Phospholipidosis as a Function of Basicity ... - ACS Publications

Mar 31, 2010 - Drug-induced phospholipidosis (PLD) is an adaptive histologic alteration that ... Using sets of PLD inducers and noninducers, we demons...
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Chem. Res. Toxicol. 2010, 23, 749–755

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Phospholipidosis as a Function of Basicity, Lipophilicity, and Volume of Distribution of Compounds Umesh M. Hanumegowda,*,† Gottfried Wenke,‡ Alicia Regueiro-Ren,§ Roumyana Yordanova,| John P. Corradi,| and Stephen P. Adams† Departments of DiscoVery Toxicology, DiscoVery Analytical Sciences, DiscoVery Chemistry, and Bioinformatics, Bristol-Myers Squibb Research and DeVelopment, 5 Research Parkway, Wallingford, Connecticut 06492 ReceiVed October 16, 2009

Drug-induced phospholipidosis (PLD) is an adaptive histologic alteration that is seen with various marketed drugs and often encountered during drug development. Various in silico and in vitro cell-based methods have been developed to predict the PLD-inducing potential of compounds. These methods rely on the inherent physicochemical properties of the molecule and, as such, tend to overpredict compounds as PLD inducers. Recognizing that the distribution of compounds into tissues or tissue accumulation is likely a key factor in PLD induction, in addition to key physicochemical properties, we developed a model to predict PLD in vivo using the measures of basicity (pKa), lipophilicity (ClogP), and volume of distribution (Vd). Using sets of PLD inducers and noninducers, we demonstrate improved concordance with this method. Furthermore, we propose a screening paradigm that includes a combination of various methods to predict the in vivo PLD-inducing potential of compounds, which may be especially useful in lead identification and optimization processes in drug discovery. Introduction Excessive accumulation of phospholipids in cells, termed phospholipidosis (PLD), is a phenomenon often encountered in animals and humans with repeated administration of cationic amphiphilic compounds, including many marketed drugs (1, 2). These compounds share similar structural featuressa hydrophobic ring structure and a hydrophilic side chain with a basic amine (1). Few drugs are reported to induce PLD in humans because the dosage is lower and the treatment duration is likely shorter than in animals. Test articles are given to animals at higher dosages for longer durations in toxicity studies, where PLD would be observed histologically by a vacuolated appearance in cells (2). Demonstration of concentric membranous lamellar inclusion bodies in cells by electron microscopy is considered definitive evidence of PLD (2). There is no strong evidence that drug-induced PLD is harmful for human health. PLD is considered primarily an adaptive response; however, given the possibility of functional compromise of affected tissues, concomitant toxicological changes in affected tissues, and the similarity with phospholipid storage disorders, PLD is also considered adverse (3, 4). This perception and the frequency of this change underscore the need to evaluate the potential of a drug candidate to induce PLD early in drug discovery. Toward this end, many in silico and in vitro biochemical and cell-based methods to evaluate PLD-inducing potential have been developed (5-12). These predictive tests range from simple computational methods to complex transcriptomic signatures. In silico and cell-based assays, however, detect several noninducers as PLD inducers, and not all of the compounds inducing PLD in vitro do so in vivo. The only way * To whom correspondence should be addressed. Tel: 203-677-6248. E-mail: [email protected]. † Department of Discovery Toxicology. ‡ Department of Discovery Analytical Sciences. § Department of Discovery Chemistry. | Department of Bioinformatics.

to confirm this potential and establish a safety margin for this effect is in animal toxicity studies spanning several weeks. Therefore, any method or combination of methods to enhance the prediction of PLD induction early in drug discovery programs will facilitate structure-activity relationships (SARs) to reduce or eliminate this liability. Ploemen et al. and Tomizawa et al. have described in silico models to predict the PLD-inducing potential of compounds (6, 12). Both of the models utilize physicochemical propertiess basicity (pKa of the most basic groupspKa-MB) and lipophilicity (ClogP) for the method of Ploemen et al. and lipophilicity (ClogP) and net charge (NC) at pH 4.0 for the method of Tomizawa et al. With minimal information, these methods predict the majority of compounds with PLD-inducing potential. These methods, however, predict several noninducers as PLD inducers. Recognizing that PLD in vivo occurs not only from inherent physicochemical properties of the compound but also likely from residence of compounds in tissues, we empirically developed a computational method that uses the volume of distribution (Vd) as the parameter describing such disposition. We demonstrate that combining Vd with the pKa of the most basic group and the ClogP for compounds provides greater concordance in predicting PLD in vivo than the Ploemen or Tomizawa methods do alone.

Experimental Procedures Data Set. Compounds that induce (positive set) and those that do not induce (negative set) PLD in either humans, animals, or cells in culture were listed (when available) based on previous publications on PLD (1-12) and by mining published literature. In addition, Bristol-Myers Squibb proprietary compounds (designated compounds A-U) for which the Vd and PLD status in vivo have been established are included in the test sets. In total, the positive set (PLD inducing) consisted of 53 compounds, and the negative set (non-PLD inducing) consisted of 50 compounds (Table 1).

10.1021/tx9003825  2010 American Chemical Society Published on Web 03/31/2010

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Table 1. List of PLD-Inducing or Noninducing Compounds, Their Physicochemical Properties, Vd, and Model Predictions compound abacavir acetaminophen amikacin amineptine amiodarone amitryptyline amlodipine amodiaquine amoxicillin atenolol atropine azithromycin bupropion carbamazepine chloramphenicol chloroquine chlorpromazine chlortetracycline cimetidine citalopram clomipramine clozapine compound A compound B compound C compound D compound E compound F compound G compound H compound I compound J compound K compound L compound M compound N compound O compound P compound Q compound R compound S compound T compound U cyclizine demeclocycline desipramine diazepam diclofenac disopyramide doxapram doxepin doxycycline erythromycin famotidine fenfluramine fluoxetine furosemide gemfibrozil gentamicin haloperidol hydroxychloroquine hydroxyzine imipramine indoramin isoniazid ketoconazole labetalol lidocaine loratadine maprotiline mefloquine methadone methapyrilene methyldopa mianserin paroxetine pentamidine perhexiline phenacetin phenobarbital piroxicam procaine propranolol quinacrine quinidine

PLD in PLD in Ploemen Tomizawa vivoa vitrob pKa-MBc ClogP pKa-MB2 + ClogP2 pred.d NCe pred.f N N Y(R) N Y (H,R) Y (R) N Y (H) N N N Y (R) N N N Y (H,R) Y (R) N N Y (R) Y (R) Y (R) Y (R) Y (R) Y (R) Y (R) N Y (R) Y (R) Y (R) Y (R) Y (R) Y (R) N Y (R) N N N N N Y (R) Y (R) N Y (R) N Y (R?) N N N N Y (R?) N Y (R) N Y (R) Y (H,R) N N Y (H,R) Y (R?) Y (H) Y (R) Y (R) Y (R) N Y (M) Y (R) N Y (R) Y (R) Y (R) N N N N Y (R) Y (H) Y (H,R) Y (R) N N N N Y (R) ?

N Y Y

N Y Y

Y Y N Y Y Y Y

Y Y Y Y N Y

Y Y Y N Y Y Y Y Y

Y Y Y

Y Y Y

6.53 1.72 9.52 8.82 9.37 9.18 8.97 9.43 1.5 9.16 9.98 8.59 7.16 –0.49 –1.73 10.47 9.41 11.02 7.13 9.57 9.46 10.09 10.02 9.09 9.87 6.95 7.48 10.29 10.36 10.36 10.36 10.22 10.36 2.68 9.07 –0.51 8.19 1.73 1.99 3.37 9.11 8.7 8.19 7.97 5.37 10.4 3.4 –2.26 10.1 7.58 9.19 10.84 8.16 7.93 10.23 10.06 –2.49 0 9.77 8.25 8.87 6.12 9.49 9.01 3.79 6.88 9.2 8.53 4.81 10.62 10.04 9.05 8.9 9.3 8.26 8.98 12.5 11.2 1.42 0 3.8 9.24 9.14 10.47 9.28

0.8 0.49 –6.3 2.5 8.95 4.85 3.43 5.5 –1.87 –0.11 1.3 2.64 3.2 2.4 1.28 5.08 5.3 –0.1 0.38 3.13 5.92 3.71 3.26 5.51 8.29 6.06 3.44 3.72 2.61 3.54 4.1 2.48 2.31 9.9 4.65 1.66 7.01 3.11 2.93 3.06 4.93 5.63 6.2 3.8 –0.6 4.47 2.96 4.73 2.58 3.2 4.09 –0.5 1.61 –0.6 3.3 4.57 1.9 3.94 –2.39 3.85 4.12 4 5.04 2.85 –0.67 3.64 2.5 1.95 5.05 4.5 3.67 4.2 3 –2.3 3.76 4.33 2.31 7.15 1.8 1.36 1.9 2.5 2.75 6.7 2.79

43 3 130 84 168 108 92 119 6 84 101 81 62 6 5 135 117 121 51 101 125 116 111 113 166 85 68 120 114 120 124 111 113 105 104 3 116 13 13 21 107 107 105 78 29 128 20 27 109 68 101 118 69 63 116 122 10 16 101 83 96 53 115 89 15 61 91 77 49 133 114 100 88 92 82 99 162 177 5 2 18 92 91 155 94

Neg Neg Neg Neg Pos Pos Pos Pos Neg Neg Pos Pos Neg Neg Neg Pos Pos Neg Neg Pos Pos Pos Pos Pos Pos Neg Neg Pos Pos Pos Pos Pos Pos Neg Pos Neg Pos Neg Neg Neg Pos Pos Pos Neg Neg Pos Neg Neg Pos Neg Pos Neg Neg Neg Pos Pos Neg Neg Neg Neg Pos Neg Pos Neg Neg Neg Pos Neg Neg Pos Pos Pos Neg Neg Neg Pos Pos Pos Neg Neg Neg Pos Pos Pos Pos

1 0 4 0.85 1 1 1 1.98 –0.97 1 1 2 1 0 0 2 1 1.78 1.01 1 1 2 1 1 1 1 1.97 1.97 1.06 1.97 1.95 1 1.97 0.05 1.03 0 0.53 0 0 0.19 1 1.05 0.73 1.03 0.72 1 0.2 –0.4 1.34 1 1 0.76 1 1.05 1 1 –0.9 –0.9 5 1 2 1.01 1 1 0.39 1.97 1 1 0.87 1 1 1 1.04 0.02 1.05 1 2 1 0 0 0.15 1.01 1 2 1.85

low none ND none high high high high none low low high high none none high high high low high high high high high high high high high high high high med high none high none none none none none high high none high none high none none high high high none med low high high none none ND high high high high high none high med med none high high high high none high high high high none none none med high high high

Vdg

PLD scoreh

pred.i

refs

0.84 1 0.16 2.4* 60, 72.3 8.7, 12 17 17 0.25, 0.42 0.95, 3.41 3.3 33, 84 20* 1.4* 0.94 140 10, 29.1 0.9 1.2, 1.36 12, 21 13 5.4* 21.6 13.8 3.8 4.3 0.67 11.3 8.6 7.1 12.5 6.8 5.4 0.4 12 5.3 1.6 24 2.7 4.33 13 10 2.7 16.5* 1.7 15 1 0.22 0.52, 6.2 1.2 12 0.69 0.95, 9.3 1.2, 1.65 7.3 35*, 17.1 0.12, 0.16 0.14* 0.33, 0.6 17, 35 >100 22.5* 12, 17.5 7.4* 0.82, 0.63 2.4, 0.66 5–9.0* 1.8, 2.5 120* 45 13–29* 4.4 3.9* 0.69 3.45 18 53 42* 1.4 0.54 0.13* 1.9* 3.1, 5.3 45 2.7*, 6–25.8

4 1 –10 53 5032 387 523 882 –1 –1 43 748 458 –2 –2 7446 499 –1 3 359 728 202 706 691 311 181 17 433 233 260 531 172 129 11 506 –4 92 129 16 45 584 490 137 500 –5 697 10 –2 14 29 451 –4 12 –6 246 1609 –1 0 –8 540 3654 551 574 190 –2 60 207 30 2915 2151 1069 167 104 –15 107 700 1530 3363 4 0 1 44 78 3157 668–155

Neg Neg Neg Neg Pos Pos Pos Pos Neg Neg Neg Pos Pos Neg Neg Pos Pos Neg Neg Pos Pos Pos Pos Pos Pos Pos Neg Pos Pos Pos Pos Neg Neg Neg Pos Neg Neg Neg Neg Neg Pos Pos Neg Pos Neg Pos Neg Neg Neg Neg Pos Neg Neg Neg Pos Pos Neg Neg Neg Pos Pos Pos Pos Pos Neg Neg Pos Neg Pos Pos Pos Neg Neg Neg Neg Pos Pos Pos Neg Neg Neg Neg Neg Pos Pos/Neg

14 7, 14 14, 18 19 12, 14, 20, 21 7, 14, 21, 22 14 23, 24 14, 20 12, 14, 20 12, 14 14, 25–27 28 13 14 12, 14, 29, 30 7, 14, 20, 31 32 12, 14, 20 14, 33–35 12, 14, 22 5, 13, 33 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 29, 37 32 14, 38, 39 14, 15 14 12, 14, 35 14 4, 14, 39 14 4, 14, 35, 40 12, 14, 20 41, 42 13, 33, 43 14, 35 13 14, 20, 44, 45 14, 46–48 14, 49 29, 50 12, 14, 20, 51 5, 15 12, 14, 20 7, 20, 52 10, 12, 15 12, 14, 20 7, 15, 53 14, 33, 54, 55 15, 56 14 57 14 33, 58 14, 59 10, 12, 14, 60, 61 12, 62–64 14, 65 14 15 13 12, 14, 20 14, 31, 33 13, 20, 33, 66

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Table 1. Continued compound

PLD in vivoa

quinine ranitidine rifampin rolitetracycline sertindole sertraline sotalol sulindac tacrine tamoxifen tetracycline thioridazine tobramycin trimipramine valproic acid warfarin zidovudine zimelidine

? N N N Y (R) N N N N Y (R) N Y (R) Y (H,R) Y (R?) N N N Y (R)

PLD in Ploemen vitrob pKa-MBc ClogP pKa-MB2 + ClogP2 pred.d Y

Y N Y Y N N Y

9.28 8.4 7.42 9.42 9.06 9.47 9.18 0 9.64 8.69 11.02 9.64 9.52 9.38 0 0 0 7.91

2.79 0.67 3.7 0.48 5.27 5.35 0.23 3.16 3.3 6.82 –0.9 6 –4.7 5.44 2.76 2.9 0 3.2

94 71 69 89 110 118 84 10 104 122 122 129 113 118 8 8 0 73

Pos Neg Neg Neg Pos Pos Neg Neg Pos Pos Neg Pos Neg Pos Neg Neg Neg Neg

NCe

Tomizawa pred.f

Vdg

1.85 1.02 1.87 1.72 1 1 1 –0.35 1 1 0.76 1 5 1 –0.13 –0.24 0 1.76

high low high high high high low none high high none high ND high none none none high

1.8*, 7.7–28.9 1.2 0.97 0.54 20–40, 55 20*, 25 1.3 1.32* 4.98 50* 1.3 18* 0.23 9.38 0.14, 0.66 0.13, 0.22 1.8, 1.66 3.2

PLD scoreh pred.i 748 7 27 2 2626 1013 3 0 158 2963 –13 1041 –10 479 0 0 0 81

Pos Neg Neg Neg Pos Pos Neg Neg Neg Pos Neg Pos Neg Pos Neg Neg Neg Neg

refs 12, 13, 66, 67 14 14 14 68, 69 7, 15, 70 9, 14 71 72 12, 13, 73 32 7, 15, 22 14, 18, 74, 75 14, 39 12, 14, 35 12, 14, 20 14, 35 7, 76, 77

a

PLD in vivo; Y, yes; N, no; “?”, literature information not clear; Sp, species; H, human; R, rat; and M, mouse. b PLD from cell-based assays only. pKa-MB for compounds without a basic group is considered “0”. d In silico prediction based on Ploemen’s method; see the text for criteria; Pos, positive; Neg, negative. e NC at pH 4.0. f In silico prediction of PLD risk based on Tomizawa’s method; see the text for criteria; Med, medium; ND, not determined. g Vdss in human (first value) and/or rat (second value) (when available) with the exception of compounds A-U, fenfluramine, which are Vdss in rat; *Varea/F or unclear if Vd is from intravenous dosing; units in L/kg. h PLD score obtained as a product of pKa-MB, ClogP, and Vd. i Prediction based on our method; see the text for criteria; Pos, positive; Neg, negative. Entries in bold indicate results contrary to in vivo findings. c

Vd and Physicochemical Properties. Vd values of these compounds in human and rat (when available) were obtained from published literature including textbooks (13-15). Where possible, Vd values were Vdss from intravenous dosing; however, because of the lack of availability of such data for some compounds, Vdbeta or Vd/F were used. These are noted in Table 1. The calculated parametersspKa-MB and ClogPswere obtained using pKa calculator version 8.0 (Advanced Chemistry Development, Inc., Toronto ON, Canada) and BioByte module within SYBYL 7.2 (Tripos Inc. St. Louis, MO), respectively. The NC at pH 4.0 was calculated from the pKa and pH (4.0) using the Henderson-Hassalbach equation. Data Analyses. The trends in the data set were visually observed, and criteria were set empirically to provide best differentiation of PLD inducers from noninducers. The data set was also analyzed using Support Vector Machines (SVM) as implemented in R package “e1071” and Decision tree classifier (in “rpart”) (http:// cran.r-project.org/) to confirm the empirical method and/or improve upon the empirical method.

Analysis and Discussion Several methods for screening compounds for PLD-inducing potential are described in the literature (5-12). Compounds can be screened in a high-throughput mode using in silico methods and in a medium-throughput mode using cell-based and biochemical-based methods. All of these methods predict and/ or detect PLD induction potential based on the inherent physicochemical properties of the compound. However, not every compound predicted positive for PLD by in silico or in vitro methods induces PLD in animals. The in vitro to in vivo disconnect, and perhaps the severity of PLD in vivo, could be explained by the differences in the in vivo disposition of compounds. Because extensive tissue distribution is likely required for PLD to occur, we consider a measure of in vivo disposition, such as Vd, important for enhanced predictability of PLD induction. Ploemen et al. clearly demonstrated the importance of basicity and lipophilicity in their in silico PLD prediction model using the calculated parameters most basic pKa (pKa-MB) and ClogP, respectively (6). Using a set of 41 compounds, they differentiated PLD inducers from noninducers. According to this method, compounds are

- predicted positive if [(pKa-MB)2 + (ClogP)2] g 90, provided that pKa g 8 and ClogP g 1. - predicted negative if [pKa-MB)2 + (ClogP)2] < 90, or pKa < 8, or ClogP < 1. Tomizawa et al. introduced NC at pH 4.0 instead of pKa-MB and demonstrated improved predictability (12). Using a set of 63 compounds, they differentiated PLD inducers from noninducers with 98% accuracy. According to this method, compounds with high ClogP (>1) and high NC (1 e NC e 2) tended to be PLD inducers. Compounds with NC > 2 were not included in their data set. By partition statistics, they generated PLD risk ratings as - none if NC < 1. - low if NC ) 1 and ClogP < 1.61. - medium if NC ) 1 and ClogP g 1.61 and 1 and e2. We developed a method to include observed Vd of the compounds with the most basic pKa and ClogP for which a “PLD score” for a given compound is the product of those three values. We used Vd values from either human or rat depending upon availability from the literature or from in-house studies and assumed that Vd in rat and human is similar for a given compound. PLD scores obtained by either rat or human Vd values were similar and followed a similar trend. The relevant data and PLD scores for positive or negative compounds calculated by our method are presented in Table 1, along with predicted outcomes using in silico methods (Ploemen et al. and Tomizawa et al.) and using cell-based assays (when available). Prediction criteria and outcomes by all of the three methods are also depicted by graphs in Figure 1. There is a clear correlation of the PLD score with PLD in vivo, with higher scores associated with PLD inducers and lower scores with noninducers. On the basis of these data, we empirically set the criteria for distinguishing PLD inducers from noninducers as follows: - predicted positive if (pKa-MB × ClogP × Vd) g 180, provided that ClogP g 2. - predicted negative if (pKa-MB × ClogP × Vd) < 180, or ClogP < 2. On the basis of these criteria, 42/51 positive set compounds were identified correctly as PLD-inducing, and 47/50 negative

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Hanumegowda et al. Table 2. Predictive Statistics of Various Models

b

model

sensitivity (%)

specificity (%)

concordance (%)

Ploemena Tomizawab oura

74 96 82

80 68 94

77 82 88

a Data set of 101 compounds (51 PLD inducers and 50 noninducers). Data set of 98 compounds (48 PLD inducers and 50 noninducers).

Figure 2. Higher Vd is correlated with PLD inducers. Analysis of 716 compounds indicated that ∼16% of the compounds had a Vd of g5.4 L/kg, and this subset included the majority of known PLD inducers. The remainder of the compounds (∼64% of the subset or ∼10% of the total) included several noninducers and compounds for which toxicology profiles were not readily available.

Figure 1. Criteria and predicted outcomes of the three models. Prediction criteria and outcomes for the Ploemen model (A), Tomizawa model (B), and our model (C); 2, PLD inducers; 4, noninducers. The criteria for PLD inducers in the Ploemen model (A) are [(pKa-MB)2 + (ClogP)2] g 90, provided that pKa g 8 and ClogP g 1 (note that the criterion of pKa g 8 is not depicted in the graph). The criteria for the Tomizawa model (B) are NC ) 1 and ClogP g 1.61 or NC is g1 and e2, and the criteria for our model (C) are (pKa-MB × ClogP × Vd) g 180, provided that ClogP g 2.

set compounds were identified correctly as non-PLD-inducing. Overall, the sensitivity (PLD inducers identified as positive) of this model is 82%, and the specificity (PLD noninducers identified as negative) of the model is 94% (Table 2). Our model’s sensitivity is likely underestimated because of noninclusion of PLD inducers such as chlorphentermine, chlorcyclizine, tilorone, homochlorcyclizine, norchlorcyclizine, meclizine, cloforex, and disobutamide due to nonavailability of Vd for these compounds in the literature. All of these compounds are described to be extensively distributed outside the plasma

compartment. In comparison, for the same set of compounds, the sensitivity and specificity of the in silico prediction using Ploemen’s model were 74 and 80%, and Tomizawa’s model were 96 and 68%, respectively (Table 2). It should be noted that for prediction statistics of Tomizawa’s model, three compounds with NC > 2 could not be included; compounds with low and no risk were binned as negative and medium and high risk as positive. Prediction from cell-based assays is not available for all of the compounds; however, within the small set of compounds for which data are available, there are several compounds that induce PLD in cells in vitro but not in vivo (Table 1). Quinine and quinidine were excluded from this analysis as their induction of PLD in vivo is not clear from the available literature. Further analyses of the data set confirmed empirically derived score and criteria as a good predictor of PLD-inducing potential. Analyses also highlighted the contribution of Vd in predicting PLD-inducing potential. A Vd value of 5.4 L/kg alone differentiated PLD inducers from noninducers to the same extent as the empirically derived PLD score method. To ascertain the contribution of Vd in differentiating inducers from noninducers, we looked into the list of 670 drugs for which intravenous pharmacokinetics parameters including Vd were published by Obach et al. (14). In combination with our data set, there were 716 compounds for which Vd data were available. Of these 716 compounds, 117 compounds (16%) had a Vd of g5.4 L/kg and included 42 PLD inducers. The remaining 72 compounds with Vd values of g5.4 L/kg included several known noninducers and compounds for which the toxicology profile was not completely available to us (Figure 2). Of these known noninducers that had Vd g 5.4 L/kg, PLD scores effectively categorized them as noninducers, suggesting that Vd alone is not adequate to differentiate noninducers from PLD inducers (Table 3). Although our method provides improved concordance (88%) over Ploemen’s (77%) or Tomizawa’s (82%) methods, we encounter several false negatives including compounds (1) with a relatively low Vd such as ketoconazole; (2) with a relatively

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Table 3. PLD Noninducers with High Vd and Model Prediction compound amphetamine bortezomib colchicine doxorubicin epirubicin idarubicin medroxalol mexiletine naltrexone nisoldipine ribavirin triamterene

PLD in vivoa pKa-MB ClogP N N N N N N N N N N N N

9.94 –0.47 –0.9 8.68 8.68 8.67 9.2 8.58 7.53 2.67 0.21 6.3

1.74 0.78 1.2 0.32 0.32 0.9 2.46 2.57 0.36 4.58 –2.85 1.61

Vdb 6.1 10 6.1 22 45 38 7.9 5.9 7.6 5.5 14 13

PLD scorec pred.d 106 –4 –7 61 125 297 179 130 21 67 8 132

Neg Neg Neg Neg Neg Neg Neg Neg Neg Neg Neg Neg

a PLD in vivo; N, no. b Vdss in human from ref 14; units in L/kg. The PLD score was obtained as a product of pKa-MB, ClogP, and Vd. d Prediction based on our method; see the text for criteria; Neg, negative. c

low ClogP such as erythromycin; or (3) both low ClogP and Vd such as aminoglycoside antibiotics, which are associated with PLD in kidneys likely because they are highly distributed to and excreted by kidneys; and (4) that are extensively metabolized to PLD inducers [e.g., zimelidine to norzimelidine (16); ketoconazole to de-N-acetyl ketoconazole] (17). In comparison, the Bayesian model developed by Pelletier et al. (5), which incorporated several descriptors including proprietary structural fingerprint, accurately predicted aminoglycoside antibiotics as PLD inducers. In a set of 201 compounds, their model demonstrated sensitivity and specificity of 92 and 77%, respectively, with an overall concordance of 83%. Because our model is highly dependent upon Vd, false positives in our model would be the noninducers with high Vd with relatively high ClogP such as bupropion and amlodipine. Although these compounds possess the physicochemical properties favorable for PLD induction, the reason why they do not induce PLD in vivo is not clear. Perhaps other preceding dose-limiting toxicities may prevent occurrence of PLD with these compounds. The in silico methods of Ploemen et al. and Tomizawa et al. predict compounds with PLD-inducing potential with minimal information and therefore are very valuable early in discovery if a cationic lipophilic compound/series is of interest. Cell-based methods although medium-to-low throughput are highly sensitive in detecting PLD inducers. Because these methods depend on the inherent physicochemical properties of the compound, their prediction of compounds as PLD inducers or noninducers is exclusively determined by these properties. Inclusion of Vd in the prediction improves overall predictability by effectively eliminating those compounds that qualify as PLD inducers in vitro but do not induce PLD in vivo likely because they are not extensively distributed in vivo. It is possible that lipophilicity, basicity, or NC that influence uptake and distribution are better defined by Vd, which in addition combines disposition in vivo, and as such, inclusion of Vd provides a better prediction of PLD induction than ClogP, pKa, or NC alone. With these observations, we propose a screening paradigm (Figure 3) that includes in silico (methods of Ploemen et al. and Tomizawa et al.) methods, cell-based methods, and our method (to account for in vivo disposition) to predict more effectively the in vivo PLD-inducing potential of a compound. This combination of the three methods could potentially help overcome the shortcomings of the individual methods and facilitate (1) selection of lead series and (2) rank order compounds within a series with PLD-inducing potential. Applying this paradigm (without cell-based methods) to the set of 101 compounds, in silico methods predicted 77 compounds

Figure 3. Proposed screening paradigm to optimize the prediction of PLD-inducing potential. Predicting the PLD-inducing potential and triaging compounds based on the prediction could be initiated early in discovery using in silico calculation (Ploemen’s and/or Tomizawa’s models). Compounds predicted positive in silico could be evaluated in cell-based methods to further confirm this potential. Compounds with negative outcomes in the cell-based assays could proceed to further development with minimal risk of inducing PLD, while those with positive outcomes could be checked by our method to obtain the relevance of disposition using the Vd data from rat PK. Those compounds with negative outcomes at this stage could proceed to further development with minimal risk of inducing PLD, while those with positive outcomes could be confirmed using cell-based assays. As indicated by dashed arrows, the decision tree could also be independent of in vitro cell-based assays. If the final outcome is negative, compounds could proceed to further development with minimal risk of inducing PLD. If the final outcome is positive, compounds are assumed to have a risk of inducing PLD in vivo and the decision to proceed with caution or to terminate could be made. 1{[pKa-MB)2 + (ClogP)2] < 90, or pKa < 8, or ClogP < 1} and {ClogP < 1 and NC < 1}. 2{[(pKa-MB)2 + (ClogP)2] g 90, provided that pKa g 8 and ClogP g 1} and/or {NC ) 1 and ClogP g 1.61 or NC is g1 and e2}. 3(pKa-MB × ClogP × Vd) < 180 or ClogP < 2. 4(pKa-MB × ClogP × Vd) g 180, provided that ClogP g 2.

correctly and 24 compounds incorrectly. Inclusion of Vd improved the prediction by recognizing 89 compounds correctly. Twelve compounds were predicted incorrectly, of which three compounds were predicted incorrectly by both in silico and Vd methods, seven compounds by either of the in silico methods and Vd methods, and only two compounds by Vd method alone. Improved concordance with inclusion of Vd demonstrates the usefulness of this paradigm in effectively differentiating PLD inducers from noninducers. Overall, “PLD score” as derived from our method demonstrates clearly the contribution of in vivo disposition in addition to physicochemical properties of compounds to PLD induction in vivo. Although refinement in predictability requires a pharmacokinetic study in rats, the resources expended are considerably less than those for repeat-dose toxicity studies of several weeks duration. Recently, methods have been introduced that can predict Vd in silico, which could be potentially used in this prediction method, thus precluding the need for an in vivo pharmacokinetic study (78). However, this needs to be tested. Furthermore, we consider that a combination of in silico, cell-based, and in vivo information can provide a meaningful testing tier for lead identification and optimization process in drug discovery.

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