The early detection of possible toxicity is important evenin the early drug discovery stage. In particular, QT prolonga-tion is a critical issue in drug discovery and development.1)

In a very small percentage of patients, drug-induced QT pro-longation is associated with a potentially fatal cardiac ar-rhythmia called Torsades de Pointes, which may degenerateinto ventricular fibrillation and sudden death.2,3) This resultedin a number of drugs being withdrawn from the market andrepresents a significant regulatory hurdle for new chemicalentities in development.2) It is expected that the prediction ofthe fatal risk such as QT prolongation and proposal of itsavoidance is expected to lead to not only the improvement ofthe success rate of drug development but also the efficientand effective drug discovery study with a rational drug de-sign.

Human ether-a-go-go-related gene (hERG) potassium(K�) channels mediate the rapidly activating delayed rectifierK� current (IKr), that plays a key role in repolarization of theventricular action potential.1,4) Inhibition of IKr has been shownto elongate the cardiac action potential, a phenomenon asso-ciated with an increased risk of arrhythmia.1,4) Most of thedrug-induced QT-prolongation have been shown to reduce IKr

by inhibiting the hERG K� channel.1,4) Therefore, blockadeof the hERG K� channel is the most important mechanism indrug-induced QT-prolongation, and the correlation analysisbetween hERG K� current inhibition (hERG inhibition) andphysicochemical properties is important for efficient drugdiscovery such as the contribution to drug design.

The prediction of hERG inhibition is thought to be ex-tremely difficult. One possible reason is considered to be thelarger inner vestibule of the hERG channel, thus providingmore space for structurally diverse drugs to block this hERGchannel.5) A detailed structure for hERG based on X-raycrystallography is not yet available, so structural details for

hERG are suggested by analogy with other ion channels,pharmacology, computer models, and mutagenesis studies.6)

On the other hand, so far, there are many reports regardingthe correlation among hERG inhibition, chemical structure,and physicochemical properties.2,3,7—12) It was previously re-ported that the simple pharmacophore models of hERGblockers typically contain a basic nitrogen center, which ispositively charged under a physiological condition, flankedby aromatic or hydrophobic groups attached with flexiblelinkers.7) Moreover, an uncharged pharmacophore modelunder a physiological condition, which was composed ofthree hydrophobic groups/aromatic rings and two or three hy-drogen bond acceptors, was also reported previously.7) In ad-dition, representative binding sites for hERG blockers havebeen mapped within the inner cavity of the channel and in-clude aromatic residues in the S6 helix (Tyr-652, Phe-656)and residues in the pore helix (Thr-623, Ser-624, Val-625).13—16) The decrease of molecular weight, adjustment oflipophilicity, conformational change, adjustment of basic dis-sociation constant (pKa), and so on are considered to be use-ful as the process for reducing hERG inhibition.2,3,7—12,17,18)

In addition, the in-silico prediction model on hERG inhibi-tion was also proposed.19)

Thus, the physicochemical approaches were examined asthe suitable method to better understand hERG inhibition.The correlation analysis based on actual values of physico-chemical properties, which can be expected to provide higheraccurate liability, is considered to be important for trendanalysis. However, in-silico predicted values of physico-chemical properties are widely used. Besides, lipophilicityand basicity are considered to be a particularly important fac-tor on hERG inhibition, although the details of relative andquantitative relationship among lipophilicity, basicity, andhERG inhibition are not clarified. Compounds that possess a

1110 Vol. 59, No. 9Regular Article

A Risk Assessment of Human Ether-a-Go-Go-Related Gene PotassiumChannel Inhibition by Using Lipophilicity and Basicity for DrugDiscovery

Yukinori KAWAI,* Shinsaku TSUKAMOTO, Junko ITO, Katsuya AKIMOTO, and Masayuki TAKAHASHI

Drug Metabolism & Pharmacokinetics Research Laboratories, Daiichi Sankyo Co., Ltd.; 1–2–58 Hiromachi, Shinagawa-ku, Tokyo 140–8710, Japan. Received April 19, 2011; accepted June 9, 2011; published online June 14, 2011

The blockade of human ether-a-go-go-related gene (hERG) potassium channels is widely regarded as thepredominant cause of drug-induced QT prolongation. The correlation analysis between the inhibition of thehERG channel (hERG inhibition) and physicochemical properties was investigated by use of in-house quinoloneantibiotics as model compounds. In order to establish a simple prediction model of hERG inhibition, we focusedon the comprehensible physicochemical parameters such as lipophilicity (log P) and basicity (pKa). At first, therisk associated with increasing log P and pKa was examined by statistical analysis. It was demonstrated that therisk associated with increasing log P and pKa by one unit, respectively, almost identically increased. Conse-quently, equal attention should be paid to both parameters on hERG inhibition. Next, a prediction model ofhERG inhibition which was represented by log P and pKa was investigated. As a result, we built the stepwise dis-criminant prediction model which took advantage of the risk judgment by zone classification. In conclusion, theimpact of log P and pKa on hERG inhibition was clarified relatively and quantitatively. The quantitative risk as-sessment established based on both parameters, was considered to be a practical and useful tool in avoidinghERG inhibition and in the rational drug design for drug discovery, especially in lead optimization. Moreover,we also carried out a trend analysis using a different derivative and demonstrated that both parameters wereequally significant for hERG inhibition.

Key words human ether-a-go-go-related gene; lipophilicity; basicity

Chem. Pharm. Bull. 59(9) 1110—1116 (2011)

© 2011 Pharmaceutical Society of Japan∗ To whom correspondence should be addressed. e-mail: kawai.yukinori.f5@daiichisankyo.co.jp

similar chemical structure to a lead compound are generallysynthesized in the lead optimization stage. Therefore,lipophilicity and basicity seem to be the key factors related tothe hERG inhibition in the lead optimization process.

As mentioned above, it is considered that reduction oflipophilicity and basicity are typically the first line ap-proaches to avoid hERG inhibition. In particular, we consid-ered that the reduction of these physicochemical parameterswas important in the lead optimization process in which it isdifficult to drastically modify chemical structure. However,the relative and quantitative relationship between both pa-rameters based on measured values is hardly reported. More-over, it is poorly understood which parameter is more effec-tive on hERG inhibition. In addition, there are few reportsabout correlation analysis by use of comprehensible physico-chemical properties, because correlation analysis aimed atstructurally diverse drug is mainly discussed.

In this study, in order to obtain a simplified and compre-hensible correlation, we focused on the partition coefficient(log P) and pKa values for the index of lipophilicity and ba-sicity, respectively. A risk assessment of hERG inhibitionwas investigated by profiling using log P and pKa based onmeasured values. In-house quinolone antibiotics were used asmodel compounds as shown in Chart 1.20) At first, the extentof influence of log P and pKa on hERG inhibition was studiedquantitatively by statistical analysis. Secondary, the predic-tion model on hERG inhibition by using both parameters,which is comprehensive for medicinal chemists, was con-structed. Finally, we also validated the significance of bothparameters by using benzoxazole derivatives as in-house antifungal agents as shown in Chart 2.21)

ExperimentalMaterials In-house and launched quinolone antibiotics and in-house

benzoxazole derivatives as antifungal agents, were used as test compounds.In-house compounds were synthesized by the Medicinal Chemistry Labora-tory, Daiichi Sankyo (Tokyo, Japan).

pKa pKa was determined by the pH-metric/UV-pH titration method. ThepH titration of solution containing test compounds with 0.5 mol/l sodium hy-drochloride was carried out under a nitrogen atmosphere at 25 °C. pH titra-tion was performed with a GLpKa automated analyzer (Sirius Analytical In-struments Ltd., Forest Row, U.K.).

Partition Coefficient The data of chloroform/n-octanol-pH 7.4 distribu-tion coefficient (log D7.4) measured by the shake-flask method was obtainedfrom the in-house database. Log D7.4 was calculated from Eq. 1.

(1)

The log P was calculated with measured pKa and log D7.4 values. Namely,log P of quinolone antibiotics and benzoxazole derivative as antifungalagents were calculated by Eq. 2 and Eq. 3, respectively. Where, pKa1 andpKa2 refer to the pKa value of carboxylic acid and amino group, respectively.Log P is defined as lipophilicity of zwitterion forms for quinolone anti-biotics and neutral forms for benzoxazole derivatives, respectively.

(2)

(3)

hERG Inhibition The data of hERG inhibition measured in hERGtransfected human embryonic kidney 293 cells by the whole-cell patch-clamp technique, was obtained from the in-house database. The tested con-centration of quinolone antibiotics and benzoxazole derivative as antifungalagents was 300 mM and 10 mM, respectively. The compounds were dividedinto two groups, “positive” or “negative,” based on the threshold level forthe hERG inhibition of a benchmark compound which is the most importantcompound for our drug design strategies.

Discriminant Analysis for the Influence of Log P and pKa on hERGInhibition Log P and pKa were used as an index of lipophilicity and basic-ity, respectively. The influence of both parameters on hERG inhibition wasinvestigated by a linear discriminant function analysis (LDA) and by the useof the dataset of 46 in-house and launched quinolone antibiotics. The slopein LDA at zero was studied as an index of the extent of influence of both pa-rameters on hERG inhibition. This LDA was performed by the lda functionin free statistical software R.22)

Building of Prediction Model on hERG Inhibition A predictionmodel was examined by using the same dataset with that of LDA. A logisticregression analysis was carried out by fixation of the ratio of coefficient oflog P to that of pKa (log P/pKa ratio) obtained by LDA. Namely, the value ofa and b was calculated using Eq. 4 by the nonlinear least squares fit method.Where g is the log P/pKa ratio obtained by LDA and the value of g is fixedas a key precondition in the logistic regression analysis. The logistic regres-sion analysis was performed by nls function in free statistical software R.

(4)

An application of the risk judgment by using zone classification was exam-ined in this prediction model. The prediction performance for the risk judg-ment was studied by changing the threshold levels for discrimination every10%. The receiver operating characteristics (ROC) curve, which is a plot ofthe true positive rate versus false positive rate, was examined by classifieddata based on discriminant criteria (see Results and Discussion). The truepositive rate represents the probability of a predicted positive compound asbeing “positive.” On the other hand, the false positive rate indicates the prob-ability of a predicted negative compound as being “positive.” Then, a step-wise discriminant prediction model was studied based on the results of theROC curve. In order to verify a stepwise discriminant prediction model, atest set of 23 in-house quinolone antibiotics, which was different from thetraining set, was used as model compounds.

Correlation between hERG Inhibition and Physicochemical Proper-ties on In-house Benzoxazole Derivatives as Antifungal Agents A logis-tic model as shown in Eq. 5 was examined by a dataset of 20 in-house ben-zoxazole derivatives as antifungal agents. Each value of a , b and g was cal-culated using Eq. 5 by a nonlinear least squares fit method. The logistic re-gression analysis was performed by the nls function in free statistical soft-ware R.

(5)

Results and DiscussionExtent of Influence of Log P and pKa on hERG Inhibi-

hERG inhibition (%)predicted ��

100

1[ exp(α β⋅ ⋅ pp aK P�γ ⋅ log )]

hERG inhibition (%)predicted ��

100

1[ exp(α β⋅ ⋅ pp aK P�β γ⋅ ⋅ log )]

log log log[ ].(P D K� � � �

7 4 1 10 p pH 7.4)a2

log log log[.( (P D K� � � ��

7 4 1 10 10p pH 7.4) pH 7.a1 44 p a2� K ) ]

log log.D7 4 �(concentration of organic phase)

((concentration of aqueous phase)

⎡

⎣⎢⎢

⎤

⎦⎥⎥

September 2011 1111

R1: Substituted group such as cyclopropyl group, R2: Substituted group such asmethoxy group, R3: Substituted group including the amino group, R4: Substitutedgroup such as F.

Chart 1. Basic Structure of In-house Quinolone Antibiotics

R1: Substituted group such as t-butyl group, R2: Substituted group such asphenyl group, R3: Substituted group including the amino group.

Chart 2. Basic Structure of In-house Benzoxazole Derivative as Anti-fungal Agent

tion The data of physicochemical properties and hERG in-hibition at 300 mM, and judgment predicted by a linear dis-criminant analysis of 46 in-house and launched quinoloneantibiotics used in this study are summarized in Table 1. Fig-ure 1 shows the relationship of risk of hERG inhibition withlog P and pKa. As the index of lipophilicity independent ofpKa and pH, log P was used instead of log D7.4. The com-pounds were divided into two groups, “positive” or “nega-tive,” based on the threshold level for the hERG inhibition ofa benchmark compound which is the most important com-pound for our drug design strategies. Judgment of this studywas set in order to examine the trend analysis. Compounds

which have higher log P and pKa tend to be at higher risk ofhERG inhibition as shown in Fig. 1.

The quantitative extent of influence of log P and pKa onhERG inhibition was an attempt to directly examine by theuse of the logistic regression model. However, the regressionequation was unable to be calculated (data not shown). Thus,the influence of log P and pKa was investigated by discrimi-nant analysis which is a technique that classifies two groupsinto “positive” or “negative,” by a linear discriminant func-tion (LD). The LD is written as shown in Eq. 6. The signifi-cance of both parameters was confirmed by 0.01 significancelevel as shown in Table 2.

1112 Vol. 59, No. 9

Table 1. Physicochemical Properties and hERG Inhibition of Quinolone Antibiotics

Physicochemical properties hERG inhibition at 300 mM

CompoundspKa

Observed Judgment

(amino group)Log P inhibition

Observeda) Predicted by the linear (%)

discriminant functionb)

1 7.82 1.36 0.0 Negative Negative2 7.54 1.81 0.0 Negative Negative3 7.11 1.35 4.5 Negative Negative4 8.91 0.96 6.7 Negative Negative5 7.82 1.85 6.7 Negative Positive6 8.59 1.52 7.6 Negative Positive7 8.15 1.77 8.4 Negative Positive8 7.28 1.68 8.9 Negative Negative9 8.41 1.47 10.3 Negative Positive

10 (Levofloxacin) 8.11 0.79 11.9 Negative Negative11 8.24 0.50 12.2 Negative Negative12 7.41 2.11 15.3 Negative Negative13 8.29 0.62 18.4 Negative Negative14 8.89 1.20 21.3 Negative Positive15 8.85 1.17 22.8 Negative Positive16 7.49 1.60 23.4 Negative Negative17 8.65 1.05 24.0 Negative Negative18 7.58 1.93 24.2 Negative Negative19 8.92 0.93 24.8 Negative Negative20 9.21 0.73 24.9 Negative Negative21 8.05 0.97 26.1 Negative Negative22 8.78 0.94 26.5 Negative Negative23 (Benchmark) 9.20 1.32 28.8 Negative Positive24 7.30 2.00 30.1 Positive Negative25 8.13 1.85 32.0 Positive Positive26 7.25 2.10 33.0 Positive Negative27 9.33 0.87 36.7 Positive Positive28 8.67 0.98 38.6 Positive Negative29 (Gatifloxacin) 8.99 0.81 39.3 Positive Negative30 8.36 1.94 44.0 Positive Positive31 8.78 1.38 46.1 Positive Positive32 9.25 0.71 47.6 Positive Negative33 (Garenoxacin) 8.95 1.27 52.3 Positive Positive34 (Gemifloxacin) 8.56 1.13 53.2 Positive Negative35 9.23 1.22 54.8 Positive Positive36 8.98 1.53 56.4 Positive Positive37 8.64 1.88 60.1 Positive Positive38 8.05 1.77 64.1 Positive Positive39 (Moxifloxacin) 9.22 1.77 65.6 Positive Positive40 8.19 1.98 67.5 Positive Positive41 8.60 1.64 68.4 Positive Positive42 8.52 1.84 73.3 Positive Positive43 8.20 1.67 79.4 Positive Positive44 8.40 1.67 79.9 Positive Positive45 9.12 1.96 83.8 Positive Positive46 9.08 1.26 89.2 Positive Positive

a) Judged by the comparison of hERG inhibition of a test compound with that of the benchmark. b) Judged by the plus/minus value calculated by the linear discriminantfunction.

(6)

Equation 6 demonstrated that the risks associated with in-creasing log P and pKa by one unit, respectively, almost iden-tically increased. Consequently, equal attention should bepaid to log P and pKa for an efficient drug design in loweringhERG inhibition. It was empirically suggested thatlipophilicity and basicity strongly relate with hERG inhibi-tion. This study demonstrated the relative and quantitativesignificance of both parameters by using actual values in thisanalysis.

Risk judgment can also be predicted with the calculatedvalue of LD in Eq. 6. For example, in cases predicted as“positive,” which means higher than the benchmark of hERGinhibition, the calculated value of LD of a compound repre-sents a plus sign and its compound is plotted to the upperside of LD of Fig. 1. On the other hand, in cases predicted as“negative,” which means lower than the benchmark of hERGinhibition, the calculated value of LD of a compound repre-sents a minus sign and its compound is plotted to the lowerside of LD of Fig. 1. The results of predicted judgment areshown in Table 1, and the prediction performance of LD isalso summarized in Table 3. It was elucidated that the erro-neous discrimination was ca. 30% (�13 compounds/46 com-pounds) and the advantage utilized of LDA was not so high.Therefore, logistic regression was reinvestigated in order toestablish a more practical prediction model on hERG inhibi-tion for effective drug design.

Statistical Prediction Model by Using Logistic Regres-sion Analysis A dataset of 46 quinolone antibiotics wasused as the training set, which is the same dataset as thatused in LDA. The log P/pKa ratio obtained by LD was fixedas a key precondition in the logistic regression analysis. Theprediction model of hERG inhibition produced is written asshown in Eq. 4 and Table 4, and is represented by the three-

dimensional image as shown in Fig. 2.

(4)

The correlation of measured and predicted hERG inhibi-tion is shown in Fig. 3. The plot gave a good correlation tosome extent (correlation coefficient�0.67). However, inorder to obtain a more practical prediction model, refinementof this model was considered to be necessary.

hERG inhibition (%)predicted ��

100

1[ exp(α β⋅ ⋅ pp aK P�β γ⋅ ⋅ log )]

LD p a� � �2 57 1 89 19 51. log . .⋅ ⋅P K

September 2011 1113

Fig. 1. Linear Discriminant Function Analysis of hERG Inhibition

�: Negative (not more than the threshold level for the hERG inhibition of a bench-mark compound). �: Positive (over the threshold level for the hERG inhibition of abenchmark compound).

Table 2. Test of Significance of Explanatory Variable

Partial F value p-value

pKa 14.9 �0.01Log P 14.1 �0.01

Table 3. Prediction Performance of the Linear Discriminant FunctionAnalysis

ObservedPredicted

Negative Positive

Negative 16 7Positive 6 17

The number of compounds that were predicted for each judgment by the linear dis-criminant function is listed.

Table 4. Parameters Calculated by Logistic Regression Analysis

Estimated value Standard error t-value p-value

a 342�104 107�105 0.32 0.75b �1.39 0.30 �4.68 �0.01bga) �1.89 —b) — —

a) The value of g was set to be 1.36 by LDA. b) Not calculated.

Fig. 2. Three-Dimensional Image of Prediction Model of hERG Inhibition

Fig. 3. Correlation of Measured and Predicted hERG Inhibition

The stepwise discriminant prediction model of hERG inhi-bition was investigated so as to obtain a more practicalmodel. Namely, an application of the risk judgment by usinga zone classification was attempted in this prediction model.The precision of this prediction was investigated by changingthe threshold levels for discrimination every 10%. The re-sults which classified based on the following discriminantcriteria are summarized in Table 5.

True positive: Observed judgment is positive and pre-dicted inhibition is higher than the thresh-old level.

True negative: Observed judgment is negative and pre-dicted inhibition is lower than the thresh-old level.

False positive: Observed judgment is negative, but pre-dicted inhibition is higher than the thresh-old level.

False negative: Observed judgment is positive, but pre-dicted inhibition is lower than the thresh-old level.

Moreover, these results were illustrated as an ROC curvefor the predicted model of hERG inhibition as shown in Fig.4. The true positive rate represents the probability of pre-dicted positive compounds as being “positive.” On the otherhand, the false positive rate indicates the probability of pre-dicted negative compounds as being “positive.” If the ROCcurve was drawn in the upper left of Fig. 4, which means that

a higher true positive rate and lower false positive rate, thediscriminant performance was determined as being ex-tremely high. As shown in Fig. 4, the ROC curve suggestedthat the most effective threshold level in risk judgment was40%. Whereas, 20% of the threshold level did not overlookthe positive compounds, therefore was set as the practicalrisk judgment. Based on the results of the ROC curve, therisk judgment was defined as follows:

Negative zone (predicted inhibition �20%): predicted as alower risk than the benchmark.

Gray zone (predicted inhibition 20—40%): difficult to pre-dict the risk.

Positive zone (predicted inhibition �40%): predicted as ahigher risk than the benchmark.

1114 Vol. 59, No. 9

Table 5. Effects of Threshold on the Detection of Positive Compounds

Itema)Threshold (inhibition %)

10 20 30 40 50 60 70 80 90

Count: True positive 23 23 18 14 9 3 2 0 0True negative 4 8 15 20 22 23 23 23 23False positive 19 15 8 3 1 0 0 0 0False negative 0 0 5 9 14 20 21 23 23

True positive rate 1 1 0.78 0.61 0.39 0.13 0.09 0 0True negative rate 0.17 0.35 0.65 0.87 0.96 1 1 1 1False positive rate 0.83 0.65 0.35 0.13 0.04 0 0 0 0False negative rate 0 0 0.22 0.39 0.61 0.87 0.91 1 1

a) The true positive rate represents the probability of predicted positive compounds as being “positive.” The false positive rate indicates the probability of predicted negativecompounds as being “positive.”

Fig. 4. Receiver Operating Characteristics Curve for the Prediction Modelof hERG Inhibition

%: Threshold level for predicted hERG inhibition.Fig. 5. Stepwise Discriminant Prediction Model of hERG Inhibition

�: Negative (not more than the threshold level for the hERG inhibition of a bench-mark compound). �: Positive (over the threshold level for the hERG inhibition of abenchmark compound). Predicted risk judgment: negative zone, �20%; gray zone,20—40%; positive zone, �40%.

Table 6. Summary of the Prediction of hERG Inhibition Using the PresentModel

ObservedPredicted

Correct predictionNegative Gray Positive

Negative 4 6 0 4/10Positive 0 2 11 11/13

The number of compounds that were predicted for each risk judgment is listed.

Furthermore, the stepwise discriminant prediction model,which took advantage of the risk judgment by zone classifi-cation, was established as shown in Fig. 5. Thus, we suc-ceeded in building a more practical stepwise discriminantprediction model.

Verification of Stepwise Discriminant Prediction Modelof hERG Inhibition The established discriminant predic-tion model was verified by a test set of 23 in-house quinoloneantibiotics. The correct prediction of positive compoundswas 85% (�11 compounds/13 compounds) as shown inTable 6, and the detectable level of positive compoundsseems to be high enough. From the viewpoint of not over-looking any the positive compounds, this model was suffi-cient in judging the risk of hERG inhibition. Thus, the estab-lished prediction model had a high prediction accuracy to de-tect “positive” which represents a higher hERG risk. Accord-ingly, this present model was verified to be a practical anduseful tool in avoiding positive compounds.

Thus, we established the prediction model by the use ofsimple parameters such as log P and pKa. This model was ad-vanced by the application of stepwise risk judgment by zoneclassification. Moreover, this model is expected to be a com-prehensive tool for medicinal chemists.

Correlation between hERG Inhibition and Physico-chemical Properties on In-house Benzoxazole Derivativesas Antifungal Agents In quinolone antibiotics, it was clar-ified that both log P and pKa were contributed on hERG inhi-bition relatively and quantitatively, moreover, we validatedthe usefulness of the correlation analysis based on both pa-rameters by using a different derivative. The dataset of 20 in-house benzoxazole derivatives as antifungal agent was usedas model compounds as shown in Chart 2. The data ofphysicochemical properties and hERG inhibition at 10 mM

are summarized in Table 7, and Fig. 6 shows the relationship

on the risk of hERG inhibition between log P and pKa. In ac-cordance with the higher log P and pKa, the risk of hERG in-hibition tended to be higher as shown in Fig. 6. This ten-dency was similar with the quinolone antibiotics. The quanti-tative extent of the contribution of both parameters was in-vestigated by a logistic regression analysis. The result is writ-

September 2011 1115

Table 7. Physicochemical Properties and hERG Inhibition of Benzoxazole Derivatives as Antifungal Agents

Physicochemical properties hERG inhibition at 10 mM

CompoundspKa Observed inhibition

Judgment

Log PPredicted by the logistic (amino group) (%)

Observeda)

regression analysisb)

1 7.89 1.16 9.6 Negative Negative2 8.75 1.63 16.4 Negative Negative3 7.52 2.17 31.6 Negative Negative4 8.63 1.14 31.6 Negative Negative5 7.2 1.81 36.0 Negative Negative6 7.9 3.12 38.7 Negative Negative7 7.1 5.08 41.5 Negative Positive8 6.84 2.19 43.4 Negative Negative9 8.67 2.00 50.7 Negative Negative

10 (Benchmark) 8.74 1.66 51.1 Negative Negative11 7.55 1.78 56.3 Positive Negative12 8.96 3.97 60.8 Positive Positive13 8.56 3.29 70.6 Positive Positive14 9.3 3.41 78.5 Positive Positive15 8.1 5.38 82.7 Positive Positive16 8.84 3.36 85.1 Positive Positive17 9.96 3.79 87.7 Positive Positive18 8.93 4.44 89.9 Positive Positive19 9.4 5.10 93.1 Positive Positive20 8.44 3.78 99.5 Positive Positive

a) Judged by the comparison of hERG inhibition of a test compound with that of the benchmark. b) Judged by the comparison of calculated inhibition of a test compoundwith the threshold level at 55%.

Table 8. Parameters Calculated by Logistic Regression Analysis

Estimated value Standard error t-value p-value

a 539 1140 0.47 0.64b �0.59 0.25 �2.34 �0.05g �0.56 0.15 �3.69 �0.01

Fig. 6. Correlation among hERG Inhibition, Log P and pKa on In-houseAntifungal Agents

�: Negative (not more than the threshold level for the hERG inhibition of a bench-mark compound). �: Positive (over the threshold level for the hERG inhibition of abenchmark compound).

ten as shown in Eq. 5 and Table 8.

(5)

The number of dataset can be considered to be insufficienton the rigorous statistic analysis, but this result indicated thatboth parameters were related significantly on hERG inhibi-tion as same as the quinolone antibiotics. Moreover, the pre-dicted risk judgment based on the threshold level of 55%well fitted the observed judgment as shown in Table 7. Com-pound 11 was predicted as false negative. Compared to thechemical structure of other observed negative compounds,compound 11 has a highly lipophilic substituent which is lo-cated outside of the molecule comparatively. Therefore, wespeculated that the observed inhibition was higher than thepredicted inhibition in compound 11. Considering the com-mon chemical structure of quinolone and benzoxazole deriv-atives, each amino group is located outside of the moleculecomparatively. Therefore, we speculated that the hERG inhi-bition of benzoxazole derivatives was also equally influencedby log P and pKa like quinolone antibiotics.

ConclusionIt was relatively and quantitatively clarified, that the log P

and pKa were contributed to hERG inhibition equivalently inquinolone antibiotics. We established the simplified predic-tion model by the use of comprehensible parameters such aslog P and pKa for medicinal chemists. This risk assessment isa practical and useful tool in avoiding hERG inhibition forefficient drug discovery, especially in lead optimization. Wealso demonstrated that both parameters were almost equallysignificant for hERG inhibition on a different derivative, viz.,benzoxazole derivatives. As the next step in order to improvethe present prediction performance, the prediction modelconsidering other parameters such as molecular size (e.g.,van del Waals volume) and electric parameters (e.g., highestoccupied molecular orbital and lowest unoccupied molecularorbital), and/or adoption of some machine learning models(e.g., artificial neural network, support vector machine and soon), are considered to be necessary.

In this study, the correlation analysis on hERG inhibitionwas focused on charged compounds under the physiologicalcondition. A detailed correlation analysis that focuses on un-charged compounds under physiological condition is consid-

ered to be necessary in the future. Moreover, in order to un-derstand the phenomenon of hERG inhibition clearly and ac-curately, we need to focus on the other features of the basicstructure of derivative such as hydrogen bond acceptor, con-formation, and so on.

Acknowledgments The authors would like to thank the project mem-bers at Daiichi Sankyo for providing data on antibacterial and antifungalagents.

References1) Takasuna K., Chiba K., Manabe S., Biomol. Ther., 17, 1—11 (2009).2) Waring M. J., Johnstone C., Bioorg. Med. Chem. Lett., 17, 1759—

1764 (2007).3) Aronov A. M., Goldman B. B., Bioorg. Med. Chem., 12, 2307—2315

(2004).4) Ponti D. F., Poluzzi E., Cavalli A., Recanatini M., Montanaro N., Drug

Saf., 25, 263—286 (2002).5) Milnes J. T., Crociani O., Arcangeli A., Hancox J. C., Witchel H. J., Br.

J. Pharmacol., 139, 887—898 (2003).6) Mitcheson J. S., Chem. Res. Toxicol., 21, 1005—1010 (2008).7) Aronov A. M., J. Med. Chem., 49, 6917—6921 (2006).8) Diller J. D., Curr. Comput.-Aided Drug Des., 5, 106—121 (2009).9) Gleeson P., Bravi G., Modi S., Lowe D., Bioorg. Med. Chem., 17,

5906—5919 (2009).10) Gleeson M. P., J. Med. Chem., 51, 817—834 (2008).11) Waring J. M., Expert Opin. Drug Discov., 5, 235—248 (2010).12) Matasi J. J., Caldwell J. P., Zhang H., Fawzi A., Higgins G. A., Cohen-

Williams M. E., Varty G. B., Tulshian D. B., Bioorg. Med. Chem. Lett.,15, 3675—3678 (2005).

13) Fernandez D., Ghanta A., Kauffman G. W., Sanguinetti M. C., J. Biol.Chem., 279, 10120—10127 (2004).

14) Farid R., Day T., Friesner R. A., Pearlstein R. A., Bioorg. Med. Chem.,14, 3160—3173 (2006).

15) Masetti M., Cavalli A., Recanatini M., J. Comput. Chem., 29, 795—808 (2008).

16) Recanatini M., Poluzzi E., Masetti M., Cavalli A., De Ponti F., Med.Res. Rev., 25, 133—166 (2005).

17) Thai K.-M., Ecker G. F., Curr. Med. Chem., 14, 3003—3026 (2007).18) Aronov A. M., Curr. Opin. Drug Discov. Devel., 11, 128—140 (2008).19) Johnson S. R., Yue H., Conder M. L., Shi H., Doweyko A. M., Lloyd

J., Levesque P., Bioorg. Med. Chem., 15, 6182—6192 (2007).20) Kuroyanagi J., Kanai K., Sugimoto Y., Horiuchi T., Achiwa I.,

Takeshita H., Kawakami K., Bioorg. Med. Chem., 18, 7593—7606(2010).

21) Miyauchi R., Kawakami K., Ito M., Matsuhashi N., Ohki H., InagakiH., Takahashi H., Takemura M., Bioorg. Med. Chem., 17, 6879—6889(2009).

22) R Development Core Team: “R: A Language and Environment for Sta-tistical Computing. R Foundation for Statistical Computing,” Vienna,Austria, 2011. ISBN 3-900051-07-0, URL http://www.R-project.org/.

hERG inhibition (%)predicted ��

100

1[ exp(α β⋅ ⋅ pp aK P�γ ⋅ log )]

1116 Vol. 59, No. 9