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Published in final edited form as: Toxicol Lett. 2016 Oct 20;263:6–10. doi: 10.1016/j.toxlet.2016.10.010

Prediction of pharmacokinetic and toxicological parameters of a 4-phenylcoumarin isolated from geopropolis: In silico and in vitro approaches

Marcos Guilherme da Cunha a,c, Gilson César Nobre Franco b, Marcelo Franchin a, John A Beutler c, Severino Matias de Alencar d, Masaharu Ikegaki e, Pedro Luiz Rosalen a,*
PMCID: PMC7755166  NIHMSID: NIHMS1653085  PMID: 27773722

Abstract

In silico and in vitro methodologies have been used as important tools in the drug discovery process, including from natural sources. The aim of this study was to predict pharmacokinetic and toxicity (ADME/Tox) properties of a coumarin isolated from geopropolis using in silico and in vitro approaches. Cinnamoyloxy-mammeisin (CNM) isolated from Brazilian M. scutellaris geopropolis was evaluated for its pharmacokinetic parameters by in silico models (ACD/Percepta™ and MetaDrug™ software). Genotoxicity was assessed by in vitro DNA damage signaling PCR array. CNM did not pass all parameters of Lipinski’s rule of five, with a predicted low oral bioavailability and high plasma protein binding, but with good predicted blood brain barrier penetration. CNM was predicted to show low affinity to cytochrome P450 family members. Furthermore, the predicted Ames test indicated potential mutagenicity of CNM. Also, the probability of toxicity for organs and tissues was classified as moderate and high for liver and kidney, and moderate and low for skin and eye irritation, respectively. The PCR array analysis showed that CNM significantly upregulated about 7% of all DNA damage-related genes. By exploring the biological function of these genes, it was found that the predicted CNM genotoxicity is likely to be mediated by apoptosis. The predicted ADME/Tox profile suggests that external use of CNM may be preferable to systemic exposure, while its genotoxicity was characterized by the upregulation of apoptosis-related genes after treatment. The combined use of in silico and in vitro approaches to evaluate these parameters generated useful hypotheses to guide further preclinical studies.

Keywords: In silico, in vitro, Pharmacokinetics, Genotoxicity, Coumarin, Geopropolis

1. Introduction

Drug discovery from natural sources is still an expensive time and effort-consuming process. Even more daunting is the fact that 50% of drug candidates fail in initial clinical trials, most of them due to unacceptable ADME/Tox results (Bugrim et al., 2004). To overcome these issues, many tools and technologies have been proposed to predict the ADME/Tox characteristics of new drug lead in a fast, comprehensive and low-cost manner. These alternatives also address pressures to limit animal experimentation, especially in pharmaceutical investigations where it is suggested that the application of the principle of the 3Rs – replace, reduction and refinement (Russell, 1995) be increased. In silico and in vitro methodologies have been shown to be feasible for judging candidate suitability for pharmacokinetic and pharmacodynamic studies (Ekins et al., 2006a). Although not sufficient by themselves, these methodologies can offer valuable direction prior to commencing in vivo studies.

Since natural products are an important source of candidates for drug leads (Newman and Cragg, 2016) in silico and in vitro methodologies are perfect approaches to combine in order to rapidly and efficiently optimize the process of understanding new bioactive molecules, their targets and possibilities of success. Propolis is a well known natural resin collected from plants by honeybees; we have investigated a specific product known as geopropolis. This propolis is a resin collected by a native Brazilian stingless bee, Melipona scutellaris, and its pharmacological activities have been extensively studied by our research group (Franchin et al., 2012, 2013; da Cunha et al., 2013b, 2013a). A detailed study of its chemical composition led to the isolation of cytotoxic coumarins and benzophenones as major compounds (da Cunha et al., 2016). Among these compounds, CNM was identified as the most abundant component. CNM, first described by Cruz et al. (2008) as a constituent of the native Northeast Brazil plant Kielmeyera reticulata, had not been previously studied biologically. In our hands it has shown promising pharmacological activity (Franchin et al., 2016), however there is no information in the literature on pharmacokinetic and toxicity properties of this coumarin.

The aim of this report is to predict the main ADME/Tox characteristics of CNM using in silico and in vitro approaches. We seek to exemplify and briefly discuss how these methodologies can be used to obtain preliminary information about basic features of a single poorly studied (or unknown) naturally occurring compound.

2. Material and methods

2.1. Compound source

CNM (C34H32O7; Fig. 1) was isolated and identified as previously detailed by us (da Cunha et al., 2016). Briefly, crude geopropolis was extracted using aqueous ethanol 70%, in a proportion of 1:7 (m/v). The ethanolic extract was fractioned on diol bonded phase media using five solvents of increasing polarity. The dichloromethane fraction was further fractionated in a Sephadex LH-20 column eluted with CH2Cl2/MeOH (1:1, v/v) yielding three subfractions A, B and C. Fractions B and C were pooled and then purified by normal phase HPLC using a cyano column under a hexane/isopropanol gradient. CNM (≥95% pure) was isolated with a final yield of 0.5%. HREIMS and NMR data are presented as Supplementary material.

Fig. 1.

Fig. 1.

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Structure of CNM.

2.2. Drug likeness screening studies for ADME/Tox compliance

The prediction of pharmacokinetic and toxicology properties of CNM was performed with ACD/Percepta™ (Advanced Chemistry Development, Inc. – ACD/Labs, Canada) and MetaDrug™ (Thomson Reuters, USA) software. For the analysis, the software was loaded with the virtual structure generated in the “.sdf” format. The predictions were obtained based on QSAR models, created from previously published data or from databases of bioactive molecules (Peach et al., 2012; Ekins et al., 2005, 2006b). For ACD/Percepta™ data, a RI higher than 0.75 while for MetaDrug™ a TP higher than 50 were considered reliable.

2.3. Prediction of genotoxic profile by DNA damage signaling PCR array

In order to evaluate the genotoxic profile of CNM, the RT2 Profiler™ PCR Array Mouse DNA Damage Signaling Pathway (PAMM-029A) from Qiagen (Hilden, German) containing 84 genes was used. Briefly, RAW 264.7 cells were seeded at 5×105 cell/mL in RPMI with 10% FBS and incubated at 37 °C, 5% CO2 for 18 h. Thereafter, cells were incubated with 2.0 μM CNM, in nonsupplemented RPMI media for 24 h under the same conditions. This concentration was chosen based on a cytotoxicity assay carried out prior to genotoxicity evaluation (see Supplementary material). After the incubation period, total RNA was extracted and cDNA was synthesized according to kit instructions. The assay was carried out in triplicate, in three independent experiments. Data were analyzed using the RT2 Array Data Analysis package, version 3.5 at http://pcrdataanalysis.sabiosciences.com/pcr/arrayanalysis.php, using the housekeeping genes Actb (β-actin), B2 m (β−2 microglobulin), Gapdh (glyceraldehyde-3-phosphate dehydrogenase), Gusb (β-glucuronidase), and Hsp90ab1 (heat shock protein 90 alpha) for normalization, according to the manufacturer’s instructions. The ΔΔCT fold change values for each gene in the control (vehicle-treated) and treatment (2.0 μM CNM) groups was calculated. Student’s t-test was used to determine statistical differences (p < 0.05) in genes with fold change higher than 2.0.

3. Results and discussion

Drug likeness and ADME/Tox properties were predicted using the ACD/Percepta™ and MetaDrug™ software, as shown in Table 1. Thus, the physicochemical properties were estimated in order to evaluate the drug likeness of CNM based on Lipinski’s “rule of five” (Lipinski et al., 2001). This rule states that a desirable oral absorption and permeation of a drug candidate are expected when the log P (lipophilicity expressed as a ratio of octanol/water solubility) is ≤5; the number of hydrogen bond acceptors ≤10; number of hydrogen bond donors ≤5, number of rotatable bonds ≤10 and molecular weight ≤500 g/mol. When the drug candidate fails one of these criteria, the oral bioavailability is likely to be impaired. Since CNM meets only 2 of these requirements, the compound is predicted to have poor oral bioavailability. That prediction was confirmed by the software estimate of the oral absorption after administration of 10 mg of the compound, which predicted only 4.84% absorption. The probability of low bioavailability for CNM can be explained by its high log P, number of rotatable bonds and molecular weight. First, the log P observed for CMN (7.31) suggests a slight preference of the compound for lipophilic environments, what could lessen its passage to hydrophilic compartments such as blood (Lipinski et al., 2001). The number of rotatable bonds, which reflects the molecule flexibility and has been correlated to low bioavailability of drug candidates (Wenlock et al., 2003; Veber et al., 2002), also is a property which can impair CNM oral use. Likewise, CNM molecular weight (552 g/mol) does not meet the requirements for a good oral bioavailability. According to (Lipinski et al., 2001), drug leads with molecular weight greater than 500 g/mol are more likely to show unsatisfactory oral availability due to their poorer membrane penetration.

Table 1.

Drug likeness and theoretical ADME/Tox properties of CNM acquired on ACD/Percepta™ and MetaDrug™ database softwares.

Parameter Value Acceptable valuesa Interpretation
Lipinski’s rule: Failure
 log P 7.31 ≤5.0 Failure
 No. of Hydrogen Bond Acceptors 7 ≤10 Good
 No. of Hydrogen Bond Donors 2 ≤5 Good
 No. of Rotatable Bonds 11 ≤10 Failure
 Molecular Weight (g/mol) 552 ≤500 Failure
Oral bioavailability of 10 mg (%) 4.84 ≥50 Poor
Plasmatic protein binding (%) 99.6 10 ≤ X ≤ 90 Failure
log BB −0.09 ≥−0.5 Good
CYP2D6 substrate (probability)b 0.02 ≥0.5 Poor affinity
CYP2D6 inhibition (probability)b 0.03 ≥0.5 Poor affinity
CYP3A4 substrate (probability)b 0.45 ≥0.5 Poor affinity
CYP3A4 inhibition (probability)b 0.42 ≥0.5 Poor affinity
Probability of positive Ames test 0.20 ≤0.5 Positive
Probability of hepatotoxicity 0.88 ≤0.9 Moderate
Probability of nephrotoxicity 0.98 0.5 ≤ X ≤ 0.9 High
Probability of skin irritation 0.54 0.5 ≤ X ≤ 0.8 Moderate
Probability of eye irritation 0.38 0.5 ≤ X ≤ 0.8 Low
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a

Interpretation based on reference values provided by the software, obtained from literature (Peach et al., 2012; Ekins et al., 2005, 2006b).

b

Data acquired using MetaDrug software.

The plasma protein binding of CNM was predicted to be up to 99.6%, which could imply a (desirable or not) prolonged effect on the organism. On the other hand, CNM showed predicted blood brain barrier penetration, shown by the logarithm of the blood to brain concentration ratio (log BB) of −0.09, higher than the stated reference value of −0.5. This supports use of CNM that might require transport across the brain/blood barrier, such as in mental diseases or meningitis (Bartzatt, 2011). On the other hand, this could be undesirable for other indications. Although these results do not predict that CNM is a promising drug, they indicate that designed analogues might meet the physicochemical requirements and enhance the biological activities of CNM. Although out of the scope of this study, it is important to highlight that the synthesis and biological evaluation of structure-related analogues must be conducted aiming to provide chemical compounds with satisfactory ADME/Tox parameters as well as desirable pharmacological activity. In addition, it has been recorded that some classes of successful drugs in the market, particularly natural products, do not fully satisfy Lipinski’s rule of five, indicating that is not mandatory for this purpose (Lipinski et al., 2001).

Preliminary biotransformation parameters were also predicted (Table 1). We calculated the probability that CNM might be a substrate or inhibit two important members of the cytochrome P450 family. According to the prediction, CNM showed likely poor affinity for both CYP2D6 and CYP3A4 isoforms as substrate or inhibitor. CYP2D6 is one of the most studied members of the CYP complex and is responsible for the metabolism of lipophilic drugs such as antidepressants, antipsychotics, antiarrhythmic, beta-blockers and opioids, representing 25% of the current drugs in the market (Teh and Bertilsson, 2012). CYP3A4 is the most abundant P450 member in the liver and small intestine and is recognized as one of the most important responsible for the biotransformation of drugs and xenobiotics (Basheer and Kerem, 2015). Take into account these facts, our data give important information on how likely CNM might be to interact with other class of drugs, such as selective serotonin reuptake inhibitors, which are potent at CYP2D6 (Caraci et al., 2011), as well as ketoconazole and erythromycin, well known CYP3A4 inhibitors (Guengerich, 2008). Since CYP2D6 and 3A4 are abundant isoforms, these data suggest that CNM probably utilizes another biotransformation pathway, and consequently has low liability for metabolic interaction with most available drugs, such as those listed above.

Once the basic pharmacokinetic parameters of CNM were evaluated, its possible toxicological profile could be explored. As shown in Table 1, the indication of a likely positive Ames test indicates potential mutagenicity of CNM. This mutagenicity assay proposed by Ames et al. (1973) is based on the ability of a given compound to lead to a mutation on histidine-dependent strain of Salmonella and has been used to evaluate the genotoxic potential of herbal medicine products (Kelber et al., 2014). Likewise, the possible toxic properties of CNM on organs and tissues were evaluated (Table 1). Despite the predicted liver and kidney toxicity, a possible moderate hepatotoxicity and high risk on nephrotoxicity were predicted. On the other hand, the skin irritation was predicted to be moderate while the probability of eye irritation was defined as low. In summary, all these predicted pharmacokinetic data suggest that the oral use of CNM must be carefully studied while external applications, such as topical applications may represent a lesser risk. Of course, in vitro and in vivo studies must be carried out in order to confirm the predictions.

As outlined above, mutagenic potential was predicted for CNM. In order to characterize the possible mutagenic mechanisms at the molecular level, we carried out an analysis of the expression profile of 84 genes included in the DNA Damage Signaling Pathway PCR Array (Fig. 2). A detailed analysis of the set of data indicated that only six genes – Abl, Ddit3, Gadd45a, Gadd45 g, Mlh3 and Ppp1r15a (about 7% from 84 genes) were significantly upregulated (p < 0.05) (Table 2). Interestingly, none of the other 78 investigated genes was significantly down-regulated after treatment with CNM, when compared to vehicle control (p > 0.05). Among the genes cited in Table 2, it is important to highlight that their upregulation is interpreted as a consequence of drug induced DNA damage. For instance, both Abl and Mlh family members have their products over expressed in cases of drug exposure, such as cisplatin (Nehmé et al., 1997; Vaisman et al., 1998). These gene products are present in a wide range of signaling pathways and are reported as important mismatch base repair operators (Stojic et al., 2004). Similarly, we observed Ddit3 gene upregulation. This gene encodes a nuclear transcription factor related to DNA damage signaling and stress, leading to cell cycle arrest and apoptosis (Jauhiainen et al., 2012). We also observed a significant upregulation of two member of an important gene family, Gadd45. These highly conserved genes encode proteins which are implicated in an extensive variety of signaling processes, including growth regulation and apoptosis (Salvador et al., 2013). Their upregulation has been reported after UV exposure and other types of DNA-damage induction (Fornace et al., 1988). Last, the treatment with CNM caused an upregulation of the Ppp1r15 gene, also known as Gadd34. Overexpression of Ppp1r15 protein has been related to ionizing radiation and DNA damage by drugs such as methyl methanesulfonate, leading to apoptosis following the exposure, which suggests an inhibitory effect on cellular growth through complementary pathways (Hollander et al., 1997). Thus, this study of the genotoxicity of CNM implicates likely molecular mechanisms underlying its mutagenicity. By exploring the whole data set it is possible to suggest that an important part of the CNM toxicity is due to its potential to lead to apoptosis following treatment. Although this assay has limitations, such as the inability to observe the effect of the compound on gene expression in the whole organism rather than a single cell line, the increased costs and limited number of genes per analysis, it yields valuable and reliable information that can be used as a preliminary guide and base for future evaluations.

Fig. 2.

Fig. 2.

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Gene expression profile of murine macrophages treated with 2.0 μM CNM. (A) Heat map of the fold changes between the CNM-treated cells compared to controls. Red colored squares represent upregulated genes, while green color indicates down-regulated genes. (B) Full layout of the 84 genes in the RT2 Profiler™ PCR Array Mouse DNA Damage Signaling Pathway.

Table 2.

Fold change (CNM/control) and p value of differentially expressed genes in the PCR array assay.

Gene Description Fold change p valuea
Abl1 C-abl oncogene 1, non-receptor tyrosine kinase 4.36 0.000707
Ddit3 DNA-damage inducible transcript 3 11.32 0.000006
Gadd45a Growth arrest and DNA-damage-inducible 45 alpha 4.14 0.000091
Gadd45g Growth arrest and DNA-damage-inducible 45 gamma 4.09 0.026577
Mlh3 MutL homolog 3 (E. coli) 2.28 0.013522
Ppp1r15a Protein phosphatase 1, regulatory (inhibitor) subunit 15A 2.84 0.025986
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a

Only p values < 0.05 selected; n = 3; Student’s t-test.

Taken together, the ADME/Tox predictions and genotoxic results show that the proposed methodologies can provide powerful information at this early stage of investigation of a recently described drug from a natural source. Notably, the predictions and data were acquired rapidly and at a relatively low cost when compared to traditional methodologies for ADME/Tox and genotoxic profile assessment. The logical sequence of methodologies proposed herein as well as the rational analysis of the data can be applied to high-throughput screening selected hits, in order to optimize the chance of generating better drug leads.

4. Conclusion

Overall, the predicted ADME/Tox predictions showed that CNM may be compatible with external application, especially topical use, while possible low oral bioavailability and expected organ toxicity argue against systemic applications. In addition, the genotoxicity prediction was supported by the upregulation of apoptosis-related genes following treatment of macrophages with CNM. The combined in silico and in vitro analysis appears to be a valuable alternative to determine the basic ADME/Tox profiles of unknown or poorly studied compounds. This may be used to guide choices of further pre-clinical studies of a given molecule.

Supplementary Material

Supplemental figures table

HIGHLIGHTS.

  • A poorly studied 4-phenyl coumarin isolated from Melipona scutellaris geopropolis.

  • Combined in silico and in vitro approaches for preliminary evaluation of drug leads.

  • In silico prediction of pharmacokinetic/toxicology parameters.

  • In vitro assessment of genotoxicity using PCR array.

Acknowledgments

The authors are grateful to Mr. José Emídio Borges de Souza for providing the geopropolis samples. We also thank Dr. Eduardo Paganni from LNBio/CNPEM for the support on the in silico analysis as well as S. Tarasov and M. Dyba (Biophysics Resource Core, Structural Biophysics Laboratory, CCR/NCI) and H. Bokesch (MTL/NCI) for assistance with high resolution mass spectrometry.

Financial support

This research was supported by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research and by FAPESP (#2011/23635-6 and #2012/22002-2).

Abbreviations:

ADME/Tox

absorption, distribution, metabolism, excretion and toxicity

CNM

cinnamoyloxy-mammeisin

HPLC

high performance liquid chromatography

HREIMS

high-resolution electron-spray ionization mass spectrometry

MeOH

methanol

NMR

nuclear magnetic resonance

QSAR

quantitative structure-activity relationship

RI

reliability index

TP

Tanimoto index

RPMI

Roswell Park Memorial Institute medium

FBS

fetal bovine serum

Footnotes

Conflict of interest

The authors declare no conflict of interest.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.toxlet.2016.10.010.

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Supplementary Materials

Supplemental figures table
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