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Automated identification of toxic substances in poisoned human fluids by a retention prediction system in reversed-phase liquid chromatography
Journal of Chromatography, 436 (1988) 1 l-21
Elsevier Science Publishers B.V., Amsterdam -
Printed in The Netherlands
CHROM. 20 095
AUTOMATED IDENTIFICATION OF TOXIC SUBSTANCES IN POISONED HUMAN FLUIDS BY A RETENTION PREDICTION SYSTEM IN REVERSED-PHASE LIQUID CHROMATOGRAPHY
K. JINNO* and M. KUWAJIMA School of Materials Science, Toyohashi University of Technology. Toyohashi 440 (Japan)
M. HAYASHIDA and T. WATANABE Department of Legal Medicine. Nippon Medical School, Tokyo 113 (Japan)
and T. HONDO Jasco, Japan Spectroscopic Co., Ltd., Hachioji 192 (Japan)
(First received July 14th, 1987; revised manuscript received September 19th, 1987)
SUMMARY
An automated identification system based on the retention prediction concept has been constructed for toxic substances. The system performance has been evaluated for the identification of toxic compounds in poisoned human fluids.
INTRODUCTION
When a doctor is confronted with a patient suffering from a drug overdose he must attempt to determine what kind of drugs are involved and then decide what kind of medicines to administer. These very important decisions should be made as early as possible, although this is a difficult task without any instrumental assistance. Recent developments in liquid chromatography (LC) may enable its use for this purpose. However, the disadvantage of this technique is the identification, because only retention information is obtainable. If UV multichannel detectors can be employed instead of conventional single-channel detectors, then spectral information can be obtained but this is still not enough to identify toxic substances because UV spectra are very similar in some cases. Therefore, other identification methods are required’. One such method is automated identification in reversed-phase LC, as recently proposed by one of the author$, which is based on the concept of retention prediction3-’
k’ = f(Pi)
(1)
where k’ is the capacity factor of a solute and Pi is a physicochemical parameter of the solute. Equations such as eqn. 1 can be derived for various chromatographic systems and then the retention of solutes can be predicted. 0021-9673/88/%03.50
0
1988 Elsevier Science Publishers B.V.
12
K. JINNO et a/.
Eqn. 1 can be useful in the confirmation of approximately identified compounds automatically by the use of retention prediction in reverse. Having obtained k’ for a peak from an experiment, the system looks for the appropriate descriptors for the peak, using the data files of descriptors for several compounds as the standard basis. In this case, the descriptor is the retention parameter which is defined in the Discussion section (closely related to the hydrophobicity of the compounds) and compounds are medicaments which are typically psychotropic drugs. Then the names of the compounds corresponding to the calculated retention parameter are listed on a cathode ray tube (CRT) or line printer. It is the purpose of this communication to demonstrate this concept for automated identification of toxic substances in poisoned human fluids by reversedphase LC. EXPERIMENTAL
The LC system used comprised a Model 880-PU pump and Multi-320 multichannel UV detector (Jasco, Tokyo, Japan). The column was a Jasco Finepak Sil Cl8 S (250 mm x 4.6 mm I.D.) set at 50°C. The mobile phases were mixtures of 10 mM perchloric acid, 10 mM sodium perchlorate and acetonitrile and the flow-rate was I ml/min. The standard samples were dissolved in acetonitrile to a concentration of 100 pg/ml. Aotual human fluids such as gastric contents, sera and urines from poisoned patients were collected at the Critical Care Medical Center (CCMC), Nippon Medical School, Tokyo. The data handling was performed by a NEC 9801 VM2 microcomputer (Nippon Electric, Tokyo, Japan). RESULTS AND DISCUSSION
Determination of retention parameter
To obtain the retention prediction equation, first one has to find an appropriate descriptor. In this case, it is reasonable to consider that the retention of the toxic substances is controlled by their hydrophobicities *- lo, but accurate estimation of the hydrophobicities is a difficult task even when the methods of Rekkerr’ or Hansch and Leolz are used. Therefore, we used the most convenient way to perform this task, i.e., the use of a retention parameter determined,by the hydrophobic parameters of selected standards. The hydrophobic parameters of barbital (log Pd = 0.67, where Pd is the octanol-water partition coefficient), phenobarbital (1.48), phenacetin (1.58) and triazolam (2.42) obtained from the data base of Hansch and LeolZ were correlated to their log k’ values measured in buffer-acetonitrile (70:30), -0.058, 0.257, 0.390 and 1.086, respectively. Thus eqn. 2 is obtained: log k’ = 0.6614 log Pd - 0.571
(2)
The capacity factors, k’, of 48 representative toxic substances were determined experimentally under the same elution conditions and are summarized in Table I. Eqn. 2 can be converted into log P, = (log k’ f 0.571)/0.6614
(3)
AUTOMATED IDENTIFICATION
0
5
10
15
13
OF TOXIC SUBSTANCES
20 Time
25 C min
30 >
3s
40
45
Fig. 1. Chromatograms of human fluids sampled from a patient who had taken some poisons. (A) gastric contents, (B) serum. Mobile phase: buffer-acetonitrile (70:30). Detection: UV, 215 nm.
where log P, is the retention parameters for each substance obtained by inserting each k’ into eqn. 3. These values are also listed in Table I. Construction of retention prediction system The construction of the retention prediction system has been described previously2*7, and therefore only the basics will be mentioned in this work. Tf there is an highly correlated relationship between log k’ and log P, for toxic
14
K. JINNO el al.
TABLE I DATA TABLE FOR 48 TOXIC COMPOUNDS: ESTIMATED log P, No. 1
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
RETENTION TIME, CAPACITY FACTOR AND
Compound
Index
tR
k
log k
log P*
Acetaminophen Caffeine Barbital Sulpiride Acetylsalicylic acid Phenobarbital Bromvalerylurea Etheenzamide Bromazepam Phenacetin Cloxazolam Oxazolam Chlormezanone Chlordiazepoxide Pentobarbital Nitrazepam Amobarbital Phenytoin Secobarbital Carbamazepine Glutethimide Oxazepam Nimetazepam Estazolam Diazepam Flunitrazepam Flurazepam Chlordiazepoxide Alprazolam Medazepam Haloperidol Prepericyazine Triazolam Bromperidol Promethazine Desipramine Carpipramine Maprotyline Nortriptyline Hydroxyzine Imipramine Trihexyphenidyl Amitriptyline Trimipramine Levomepromazine Clocapramine Chlorpromazine Clomipramine
AA
2.410 2.870 3.000 3.220 4.020 4.490 4.830 5.060 5.060 5.530 5.870 6.220 6.450 7.020 7.370 7.370 7.610 7.610 9.100 9.100 9.100 9.920 11.880 12.220 12.800 14.650 15.650 15.670 16.490 16.710 18.000 18.570 21.100 21.220 20.370 21.920 25.020 25.370 25.490 26.180 27.220 27.450 31.610 33.100 34.370 42.690 43.020 49.490
0.506 0.794 0.875 1.013 1.513 1.806 2.019 2.163 2.163 2.456 2.669 2.888 3.031 3.388 3.606 3.606 3.756 3.756 4.688 4.688 4.688 5.200 6.425 6.638 7.000 8.156 8.781 8.794 9.306 9.444 10.250 10.606 12.188 12.263 11.731 12.700 14.638 14.856 14.931 15.363 16.013 16.156 18.756 19.688 20.48 1 25.681 25.888 29.93 1
-0.296 -0.100 -0.058 0.005 0.180 0.257 0.305 0.335 0.335 0.390 0.426 0.461 0.482 0.530 0.557 0.557 0.575 0.575 0.671 0.671 0.671 0.716 0.808 0.822 0.845 0.911 0.944 0.944 0.969 0.975 1.011 1.026 1.086 1.089 1.069 1.104 1.165 1.172 1.174 1.186 1.204 1.208 1.273 1.294 1.311 1.410 1.413 1.476
0.418 0.713 0.777 0.873 1.137 1.253 1.326 1.371 1.371 1.455 1.510 1.561 1.593 1.666 1.707 1.707 1.734 1.734 1.879 1.879 1.879 1.948 2.086 2.108 2.143 2.243 2.292 2.293 2.330 2.339 2.393 2.416 2.507 2.511 2.482 2.534 2.627 2.637 2.640 2.659 2.686 2.692 2.790 2.822 2.848 2.996 3.002 3.097
CAF BAL SUL ASA PHB BVU ETM BMZ PNC cxz oxz CM CD PTB NTZ AMB PHT SEB CBM GLT OPZ NMZ ESZ DIZ FNZ FLZ CD APZ MDZ HP PPC TRZ BP PM DEP CAP MPT NTP HX IMP THPH ATP TM0 LMP CCP CP CMP
AUTOMATED
IDENTIFICATION
15
OF TOXIC SUBSTANCES
TABLE II EXPERIMENTAL
DATA FOR OBTAINING EQN. 4a IN THE TEXT
Standard compound
Capacity factor,
log P.
k’
Mobile phase composition,
Barbital Phenobarbital Phenacetin Triazolam
0.171 I .253 1.455 2.501
buffer-acetoniirile
60:40
65:35
70:30
74:25
0.581 1.063 1.450 4.183
0.719 1.394 1.919 7.163
0.875 1.881 2.554 12.431
1.106 2.615 3.606 22.866
substances, multiple regression analysis on the data set shown in Table II gives log k’ = f1(X)log P, + fi(X)
(4)
(the results of the regression analysis are shown in Table III, where A and B are regression coefficients) where X is the volume fraction of the organic solvent (in this case, acetonitrile) in the mobile phase; the experimentally determined relationship is: log k’ = (-0.0178X
+ 1.2003) log P, - (0.0034X + 0.4562)
(4a)
Eqns. 4 and 4a mean that, if X and log P, of a substance are known, the logarithm of the capacity factor can be predicted under given chromatographic conditions. This is the basic concept of the retention prediction for toxic compounds investigated here. Eqn. 4a can be stored in the program of the retention prediction system3-’ and also in the automated identification systemz. Automated ident$cation system Eqn. 4a can be used, in turn, to obtain log P, for toxic compounds by substituting the measured retention information, k’. This concept can easily be systemized in a microcomputer yielding an interactive identification tool for toxic compounds. The procedure is as follows: if one measures the k’ value of a peak, the computerized system establishes the appropriate log P, for the peak, using the data file TABLE III RESULTS OF REGRESSION ANALYSIS FOR THE DATA SET IN TABLE II log k’ = A log P. + B. F = statistical F ratio; R = correlation coefficient. Mobile phase composition
A
B
F
R
6wo 65:35 70:30 75125
0.4899 0.5742 0.6635 0.7571
-0.5904 -0.5753 -0.5644 -0.5373
310 1306 7130 5066
0.997 0.999 1.000
1.000
16
A
K. JINNO et al. SAWLE : 1306~s~ 1 #t##W#b##t##wt#u
Search
Version
3.0
Concentrat icn of CH3Ch Retention Time of Unknwn Solute Elut ion Time of Unretaimd Solute Lwarith of Capacity Factor Naxiinum Value of Relative Error
30.00~“0& 4.53 min 1.60 nln 0.26 10.00 Y
--_--__
Candidates
______-___-____
___ ____ ____
samnle name Phenobarbital #-H-w
____ _ ______ _________________
index PH6
SAklPLE : 1306SAT 2 #11;###t########(
Search
Concentration of CH3Ch Retention Time of Unknwn Solute Elution Tia of Unretained Solute Lwarithn of Capacity Factor t!aximum Value of Relative Error
LwP 1.253
Version 30.00 5.32 1.60 0.37 l?.OD
Search
x
Version
______-______
Candidates
1
__________________
__
caff. 0.732 0.732 0.081
VOlX min nin x ____________________--_-____
,.
saaple name oentobarbital nitrazspam wbarbital Phenvtoin chlwdiazewxide
:
I
SMPLE : 1306AST4 #################W Search
index PTE NT2
LogP 1.707 1.707
AM
1.73
PHT CD
1.734 1.664
30.00 14.50 1.60 0.91 10.00
____________________--
Candidates
______ _____
sample name flunitrazepam diazepam ########t#)##t##u~
index FNZ DIZ
1-k 0.578 0.578 0.5% 0.5% 0.551
caff. 0.938 0.938 0.516 0.516 0.448
3.0 ####tt####wt#t#tw
Version
Concentration of CH3CN Retention Tine of Unknown SoLute Elution Tine of Unretained Solute Lwarithil of Capacity Factor kximw Value of Relative Error
Fig. 2.
leak’ *0.355 0.355 0.325
3.0 ###ww#w#######m
30.00 7.62 1.60 0.58 10.00
i
____________________--_____ Lo9P 1.371 1.371 1.326
Concentration of CiW Retention Time of Unkwn SoLute Etut ion Time of Unretained Solute Logarithm of Capacity Factor !aximum Value of Relative Error
,.
cceff. 0.692
VOLX min nin
index EM M2 8VU
SM’LE : 13LW3 ###t#t#######m# :
Logk’ 0.276
3.0 #######lW#####W##
Candidates samle nane ethentamide brazepan brmalervlurea -###########~
####m###t###tt#
VOLX min min x ________________ 1wP 2.243 2.143
logk’ 0.935 0.866
y---------coeff. 0.323 0.154
SAMPLE : lJOGAST5 tttW#itPBOtt##Ptii##ttPt#
Search
Version
3.0
Concentrat io:l of CHEN Relent ion Time of Unknow Zoiute ELut ion Time of Unretatned Solute Logarithm cf Capacity Factor Maximum Value of Re!ar ive Error
30.00 21.60 1.60 1.10 lO.O?
--_--___-------__-___--___-_-____
Candidates
VDLI min min I ____________________________
Index WI TRZ BP DEP
sampie name promethazine tr ia:oLam bromperidoi desipramine
t#t###tYtUt#####ttttl#PIY
LwP 2.482 2.507 2.511 2.534
Lo+’ 1.094 1.110 1.113
coeff, D.929 0.683
.0.619
I.128 0.246 ttXtttttWtY#tWtttt#tt#tttXtttttlYtffttXlt##~~~#~~###~$#~##~###~#~~ SAMPLE : 130GAST 6 ##t#t#####t###~Xtttt(rttt Search
Version
3.0 ##tlt#XX~rmtt##XtttY(#Utltl
Concentration of CMCN Retention Time of Unknown Solute Elution Time of Unretained Solute Logarithm of Capacity Factor laximum Value of Relative Error
30.00 33.03 1.60 1.29 10.00
_-_--__
Candidates
_--___-_-__
___ ____ ______-_
01% min min X ___-________________________
sample name index LwP Logk’ coeff. levcwprcmazine LRP 2.868 1.294 0.981 trimipramine TM0 2.822 1.277 0.637 amitriptyline ATP 2.790 1.256 0.188 ####MliX##XYP##Mt#~~#~~~##~~~~##~###~##~#~~####~~~~~~#~####~~
B
SAiw_E
: 130SERW.1 W##W#tlW#ttt##tr
Search
Version
3.0
Concentration of CH3CN Retention Time of Unknwn Solute Elution Time of Unretained Solute Logarithm of Capacity Factor Maximum Value of Relative Error
30.00 5.32 1.60 0.37 10.00
_________________________________
Candidates
###~~##t###l#tt#~lttt
VOlY min min X ___-___ --__ _ _-____-_
sample name index 1wP Logk’ ethenzamide 1.371 0.355 ETR brmzepam EN2 1.371 0.355 bromvalerylurea 1 326 0 325 tn#w4nwrurKI*rt#Hxt~~~~*~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SANPLE : 13OSERUM 2 Il###tW#X###Mtt###ttXtft Search
‘Jer s ion 3.0
Concentration of CH3CN Retention Time of Unknovn Mute Elution Time of Unretained So!ute Lwarithm of Capacity Factor Maximum VaLue of Relative Error
30.00 7.62 1.60
____________---___________-____--
Candidates
________
cmff 0.732 0.732
0.081
tta#~###tlll###t##t##tttttXt
VOlX min min
0.5s lD.00
sampie name index pentobarbital PT6 nitrazepam NT2 amobarbital AHG phenytoin PHI chlord iazepoxide CD tM#(W#t#tlM~#Y#tt)#tfftWtYtttX#XtffmXt
x ____________________________ 1osP 1.707 1.707 1.734 1.734 1.666
logk’
Coeff,
0.578
0.938
0.576 0.596 0.596 0.551
0.938 0.516 0.516 0.448
Fig. 2. Output of the automated identification system for peaks in the chromatograms gastric contents, (B) serum. Each number corresponds to the peak number in Fig. 1.
of Fig. I. (A) For
18
K. JINNO er al.
TABLE IV COMPARISON BETWEEN THE AUTOMATICALLY SCRIBED DRUGS
IDENTIFIED
DRUGS AND THE PRE-
Sample fluid
Drugs identified by the system
Drugs prescribed
Gastric fluid
Phenobarbital Ethenzamide Bromazapam Pentobarbital Nitrazepam Promethazine Triazolam Levomepromazine
Bromazepam Flunitrazepam Triazolam Levomepromazine Trihexylphenidyl Pentobarbital Nitrazepam Thioridazine*
Serum
Ethenzamide Bromazepam Pentobarbital Nitrazepam
Timiperone’ Clofedanol*
Urine
Bromvalerylurea Oxazolam * Drug not included in the library.
of log P, for several toxic compounds as the standard basis. Then the names of the compounds having values closest to the calculated log P, value (within the inputted relative error) are listed on the CRT or the line printer, together with the corresponding correlation coefficients. This coefficient is a measure of the probability of the predicted identification.
250
0
5
10
Time
15 <
min
>
213
25
G
Fig. 3. Chromatogram of gastric contents sampled from a woman who had taken poison. Experimental conditions as in Fig. 1.
AUTOMATED SMPLE
IDENTIFICATION
OF TOXIC SUBSTANCES
: Version
Conocntrat ion of CR3C?l Retention Time of Unknwn Solute Elution Time of Unrctaincd Solute Logarithm of Caoaoity Factor fbximum Value of Relative Error
3.0
30.00 votx 4.49 nin 1.60 min 0.26 10.00 x
~-________~__~__~___~~~~~~~~~~~~_ Candidates sams!c name phenobarbital
______________________----_-
index PHB
SAMPLE : 139GPST 2 #####1###H#####
Search
1osP lwk’ 1.253 0.276
Version
3.0
Conccntrat ion of CMCN Rctcnt ion Tine of Unknwn Solute E!ution Time of Unrcraincd Solute Locarirhm of Capacity Factor llaximum Vatuc of Relative Error
30.00 voLX 4.96 sin 1.60 min 0.32 . 10.00 x
____________________________-____
Candidates
samole name brmvalcrylurca cthenzamide brmszcpam t####t#t###)t#####
Search
Version
__ __________-___
Candidates
Starch
coeff . 0.99b 0.289 0.289
volX nin nin x _________________
index Rnz ETN PNC
SMPLE : 139X74 #H#############M
Losk’ U.325 0.355 0.355
3.0 ##t###H#####1##
30.00 5.bO 1.60 0.38 10.00
sacolc name brmcwam cthcnzamidc shcnacct in
#####l#####t-
LosP 1.326 1.371 1.371
Conocntrat ion @f CH3CR Rctcnt ion Time of Unknwn Solute Elution Tile of Unrctaincd Solute Lwarithm of Capacity Factor kxiaun Va!ue of Rclat ivc Error __________ _____ __
coeff.
0.549
_________________________-__
index BVU EM RI42 ##wwt#-
SAWLE : 1396AST 3 ######t####tt##l
m
1osP 1.371 1.371 1.455
Version
lock’ 0.355 0.355 0.410
_ _________ _ oceff. 0.527 0.527 0.170
3.0 W###########?#########t
Concentration of CH3C-A Retention Tiae of Unknwn SoLute Elurion Time of Unrctaincd Solute Lwarithm of Capacity Factor kxinum Value of Relative Error
30.00 21.00 1.60 1.08 10.00
-~________~_~_~_____~~_~~___~~___
Candidates
VOlX min nin x ____________________________
sample name index 1ogP Losk’ cocff. prmethazinc PI Z.&S2 I.094 0.76c tr iazolan TRZ 2.507 1.110 0.363 brasccr idol BP 2.511 1.113 0.2% propericyazine PPC 2.416 1.050 0.24S #####ttU#######t###########tt######t##l#1##1#
Fig.
4.
Output
of the
automated
identification
system for the chromatogram in Fig. 3.
19
K. JINNO et al.
20
Application of the system to actual samples The first application is to analysis of and serum of who had to hospital on The chromatograms of are shown in Fig. 1A gastric contents and Fig. 1B serum, respectively. A peak at 11.2 min which in all chromatograms is not due to in the chromatograms of pooled control samples. In Fig. 2, output of automated identification for peaks in chromatograms is The gastric sample is most suitable to judge of toxic substances the chromatographic by the automated identification it highly possible the patient one some of drugs listed in IV. Table IV, drugs he had for his psychiatric disease also indicated. it considered he took of the drugs which been prescribed, be included in list. by the automated system found in prescribed drugs and therefore the analysis be assisted by system output. The second example is gastric contents taken from who took a drug on she stated she had taken chlordiazepoxide, although it was reported she had bromvaleryurea. The chromatogram and the output of automated system are shown in and respectively. The result of the identification clearly she had taken bromvaleryurea chlordiazepoxide. The chromatagram of gastric sample indicates no trace of
in reversed-phase LC
for toxic been described. To construct the system,
user
or and selected standards so as obtain 4a, and day-to-day fluc. of retention can corrected. Although the system in its present form will leave many problems to be solved in practice, the concept opens a new dimension of clinical toxicological analysis. If the system is coupled to the spectral-matching function of multichannel detectors, its reliability is expected to increase, and: this is under investigation in our laboratory. An expansion of the log P, data file on toxic substances is also required for much ~ I* wider application of the system. REFERENCES 1 A. C. Moffat (Editor), Clarke’s Isolation and Ident#ication of Drugs in Pharmaceuticals, Body Fluid, and Post-Mortem Material, The Pharmaceutical Press, London, 2nd ed., 1986. 2 K. Jinno and M. Kuwajima, Chromatographia, 21 (1986) 622. 3 K. Jiwo, Proceedings of the 8th International Symposium on Capillary Chromatography, Vol. 2, Hiithig, Htiidelberg, 1987, pp. 1143-l 152. 4 K. Jinnoand K. Kawasaki, J. Chromatogr., 316 (1984) 1. 5 K. Jinno and K. Kawasaki, J. Chromatogr., 298 (1984) 326. 6 K. Jinno and K. Kawasaki, ACS Symp. Ser., 297 (1986) 167-187.
AUTOMATED IDENTIFICATION
OF TOXIC SUBSTANCES
21
7 K. Jinno, T. Hoshino, T. Hondo, M. Saito and M. Senda, Anal. Chem., 58 (1986) 2696. 8 Cs. HorvPth, W. Melander and I. MolnBr, J. Chromatogr., 125 (1976) 129. 9 W. Melander, J. Stoveken and Cs. Horvlth, J. Chromatogr., 199 (1980) 35. 10 L. Hafkenscheid, Ph.D. Thesis, University of Amsterdam, Amsterdam, 1983. 11 F. Rekker, The Hydrophobic Fragmental Constunr, Elsevier, Amsterdam, 1977. 12 C. Hansch and A. Leo, Substituenr Constants for Correlation Analysis in Chemistry and Biology, Wiley, New York, 1979.
Elsevier Science Publishers B.V., Amsterdam -
Printed in The Netherlands
CHROM. 20 095
AUTOMATED IDENTIFICATION OF TOXIC SUBSTANCES IN POISONED HUMAN FLUIDS BY A RETENTION PREDICTION SYSTEM IN REVERSED-PHASE LIQUID CHROMATOGRAPHY
K. JINNO* and M. KUWAJIMA School of Materials Science, Toyohashi University of Technology. Toyohashi 440 (Japan)
M. HAYASHIDA and T. WATANABE Department of Legal Medicine. Nippon Medical School, Tokyo 113 (Japan)
and T. HONDO Jasco, Japan Spectroscopic Co., Ltd., Hachioji 192 (Japan)
(First received July 14th, 1987; revised manuscript received September 19th, 1987)
SUMMARY
An automated identification system based on the retention prediction concept has been constructed for toxic substances. The system performance has been evaluated for the identification of toxic compounds in poisoned human fluids.
INTRODUCTION
When a doctor is confronted with a patient suffering from a drug overdose he must attempt to determine what kind of drugs are involved and then decide what kind of medicines to administer. These very important decisions should be made as early as possible, although this is a difficult task without any instrumental assistance. Recent developments in liquid chromatography (LC) may enable its use for this purpose. However, the disadvantage of this technique is the identification, because only retention information is obtainable. If UV multichannel detectors can be employed instead of conventional single-channel detectors, then spectral information can be obtained but this is still not enough to identify toxic substances because UV spectra are very similar in some cases. Therefore, other identification methods are required’. One such method is automated identification in reversed-phase LC, as recently proposed by one of the author$, which is based on the concept of retention prediction3-’
k’ = f(Pi)
(1)
where k’ is the capacity factor of a solute and Pi is a physicochemical parameter of the solute. Equations such as eqn. 1 can be derived for various chromatographic systems and then the retention of solutes can be predicted. 0021-9673/88/%03.50
0
1988 Elsevier Science Publishers B.V.
12
K. JINNO et a/.
Eqn. 1 can be useful in the confirmation of approximately identified compounds automatically by the use of retention prediction in reverse. Having obtained k’ for a peak from an experiment, the system looks for the appropriate descriptors for the peak, using the data files of descriptors for several compounds as the standard basis. In this case, the descriptor is the retention parameter which is defined in the Discussion section (closely related to the hydrophobicity of the compounds) and compounds are medicaments which are typically psychotropic drugs. Then the names of the compounds corresponding to the calculated retention parameter are listed on a cathode ray tube (CRT) or line printer. It is the purpose of this communication to demonstrate this concept for automated identification of toxic substances in poisoned human fluids by reversedphase LC. EXPERIMENTAL
The LC system used comprised a Model 880-PU pump and Multi-320 multichannel UV detector (Jasco, Tokyo, Japan). The column was a Jasco Finepak Sil Cl8 S (250 mm x 4.6 mm I.D.) set at 50°C. The mobile phases were mixtures of 10 mM perchloric acid, 10 mM sodium perchlorate and acetonitrile and the flow-rate was I ml/min. The standard samples were dissolved in acetonitrile to a concentration of 100 pg/ml. Aotual human fluids such as gastric contents, sera and urines from poisoned patients were collected at the Critical Care Medical Center (CCMC), Nippon Medical School, Tokyo. The data handling was performed by a NEC 9801 VM2 microcomputer (Nippon Electric, Tokyo, Japan). RESULTS AND DISCUSSION
Determination of retention parameter
To obtain the retention prediction equation, first one has to find an appropriate descriptor. In this case, it is reasonable to consider that the retention of the toxic substances is controlled by their hydrophobicities *- lo, but accurate estimation of the hydrophobicities is a difficult task even when the methods of Rekkerr’ or Hansch and Leolz are used. Therefore, we used the most convenient way to perform this task, i.e., the use of a retention parameter determined,by the hydrophobic parameters of selected standards. The hydrophobic parameters of barbital (log Pd = 0.67, where Pd is the octanol-water partition coefficient), phenobarbital (1.48), phenacetin (1.58) and triazolam (2.42) obtained from the data base of Hansch and LeolZ were correlated to their log k’ values measured in buffer-acetonitrile (70:30), -0.058, 0.257, 0.390 and 1.086, respectively. Thus eqn. 2 is obtained: log k’ = 0.6614 log Pd - 0.571
(2)
The capacity factors, k’, of 48 representative toxic substances were determined experimentally under the same elution conditions and are summarized in Table I. Eqn. 2 can be converted into log P, = (log k’ f 0.571)/0.6614
(3)
AUTOMATED IDENTIFICATION
0
5
10
15
13
OF TOXIC SUBSTANCES
20 Time
25 C min
30 >
3s
40
45
Fig. 1. Chromatograms of human fluids sampled from a patient who had taken some poisons. (A) gastric contents, (B) serum. Mobile phase: buffer-acetonitrile (70:30). Detection: UV, 215 nm.
where log P, is the retention parameters for each substance obtained by inserting each k’ into eqn. 3. These values are also listed in Table I. Construction of retention prediction system The construction of the retention prediction system has been described previously2*7, and therefore only the basics will be mentioned in this work. Tf there is an highly correlated relationship between log k’ and log P, for toxic
14
K. JINNO el al.
TABLE I DATA TABLE FOR 48 TOXIC COMPOUNDS: ESTIMATED log P, No. 1
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
RETENTION TIME, CAPACITY FACTOR AND
Compound
Index
tR
k
log k
log P*
Acetaminophen Caffeine Barbital Sulpiride Acetylsalicylic acid Phenobarbital Bromvalerylurea Etheenzamide Bromazepam Phenacetin Cloxazolam Oxazolam Chlormezanone Chlordiazepoxide Pentobarbital Nitrazepam Amobarbital Phenytoin Secobarbital Carbamazepine Glutethimide Oxazepam Nimetazepam Estazolam Diazepam Flunitrazepam Flurazepam Chlordiazepoxide Alprazolam Medazepam Haloperidol Prepericyazine Triazolam Bromperidol Promethazine Desipramine Carpipramine Maprotyline Nortriptyline Hydroxyzine Imipramine Trihexyphenidyl Amitriptyline Trimipramine Levomepromazine Clocapramine Chlorpromazine Clomipramine
AA
2.410 2.870 3.000 3.220 4.020 4.490 4.830 5.060 5.060 5.530 5.870 6.220 6.450 7.020 7.370 7.370 7.610 7.610 9.100 9.100 9.100 9.920 11.880 12.220 12.800 14.650 15.650 15.670 16.490 16.710 18.000 18.570 21.100 21.220 20.370 21.920 25.020 25.370 25.490 26.180 27.220 27.450 31.610 33.100 34.370 42.690 43.020 49.490
0.506 0.794 0.875 1.013 1.513 1.806 2.019 2.163 2.163 2.456 2.669 2.888 3.031 3.388 3.606 3.606 3.756 3.756 4.688 4.688 4.688 5.200 6.425 6.638 7.000 8.156 8.781 8.794 9.306 9.444 10.250 10.606 12.188 12.263 11.731 12.700 14.638 14.856 14.931 15.363 16.013 16.156 18.756 19.688 20.48 1 25.681 25.888 29.93 1
-0.296 -0.100 -0.058 0.005 0.180 0.257 0.305 0.335 0.335 0.390 0.426 0.461 0.482 0.530 0.557 0.557 0.575 0.575 0.671 0.671 0.671 0.716 0.808 0.822 0.845 0.911 0.944 0.944 0.969 0.975 1.011 1.026 1.086 1.089 1.069 1.104 1.165 1.172 1.174 1.186 1.204 1.208 1.273 1.294 1.311 1.410 1.413 1.476
0.418 0.713 0.777 0.873 1.137 1.253 1.326 1.371 1.371 1.455 1.510 1.561 1.593 1.666 1.707 1.707 1.734 1.734 1.879 1.879 1.879 1.948 2.086 2.108 2.143 2.243 2.292 2.293 2.330 2.339 2.393 2.416 2.507 2.511 2.482 2.534 2.627 2.637 2.640 2.659 2.686 2.692 2.790 2.822 2.848 2.996 3.002 3.097
CAF BAL SUL ASA PHB BVU ETM BMZ PNC cxz oxz CM CD PTB NTZ AMB PHT SEB CBM GLT OPZ NMZ ESZ DIZ FNZ FLZ CD APZ MDZ HP PPC TRZ BP PM DEP CAP MPT NTP HX IMP THPH ATP TM0 LMP CCP CP CMP
AUTOMATED
IDENTIFICATION
15
OF TOXIC SUBSTANCES
TABLE II EXPERIMENTAL
DATA FOR OBTAINING EQN. 4a IN THE TEXT
Standard compound
Capacity factor,
log P.
k’
Mobile phase composition,
Barbital Phenobarbital Phenacetin Triazolam
0.171 I .253 1.455 2.501
buffer-acetoniirile
60:40
65:35
70:30
74:25
0.581 1.063 1.450 4.183
0.719 1.394 1.919 7.163
0.875 1.881 2.554 12.431
1.106 2.615 3.606 22.866
substances, multiple regression analysis on the data set shown in Table II gives log k’ = f1(X)log P, + fi(X)
(4)
(the results of the regression analysis are shown in Table III, where A and B are regression coefficients) where X is the volume fraction of the organic solvent (in this case, acetonitrile) in the mobile phase; the experimentally determined relationship is: log k’ = (-0.0178X
+ 1.2003) log P, - (0.0034X + 0.4562)
(4a)
Eqns. 4 and 4a mean that, if X and log P, of a substance are known, the logarithm of the capacity factor can be predicted under given chromatographic conditions. This is the basic concept of the retention prediction for toxic compounds investigated here. Eqn. 4a can be stored in the program of the retention prediction system3-’ and also in the automated identification systemz. Automated ident$cation system Eqn. 4a can be used, in turn, to obtain log P, for toxic compounds by substituting the measured retention information, k’. This concept can easily be systemized in a microcomputer yielding an interactive identification tool for toxic compounds. The procedure is as follows: if one measures the k’ value of a peak, the computerized system establishes the appropriate log P, for the peak, using the data file TABLE III RESULTS OF REGRESSION ANALYSIS FOR THE DATA SET IN TABLE II log k’ = A log P. + B. F = statistical F ratio; R = correlation coefficient. Mobile phase composition
A
B
F
R
6wo 65:35 70:30 75125
0.4899 0.5742 0.6635 0.7571
-0.5904 -0.5753 -0.5644 -0.5373
310 1306 7130 5066
0.997 0.999 1.000
1.000
16
A
K. JINNO et al. SAWLE : 1306~s~ 1 #t##W#b##t##wt#u
Search
Version
3.0
Concentrat icn of CH3Ch Retention Time of Unknwn Solute Elut ion Time of Unretaimd Solute Lwarith of Capacity Factor Naxiinum Value of Relative Error
30.00~“0& 4.53 min 1.60 nln 0.26 10.00 Y
--_--__
Candidates
______-___-____
___ ____ ____
samnle name Phenobarbital #-H-w
____ _ ______ _________________
index PH6
SAklPLE : 1306SAT 2 #11;###t########(
Search
Concentration of CH3Ch Retention Time of Unknwn Solute Elution Tia of Unretained Solute Lwarithn of Capacity Factor t!aximum Value of Relative Error
LwP 1.253
Version 30.00 5.32 1.60 0.37 l?.OD
Search
x
Version
______-______
Candidates
1
__________________
__
caff. 0.732 0.732 0.081
VOlX min nin x ____________________--_-____
,.
saaple name oentobarbital nitrazspam wbarbital Phenvtoin chlwdiazewxide
:
I
SMPLE : 1306AST4 #################W Search
index PTE NT2
LogP 1.707 1.707
AM
1.73
PHT CD
1.734 1.664
30.00 14.50 1.60 0.91 10.00
____________________--
Candidates
______ _____
sample name flunitrazepam diazepam ########t#)##t##u~
index FNZ DIZ
1-k 0.578 0.578 0.5% 0.5% 0.551
caff. 0.938 0.938 0.516 0.516 0.448
3.0 ####tt####wt#t#tw
Version
Concentration of CH3CN Retention Tine of Unknown SoLute Elution Tine of Unretained Solute Lwarithil of Capacity Factor kximw Value of Relative Error
Fig. 2.
leak’ *0.355 0.355 0.325
3.0 ###ww#w#######m
30.00 7.62 1.60 0.58 10.00
i
____________________--_____ Lo9P 1.371 1.371 1.326
Concentration of CiW Retention Time of Unkwn SoLute Etut ion Time of Unretained Solute Logarithm of Capacity Factor !aximum Value of Relative Error
,.
cceff. 0.692
VOLX min nin
index EM M2 8VU
SM’LE : 13LW3 ###t#t#######m# :
Logk’ 0.276
3.0 #######lW#####W##
Candidates samle nane ethentamide brazepan brmalervlurea -###########~
####m###t###tt#
VOLX min min x ________________ 1wP 2.243 2.143
logk’ 0.935 0.866
y---------coeff. 0.323 0.154
SAMPLE : lJOGAST5 tttW#itPBOtt##Ptii##ttPt#
Search
Version
3.0
Concentrat io:l of CHEN Relent ion Time of Unknow Zoiute ELut ion Time of Unretatned Solute Logarithm cf Capacity Factor Maximum Value of Re!ar ive Error
30.00 21.60 1.60 1.10 lO.O?
--_--___-------__-___--___-_-____
Candidates
VDLI min min I ____________________________
Index WI TRZ BP DEP
sampie name promethazine tr ia:oLam bromperidoi desipramine
t#t###tYtUt#####ttttl#PIY
LwP 2.482 2.507 2.511 2.534
Lo+’ 1.094 1.110 1.113
coeff, D.929 0.683
.0.619
I.128 0.246 ttXtttttWtY#tWtttt#tt#tttXtttttlYtffttXlt##~~~#~~###~$#~##~###~#~~ SAMPLE : 130GAST 6 ##t#t#####t###~Xtttt(rttt Search
Version
3.0 ##tlt#XX~rmtt##XtttY(#Utltl
Concentration of CMCN Retention Time of Unknown Solute Elution Time of Unretained Solute Logarithm of Capacity Factor laximum Value of Relative Error
30.00 33.03 1.60 1.29 10.00
_-_--__
Candidates
_--___-_-__
___ ____ ______-_
01% min min X ___-________________________
sample name index LwP Logk’ coeff. levcwprcmazine LRP 2.868 1.294 0.981 trimipramine TM0 2.822 1.277 0.637 amitriptyline ATP 2.790 1.256 0.188 ####MliX##XYP##Mt#~~#~~~##~~~~##~###~##~#~~####~~~~~~#~####~~
B
SAiw_E
: 130SERW.1 W##W#tlW#ttt##tr
Search
Version
3.0
Concentration of CH3CN Retention Time of Unknwn Solute Elution Time of Unretained Solute Logarithm of Capacity Factor Maximum Value of Relative Error
30.00 5.32 1.60 0.37 10.00
_________________________________
Candidates
###~~##t###l#tt#~lttt
VOlY min min X ___-___ --__ _ _-____-_
sample name index 1wP Logk’ ethenzamide 1.371 0.355 ETR brmzepam EN2 1.371 0.355 bromvalerylurea 1 326 0 325 tn#w4nwrurKI*rt#Hxt~~~~*~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SANPLE : 13OSERUM 2 Il###tW#X###Mtt###ttXtft Search
‘Jer s ion 3.0
Concentration of CH3CN Retention Time of Unknovn Mute Elution Time of Unretained So!ute Lwarithm of Capacity Factor Maximum VaLue of Relative Error
30.00 7.62 1.60
____________---___________-____--
Candidates
________
cmff 0.732 0.732
0.081
tta#~###tlll###t##t##tttttXt
VOlX min min
0.5s lD.00
sampie name index pentobarbital PT6 nitrazepam NT2 amobarbital AHG phenytoin PHI chlord iazepoxide CD tM#(W#t#tlM~#Y#tt)#tfftWtYtttX#XtffmXt
x ____________________________ 1osP 1.707 1.707 1.734 1.734 1.666
logk’
Coeff,
0.578
0.938
0.576 0.596 0.596 0.551
0.938 0.516 0.516 0.448
Fig. 2. Output of the automated identification system for peaks in the chromatograms gastric contents, (B) serum. Each number corresponds to the peak number in Fig. 1.
of Fig. I. (A) For
18
K. JINNO er al.
TABLE IV COMPARISON BETWEEN THE AUTOMATICALLY SCRIBED DRUGS
IDENTIFIED
DRUGS AND THE PRE-
Sample fluid
Drugs identified by the system
Drugs prescribed
Gastric fluid
Phenobarbital Ethenzamide Bromazapam Pentobarbital Nitrazepam Promethazine Triazolam Levomepromazine
Bromazepam Flunitrazepam Triazolam Levomepromazine Trihexylphenidyl Pentobarbital Nitrazepam Thioridazine*
Serum
Ethenzamide Bromazepam Pentobarbital Nitrazepam
Timiperone’ Clofedanol*
Urine
Bromvalerylurea Oxazolam * Drug not included in the library.
of log P, for several toxic compounds as the standard basis. Then the names of the compounds having values closest to the calculated log P, value (within the inputted relative error) are listed on the CRT or the line printer, together with the corresponding correlation coefficients. This coefficient is a measure of the probability of the predicted identification.
250
0
5
10
Time
15 <
min
>
213
25
G
Fig. 3. Chromatogram of gastric contents sampled from a woman who had taken poison. Experimental conditions as in Fig. 1.
AUTOMATED SMPLE
IDENTIFICATION
OF TOXIC SUBSTANCES
: Version
Conocntrat ion of CR3C?l Retention Time of Unknwn Solute Elution Time of Unrctaincd Solute Logarithm of Caoaoity Factor fbximum Value of Relative Error
3.0
30.00 votx 4.49 nin 1.60 min 0.26 10.00 x
~-________~__~__~___~~~~~~~~~~~~_ Candidates sams!c name phenobarbital
______________________----_-
index PHB
SAMPLE : 139GPST 2 #####1###H#####
Search
1osP lwk’ 1.253 0.276
Version
3.0
Conccntrat ion of CMCN Rctcnt ion Tine of Unknwn Solute E!ution Time of Unrcraincd Solute Locarirhm of Capacity Factor llaximum Vatuc of Relative Error
30.00 voLX 4.96 sin 1.60 min 0.32 . 10.00 x
____________________________-____
Candidates
samole name brmvalcrylurca cthenzamide brmszcpam t####t#t###)t#####
Search
Version
__ __________-___
Candidates
Starch
coeff . 0.99b 0.289 0.289
volX nin nin x _________________
index Rnz ETN PNC
SMPLE : 139X74 #H#############M
Losk’ U.325 0.355 0.355
3.0 ##t###H#####1##
30.00 5.bO 1.60 0.38 10.00
sacolc name brmcwam cthcnzamidc shcnacct in
#####l#####t-
LosP 1.326 1.371 1.371
Conocntrat ion @f CH3CR Rctcnt ion Time of Unknwn Solute Elution Tile of Unrctaincd Solute Lwarithm of Capacity Factor kxiaun Va!ue of Rclat ivc Error __________ _____ __
coeff.
0.549
_________________________-__
index BVU EM RI42 ##wwt#-
SAWLE : 1396AST 3 ######t####tt##l
m
1osP 1.371 1.371 1.455
Version
lock’ 0.355 0.355 0.410
_ _________ _ oceff. 0.527 0.527 0.170
3.0 W###########?#########t
Concentration of CH3C-A Retention Tiae of Unknwn SoLute Elurion Time of Unrctaincd Solute Lwarithm of Capacity Factor kxinum Value of Relative Error
30.00 21.00 1.60 1.08 10.00
-~________~_~_~_____~~_~~___~~___
Candidates
VOlX min nin x ____________________________
sample name index 1ogP Losk’ cocff. prmethazinc PI Z.&S2 I.094 0.76c tr iazolan TRZ 2.507 1.110 0.363 brasccr idol BP 2.511 1.113 0.2% propericyazine PPC 2.416 1.050 0.24S #####ttU#######t###########tt######t##l#1##1#
Fig.
4.
Output
of the
automated
identification
system for the chromatogram in Fig. 3.
19
K. JINNO et al.
20
Application of the system to actual samples The first application is to analysis of and serum of who had to hospital on The chromatograms of are shown in Fig. 1A gastric contents and Fig. 1B serum, respectively. A peak at 11.2 min which in all chromatograms is not due to in the chromatograms of pooled control samples. In Fig. 2, output of automated identification for peaks in chromatograms is The gastric sample is most suitable to judge of toxic substances the chromatographic by the automated identification it highly possible the patient one some of drugs listed in IV. Table IV, drugs he had for his psychiatric disease also indicated. it considered he took of the drugs which been prescribed, be included in list. by the automated system found in prescribed drugs and therefore the analysis be assisted by system output. The second example is gastric contents taken from who took a drug on she stated she had taken chlordiazepoxide, although it was reported she had bromvaleryurea. The chromatogram and the output of automated system are shown in and respectively. The result of the identification clearly she had taken bromvaleryurea chlordiazepoxide. The chromatagram of gastric sample indicates no trace of
in reversed-phase LC
for toxic been described. To construct the system,
user
or and selected standards so as obtain 4a, and day-to-day fluc. of retention can corrected. Although the system in its present form will leave many problems to be solved in practice, the concept opens a new dimension of clinical toxicological analysis. If the system is coupled to the spectral-matching function of multichannel detectors, its reliability is expected to increase, and: this is under investigation in our laboratory. An expansion of the log P, data file on toxic substances is also required for much ~ I* wider application of the system. REFERENCES 1 A. C. Moffat (Editor), Clarke’s Isolation and Ident#ication of Drugs in Pharmaceuticals, Body Fluid, and Post-Mortem Material, The Pharmaceutical Press, London, 2nd ed., 1986. 2 K. Jinno and M. Kuwajima, Chromatographia, 21 (1986) 622. 3 K. Jiwo, Proceedings of the 8th International Symposium on Capillary Chromatography, Vol. 2, Hiithig, Htiidelberg, 1987, pp. 1143-l 152. 4 K. Jinnoand K. Kawasaki, J. Chromatogr., 316 (1984) 1. 5 K. Jinno and K. Kawasaki, J. Chromatogr., 298 (1984) 326. 6 K. Jinno and K. Kawasaki, ACS Symp. Ser., 297 (1986) 167-187.
AUTOMATED IDENTIFICATION
OF TOXIC SUBSTANCES
21
7 K. Jinno, T. Hoshino, T. Hondo, M. Saito and M. Senda, Anal. Chem., 58 (1986) 2696. 8 Cs. HorvPth, W. Melander and I. MolnBr, J. Chromatogr., 125 (1976) 129. 9 W. Melander, J. Stoveken and Cs. Horvlth, J. Chromatogr., 199 (1980) 35. 10 L. Hafkenscheid, Ph.D. Thesis, University of Amsterdam, Amsterdam, 1983. 11 F. Rekker, The Hydrophobic Fragmental Constunr, Elsevier, Amsterdam, 1977. 12 C. Hansch and A. Leo, Substituenr Constants for Correlation Analysis in Chemistry and Biology, Wiley, New York, 1979.