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Computational discovery of novel anthelmintic natural compounds from Agave Brittoniana trel. Spp. Brachypus
Yeniel González-Castañeda,1 Yovani Marrero-Ponce,1-3*Jose O. Guerra,4 Yunaimy Echevarría-Díaz,1,2 Noel Pérez,5 Facundo Pérez-Giménez,3 Ana M. Simonet,6 Francisco A. Macías,6 Clara M. Nogueiras,7 Ervelio Olazabal,8 and Hector Serrano.8
1Universidad San Francisco de Quito, Grupo de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA), Av. Interoceánica Km 12 1/2 y Av. Florencia, 17-1200-841 Quito, Ecuador.
2Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja California 22860, Mexico.
3Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia, Spain.
4Chemistry Department, Faculty of Chemistry-Pharmacy. Universidad Central “Marta Abreu” de Las Villas, Santa Clara, 54830, Villa Clara, Cuba.
5Colegio de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito (USFQ), Quito, Ecuador.
6Grupo de Alelopatía, Departamento de Química Orgánica, Facultad de Ciencias, Universidad de Cádiz, C/República Saharaui, s/n, 11510 Puerto Real, Cádiz, España.
7Departamento de Química Orgánica, Facultad de Química, Universidad de La Habana, C/Zapata s/n entre G y Carlitos Aguirre, Vedado, Plaza de la Revolución, 10400, Ciudad de La Habana, Cuba.
8Chemical Bioactive Center. Universidad Central “Marta Abreu” de Las Villas, Santa Clara, 54830, Villa Clara, Cuba.
*Corresponding author. [email protected] or [email protected]; Tel.: +593-2-297-1700 (ext. 4021).
Available from: http://dx.doi.org/10.21931/RB/2022.07.04.53
Helminth infections are a medical problem in the world nowadays. This report used bond-based 2D quadratic indices, a bond-level QuBiLs-MAS molecular descriptor family, and Linear Discriminant Analysis (LDA) to obtain a quantitative linear model that discriminates between anthelmintic and non-anthelmintic drug-like organic-compounds. The model obtained correctly classified 87.46% and 81.82% of the training and external data sets, respectively. The developed model was used in a virtual screening to predict the biological activity of all chemicals (19) previously obtained and chemically characterized by some authors of this report from Agave brittoniana Trel. spp. Brachypus. The model identified several metabolites (12) as possible anthelmintics, and a group of 5 novel natural products was tested in an in vitro assay against Fasciola hepatica (100% effectivity at 500 µg/mL). Finally, the two best hits were evaluated in vivo in bald/c mice and the same helminth parasite using a 25 mg/kg dose. Compound 8 (Karatavinoside A) showed an efficacy of 92.2% in vivo. It is important to remark that this natural compound exhibits similar-to-superior activity as triclabendazole, the best human fasciolicide available in the market against Fasciola hepatica, resulting in a novel lead scaffold with anti-helminthic activity.
Keywords: TOMOCOMD-CARDD Software; QuBiLs-MAS, nonstochastic and stochastic bond-based quadratic indices; LDA-based QSAR model; Computational Screening, Anthelmintic Agent; Agave brittoniana Trel. spp. Brachypus, Fasciola hepatica.
Helminths remain among the most common chronic infections, with more than one-third of the world’s population infected at any time.1 Currently, the high cost and toxicity of anthelmintics as well as the emergence of resistant strains of pathogenic helminths, have stimulated the desire to search for additional chemotherapeutic agents allowing a more efficient control of these parasites.2-4 A practical solution to this problem is to develop effective drugs from less expensive and more available raw materials.5 Natural products (NP) can be one of these materials for various reasons: 1) They inspired most of the active ingredients in medicines, 2) NP exhibit enormous structural diversity, 3) NP are the result of centuries of evolutionary pressure to create biologically active molecules, 4) the structural similarity of protein targets across many species, and so on. 5) It is extensively known that NP share more similar than synthetic compounds to the ‘chemical space’ of drug molecules.6-18 Unfortunately, only a small proportion of that diversity has been extensively explored for its pharmacological potential so far.19-21
Until now, the search for new anti-helminthic compounds from natural origin has generally been based on traditional trial-and-error methods.5,22 Unfortunately, these methods are highly inefficient and expensive.9,23 For this reason, new technologies have emerged to replace these old “hand-crafted” approaches for synthesis and testing new chemical entities.12,24-26 Virtual screening is an example of these modern approaches. Specifically, Quantitative Structure-Activity Relationships (QSAR) predictive models have been extensively used to filter large databases of compounds to identify new bioactive chemicals.27-34 Compared to other areas of pharmaceutical research; however, the screening of NPs has suffered from a lack of data in an appropriate format. Such information can significantly impact virtual screening, where new natural agents would be identified as potential therapeutic anthelmintics.
On the other hand, some authors of this report used an in-house computational approach to discover new anthelmintic synthetic compounds with rather good results35-37. A similar approach has been used to find new tyrosinase inhibitors from natural origin.38,39 However, no scientific report about discovering NPs with an anti-helminthic activity using an analogous computational strategy has been published.
This report presents the creation/validation of the QSAR model able to identify potential anthelmintic compounds. Next, we used this model in the virtual screening of NPs previously obtained and chemically characterized from Agave brittoniana Trel. spp. Brachypus. Finally, the identification/selection of the most promising anti-helminthic NPs for in vitro and in vivo experimental evaluation and the results of these evaluations are presented.
MATERIALS AND METHODS
Materials. Compounds 1-5 were derived from previous studies made with Agave brittoniana Trel. spp. Brachypus.63 The rest of the chemicals were obtained using a similar approach described by the same research team.63 The extraction and purification of all compounds with a purity higher than 99% were carried out employing previously described methods.63 To obtain the initial dissolutions, each product dissolved in water at a concentration of 10 mg/mL (1%). The insoluble products were first dissolved in dimetylsulphoxide (DMSO) so that the concentrations of this product in the final solution did not exceed 1%. The necessary dilutions of each product to make possible the biological evaluation was obtained starting from the initial solutions. In addition, a solution of TCB was utilized as reference drug.
Animals. Healthy balb/c mice of both sexes (body weight: 0.018±0.001 Kg) and food were purchased from the National Center for Laboratory Animal Production (CENPALAB, Havana, Cuba). Quarantine, labeling, climatization and good maintenance conditions of animals were strictly obeyed.
General Experimental Procedures. To measure the chemical effectiveness against F. hepatica, an experimental technique reported in the literature was selected for biological material processing and F. hepatica egg extraction.66 Mitterpak et al.’s technique for the host (Lymnaea cubensis) invasion was carried out.67 Afterwards, we followed the steps reported by Olazábal et al.68 to obtain the metacercariae. Metacercariae were conserved in the cold until the in vivo experiment.66
Biological Experiments. The anthelmintic activity of the compounds was evaluated, first, against F. hepatica in an in vitro assay using an earlier described procedure and second, against metacercariae of the same pathogen in an in vivo experiment, applying another well-established procedure.
Several treatment groups with ten mice per group were created. One group (infected control group) was treated with Miglyol 810N (administration vehicle). The second group was neither infested nor treated. The remaining groups were treated with new chemicals. All mice received the new compounds through an oral route. Mouse invasion with metacercariae of F. hepatica, 2 weeks old, 14 days before drug administration, was carried out by Corba et al.’s method.69 The effectiveness was evaluated based on the following:
1) determination of the E% index. This is a quantitative indicator of effectiveness introduced by Steward70 and defined as E% = [(XC–XT)/XC] × 100.71 Here, E% is the percentage of effectiveness, XC is the average amount of Fasciola in the control group, and XT is the average amount of Fasciola in the treated group. Effectiveness was measured based on the elimination or not of F. hepatica, in its juvenile stage, as shown by laboratory diagnostics, using the helminthological necropsy on day 7 after the inoculated treatment.69
2) Determination of the hepatic index,72 by mean of the formula A = (B/C) × 100. In this case, A = hepatic index, B = liver weight and C = body weight.
3) Degrees of lesions of the liver.69
4) Spleen relative weight.73
5) Intensity of invasion making use of the formula I = A/B, where A = total amount of parasites, B = total amount of positives.
6) Extension of invasion by use of the formula %E.I = [T(t)/T(a)] × 100, where %E.I is the percent of invasion extensity, T(t) = number of total positives, and T(a) = total of infected animals.74
7) Gain of weight (final weight) (initial weight).
From these different effectiveness indexes,72-74 the E% index was selected.
In the present report, we used a defined mathematical algorithm, which is characterized in this case by bond-based QuBiLs-MAS (acronym for Quadratic, Bilinear and N-Linear mapS based on graph–theoretic electronic-density Matrices and Atomic weightingS) MDs family (bond-level nonstochastic quadratic indices) to encode the chemical information in numbers.50-52 The CARDD extension of the TOMOCOMD approach has been previously successfully used to discover new bioactive molecular entities.35,36,38,44-49 The general principles of these indices and the main steps for the application of the QuBiLS-MAS50 software (http://tomocomd.com/software/qubils-mas) in QSAR/QSPR for drug design have been described in detail elsewhere.35,36,38,44-49
To find the classification function that discriminates between active and inactive compounds, we select the LDA because it is one of the most broadly used and straightforward techniques to obtain QSAR equations.35,36,48,49,75-85 It was carried out with the STATISTICA software.53 Forward-stepwise and best subset search procedures were fixed as the strategy for variable selection. The best model was selected considering the principle of parsimony (Occam’s razor). The considered tolerance parameter was the default value for minimum acceptable tolerance, which is 0.01. The quality of the model was determined by examining Wilks’ λ parameter (U statistic), the square Mahalanobis distance (D2), the Fisher ratio (F), and the corresponding p level [p(F)] as well as the percentage of good classification (accuracy) in the training and test sets (see Schemes 1 and 2). The classification of cases was performed by means of the posterior classification probabilities where one compound can then be classified as active if ΔP% > 0, being ΔP% = [P(Active) - P(Inactive)] >100, or as inactive otherwise. P(Active) and P(Inactive) are the probabilities with which the equation classifies a compound as active or inactive, respectively. On the other hand, the probability density approach implemented in the Ambit Disclosure software was used to evaluate the applicability domain of the model developed.60
RESULTS AND DISCUSSION
In silico study and virtual screening
Developing and validating linear QSAR models
To obtain a mathematical relationship between chemical structures and biological activity, the chemical information contained in many compounds must be statistically processed. Therefore, we build a data set containing 21240-43 and 30540,41 inactive compounds from the literature. It was build including 517 (active + inactive) compounds and was randomly divided into two subgroups: a set of 352 compounds (138 active and 214 inactive) that was used as the training set for developing the classification model and a second set of 165 compounds (74 active and 91 inactive) that was used as a test set for testing the predictive power of the model developed (see figure 1).
Figure 1. Schematic representation of the process used to design training and test sets.
Each structure was parameterized by using one TOMOCOMD-CARDD35,36,38,44-49 molecular descriptor (MDs) family, named bond-based nonstochastic 2D quadratic indices (QuBiLs-MAS Software)50-52 (see the experimental section for more details). Linear discriminant analysis (LDA), implemented on the STATISTICA software, was used as the statistical technique for model building.53 The best classification model obtained is given below, together with the LDA-statistical parameters:
where, N is the number of compounds, λ is the Wilks’ statistic, D2 is the squared Mahalanobis distance and F is the Fisher ratio.
The Wilks’ parameter is equal to the proportion of the total variance in the discriminant scores not explained by differences among the groups. Smaller values of Wilks’ lambda indicate the greater discriminatory ability of the function. Its statistic parameter can take values in the range of 0 (perfect discrimination) to 1 (no discrimination).54 That is, Wilks’ lambda is a direct measure of the proportion of variance in the combination of dependent variables unaccounted for by the independent variable (the grouping variable). Suppose a large proportion of the variance is accounted for by the independent variable. In that case, it suggests an effect from the grouping variable and that the groups (active and inactive) have different mean values. The Mahalanobis distance is a statistical technique that can be used to measure how distant a point is from the centre of a multivariate normal distribution, and its parameter indicates the separation between the respective groups.55 It shows whether the model has an appropriate discriminatory power for differentiating between the two respective groups. The classification of cases was carried out by means of the posterior classification probabilities. Using the Mahalanobis distances to do the classification, we can now derive probabilities. The probability that a case belongs to a particular class is basically proportional to the Mahalanobis distance from that group centroid. In summary, the posterior probability is the probability, based on our knowledge of the values of other variables, that the respective case belongs to a particular group.
This equation can correctly classify 87.46% (307/352) of the compounds in the training set and showed values of the Matthews correlation coefficients of 0.74 on it. More important, the model achieves a balanced classification accuracy in each group.
The results of the most relevant statistical parameters for this model are presented in Table 1, and the classification of compounds in the training set using Eq. 1 is presented in Table 2.
Table 1. Prediction Performances and Statistical Parameters for QSAR Models in the Training and Test Sets.
Table 2. Results of the Classification of Compounds in the Training and Test Set using QSAR Models.
Once a model is trained, its validation is another crucial aspect in this kind of analysis which can be performed by internal and external validation techniques (see Scheme 2).56,57 Here, a leave-many-out (LMO) cross-validation technique was carried out where groups of 176, 117, 70, 35, and 17 compounds of the training data (352 chemicals) were taken like cancellation groups and at each step. Then, the newly trained model was used to predict the left-out compounds. The results of this analysis are shown in Table 3, and the model’s parameters and predictions are rather stable when a perturbation is applied to the training set. This proofs that our model is robust.
In addition, to check the possibility of random correlations, the Y-randomization test (Y-scrambling) was performed by calculating the quality of the model randomly modifying the sequence of the response vector y (binary response: active or inactive) of the 5%, 10%, 20%, 30% y 40% of the compounds in the training set and recalculating the statistical parameters of the obtained models.57 The final conclusions of this test are present in Figure 2, indicating that the achieved level of random correlation is significantly lower than the original regression, leading to the conclusion that the models are not random.
Figure 2. Chemical Structures of Compounds Evaluating in the in silico Experiment from Agave brittoniana Trel. spp. Brachypus.
Table 3. Results of the Leave-Many-Out (LMO) Cross-Validation Analysis.
A more strict performance evaluation of a model is provided by an external validation where the model predictively is a challenge by compounds (external test set) that were not used in the model training (see Figure 3).57 Therefore, the equation obtained was evaluated in the test set (external prediction), showing accuracies of 81.82 % (135/165) and values of the Matthews correlation coefficients of 0.64. In addition to the external validation, the results of the statistical parameters described in Table 1 show that our model is not only robust but also predictive; therefore, it can be used in ligand-based virtual screening. The classification of both compounds in the external prediction set are depicted in Table 2.
Figure 3. General overview of the computational procedure.
Figure 4. Behavior of the Percentage of Good Classification in the Y-scrambling Analysis.
Finally, to define the applicability domain57 of Eq 1, a city-block distance-based approach58,59 implemented in the Ambit program60 was used. The model's applicability domain was defined from the training set, and all compounds belonging to the external test series were inside it.
In silico identification of active compounds from natural products. Taking into consideration that NPs have inspired most of the active ingredients in medicines,10 in the last years a number of recent investigation was carried out to discover new active compounds from the natural origin using computational strategy.61 In our research, the developed model (Eq. 1) was used to filter an extensive database of NPs. All details of this database and other active (anthelmintics) NPs discovered by using our approach will be shown in the following reports.
Here, we only present the discovery of novel anthelmintic compounds from Agave brittoniana Trel. spp. Brachypus: a plant that grows like one of two endemic subspecies (ssp. Brachypus and ssp. Spirituana) of Agave brittoniana Trel. in the central region of Cuba.62 A group of nineteen compounds composed by 12 steroidal saponins, 6 steroidal sapogenins and 1 phytosterol (see Figure 4) that have been previously obtained and chemically characterized from this subspecies of Agave was evaluated in silico using the Eq. 1. These compounds were: agabrittonosides A–D,63 agabrittonosides E–K, karatavioside A,64 Diosgenin, Chlorogenin, Hecogenin, Tigogenin, Rockogenin, and β-Sitosterol.
As result of this virtual screening, twelve compounds were identified by the model as potential anti-helminthic hits (see Table 4).
Table 4. Results of the in silico Classification and Percentages of Anthelmintic Activity of the Selected Compounds from Agave brittoniana Trel spp. Brachypus in vitro and in vivo Assayed.
However, it is generally acknowledged that QSARs are valid only within the same domain for which they were developed. Even if the models are developed on the same chemicals, the DA for new chemicals can differ from model to model, depending on the specific MDs. One of the present reports aims is to develop a model for predicting the anthelmintic activity of NP at the early stages of the drug discovery and development pipelines. Therefore, the chemicals selected in this study were only evaluated in vitro after plotting them into the model's previously obtained AD. In this analysis, all compounds were inside the DA of the model, ensuring excellent reliability for the prediction of this kind of lead used in the virtual screening. Moreover, all new leaders fall within the model's DA, so the predictions are reliable.
In vitro assay. Compounds were limited in availability; therefore, not all compounds were experimentally tested. Only three of the compounds detected in silico as potential anti-helminthic hits (Karataviosido A, Agabrittonósido A, Agabrittonósido B) and a mixture of Agabrittonósidos D and Agabrittonósidos E could be tested in vitro against F. hepatica at 5×10-1, 5×10-2, 5×10-3, 5×10-4, 5×10-5 and 5×10-6 mg/mL. Triclabendazole (TCB) was included in this experiment as a reference drug because it is the one of choice in treating human fascioliasis.65 Besides, Yucagenin (predicted as inactive) was also included in determining the influence of the glycoside moiety in the anti-helminthic activity. The biological in vitro evaluation results can also be seen in Table 4.
The experimental results agreed with the virtual screening predictions. As predicted, Yucagenin is not active at any test concentrations. However, its glycoside derivative (8, 9) had a bioactivity profile as TCB. This first saponin (8) has a glycoside rest joined to the C-3 atom identical to compound 9, its structural difference in the opening of the ring F and the glycosidation in the C-26 atom. The responsible for the little activity of 10, can be this structural modification or the increase of polarity of this zone. The mixture of compounds 12 and 13 presented in vitro activity higher than that observed for TCB. Compounds 12 and 13 are very similar structurally; both have the diosgenin-like central scaffold, but in compound 13 one xylose unit in 12 is substituted by a rhamnose group. In addition, 12 have a hydroxyl moiety in C-2, which is the only difference from 9. The combination of these subtle changes notably increases the activity of 12 and 13 concerning 9.
In vivo assay. An in vivo experiment using Bald/c mice-like biological models was conducted to obtain more profound conclusions about the pharmacological activity of in vitro hits. In this case, we only include in this experiment the two more active and pure substances (8 and 9) at doses of 3 mg/Kg. Table 4 shows the results of this study, where compound 8 was more active (92.16 % of efficacy) than 9 (52.94 %). The in vivo efficacy of compound 8 was identical to that of the control TCB. It is important to emphasize that this experiment was performed with a reduced dose (3 mg/kg). For instance, the TCB (the best human fasciolicide on the market65) is only wholly effective at 10 mg/kg. In addition, the few injuries in the livers and low inflammation of the spleens observed during the postmortem examination are qualitative criteria that positively appraise the effect of the tested compounds.
Today virtual screening has become an essential tool in drug discovery protocols. Here, bond-level quadratic indices (QuBiLs-MAS software, http://tomocomd.com/software/qubils-mas) and LDA were used to obtain a QSAR model that discriminates anthelmintic from inactive ones. Virtual screening of several metabolites from Agave brittoniana Trel. spp. Brachypus was carried out to discover new lead scaffold anthelmintics, and experimental corroboration showed that Karatavinoside A (8) exhibits similar-to-superior activity as triclabendazole (fasciolicide reference drug), with 100% in vitro effectivity (at 500 µg/mL) against Fasciola hepatica and 92.2% in vivo efficacy (25 mg/kg). This natural compound has been identified as a promising starting point for the rational optimization/design of new chemical derivatives with more potent anthelmintic activity.
Program availability: The QuBiLS-MAS software (portable standalone) and the respective user manual are freely available online at http://tomocomd.com/software/qubils-mas50
Acknowledgments. One of the present authors (M-P. Y) thanks the program ‘Estades Temporals per a Investigators Convidats’ for a fellowship to work at Valencia University (2020). Y.M.-P. and Noel Pérez acknowledge the support from Collaboration Grant 2019–2020 (Project ID16897) and Med Grant 2019-2020 (Project ID16911).
Competing interests: The authors declare no conflict of interest.
Author Contributions Statement: YG-C, FP-G, JOG, YE-D, NP and YM-P proposed the computational applications, QSAR modeling and performed the statistical analysis as well as prepared the manuscript. AMS, JOG, FAM, and CMN worked in the chemical methods and prepared the manuscript. EO and HS worked in the Parasitology tests. YG-C, FP-G, YE-D, NP and YMP worked in the QSAR modeling and performed the statistical analysis. All authors read and approved the final manuscript.
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Received: January 25, 2022 / Accepted: October 22, 2022 / Published:15 November 2022
Citation: González-Castañeda Y, Marrero-PonceY, Guerra J O, Echevarría-Díaz Y, Pérez N, Pérez-Giménez F, Simonet A M, Macías F A, Nogueiras C M, Olazabal E, Serrano H. Computational discovery of novel anthelmintic natural compounds from Agave Brittoniana trel. Spp. Brachypus. Revis Bionatura 2022;7(4) 53. http://dx.doi.org/10.21931/RB/2022.07.04.53