Constructing an In Silico Three-Class Predictor of Human Intestinal Absorption With Caco-2 Permeability and Dried-DMSO Solubility

Journal of Pharmaceutical Sciences xxx (2019) 1-10

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Journal of Pharmaceutical Sciences journal homepage: www.jpharmsci.org

Pharmaceutics, Drug Delivery and Pharmaceutical Technology

Constructing an In Silico Three-Class Predictor of Human Intestinal Absorption With Caco-2 Permeability and Dried-DMSO Solubility Tsuyoshi Esaki 1, *, Rikiya Ohashi 1, 2, Reiko Watanabe 1, Yayoi Natsume-Kitatani 1, 3, Hitoshi Kawashima 1, Chioko Nagao 1, 3, Hiroshi Komura 4, Kenji Mizuguchi 1, 3, * 1

Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka 567-0085, Japan Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda, Saitama 335-8505, Japan 3 Laboratory of In-silico Drug Design, Center of Drug Design Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka 567-0085, Japan 4 University Research Administrator Center, Osaka City University, 1-2-7 Asahi, Abeno-ku, Osaka 545-0051, Japan 2

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 April 2019 Revised 6 July 2019 Accepted 17 July 2019

Absorption of drugs is the first step after dosing, and it largely affects drug bioavailability. Hence, estimating the fraction of absorption (Fa) in humans is important in the early stages of drug discovery. To achieve correct exclusion of low Fa compounds and retention of potential compounds, we developed a freely available model to classify compounds into 3 levels of Fa capacity using only the chemical structure. To improve Fa prediction, we added predicted binary classification results of membrane permeability measured using Caco-2 cell line (Papp) and driededimethyl sulfoxide solubility (accuracy, 0.836; kappa, 0.560). The constructed models can be accessed via a web application. © 2019 Published by Elsevier Inc. on behalf of the American Pharmacists Association.

Keywords: absorption permeability solubility machine learning in silico modeling

Introduction Oral drug administration is one of the most preferred delivery routes and is currently the goal of new drug development owing to the ease of administration and patient compliance. Approximately

Abbreviations used: HIA, human intestinal absorption; Fa, the fraction of absorption; Caco-2, human colon adenocarcinoma; MDCK, Madin-Darby canine kidney; 2/4/A1, rat duodenal immortalized; PAMPA, parallel artificial membrane permeability assay; Papp, membrane permeability measured using Caco-2 cell line; D-Sol, solubility measured using dried-DMSO method; PCA, principal component analysis; MW, molecular weight; HBA, hydrogen bond acceptor count; HBD, hydrogen bond donor count; DataFa, data with Fa; DataPS, data without Fa (having only Papp or D-Sol data; RF, random forest; Radial SVM, support vector machine with radial basis kernel function; Linear SVM, support vector machine with linear kernel. Current addresses for Dr. Tsuyoshi Esaki: The Center for Data Science Education and Research, Shiga University, 1-1-1 Banba, Hikone, Shiga, 522-8522, Japan. This article contains supplementary material available from the authors by request or via the Internet at https://doi.org/10.1016/j.xphs.2019.07.014. * Correspondence to: Tsuyoshi Esaki (Telephone: þ81-749-27-1081) and Kenji Mizuguchi (Telephone: þ81-72-849-9890). E-mail addresses: [email protected] (T. Esaki), kenji@nibiohn. go.jp (K. Mizuguchi).

90% of all drugs reaching the market are orally administered, but an estimated 10% of them fail in the development process because of poor pharmacokinetic properties.1,2 In the drug discovery stage, lead compounds with high intestinal absorption have been found to be very important because intestinal absorption is the first step after dosing, and it largely affects bioavailability. Orally administered drugs reaching the intestine pass through the intestinal epithelial cell membrane to the circulating blood and interact with targets such as receptors, channels, and enzymes in several tissues. The administration of compounds with poor human intestinal absorption (HIA) may cause substantial individual variation in pharmacokinetics and may affect the desired pharmacological effects. Furthermore, the structure of recent medicinal drugs has shifted from small molecules to mid-sized compounds, and thus, predicting the HIA of compounds in the current chemical space is difficult. Developing a model to predict the fraction of absorption (Fa) in humans using a data set that covers a wide chemical space is helpful to provide more insights on a compound’s potential as a new drug in the early stage of drug discovery. Although several acceptable Fa prediction models have been published, there are still some issues related to their usage in the early stage of drug discovery. First, almost all previously described

https://doi.org/10.1016/j.xphs.2019.07.014 0022-3549/© 2019 Published by Elsevier Inc. on behalf of the American Pharmacists Association.

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T. Esaki et al. / Journal of Pharmaceutical Sciences xxx (2019) 1-10

prediction models are 2-class classifiers.3-9 It is important to retain ambiguous compounds that are difficult to clearly classify into low or high Fa, as their absorption can be improved by structural optimization. Thus, a “moderate” class between low- and highabsorption ability is required to predict the absorption ability of compounds, and it plays an important role in deciding whether to proceed or optimize lead compounds. Second, almost all models have not been reproducible by researchers, as the data set or software is either commercial or in-house and has not been made freely available.4,6,8-10 Third, membrane permeability and solubility of compounds are not considered although the Fa is a parameter significantly affected by these physicochemical properties. One study used membrane permeability and solubility data for Fa prediction; however, the membrane permeability data were obtained by combining the experimentally determined values from several cell lines including human colon adenocarcinoma (Caco-2) and solubility data were obtained using ambiguous protocols.8 The simple concatenation of data obtained using different protocols can be noisy when constructing models because it is rather difficult to merge data obtained using different protocols to predict solubility. This is because thermodynamic and kinetic solubility have different values in the lower range.11 Thus, to correctly evaluate the Fa of target compounds, the use of a single protocol to measure membrane permeability and solubility is necessary. Several in vitro membrane permeability assays to estimate HIA have been developed such as Caco-2,12 MadineDarby canine kidney,13 rat duodenal immortalized (2/4/A1) cell lines,14 and parallel artificial membrane permeability assay.15 Of these, Caco-2 cell is the most widely used to evaluate membrane permeability in humans despite the time-consuming process of culturing.16 This is because Caco-2 cells possess the following characteristics: ability to differentiate into monolayers, ability to generate transepithelial electrical resistance, and expression of transporters present in the intestinal epithelium. Furthermore, the Caco-2 model has been recommended to demonstrate membrane permeability of compounds to be classified in the Biopharmaceutics Classification System by the US Food and Drug Administration and the solubility of compounds.17 Solubility is also one of the most important factors for Fa and recommended in the Biopharmaceutics Classification System. Several compounds in the early stage of drug discovery tend to have higher lipophilicity,18 and highly lipophilic compounds are difficult to absorb because they are rarely dissolved in the intestine. Solubility has been reported as one of the difficult properties to adequately predict.19 One of the reasons for this is the different protocols used to measure solubility, such as thermodynamic, kinetic,20,21 and dried dimethyl sulfoxide (DMSO) solubility methods.22 Recently, the dried-DMSO method has been widely used in pharmaceutical companies, and it provides information required to effectively reduce the number of synthesized compounds with poor solubility. Therefore, we developed a freely available model classifying compounds using a 3-level prediction capacity for human Fa with membrane permeability measured using Caco-2 cell line (Papp) and solubility measured using dried-DMSO method (D-Sol); only the chemical structure of compounds was used. To support Fa prediction, binary classification models of Papp and D-Sol were constructed. The largest amount of experimental Fa, Papp, and DSol data were collected from an open database to cover a wide chemical space. To construct the Fa prediction model with high performance, we compared several machine learning methods and descriptors such as physicochemical, substructure, and pharmacokinetics (Papp and D-Sol) parameters and found that adding both predicted Papp and D-Sol was effective in classifying Fa. Furthermore, adding compounds measured in the early stage of drug

discovery allowed us to widely cover the chemical space compared to that with Fa-only data sets. This plays an important role in predicting the Fa of compounds in the early stage of discovery. The data set used in this study has been made freely available as Supplementary Data and our 3 constructed models (Fa, Papp, and D-Sol) can be accessed as an open web application at https:// drumap.nibiohn.go.jp/fa. Materials and Methods Data Sets Fraction of Human Intestinal Absorption (Fa) To collect in vivo experimental data of Fa, 13,342 entries (4899 compounds) were collected from ChEMBL (Ver. 23)23 using keywords such as “absorption” and “Fa”. Data identified as nonhuman intestinal absorption, in vitro data, data lacking values, other animals’ data (such as, dogs and pigs), or unequal relations were removed; thus, 2674 entries (779 compounds) were retained. Original literature of the retained entries was reviewed and 351 compounds with Fa value were collected after unifying data with multiple entries. Furthermore, adding data used in a previous study8 and removing duplicate entries resulted in 946 compounds for use in the study. The structure data file of these compounds was also extracted from ChEMBL. To estimate the amount of drug that reaches the systemic circulation, we calculated bioavailability (BA) using BA ¼ Fa (absorption ratio)  Fh (hepatic availability), based on the simplified hypothesis that an administered drug is taken up into systemic circulation via absorption in the intestine (the Fa does not include intestinal metabolism) and first-pass effects in the liver. In the setting, Fh ¼ 0.5, if a compound had Fa ¼ 0.7 and BA ¼ 0.35; this indicated that more than 30% of the drug reached the blood. If Fa < 0.2 in the same setting, BA < 0.1 meant that the amount of drug circulating in the body is less than 10% of the administered drug. In other words, over 90% of the ingested drug is lost before reaching the systemic circulation. Values of 0.2 and 0.7 were selected as the cutoff to define “Low absorption (Fa < 0.2)”, “Moderate absorption (0.2  Fa < 0.7)”, and “High absorption (0.7  Fa)” in this study. In Vitro Caco-2 Membrane Permeability (Papp) To collect in vitro experimental membrane permeability data measured using Caco-2 cell line, 13,342 entries (4899 compounds) were collected from ChEMBL using “permeability”, “papp”, and “caco-2” as keywords. Data that were not measured using Caco-2 cells, lacked values, or had unequal relations were removed; thus, 4789 entries (4429 compounds) were retained. Original literature of the retained entries was checked and 4415 compounds with a Papp value were collected after unifying data with multiple entries. These compounds were used to construct binary Papp models. To classify the compounds into 3 Fa classes (Low: Fa < 20%, Moderate: 20  Fa < 70%, High: 70%  Fa) at a high performance, the most suitable criteria for Papp binary classification were evaluated. To predict these parameters, 4 criteria were used (0.1, 1, 10, and 100  106 cm/s). Binary classification models of Papp were constructed using these criteria (Low: Papp < criteria and High: Papp  criteria). Solubility Measured Using Dried-DMSO Method (D-Sol) To obtain the experimental aqueous solubility measurements obtained using the dried-DMSO method, entries (500 compounds) were extracted using “solubility” as a keyword to filter the results from ChEMBL. Entries lacking values and units or having unequal relations were removed. The 500 entries (496 compounds) that were clarified as solubility measurements using the dried-DMSO method at room temperature and near pH 7.4 were retained;

T. Esaki et al. / Journal of Pharmaceutical Sciences xxx (2019) 1-10

RF) or “10-fold cv” (10-fold cross validation for radial SVM and linear SVM), and “centering and scaling”. As there is a parameter for optimization, we attempted different descriptors at each decision point (mtry) to obtain RF. For SVM, radial basis kernel as a nonlinear function and linear kernel as a linear function were used. Two parameters, in-sensitive loss function (sigma) and cost of constrains violation (C), for radial SVM and one parameter, C, for linear SVM had to be optimized. The parameters of these 3 algorithms were optimized with “train” function in caret and used to construct the prediction models.

Table 1 Basic Statistics of Each Data Set Parametera

Data Set

Number of Compounds

Range

Mean

S.D.b

Fa

All Training Test All Training Test All Training Test

946 756 (79.9%) 190 (20.1%) 4415 3532 (80.0%) 883 (20.0%) 367 293 (79.8%) 74 (20.2%)

0.0-1.0 0.0-1.0 0.0-1.0 0.00016-880 0.00016-880 0.00167-300 0.11442-586 0.11442-586 0.116037-429

0.757 0.756 0.760 13.605 13.695 13.243 46.384 46.978 44.029

0.322 0.323 0.317 32.664 34.898 21.534 88.207 92.467 69.294

Papp

D-Sol

a b

3

Statistical Evaluation

Units for each data are Fa: %, Papp: 106 cm/s, and D-Sol: mg/mL. Standard deviation.

thus, other entries obtained using other methods were removed. A total of 367 compounds were retained as the D-Sol data set. To correctly place the Fa ability of compounds into the 3 classes, the best suited criteria for D-Sol binary classifications were also evaluated. In D-Sol binary classification, 3 criteria were used (1, 10, and 100 mg/mL). Binary D-Sol classification models also classify the compounds using these criteria (Low: solubility < criteria and High: solubility  criteria). Approved Drug Data for Comparison with Our Data Sets Approved drugs were collected from KEGG DRUG24 with the filters “molecular weight is not more than 1000” and “no inner salt” as representative drug-like compounds. Thus, 5862 compounds were collected for use as the approved drug data set. Their structure data file was also extracted from ChEMBL. To compare the diversity of approved drugs from our collected compounds, principal component analysis (PCA) was performed.

To compare the performance of each method, statistical parameters were calculated to represent their predictive ability (Supplementary Data, Table S1). In the classification models, values for specificity, sensitivity, F-measure, positive precision, negative precision, balanced accuracy, accuracy, and kappa were obtained.33 A kappa value greater than 0.4 was considered to have useful predictive power.34 Each score for the different models was calculated using “train” (for cross validation) or “confusionMatrix” (for test set) function in caret. Our Fa data set was, for the most part, imbalanced (Low: 12.5%, Moderate: 13.4%, High: 74.1%) owing to the difficulty faced in collecting lower HIA data. For instance, if a new classifier predicted 100 compounds (observed: 10 low and 90 high compounds; predicted: 0 low and 100 high; an imbalanced data set) as high Fa, the accuracy was 0.90. In this case, the kappa score, which is a predictive score removing accidental success, was 0, and this was not an acceptable model. This is because the low compounds have been ignored, which results in the time and expenses allocated to drug development being wasted. From this example, we used kappa as the first priority for the test set as it is more useful to reveal a model’s utility.

Calculation of Molecular Descriptors To obtain the features of the collected compounds, CDKDescUI25 (Ver. 1.4.8), Mordred26 (Ver. 1.0.0), and PaDEL-Descriptor27 (Ver. 2.21) were used. Mordred and CDKDescUI calculated 286 and 1612 2D physicochemical properties, respectively. PaDEL-Descriptor was used to obtain 6010 fingerprints divided into 3 types (count of substructure: 370, count of Klekatol-Roth28: 4860, and count of 2D atom pair: 780) for the collected compounds in our data sets. In total, 7908 descriptors for each compound were generated. If an entry had 2 disconnected components, the larger one was used for descriptor calculation using the software. Machine Learning Methods To construct the prediction models, random forest (RF),29 support vector machine with radial basis kernel (radial SVM), and linear kernel (linear SVM)30 were used in the caret package31 in the statistical environment R.32 The training parameters were as follows: tune length ¼ 10, validation method ¼ “oob” (out-of-bag for

Results and Discussions Distribution of Training and Test Sets The collected data sets were divided into a training set and test set to construct a prediction model and evaluate its predictive ability. To determine the efficacy of adding Papp and D-Sol data for Fa prediction, 190 compounds (20.1% of Fa data set) were randomly separated to serve as the Fa test set. In the Papp and D-Sol data sets, approximately 20% of compounds in each data set was randomly extracted and used as the test set. The estimated remainder of 80% for each data set was used as the training set. The range of experimental values, average, and standard deviation of each data set are shown in Table 1 (the distributions of Fa, Papp, and D-Sol data sets are shown as histograms in Supplementary Data, Fig. S1; A: Fa, B: Papp, and C: D-Sol). The training and test sets for each parameter displayed no significant differences (p-values: Fa, 0.878; Papp, 0.628; and D-Sol, 0.762). In addition, the similar distribution indicates that the division into sets was random without bias.

Table 2 Number of Selected Descriptors and the Performance of Papp Prediction for the Test Set Papp Criteria [106 cm/s]

Number of Data (Low/High)a

RF Accuracy

Kappa

Accuracy

Kappa

Accuracy

Kappa

0.1 1 10 100

81/3451 746/2876 2157/1375 3517/15

0.976 0.829 0.809 0.994

0.150 0.394 0.590 0.442

0.981 0.854 0.810 0.994

0.186 0.510 0.601 0.284

0.981 0.824 0.752 0.995

0.186 0.401 0.475 0.598

a b

R-SVM

Number of compounds used to train Papp binary classifiers. Selected using Boruta and used to train Radial SVM and Linear SVM. For RF, 521 descriptors were used.

Number of Descriptorsb

L-SVM

119 380 382 56

4

T. Esaki et al. / Journal of Pharmaceutical Sciences xxx (2019) 1-10

Table 3 Number of Selected Descriptors and the Performance of D-Sol Prediction in the Test Set D-Sol Criteria [mg/mL]

1 10 100 a b

Number of Data (Low/High)a

39/254 155/138 251/42

RF

R-SVM

Number of Descriptorsb

L-SVM

Accuracy

Kappa

Accuracy

Kappa

Accuracy

Kappa

0.878 0.730 0.838

0.335 0.463 0.101

0.878 0.811 0.662

0.335 0.619 0.064

0.878 0.811 0.838

0.000 0.628 0.000

17 65 20

Number of compounds used to train the D-Sol binary classifiers. Selected using Boruta and used to train Radial SVM (R-SVM) and Linear SVM (L-SVM). For RF, 521 descriptors were used.

Descriptor Selection Although the predictive ability of the constructed models depends on the number of descriptors, an excessive amount can result in overfitting. Thus, to remove dispensable descriptors, those with near-zero-variance and a high correlation with other descriptors were removed using the “nearZeroVar” and “findCorrelation” (cutoff ¼ 0.95) functions in caret. Therefore, 521 descriptors were retained. To select the suitable descriptors for prediction, Boruta package35 was used to automatically select the most important descriptors. For Fa prediction, 107 descriptors were selected as important by Boruta for Radial and Linear SVM (the number of descriptors selected for other prediction models is shown in Tables 2 and 3. Among the 20 most important parameters in 107 descriptors for Fa prediction (Supplementary Data, Table S2), physicochemical descriptors (18 of the 20 most important descriptors) were more than substructure descriptors (2 of the top 20) in spite of the generated number of descriptors (physicochemical: 1898, substructure: 6010). The most important descriptor was the topological polar surface area (TopoPSA), followed by the number of hydrogen bond donors (nHBDon). The lipophilicity parameters such as ALogP were also included with electric parameters. They seemed reasonable descriptors of water solubility and membrane permeability. For RF training, the 521 previous descriptors were used. Distribution of Collected Data Sets To assess the diversity of collected compounds in our data sets, chemical spaces were compared to those of approved drugs by PCA to assess the diversity of our data sets. We applied Boruta, a wrapper of random forest algorithm for feature selection, to the data sets to be compared and 107 descriptors were selected as relevant. These

descriptors were used for the comparison of chemical space by PCA. Figure 1 shows the 2-dimensional PCA plot of approved drug data, data with Fa (DataFa), and data without Fa (having only Papp or D-Sol data; DataPS). To show differences in the distribution of DataFa and DataPS against approved drugs, a PCA plot was divided into 2 plots to show the localization of training and test compounds in DataFa (A) and DataPS (B) against approved drugs. Two principal components contributed more than 43% to the analyzed data. Compounds in our data sets (DataFa and DataPS) covered most of the distribution of approved drugs. This indicates that our collected data sets have acceptable diversity to construct prediction models for general approved drugs based on the descriptors selected using Boruta. By comparing the diversity of compounds by DataFa, DataPS, and approved drugs, marginal differences were revealed. From PCA, compounds of DataFa gathered slightly in the upper right region representing approved drugs, and DataPS were slightly in the lower left. The compounds in DataFa combined with that in DataPS covered the entire area of approved drugs. This shows that it is insufficient to use only Fa data to cover the chemical space of approved drugs and that data without Fa can efficiently cover the space. In terms of distribution of training and test sets in both DataFa and DataPS, the test compounds were scattered in the cloud of training compounds, implying that the training and test compounds were successfully separated without bias. Performance of the Three-Level Fa Classification With CrossValidation and Test Set Descriptor Selection The prediction models trained using 521 (RF) and 107 (radial and linear SVM) descriptors for the 756 compounds in the training set were cross-validated. In addition, Papp and D-Sol predicted using several criteria (details are presented in Materials and

Figure 1. PCA of approved drugsdtraining and test compounds in DataFa (collected data with Fa) and DataPS (collected data without Fa). The black, orange, and green circles represent approved drugs, training compounds, and test compounds in DataFa, respectively (a). The black, blue, and yellow circles represent approved drugs, training compounds, and test compounds in DataPS, respectively (b). These plots essentially show the same distribution and were described to clarify the localization of both training and test compounds in DataPS and DataFa against approved drugs.

T. Esaki et al. / Journal of Pharmaceutical Sciences xxx (2019) 1-10

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Table 4 List of Performances Used to Classify Compounds Into the Three Fa Classes (Low: Fa < 20%, Moderate: 20  Fa < 70%, High: 70%  Fa) in the Test Set Algorithmsa

Descriptorsb

RF

Phys Phys Phys Phys Phys Phys Phys Phys Phys Phys Phys Phys

R-SVM

L-SVM

a b

D-Sol [mg/mL]

Criteria (Algorithms) Papp [106 cm/s]

þ Papp þ D-Sol þ Papp þ D-Sol

10 (RF)

þ Papp þ D-Sol þ Papp þ D-Sol

10 (L-SVM)

þ Papp þ D-Sol þ Papp þ D-Sol

1 (RF)

10 (L-SVM) 100 (R-SVM)

1 (RF)

100 (RF) 10 (L-SVM)

1 (RF)

100 (L-SVM) 1 (RF)

1 (RF)

Cross-validation Accuracy

Kappa

Test Accuracy

Kappa

0.812 0.806 0.811 0.810 0.815 0.814 0.814 0.813 0.794 0.799 0.787 0.802

0.463 0.462 0.458 0.472 0.495 0.504 0.494 0.513 0.434 0.451 0.422 0.506

0.800 0.816 0.816 0.832 0.784 0.789 0.789 0.789 0.763 0.789 0.784 0.805

0.437 0.500 0.504 0.550 0.434 0.474 0.452 0.498 0.378 0.452 0.424 0.493

RF, R-SVM, and L-SVM represent Random Forest, Support Vector Machine with Radial Based Function, and Linear Kernel, respectively. Phys, Papp, and D-Sol represent physicochemical properties, predicted Papp, and predicted D-Sol, respectively.

methods) were added to these descriptors, and the Fa predicting models were cross-validated. Table 4 shows the best performance of models in each trial based on kappa (all results in the test set are shown in Supplementary Data, Table S3). The radial SVM model had a higher performance compared with that of RF and linear SVM among all models in cross-validations. An external validation for the test compounds was performed for each Fa prediction model. The statistical scores obtained with the test set are shown in Table 4. RF had higher scores than radial and linear SVM. For the scores of the test set, adding both predicted Papp and DSol as descriptors for Fa prediction improved the predicting performance in all algorithms. Thus, for Fa prediction, RF or radial SVM including both Papp and D-Sol was effective based on the results of the training and test sets. Critical Mispredictions Using the Three-Class Fa Prediction Model In the early stage of drug discovery, it is acceptable to predict low and high Fa compounds as “Moderate”, as the user can avoid discarding potential compounds while retaining compounds that cannot be used. Similarly, it is also acceptable to predict moderate Fa compounds as low or high, as potential compounds would again be retained as well as those deemed unusable. By contrast, it is not acceptable to retain low Fa compounds mispredicted as high Fa because researchers have to spend time and money on unusable compounds. Furthermore, if the compound categorized as having high Fa was reported as a low Fa compound, then a potential compound would be discarded. As this demonstrates that binary classifiers have risks, these were defined as “critical misprediction”. We considered that it is important to retain ambiguous compounds that are difficult to

clearly classify into the low or high categories. Thus, we constructed a 3-level Fa classification model where compounds were categorized as “Low”, “Moderate”, or “High” to construct a helpful model to prioritize compounds during synthesis and carry out experiments in the early stage of drug discovery. Furthermore, we calculated and used the percentage of misprediction to monitor and evaluate the 3-class classifiers.36 Critical misprediction was calculated as: (number of “High” Fa compounds predicted as “Low” þ number of “Low” Fa compounds predicted as “High”)/(total number of test compounds) in this study (Table 5). By adding predicted Papp and D-Sol to the Fa model, mispredictions such as low Fa compounds classified as high decreased (number of misprediction decreased from 7 to 4), and the model possessed high classification ability to correctly distinguish low from moderate compounds compared to the direct Fa-only prediction model. Nevertheless, when the predicted Papp and D-Sol were used for Fa prediction, 7 compounds failed to be correctly placed into one of the 3 classes based on their Fa ability (Table 6). Pyridostigmine and ipratropium contain a quaternary ammonium and a positive charge in their structures. Pyridostigmine, which is an acetylcholinesterase inhibitor, is orally administered to treat myasthenia gravis.37,38 Ipratropium, an inhalation drug, is highly selective in its effect on bronchial smooth muscle in the treatment of chronic obstructive pulmonary disease or asthma.39,40 Although these compounds were predicted to have high Papp, their quaternary ammonium makes absorption difficult because of the positive charge. Lipophilicity, which is highly related to membrane permeability,12 is generally predicted using a combination of the substructure of compounds. The prediction of Fa for compounds with a positive charge due to the quaternary ammonium may not be considered well and may make Fa prediction difficult. Imipenem is a b-lactam antibiotic41,42 and vidarabine is an anti-herpes virus

Table 5 Confusion Matrix and Critical Misprediction (%) of the Best Performance of RF Models Using Only Physicochemical Data (the Upper Model) and Physicochemical, and Predicted Papp and D-Sol Data (the Lower Model) Descriptorsb

Criteriaa

Phys

Descriptorsb Phys þ Papp þ D-Sol

a

Criteriaa Papp: 1 [106 cm/s] (RF) D-Sol: 100 [mg/mL] (R-SVM)

Obs.

Pred. Low

Moderate

High

Mispredictionc (%)

Low Moderate High Obs. Low Moderate High

14 3 3 Pred. Low 17 3 3

2 4 4 Moderate 2 7 4

7 19 134 High 4 16 134

5.26

Mis-predictionc (%) 3.68

RF and R-SVM using the machine learning algorithms, Random Forest and Support Vector Machine with Radial bases function, respectively. Phys, Papp, and D-Sol represent physicochemical properties, predicted Papp, and predicted D-Sol, respectively. The percentage of misprediction is calculated as: (number of high Fa compounds predicted as low þ number of low Fa compounds predicted as high)/total number of test compounds). b c

6

T. Esaki et al. / Journal of Pharmaceutical Sciences xxx (2019) 1-10

Table 6 Structures of Seven Incorrectly Predicted Compounds Observed Fab

Predicted Fac

Imipenem (ChEMBL148)

Low

High

Vidarabine (ChEMBL1090)

Low

High

Pyridostigmine (ChEMBL1115)

Low

High

Ipratropium (ChEMBL3085123)

Low

High

Mesna (ChEMBL975)

High

Low

Cefazolin (ChEMBL1435)

High

Low

Canagliflozin (ChEMBL2103841)

High

Low

Name

Chemical Structurea

a

Chemical structure of compounds was obtained from ChEMBL as SDF and displayed using MarvinSketch software (ChemAxon, Budapest, Hungary). Observed Fa was categorized into 3 classes (Low: Fa < 0.2, Moderate: 0.2  Fa < 0.7, and High: 0.7  Fa). c Predicted Fa was estimated using predicted Papp and D-Sol as descriptors. If the entries had more than 2 components in the SDF obtained from ChEMBL, the smaller component was removed, and the larger component was used for prediction. b

drug,43 both of which are used as intravenous drugs. Imipenem has a substructure composed of 5- and 4-membered rings, and vidarabine is synthesized from a type of nucleoside. These structures are rare, and as there is a small number of low Fa compounds in our collected data set, it is difficult to distinguish between these compounds. Canagliflozin is an oral treatment for type 2 diabetes and one of the inhibitors of sodium-glucose co-transporter 2.44 The inhibitor of sodium-glucose co-transporter 2 commonly has the specific substructure, C-glucoside, as a basic structure, which may make it difficult to predict Fa because there are no compounds with the same substructures in our training set. Mesna (2mercaptoethane sulfonate Na) is a type of adjuvant taken with cyclophosphamide or ifosfamide to decrease the risk of hemorrhagic cystitis or hematuria via the intravenous or oral route.45 Mesna does not contain aromatic, 5-, or 6-membered rings, aromatic atoms, bonds, and a hydrogen bond donor. As Mesna was simple and this feature was very rare in our data set, this can make it difficult to predict the Fa capacity of Mesna. Cefazolin is used to

treat bacterial infections as an intravenous antibiotic drug.46 We confirmed the results of the previous report using cefazolin where the Fa of this compound was classified as high (Fa ¼ 100%).47 Cefazoline was used as an intravenous or intramuscular drug in previous studies48-50 and in the Food and Drug Administration approval package document.51 As the Fa of cefazolin may have been incorrectly included, it seemed appropriate to remove this entry to properly evaluate Fa prediction. After removing cefazolin (ChEMBL1435) from the initial test set as it may have been incorrectly collected, the scores of our model using Papp and D-Sol in a new test set were recalculated: accuracy ¼ 0.836, kappa ¼ 0.560, and the percentage of misprediction ¼ 3.17%. Relationship Between Fa and Papp Data To classify the Fa ability of compounds into 3 classes (Low: Fa < 20%, Moderate: 20  Fa < 70%, High: 70%  Fa) for high

Table 7 Comparison of the Scores Obtained in Our Model With Those of Previous Studies Methods, Year

Criteria (%)

Training Accuracy

Test a

Kappa

a

Accuracy

50

0.80

Bai, 200410

6 classes

0.79-86

4

Kappa

30

0.973

0.98

Obrezanova, 20106 Shen, 20107 Suederhauf, 201163

30 50 70 30 30 <30 or 80 <

0.90 0.84 0.74 0.89-1.00 0.986

0.89 0.81 0.69 0.81-0.91 0.999 0.883

0.751

Newby, 20158

30

0.672, 0.9086

0.800, 0.881

0.461

Basant, 20165

30

0.9975, 0.963

0.977, 0.9885

0.95

30

0.885

20, 70

0.810

Hou, 2007

Guerra, 201062

Wang, 2017 Our data

9

0.831

0.866 0.472

0.836

0.560

Descriptor Calculationb,c

Data

Merits/Disadvantages

CLOGP and In-house programs 28 descriptors from literatures ACDLAB

In-house 86 data

Ease of discussion for descriptors/Not reproducible (Descriptor calculator and data are not freely available.) Large number of data/Not reproducible (Data is not freely available.)

a

1260 from commercial database 648 data from literatures

CODES

367 (202 data from literatures)

StarDrop OpenBabel CDK, OpenBabel

235 from literatures 578 from literatures 458 from FDA package documents 932 from literatures and Drugs@FDA database4 577 from literatures

TSAR 3D, MDL QSAR, MOE and ACDLAB MOSES Descriptor Community Edition MOE, MMFF94x, ChemDes CDK, Mordred, PaDEL-Descriptors

970 from literatures 946 from literatures Papp: 4415, D-Sol: 367

High accuracy for test set/Not reproducible (Descriptor calculator and data are not freely available.) Three patterns are helpful for users/Not reproducible (Descriptor calculator and data are not freely available.) Not reproducible (Descriptor calculator is not freely available.) High accuracy for test set/Not reproducible (Data are not freely available.) Reliable data from FDA/Moderate class (30 & Fa & 70%) is ignored. Reliable data from FDA/Not reproducible (Descriptor calculator is not freely available.) High accuracy for test set/Not reproducible (Descriptor calculator is not freely available.) Ease of discussion for descriptors (usage of CART)/Not reproducible (Descriptor calculator is not freely available.) Freely reproducible (Web application), wide chemical space, and related parameters (membrane permeability and solubility) can be estimated simultaneously/Uploading of chemical structure to web is required.

T. Esaki et al. / Journal of Pharmaceutical Sciences xxx (2019) 1-10

Niwa, 20033

a

a

Collected from articles; several scores with no calculation were left blank. Commercial software: CLOGP (Daylightn Chemical Information Inc.), MOE (Molecular Operating Environment, Chemical Computing Group Inc.), TSAR 3D (Accelrys Inc.), MDL QSAR (Accelrys Inc.), ADCLAB (Advanced Chemistry Development Laboratories), StarDrup (Optibrium), and MOSES Descriptor Community Edition. c Freely available software: CLOGP, CDK (Chemistry Developers Kit), OpenBabel, MMFF94 (Merch Molecular Force Field 94), ChemDes,64 Mordred, and PaDEL-Descriptor. b

7

8

T. Esaki et al. / Journal of Pharmaceutical Sciences xxx (2019) 1-10

performance, suitable criteria for Papp binary classification were selected (Table 2). The proper criterion for Papp classification was 10.0  106 cm/s, and it is reasonable to classify compounds into the “Low” membrane permeability category based on a previous study.52 This model displayed high accuracy for the test set compared to that of other studies (accuracy of this study: 0.854, that of other studies: 0.786-0.83953-55 despite the different situations). In Papp prediction, the same 9 descriptors of Fa prediction were selected (Supplementary Data, Table S2). The most important descriptor was nHBDon, followed by TopoPSA, similar to that in Fa prediction. The process of Fa is hugely affected by Papp. From this table, effective descriptors for prediction of both Fa and Papp. However, the criterion of 1.0  106 cm/s was most effective to improve the predictive capacity of Fa (Table 4, the rationale for the selected descriptors are shown in Supplementary Data, Table S2). To associate the distribution of Fa and Papp with the criteria of Papp binary classification, 2 histograms of logarithm of Papp and Fa were generated. One scatter diagram of 98 compounds having both Fa and Papp data was also generated (Supplementary Data, Fig. S3) and Pearson’s correlation coefficient (R) score between the logarithm of Papp and Fa was found to be highly positive, 0.711. The criterion of 10.0  106 cm/s, which was the best performer based on kappa for Papp binary classification, separated the Papp data set with a higher balance (Low: 2157 compounds [61%], High: 1375 compounds [39%]) than the other criteria. This high balance in the data set seems to improve the performance of Papp prediction. The criterion of 1.0  106 cm/s, which was more suitable to distinguish the Fa ability of compounds, appeared to be effective in dividing compounds into “Moderate” or “Low” Fa based on the confusion matrix from a previous discussion (Table 5). It appeared that the criterion, 1.0  106 cm/s, was the most suited to remove high Fa compounds, and before Fa prediction, to propose whether compounds are “Moderate” or “Low”. We also observed an increase in the total performance. Relationship Between Fa and Sol Data For higher performance of Fa in the 3-level classification model, suitable criteria for binary D-Sol classification were also derived (Table 3). The suitable criterion for D-Sol classification in high performance was 10.0 mg/mL. This criterion was also reasonable when considering compounds of “Low” solubility because it aligns with that predicted by the Burnham Center for Chemical Genomics depositors.56,57 This model displayed an acceptable accuracy for the test set compared with that of other studies (accuracy in this study, 0.811; that in other studies, 0.73e0.8457-60 despite the performance of measurements with different protocols). In Sol prediction, several descriptors were different from Fa and Papp predictions (Supplementary Data, Table S2). Sol was required for function aspects such as evaluating Kier & Hall Chi chain indices. These indices represent atom connectivity within a compound, and they have good correlations with some physicochemical and biological endpoints. Here, we have to consider that the descriptors were top 20 for each prediction. The ranges of meanImp in Fa and Sol prediction were 6.51e10.56 and 4.19e6.63, respectively. This implies that descriptors similar to Kier & Hall Chi chain indices, which were used for Sol prediction, may be included in the lower priorities for Fa prediction. To discuss the relationships of descriptors used for prediction, it is important to collect additional experimental data of both Fa and Sol. For the 3-classification of Fa, the criterion of 100.0 mg/mL was the most effective to improve the performance (Table 4). The criterion of 10.0 mg/mL, which was the best performer based on kappa for D-Sol binary classification, separated the D-Sol data set with a higher balance (Low: 155 compounds [53%], High: 138 compounds

[47%]) than the other criteria. However, in Fa prediction, the D-Sol criterion of 100.0 mg/mL (Low: 251 compounds [86%], High: 42 compounds [14%]) was an effective cutoff value (the rationale for the selected descriptors are shown in Supplementary Data, Table S2). To analyze the relationship between Fa and the solubility of the collected compounds, 46 compounds of the experimental solubility data measured using other protocols were collected from ChEBML; this is because the amount of data having both Fa and D-Sol was too low (27 compounds) to interpret the relationship between these values. Similar to Papp, the distribution of 73 compounds having both solubility and Fa data was associated with the criteria (Supplementary Data, Fig. S4). As it was difficult to propose a reason for this outcome as the experimental data were measured using the dried-DMSO and other protocols, the relationship was plotted. The score for R between logarithm of solubility (including measurement using dried-DMSO and other protocols, Sol in Fig. S3) and Fa was a low positive (0.252). From previous discussions (Table 5), adding predicted D-Sol as a descriptor was effective to distinguish whether the compounds had “Moderate” or “Low” Fa. It seems that the criterion of 100.0 mg/mL is the most suited to predict whether compounds were “Moderate”. We also observed an increase in the total performance. We certainly have to consider the dose of a drug in the relationship between the Fa and solubility. However, it is difficult to include drug dose in the relationship because the dose of drugs with the Fa is already optimized for use. However, the “Low” solubility compounds have possibilities of being a new drug considering the dosage and membrane permeability. Lipinski61 proposed the minimum solubility for a projected dose in mg/kg with respect to membrane permeability. This implies that it is useful to take priorities depending on the required dosage of a drug. Thus, the present model is required to reconfirm that solubility affects the Fa and that it is important to roughly predict the degree of Fa in the 3 classes to decide the possibility in the very early stage of drug discovery. Although the solubility data were obtained using different protocols in this study, it is important to collect data of both Fa and D-Sol and to discuss the solubility of compounds based on dose and membrane permeability. Comparison With Other Fa Prediction Models Proposed in Previous Reports Several studies described in Introduction have published the statistical scores used to predict the Fa (Table 7 shows the statistical scores of previous studies and our RF model). It is difficult to directly compare our model with others found in the literature as they used different criteria for classification and were developed for different purposes. Furthermore, the performance of these models depends on the number, composition, and diversity of the training compounds. Thus, we simply presented the accuracy and kappa for the test set to place our model in the context of published studies. In the test set, the scores for accuracy and kappa for all models were higher than 0.69 and 0.461, respectively; therefore, we considered that these scores as useful criteria for acceptable performance. Our RF model, which divides the compounds into 3 Fa classes, had an accuracy of 0.832 and kappa of 0.550 for the test set; thus, satisfying the accuracy and kappa criteria. Most previous models can predict whether the Fa of compounds is low (less than 30%) and similarly, we considered our model as a binary classification to predict whether the Fa was low (less than 20%) or moderate/high (Fa  20%, moderate or high Fa in our categories) and recalculated accuracy and kappa by referring to Table 5. For the test set, accuracy was 0.942 and kappa was 0.723. As these scores were higher than the criteria, it implied that our model displayed an

T. Esaki et al. / Journal of Pharmaceutical Sciences xxx (2019) 1-10

acceptable performance. A priority in the early stage of drug discovery entails removing compounds lacking the potential for use as new drugs. Our model is a 3-level classification model with the capacity to predict the Fa and covers a wide chemical space. It is therefore useful when deciding whether a compound should be discarded or retained. Generally, the Fa is measured at the late stage of drug discovery. As these compounds have already been subjected to solubility and membrane permeability studies, these Fameasured compounds were confirmed to have acceptable solubility and membrane permeability for use as a new medicine. If only Fa data were collected to build an Fa prediction model, it would also confirm that these compounds have acceptable solubility and permeability. However, predicting the Fa in the early stage rather than the late stage of drug discovery is more effective as cost is reduced. One of the most important challenges in this stage is to correctly exclude low HIA compounds without discarding potential compounds. Accordingly, to construct a prediction model with high accuracy, it is important to retrieve a large amount of low Fa data despite the difficulty associated with this retrieval. Conclusions In the present study, we constructed an in silico model to divide compounds into 3 classes by their HIA ability using only their chemical structure. In the first step, we developed in silico Papp and D-Sol binary classifiers to support human Fa prediction and succeeded in improving the performance of Fa prediction by adding the results of these 2 classifications (Papp and D-Sol) as descriptors. All experimental data of Fa, Papp, and D-Sol were collected from an open database and have been made available as Supplementary Data. The constructed models are fully reproducible and can be accessed at https://drumap.nibiohn.go.jp/fa. Our study can allow researchers to estimate the behavior of orally administered drugs in the early stages of drug discovery. Acknowledgments This work was conducted as part of the “Development of Drug Discovery Informatics System”. The authors thank Mr. Toshiyuki Oda and Mr. Daisuke Sato at Lifematics Inc. for assisting us with data collection, rigorous curation, and web interface creation. Finally, they would like to thank Editage (www.editage.jp) for their assistance in editing and enhancing the language of the manuscript. This work was supported by the Japan Agency for Medical Research and Development [grant number JP17nk0101101]. References 1. Kennedy T. Managing the drug discovery/development interface. Drug Discov Today. 1997;2(10):436-444. 2. Caldwell GW, Yan Z, Tang W, Dasgupta M, Hasting B. ADME optimization and toxicity assessment in early- and late-phase drug discovery. Curr Top Med Chem. 2009;9(11):965-980. 3. Niwa T. Using general regression and probabilistic neural networks to predict human intestinal absorption with topological descriptors derived from twodimensional chemical structures. J Chem Inf Comput Sci. 2003;43(1):113-119. 4. Hou T, Wang J, Zhang W, Xu X. ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification. J Chem Inf Model. 2007;47(1):208-218. 5. Basant N, Gupta S, Singh KP. Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches. Comput Biol Chem. 2016;61:178-196. 6. Obrezanova O, Segall MD. Gaussian processes for classification: QSAR modeling of ADMET and target activity. J Chem Inf Model. 2010;50(6):1053-1061. 7. Shen J, Cheng F, Xu Y, Li W, Tang Y. Estimation of ADME properties with substructure pattern recognition. J Chem Inf Model. 2010;50(6):1034-1041.

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