Quantitative Structure—Plasma Protein Binding Relationships of Acidic Drugs

ZVETANKA ZHIVKOVA, IRINI DOYTCHINOVA Faculty of Pharmacy, Medical University of Soﬁa, Soﬁa 1000, Bulgaria

Received 21 June 2012; revised 24 July 2012; accepted 2 August 2012 Published online 7 September 2012 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.23303

ABSTRACT: One of the most important factors, affecting significantly the overall pharmacokinetic and pharmacodynamic profile of a drug, is its binding to plasma protein (PPB). In the present study, we focus on a set of 132 diverse acidic drugs binding to plasma proteins to different extent and develop quantitative structure-plasma protein binding relationships (QSPPBR) to predict their unbound fraction in plasma (fu) using 178 molecular descriptors. QSPPBR models were derived after variable selection by genetic algorithm followed by stepwise regression and tested by cross- and external validation. The final model has r2 value of 0.771, q2 value of 0.737, and four outliers. It predicts 57% of the fu values with less than twofold error. Ac- cording to the molecular descriptors selected as the most predictive for PPB, the lipophilicity of the drugs, the presence of aromatic rings, cyano groups, and H-bond donor–acceptor pairs increase the PPB, whereas the presence of tertiary carbon atoms, four-member rings, and iodine atoms decrease PPB. These descriptors were summarized into a short seven-item checklist of criteria responsible for PPB. The checklist could be used as a guide for evaluation of PPB of acidic drug candidates, similarly to the Lipinski’s rule of five used for evaluation of oral permeability of drugs. © 2012 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 101:4627–4641, 2012 Keywords: protein binding; plasma protein binding (PPB); computational ADME; QSAR; fraction of unbound drug (fu); fu prediction; acidic drugs; complexation; drug design

INTRODUCTION discovery prior to chemical synthesis, allowing prediction to be made even on virtual compounds. The analysis of the main reasons for attrition in drug One of the most important factors, affecting sig- development, published in the late 1990s, reveals that nificantly the overall pharmacokinetic and pharma- almost 39% of the costly late-stage failures are caused codynamic profile, is the binding of drugs to serum by poor pharmacokinetics.1 The recognized need for proteins. Although the significance of this phenom- estimation of pharmacokinetic behavior of drug candi- ena remains a source of sheer never-ending debate,6 dates as early as possible led to considerable progress it is generally accepted that protein binding is one in computational techniques and to development of of the factors controlling the concentration of free, extensively growing number of models for prediction therapeutically active drug in plasma. Drugs with an of various pharmacokinetic properties. As a result, by excessively high affinity for plasma proteins (>95% 2000, the negative contribution of pharmacokinetic bound) may require higher doses to achieve the ef- factors has decreased dramatically to about 10% of fective concentrations in vivo, and usually have re- the drug failures.2 The contemporary state of the mat- stricted distribution to sites of action. Changes in the ter is reviewed in several articles and books.3–5 These binding affinity may result in unforeseen changes in studies highlighted the potential of in silico approach the key parameters for setting up dosing regimen— to provide key information at early stages in drug apparent volume of distribution, clearance, half-life, and bioavailability.6–8 The development of computational models for the Correspondence to: Zvetanka Zhivkova (Telephone: +359-2- prediction of plasma protein binding (PPB) affinity is 9236514; Fax: +359-2-9879874; E-mail: zzhivkova@pharmfac. acad.bg) an attractive goal from theoretical and practical point Journal of Pharmaceutical Sciences, Vol. 101, 4627–4641 (2012) of view.From a theoretical point of view,the structure- © 2012 Wiley Periodicals, Inc. and the American Pharmacists Association PPB relationships gain insights into the chemical

JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 4627 4628 ZHIVKOVA AND DOYTCHINOVA nature of drug–protein interactions. The practical ap- occupy all known binding sites, each with different plications of these models in the process of drug de- affinity and different pharmacological relevance.18 velopment allow the design of new compounds with Considering all these facts, it seems that the ex- reduced PPB and less variable pharmacokinetic be- tent of protein binding (expressed as free, unbound havior. Recently, this strategy has been successfully fraction of the drug fu) is the most reliable parame- applied by Mao et al.9 ter characterizing the overall protein-binding behav- A good number of studies on the analysis and pre- ior in vivo. diction of PPB have been performed during the last The binding of drugs to various plasma pro- 10–15 years. They could be classified according to sev- teins seems to be governed by different driving eral criteria: data, descriptors, and methods. Accord- forces. Hydrophobic and electrostatic interactions ing to the data used, studies are based on congeneric are involved in complexation of drugs with HSA,29 sets of drugs10–13 or on diverse drugs.14–24 PPB affin- whereas binding to AAG and lipoproteins is mainly ity is usually expressed as a percent of protein-bound hydrophobic.30–32 Recently, it was suggested that the drug, or as an equilibrium association (dissociation) influence of the lipophilicity on binding to HSA is constant. The descriptors used in the models are larger for acids than for bases.15 It is clear that the constitutional, topological,21 electrotopological,10,16 lipophilicity and the ionization state of the ligand are chemical, physicochemical, quantum chemical, molec- important for the mode of binding to plasma proteins ular docking based,24 and combinations of them. The and contribute to the different behavior of acidic and repertoire of methods includes multiple linear regres- basic drugs in plasma. sion with variable selection,12–14,19 artificial neural In the present study, we focus on a set of 132 di- networks,22,23 support vector machines (SVMs),11 ant verse acidic drugs binding to plasma proteins and colony optimization,19 pharmacophoric similarity and develop QSPPBR models to predict their unbound 17 18 fingerprints, cluster analysis, and so on. fraction in plasma (fu) using 178 molecular descrip- All of the above-mentioned studies use ligand- tors grouped into several groups: molecular connec- based methodologies. The only structure-based study tivity P (chi) indices; molecular shape indices; elec- to our knowledge is an SVM model combined with au- trotopological state (E-state) indices; and two- and tomated molecular docking calculations that are able three-dimensional molecular properties. Apart from to predict whether albumin binds the query ligand, prediction, the QSPPBR models are used to explore to determine the probable binding site on albumin, the structural features of drugs important for their to select the albumin X-ray structure complexed with PPB. The most relevant descriptors are translated the most similar ligand and to calculate the geometry into a simple, easy to use, checklist of criteria govern- of the complex ligand-albumin.25 ing the PPB. Prediction of binding affinity of drugs to plasma proteins is a rather complicated task. The quality of the model depends on the quality of utilized dataset. MATERIALS AND METHODS Like the other pharmacokinetic parameters, binding Dataset affinity data vary significantly from report to report The dataset used in the present study was com- as a result of differences in methodology, experimen- 33 tal conditions, and mathematical approaches. This is piled from Obach’s database which is considered as especially true for the affinity constants K, which are, the largest and the best curated database for phar- in general, determined in vitro. In addition, published macokinetic parameters in humans. The mol files of the drugs were retrieved from DrugBank34 and data for K are restricted to affinity toward a specified 35 plasma protein, and even toward a single binding site ChemicalBook and their pKa values were calculated in the protein. by ACD/LogD software (Advanced Chemistry Devel- However, the pharmacokinetic behavior of drugs is opment Inc., Ontario, Canada). The fraction of a drug controlled by the free, unbound fraction that is deter- ionized at pH 7.4 as acid (fA) or base (fB) were calcu- mined by the possible interactions with all proteins in lated according to the equations: plasma: human serum albumin (HSA), alpha-1-acid 1 glycoprotein (AAG), lipoproteins, globulins, erythro- for acid : f A = + (pKa−7.4) cytes, and so on. Although it is accepted that HSA is 1 10 primarily responsible for the binding of acidic drugs = 1 for base : f B − and AAG displays greater affinity for basic drugs, 1 + 10(7.4 pKa) there are several examples for binding of drugs both to HSA and AAG irrespectively on their ionization In case of more than one acidic or basic center, the 7,26,27 state. Lipoproteins also play a significant role in pKa of the strongest one was taken. A drug was con- drug binding, especially for hydrophobic drugs.28 By sidered as an acid if met at least one of the two condi- increasing the ligand concentration, the ligands can tions: (1) fA exceeded 10% and fB ≈ 0; or/and (2) fA was

JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 DOI 10.1002/jps QUANTITATIVE STRUCTURE—PLASMA PROTEIN BINDING RELATIONSHIPS OF ACIDIC DRUGS 4629

considerably higher than fB and close to 1. One hun- Variable Selection dred and thirty-two drugs met one or both of these A genetic algorithm (GA),38 as implemented in the conditions and entered the dataset used in the study. MDL QSAR package, was used as a variable selec- The data for f in Obach’s database were used as u tion procedure in the present study. GA allows one a quantitative measure for plasma protein binding to select a subset of the most significant predictors of drugs. This parameter represents the fraction of using two evolutionary operations: random mutation unbound drug in plasma and is given by the equation: and genetic recombination (crossover). The algorithm was used in the study with default values for the size Cu fu = , of initial population (32), choice of parents (tourna- C ment selection), types of crossover (uniform crossover) and mutation (one-point mutation), and fitness func- where Cu is the unbound drug concentration and C is tion (Friedman’s lack-of-fit scoring function with two the total drug concentration in plasma. For QSPPBR parameters).39 The selected variables entered a step- =− purposes, it was used as pfu log fu. Thus, higher wise linear regression. pfu values correspond to higher PPB. The whole dataset was ranked in an ascending or- QSPPBR by Stepwise Linear Regression der of pf values and was divided into six subsets of u The QSPPBR models in the present study were de- 22 drugs each by allocating one of every six drugs into rived by a stepwise linear regression, as implemented a different subset. Every subset was used as a test set in the MDL QSAR package. It was used in a forward whereas the remaining five subsets were combined mode with default value for F-to-enter (4.00) and F- into a training set. The training sets were used to to-remove (3.99). Final models were assessed by ex- develop and cross-validate the QSPPBR models, and plained variance (r2) and standard error of estimate the test sets were used for external validation. (SEE), according to the following equations: Molecular Descriptors n The chemical structure of the drugs used in the (pfu,obs,i − pfu,calc,i) present study was described by 178 molecular de- 2 = − i=1 r 1 n , scriptors computed using the software packages ACD/ (pfu,obs,i − pfu,obs,mean) LogD version 9.08 (Advanced Chemistry Develop- i=1 ment Inc., Ontario, Canada) and MDL QSAR version 2.2 (MDL Information Systems, Inc., San Leandro, California). The descriptors were grouped into five types: n (pfu,obs,i − pfu,calc,i) = SEE = i 1 , (i) Molecular connectivity P (chi) indices,36 which n − d − 1 represent molecular structure by encoding significant topological features of whole molecule. where pfu,obs, i is the observed pfu of the ith drug, There are five categories of structural informa- pfu, calc,i is the calculated by the model pfu of the ith tion described by P indices: degree of branching drug, n is the number of drugs in the dataset, and d (low-order P indices), variable branching pat- is the number of molecular descriptors in the model. tern (path P indices), position and influence of Fisher statistics (F) for models was also calculated. heteroatoms (valence P indices), patterns of ad- jacency (P cluster and path/cluster indices), and Validation degree of cyclicity (P chain indices). Two validation procedures were applied in the study (ii) 6 shape indices—a family of graph-based struc- to access the predictive ability of the QSPPBR mod- ture descriptors that represent shape.37 els: leave-one-out cross-validation (LOO-CV) in the (iii) E-state indices, which are atom-level molecu- training sets and external validation by test sets. The lar descriptors computed for each atom in the models were assessed by the coefficients q2 and molecule.37 They represent the electron density LOO-CV r2 , respectively, according to the following equa- at each atom and the ability of those electrons pred tions: to participate in intermolecular interactions. (iv) Molecular properties—weight, log P,logD , 7.4 n number of rings, number of hydrogen bond (pfu,obs,i − pfu,pred,i) donors and acceptors, and so on. 2 i=1 q − = 1 − , (v) Three-dimensional molecular properties such as LOO CV n (pfu,obs,i − pfu,obs,mean) polarizability, surface area, volume, and so on. i=1

DOI 10.1002/jps JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 4630 ZHIVKOVA AND DOYTCHINOVA

n − (pfu,obs,itest pfu,pred,itest ) 2 = − i=1 rpred 1 n , − (pfu,obs,itest pfu,obs,meantest ) i=1 where pfu,pred,i is the predicted by the model pfu of the ith drug. In the LOO-CV procedure, one drug was excluded from the training subset, the model was derived based on the remaining n − 1 drugs and used to predict the pfu,pred,i of the excluded ith drug. In the external validation, pf is the pfu value of a u,pred,itest Figure 1. Histogram of the experimental f values. drug from the test subset predicted by the model der- u ived on the basis of the training subset. Fold error of prediction for the test sets was calcu- This initial model was cross-validated by LOO and it lated according to the equation: is given below as Model (1):

| − | FEP = 10 pfu,obs,i pfu,pred,i

pf u = 0.177 log P (±0.021) + 0.082 (±0.020)SaasC acnt and the average value of FEPs for the test drugs is given as mean fold error MFEP. + 0.024(±0.008)SaaCH + 0.057(±0.015)StN The accuracy of prediction is given as a percent of + 0.04(±0.014)SsCl + 0.071(±0.018)SHBint4 acnt the total number of drugs predicted with less than twofold error. + 0.101(±0.017)SHBint5 acnt + 0.492xvp10 − 0.405(±0.097)SsI acnt

RESULTS − 0.063(±0.013)SHBint6 acnt − 0.334(±0.068)SssssC acnt Dataset Analysis − 0.155(±0.050)SsssN + 0.441. Model,(1) The dataset of acidic drugs used in the present study n = 126, r2 = 0.822, SEE = 0.371, F = 43.41 q2 = 0.744, encompasses a wide variety of structures, therapeutic actions, and physicochemical properties. A drug was MFEP = 2.17 ± 1.33, accuracy = 61%. considered as an acid if it met at least one of the two conditions: (1) fA exceeded 10% and fB ≈ 0; or/and (2) fA was considerably higher than fB and close to 1. The Six outliers were detected and removed from the dataset consisted of 132 drugs. dataset (5-aminosalicylic acid, bromfenac, ceftriax- The molecular weight (M) of the analyzed drugs one, telmisartan, tesaglitazar, and verlukast). varies between 126 and 1297 g/mol with an average To derive a robust model, the whole set of 132 value of 379.4 g/mol. M exceeds 500 g/mol for 14 drugs was divided into six training subsets, each consist- and two of them (micafungin and suramin) have M ing of 110 molecules, and six corresponding test sub- higher than 1200 g/mol. sets, each containing 22 molecules, as described in the The values of lipophilicity parameters log P and section Materials and Methods. The training subsets log D7.4 also vary significantly. Log P ranges between were used for the development and cross-validation −7.48 (micafungin) and 7.4 (montelukast) and the of QSPPBR models and the test subsets were used for same drugs have extreme values of log D7.4 (−11 and external validation of the models. The robust model- 5.19, respectively). ing of pfu was performed in three steps. The values of fu range from 0.0004 (pfu = 3.398) for As a first step, variable selection procedure by GA verlukast to 1 (pfu = 0) for atovaquone, cidofovir, flucy- followed by a stepwise linear regression was applied tosine, and fosfomycin with an average value of 0.30 to every training subset using the whole descriptor and median value of 0.15. The histogram of the data set. The aim of this step was to select the most pre- distribution of the experimental fu values is shown in dictive descriptors. Figure 1. Second, from the pool of the most predictive descriptors were selected the most frequently emerged QSPPBR Models for pf u ones. As most frequently emerged descriptors were A preliminary or an initial model was derived based defined those which appear at least in two of the six on the whole set of 132 acidic drugs and using the models. They were used as a new input descriptor set whole set of 178 descriptors. The descriptors were se- and underwent variable selection by GA followed by lected by GA followed by a stepwise linear regression. stepwise regression for every training subset.

JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 DOI 10.1002/jps QUANTITATIVE STRUCTURE—PLASMA PROTEIN BINDING RELATIONSHIPS OF ACIDIC DRUGS 4631

Finally, a consensus model was derived based on drogen bond donors and hydrogen bond accep- the whole set of 132 acidic drugs using the set of the tors separated by four skeletal bonds (of inter- most frequent descriptors. nal hydrogen bond with four skeletal bonds be- If a pair of intercorrelated descriptors (r > 0.65) tween donor and acceptor). They emerged in two appeared in a model, the less signiﬁcant descriptor models. was omitted and the model was rebuild. The results at each step are considered below. These 16 most frequently emerged descriptors form a new descriptor set, which was used in the next step QSPPBR Models for pfuUsing the Whole Descriptor Set of pfu modeling. The set of 178 descriptors underwent variable selec- QSPPBR Models for pf Using the Most Frequently tion by GA followed by a stepwise regression for ev- u Emerged Descriptors ery training subset of drugs. The derived models were cross-validated and externally validated by the corre- The new set of 16 descriptors underwent variable se- sponding test sets. Drugs were considered as outliers lection by GA followed by a stepwise regression for ev- if the residuals did not obey normal distribution law. ery training subset of drugs. The newly derived mod- They were removed from the training sets, and the els were cross-validated and externally validated by models were rebuilt. The statistics of the best per- the corresponding test sets. The statistics of the best forming models is given in Table 1. performing models is given in Table 2. The descriptors that have been selected in more The comparison between the two sets of models than one model were as follows: shows that reducing the set of descriptors slightly 2 2 decreases the mean values of r and q LOO-CV but im- • log P or log D. They intercorrelate with r2 = 0.748 proves the overall prediction by decreasing the MFEP, and emerged in all models. increasing the accuracy and reducing the number of • SaasC acnt or SaaCH acnt. These descriptors outliers. count the number of aromatic nonconjugated Consensus QSPPBR Model for pf Using the Most substituted and nonsubstituted carbon atoms in u Frequently Emerged Descriptors molecule, respectively. They intercorrelate with 2 r = 0.512 and were found in ﬁve of the six At the last step of pfu modeling, a consensus model models. was derived using the set of 16 frequently emerged • StN acnt, StN,orStsC. These descriptors ac- descriptors and based on the whole set of 132 acidic count for the presence of cyano (-CN) group. They drugs. The derived consensus model is given below as exist in four models. Model (2): • SssssC acnt. This descriptor counts the number of aliphatic tertiary carbon atoms in molecule. It

was selected in three models. pf u = 0.208(±0.020) log P + 0.125(±0.019)SaasC acnt • SsCl acnt. This descriptor counts the number of + 0.276(±0.081)StsC + 0.046(±0.013)SHBint4 acnt Cl atoms in molecule. It appeared in two models. • xch4. This descriptor accounts for the presence + 0.074(±0.017)SHBint5 acnt − 0.193(±0.073)SssssC acnt of four-member ring. It was found in two of the − 3.104(±0.760)xch4 − 0.498(±0.101)SsI acnt models. • SsI acnt counts the number of I atoms in + 0.419, Model,(2) molecule and it is important descriptor in two n = 128, r2 = 0.771, SEE = 0.410, F = 49.98, of the models. • SsssN. This descriptor gives the sum of all (>N-) q2 = 0.737, MFEP = 2.45 ± 1.66, accuracy = 57%. E-state values in molecule. It was presented in two of the models. • SaaN. It shows the sum of all E-state values of Model (2) had statistics close to this of Model (1) but the aromatic N atoms in molecule and appeared with fewer descriptors and only four outliers: brom- in two models. fenac, suramin, tesaglitazar, and verlukast. • SHBint 5 acnt counts the number of pairs of hy- Checklist for PPB Prediction drogen bond donors and hydrogen bond acceptors separated by ﬁve skeletal bonds (internal hydro- The descriptors involved in Model (2) account for con- gen bonds with ﬁve skeletal bonds between donor crete structural features and reveal their contribution and acceptor). It is an important descriptor in two to PPB of acidic drugs. The lipophilicity of molecules models. (expressed as log P) and the presence of aromatic sub- • SHBint4 or SHBint4 acnt. These descriptors ac- stituted carbon atoms (descriptor SaasC acnt), cyano count for the presence or number of pairs of hy- groups (descriptor StsC), and/or H-bond donors and

DOI 10.1002/jps JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 4632 ZHIVKOVA AND DOYTCHINOVA MFEP Accuracy (%) Outliers pred 2 r LOO-CV 2 Fq SEE 2 r 0.822 0.387 35.38 0.771 0.698 2.54 45 8 0.823 0.358 54.55 0.782 0.115 2.84 32 10 0.826 0.373 50.12 0.782 0.660 2.51 46 9 0.847 0.353 58.56 0.811 0.771 2.98 45 9 0.819 0.370 73.01 0.796 0.683 2.47 41 9 0.834 0.370 43.04 0.781 0.888 2.27 55 9 0.809 0.400 36.15 0.748 0.803 1.94 55 7 + + − 9 − + acnt − 590 int4 xvch . 576 . 0 acnt 08 0 . SdssS StN acnt + SHB + 49 871 433 . . + 007 − 0 . 0 int6 − 3 SssNH + 0 SsssN − + ka + acnt SHB 248 SHssNH acnt 252 . 342 acnt . . 0 087 acnt int3 0 2 . StN 112 0 077 − int4 . − . − StN 0 0 + SsI 051 SHB . SssssC 3 − − 0 acnt SHB 057 acnt Acnt . 446 026 + . 0 . 274 . 0 0 acnt 083 0 SsCH . + int5 − 0 − int8 4 acnt − SaaCH 4 812 + SdsCH 116 . . SHB xch 0 0 SHB SsI 030 xch SssssC . int4 132 + − 0 . acnt 091 550 0 3 . 2 102 . SHo the r 520 312 363 + . k . 0 . . 2 SHB 0 − 0 5 0 + SsCl − 042 + − . 056 − − acnt . SsNH 009 D min 0 . 0 469 0 H 698 . + acnt . + acnt acnt 074 acnt acnt 0 + 0 . 0 373 + . + max SaaaC 0 134 log − StsC H . 27 10 0 . + SaasC StN SssssC SsssN SaasC SaasC SaaCH 10 0 301 + 346 . . xvp − 0 209 072 1 194 186 107 192 066 xch ...... 0 0 + 0 0 0 0 0 + acnt 983 46 . . + + − − + + + Acnt 0 45 P P P D D acnt acnt + + 2 acnt acnt int5 SaaCH SaaN SpcPolarizability SssssC SsCl SsCl SsNH SHB SaaN 169 log 128 log 180 log 107 log 194 log 083 ...... 0 0 0 0 0 0 550 . 217 0422 380 370 266 105 022 593266 ======...... 0 0 0 0 0 0 doxifluridine, and tesafglitazar. Outliers frompravastatin, the and test tolcapone. set: 5-aminosalicylic acid, glyburide, tesaglitazar, tolcapone, and verlukast.bromfenac, Outliers meloxicam, from and the tenoxicam. test set: ceftriaxone, enalaprilat, and tesaglitazar. Outliersglimepiride, from irbesartan, the and test verlukast. set: cefotetan, 0 furosemide, tesaglitazar, and verlukast. Outliersfosfluconazole, from iothalamic the acid, test and set: micafungin. 0 tesaglitazar. Outliers from the testcephaloridine, set: epristeride, acetylsalicylic and acid, folinic ceftriaxone, acid. 14 Outliers from the test set: ibuprofen, suramin, and tesaglitazar. u u u u u u Outliers from the training set: acetylsalicylic acid, acetazolamide, bromfenac, pf Outliers from the training set: acetylsalicylic acid, ceftriaxone, diflunisal, Outliers from the training set: 5-aminosalicylic acid, acetylsalicylic acid, pf Outliers from the training set: acetylsalicylic acid, amidotrizoate, folinic acid, pf Outliers from the training set: bromfenac, glimepiride, pravastatin, and pf pf Outliers from the training set: bromfenac, ceftriaxone, glimepiride, and suprofen. pf Cross–Validation of the QSPkR Models Based on the Whole Descriptor Set Table 1. Training Set1 Model 6 4 2 3 Mean 5

JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 DOI 10.1002/jps QUANTITATIVE STRUCTURE—PLASMA PROTEIN BINDING RELATIONSHIPS OF ACIDIC DRUGS 4633 MFEP Accuracy (%) Outliers pred 2 r LOO-CV 2 Fq SEE 2 r 0.776 0.406 52.08 0.731 0.660 2.15 48 6 0.763 0.418 45.11 0.653 0.514 2.54 50 5 0.789 0.395 61.58 0.764 0.775 2.26 450.787 8 0.403 60.33 0.7610.745 0.731 0.427 48.27 2.33 0.710 0.605 50 2.27 7 50 5 0.818 0.368 52.08 0.777 0.656 2.81 45 8 0.751 0.423 50.31 0.719 0.678 2.68 50 5 − + + + int4 acnt + + 522 . StN 0 StN SHB StN StN StN + 053 05075 009 485 053 . 086 . . . . . 0 0 0 0 0 0 SsssN + + + + + + 234 . acnt acnt acnt acnt acnt acnt 0 − SsI SsI SsI SsI SsI SsI 32 acnt . 547 429 489 . . . 5384 0 5265 . 0 0 0 . 0 − 0 − − − SsCl − 4 − 4 4 4 4 4 351 xch . xch xch xch xch 0 426 xch . 069 0 + . 785 203 725 01 . . . . 077 2 + . 4 3 3 4 4 − acnt − − − − − acnt acnt acnt acnt acnt acn acnt SsCl SssssC 454 250 418 426 243 . . . . . SaasC SaasC SaasC SaasC SaasC SaaCH 0 0 0 0 0 589 . + + + + − 0 139 102 135 138 132 081 ...... 0 0 0 0 0 0 + + + + + + + acnt acnt acnt acnt acnt P P P P P P acnt int5 int5 int5 int5 int5 SssssC SHB SHB SHB SHB SHB 187 log 197 log 168 log 192 log 192 log 177 log ...... 0 0 0 0 0 0 215 099 075 092 109 088 ======...... Outlier from the test set: bromfenac. 0 0 Outliers from the test set: cefotetan, glimepiride, irbesartan, and verlukast. verlukast. Outliers from the test set: suramin and tesaglitazar. Outlier from the test set: suprofen. 0 0 0 suramin, tesaglitazar, and verlukast. Outliersdiﬂunisal. from the test set: ceftriaxone and the test set: fosﬂuconazole and micafungin. 0 u u u u u u Outliers from the training set: suprofen, suramin, tesaglitazar, and verlukast. pf Outliers from the training set: bromfenac, ceftriaxone, suramin, and tesaglitazar. pf Outliers from the training set: bromfenac, ceftriaxone, glimepiride, irbesartan, and pf Outliers from the training set: bromfenac, suramin, tesaglitazar, and verlukast. pf pf Outliers from the training set: 5-aminosalicylic acid, bromfenac, glimepiride, Outliers from the training set: bromfenac, tesaglitazar, and verlukast. Outlierspf from Cross-Validation of the QSPkR Models Based on the Most Frequently Emerged Descriptors 4 2 5 6 Table 2. Training Set1 Model Mean 3

DOI 10.1002/jps JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 4634 ZHIVKOVA AND DOYTCHINOVA

Table 3. Checklist of Criteria for Plasma Protein Binding

Number Presence of PPB Increases PPB Decreases √ ≥ 1LogP 3 √ ≥ 2 Aromatic nonconjugated rings 2 √ ≥ 3 Cyano (CN) group 1 √ ≥ 4 H-bond donors and acceptors separated by four or five skeletal bonds 3 √ ≥ 5 Tertiary C atoms 1 √ ≥ 6 Four-member ring 1 √ 7Iatom≥1 acceptors separated by four or five skeletal bonds ber of positive and negative criteria at least 2. The (descriptors SHBint4 acnt and SHBint5 acnt)in- options for a difference higher or equal to 2 are as fol- crease the PPB. The presence of tertiary C atoms (de- lows: four positive criteria (none of the drugs), three scriptor SssssC acnt), four-member rings (descriptor positive without any negative (bromfenac, tolcapone, xch4) and/or I atoms (descriptor SsI acnt) decrease fluvastatin, and glyburide), two positive without any the PPB ability of drugs. The above descriptors were negative (most of the high binders), four positive and summarized into a short seven-item checklist of crite- two negative (none of the drugs), and three positive ria responsible for PPB (Table 3). Although the num- and one negative (none of the drugs). ber of nonsubstituted aromatic carbon atoms (encoded with the descriptor SaaCH acnt) was not involved in DISCUSSION Model (2) it was considered together with SaasC acnt because of its high positive impact on PPB. The pres- The present study is focused on the quantitative re- ence of aromatic substituted and nonsubstituted car- lationships between the molecular structure of 132 bon atoms was expressed as presence of aromatic non- acidic drugs and their PPB affinity without con- conjugated rings. sidering explicitly any particular plasma protein or The checklist was applied to the dataset of acidic binding site. PPB affinity was expressed as pfu and drugs (Appendix 1). It was found the cutoff for each QSPPBR models were built starting from 178 molecular descriptors. A good QSPPBR model (Model 1) was descriptor distinguishing between high binders (fu ≤ 0.05) and the rest of binding drugs and defined as a derived even in the first run on the whole dataset. To criterion for PPB. To bind to plasma proteins, an acidic avoid any chance correlation and to develop a robust drug needs log P higher than 3, more than two aro- model, the whole dataset was divided systemically matic nonconjugated rings and/or cyano group. Low into six training and six test subsets, which under- lipophilicity (log P < 1.5) as well as the presence of went variable selection by GA followed by a stepwise tertiary carbon atoms, four-member rings, and/or io- regression. The derived models were cross- and exter- dine atoms result in decrease in PPB of acidic drugs. nal validated and showed good predictive ability. A The checklist was applied on the set of acidic drugs subset of 16 most frequently emerged in the models and a minimum number of criteria for good PPB was descriptors was selected and used to derive a consensus model for PPB prediction. The descriptors in- defined (Fig. 2). For PPB above 95% (fu = 0.05, pfu = 1.301), drugs should have difference in the num- cluded in this model were converted into a seven-item checklist of criterion for PPB prediction. A prerequisite for a good QSAR model is the molecules under study to bind the same binding site on the target biomacromolecule. This is not the case with drug binding in plasma where specific and nonspecific, reversible, and nonreversible interactions with many proteins are possible. The interpretation of models based on such data is further complicated by the presence of numerous secondary binding sites, allosteric interactions between bound drugs, and especially interactions with endogenous ligands arising in vivo. In practice, the mechanisms of drug–plasma protein interactions are as diverse, as the drugs them- selves, and their binding affinities are determined by different structural features.

Figure 2. Relationship between pfu and the difference be- In plasma, the acidic drugs bind mainly to albumin. tween the number of positive and negative criteria. The Binding to AAG and lipoproteins is in general low and outlies from Model (2) are given as open squares. The cor- inconsequential, governed by nonspeciﬁc hydropho- relation coefﬁcient is 0.704 (including outliers). bic interactions.30–32 The X-ray structure of HSA in

JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 DOI 10.1002/jps QUANTITATIVE STRUCTURE—PLASMA PROTEIN BINDING RELATIONSHIPS OF ACIDIC DRUGS 4635 complexes with several drugs29,40,41 revealed the ar- acid and amidotrizoate). Both show negligible PPB chitecture of the binding sites and provided an in- with fu 0.98 and 1, respectively. Negative influence sight into the binding mechanisms. There are at least on the binding affinity has also the number of substi- six binding sites on HSA, most of which are inher- tuted quaternary aliphatic C atoms in the molecule ent for fatty acids but also accommodate drugs such encoded by the descriptor SssssC acnt. Aromatic and as azapropazone, indomethacin, diflunisal, ibuprofen, tertiary N atoms, described by SsssN and SaaN,also and so on. The complexation of drugs occurs pre- decrease PPB. dominantly at two specific sites defined as Site 1 Many drugs appear as outliers in the models de- and Site 2.42 They are topologically similar but not rived in the present study. However, only four drugs identical. They both contain largely preformed hy- do not obey the final consensus Model (2): bromfenac, drophobic cavities with distinct polar features. Site suramin, tesaglitazar, and verlukast. All of them have 1 is larger, with three compartments and two polar fu values in the lowest range (fu < 0.003) correspond- clusters, whereas Site 2 is narrower, with a single ing to PPB higher than 99.7%. Such drugs usually polar cluster. Hydrophobic and specific interactions have extremely low plasma levels for the free frac- with the polar residuals are responsible for the inter- tion and require high-sensitive and specific bioassay actions of acidic drugs with albumin. At Site 1 most methods to detect it. Frequently, the free drug plasma of the studied drugs have a planar aromatic moiety concentrations depend on the assay methodology, as snugly pinned between the nonpolar residuals at the it has been demonstrated for ceftriaxone.44 Therefore, bottom of the pocket, another hydrophobic part pro- the deviations of the outliers may be as a result of a jected in the left or right compartment, and make a measurement error in the determination of the un- number of hydrogen bonds with the polar residuals bound fraction, or to saturation of a low-capacity high- (Tyr150,His242,Lys199,andArg222). The presence of a affinity binding site in plasma. planar hydrophobic structure and two negative cen- The quantitative structure/pfu relationships de- ters separated by five or six bonds is a prerequisite for rived in the present study allow defining of several binding of drugs to Site 1. The complexation of drugs clear criteria for binding of acidic drugs to plasma at Site 2 occurs through clustering of an aromatic proteins. The lipophilicity of drugs is the most impor- ring in the center of the pocket and hydrogen bonds tant of them. As more lipophilic is a drug as it binds with Tyr411,Ser489, and Leu430. Obviously, the pres- to plasma proteins in a greater extent (Fig. 3). Among ence of planar hydrophobic structure and two nega- the high binders (PPB ≥ 95%, fu ≤ 0.05, pfu ≥ 1.301), tive centers separated by a few skeletal bonds is a 81% have log P higher than 2 and 53% have higher prerequisite for binding of drugs to both Site 1 and than 3. Micafungin is an outlier with log P of −7.48, Site 2. These structural features are encoded in our but its low log P value is compensated by another models by the descriptors SaasC acnt, SaaCH acnt, positive criteria. The second very important criterion SHBint4 acnt,andSHBint5 acnt. for good PPB is the presence of at least 2 nonconju- The models contain also the lipophilicity descrip- gated aromatic rings. The aromatic ring is part of the tors log P and log D. Higher lipophilicity of drugs biophore for Site 1 and Site 2 of HSA.29 Estrada et 21 corresponds to higher pfu values and PPB. This is al. also have found a high fragment contribution of in a good agreement with the general assumption benzene to HSA binding affinity. The third criterion that binding of drugs to plasma proteins is governed favoring PPB is the presence of cyano group. The stud- by nonspecific hydrophobic interactions. The presence ied set includes five CN-containing drugs: cilomilast of cyano group encoded by the descriptors StN acnt, (fu = 0.006), entacapone (fu = 0.02), levosimendan (fu StN,andStsC favors PPB. The positive contribution = 0.02), milrinone (fu = 0.035), and cefmetazole (fu = of this structural feature has been already reported.43 0.15). Four of them are high binders. The fourth crite- Another structural feature with positive contribution rion for PPB is the presence of at least three pairs of in PPB is the presence of chlorine atom, encoded H-bond donor and acceptor separated by four or five by the descriptor SsCl acnt. Among the molecular skeletal bonds. The two negative centers separated descriptors with negative impact on the PPB affin- by a few skeletal bonds are parts of the biophore for 29 ity is the simple four-order chain connectivity in- both Site 1 and Site 2 on HSA. The plot pfu ver- dex xch4, reflecting the presence and substitution in sus donor–acceptor pairs is quite scattered (Fig. 4). four-membered ring. This structure is common in 43 This means, that this criterion is not enough to dis- molecules used in the present study, mainly $-lactam criminate between high, moderate, and low binders; antibiotics, and reflects the low (with few exceptions) and it is recommended to be used only in combination protein binding of this therapeutic class. The pres- with other positive criteria. Micafungin has 37 such ence and the number of iodine atoms in the molecule pairs, which compensate its low log P. The last three reduce the PPB affinity implied by the negative coef- criteria concern the presence of nonfavorite for PPB ficient of SsI acnt in the models. There are only two structural features: tertiary carbon atoms, four- drugs in the dataset containing I-atoms (iothalamic member rings, and iodine atoms. Five drugs (9%)

DOI 10.1002/jps JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 4636 ZHIVKOVA AND DOYTCHINOVA ) Sum − ) + Continued ( ) Descriptors − ( Sum ( ) − Sum ( Descriptors ) + 20 2 20 2 10 1 20 2 20 2 20 2 10 1

Figure 3. Relationship between pfu and log P. The outlier Sum ( micafungin (log P =−7.48) is given as an open square. The Descriptors correlation coefﬁcient is 0.717 (excluding outlier). Atom I Atom Tertiary C ∗ Ring Four-Member 3 ≥

Figure 4. Relationship between pfu and the number of H-bond donor/acceptor pairs. Micafungin (SHBint4 acnt+ H-Bond SHBint5 acnt = 37) is given as an open square. No correlation exists. Donor–Acceptor

among the high binders contain one negative frag- CN Group ment and only two of them (4%) have two negative 2 fragments. The seven-item checklist of criteria de- ≥ rived in the present study could be used as a guide Positive Fragments Negative Fragments Rings for evaluation of PPB of acidic drug candidates, simi- Aromatic

larly to the Lipinski’s rule of ﬁve used for evaluation 3 45 ≥ of oral permeability of drugs. P Log CONCLUSIONS u f 001 1 1 0004 1 1 0011 1 0011 1 1 1 3 0 3 0012 1 1 1 3 0 3 002 1 1 1 2 1 1 0016 1 1 0023 1 1 2 0 2 003 1 1 0 1 003004 1 1 1 1 2 0 2 005 1 1 0 1 005 1 1 006006 1 1 1 1 1 2 2 1 0 1 2 The QSPPBR models derived in the present study 006 1 ...... show that PPB of the acidic drugs depends on several structural features. The lipophilicity, the presence of u 000 0 398 0 959 0 959 0 921 0 699 0 796 0 638 0 523 0 523398 0 0 301 0 301 0 222222 0 0 222 0 ...... aromatic rings, cyano groups, and H-bond donor–ac- pf ceptor pairs increase the PPB. The presence of tertiary carbon atoms, four-member rings, and iodine atoms decrease PPB. The seven-item checklist of criteria could be used as a guide for PPB prediction. The Seven-Item Checklist of Criteria for Plasma Protein Binding Applied to the Dataset of Acidic Drugs APPENDIX 1 99% > Table A1. The seven-item checklist of criteria for PPB Verlucast 3 Bromfenac 2 Atovaquone 3 Tolcapone 2 Tesaglitazar 2 Diﬂunisal 2 Montelucast 2 MicafunginMeloxicam 2 2 Suramin 2 Diclofenac 2 Telmisartan 2 Cilomilast 2 Glimepiride 2 Fosinoprilat 2 Ibuprofen 2 applied to the dataset of acidic drugs. Table A1. Name PPB

JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 DOI 10.1002/jps QUANTITATIVE STRUCTURE—PLASMA PROTEIN BINDING RELATIONSHIPS OF ACIDIC DRUGS 4637 ) Sum − 1 2 1 2 ) − − − − + Continued ( ) Descriptors − ( Sum ( ) − Sum ( Descriptors ) + 00 0 00 0 00 0 00 0 00 0 10 1 10 1 10 1 00 0 10 1 20 2 10 1 20 2 20 2 20 2 10 1 20 2 20 2 Sum ( Descriptors Atom I Atom Tertiary C Ring Four-Member 3 ≥ H-Bond Donor–Acceptor CN Group 2 ≥ Positive Fragments Negative Fragments Rings Aromatic 3 ≥ P Log u f 02 1 1 0 1 02 1 1 2 0 2 05 02 02 006 07 1 1 1 1 0 052 1 0 1 024 1 1 2 0 2 021 1 1 1 3 0 3 0079 1 1 1 3 0 3 0068 07 1 1 0 2 058 1 03 008 1 029 1 0085 1 1 0 1 033 1 1 1 1 2 031 1 1 2 0 2 03 1 1 1 1 0 0085 1 04 1 1 0 1 035035 1 1 1 1 0 0 1 1 01 1 01 1 1 2 0 2 009 1 1 2 0 2 042 1 1 1 1 0 037 1 1 1 1 0 01 1 1 043 1 1 0 2 04 1 1 01 1 1 2 0 2 01 1 1 012 1 1 2 0 2 043 1 1 2 0 2 015 1 1 017 1 017 1 1 015 1 1 ...... u 699 0 301 0 699 0 699 0 222 0 155 0 284 0 620 0 678 0 102 0 167 0 155 0 237 0 523 0 097 0 538 0 071 0 481 0 509 0 523 0 071 0 398 0 456456 0 0 000 0 000 0 046 0 377 0 432 0 000 0 367 0 398 0 000 0 000 0 921 0 367 0 824 0 770 0 770 0 824 0 699 0 ...... pf Continued Levosimendan 1 Glipicide 1 Suprofen 2 Ceftriaxone 1 Glyburide 1 Pantoprazole 1 Fluvastatin 2 Ketorolac 2 Oxacillin 1 Cefoperazone 1 Piretanide 1 Dexloxiglumide 1 Ketoprofen 2 Nateglinide 1 Tenoxicam 2 ChlorpropamideEpristeride 1 1 Tezosentan 2 Dicloxacillin 1 Bumetadine 1 IsoxicamMilrinone 1 1 Quercetin 2 BosentanAcetazolamide 1 1 Chlorambucil 2 Cerivastatin 2 Fosﬂuconazole 1 Valsartan 1 Torsemide 2 Losartan 2 Indomethacin 2 Flucloxacillin 1 Furosemide 1 Lovastatin 1 Repaglinide 1 Tolbutamide 1 Eprosartan 1 Warfarin 1 Sulﬁnpyrazone 1 Entacapone 1 Table A1. Name PPB 90–95% PPB 95–99%

DOI 10.1002/jps JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 4638 ZHIVKOVA AND DOYTCHINOVA ) Sum − 1 1 2 2 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 2 1 2 1 ) − − − − − − − − − − − − − − − − − − − − − − − + Continued ( ) Descriptors − ( Sum ( ) − Sum ( Descriptors ) + 00 0 00 0 00 0 10 1 00 0 00 0 00 0 00 0 00 0 00 0 Sum ( Descriptors 101 101 101 Atom I Atom Tertiary C Ring Four-Member 3 ≥ H-Bond Donor–Acceptor CN Group 2 ≥ Positive Fragments Negative Fragments Rings Aromatic 3 ≥ P Log u f 23 39 1 1 0 1 22 1 0 1 079 45 1 1 1 1 2 08 41102 39 1 1 0 2 39 1 1 1 1 0 25 25 1 0 1 12 1 1 0 1 087 1 1 0 1 43 1 1 1 1 2 39 25 1 1 0 2 13 1 1 1 1 2 11 1 1 2 1 1 44 1 1 0 1 27 13 1 49 1 1 1 1 0 41102 15 1 1 1 1 2 14 1 1 1 1 0 4101 37 1 1 0 1 36 1 0 1 31 1 0 1 511112 16 15 1 1 1 1 2 511112 5 17 52 1 1 0 2 18 17 17 55 1 0 1 55 1 1 0 2 18 1 0 1 ...... u 638 0 409 0 102 0 347 0 097 0 398 0 409 0 409 0 602 0 602 0 921 0 060 0 367 0 409 0 602 0 886 0 000 0 357 0 569 0 886 0 310 0 398 0 824 0 854 0 398 0 432 0 444 0 509 0 301 0 796 0 824 0 301 0 301 0 770 0 284 0 745 0 770 0 770 0 260 0 658 0 260 0 745 0 ...... pf acid 0 Continued G0 Sulfamethoxazole 0 Sulﬁsoxazole 1 Cefadroxil 0 Artesunate 0 Folinic acid 1 Valproic acid 1 Methicillin 0 Moxalactam 0 Cefamandole 0 Nafcillin 0 IrbesartanRosuvastatin 1 0 Pentobarbital 0 Phenethicillin 0 Probenicid 0 Phenoxymethylpenicillin 0 Sulfadiazine 0 Aztreonam 0 Methohexital 0 Thiopental 0 Phenobarbital 0 Cefatrizine 0 Cefuroxime 0 Ceﬁxime 0 Cefmetazole 0 Piperacillin 0 Methotrexate 0 Cefotetan 0 Pravastatin 0 Penicillin 5-Aminosal Perindoprilat 0 Sulbenicillin 0 Acetylcysteine 0 Carbenicillin 0 Betamipron 0 Cephapirn 0 Nitrofurantoin 0 Ticarcillin 0 Cephalothin 0 Propylthiouracil 0 Cefazolin 0 Table A1. Name PPB 30–90%

JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 DOI 10.1002/jps QUANTITATIVE STRUCTURE—PLASMA PROTEIN BINDING RELATIONSHIPS OF ACIDIC DRUGS 4639 Sum − 1 2 2 1 1 1 1 2 1 1 2 2 1 2 1 ) − − − − − − − − − − − − − − − + ) Descriptors − ( Sum ( ) − Sum ( Descriptors ) + 00 0 00 0 00 0 00 0 00 0 00 0 00 0 00 0 Sum ( Descriptors 101 Atom I Atom Tertiary C Ring Four-Member 3 ≥ H-Bond Donor–Acceptor CN Group 2 ≥ Positive Fragments Negative Fragments Rings Aromatic 3 ≥ P Log u f 68 85 59611110 1 1 1 1 0 91 1 0 1 85 1 1 0 2 70 1 1 0 2 61 6 72 1 1 1 1 0 62 1 1 1 1 2 98 1 1 1 1 0 93 95 1 0 1 72 1 0 1 00 1 1 1 1 0 762 1 1 1 1 0 762 1 1 0 1 73 73 1 1 0 2 64 1 1 0 1 62 78 1 0 1 78 1 0 1 0000 1 1 0 1 79 1 1 0 2 78 1 1 1 1 0 65 1 1 0 2 00 8101 85 1 1 0 2 85 1 0 1 ...... u 167 0 229222 0 0 041 0 071 0 155 0 215 0 222 0 143 0 208 0 009 0 032022 0 0 143 0 000 1 119 0 119 0 137137 0 0 194 0 208 0 108 0 108 0 000000 1 1 102 0 108 0 187 0 000 1 097 0 071 0 071 0 071 0 ...... pf acid 0 Continued sal 30% < The presence of a fragment is denoted by 1. ∗ CefprozilCefotaxime 0 0 Ampicilin 0 Mezlocillin 0 Cilastatin 0 Clavulanic acid 0 Sulbactam 0 Doxiﬂuridine 0 TenofovirCephradine 0 0 Ceftizoxime 0 Carumonam 0 Iothalamic acid 0 CaptoprilCefoxitin 0 0 Enalaprilat 0 Amidotrizoate 0 Cefetamet 0 Cefepime 0 Risedronate 0 Fludarabine 0 5-Fluorouracil 0 CidofovirFlucytosine 0 0 Zoledronate 0 Azlocillin 0 Fosfomycin 0 Ceftazidime 0 Acetyl Cephaloridine 0 Amoxicillin 0 Cephalexin 0 Foscarnet 0 Table A1. Name PPB

DOI 10.1002/jps JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 4640 ZHIVKOVA AND DOYTCHINOVA

REFERENCES 22. Deeb O, Hemmateenehad B. 2007. ANN-QSAR model of drug binding to human serum albumin. Chem Biol Drug Des 1. Kennedy T. 1997. Managing the drug discovery/development 70:19–29. interface. Drug Disc Today 2:436–444. 23. Deeb O. 2010. Correlation ranking and stepwise regression 2. Kola I, Landis J. 2004. Can the pharmaceutical industry re- procedures in principal components artificial neutral networks duce attrition rates? Nat Rev Drug Discov 3:711–716. modeling with application to predict toxic activity and hu- 3. Van de Waterbeemd H, Gifford E. 2000. ADMET in silico man serum albumin binding affinity. Chem Intell Lab Sys modeling: Towards prediction paradise? Nat Rev Drug Discov 104:181–194. 2:192–204. 24. Chen L, Chen X. 2012. Results of molecular docking as de- 4. Chohan KK, Pain SW, Waters, NJ. 2008. Advancements in pre- scriptors to predict human serum albumin binding affinity. J dictive in silico models for ADME. Curr Chem Biol 2:215–228. Mol Graph Model 33:35–43. 5. Wang J, Hou T. 2009.Recent advances on in silico ADME mod- 25. Zsila F, Bikadi Z, Malik D, Hari P, Pechan I, Berces A, Hazai eling. Vol. 5. In Annual reports in computational chemistry; E. 2011. Evaluation of drug-human serum albumin binding Elsevier, Amsterdam, San Diego pp 102–127. interactions with support vector machine aided online auto- 6. Schmidt S, Gonzales D, Derendorf H. 2010. Significance of mated docking. Bioinformatics 27:1806–1813. protein binding in pharmacokinetics and pharmacodynamics. 26. Meletiadis J, Chanock S, Walsch T. 2006. Human pharmacoge- J Pharm Sci 99:1107–1122. nomic variations and their implication for antifungal efficacy. 7. Mehvar R. 2005. Role of protein binding in pharmacokinetics. Clin Microbiol Rev 19:763–787. Am J Pharm Edu 69:1–8. 27. Martinez-Gomez MA, Sagrado S, Villanueva-Camanas RM, 8. Kerns E, Di L. 2008. Drug-like properties: Structures, design Medina-Hernandez MJ. 2006. Characterization of basic drug- and methods. 1st ed. Elsevier, Burlington, San Diego, London human serum albumin interactions by capillary electrophore- pp. 187–196. sis. Electrophoresis 27:3410–3419. 9. Mao H, Hajduk PJ, Craig R, Bell R, Bore T, Fesik SW. 2001. 28. Wasan KM, Brocks DR, Lee SD, Sachs-Barrable K, Thornton Rational design of diflunisal analogues with reduced affin- SJ. 2008. Impact of lipoproteins on the biological activity and ity to human serum albumin. J Am Chem Soc 123:10429– disposition of hydrophobic drugs: Implications for drug discov- 10435. ery. Nat Rev Drug Discov 7:84–99. 10. Hall LM, Hall LB, Kier LB. 2003. QSAR modeling of beta- 29. Ghuman J, Zunzain PA, Petipas I, Bhattacharya AA, Otagiri lactam binding to human serum proteins. J Comput Aided M, Curry S. 2005. Structural basis of the drug-binding speci- Mol Des 17:103–118. ficity of human serum albumin. J Mol Biol 353:38–52. 11. Xue CH, Zhang RS, Liu HX, Yao XJ, Liu MC, Hu ZD, Fan BT. 30. Otagiri M, Yamamichi R, Maruyama T, Imai T, Suenaga A, 2004. QSAR model for the prediction of binding affinities to Imamura Y, Kimachi K. 1989. Drug binding to alpha-1 acid human serum albumin using the heuristic method and support glycoprotein studied by circular dichroism. Pharm Res 6:156– vector machine. J Chem Inf Comput Sci 44:1693–1700. 159. 12. Ramamurthi N, Gunturi SB. 2005. In silico ADME modeling: 31. Israili ZH, Dayton PG. 2001. Human alpha-1-glycoprotein $ QSPR models for the binding of -lactams to human serum and its interactions with drugs. Drug Metab Rev 33:161– proteins using genetic algorithms. ARKOVOC XI:102–123. 235. 13. Seedher N, Bhatia S, Singh B. 2008. Quantitative correlation 32. Yamazaki K, Kanaoka M. 2004. Computational prediction of between theoretical molecular descriptors and drug-HSA bind- the plasma protein binding percent of diverse pharmaceutical ing affinities for various cox-2 inhibitors. Chem Biol Drug Des compounds. J Pharm Sci 93:1480–1494. 72:297–302. 33. Obach RS, Lombardo F, Waters NJ. 2008. Trend analysis 14. Colmenarejo G, Alvarez-Pedraglio A, Lavandera J-L. 2001. of a database of intravenous pharmacokinetic parameters Cheminformatic models to predict affinities to human serum in humans for 670 drug compounds. Drug Metab Dispos albumin. J Med Chem 44:4370–4378. 36:1385–1405. 15. Kratochwil AN, Huber W, Mueller F, Kansy M, Gerber PR. 34. Drug Bank Accessed June 18, 2012, at: http://www 2002. Predicting plasma protein binding of drugs—A new ap- .drugbank.ca. proach. Biochem Pharmacol 64:1355–1374. 35. Chemical Book Accessed June 18, 2012, at: http://www 16. Hall LM, Hall LH, Kier LB. 2003. Modeling drug-albumin .chemical book.com. binding affinity with E-state topological structure represen- 36. Hall LH, Kier LB. 1999. Molecular connectivity chi indices for tation. J Chem Inf Comoput Sci 43:2120–2128. database analysis and structure–property modeling. In Topo- 17. Liu J, Yang L, Li Y, Pan D, Hopfinger AJ. 2005. Prediction logical indices and related descriptors in QSAR and QSPR; of plasma protein binding of drugs using Kier–Hall valence Devillers J, Balaban A, Eds. London: Gordon and Breach, pp connectivity indices and 4D-fingerprint molecular similarity 307–360. analyses. J Comput Aided Mol Des 19:567–583. 37. Hall LH, Kier LB. 1999. Electrotopological state: Structure 18. Liu J, Yang L, Li Y, Pan D, Hopfinger AJ. 2006. Constructing modeling for QSAR and database analysis. In Topological in- plasma protein binding model on a combination of cluster anal- dices and related descriptors in QSAR and QSPR; Devillers ysis and 4D-fingerprint molecular similarity analyses. Bioorg J, Balaban A, Eds. London: Gordon and Breach, pp 491– MedChem 14:611–621. 562. 19. Gunturi SB, Ramamurthi N, Khandelwal A. 2006. In silico 38. Leardi R, Boggia R, Terrile M. 1992. Genetic algorithms as a ADME modeling. Computational models to predict human strategy for feature selection. J Chemom 6:267–281. serum albumin binding affinity using ant colony systems. 39. Friedman J. 1988. Multivariate adaptive regression splines. Bioorg Med Chem 14:4118–4129. Technical report No. 102. Stanford University, Stanford, Cal- 20. Votano JR, Parham M, Hall LM, Hall LH, Kier LB, Oloff S, ifornia: Laboratory for Computational Statistics, Department Tropsha A. 2006. QSAR modeling of human serum protein of Statistics. binding with several modeling techniques utilizing structure- 40. He XM, Carter DC. 1992. Atomic structure and chemistry of information representation. J Med Chem 49:7169–7181. human serum albumin. Nature 358:209–215. 21. Estrada E, Uriarte E, Molina E, Simon-Manso Y, Milne GW. 41. Petitpas I, Bhattacharya AA, Twine S, East M, Curry S. 2001. 2006. An integrated in silico analysis of drug binding to human Crystal structure analysis of warfarin binding to human serum serum albumin. J Chem Inf Model 46:2709–2724. albumin. J Biol Chem 276:22804–22809.

JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 101, NO. 12, DECEMBER 2012 DOI 10.1002/jps QUANTITATIVE STRUCTURE—PLASMA PROTEIN BINDING RELATIONSHIPS OF ACIDIC DRUGS 4641

42. Sudlow G, Birkett DJ, Wade DH. 1975. Characterization of 44. Kohlhepp SJ, Gilbert DN, Leggett JE. 1988. Inﬂuence of assay two speciﬁc drug binding sites on human serum albumin. Mol methodology on the measurement of free serum ceftriaxone Pharmacol 12:1052–1061. concentration. Antimicrob Agents Chemother 42:2259–2261. 43. Hajdik PJ, Mendoza R, Petros AM, Huth JR, Bures M, Fesik 45. Lipinski CA, Lombardo F, Dominy BW, Feeney PG. 2001. Ex- W, Martin YC. 2003. Ligand binding to domain-3 of human perimental and computational approaches to estimate solu- serum albumin: A chemometric analysis. J Comput Aided Mol bility and permeability in drug discovery and development Des 17:93–102. settings. Adv Drug Del Rev 46:3–26.

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