Qantitative structure - pharmacokinetic relationship of antimicrobial agents drug plasma protein binding

Transcription

1 W C J P S 2003,18(1) : : 61 - (QSPR) ; 51 leave - one - out 10, QSPR : ; ; ; :R969. 1,R911 :A : (2003) Qantitative structure - pharmacokinetic relationship of antimicrobial agents drug plasma protein binding ZHOU Lu,SU Yi,ZUO Zhi - li,zhao Cai - bin,xia Kun - hua Department of Pharmaceutical Engineering, College of Chemical Engineering, Sichuan University, Chengdu , China Abstract :OBJECTIVE To study and demonstrate the application of neural network to research the quantitative structure - pharmacokinetic relationship (QSPR)of 61 kinds of antimicrobial agents. METHODS Firstly,three - layer back propagation system network was established in2 cluding an input layer, a hidden layer and an output layer. The input layer of the neural network is the quantum chemistry parameter, physical - chemistry parameter and molecular connectivity index of drug. The output of the neural network is the pharmacokinetic property - drug plas2 ma protein binding(dppb) of antimicrobial agents which is derived from the experimental data. Secondly, the prediction ability of the network is tested by the leave - one - out method. RESULTS The test involved the prediction of the pharmacokinetic properties(dppb) of ten com2 pound which have never been seen by the network. The predicted by neural network values shew good agreement with the experimental values. CONCL USION This result indicates that the neural network described in this study is proper and effective for QSPR research. Key words :Pharmacokinetics ;Drug molecular structure ;Drug plasma protein binding ;Neural network ;Antimicrobial agent CLC number :R969. 1,R911 Document code :A Article ID : (2003) [1 ] 40 %, ( quantitative structure pharmacokinetic relationslip, : (01GY051-55) : ( ),,E - mail :zhoulu - com

2 2 18 QSPR) (QSAR),QSPR [2 7 ] energy [8 ], :, 1 - ( - ) / / ,, (tetracycline),qspr 461 Hyperchem Table 1 Physical - chemistry parameter values of antimicrobial agent No. Hydration Logp Refractivity Polarizability Mass Total energy (ampicillin) ;21 (oxacilin) ;31 (sulbencillin) ; 41 (cloxacillin) ; 51 ( amoxicillin) ; 61 G (penicillin G) ;71 (carbenicillin) 81 V (penicillin V) ; 91 (hetacillin) ;101 (cyclacillin) ;111 (piperacillin) ;121 (ticarcillin) ;131 (mezlocillin) ; 141 (axlocillin) ;151 (diclxacillin) ;161 (nafcillin) ;171 (methicillin) ;181 (mecillinam) ;191 (bacampicillin) ;201 (minocydine) ;211 (methacycline) ;221 (chlortetracycline) ;231 (doxycy2 cline) ;241 ;251 (oxytetracycline) ;261 (cephacothin) ;271 (cephacexin) ;281 (ce2 furoxine) ;291 (cephracline) ;301 (cafamandole) ;311 (cephapivin) ;321 (cefadroxic) ; 331 (cephaloridine) ;341 (cepfotaxine) ;351 (cefazolin) ; 361 (cefaclor) ;371 (ceforanide) ;381 (ce2 faxintin) ;391 (cefuroximeaxetil) ;401 (cefotetan) ; 411 (catamoxef) ;421 (cefoperazone) ;431 (ceftizoxime) ;441 (ceftriaxone) ;451 (ceftazidime) ; (imipenem) ;471 (aztreonam) ;481 (gen2 tamicin) ;491 (kanamycin) ;501 (steptomycin) ;511 (tobramycin) ;521 (amikacin) ;531 (netilmich) ; (erythromycin) ; 551 (lincomycin) ; 561 (clindymycin) ;571 ( demeclocycline) ; 581 ( minocy2 cline) ;591 (nalidixic acid) ;601 (norfloxacin) ;611 (ciprofloxacin)

3 1 3 2 Table 2 Quantum chemistry parameter values of antimicrobial agent No. Binding Iso - atomic Electronic c - c energy energy energy interaction HOF HOMO LUMO No are the same as table 1 ;C - C interaction = core - core interation,hof = heat of formation,homd = hightest occupied molecular orbit,lumo = lowest unoccupied molecular orbit

4 Table 3 Molecular connectivity index values of antimicrobial agent Table 4 Train result analysis of neural network No1 X 0 X 1 X 2 X 3 X 4 X No are the same as table 1 No. EV NLV RD AV RD E E E E E E E E E R s No are the same as table 1 ; EV = experimental values,nlv = network learning values,rd = residual difference,av = anticipation values 2 Matlab,, 19,

5 1 5 [9] : lr = 0. 1, lr (i) = , lr (d) Leave - one - out = m = 0. 9, R s ; 40,, 30 30, QSPR, QSPR 1 2 (1 ) leave - one - out %, R = s = QSPR 5 Table 5 Prediction ability analysis of neural network No. EV AV RD RE/ % R s No are the same as table 1 ; EV,AV and RD are the same as table 4, RE = relative error 1 Obach RS,Baxter J G, Liston TE, et al. The prediction of human pharma2 cokinetic parameters from Pheclinical and in vitro metabolism data [J ]. J Pharmacol Exp Ther,1997,283 :46 2. [J ].,2000, 32 (3) : [ M]. :, [M]. :, Blakey GE. Quantitative based model to characterize changes in pharma2 cokinetics across a homologous series of barbiturates in the rat[j ]. Journal of Pharmakokinetic Biopharmaceutical,1997,25 :277 6 Ivan Nestorov. Quantitative structure - pharmacokinetics relationships :A mechanistically based model to evaluate the relationship between tissue distribution parametersand compound lipophilicity[j ]. Journal of Pharma2 cokinetic and Biopharmaceutical,1998,26(5) :521 7 Jogarao V, Gobburu S,William H, et al. Quantitative structure - pharma2 ceutical relationship (QSPR) of beta blocker derived using neural network [J ].Journal of Pharmacutical Science,1995,84 (7) : [ M]. :, Kuemmerle, HP.. [M]. :, :