Pharmacokinetics and Toxicity Predictors of New s-Triazines

Aug 5, 2014 - Hence, development of new s-triazine derivatives that have herbicide activity but fewer side effects is justified. It can be very inform...
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Pharmacokinetics and Toxicity Predictors of New s‑Triazines, Herbicide Candidates, in Correlation with Chromatogrpahic Retention Constants Nataša Milošević,*,† Nataša Janjić,‡ Nataša Milić,† Maja Milanović,† Jovan Popović,§ and Dušan Antonović∥ †

Department of Pharmacy, ‡Department of Orthopaedic Surgery and Traumatology, and §Department of Pharmacology and Toxicology, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000 Novi Sad, Serbia ∥ Department of Organic Chemistry, Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11000 Belgrade, Serbia ABSTRACT: Herbicides, which are ubiquitously present in soil and food, have been proven to cause human health hazard effects, hence development of new herbicide-active compounds is recommended. In this paper, nine 2,4-bis(cycloalkyl)-6-chloros-triazines were considered as herbicide candidates and their pharmacokinetics and toxicity were reviewed on the basis of in silico descriptors. Both, pharmacokinetic and toxicity predictors were presented as functions of their lipophilicity, quantified with retention constants that were obtained by liquid chromatography. None of the candidates investigated has functional groups for genotoxicity hazards and endocrine disruptions; they have acceptable toxicity and favorable pharmacokinetic properties based on computer-aided analyses. Two candidates have been selected as lead compounds for further research. KEYWORDS: herbicide candidates, triazines, in silico, pharmacokinetics, toxicity

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INTRODUCTION s-Triazines are compounds with pharmaceutical, chemosterilant and industrial importance. They are widely used as herbicides in agriculture and industry, and some of the compounds also have fungicidal properties.1 Several of the most widely used herbicides (the class of 2-chloro-s-triazines) have been proven to induce the human aromatase enzyme (which converts androgens to estrogens) in vitro at relatively low concentrations. During critical developmental periods, aromatase induction may contribute to estrogen-mediated toxicities and inappropriate sexual differentiation.2,3 Hence, development of new s-triazine derivatives that have herbicide activity but fewer side effects is justified. It can be very informative to apply the main postulates of a drug designing process in an herbicide candidate screening process and to determine both compounds that have preferable pharmacokinetic and biological properties and to alert for those compounds with highly undesirable behavior in the human body in order to avoid late-stage attrition.4 The implementation of appropriate management strategies requires that adequate information be developed regarding the types, physical and chemical properties, and toxicity of these compounds. The biological activity of a compound depends, among other factors, on its ability to reach the intended site of action.5−7 The physicochemical properties of a drug or pesticide have an important impact on its pharmacokinetic and metabolic fate in the body. Measurement and prediction of physicochemical characteristics are crucial in the development process of in silico models that allow early estimation on absorption, distribution, metabolism, and excretion (ADME) and toxicity data (together called ADMET data). Today, simple multiple linear regression and modern multivariate analysis techniques are now being © 2014 American Chemical Society

applied to the analysis of ADME data and present the most applied quantitative structure−activity (or property) relationship [QSA(P)R] strategies.8 One of the most commonly referred descriptors in QSA(P)R analyzes is lipophilicity of a substance. It is usually quantified as the logarithm of the water−octanol partition coefficient for partitioning of the agent between the two immiscible solvent phases and expressed as log P. Partition coefficient, P, quantified as log P, can be precisely computed by using atomic or fragment-addition approaches or experimentally determined by a traditional shake-flask method or by application of chromatographic methods. In addition, the presence of more than one species (ionizable and molecular) in solutions with different pH values results in an apparent partition coefficient or distribution coefficient D, expressed as log D.9 The chromatographic methods are a useful alternative for lipophilicity quantification of drug or pesticide candidates. Net of bonds and interactions of the molecule with nonpolar stationary and polar mobile phase are very similar to the partitioning between the phospholipids bilayer and body fluids, which influences pharmacokinetic properties.10,11 Thin-layer chromatography (TLC) allows the possibility of simultaneous analysis of several analytes, it is simple and economical, and it equalizes the differences that arise from the application of various organic solvent−water mixtures because extrapolated values to 0% of the organic solvents are applied; so it has still been an attractive method for lipophilicity quantification.12 In Received: Revised: Accepted: Published: 8579

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this paper some s-triazines are overviewed as herbicides candidates, their pharmacokinetic and toxicity descriptors are discussed and presented as a function of lipophilicity described as retention constants.

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equations derived for individual compounds are characterized by different slopes, which means that changes in mobile-phase composition have different effects on the RM values and that the slopes, b, correlate with the eluent strength of organic modifier. Linear relationships between the intercept, RM0, and the volume fraction of the organic modifier were characterized by high correlation coefficients (Table 2).

MATERIALS AND METHODS

A new series of nine s-triazine derivatives (Table 1) has been studied for their retention behavior on reversed-phase thin layer chromatog-

Table 2. Intercepts and Slopes of the Linear TLC Equation Calculated for Each Analyzed Compounda

Table 1. Structures of Analyzed s-Triazines

RM = RM0 + bφ

compd

name

1

2,4-bis(cyclopropylamino)-6-chloro-striazine 2,4-bis(cyclobutylamino)-6-chloro-s-triazine 2,4-bis(cyclopentylamino)-6-chloro-striazine 2,4-bis(cyclohexylamino)-6-chloro-s-triazine 2,4-bis(cycloheptylamino)-6-chloro-striazine 2,4-bis(cyclooctylamino)-6-chloro-s-triazine 2,4-bis(cyclodecylamino)-6-chloro-s-triazine 2,4-bis(cycloundecylamino)-6-chloro-striazine 2,4-bis(cyclododecylamino)-6-chloro-striazine

2 3 4 5 6 7 8 9

no. of C atoms in rings 3 4 5

a

6 7 8 10 11

slope (b)

adj r2

1 2 3 4 5 6 7 8 9

1.695 2.023 2.553 3.055 3.678 4.121 5.242 5.325 5.860

−2.885 −3.667 −3.745 −3.875 −4.599 −4.610 −6.117 −5.652 −6.640

0.998 0.986 0.985 0.986 0.989 0.992 0.989 0.990 0.996

p < 0.0001.

RM 0 = −1.754 − 1.181b

12

where adj r2 = 0.949, F = 148.628, and p < 0.0001. Sum effect of intermolecular interactions between analyte, stationary phase, and mobile phase define the chromatographic behavior of a molecule. Hydrophobic interactions dominantly guide the retention process of agents in reversed-phase liquid chromatography, hence good correlations (linear or parabolic) between the retention factor, RM0 and the standard lipophilicity parameter, log P, can be expected.18 Linear correlation was obtained between retention constants determined in RP TLC and log P (ClogP and ACDlogP calculated by different software packages; Table 3) and more importantly with log D (Table 3), which is consistent with literature data19 in which a linear relationship between the retention constant log kw determined in HPLC and different log P values has been described. Since the relationship between slopes b and intercepts RM0 of the RP TLC equations has excellent statistical quality, the slopes can be considered alternative to intercepts for lipophilicity parameters. The slopes b of RP TLC equations were correlated with log P and log D from calculation chemistry. It is evident that the slopes b of TLC equation (eq 2) may be applied for lipophilicity expression (Table 4) of the compounds investigated with similar reliability like the intercepts, which well agrees with a report by Valko.20 Both retention constants RM0 and b also vary with the size of the molecules quantified as molecular weight (MW)that is, the number of C atoms (nC) in the substituent ringssince other physicochemical properties of the analytes do not alter with the change of their structure. Namely, the polarity of the molecules, described as total polar surface area (TPSA), number of hydrogen-bond donors (NHBD) and acceptors (NHBA), and elasticity of the molecules, quantified as number

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RESULTS AND DISCUSSION The retention behavior of s-triazines was investigated by changing the amount of organic modifier in the mobile phase and has been already previously reported.13−16 The RM value for each compound was calculated as

(1)

Relationships between retention values, RM, of the compound and the volume fraction, φ, of organic modifier in the mobile phase were, as expected, linear and are expressed by the wellknown equation:

RM = RM 0 + bφ

intercept (RM0)

In partition chromatography there is usually a linear correlation between RM0 and slope b. If linearity is observed, the compounds form a congeneric class.17 Linear relationship between intercept RM0 and slope b is given as

raphy (RP-TLC), and the obtained retention constants RM0, which reflect their lipophilicity, have been correlated to pharmacokinetic and toxicological descriptors obtained with acd/i-lab-2.0 software package. RP-TLC was performed on silica gel C18 (Macherey-Nagel) with five different volume fractions (φ) of acetone in water−acetone mobile phase. Solutions for chromatographic investigations were prepared by dissolving in chloroform and were spotted on the plates with micropipette. Approximately 0.2 μL of each freshly prepared solution was spotted on the plates. Ascending technique has been used for chromatographic development. Dried plates after development were examined at 254 nm. Rf values were calculated as an average out of three measurements.

⎛1 ⎞ RM = log⎜⎜ − 1⎟⎟ ⎝ Rf ⎠

compd

(2)

0

where RM (intercepts) are the extrapolated values corresponding to φ = 0% and b are the slopes of the linear plot. Regression 8580

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Table 3. Main Physicochemical Properties of the Analytesa nC

NHBD

NHBA

NRB

TPSA

MW

ClogP

ACDlogP

log S (pH = 7.4)

log D (pH = 7.4)

3 4 5 6 7 8 10 11 12

2 2 2 2 2 2 2 2 2

5 5 5 5 5 5 5 5 5

4 4 4 4 4 4 4 4 4

62.73 62.73 62.73 62.73 62.73 62.73 62.73 62.73 62.73

225.68 253.73 281.78 309.84 337.89 365.94 422.05 450.1 478.15

2.5 3.16 4.28 5.4 6.52 7.63 9.87 10.99 12.11

1.96 3.08 4.21 5.34 6.47 7.6 9.85 10.98 12.11

−4.18 −4.68 −5.17 −5.63 −6.06 −6.47 −7.23 −7.57 −7.89

2.62 3.04 4.16 4.80 6.27 7.34 8.53 9.23 9.89

a

nC, number of C atoms in substituent rings; NHBD, number of hydrogen-bond donors; NHBA, number of hydrogen-bond acceptors; NRB, number of rotatable bonds; TPSA, total polar surface area; MW, molecular weight.

Table 4. Retention Constants RM0 and b, Presented as Functions of Computer-Calculated ClogP, ACDlogP, and log D (pH = 7.4) adj r2

p-value

RM0

= 0.697 + 0.437ClogP b = −2.196 − 0.353ClogP

0.991 0.936

1.074 × 10−8 1.220 × 10−5

RM0 = 0.827 + 0.424ACDlogP b = −2.298 − 0.343ACDlogP

0.992 0.940

7.951 × 10−9 9.855 × 10−6

RM0 = 0.256 + 0.559 log D b = −1.870 − 0.447 log D

0.991 0.913

1.242 × 10−8 3.681 × 10−5

equation

acceptors and donors in a molecule.23−25 Lipinski et al.26 have used these molecular properties to formulate the “rule of five”. The rule states that most molecules with good membrane permeability have ClogP ≤ 5, molecular weight ≤ 500, number of hydrogen-bond acceptors ≤ 10, and number of hydrogenbond donors ≤ 5. Investigated s-triazines have molecular weight under 500, two hydrogen-bond donors, five hydrogen-bond acceptors, and four rotatable bonds (Table 3). However, only three compounds have ClogP under 5, for which good oral absorption can be assumed. Compounds with more than 6 C atoms in substituent rings have ClogP over 5 and violate one rule; hence, for these compounds low membrane permeability can be expected, due to very low solubility (Table 3), and consequently very poor absorption. Nevertheless, absorption constants for compounds investigated are predicted to be low (0.048−0.06 min−1, Table 5); that is, they have short half-times of absorption (11−15 min), which practically guarantee unomitted permeation through enterocytes. However, very low solubility for over 6C-in-rings-compounds should not be neglected for herbicide candidates. The absorption constant defines the rate of transport through membrane and it is completely associated with lipophilicity quantifiers RM0 and b:

of rotatable bonds (NRB), are constant for the analytes observed in this paper (Table 3), so the factors that influence the retention behavior of molecules are their lipophilicity and molecular size (Figure 1). Pharmacokinetic predictors, calculated by use of computation program i-lab 2.0, are presented in Table 5. High oral bioavailability is an important factor for the development of bioactive molecules as therapeutic agents but is undesirable for any xenobiotic that can be ingested. The rate and extent of intestinal absorption are mainly dependent on (i) dissolution rate of the drug (xenobiotics) in gastrointestinal fluids and (ii) rate of transport across the intestinal membrane. In light of present knowledge, it is probable that the predominant process of absorption for most conventional drugs (and xenobiotics) is passive diffusion. Varying types of approaches are used to predict the rate of passive intestinal absorption of drug candidates (or xenobiotics).21 Models applied in the early discovery phase typically allow rapid screens of a large number of compounds. The highest throughput can be obtained with computational quantitative structure−property relationship (QSPR) models.22 Models developed to predict passive intestinal absorption can be evaluated by employing them to predict the intestinal absorption of a set of passively absorbed compounds with known values of the percentage of dose absorbed (abs%) in humans or constant of absorption ka, defined as the ratio of natural logarithm of 2 and the half-time of compound absorption (ta1/2): ka = ln 2/(ta1/2). Compared with predicted abs%, predicted ka is more informative in the drug discovery process because the rate of absorption among completely absorbed drugs can vary.9 Properties of molecules such as bioavailability or membrane permeability have often been connected to simple molecular descriptors such as log P, MW, or counts of hydrogen-bond

ka = 0.066 − 0.004RM 0

where adj r2 = 0.983, F = 466.892, and p < 0.0001, and ka = 0.072 + 0.004b

where adj r2 = 0.902, F = 74.658, and p < 0.0001, although only compounds with 3, 4, 5, and eventually 6 carbons in the rings should be considered for further pharmacokinetic profiling on the basis of violation of Lipinski’s rule for the rest of the compounds. This good and fast absorption for compounds can be expected for these four molecules, so their further action in the human body will be the key in the selection process of herbicide candidates with the least side effects. The change of pharmacokinetic behavior of all considered compounds, however, is very informative, and all compounds will be considered. The concentration of drug (or xenobiotic) in the plasma or tissues depends on the amount of xenobiotic systemically absorbed and the volume in which the xenobiotic is distributed. The apparent volume of distribution in the body, Vd, is the most important pharmacokinetic parameter that determines the extent of drug or any other xenobiotic distribution. It is simply a proportionality constant that relates the amount of xenobiotic in the body and/or compartments of the body (peripheral compartments) to its plasma (central compartment) concen8581

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Figure 1. Linear dependence of retention constants RM0 from (a) number of C atoms in substituent rings, given as RM0 = 0.222 + 0.478nC [adj r2 = 0.992, F = 1004.25, p = 8.053 × 10−9] or from (b) molecular weight, described as RM0 = −2.191 + 0.017MW [adj r2 = 0.992, F = 1004.56, p = 8.044 × 10−9]. Linear dependence of slope b from (c) number of C atoms in substituent rings, characterized as b = −1.808 − 0.387nC [adj r2 = 0.940, F = 126.75, p = 9.744 × 10−6] or from (d) molecular weight, estimated as b = 0.143 − 0.014MW [adj r2 = 0.940, F = 126.75, p = 9.744 × 10−6].

Table 5. In Silico-Determined Pharmacokinetic Parametersa compd

ka (min−1)

Vd (L/kg)

PPB (%)

log BBB

1 2 3 4 5 6 7 8 9

0.061 0.059 0.057 0.054 0.053 0.051 0.048 0.047 0.046

1.80 2.16 2.48 2.85 3.93 4.26 5.63 6.48 7.10

91.77 92.23 95.97 96.99 96.89 98.16 99.49 99.62 99.69

−0.17 0.06 0.37 0.53 0.77 0.60 0.3 0 0

in everyday life is an important subject. These homologous series of investigated compounds have moderate predicted Vd values (range 1.8−7.1 L/kg) (Table 5), indicating that no serious accumulation of compound in fat tissue occurs. Vd can be larger for very hydrophobic compounds, which accumulate in the fat tissue. Lipophilicity of analytes has a leading role in the distribution process (as in absorption process) of investigated triazines. Figure 2 shows the change of Vd with lipophilicity of the triazines investigated. The most important observation is that compounds which should have good oral absorption (1−4) are the least lipophilic compounds and are presumed to have the smallest Vd, which is highly preferable. In the drug development process, it is desirable to recognize those drug candidates which are potential high binders for plasma proteins in early virtual screening procedures. Drug bound to plasma proteins is not active i.e. that is, it is not able to cross membranes, pass the central compartment, and diffuse into the interstitial spacehence it cannot permeate to the site of action nor bind to receptors.29 Although drugs require low binding values for xenobiotics, it is preferable to have a small free compound concentration in plasma in order to prevent possible side effects and remain nonpharmacologically active in the human body. From Table 5 it can be observed that compounds investigated are expected to have

a ka, constant of absorption; Vd, volume of distribution; PPB, plasma protein binding affinity; log BBB, logarithm of blood−brain barrier permeation.

tration. Vd is assumed to be constant for a xenobiotic that follows a one-compartment pharmacokinetic model and is calculated as the dose taken divided by the plasma concentration of the compound at time zero (C0):27,28 Vd = dose/C0. Since accumulation of xenobiotic in fat tissue causes major problems and for many xenobiotics is the main health concern, Vd of newly synthesized compounds that may have frequent use 8582

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PPB = 72.027 − 8.719b − 0.686b2

where adj r2 = 0.807, F = 17.718, and p < 0.005. It should be emphasized that high affinity for plasma proteins and very small free plasma concentration is characteristic for these sets of compounds, including those that are expected to absorb easily and in great percentage. The blood−brain barrier is the formidable obstacle that restricts the brain entry of most hydrophilic compounds. Permeation of drugs through the blood−brain barrier is usually described as a logarithmic value. For many neurological drugs, a positive value for log BBB that tends to reach 1 is preferable, while for most xenobiotics, a negative value for log BBB or values close to zero indicate that those compounds are unable to gain access to the brain or lack sufficient concentration for an appropriate time; hence neurotoxicity should be diminished.31,32 The novel set of compounds observed in this paper, as demonstrated in Table 5, have log BBB values that vary from −0.17 up to 0.77. It can be assumed that compounds with only 3 and 4 C atoms or those with more than 10 C atoms in the rings are expected to have very low log BBB. Nevertheless, compounds with 5−8 C atoms in rings have moderate log BBB, and for them poor penetration in the brain can be predicted. These assumptions agree with Clark’s33 rule of thumb, on which basis all compounds violate at least one of the following rules: • Rule 1: The sum of nitrogen and oxygen (N + O) atoms in a molecule should be five or fewer to facilitate brain permeation. • Rule 2: ClogP − (N + O) > 0 is recommended. • Rule 3: Polar surface area (PSA) of the compound should be below 90 Å2 (60−70 Å2). • Rule 4: If molecular weight (MW) is below 450, then the molecule has a high chance of entering the brain. • Rule 5: If log D value is in the range 1−3, then log BBB is likely to be positive. These compounds do have polar surface area around 60 Å2 and molecular weight around 450 and less, as well as a sum of nitrogen and oxygen atoms 5 or fewer. However, all compounds violate rule 1 or rule 5. Compounds with 5 or fewer C atoms in the substituent rings do not obey the rule ClogP − (N + O) > 0, while rule 5, log D = 1−3, is broken by compounds with 5 or more C atoms in rings. For potential herbicides that can be absorbed in the human body, low permeation in the brain is desirable as it is predicted. The most important is the small brain permeability by compounds with 3C, 4C and 5C atoms in the substituent rings, which have the best features for herbicide candidates. The generally accepted physicochemical models of BBB permeability have recognized as their primary determinants for passive transport the analyte’s lipophilicity and polarity.34 Having in mind the constant polarity of the compounds (Table 3), log BBB should be associated only with the lipophilicity of the analytes. For this set of compounds, parabolic correlation with high statistical quality has been established between log BBB and lipophilicity of the solutes:

Figure 2. In silico estimated volume of distribution, Vd, presented as linear function of (a) lipophilicity of the analytes, quantified as RM0 and given as Vd = −0.642 + 1.266RM0 [adj r2 = 0.970, F = 262.309, p = 8.324 × 10−7], and (b) slopes b, presented as Vd = −2.914 − 1.506b [adj r2 = 0.936, F = 118.626, p = 1.215 × 10−5].

very low free concentration (only 8% or less) in plasma, which guarantees small or no pharmacological effectiveness in the human body. It is widely accepted that protein binding and especially binding of drugs and other xenobiotics to serum albumin is generally nonspecific and strongly influenced by hydrophobic interactions within small congeneric series of compounds. However, lipophilicity seems not to be the dominant chemical property for albumin binding affinity when structurally different drugs or xenobiotics are compared.30 Since we are dealing with structurally similar compounds, the examination of dependence between PPB and lipophilicity is strongly recommended. Statistically high square function was determined between lipophilicity and PPB. As the number of C atoms in the ring increases, both lipophilicity and affinity for albumins elevate until the highest level (over 99% bound for human plasma albumins) is achieved for compounds that have 10 or more C atoms in rings:

log BBB− 1.981 + 1.384RM 0 − 0.182(RM 0)2

where adj r2 = 0.864, F = 26.350, and p < 0.005, and

PPB = 83.572 + 5.727RM 0 − 0.512(RM 0)2

log BBB = − 4.634 − 2.157b − 0.220b2

where adj r2 = 0.941, F = 64.256, and p < 0.0001, and 8583

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where adj r2 = 0.802, F = 15.151, p < 0.01, and compound 8 is excluded. The increment of C atom number in the rings leads to elevation of the molecular weight and lipophilicty of the molecules which at first facilitates permeability into the brain. Further elevation of lipophilicity diminishes their permeation, probably due to low solubility and dissociation in water body fluids. The critical point in drug development programs, which is recognized by the pharmaceutical industry and safety assessment regulatory authorities, is the early identification of serious toxicological issues in order to prevent significant investment of time and resources for new drugs in late stages of clinical development.35 For new herbicide candidates, low animal and human toxicity is one of the most important issues. On the basis of in silico calculations, predicted acute toxicity of the compounds investigated (Table 6) compared to data for

chloro-s-triazine herbicides (atrazine, simazine, and propazine) induce the human aromatase enzyme (which converts androgens to estrogens) in vitro at relatively low concentrations. A logical concern would be that exposure of wildlife and humans to triazine herbicides, which are produced and used in large quantities and are ubiquitous environmental contaminants, may similarly contribute to estrogen-mediated toxicities and inappropriate sexual differentiation.50,51 Investigated derivatives of s-triazine, according to in silico analyses, do not have functional groups for genotoxicity hazards and endocrine disruptions. On the basis of the obtained results, it can be concluded that for further herbicide candidates, compounds 1 and 2 with 3C and 4C atoms in the substituent rings can be reviewed.

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Corresponding Author

*Telephone/fax +381(0)21-422-760; e-mail milosevic_ [email protected] or [email protected].

Table 6. Predicted LD50 Values for Compounds Investigateda

Funding

This study was performed with the support of the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project OI 172013.

LD50 (mg/kg) mouse

a

AUTHOR INFORMATION

rat

compd

ip

po

iv

sc

ip

po

1 2 3 4 5 6 7 8 9

240 240 260 240 150 180 140 130 120

540 920 1900 1900 1300 550 1400 1200 1100

81 43 61 49 31 20 16 12 10

28 61 35 33 33 16 98 76 80

38 530 98 49 98 110 50 48 46

400 3200 850 960 3300 1700 3300 3900 4500

Notes

The authors declare no competing financial interest.

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REFERENCES

(1) Martin, H., Worthing, C. R., Eds. Pesticide Manual, 4th ed.; British Crop Protection Council: Worcestershire, England, 1974. (2) Benachour, N.; Moslemi, S.; Sipahutar, H.; Seralini, G. E. Cytotoxic effects and aromatase inhibition by xenobiotic endocrine disrupters alone and in combination. Toxicol. Appl. Pharmacol. 2007, 222, 129−140. (3) Vandenberg, L. N.; Colborn, T.; Hayes, T. B.; Heindel, J. J.; Jacobs, D. R., Jr.; Lee, D. H.; Shioda, T.; Soto, A. M.; vom Saal, F. S.; Welshons, W. V.; Zoeller, R. T.; Myers, J. P. Hormones and endocrinedisrupting chemicals: Low-dose effects and nonmonotonic dose responses. Endocr. Rev. 2012, 33 (3), 378−455. (4) Cimmino, A.; Zonno, M. C.; Andolfi, A.; Troise, C.; Motta, A.; Vurro, M.; Evidente, A. Agropyrenol, a phytotoxic fungal metabolite, and its derivatives: A structure−activity relationship study. J. Agric. Food Chem. 2013, 61 (8), 1779−1783. (5) Henchoz, Y.; Bard, B.; Guillarme, D.; Carrupt, P. A.; Veuthey, J. L.; Martel, S. Analytical tools for the physicochemical profiling of drug candidates to predict absorption/distribution. Anal. Bioanal. Chem. 2009, 394, 707−729. (6) Kerns, E. H.; Di, L. Pharmaceutical profiling in drug discovery. Drug Discovery Today 2003, 8 (7), 316−323. (7) Li, A. P. Screening for human ADME/Tox drug properties in drug discovery. Drug Discovery Today 2001, 6 (7), 357−366. (8) van de Waterbeemd, H.; Gifford, E. ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discovery 2003, 2 (3), 192−204. (9) Hou, T.; Wang, J.; Zhang, W.; Wang, W.; Xu, X. Recent advances in computational prediction of drug absorption and permeability in drug discovery. Curr. Med. Chem. 2006, 13 (22), 2653−2667. (10) Lambert, W. J. Modeling oil-water partitioning and membrane permeation using reversed-phase chromatography. J. Chromatogr. A 1993, 656, 469−484. (11) Gocan, S.; Cimpan, G.; Comer, J. Lipophilicity measurements by liquid chromatography. Adv. Chromatogr. 2006, 44, 79−176. (12) Komsta, L.; Skibinski, R.; Berecka, A.; Gumieniczek, A.; Radkiewicz, B.; Radon, M. Revisiting thin-layer chromatography as a lipophilicity determination tool: A comparative study on several techniques with a model solute set. J. Pharm. Biomed. Anal. 2010, 53 (4), 911−918.

ip, intraperitoneal; po, per os; iv, intravenous; sc, subcutaneous.

toxicity of atrazine,36,37 simazine,38−42 propazine,43−47 and terbuthylazine48,49 indicates slight to moderate toxicity to humans and other animals for the compounds investigated; that is, they have acceptable toxic properties. In addition, not only do predicted oral absorption and distribution of compounds investigated depend on their lipophilicity, but values of toxicity predictors are also changed in accordance with lipophilicity alternation. There is a linear increment of both intravenous and intraperitoneal toxicity in mouse (i.e., decrement of LD50 dosages predicted on the basis of structure) with the elevation of lipophilicity of the analytes experimentally quantified as retention constants RM0 and b: LD50 (ip mouse) = 315.490 − 33.959RM 0

where adj r2 = 0.825, F = 38.646, and p < 0.0005; LD50 (iv mouse) = 90.866 − 14.747RM 0

where adj r2 = 0.811, F = 35.259, and p < 0.001; LD50 (ip mouse) = 376.480 + 40.400b

where adj r2 = 0.796, F = 32.123, and p < 0.001; and LD50 (iv mouse) = 118.021 + 17.688b

where adj r2 = 0.797, F = 32.441, and p < 0.001. Finally, investigated compounds were tested in silico as possible causes of human endocrine system disorders and genotoxic hazards. Namely, several members of the class of 28584

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dx.doi.org/10.1021/jf502405k | J. Agric. Food Chem. 2014, 62, 8579−8585