Theoretical prediction of the native fluorescence of pharmaceuticals

Talanta 79 (2009) 412–418

Contents lists available at ScienceDirect

Talanta journal homepage: www.elsevier.com/locate/talanta

Theoretical prediction of the native fluorescence of pharmaceuticals J.R. Albert-Garcia c , G.M. Antón-Fos a , M.J. Duart b , L. Lahuerta Zamora a , J. Martínez Calatayud a,∗ a

Departamento de Química, Bioquímica y Biología Molecular, Universidad Cardenal Herrera-CEU, Moncada, Valencia, Spain Departamento de Ingeniería, División de Farmacia y Tecnología Farmacéutica, Facultad de Farmacia, Universidad Miguel Hernández, Alicante, Spain c Departamento de Química Analítica, Universidad de Valencia, Valencia, Spain b

a r t i c l e

i n f o

Article history: Received 19 September 2008 Received in revised form 30 March 2009 Accepted 1 April 2009 Available online 10 April 2009 Keywords: Organic compounds Pharmaceuticals Flow Molecular connectivity Fluorescence

a b s t r a c t At present, to search fluorescent compounds or to increase the native fluorescence is an active research line specially and not only with analytical purposes. On some analytical areas and from the early times of applications of fluorescence (mid-fifties) the fluorimeter was defined as the suitable detector for determination of pharmaceuticals and subsequently, this detection mode has been widely applied. Therefore, it is mandatory to develop new strategies to discover or to enhance in a simple way the native fluorescence of organic compounds to increase the number of analytes to be determined by direct fluorescence. In the present paper are studied further applications of a new tool suitable to increase the research in analytical field. Calculations on molecular connectivity and discriminant analysis are applied to a certain number of pharmaceuticals (and some pesticides) on which fluorescence behaviour was observed in an experimental screening or obtained from scientific literature. The screening tests were based on the on-line fluorimetric measurement by using a continuous-flow assembly. The screening comprised pre-selected compounds with different molecular structures. The theoretical predictions agree with the empirical results from the screening test. © 2009 Elsevier B.V. All rights reserved.

1. Introduction In Molecular Topology or more strictly speaking molecular connectivity, a molecule is assimilated to a graph, where each atom is represented by a dot (vertex) and its connections, the bonds, are represented by linear segments (edges between vertices). Bearing in mind these inter-connexions between vertexes, a topological matrix can be built; elements ij take the values 1 or 0, depending whether a given vertex i is connected to the vertex j or not, respectively. The mathematical manipulation of this matrix resulted in a set of topological (indices) descriptors. These indices, whether well chosen, are a unique characterization of the molecular structure. Furthermore, they can be correlated with many physical, chemical and biological properties of molecules [1–4]. This topological method has been applied to the determination of biological activities [5], physical characteristics [6], chemical properties [7] and to design new drugs into different families of therapeutical activity [8–12]. Recently the molecular connectivity has been applied to predict the analytical behaviour of organic molecules (pharmaceuticals and pesticides) as chemiluminescent with the aid of a given redox reaction [13] or by previous on-line UV-irradiation [14].

∗ Corresponding author at: Departamento de Química Analítica, Universidad de Valencia, 46100 Valencia, Spain. Tel.: +34 96 354 40 62; fax: +34 96 354 40 62. E-mail address: [email protected] (J. Martínez Calatayud). 0039-9140/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.talanta.2009.04.006

Fluorimetry from the early fifties has been among the most frequently used techniques for determining both therapeutical and abuse drugs; probably due to its excellent selectivity and the low detection limits. Also, the fluorimetric technique is the recommended choice for quantifying the purity of active principles. The research on analytical fluorescence applications is in continuous expansion to new automated or semi-automate processes like classic or emergent methodologies on the continuous-flow field; on this way, fluorescence-based methods have found a wide range of analytical applications [15,16]. In addition to its analytical applications, fluorimetry is a multidimensional technique providing different types of information in addition to spectral and output intensities. Measurements of fluorescence polarization (the fluorescence of a molecule can be partially or totally polarized) can provide important information and it is of widespread use in biological and polymer science. Also measurements of time-resolved spectra (the fluorescence rate of a molecule can be measured) and decay times can provide fundamental information in the study of very fast chemical and physical phenomena. Properties as pH, viscosity, polarity, etc. may be inferred from measurements of fluorescence spectra or decay times of suitable probe molecules; these fluorescent probes are widely used in biology and material sciences to obtain information concerned with the nature and accessibility of binding sites in biological macromolecules, in homogeneities of polymer chains, in properties of micelles, counting particles in flow cytometry, concentration of ions in cells, remote sensing using fiberoptic sensors, the study of solid surfaces

J.R. Albert-Garcia et al. / Talanta 79 (2009) 412–418

413

Table 1 Experimental results obtained with the assayed pharmaceuticals and pesticides. Analyte

exc

em

Imax

Ascorbic acid Aspartic acid 3-Indolyl acetic acid Acetylcysteine L–D Alachlor Alanine ␣ (D–L) Alanine ␤ Amitriptyline Amoxicillin Ampicillin Atropine Benfuresate Benzalkonium HCl Benzocaine Biguanide Bromacil Brompyrazon Buminafos Caffeine Captopril Chlordimeform HCl Chlorpromazine Ciprofloxacine Clotrimazole Colesterine Cycloate Cysteine Diazepam Diphenamid DNOC Dopamine Fenobucarb Fenoprop Fluometuron Furalaxyl Glutamic acid Isoleucine Isoprocarb Karbutilate l-Alanine l-Arginine l-Asparagine l-Glutamine Lidocaine l-Leucine Lysine HCl Metobromuron Minoxidil Novocaine Ofurace Oxasulfuron Phenylamine Phenylephrine Piroxicam Prazosin HCl Prednisolone Promethazine Propanil Propranolol HCl Spiroxamine Sulphadiazine Sulphamethazine Sulphathiazole Threonine Trimethoprim Tryptophan Tyrosine Umbelliferone Valine Vit.B1 thiamine Vit.B2 riboflavine Vit.B3 nicotinamide Vit.B5 pantothenic ac. Vit.B6 pyridoxine

280 270 280 260 280 285 250 260 290 345 260 278 265 285 285 240 280 280 280 265 280 270 260 280 280 275 280 275 260 265 280 270 280 248 280 275 285 265 280 285 275 245 270 280 280 280 285 285 285 280 290 257 275 330 244 300 270 285 290 275 285 260 260 265 265 280 274 325 265 370 370 320 285 290

335 355 364 306 309 361 436 346 270 421 285 316 292 352 357 355 310 367 309 340 310 457 435 330 400 307 378 426 566 320 319 300 310 333 311 345 311 296 315 369 310 329 299 340 333 363 317 414 354 310 351 282 302 440 389 431 379 315 352 302 352 345 441 342 341 348 303 418 358 432 527 387 345 398

12.35 9.57 >1000 6.02 80.93 3.28 5.21 3.65 40 40.2 24.5 310.3 37.8 705 46.7 56.38 45.8 67.84 25.6 17.2 64.79 489.6 >1000 49.2 30.85 17.14 18.17 125.7 34.04 20.43 >1000 172.5 40.1 725.9 16.03 11.24 7.51 69.09 21.12 2.51 4.35 4.02 22.1 7.25 7.63 21.13 15.14 82.6 98.7 19.12 201.1 – >1000 9 14 6.02 339.4 10.95 >1000 7.92 18.5 6 26.51 16.15 872.15 – – 5 31.25 24.6 >1000 50.7 14.7 >1000

Imax ≥3 × Iblank

x

x x

x x

x

x x x

x

x x x x x

x x

x x x

x x x

Medium HClO4 ×10−6 M HClO4 ×10−6 M Pure water NaOH ×10−4 M NaOH ×10−6 M HClO4 ×10−6 M HClO4 ×10−6 M NaOH ×10−6 M NaOH ×10−2 M NaOH ×10−2 M NaOH ×10−2 M Pure water NaOH ×10−4 M NaOH ×10−2 M NaOH ×10−4 M HClO4 ×10−6 M Pure water NaOH ×10−6 M NaOH ×10−2 M HClO4 ×10−4 M HClO4 ×10−6 M NaOH ×10−2 M NaOH ×10−4 M HClO4 5 × 10−2 M Ethilic ether NaOH ×10−6 M HClO4 ×10−4 M HClO4 ×10−6 M HClO4 ×10−6 M HClO4 ×10−6 M HClO4 ×10−4 M Pure water Pure water Pure water NaOH ×10−6 M HClO4 ×10−6 M HClO4 ×10−6 M HClO4 ×10−4 M NaOH ×10−6 M NaOH ×10−4 M NaOH ×10−6 M NaOH ×10−4 M HClO4 ×10−4 M NaOH ×10−6 M NaOH ×10−4 M HClO4 ×10−6 M HClO4 ×10−6 M NaOH ×10−2 M HClO4 5 × 10−2 M NaOH ×10−6 M Pure Water Pure Water HClO4 ×10−4 M HNO3 0.5 M Pure Water HClO4 ×10−6 M HClO4 5 × 10−2 M Pure Water Pure Water HClO4 ×10−6 M NaOH ×10−6 M Pure Water HClO4 ×10−6 M HClO4 ×10−4 M HClO4 ×10−4 M Pure Water Pure Water Pure Water/methanol (50:50) NaOH ×10−4 M NaOH ×10−2 M HClO4 5 × 10−2 M HClO4 5 × 10−2 M HClO4 ×10−4 M HClO4 5 × 10−2 M

Maximum observed fluorescence intensity (Imax ) corresponds to the displayed medium. The fifth column shows fluorescent compounds, namely, those whose maximum fluorescence intensity was over threefold the one from its own blank.

414

J.R. Albert-Garcia et al. / Talanta 79 (2009) 412–418

Fig. 1. Flow assembly for the experimental screening.

and many other applications (a very long list) no merely analytical. In former papers [7,13,14,17,18] we introduced the molecular connectivity calculations into the experimental work of Analytical Chemistry; previously developed topics were used as reported in above paragraphs, to predict the chemiluminescence behaviour of organic compounds with reaction of strong inorganic oxidants; and, the photo-induced chemiluminescence. As far as authors know (analytical references), those papers aided to investigate and publish new analytical procedures from other laboratories. The present Table 2 Bibliographic information [19] about native fluorescent compounds. Analyte

exc (nm)

em (nm)

Medium: water (pH)

Aminopterin Amobarbital Antimycin A Azoguanine Bromolysergic acid diethylamide Brucine Bufotenine Cinchonidine Cinchonine Codeine Deserpidine Desipramine Epinephrine Ethacridine Gentisic acid Griseofulvin Harmine Hydroxyamphetamine Imipramine Indoleacetic acid Indomethacin Levo-dromoran Mephenesin Morphine Nalorphine Naphthalene acetic acid Naphthaleneacetamide Norepinehrine p-Aminosalicylic acid Pentobarbital Phenobarbital Phenylenepyrene Physostigmine Piperoxan Podophyllotoxin Procainamide Procaine Psilocin Quinacrine Quinidine Quinine Rescinnamine Reserpine Salicylic acid Scoparone Scopoletin Synephrin Thiamylal Yohimbine Zoxazolamine

280 264 350 285 315 305 292 315 320 285 280 295 285 370 315 295 300 275 295 295 300 275 280 285 285 270 270 285 300 265 265 270 265 290 280 295 275 292 285 350 250 310 300 310 350 365 270 310 270 280

460 410 420 405 460 500 520 445 420 350 365 415 325 515 440 450 400 300 415 345 410 320 315 350 350 327 327 325 405 440 440 305 315 325 325 385 345 314 420 450 450 400 375 400 430 460 310 530 360 320

7 14 8 7 1 7 <0 1 1 7 1–2 14 7 2 7 7 1 1 8 13 1 1 7 7 11 11 7 11 13 13 1 7 7 11 11 11 7 11 1 1 1 1 10 10 10 1 13 1 11

paper follows this attempt by proposing a new strategic tool to predict the native fluorescence of organic molecules. For this purpose, the molecular connectivity indices of different organic substances (pharmaceuticals and pesticides) were calculated. We faced the problem of finding out molecules which resulted or not in native fluorescence emission in liquid phase and different pH values by searching in the analytical literature. It was relatively easy to find molecules described as fluorescent [19] but it is not so easy to find non-fluorescence molecules as these “negative” results are not usually reported. The bibliographic screening was implemented with the results from experimental assays on pharmaceuticals and some pesticides. The work presented in this paper was focused to present a new tool for enhancing the research yield on new analytical applications of fluorescence. 2. Experimental 2.1. Reagents All reagents were analytically pure unless stated otherwise and prepared in purified water by reverse osmosis and then deionized (1.8 × 10−4 S m−1 ) by using a Sybron/Barnstead Nanopure II water purification system. Pharmaceuticals were obtained from different manufacturers, most of them from Guinama (Valencia, Spain). Pesticides were obtained from Dr. Ehrenstorfer (Augsburg, Germany). 2.2. Flow assembly and screening procedures The flow manifold used for experimental screening (see Fig. 1) comprised a PTFE coil of 0.8 mm i.d. and a Gilson Minipuls 2 (Worthington, OH, USA) peristaltic pump. The detector was the fluorimeter model FP 750 supplied by Jasco (Spain) and provided with a Hellma (Germany) flow cell of 1 cm path length. Experimental screening was performed at different pH values. Aqueous analyte solutions (50 mg L−1 ) merged with the medium

Fig. 2. Distribution diagrams for emission fluorescent activity. White line: nonfluorescence emission. Black line: fluorescence emission. Upper: training group. Lower: testing group.

J.R. Albert-Garcia et al. / Talanta 79 (2009) 412–418

415

Table 3 Symbols and definitions of topological indices. Symbol

k

t

k = 0–10 t = p, c, pc

k

vt

k = 0–10 t = p, c, pc k Dt k = 0–4 t = p, c, pc

Gk

Name

Definition

´ Randic-like indices of order k and type path (p), cluster (c) and path-cluster (pc)

k

Kier-Hall indices of order k and type path (p), cluster (c) and path-cluster (pc) Connectivity differences of order k and type path (p), cluster (c) and path-cluster (pc) Topological charge indices of order k

⎛

Refs.

⎞−1/2

kn t   ⎝ ıi ⎠ t =

j=1

[20]

i ∈ Sj

ıi , number of bonds,  or , of the atom i to non-hydrogen atoms Sj, jth sub-structure of order k and type t

⎛ ⎞−1/2 kn t   k v ⎝ ıvi ⎠ t = j=1

[21]

i ∈ Sj

ıvi , Kier-Hall valence of the atom i Sj, jth sub-structure of order k and type t k

Dt = k t − k vt

Gk =

[21]

N N−1     M ij − M ji  ı(k, Dij )

[22]

i=1 j=i+1

k = 1–5

M = AQ, product of the adjacency and inverse squared distance matrices for the hydrogen-depleted molecular graph D, distance matriz; ı, Kronecker delta

Gkv

Gkv =

Valence topological charge indices of order k

N N−1    v  M ij − M vji  ı(k, Dij ) i=1 j=i+1

[22]

Mv = Av Q, product of the electronegativity-modified adjacency and inverse squared distance matrices for the hydrogen-depleted molecular graph D, distance matriz; ı, Kronecker delta

k = 1–5

Gk , N−1

Pondered topological charge indices of order k

Jk =

Si

Sum-electrotopological indexes type Length PR1 to PR4

Si = Ii + Ii

[24]

Maximal distance between atoms in terms of bonds Number of pairs of ramifications separated by i edges Difference of 3 vc and 4 pc and difference of 3 vc and 4 vpc , respectively

[23] [23] [2]

Index based on the information theory Total number of bonds between all the pairs of atoms of the graph

[26] [25]

L PRi knotpv knot IShannon W

Shannon index Wiener index

Jkv =

Gv

Jk , Jkv k = 1–5

k

N−1

[22]

Topological descriptors were calculated for each compound by using MOLCONN-Z and DESMOL13 programs.

stream formed by solutions of HClO4 (5 × 10−2 , 10−4 and 10−6 M) or NaOH (10−2 , 10−4 and 10−6 M) or pure water. Both solutions were flowing at the flow-rate 1.5 mL min−1 (final analyte concentration 25 mg L−1 ) and after being mixed into the reactor tubing (length 10 cm) were leaded to the detector flow-cell. The emission output was recorded at 8000 nm min−1 and wavelength intervals over the range exc 220–400 nm and em 220–600 nm for excitation and emission, respectively. Analyte stream replaced for pure water merging with the media stream was used to obtain the corresponding blank signals. The working standard solutions were freshly prepared and protected against room light and temperature changes. Screening was carried out with different compounds and fluorimeter outputs were compared with the corresponding blank signal. Some pesticides were included in the study to implement the information obtained form the screening of pharmaceuticals. Pesticides were assayed at 10 mg L−1 ; final tested solution 5 mg L−1 . Table 1 depicts the results obtained with the assayed compounds. A compound was considered not fluorescent when its signal was below threefold the one from its own blank. Bibliographic information [19] also used for molecular connectivity calculations is depicted in Table 2 (native fluorescent compounds).

2.3. Molecular connectivity and topological descriptors A set of 170 descriptors was calculated for each selected compound by using Hall’s MOLCONN-Z [L. H. Hall, MOLCONN-Z software, 1995, Eastern Nazarene College, Quincy, Massachusetts], and DESMOL13 [DESMOL13 software, 2000, Unidad de Investi˜ de Fármacos y Conectividad Molecular, Facultad gación de Diseno de Farmacia, Universitat de València, Spain] programs which is a mandatory way to characterize the molecular structure. These calculated indices could be considered as forming part of the groups: Randi–Kier–Hall subgraph connectivity indices m t [20] up to order 10, and their corresponding valence indices [21], topological charge indices [22], topological constitutional indices [23] and atom type E-state indices [24] (see Table 3). 2.4. Linear discriminant analysis Stepwise linear discriminant analysis, SLDA is a patternrecognition method which facilitates the evaluation of the ability to distinguish among two or more groups of molecules. In the present paper the independent variables were the topological indices and the discrimination property was the native fluorescent activity according to previously established criteria. The corresponding SLDA study was carried out on two separated groups of substances;

416

J.R. Albert-Garcia et al. / Talanta 79 (2009) 412–418

Table 4 Results obtained in the LDA study and classification of the compounds from pattern of fluorescent emission. Training group and test group; both groups included active and inactive compounds. Compound

DFa

Training group—active Amobarbital Antimycin A Azoguanine Bromolysergic acid diethylamide Bufotenine Cinchonidine Cinchonine Deserpidine Desipramine Epinephrine Gentisic acid Griseofulvin Harmine Hydroxyamphetamine Imipramine Indomethacin Levo-dromoran Morphine Nalorphine Naphthaleneacetamide

−1.228 0.443 1.569 −0.721 2.500 5.158 5.158 4.204 2.200 3.445 3.030 2.068 3.562 1.074 0.558 5.056 2.498 5.283 3.389 2.121

Training group—inactive Acetylcysteine Alachlor Alanine Amitryptiline Amoxicillin Ampicillin Ascorbic acid Asparaginine Aspartic acid Atropine Biguanide Bromacil Brompyrazon Buminafos Caffeine Captopril Chlordimeform Clotrimozole Colesterine Cycloate Test group—active Aminopterin Brucine Codeine Ethacridine Indoleacetic acid Mephenesin Naphthalene acetic acid Phenylenepyrene Thiamylal Zoxazolamine a b c

Probb

Classc

Compound

DFa

Probb

Classc

0.188 0.539 0.792 0.273 0.880 0.990 0.990 0.981 0.892 0.950 0.927 0.858 0.969 0.65 0.614 0.989 0.876 0.991 0.943 0.979

− + + − + + + + + + + + + + + + + + + +

Norepinehrine p-Aminosalicylic acid Pentobarbital Phenobarbital Physostigmine Piperoxan Podophyllotoxin Procainamide Procaine Psilocin Quinacrine Quinidine Quinine Rescinnamine Reserpine Salicylic acid Scoparone Scopoletin Synephrin Yohimbine

2.947 3.232 0.942 2.534 4.215 2.071 8.821 −0.161 −0.016 1.630 1.534 5.373 5.373 4.217 4.264 3.028 3.132 4.655 3.015 3.485

0.921 0.939 0.670 0.903 0.982 0.876 1.000 0.403 0.439 0.749 0.803 0.991 0.991 0.981 0.982 0.927 0.949 0.984 0.925 0.948

+ + + + + + + − − + + + + + + + + + + +

−3.299 0.812 −6.049 −0.944 −1.731 −1.408 −1.520 −4.364 −4.263 1.965 −5.146 −4.822 −1.351 −1.668 −2.310 −2.027 −2.446 −0.900 −2.389 −2.551

0.977 0.360 0.998 0.735 0.906 0.875 0.882 0.992 0.991 0.191 0.995 0.994 0.829 0.874 0.927 0.926 0.929 0.740 0.931 0.942

− + − − − − − − − + − − − − − − − − − −

Diphenamid DNOC Furalaxyl Glutamic acid Glutamine Karbutilate Lysine Metobromuron Minoxidil Novocaine Prednisolone Propanil Spiroxamine Sulphadiazine Sulphamethazine Sulphatizaole Threonine Valine Vitamine B1 Vitamine B5

−0.244 3.478 −0.661 −4.134 −4.239 −0.938 −1.277 −1.634 0.795 −0.016 −1.894 −0.322 −6.729 −5.002 −7.140 −7.938 −3.650 −4.590 −2.266 −1.514

0.620 0.050 0.714 0.990 0.991 0.763 0.851 0.864 0.336 0.561 0.925 0.631 0.999 0.995 0.999 1.000 0.984 0.994 0.940 0.885

− + − − − − − − + − − − − − − − − − − −

4.637 4.077 6.054 4.510 2.505 2.219 4.308 5.559 −0.160 1.192

0.984 0.978 0.996 0.988 0.884 0.850 0.871 0.996 0.401 0.746

+ + + + + + + + − +

Test group—inactive Arginine Benzalkonium Cysteine Fenoprop Isoleucine Leucine Lidocaine Ofurace Umbelliferone Vitamina B3

−2.065 −2.139 −3.514 −0.446 −2.619 −3.691 −2.078 −0.890 3.477 −0.433

0.927 0.898 0.981 0.718 0.956 0.985 0.910 0.755 0.048 0.653

− − − − − − − − + −

Value of the DF (discriminant function) for each compound. Probability of activity. The compounds are classified either as active (+) or inactive (−) according to the value of column DF.

the first is the known as “the training group”, and it includes compounds presenting native florescence (active or positive molecules), and not fluorescent compounds (the negative ones). The other group is the so-called “the test group” which also comprises active and inactive molecules, these compounds were randomly selected from the first total population. The election of connectivity functions was performed with the aid of the BMDP Biomedical package [Dixon, W.J. BMDP Statistical software, University of California, Berkeley, 1990] after the selection of the descriptors with the FSnedecor parameter. The criterion for the classification was the minimum value of the Mahalanobis distance. The quality of the discriminant function was evaluated through Wilk’s U-statistical parameter  (also known as U-statistic), which is obtained by a

multivariate analysis of variance statistic that tests the equality of group means for the variables in the discriminant functions (the files containing the values of all the descriptors used in this work are at readers’ disposal upon request). After selecting the discriminant function, the corresponding pharmacological distribution diagrams (PDD) were built up. These plots are useful to determine the intervals of the discriminant function in which the expectancy, E, to find active compounds is maximum. PDDs are histogram-like plots of connectivity functions in which the expectancies appear on the ordinate axis. For an arbitrary interval of values of a given function, we can define the expectancy of activity as Ea = a/(i + 1), where “a” is the number of active compounds in the interval divided by the total number of

J.R. Albert-Garcia et al. / Talanta 79 (2009) 412–418

417

Table 5 Classification and experimental results for tested compounds. Compound

DF

Prob

Theoretical Class

Experim. result

exc

em

Imax

Medium

3-Indolylacetic acid Vitamine B2 Vitamine B6 Benfuresate Benzocaine Chlorpromazine Ciprofloxacine Diazepam Dopamine Fenobucarb Fluometuron Isoprocarb Oxasulfuron Phenylamine Phenylephrine Promethazine Propanolol Trimethroprim Tryptophan Tyrosine

2.989 1.571 0.909 −2.929 0.298 −0.077 3.023 1.823 3.464 1.337 −1.311 0.402 −4.504 0.964 4.330 −1.937 3.798 3.222 1.958 0.124

0.925 0.732 0.599 0.041 0.520 0.449 0.923 0.830 0.952 0.754 0.176 0.547 0.008 0.701 0.979 0.116 0.963 0.957 0.813 0.589

+ + + − + − + + + + − + − + + − + + + +

+ + + + + + + + + + + + + + + + + + + +

280 370 290 278 285 270 260 275 280 270 248 265 290 257 275 270 290 265 280 274

364 527 398 316 352 457 435 426 319 300 333 296 351 282 302 379 352 341 348 303

>1000 >1000 >1000 310.3 705 489.6 >1000 125.7 >1000 172.5 725.9 69.09 201.1 − >1000 339.4 >1000 872.15 − −

Water HClO4 5 × 10−2 M HClO4 5 × 10−2 M Water NaOH ×10−2 M NaOH ×10−2 M NaOH ×10−4 M HClO4 ×10−6 M HClO4 ×10−4 M Water Water HClO4 ×10−4 M Water Water HClO4 ×10−4 M HClO4 5 × 10−2 M Water HClO4 ×10−4 M Water Water

active compounds, and “i” is the number of inactive compounds. The expectancy of inactivity is defined in a symmetrical way, as Ei = i/(a + 1). 3. Results and discussion

The obtained discriminant function was applied to predict the fluorescent behaviour to a group of 20 compounds which were not previously tested neither in the test-group and the training-group. Results are illustrated in Table 5; as depicted on it, the prediction succeeds in a 75% and to point out no false positives were observed.

3.1. Molecular connectivity calculations

4. Conclusions

In this stage, a set of 100 structurally heterogeneous molecules (Table 4) was analyzed. These comprise an inactive group (50 nonnative fluorescent compounds), and an active group (50 native fluorescent compounds). Each group was divided in two groups, the training (40 compounds) and the test (10 compounds), respectively. The calculated discriminant functions were validated on the basis of these groups. The discriminant function (DF) chosen was

Fluorescence activity is a relevant property to obtain analytical (among others) applications; at present, it continues to be a very active research field especially for analysis of pharmaceuticals. The goal was to propose a new tool for finding fluorescence molecules. Based on an experimental screening on a continuous-flow assembly as well as on bibliographic data, molecular connectivity studies are applied to predict the native fluorescence of organic compounds; most of the tested were pharmaceuticals. This paper demonstrates the molecular connectivity as an effective molecular topological tool for the prediction of native fluorescence with substantial time, work and resource savings.

DF = −0.972D1  + 7.727IShannon + 2.6534 p − 2.4832 V −10.528JV2 + 0.112SHssNH + 3.479knotpv − 3.47

References N = 80

F = 13.71

U-statistics (Wilks’) = 0.432

The equation includes different type of indices bearing also different kind of information. So the indices of the type ␹i give topological and assembly information. The combination of those indices D1  is an information source in the presence and type of heteroatoms in the molecule and on the polarizability degree of this given molecule. The Ji parameters also known as charge indices are the responsible of the information on the distribution of intramolecular charge. More information is also obtained from indice IShannon which evaluates the chemical characteristics of the vertices and from the electro-topological indice SHssNH which gives two kind of information on each atom in the molecule: namely, topological and electronic. Table 4 summarizes the classification of the results obtained with DF discriminant functions for each group. A compound will be selected as active if DF > 0 or as non-active if DF < 0. As outlined in the tables, training and test groups, get an average accuracy of 90%. Moreover, in the majority of cases, we work within a success probability (See Prob. Column in Table 4) higher than 85%. The PDDs obtained with the discriminant function are shown in Fig. 2.

[1] M. Karelson, Molecular Descriptors in QSAR/QSPR, John Wiley & Sons, New York, 2000. [2] R. Mannhold, H. Kubinyi, H. Timmerman (Eds.), Handbook of Molecular Descriptors, vol. 11, Wiley VCH Verlag GmbH, Weinheim, Germany, 2000. [3] H. Hosoya, M. Gotoh, M. Murakami, S. Ikeda, J. Chem. Inf. Comput. Sci. 39 (9) (1999) 192–196. [4] L.B. Kier, L.H. Hall, L.B. Kier, J. Chem. Inf. Comput. Sci. 40 (3) (2000) 784. [5] R. García-Doménech, J.V. de Julian-Ortiz, I. Rios-Santamarina, J. Gálvez-Alvarez, C. de Gregorio-Alapont, M.J. Duart, G.M. Anton-Fos, Environm. Res. 12 (6) (2001) 237–254. [6] R. García Domenech, A. Villanueva, J. Gálvez, R. Gozalbes, J. Chim. Phys. 96 (6) (1999) 1172–1176. [7] M.C. Icardo, L. Lahuerta Zamora, G.M. Antón Fos, J. Martínez Calatayud, M.J. Duart, Trends Anal. Chem. 24 (8) (2005) 782–791. [8] I. Ríos Santamaría, R. García Doménech, J. Gálvez Álvarez, J.M. Esteban, P. Santamaría, J. Cortijo, Eur. J. Pharm. Sci. 22 (4) (2004) 271–277. [9] C. Calabuig, G.M. Antón Fos, J. Gálvez Álvarez, R. García Domenech, Int. J. Pharm. 278 (1) (2004) 111–118. [10] M.J. Duart, R. García-Doménech, J. Gálvez, P. Alemán-López, R.V. Marín-Algarra, G.M. Anton-Fos, J. Med. Chem. 49 (12) (2006) 3667–3773. [11] S. Mut Ronda, M.T. Salabert Salvador, M.J. Duart, G.M. Antón Fos, Bioorg. Med. Chem. Lett. 13 (4) (2003) 2699–2703. [12] M. Duart, G.M. Antón-Fos, P.A. Alemán, J.B. Gay-Roig, M.E. González-Rosende, J. Gálvez, R. García-Domenech, J. Med. Chem. 48 (5) (2005) 1260–1265. [13] A. Czescik, D. López Malo, M.J. Duart, L. Lahuerta Zamora, G.M. Antón Fos, J. Martínez Calatayud, Talanta 73 (4) (2007) 718–725. [14] I. Sahuquillo Ricart, J.R. Albert-García, G.M. Antón-Fos, M.J. Duart, J.V. García Mateo, L. Lahuerta Zamora, J. Martínez Calatayud, Talanta 72 (2) (2007) 378–386.

418

J.R. Albert-Garcia et al. / Talanta 79 (2009) 412–418

[15] J. Martínez Calatayud, L. Lahuerta Zamora, in: A. Townshend (Ed.), Encyclopedia of Analytical Science, Academic Press, Oxford, 1995, ISBN 0-12-226700-1. [16] E.L. Wehrym, Molecular Fluorescence and Phosphorescence Spectrometry, Plenum Press, NY, 1981 (Chapter 26). [17] B. Gómez-Taylor Corominas, G.M. Antón Fos, J.V. García Mateo, L. Lahuerta Zamora, J. Martínez Calatayud, Talanta 60 (2–3) (2003) 623–628. [18] E. Polo Martí, M. Catalá Icardo, L. Lahuerta Zamora, G.M. Antón Fos, J. Martínez Calatayud, Anal. Chim. Acta 527 (2) (2004) 177–186. [19] S.G. Schulman, Fluorescence and Phosphorescence Spectroscopy: Physicochemical Principles and Practice, Pergamon Press, Oxford, 1976.

[20] L.B. Kier, W.J. Murray, M. Randic, L.H. Hall, J. Pharm. Sci. 65 (8) (1976) 1226–1930. [21] B. Kier, L.H. Hall, J. Pharm. Sci. 65 (1976) 1806–1809. [22] J. Gálvez, R. García–Doménech, M.T. Salabert, R. Soler, J. Chem. Inf. Comput. Sci. 34 (1) (1994) 520–528. [23] R. Moliner, F. Garcia, J. Galvez, R. Garcia-Domenech, C. Serrano, Ann. Real Acad. Farm. 57 (1991) 287–298. [24] L.B. Kier, L.H. Hall, Pharm. Res. 7 (2) (1990) 801–805. [25] H. Wiener, J. Am. Soc. 69 (8) (1947) 17–21. [26] C.E. Shannon, Bell. Syst. Tech. J. 27 (2) (1948) 229–235.