated molecularparameters was tested. Pairs of molecular parameters with inter-correlation higher than 0.99 (for PLS analysis) or higher than 0.90(for ANN and MLR analysis) were examined and those with stron-ger influence on dependent variables (logPe and R) were retainedfor modeling. After the completion of intercorrelation test, 279molecular parameters were used for further MLR(logPe),ANN(logPe), MLR(R) and ANN(R) analysis, whereas 654 molecularparameters were used for further PLS(logPe) and PLS(R) analysis.

STATISTICA package with the neural network module was usedfor stepwise MLR and ANN modeling (StatSoft Inc., 1998). The SoftIndependent Modeling of Class Analogy SIMCA P + 12.0 program(Umetrics AB, 2008) was used for the PLS analysis (Erikssonet al., 2001; Umetrics AB, 2008). In the PLS analysis, variables wereselected according to the variable importance in the projection(VIP) parameter (Umetrics AB, 2008).

Single factor ANOVA was performed in Microsoft Office Excel(Microsoft Corporation, 2010) in order to identify compounds withsignificantly higher retention than starting corticosteroids.

3. Results and discussion

3.1. Selection and synthesis of 17b-carboxamide steroids

The affinity for glucocorticoid receptor depends on the nature of17b side chain. According to X-ray crystal structure studies (Duaxet al., 1982), presence of hydrogen bond donor groups in the 17bside chain is responsible for glucocorticoid activity, whereas lackof these groups leads to antiglucocorticoid activity of a compound.According to molecular docking calculations, amides of cortienicacids with esterified L-amino acids might have glucocorticoid

Table 1Molecular parameters calculated in MarvinSketch and Chem3D Ultra 9.0.1.

Programs Molecular parameters

MarvinSketch Platt index, Randic index, Harary index, hyper wiener index, Szeged index, Wiener index, Wiener polarity, dreiding energy, minimal projection area,maximal projection area, minimal projection radius, maximal projection radius, polar surface area, molecular surface area

Chem3D Ultra9.0.1

Dipol, charge at C1 atom, charge at C2 atom, electronic density at C1 atom, electronic density at C2 atom, highest occupied molecular orbital(HOMO) energy, lowest unoccupied molecular orbital (LUMO) energy, chemical potential, electronegativity, hardness, global softness, steric energysummary, Connoly accessible area, Connoly molecular area, Connoly solvent excluded volume, molecular weight, ovality, Balaban index, clustercount, diameter, radius, molecular topological index, shape attribute, shape coefficient, sum of degrees, sum of valence degrees, total connectivity,total valence connectivity, Wiener index, molar refractivity, partition coefficient, bend energy, non-1,4-VDW energy, stretch energy, stretch bendenergy, torsion energy, total energy, VDW 1,4 energy, electronic energy

98 V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108

activity (Dobricic et al., 2012). Binding energies of these com-pounds are lower than binding energies of corresponding cortienicacids. Three-dimensional simulation of interaction between thesecompounds and receptor for dexamethasone shows that carbonyloxygen of L-amino acids forms a hydrogen bond with ASN564.Additionally, this interaction is strengthen by hydrophobic interac-tions between LEU563 and alkyl group used for esterification of theL-amino acid. These interactions might be important for receptoractivation and potential glucocorticoid activity of these com-pounds. Interaction of amide of cortienic acid derived from hydro-cortisone and methyl ester of L-glycine with the receptor fordexamethasone is presented in Fig. 2.

Twenty-two L-amino acid amides of cortienic acids obtainedfrom hydrocortisone, prednisolone, methylprednisolone, dexa-methasone and betamethasone were synthesized and structurallycharacterized. L-amino acids used for the synthesis are: methyl es-ter of L-glycine, methyl ester of L-alanine, ethyl ester of L-glycine,ethyl ester of b-alanine and methyl ester of L-phenylalanine. L-ami-no acids were chosen because they are natural constituents of hu-man cells. As expected metabolites of these compounds, they aregood choice because they are not toxic. Synthesis of these com-pounds is presented in Fig. 3.

Compounds DG and DF (Fig. 3) had already been synthesizedusing N,N0-dicyclohexylcarbodiimide (DCC) and HOBt accordingto Formstecher et al. (1980) and their lypophilicity (Maes et al.,1988) as well as affinity for glucocorticoid receptor (Formstecheret al., 1991) were determined. In this paper, alternative reactionemploying EDC and HOBt (Anthes et al., 2009) is used because ofthe ease of handling of EDC comparing to DCC (EDC is crystalline

Fig. 2. Three-dimensional simulation of interaction between selected compoundand receptor for dexamethasone.

powder, whereas DCC is low melting point waxy solid) and the en-hanced solubility of EDC and particularly the urea by-productformed during the reaction. Because of tert-amino group in theirstructure, EDC and its urea by-product can be easily removed byextraction under acidic conditions which facilitates purification.In addition, EDC is less sensitizing agent than DCC.

3.2. Physico-chemical characterization of synthesized compounds

3.2.1. Methyl 2-(11b,17a-dihydroxy-3-oxo-androst-4-en-17b-carboxamido) acetate (HG)

Light-gray crystalline solid obtained by purification with mobilephases C and D. Yield: 15.3%. Melting point: 224–227.8 �C. IR (ATR)mmax (cm�1): 1237.91 (CAO ester), 1514.96 (NAH, bend), 1616.54(C@C), 1654.84 (C@O amide), 1743.37 (C@O ester), 3424.01(NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm 0.98 (3H, s, H-18), 1.00–1.02 (1H, m, H-9), 1.11 (1H, dq, J = 9, J = 13.5, H-6),1.40–1.44 (1H, m, H-15), 1.48 (3H, s, H-19), 1.51–1.56 (1H, m, H-16), 1.72 (1H, dd, J = 2.7, J = 13.7, H-12), 1.77–1.78 (2H, m, H-14,H-15), 1.85–2.07 (4H, m, H-1, H-12, H-6, H-8), 2.20–2.33 (3H, m,H-1, H-7, H-2), 2.46–2.60 (2H, m, H-2, H-7), 2.72–2.78 (1H, m, H-16), 3.73 (3H, s, RAC(@O)OCH3), 3.95 (2H, ABq, J = 17.5,RANHACH2AC(@O)OCH3), 4.40 (1H, q, J = 3.2, H-11), 5.65 (1H, d,J = 1, H-4). m/z = 418.3 (M+�1), 368.4, 386.2, 300.8. MS [M+H]+ cal-culated for C23H33NO6 = 420.23079; observed = 420.23823.kmax(CH3OH) = 241 nm.

3.2.2. Methyl 2-(11b,17a�dihydroxy-3-oxo-androst-4-en-17b-carboxamido) propionate (HA)

White crystalline solid obtained by purification with mobilephases B and D. Yield: 17.4%. Melting point: 122–125 �C. IR (ATR)mmax (cm�1): 1210.80 (CAO ester), 1512.32 (NAH, bend), 1614.73(C@C), 1653.68 (C@O amide), 1742.92 (C@O ester), 3376.39(NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm 0.96 (3H, s, H-18), 1.00 (1H, dd, J = 3.5, J = 11, H-9), 1.07–1.15 (1H, m, H-6),1.40–1.44 (1H, m, H-15), 1.40 (3H, d, J = 7.5, RANHACH(CH3)AC(@O)OCH3), 1.48 (3H, s, H-19), 1.49–1.54 (1H, m, H-16), 1.72–1.79 (3H, m, H-12, H-14, H-15), 1.85–1.96 (2H, m, H-1, H-12),2.04–2.07 (3H, m, H-1, H-7, H-2), 2.21–2.34 (3H, m, H-1, H-7, H-2), 2.46–2.60 (2H, m, H-2, H-7), 2.72–2.77 (1H, m, H-16), 3.73(3H, s, RAC(@O)OCH3), 4.39–4.44 (2H, m, H-11, RANHACH(CH3)AC(@O)OCH3), 5.65 (1H, d, J = 0.5, H-4). m/z = 432.0 (M+�1),400.0, 382.1, 356.0. MS [M+H]+ calculated forC24H35NO6 = 434.25371; observed = 434.25358. kmax(CH3OH) =242 nm.

3.2.3. Ethyl 2-(11b,17a-dihydroxy-3-oxo-androst-4-en-17b-carboxamido) acetate (HEG)

White crystalline solid obtained by purification with mobilephases B and D. Yield: 32.2%. Melting point: 113–116 �C. IR (ATR)mmax (cm�1): 1232.70 (CAO ester), 1505.49 (NAH, bend), 1613.36(C@C), 1654.24 (C@O amide), 1739.16 (C@O ester), 3421.50(NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm 0.99 (3H, s, H-

Fig. 3. Synthesis of amides.

V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108 99

18), 1.01 (1H, d, J = 3.5, H-9), 1.12 (1H,dd, J = 4, J = 13, H-6), 1.28(3H, t, J = 7, RAC(@O)OCH2CH3), 1.43–1.45 (1H, m, H-15), 1.48(3H, s, H-19), 1.53–1.56 (1H, m, H-16), 1.74–2.08 (7H, m, H-12,H-14, H-15, H-1, H-12, H-6, H-8), 2.22–2.33 (3H, m, H-1, H-7, H-2), 2.46–2.57 (2H, m, H-2, H-7), 2.76 (1H, m, H-16), 3.94 (2H,ABq, J = 17.5, RANHACH2AC(@O)OCH2CH3), 4.20 (2H, q, J = 7.2,RAC(@O)OCH2CH3), 4.40 (1H, d, J = 3, H-11), 5.66 (1H, s, H-4). m/z = 432.3 (M+�1), 386.1, 368.2, 342.1. MS [M+H]+ calculated forC24H35NO6 = 434.25372; observed = 434.25241. kmax(CH3OH) =243 nm.

3.2.4. Ethyl 3-(11b,17a-dihydroxy-3-oxo-androst-4-en-17b-carboxamido) propionate (HEA)

White crystalline solid obtained by purification with mobilephases B and D. Yield: 31.9%. Melting point: 178.7–181.7 �C. IR(ATR) mmax (cm�1): 1181.68 (CAO ester), 1518.98 (NAH, bend),1608.81 (C@C), 1636.10 (C@O amide), 1733.96 (C@O ester),3398.50 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm 0.96

(3H, s, H-18), 0.98 (1H,dd, J = 3.5, J = 11, H-9), 1.06–1.15 (1H, m,H-6), 1.26 (3H, t, J = 7.2, RAC(@O)OCH2CH3), 1.40–1.43 (1H, m,H-15), 1.47 (3H, s, H-19), 1.50–1.57 (2H, m, H-16,H-12), 1.70–1.81 (2H, m, H-12, H-14, H-15), 1.85–1.93 (2H, m, H-1, H-12),2.04–2.06 (2H, m, H-6, H-8), 2.20–2.33 (3H, m, H-1, H-7, H-12),2.46–2.52 (1H, m, H-2), 2.55 (2H, t, J = 6.7, RANHACH2CH2

C(@O)OCH2CH3), 2.58–2.59 (1H, m, H-7), 2.73–2.78 (1H, m,H-16), 3.42 (1H, dt, J = 7, J = 14, RANHACH2CH2C(@O)OCH2CH3),3.51 (1H, dt, J = 7, J = 14, RANHACH2CH2C(@O)OCH2CH3), 4.15(1H, q, J = 7.2, RANHACH2CH2C(@O)OCH2CH3), 4.38 (1H, q,J = 3.2, H-11), 5.65 (1H, d, J = 0.5, H-4). m/z = 446.0 (M+�1), 346.1,328.2, 301.1. MS [M+H]+ calculated for C25H37NO6 = 448.26937;observed = 448.27024. kmax(CH3OH) = 242 nm.

3.2.5. Methyl 2-(11b,17a-dihydroxy-3-oxo-androst-4-en-17b-carboxamido)-3-phenyl propionate (HF)

White crystalline solid obtained by purification with mobilephases A and D. Yield: 23.3%. Melting point: 269.2–272.3 �C. IR

100 V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108

(ATR) mmax (cm�1): 1204.48 (CAO ester), 1503.51 (NAH, bend),1612.05 (C@C), 1649.52 (C@O amide), 1664.19 (C3@O), 1742.95(C@O ester), 3338.64 (NAH, stretch). 1H NMR (500 MHz, CD3OD)d ppm 0.93 (3H, s, H-18), 0.97 (1H, dd, J = 3, J = 11, H-9), 1.08–1.11 (1H, m, H-6), 1.39–1.42 (1H, m, H-15), 1.46 (3H, s, H-19),1.51 (1H, m, H-16), 1.62 (1H, dd, J = 2.5, J = 14, H-12), 1.74 (2H,m, H-14, H-15), 1.87–2.05 (4H, m, H-1, H-12, H-6, H-8), 2.20–2.32 (3H, m, H-1, H-7, H-2), 2.45–2.69 (3H, m, H-2, H-7, H-16),3.11 (2H, d, J = 6.5, RANHACH(CH2C6H5)AC(@O)OCH3), 3.70 (3H,s, RANHACH(CH2C6H5)AC(@O)OCH3), 4.67 (1H, t, J = 6.5,RANHACH(CH2C6H5)AC(@O)OCH3), 5.65 (1H, s, H-4), 7.19–7.31(5H, m, RANHACH(CH2C6H5)AC(@O)OCH3). m/z = 508.1 (M+�1),476.2, 458.1, 174.0. MS [M+H]+ calculated for C30H39NO6 =510.28502; observed = 510.28354. kmax(CH3OH) = 202 and 242 nm.

3.2.6. Methyl 2-(11b,17a-dihydroxy-3-oxo-androst-1,4-dien-17b-carboxamido) acetate (PG)

White crystalline solid obtained by purification with mobilephases C and E. Yield: 50%. Melting point: 254.5–257 �C. IR (ATR)mmax (cm�1): 1203.08 (CAO ester), 1508.78 (NAH, bend), 1603.79(C@C), 1650.59 (C@O amide), 1676.17 (C3@O), 1753.13 (C@O es-ter), 3385.72 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm1.00 (3H, s, H-18), 1.02 (1H, d, J = 4, H-9), 1.08–1.17 (1H, m, H-6),1.44–1.47 (1H, m, H-15), 1.50 (3H, s, H-19), 1.52–1.55 (1H, m, H-16), 1.70–1.77 (3H, m, H-14, H-12, H-15), 1.93 (1H, dd, J = 3.5,J = 14, H-12), 2.14–2.18 (2H, m, H-6, H-8), 2.35–2.39 (1H, m, H-7), 2.62–2.69 (1H, m, H-7), 2.75 (1H, ddd, J = 2.5, J = 11, J = 14, H-16), 3.73 (3H, s, RAC(@O)OCH3), 3.95 (2H, ABq, J = 17.5,RANHACH2AC(@O)OCH3), 4.40 (1H, q, J = 3.5, H-11), 6.00 (1H, t,J = 1.5, H-4), 6.24 (1H, dd, J = 2, J = 10, H-2), 7.48 (1H, d, J = 10, H-1). m/z = 416.1 (M+�1), 366.2, 384.3, 340.2. MS [M+H]+ calculatedfor C23H31NO6 = 418.22241; observed = 418.22262. kmax(CH3OH) =244 nm.

3.2.7. Methyl 2-(11b,17a-dihydroxy-3-oxo-androst-1,4-dien-17b-carboxamido) propionate (PA)

White crystalline solid obtained by purification with mobilephases B and E. Yield: 30.5%. Melting point: 220–223 �C. IR (ATR)mmax (cm�1): 1215.48 (CAO ester), 1513.76 (NAH, bend), 1587.81(C@C), 1648.23 (C@O amide), 1739.37 (C@O ester), 3436.39(NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm 0.98 (3H, s, H-18), 1.01 (1H, dd, J = 4, J = 11, H-9), 1.08–1.17 (1H, m, H-6), 1.40(3H, d, J = 7.5, RANHACH(CH3)AC(@O)OCH3), 1.43–1.54 (2H, m,H-15, H-16), 1.50 (3H, s, H-19), 1.67–1.80 (3H, m, H-14, H-12, H-15), 1.93 (1H, dd, J = 3.5, J = 14, H-12), 2.13–2.18 (2H, m, H-6, H-8), 2.35–2.39 (1H, m, H-7), 2.62–2.77 (2H, m, H-7, H-16), 3.74(3H, s, RAC(@O)OCH3), 4.39–4.44 (2H, m, H-11, RANHACH(CH3)AC(@O)OCH3), 6.00 (1H, t, J = 1.7, H-4), 6.24 (1H, dd, J = 2, J = 10,H-2), 7.49 (1H, d, J = 10, H-1). m/z = 430.1 (M+�1), 380.2, 398.2,98.2. MS [M+H]+ calculated for C24H33NO6 = 432.23806; ob-served = 432.23731. kmax(CH3OH) = 243 nm.

3.2.8. Ethyl 2-(11b,17a-dihydroxy-3-oxo-androst-1,4-dien-17b-carboxamido) acetate (PEG)

White crystalline solid obtained by purification with mobilephases B and E. Yield: 61%. Melting point: 133.8–136.5 �C. IR(ATR) mmax (cm�1): 1209.26 (CAO ester), 1517.24 (NAH, bend),1599.44 and 1613.71 (C@C), 1654.79 (C@O amide), 1675.99(C3@O), 1728.90 (C@O ester), 3429.72 (NAH, stretch). 1H NMR(500 MHz, CD3OD) d ppm 1.01 (3H, s, H-18), 1.00–1.03 (1H, m,H-9), 1.09–1.17 (1H, m, H-6), 1.28 (3H, t, J = 7, RAC(@O)OCH2CH3),1.44–1.48 (1H, m, H-15), 1.50 (3H, s, H-19), 1.52–1.57 (1H, m, H-16), 1.70–1.79 (3H, m, H-14, H-12, H-15), 1.92–1.95 (1H, m, H-12), 2.14–2.18 (2H, m, H-6, H-8), 2.37 (1H, dd, J = 3, J = 13, H-7),2.66 (1H, td, J = 4.7, J = 13.4, H-7), 2.75 (1H, ddd, J = 2.5, J = 11,J = 14, H-16), 3.94 (2H, ABq, J = 18, RANHACH2AC(@O)OCH2CH3),

4.20 (2H, q, J = 7.2, RAC(@O)OCH2CH3), 4.40 (1H, q, J = 3.2, H-11),6.00 (1H, s, H-4), 6.24 (1H, dd, J = 2, J = 10, H-2), 7.49 (1H, d,J = 10, H-1). m/z = 430.0 (M+�1), 384.1, 366.1, 340.3. MS [M+H]+calculated for C24H33NO6 = 432.23806; observed = 432.23758.kmax(CH3OH) = 244 nm.

3.2.9. Ethyl 3-(11b,17a-dihydroxy-3-oxo-androst-1,4-dien-17b-carboxamido) propionate (PEA)

White crystalline solid obtained by purification with mobilephases B and E. Yield: 15.6%. Melting point: 122–125 �C. IR (ATR)mmax (cm�1): 1194.74 (CAO ester), 1520.62 (NAH, bend), 1604.09(C@C), 1650.70 (C@O amide), 1717.32 (C@O ester), 3333.81(NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm 0.98 (3H, s, H-18), 0.99–1.01 (1H, m, H-9), 1.07–1.16 (1H, m, H-6), 1.26 (3H, t,J = 7.2, RAC(@O)OCH2CH3), 1.43–1.48 (1H, m, H-15), 1.50 (3H, s,H-19), 1.50–1.56 (2H, m, H-16, H-12), 1.65–1.71 (1H, m, H-14),1.74–1.80 (1H, m, H-15), 1.89 (1H, dd, J = 4, J = 14, H-12), 2.15–2.17 (2H, m, H-6, H-8), 2.37 (1H, dd, J = 3.2, 13.2, H-7), 2.55 (2H,t, J = 6.7, RANHACH2CH2C(@O)OCH2CH3), 2.62–2.68 (1H, m, H-7),2.72–2.78 (1H, m, H-16), 3.42 (1H, dt, J = 6.5, 13.5, RANHACH2

CH2C(@O)OCH2CH3), 3.51 (1H, dt, J = 6.5, 13.5, RANHACH2

CH2C(@O)OCH2CH3), 4.15 (2H, q, J = 7.2, RANHACH2

CH2C(@O)OCH2CH3), 4.38 (1H, q, J = 3.2, H-11), 6.00 (1H, s, H-4),6.24 (1H, dd, J = 2, J = 10, H-2), 7.48 (1H, d, J = 10, H-1). m/z = 444.0 (M+�1), 344.1, 326.1, 299.2. MS [M+H]+ calculated forC25H35NO6 = 446.25371; observed = 446.25302. kmax(CH3OH) =243 nm.

3.2.10. Ethyl 2-(11b,17a-dihydroxy-3-oxo-androst-1,4-dien-17b-carboxamido)-3-phenyl propionate (PF)

White crystalline solid obtained by purification with mobilephases A and E. Yield: 21.5%. Melting point: 246–249.8 �C. IR(ATR) mmax (cm�1): 1205.55 (CAO ester), 1494.47 (NAH, bend),1601.03 and 1613.54 (C@C), 1653.80 (C@O amide), 1745.58 (C@Oester), 3320.67 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm0.95 (3H, s, H-18), 0.98 (1H, dd, J = 4, J = 11, H-9), 1.10 (1H, ddd,J = 4.5, J = 13, J = 17.5, H-6), 1.40–1.47 (1H, m, H-15), 1.48 (3H, s,H-19), 1.50–1.53 (1H, m, H-16), 1.72–1.78 (1H, m, H-15), 1.83(1H, dd, J = 3.5, J = 14, H-12), 2.11–2.16 (2H, m, H-6, H-8), 2.34(1H, dd, J = 3.5, J = 13.5, H-7), 2.61–2.71 (2H, m, H-7, H-16), 3.10(2H, d, J = 6.7, RANHACH(CH2C6H5)AC(@O)OCH3), 3.70 (3H, s,RANHACH(CH2C6H5)AC(@O)OCH3), 4.35 (1H, q, J = 3.2, H-11),4.66 (1H, t, J = 6.5, RANHACH(CH2C6H5)AC(@O)OCH3), 5.99 (1H,t, J = 1.5, H-4), 6.23 (1H, dd, J = 1.7, J = 10.2, H-2), 7.18–7.29 (5H,m, RANHACH(CH2C6H5)AC(@O)OCH3), 7.46 (1H, d, J = 10, H-1).m/z = 506.3 (M+�1), 456.2, 364.9, 474.3. MS [M+H]+ calculatedfor C30H37NO6 = 508.26936; observed = 508.26784. kmax(CH3OH) =202 and 243 nm.

3.2.11. Methyl 2-(11b,17a-dihydroxy-6a-methyl-3-oxo-androst-1,4-dien-17b-carboxamido) acetate (MPG)

White crystalline solid obtained by purification with mobilephases C and E. Yield: 30%. Melting point: 142.5–144.5 �C. IR(ATR) mmax (cm�1): 1210.68 (CAO ester), 1520.43 (NAH, bend),1599.67 (C@C), 1651.32 (C@O amide), 1753.20 (C@O ester),3350.68 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm 0.81(1H, q, J = 12.2, H-16), 0.97 (1H, dd, J = 3.7, J = 11.2, H-9), 1.01(3H, s, H-18), 1.14 (3H, d, J = 6.5, CH3 at C-6), 1.45–1.49 (1H, m,H-15), 1.50 (3H, s, H-19), 1.66–1.79 (3H, m, H-14, H-12, H-15),1.93 (1H, dd, J = 3.7, J = 14.2, H-12), 2.14–2.18 (1H, m, H-16), 2.24(1H, ddd, J = 4, J = 11, J = 15, H-8) 2.73–2.78 (2H, m, H-7), 3.74(3H, s, RAC(@O)OCH3), 3.95 (2H, ABq, J = 17.5, RANHACH2

AC(@O)OCH3), 4.40 (1H, q, J = 3.2, H-11), 5.99 (1H, t, J = 1.5, H-4),6.26 (1H, dd, J = 2, J = 10, H-2), 7.50 (1H, d, J = 10, H-1). m/z = 430.1 (M+�1), 380.2, 398.2, 354.2. MS [M+H]+ calculated for C24-

V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108 101

H33NO6 = 432.23806; observed = 432.23775. kmax(CH3OH) =244 nm.

3.2.12. Methyl 2-(11b,17a-dihydroxy-6a-methyl-3-oxo-androst-1,4-dien-17b-carboxamido) propionate (MPA)

White crystalline solid obtained by purification with mobilephases B and E. Yield: 44.5%. Melting point: 198.5–201 �C. IR(ATR) mmax (cm�1): 1210.97 (CAO ester), 1503.23 (NAH, bend),1601.34 and 1613.55 (C@C), 1654.97 (C@O amide), 1745.53 (C@Oester), 3378.26 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm0.80 (1H, q, J = 12.3, H-16), 0.96 (1H, d, J = 3.5, H-9), 0.99 (3H, s,H-18), 1.13 (3H, d, J = 6.5, CH3 at C-6), 1.40 (3H, d, J = 7.5,RANHACH(CH3)AC(@O)OCH3), 1.44–1.48 (1H, m, H-15), 1.49(3H, s, H-19), 1.51–1.54 (1H, m, H-6), 1.65–1.80 (3H, m, H-14, H-12, H-15), 1.92 (1H, dd, J = 3.7, J = 13.7, H-12), 2.13–2.17 (1H, m,H-16), 2.23 (1H, ddd, J = 4.5, J = 11.5, J = 15.5, H-8), 2.72–2.77 (2H,m, H-7), 3.74 (3H, s, RAC(@O)OCH3), 4.39–4.44 (2H, m, H-11,RANHACH(CH3)AC(@O)OCH3), 5.99 (1H, t, J = 1.5, H-4), 6.25 (1H,dd, J = 1.5, J = 10, H-2), 7.49 (1H, d, J = 10.5, H-1). m/z = 444.0(M+�1), 412.1, 394.0, 368.0. MS [M+H]+ calculated forC25H35NO6 = 446.25371; observed = 446.25341.kmax(CH3OH) = 244 nm.

3.2.13. Ethyl 2-(11b,17a-dihydroxy-6a-methyl-3-oxo-androst-1,4-dien-17b-carboxamido) acetate (MPEG)

White crystalline solid obtained by purification with mobilephases B and E. Yield: 59.5%. Melting point: 119.3–122.5 �C. IR(ATR) mmax (cm�1): 1215.00 (CAO ester), 1523.54 (NAH, bend),1598.84 (C@C), 1650.75 (C@O amide), 1732.22 (C@O ester),3336.43 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm 0.81(1H, q, J = 12.3, H-16), 0.97 (1H, dd, J = 3.5, J = 11.5, H-9), 1.01(3H, s, H-18), 1.14 (1H, d, J = 6.5, CH3 at C-6), 1.28 (3H, t, J = 7.2,RAC(@O)OCH2CH3), 1.44–1.49 (1H, m, H-15), 1.50 (3H, s, H-19),1.51–1.57 (1H, m, H-6), 1.66–1.79 (3H, m, H-14, H-12, H-15),1.93 (1H, dd, J = 3.7, J = 13.7, H-12), 2.14–2.18 (1H, m, H-16), 2.24(1H, ddd, J = 4.5, J = 11.5, J = 15.5, H-8), 2.73–2.78 (2H, m, H-7),3.94 (2H, ABq, J = 17.5, RANHACH2AC(@O)OCH2CH3), 4.20 (2H, q,J = 7.2, RAC(@O)OCH2CH3), 4.40 (1H, q, J = 3.2, H-11), 5.99 (1H, t,J = 1.7, H-4), 6.26 (1H, dd, J = 1.7, J = 10.2, H-2), 7.50 (1H, d, J = 10,H-1). m/z = 444.1 (M+�1), 398.1, 380.1, 354.1. MS [M+H]+ calcu-lated for C25H37NO6 = 446.25372; observed = 446.25229.kmax(CH3OH) = 244 nm.

3.2.14. Ethyl 3-(11b,17a-dihydroxy-6a-methyl-3-oxo-androst-1,4-dien-17b-carboxamido) propionate (MPEA)

White crystalline solid obtained by purification with mobilephases B and E. Yield: 29.3%. Melting point: 168.9–171.9 �C. IR(ATR) mmax (cm�1): 1193.76 (CAO ester), 1512.49 (NAH, bend),1607.72 (C@C), 1650.94 (C@O amide), 1723.81 (C@O ester),3426.03 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm 0.80(1H, q, J = 12.3, H-16), 0.96 (1H, dd, J = 3.7, J = 11.2, H-9), 0.98(3H, s, H-18), 1.13 (1H, d, J = 6.5, CH3 at C-6), 1.26 (3H, t, J = 7,RAC(@O)OCH2CH3), 1.44–1.47 (1H, m, H-15), 1.49 (3H, s, H-19),1.50–1.56 (2H, m, H-6,H-12), 1.63–1.69 (1H, m, H-14), 1.74–1.77(1H, m, H-15), 1.88 (1H, dd, J = 3.7, J = 13.7, H-12), 2.13–2.17 (1H,m, H-16), 2.22 (1H, ddd, J = 4, J = 11, J = 15, H-8), 2.54 (2H, t,J = 6.7, RANHACH2CH2C(@O)OCH2CH3), 2.72–2.78 (2H, m, H-7),3.42 (1H, dt, J = 7, J = 14, RANHACH2CH2C(@O)OCH2CH3), 3.51(1H, dt, J = 6.5, J = 13.5, RANHACH2CH2C(@O)OCH2CH3), 4.15 (1H,q, J = 7, RANHACH2CH2C(@O)OCH2CH3), 4.38 (1H, q, J = 3.3, H-11), 5.99 (1H, t, J = 1.7, H-4), 6.25 (1H, dd, J = 2, J = 10, H-2), 7.49(1H, d, J = 10, H-1). m/z = 458.0 (M+�1), 358.1, 340.1, 440.7. MS[M+H]+ calculated for C26H37NO6 = 460.26937; ob-served = 460.26874. kmax(CH3OH) = 243 nm.

3.2.15. Methyl 2-(11b,17a-dihydroxy-6a-methyl-3-oxo-androst-1,4-dien-17b-carboxamido)-3-phenyl propionate (MPF)

White crystalline solid obtained by purification with mobilephases A and E. Yield: 19.4%. Melting point: 114.5–117.5 �C. IR(ATR) mmax (cm�1): 1116.32 (CAO ester), 1497.36 (NAH, bend),1600.80 and 1611.22 (C@C), 1651.82 (C@O amide), 1738.17 (C@Oester), 3406.46 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm0.78 (1H, q, J = 12.2, H-16), 0.93 (1H, d, J = 3.5, H-9), 0.95 (3H, s,H-18), 1.12 (1H, d, J = 6.5, CH3 at C-6), 1.42–1.45 (1H, m, H-15),1.48 (3H, s, H-19), 1.49–1.53 (1H, m, H-6), 1.60–1.67 (2H, m,H-12, H-14), 1.74–1.76 (1H, m, H-15), 1.82 (1H, dd, J = 3.5, J = 14,H-12), 2.11–2.16 (1H, m, H-16), 2.21 (1H, ddd, J = 4, J = 11, J = 15,H-8), 2.66–2.75 (2H, m, H-7), 3.10 (2H, d, J = 6.2, RANHACH(CH2C6

H5)AC(@O)OCH3), 3.70 (3H, s, RANHACH(CH2C6H5)AC(@O)OCH3),4.35 (1H, q, J = 3.2, H-11), 4.66 (1H, t, J = 6.5, RANHACH(CH2C6H5)-AC(@O)OCH3), 5.98 (1H, t, J = 1.5, H-4), 6.25 (1H, dd, J = 2, J = 10,H-2), 7.18–7.29 (5H, m, RANHACH(CH2C6H5)AC(@O)OCH3), 7.47(1H, d, J = 10, H-1). m/z = 520.0 (M+�1), 470.2, 488.3, 174.1. MS[M+H]+ calculated for C31H39NO6 = 522.28502; observed = 522.28406.kmax(CH3OH) = 202 and 243 nm.

3.2.16. Methyl 2-(11b,17a-dihydroxy-9a-fluoro-16a�methyl-3-oxo-androst-1,4-dien-17b-carboxamido) acetate (DG)

White crystalline solid obtained by purification with mobilephases C and E. Yield: 17.2%. Melting point: 248.2–250.3 �C. IR(ATR) mmax (cm�1): 1206.84 (CAO ester), 1513.79 (NAH, bend),1602.04 and 1616.75 (C@C), 1658.56 (C@O amide), 1744.96 (C@Oester), 3377.70 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm0.91 (1H, d, J = 7.5, CH3 at C-16), 1.10 (3H, s, H-18), 1.18–1.23(1H, m, H-6), 1.53 (1H, ddd, J = 5.5, J = 13, J = 18, H-15), 1.60 (3H,s, H-19), 1.64 (1H, d, J = 2, H-12), 1.76 (1H, q, J = 12, H-6), 1.86–1.91 (1H, m, H-15), 2.18–2.25 (2H, m, H-14, H-12), 2.38–2.51(2H, m, H-7, H-8), 2.69–2.76 (1H, m, H-7), 3.07–3.14 (1H, m, H-16), 3.74 (3H, s, RAC(@O)OCH3), 3.96 (2H, ABq, J = 17.5,RANHACH2AC(@O)OCH3), 4.25 (1H, dq, J = 1.8, J = 11, H-11), 6.08(1H, t, J = 1.5, H-4), 6.29 (1H, dd, J = 1.7, J = 10.2, H-2), 7.43 (1H, d,J = 10.5, H-1). m/z = 447.9 (M+�1), 396.1, 416.0, 311.0. MS[M+H]+ calculated for C24H32FNO6 = 450.22864; ob-served = 450.22679. kmax(CH3OH) = 239 nm.

3.2.17. Methyl 2-(11b,17a-dihydroxy-9a-fluoro-16a-methyl-3-oxo-androst-1,4-dien-17b-carboxamido) propionate (DA)

White crystalline solid obtained by purification with mobilephases B and E. Yield: 46%. Melting point: 267.2–269.6 �C. IR(ATR) mmax (cm�1): 1505.40 (NAH, bend), 1606.06 and 1626.00(C@C), 1645.36 (C3@O), 1665.51 (C@O amide), 1740.96 (C@O es-ter), 3398.25 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm0.89 (1H, d, J = 7, CH3 at C-16), 1.08 (3H, s, H-18), 1.18–1.23 (1H,m, H-6), 1.41 (3H, d, J = 7, RANHACH(CH3)AC(@O)OCH3), 1.53(1H, ddd, J = 5.5, J = 13, J = 18, H-15), 1.60 (3H, s, H-19), 1.64 (1H,dd, J = 1.7, J = 14.2, H-12), 1.75 (1H, q, J = 12, H-6), 1.86–1.91 (1H,m, H-15), 2.17–2.23 (2H, m, H-14, H-12), 2.38–2.51 (2H, m, H-7,H-8), 2.69–2.76 (1H, m, H-7), 3.08–3.13 (1H, m, H-16), 3.74 (3H,s, RAC(@O)OCH3), 4.25 (1H, dq, J = 1.8, J = 11, H-11), 4.42 (1H, q,J = 7.2, RANHACH(CH3)AC(@O)OCH3), 6.08 (1H, s, H-4), 6.29 (1H,dd, J = 2, J = 10, H-2), 7.43 (1H, d, J = 10, H-1). m/z = 461.9 (M+�1),430.1, 209.1, 410.1. MS [M+H]+ calculated for C25H34FNO6 =464.24429; observed = 464.24372. kmax(CH3OH) = 239 nm.

3.2.18. Ethyl 2-(11b,17a-dihydroxy-9a-fluoro-16a-methyl-3-oxo-androst-1,4-dien-17b-carboxamido) acetate (DEG)

White crystalline solid obtained by purification with mobilephases B and E. Yield: 39.5%. Melting point: 237–239.4 �C. IR(ATR) mmax (cm�1): 1206.58 (CAO ester), 1524.97 (NAH, bend),1622.17 (C@C), 1644.41 (C3@O), 1662.61 (C@O amide), 1735.59(C@O ester), 3375.09 (NAH, stretch). 1H NMR (500 MHz, CD3OD)

102 V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108

d ppm 0.90 (1H, d, J = 7.5, CH3 at C-16), 1.10 (3H, s, H-18), 1.18–1.23 (1H, m, H-6), 1.28 (3H, t, J = 7, RAC(@O)OCH2CH3), 1.52 (1H,ddd, J = 5.5, J = 13, J = 18, H-15), 1.59 (3H, s, H-19), 1.62 (1H, dd,J = 1.7, J = 14.2, H-12), 1.76 (1H, q, J = 12, H-6), 1.86–1.90 (1H, m,H-15), 2.17–2.25 (2H, m, H-14, H-12), 2.37–2.51 (2H, m, H-7, H-8), 2.69–2.75 (1H, m, H-7), 3.08–3.13 (1H, m, H-16), 3.94 (2H,ABq, J = 17.5, RANHACH2AC(@O)OCH3), 4.19 (2H, q, J = 7.2,RANHACH2AC(@O)OCH2CH3), 4.24 (1H, dq, J = 2, J = 11, H-11),6.08 (1H, t, J = 1.5, H-4), 6.28 (1H, dd, J = 1.7, J = 10.2, H-2), 7.42(1H, d, J = 10.5, H-1). m/z = 462.0 (M+�1), 396.2, 377.8, 416.1. MS[M+H]+ calculated for C25H34FNO6 = 464.24430; ob-served = 464.24286. kmax(CH3OH) = 239 nm.

3.2.19. Ethyl 3-(11b,17a-dihydroxy-9a-fluoro-16a�methyl-3-oxo-androst-1,4-dien-17b-carboxamido) propionate (DEA)

White crystalline solid obtained by purification with mobilephases B and E. Yield: 38.1%. Melting point: 113.7–116.6 �C. IR(ATR) mmax (cm�1): 1185.32 (CAO ester), 1520.27 (NAH, bend),1605.35 and 1619.51 (C@C), 1660.59 (C@O amide), 1724.87 (C@Oester), 3382.54 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm0.88 (1H, d, J = 7.5, CH3 at C-16), 1.07 (3H, s, H-18), 1.17–1.22(1H, m, H-6), 1.26 (3H, t, J = 7, RAC(@O)OCH2CH3), 1.44 (1H, dd,J = 1.5, J = 14, H-12), 1.50–1.53 (1H, m, H-15), 1.59 (3H, s, H-19),1.75 (1H, q, J = 11.3, H-6), 1.86–1.90 (1H, m, H-15), 2.17–2.19(2H, m, H-14, H-12), 2.37–2.50 (2H, m, H-7, H-8), 2.55 (2H, t,J = 6.7, RANHACH2CH2C(@O)OCH2CH3), 2.69–2.75 (1H, m, H-7),3.08–3.13 (1H, m, H-16), 3.42 (1H, dt, J = 7, J = 14, RANHACH2CH2-

C(@O)OCH2CH3), 3.53 (1H, dt, J = 7, J = 13.5, RANHACH2

CH2C(@O)OCH2CH3), 4.15 (2H, q, J = 7.2, RANHACH2AC(@O)OCH2-

CH3), 4.21–4.24 (1H, m, H-11), 6.08 (1H, s, H-4), 6.28 (1H, dd,J = 1.7, J = 10.2, H-2), 7.42 (1H, d, J = 10.5, H-1). m/z = 476.0(M+�1), 376.1, 458.8, 311.1. MS [M+H]+ calculated for C26H36-

FNO6 = 478.25995; observed = 478.25876. kmax(CH3OH) = 240 nm.

3.2.20. Methyl 2-(11b,17a-dihydroxy-9a-fluoro-16a-methyl-3-oxo-androst-1,4-dien-17b-carboxamido)-3-phenyl propionate (DF)

White crystalline solid obtained by purification with mobilephases A and E. Yield: 52.1%. Melting point: 119–122 �C. IR (ATR)mmax (cm�1): 1268.72 (CAO ester), 1485.12 (NAH, bend), 1601.87(C@C), 1657.14 (C@O amide), 1717.47 (C@O ester), 3406.91(NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm 0.83 (1H, d,J = 7, CH3 at C-16), 1.03 (3H, s, H-18), 1.15–1.20 (1H, m, H-6),1.46–1.55 (2H, m, H-12, H-15), 1.57 (3H, s, H-19), 1.72 (1H, q,J = 11.8, H-6), 1.84–1.88 (1H, m, H-15), 2.09–2.18 (2H, m, H-14,H-12), 2.36–2.48 (2H, m, H-7, H-8), 2.67–2.74 (1H, m, H-7), 2.99–3.15 (3H, m, H-16, RANHACH(CH2C6H5)AC(@O)OCH3), 3.70 (3H,s, RAC(@O)OCH3), 4.19 (1H, dq, J = 2, J = 11, H-11), 4.68 (1H, t,J = 6.7, RANHACH(CH2C6H5)AC(@O)OCH3), 6.07 (1H, s, H-4), 6.28(1H, dd, J = 2, J = 10, H-2), 7.19–7.29 (5H, m, RANHACH(CH2C6H5)AC(@O)OCH3), 7.40 (1H, d, J = 10, H-1). m/z = 538.3 (M+�1),506.3, 173.9, 486.2. MS [M+H]+ calculated for C31H38FNO6 =540.27560; observed = 540.27405. kmax(CH3OH) = 240 nm.

3.2.21. Methyl 2-(11b,17a-dihydroxy-9a-fluoro-16b-methyl-3-oxo-androst-1,4-dien-17b-carboxamido) acetate (BG)

White crystalline solid obtained by purification with mobilephases C and E. Yield: 42.9%. Melting point: 263–266 �C. IR (ATR)mmax (cm�1): 1241.31 (CAO estar), 1503.75 (NAH, bend), 1601.20and 1618.37 (C@C), 1658.96 (C@O amide), 1740.00 (C@O ester),3434.43 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm 1.14(3H, s, H-18), 1.17–1.20 (1H, m, H-6), 1.22 (3H, d, J = 7.5, CH3 atC-6), 1.52–1.58 (2H, m, H-12, H-15), 1.60 (3H, s, H-19), 1.94–2.14(4H, m, H-15, H-6, H-14, H-16), 2.19 (1H, dt, J = 3.2, J = 14, H-12),2.41 (1H, dd, J = 3.5, J = 14, H-7), 2.46–2.58 (1H, m, H-8), 2.71–2.78 (1H, m, H-7), 3.73 (3H, s, RAC(@O)OCH3), 3.93 (2H, ABq,J = 17.5, RANHACH2AC(@O)OCH3), 4.24 (1H, dq, J = 2, J = 10.5, H-

11), 6.08 (1H, s, H-4), 6.28 (1H, dd, J = 2, J = 10, H-2), 7.42 (1H, d,J = 10, H-1). m/z = 447.9 (M+�1), 398.0, 415.9, 396.0. MS [M+H]+calculated for C24H32FNO6 = 450.22864; observed = 450.22826.kmax(CH3OH) = 239 nm.

3.2.22. Ethyl 2-(11b,17a-dihydroxy-9a-fluoro-16b-methyl-3-oxo-androst-1,4-dien-17b-carboxamido) acetate (BEG)

White crystalline solid obtained by purification with mobilephases B and E. Yield: 45.1%. Melting point: 252.2–255.1 �C. IR(ATR) mmax (cm�1): 1189.99 (CAO ester), 1505.39 (NAH, bend),1604.16 and 1619.09 (C@C), 1655.90 (C@O amide), 1739.17 (C@Oester), 3395.75 (NAH, stretch). 1H NMR (500 MHz, CD3OD) d ppm1.14 (3H, s, H-18), 1.18–1.20 (1H, m, H-6), 1.22 (3H, d, J = 7.5,CH3 at C-6), 1.283 (3H, t, J = 7, RAC(@O)OCH2CH3), 1.52–1.58 (2H,m, H-12, H-15), 1.60 (3H, s, H-19), 1.93–2.14 (4H, m, H-15, H-6,H-14, H-16), 2.19 (1H, dt, J = 3.4, J = 14, H-12), 2.41 (1H, dd,J = 3.5, J = 14, H-7), 2.46–2.58 (1H, m, H-8), 2.71–2.77 (1H, m, H-7), 3.92 (2H, ABq, J = 17.5, RANHACH2AC(@O)OCH3), 4.19 (2H, q,J = 7.2, RANHACH2AC(@O)OCH2CH3), 4.24 (1H, dq, J = 2, J = 10.5,H-11), 6.08 (1H, s, H-4), 6.28 (1H, dd, J = 2, J = 10, H-2), 7.42 (1H,d, J = 10, H-1). m/z = 461.9 (M+�1), 416.2, 395.9, 398.2. MS[M+H]+ calculated for C25H34FNO6 = 464.24430; ob-served = 464.24304. kmax(CH3OH) = 239 nm.

3.3. PAMPA test

The values of permeability coefficient-logPe, permeationparameter-CA(t)/CD(0), retention factor-R and predicted humanskin permeability-logKp of tested compounds are presented inTable 2.

The results for hydrocortisone and dexamethasone obtained inthis study were in good accordance with previously reported datafor these corticosteroids (Markovic et al., 2012).

According to Ottaviani et al. (2006), compounds can be classi-fied into 3 groups based on their human skin permeability (logKp):compounds with lower logKp (logKp < �6) having negligible mem-brane retention and low permeation (I) and compounds with high-er logKp (logKp P �6) which have low or negligible membraneretention and high permeation (II) or high membrane retentionand low permeation (III). The proposed equation for correlation be-tween logKp and logPe is:

logKp ¼ ð1:34� 0:12ÞlogPeþ ð0:28� 0:56Þ ð3Þ

Using this equation, logKp values for all tested compounds werecalculated (Table 1). All tested compounds have logKp < �6 and be-long to group I (compounds with negligible membrane retentionand low permeation) (Fig. 4).

The majority of 17b-carboxamide derivatives have retentionsimilar to the retention of starting corticosteroids. The next stepwas to identify derivatives that have significantly higher retentionthan starting corticosteroids. Higher retention of a compound canfavorably affect its potential anti-inflammatory activity becausethe compound is retained in the site of action in higher amount.On the other hand, higher permeability can cause systemic side ef-fects and reduce local activity of a compound. According to thestructure and expected metabolic properties of these compounds,low systemic toxicity is expected because these compounds shouldbe chemically transformed to inactive and non-toxic metabolites.Therefore, higher permeability should negatively affect only poten-tial local anti-inflammatory activity of these compounds. Singlefactor ANOVA was used to test the triplicates of results for eachcompound, comparing their mean values in order to identify deriv-atives with significantly higher retention than corticosteroids fromwhich they were synthesized. Significantly higher retention wasobtained for MPEA and all derivatives which are amides of cortie-nic acid and methyl ester of L-phenylalanine (HF, PF, MPF, DF).

Table 2Calculated PAMPA parameters of tested compounds.

Compound CA(t)/CD(0) logPe R logKp

Hydrocortisone 0.72 ± 0.03 �6.50 ± 0.02 1.60 ± 0.17 �8.44 ± 0.02HG 0.31 ± 0.00 �6.88 ± 0.01 0.33 ± 0.14 �8.94 ± 0.01HA 0.89 ± 0.13 �6.42 ± 0.06 0.34 ± 0.23 �8.32 ± 0.09HEG 0.47 ± 0.04 �6.70 ± 0.04 0.55 ± 0.33 �8.70 ± 0.05HEA 1.01 ± 0.09 �6.36 ± 0.04 1.98 ± 0.53 �8.24 ± 0.05HF 10.99 ± 0.48 �5.23 ± 0.00 8.42 ± 3.59 �6.73 ± 0.01

Prednisolone 0.67 ± 0.02 �6.53 ± 0.02 1.26 ± 0.64 �8.48 ± 0.02PG 0.17 ± 0.01 �7.13 ± 0.03 0.75 ± 0.43 �9.27 ± 0.04PA 0.55 ± 0.08 �6.62 ± 0.06 3.28 ± 1.21 �8.59 ± 0.09PEG 0.49 ± 0.03 �6.67 ± 0.03 2.15 ± 0.49 �8.65 ± 0.04PEA 0.63 ± 0.04 �6.57 ± 0.03 0.67 ± 0.12 �8.52 ± 0.04PF 8.07 ± 0.81 �5.37 ± 0.05 11.01 ± 0.84 �6.92 ± 0.06

Methylprednisolone 1.35 ± 0.06 �6.23 ± 0.02 0.94 ± 0.17 �8.19 ± 0.03MPG 0.55 ± 0.05 �6.62 ± 0.04 1.23 ± 0.31 �8.59 ± 0.06MPA 1.37 ± 0.06 �6.22 ± 0.02 1.67 ± 0.12 �8.06 ± 0.03MPEG 0.94 ± 0.16 �6.39 ± 0.09 2.32 ± 1.81 �8.28 ± 0.11MPEA 1.37 ± 0.10 �6.20 ± 0.03 6.47 ± 1.40 �8.03 ± 0.03MPF 11.56 ± 0.19 �5.21 ± 0.01 7.89 ± 1.29 �6.70 ± 0.02

Dexamethasone 1.69 ± 0.03 �6.13 ± 0.01 1.08 ± 0.14 �7.94 ± 0.01DG 0.4 ± 0.02 �6.77 ± 0.03 1.22 ± 0.26 �8.79 ± 0.04DA 1.38 ± 0.04 �6.22 ± 0.01 0.95 ± 0.48 �8.05 ± 0.02DEG 0.72 ± 0.03 �6.50 ± 0.02 2.05 ± 0.21 �8.44 ± 0.02DEA 0.97 ± 0.02 �6.37 ± 0.01 3.19 ± 1.20 �8.25 ± 0.01DF 13.05 ± 0.42 �5.15 ± 0.01 7.15 ± 1.18 �6.63 ± 0.01

Betamethasone 1.94 ± 0.18 �6.06 ± 0.04 2.57 ± 1.17 �7.85 ± 0.05BG 0.72 ± 0.03 �6.51 ± 0.02 0.90 ± 0.51 �8.44 ± 0.03BEG 1.39 ± 0.08 �6.20 ± 0.02 4.24 ± 0.81 �8.03 ± 0.03

Fig. 4. The relationship between retention factor, R and permeation parameter,CA(t)/CD(0) of examined compounds in PAMPA test.

V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108 103

Apart from this, phenylalanine derivatives have significantly high-er permeability than starting corticosteroids. Derivative MPEAdoes not have higher permeability than methylprednisolone whichdistinguishes this compound from phenylalnine derivatives.

3.4. Stepwise MLR-, PLS-, and ANN-QSPR studies

The quantitative structure–property relationship (QSPR) studywas performed to investigate the correlations between logPe andR (dependent variables) of the examined compounds and their cal-culated constitutional, geometrical, thermodynamic, physicochem-ical and electronical molecular parameters (independentvariables). Independent variables that have the strongest influenceon dependent variables (logPe and R) and form final QSPR models(logPe and R) are defined as molecular descriptors. In order to per-

form relevant comparison between different methodologies usedto build QSPR models, same training, verification and test sets wereused. For MLR(logPe) and PLS(logPe) test set consisted of 11 com-pounds (BEG, HA, MPF, PEG, PG, HEA, HEG, HG, MPA, MPEG, MPG)while other compounds were chosen as training set. ForANN(logPe), compounds used as test set for MLR(logPe) andPLS(logPe) were divided into two groups: verification set (HEA,HEG, HG, MPA, MPEG, MPG – 6 compounds) and test set (BEG,HA, MPF, PEG, PG – 5 compounds). For MLR(R) and PLS(R) testset consisted of 11 compounds (HEA, HEG, HG, MPA, MPEG, PG,BEG, HA, MPF, MPG, PEG), while other compounds were chosenas training set. For ANN(R), compounds used as test set for MLR(R)and PLS(R) were divided into two groups: verification set (HEA,HEG, HG, MPA, MPEG, PG – 6 compounds) and test set (BEG, HA,MPF, MPG, PEG - 5 compounds). Test and verification sets wereformed in the way that logPe and R values of these compoundswere homogenously distributed in the whole range of logPe andR values.

Multilayer perceptron artificial neural network based on back-propagation training algorithm was chosen for nonlinear QSPRmodeling. The first step in ANN construction was appropriate inde-pendent variable selection. The genetic algorithm (Gupta et al.,2011), principal component analysis (Zhang, 2007), and stepwiseMLR (Filipic et al., 2013; Gonzalez-Arjona et al., 2002; Jalali-Heraviand Garkani-Nejad, 2002) had been used for independent variableselection. In this study, stepwise MLR was applied. Thus selectedindependent variables were used as inputs for ANN modeling.With forward stepwise MLR, independent variables are addedone by one into the model. These variables are evaluated at eachstep, being added or deleted from the model based on specified cri-teria (F to enter and F to remove criteria). For ANN(logPe), F to en-ter values were 7 and 8, whereas F to remove value in both caseswas 6. For ANN(R), F to enter values were 4 and 6, whereas F to re-move value was 3. Using stepwise MLR, sets of 7 and 4 indepen-dent variables for both ANN(logPe) and ANN(R) were formed.Subsequently, three-layer networks were created. The first layer

Table 4Sensitivity analysis of ANN(R) model.

Descriptor nBM RDF015m RDF145s Mor24s

Ratio a 3.19 1.60 1.29 1.78

a Sensitivity analysis parameter.

104 V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108

is input layer. Number of nodes in the input layer is equivalent tothe number of selected independent variables. The second layer isa hidden layer with optimal number of units (nodes). The thirdlayer is output layer, consisting of one node (logPe or R of theexamined compounds). The whole data set was divided into train-ing (16 compounds), verification (6 compounds) and test set (5compounds). The training set is used for network training andthe verification set is used to perform an independent check ofthe network performance during training in order to avoid overfit-ting the data. Finally, the test set is used to provide a final indepen-dent check of the network performance. The optimal number ofnodes in hidden layer, number of epochs, momentum and learningrate for both ANN(logPe) and ANN(R) modeling were previouslydetermined in initial network trainings for each set of chosendescriptors for ANN(logPe) and ANN(R), evaluating prediction er-rors for training, verification and test sets. Thus formed modelswith different number of descriptors (7 and 4 for both ANN(logPe)and ANN(R)) were analyzed and best ones were selected compar-ing Q2 (cross-validated squared correlation coefficient), r (correla-tion between observed and predicted values of test set), RMSEE(root mean squared error of estimation) and RMSEP (root meansquared error of prediction) values. The final structure of selectedmodels (number of nodes in input layer – number of nodes inhidden layer - number of nodes in output layer) was 4-6-1(ANN(logPe)) and 4-4-1 (ANN(R)).

Forward stepwise MLR models have been developed to assessthe linear relationship between calculated molecular parametersand logPe and R of tested compounds. The sets of 4 independentvariables used for ANN(logPe) and ANN(R) were employed forMLR modeling. Using forward stepwise method (the same F toenter and F to remove values and other parameters as for the inde-pendent variable selection for ANN modeling), three out of fourdescriptors from the selected ANN(logPe) model were includedin the final MLR(logPe) model, whereas entire set of four descrip-tors from the selected ANN(R) model was included in the finalMLR(R) model (Table 3).

In contrast to MLR analysis, PLS approach can analyze data withstrongly collinear, noisy, and numerous independent variables (X-variables). In PLS modeling, the importance of each X-variable isevaluated according to its VIP value. The X-variables with VIP valuelarger than 1 are the most relevant, those with 1.0 > VIP > 0.5 aremoderately influential, while X-variables with VIP value smallerthan 0.5 are not relevant for the model (Eriksson et al., 2001;Umetrics AB, 2008). X-variables with lowest VIP-values are succes-sively removed from the PLS model and each time new PLS modelis created. For each created model regression factors R2, Q2, F ratio,

Table 3Statistical parameters calculated for ANN, PLS and MLR models.

Model Regression equations/selected descriptors

ANN(logPe) logPe = f (Mor16s,nCt,C-008,CATS2D_09_DL)a

PLS(logPe) logPe = f(D/Dtr06,CATS2D_08_LL,F02[CAC],F05[CAO], MLOGP2)b

MLR(logPe) logPe = 7.88465 + 0.00009.Mor16s + 0.52423.C-008 +0.39012.CATS2D_09_DL

ANN(R) R = f(nBM,RDF015m,RDF145s,Mor24s)c

PLS(R) R = f(HOMO,HOMT,RDF130v,CATS2D_05_AL)d

MLR(R) R = 0.985602.nBM-0.001364.RDF015m + 0.000069.RDF145s + 0.000738.Mor24s

a Mor16s – signal 16/weighted by I-state (3D MoRSE descriptor); nCt – number of tCATS2D Donor-Lipophilic at lag 09.

b D/Dtr06 – distance/detour ring index of order 6; CATS_2D_08_LL – CATS2D LipophF05[CAO] – frequency of CAO at topological distance 5; MLOGP2 – squared Moriguchi

c nBM – number of multiple bonds; RDF015m – Radial Distribution Function-015/weMor24s – signal 24/weighted by I-state.

d HOMO – highest occupied molecular orbital; HOMT – HOMA total (geometrical devolume; CATS_2D_05_AL – CATS2D Acceptor-Lipophilic at lag 05.

p value, RMSEE are calculated and compared with the previousmodel. The procedure is repeated until the best model is obtained.The response permutation test (Y scrambling) is used to examinethe statistical significance of the R2 and Q2 and overfitting due tothe chance correlation (Eriksson et al., 2001). In the response per-mutation test, the Y-matrix is randomly re-ordered (100 times inthis project) whereas the X-matrix is kept intact. Model is fittedto the new Y-data and the new R2, Q2 and VIP parameters are cal-culated. All model selection steps are repeated on the scrambled Y-response data. Lines are fitted through the R2-values and throughthe Q2-values to yield two separate intercepts. For a valid model,the R2-intercept should not exceed 0.4 while the Q2-interceptshould be lower than 0.05 (Eriksson et al., 2001).

Statistical parameters calculated for QSPR models are presentedin Table 3.

Numerical values of descriptors that form final QSPR models arepresented in Supplementary Tables 2 and 3.

The quality of the obtained QSPR models was estimated usingsuch parameters, as R2 (square of the correlation coefficient), theF ratio, the p value, RMSEE, RMSEP, r, Q2 (equation (5)) and R2

pred

(equation (6)).RMSEE value is calculated for training set, whereas RMSEP value

is calculated for test and verification set (ANN models) or test set(MLR and PLS models).

Q2 is an internal validation parameter. It is calculated usingleave-one-out (LOO) cross-validation technique where each com-pound of the training set is deleted once while the remaining com-pounds are used to create a model. The value of the deletedcompound is predicted using the developed model. The procedureis repeated until all the compounds are deleted once (Ojha and Roy,2011; Snedecor and Cochran, 1967). Q2 is calculated according tothe equation (5). In this equation, �Ytraining is average logPe or R va-lue of compounds from training set, while Yobs(training) is an ob-served logPe or R value from the training set. PRESS is calculatedfor the training set according to the equation (4) after the comple-tion of the LOO procedure. QSPR models with Q2 P 0.5 are consid-ered to have good predictive capability (Eriksson et al., 2001;Umetrics AB, 2008).

RMSEE RMSEP Q2 r R2pred

R2 F p

0.102 0.195 0.671 0.922 0.8670.079 0.172 0.961 0.939 0.896 0.974 34.815 0.000010.125 0.264 0.895 0.845 0.756 0.917 56.437 <0.0000

0.746 0.830 0.888 0.950 0.8821.109 1.473 0.850 0.853 0.630 0.872 36.703 0.0000040.519 0.962 0.908 0.955 0.842 0.962 95.156 <0.0000

otal tertiary C(sp3); C-008 – CHR2X (Atom-centred fragments); CATS2D_09_DL –

ilic–Lipophilic at lag 08; F02[CAC] – frequency of CAC at topological distance 2;octanol–water partition coefficient.ighted by mass; RDF145s – Radial Distribution Function-145/weighted by I-state;

scriptor); RDF130v – Radial Distribution Function-130/weighted by van der Waals

V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108 105

Q2 value enables determination of predictive potential of a QSPRmodel for compounds that are similar to ones in the training set. Todetermine predictive potential of a QSPR model for compoundsthat differ in a certain manner from those in the training set,R2

pred is used. R2pred is an external validation parameter, calculated

according to the equation (6) (Marshall, 1994). In equation (6),Yobs(test) is an observed value of logPe or R of a test set compoundand Y

�training is mean logPe or R value of the training set compounds.

PRESS is calculated for test set according to the equation (4).

PRESS ¼Xn

i¼1

e2ðiÞ ð4Þ

Difference between observed and the predicted Y values – (e(i))

Q 2 ¼ 1� PRESSPðYobsðtrainingÞ � YtrainingÞ

2 ð5Þ

Fig. 5. VIP values of descriptors i

Fig. 6. Coefficient plot of descriptor

R2pred ¼ 1� PRESSP

ðYobsðtestÞ � YtrainingÞ2 ð6Þ

QSPR models with R2pred P 0:5 can be considered to have good pre-

dictive capability (Ojha and Roy, 2011; Tropsha, 2010).The F-test, which is based on the ratio MS Regression/MS Resid-

ual, formally evaluates significance of the model. The p value indi-cates the probability level where a model with this F value may bethe result of just chance. Models with p-value lower than 0.05 areconsidered significant (Eriksson et al., 2001).

Analyzing Q2 and R2pred for obtained QSPR models (Table 4), it can

be concluded that all of them have good predictive potential(Q2 P 0.5 and R2

pred P 0.5). Comparing other statistical parameters,the most reliable QSPR models for logPe and R were PLS(logPe) andANN(R).

n selected PLS(logPe) model.

s in selected PLS(logPe) model.

Fig. 7. Response graphs from the ANN(R) model.

Fig. 8. 3D structure with geometrical center of DG.

106 V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108

3.4.1. Interpretation of selected QSPR modelsAccording to VIP parameters of descriptors in selected

PLS(logPe) model, all of them have similar influence on the perme-ability of tested compounds through the artificial PAMPA mem-brane. VIP values of these descriptors are from 0.9 to 1.2 (Fig. 5).

Coefficient plot (Fig. 6) shows that all descriptors have positive,apart from D/Dtr06 which has negative influence on logPe.Descriptor D/Dtr06 is a ring descriptor and represents distance/de-tour ring index of order 6. Descriptors F02[CAC] and F05[CAO] rep-resent frequency of CAC at topological distance 2 and frequency ofCAO at topological distance 5. These descriptors belong to 2D atompairs descriptors. MLOGP2 is a squared Moriguchi octanol–waterpartition coefficient. CATS2D_08_LL belongs to CATS2D descrip-tors. This descriptor is defined as CATS2D lipophilic–lipophilic atlag08 (TALETE srl, 2013). MLOGP2 and CATS2D_08_LL are relatedto the lipophilicity of tested compounds. Apart fromCATS2D_08_LL, CATS2D_09_DL appears in other two models(ANN(logPe) and MLR(logPe)) and also represents lipophilicity.This indicates that lipophilicity of tested molecules is important

property that influences permeability through the artificial PAMPAmembrane.

Analyzing response graphs obtained from the ANN(R) model,which show the influence of each descriptor on retention of testedcompounds, it can be concluded that nBM, Mor24s and RDF145shave positive, whereas RDF015m has negative influence on reten-tion (Fig. 7). The same conclusion can be drawn analyzing MLR(R)model and coefficients in front of each descriptor.

According to the sensitivity analysis of the descriptors inANN(R) model (Table 4), the most influential descriptor on reten-tion of tested compounds in the PAMPA artificial membrane isnBM (number of multiple bonds) (TALETE srl, 2013). The increaseof the value of these descriptors causes the increase of retention.This can be used to explain why the majority of prednisolonederivatives (PG, PEG, PA, PF) have higher retention than corre-sponding derivatives of hydrocortisone (HG, HEG, HA, HF). In addi-tion, this can be used to explain high retention of phenylalaninederivatives (HF, PF, MPF, DF). According to this, p�p interactionbetween molecules and constituents of the artificial membrane

V. Dobricic et al. / European Journal of Pharmaceutical Sciences 52 (2014) 95–108 107

might be one of membrane retention mechanisms of thesecompounds.

Mor24s is a 3D MoRSE descriptor (TALETE srl, 2013). The 3D-MoRSE descriptors are molecule atom projections along differentangles, such as in electron diffraction. These descriptors take intoaccount 3D arrangement of the atoms in a molecule without ambi-guities. 3D MoRSE descriptors do not depend on the molecular sizeand can be applied to a large number of molecules with greatstructural variance (Duchowicz et al., 2006). They represent differ-ent views of the whole structure, but their meaning is not clear(Todeschini and Consonni, 2000; Hancock et al., 2005). RDF015mand RDF145s are Radial Distribution Function (RDF) descriptorsweighted by mass and l-state respectively (TALETE srl, 2013).RDF015m represents 3D molecular distribution of mass calculatedat radius of 1.5 Å from the geometrical center of a molecule. Thisdescriptor is actually related to steric factors at the radius of1.5 Å from the geometrical center of the molecule (Gonzalezet al., 2006). Compounds with the highest values of this descriptorare DF, DEG, DG and DEA. Most likely, this is related to the pres-ence of methyl group in a-orientation at position C16 in thesecompounds, since the distance between this group and geometricalcenters of these molecules is 2–2.5 Å. This is presented in Fig. 8 (DGis used as an example, where geometrical center (Du) and methylgroup at C16 are circled). RDF145s is a signal calculated at radius of14.5 Å from the center of molecule. BEG and MPEA have highest,whereas prednisolone and methylprednisolone have lowest valuesof this descriptor. This descriptor could be used to explain positiveinfluence of side chain length on the retention.

Instead of PAMPA experiments, formed QSPR models could beused for in silico prediction of human skin permeability and reten-tion of novel 17b-carboxamide steroids (e.g., obtained by structuralmodifications of compounds tested in this study or 17b-carboxam-ide steroids derived from other corticosteroids).

4. Conclusion

Twenty-two 17b-carboxamide derivatives of hydrocortisone,prednisolone, methylprednisolone, dexamethasone and betameth-asone were synthesized and structurally characterized. These com-pounds are L-amino acid amides of cortienic acids obtained byperiodic acid oxidation of corresponding corticosteroids and repre-sent potential soft corticosteroids with local anti-inflammatoryactivity in the skin. Their permeability and retention in the humanskin were estimated using PAMPA test and permeability coeffi-cients, permeation parameters, retention factors and predicted hu-man skin permeability values were calculated. According tocalculated permeation parameters and retention factors, all com-pounds are classified into group I (compounds with negligiblemembrane retention and low permeation). Compounds with sig-nificantly higher retention than corresponding corticosteroidswere identified. High retention of a compound in the skin can pos-itively affect its potential local anti-inflammatory activity withminimal systemic side effects. Derivatives HF, PF, MPF, DF andMPEA have significantly higher retention than corresponding corti-costeroids while compound MPEA does not have significantly high-er permeability than methylprednisolone. Geometrical,thermodynamic, physicochemical and electronical molecularparameters were calculated and stepwise MLR-, PLS-, and ANN-QSPR studies were performed. Best models that explain permeabil-ity coefficients and retention in the artificial membrane were cho-sen. Analyzing descriptors in these models, the most significantmolecular properties that influence permeability and retention inthe artificial membrane were identified. Taking this into account,further structural modifications of these compounds could be ap-plied in order to improve their retention without significant in-

crease of permeability. High retention and low permeability,together with expected metabolical inactivation of 17b-carboxam-ide steroids, should enable favorable therapeutical effects withminimal local and systemic side effects, which are common for tra-ditional corticosteroids when applied to the skin. Selected QSPRmodels provide in silico prediction of human skin permeabilityand retention of novel 17b-carboxamide steroids without perform-ing PAMPA experiments.

Acknowledgements

This work was financially supported by the Ministry of Educa-tion, Science and Technological Development, Belgrade, Serbia, aspart of Project No. 172041. We also thank the Institute of Chemis-try, Technology and Metalurgy, Belgrade, Serbia and Department ofBiochemistry, Faculty of Chemistry, University of Belgrade, Serbiafor their assistance.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.ejps.2013.10.017.

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