Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates

Int. J. Mol. Sci. 2014, 15, 17035-17064; doi:10.3390/ijms150917035 International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Article Prediction of Multi-Target Networks of Neuroprotective Compounds with Entropy Indices and Synthesis, Assay, and Theoretical Study of New Asymmetric 1,2-Rasagiline Carbamates Francisco J. Romero Durán 1, Nerea Alonso 1, Olga Caamaño 1, Xerardo García-Mera 1,* Matilde Yañez 2, Francisco J. Prado-Prado 1,3 and Humberto González-Díaz 4,5,* 1 Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela (USC), Santiago de Compostela 15782, Spain; E-Mails: fcojavier.rom[email protected] (F.J.R.D.); [email protected] (N.A.); [email protected] (O.C.); [email protected] (F.J.P.-P.) 2 Department of Pharmacology, University of Santiago de Compostela (USC), Santiago de Compostela 15782, Spain; E-Mail: m[email protected] 3 Biomedical Sciences Department, Health Sciences Division, University of Quintana Roo (UQROO), Chetumal 77039, Mexico 4 Department of Organic Chemistry II, Faculty of Science and Technology, University of the Basque Country UPV/EHU, Leioa 48940, Spain 5 IKERBASQUE, Basque Foundation for Science, Bilbao 48011, Spain * Authors to whom correspondence should be addressed; E-Mails: xerardo.garcí[email protected] (X.G.-M.); humberto.gonzálezdí[email protected] (H.G.-D.); Tel.: +34-946-013-547 (H.G.-D.); Fax: +34-946-012-748 (H.G.-D.). Received: 11 March 2014; in revised form: 19 August 2014 / Accepted: 21 August 2014 / Published: 24 September 2014 Abstract: In a multi-target complex network, the links (Lij) represent the interactions between the drug (di) and the target (tj), characterized by different experimental measures (Ki, Km, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (cj). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%–90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular OPEN ACCESS Int. J. Mol. Sci. 2014, 15 17036 targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally. Keywords: CHEMBL; neuroprotective agents; rasagiline derivatives; asymmetric synthesis; multi-target drugs; molecular information measures; Shannon entropy; Markov chains; moving averages 1. Introduction Entropy measures are universal parameters useful to codify biologically relevant information in many systems. In the 1970’s Bonchev and Trinajstic et al. published works about the use of Shannon’s entropy to calculate a structural information parameter [1–4]. Kier published another seminar works on the use of Shannon’s entropy to encoding molecular structure in Cheminformatics studies in 1980 [4]. Many other authors used Shannon’s entropy parameters for the same purpose on small molecule structure [5–10]. Graham et al. [11–16] used entropy measures to study the information properties of organic molecules. Entropy information measures were used to describe proteins [17,18], DNA sequences [19], protein networks [20], and magnetic resonance outcomes [21]. The software MARCH-INSIDE (MI) uses the theory of Markov chains to calculate the parameters θk(G). These values are the Shannon entropies of order kth of a graph G. The θk(G) values are useful quantify information about the structure of molecular systems [22]. The graph G represents a complex molecular system as a network of nodes interconnected by links (Lij = 1) or not connected (Lij = 0). MI algorithm associates a Markov matrix to the graph G in order to define the probabilities of interactions (ties or relationships) between nodes. These entropy parameters θk(G) can be calculated for many types of systems (molecular or otherwise). We have studied small molecules, RNA secondary structures, protein sequences, viral surfaces, cerebral cortex networks, metabolic networks, host-parasite networks, world trading networks, social networks, etc. In molecules, we know the information about links Lij (covalent bonds, hydrogen bonds, spatial contacts, etc.) beyond any reasonable doubt. However, we can use the information (θk(G) values)of the system to predict interactions with other systems in a network of a higher-structural level. For instance, we use the θk(G) values of drugs and targets structure to predict drug–target interactions (links) in drug–target network. In other cases, linking patterns change, are not known, or we find contradictory information. This is the case of the existence of different relationships between nodes in biological webs or social networks. In these cases, we can use the θk(G) values of known networks to find models useful to predict links in new networks [23–25]. On the other hand, the discovery of new drugs for the treatment of neurodegenerative diseases such as Alzheimer’s, Parkison’s, and Huntington’s disease, Friedreich ataxia and others, is an important Int. J. Mol. Sci. 2014, 15 17037 goal of medicinal chemistry [26–29]. The genes causing hereditary forms of some of these diseases have been identified but the molecular mechanisms of the neuronal degeneration have not been totally understood yet [30]. This picture, and some disappointing results in clinical trials, makes interesting the prediction of drug candidates with computational techniques [31,32]. In order to design these computational models we need to process chemical information from public databases. These databases have accumulated immense datasets of experimental results of pharmacological trials for many compounds. For instance, CHEMBL [33,34] is one of the biggest with more than 11,420,000 activity data for >1,295,500 compounds, and 9844 targets. This huge amount of information offers a fertile field for the application of computational techniques [34,35]. The analysis of all this data is very complex due to the presence of multi-target, multi-output, and multi-scale information. Multi-target complication emerges due to the existence of compounds with multiple targets [36–38]. This led to the formation of complex networks of drug–target interactions. We can represent drug–target networks as a graph with two types of nodes drugs (di) and targets (tj) interconnected by links (Lij). Barabasi et al. [39], constructed a drug–target network based on Food and Drug Administration (FDA) drugs and proteins linked by drug–target binary associations. Csermely et al. [40], reviewed the use of networks, including drug–target networks, for drug discovery. Multi-output feature refers to the necessity of prediction of different experimental parameters (IC50, Ki, Km, etc.) to decided whether two nodes (drug and target) interact (Lij = 1) or not (Lij = 0). Multi-scaling refers to the different structural levels of the organization of matter. In this case, the input variables quantify molecular information (drugs structure) and macromolecular information (targets). They have to quantify also cellular (cellular targets) and organism information (specie that express the target). In these models we have a high number of assays carried out in very different conditions (cq) like time, concentrations, temperature, cellular targets, tissues, organisms, etc. In a recent work, we combined the θk(G) values calculated with MI and the idea of Moving Average (MA) operators with a similar purpose [41]. In time series analysis the MA operators are average values of characteristic of the system for different seasons. In fact, MA models became popular after the initial works of Box and Jenkins [42]. In time series analysis, MA models may combine other operators I = Integrated, AR = Autoregressive, N = Non-linear operators, or X = Exogenous effects. In this sense, others models have emerged combining different operators: ARMA, ARIMA, VARIMA, ARIMAX, NARMA, etc. In multi-output modeling, we calculate the MA operators as the average of the property of the system (molecular descriptors or others) for all drugs or targets with a specific response in one assay carry out under a sub-set of conditions (cj). Consequently, our MA operator is not acting over a time domain but over a sub-set of conditions of the pharmacological assays. The idea of application of MA operators to other domains different from time is gaining adepts due to its advantages. For instance, Botella-Rocamora et al. [43] developed a model for disease mapping using spatial Box–Jenkins operators with the form of MAs, to define dependence of the risk of a disease to occur. In our models, we use MA in relation with properties of nodes of networks (drugs, proteins, reactions, laws, neurons, etc.); which form links Lij(cq) in specific sub-set of conditions (cq). For this reason, we decided to call this strategy as ALMA (Assessing Links with Moving Averages) models. Speck-Planche and Cordeiro reported different multi-target or multi-output models using the same type of ALMA models [44–46]. Int. J. Mol. Sci. 2014, 15 17038 In the specific area of neurodegenerative diseases, almost all these datasets includes also large sub-sets of assays involving potential neuroprotective drugs, targets, as well as drug-target and/or target–target interactions. The database NeuroDNet has interactive tools to create interaction networks for twelve neurodegenerative diseases. According to Vasaikar et al. [47], it is the first of its kind, which enables the construction and analysis of neurodegenerative diseases through protein interaction networks, regulatory networks and Boolean networks. In the case of neuroprotective compounds, some authors have reported multi-target ALMA models. García et al. used topological descriptors for a large series of 3370 active/non-active compounds to fit a classification function that can predict links Lij (interactions) of heterogeneous series of GSK inhibitors compounds with different neurological targets relevant to Alzheimer’s disease and parasite species. Speck-Planche et al. [48], developed a multi-target model using a large and heterogeneous database of inhibitors against five proteins associated with Alzheimer’s disease. The model correctly classified more than 90% of active and inactive compounds in the treatment of Alzheimer’s disease on both, training and prediction series. Several guidelines are offered in other paper to show how the use of fragment-based descriptors can be determinant for the design of multi-target inhibitors of proteins associated with Alzheimer’s disease [49]. In a recent work, we used the method TOPS-MODE (TM) [50] to calculate the structural parameters of drugs. The model correctly classified 4393 out of 4915 total cases with Specificity (Sp), Accuracy (Ac), and Sensitivity (Sn), of 80%–98%. We also used the method TM to develop one ALMA [51] model useful for the prediction of neuroprotective drugs. This dataset includes Multi-output assay endpoints of 2217 compounds for at least one out of 338 assays, with 148 molecular or cellular targets, and 35 types of activity measures in 11 model organisms (including human). In a third work [52], we introduced another ALMA model for neurotoxicity/neuroprotective effects of drugs based on the method MI. First, we used MI to calculate molecular descriptors of the type of stochastic spectral moments of all compounds. Next, we found a model that classified correctly 2955/3548 total cases on training and validation series with Ac, Sn, and Sp > 80%. Each data point (>8000) contains the values of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. The model has shown excellent results also in computational simulations of high-throughput screening experiments, with Ac = 90.6% for 4671 positive cases. Both models are able to predict the links Lij(cq) between ith drugs and jth targets according to the assay aq. However, we do not carried out a formal construction and a comparison of the drug-target networks for the CHEMBL data in previous papers. In any case, despite the high versatility of entropy measures to codify structural information, there is no report of a multi-target model for drug–target interactions for compounds with neuroprotective/neurotoxic effect. In this work, we report the first multi-target, multi-output, and multi-scale ALMA model for CHEMBL data of neuroprotective/neurotoxic effect of drugs. Then, we construct and compare for the first time three Multi-output assay complex networks for these CHEMBL dataset using the two previous models and the model reported in this work. From there, we reported by the first time the synthesis, characterization, and experimental assays of a new series of rasagiline carbamate derivatives not reported in previous works. We carried out three different experimental tests: assay (1) in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Finally, we used the new entropy model to predict possible outcomes for these compounds in a high number of pharmacological tests not carried out experimentally. The results presented here show the high Int. J. Mol. Sci. 2014, 15 17039 potential of entropy parameters of chemical information for the design of neuroprotective drugs, the construction of complex bio-molecular networks, and the potential of ALMA models for multi-target, multi-output, and multi-scale modeling. 2. Results and Discussion 2.1. Development of New Model for Prediction of Drug–Target Networks 2.1.1. Model Training and Validation We report a model to predicting when the ith compound may present a high (Lij(cq) = 1) or not (Lij(cq) = 0) value of the experimental parameter used to characterize interaction with a molecular or cellular target involved in a neuroprotective/neurodegenerative process. The output Sij(cq) of our multi-output model depend on both chemical structure of the ith drug di and the set of conditions selected to perform the biological assay (cq) including the jth target, of course. In consonance, the ALMA model should predict different probabilities if we change the organisms (c1), the biological assays (c2), the molecular/cellular target (c3), or the standard experimental parameter measured (c4), for the same compound [53].The best ALMA-entropy model found in this work was:       005.0007.191372.02661 ·0017.0·0202.0 ·4349.0·1993.0)(·4039.01396.1 2 11 111    pRN to ascpcS c e i t i u i x ii lqij    (1) The statistical parameters for the above equation in training are: Number of cases used to train the model (N), Canonical Regression Coefficient (Rc), Chi-square (χ2), and p-level [54]. The probability cut-off for this Linear Discriminant Analysis (LDA) model is ip1(cq) > 0.5 ≥ Lij(cq) = 1. It means that the drug di predicted by the model, with probability p > 0.5, is expected to give a positive outcome in the qth assays carry out under the given set of conditions cq. This ALMA-entropy model presents excellent performance in both training and external validation series with Sn, Sp, and Ac > 80% (see Table 1). Values higher than 75% are acceptable for LDA-QSAR models, according to previous reports [55–59]. The first term in the equation, quantify both the quality of the input data p(cl) and the information θi5 about the structure of the drug (see material and methods and previous works [51]). We can expand the Box–Jenkins MA terms in the ALMA equation in order to clearly depict all the parameters involved:                    005.0007.191372.02661 ··001660.0 ··020189.0 ··434889.0 ··199322.0 ··403994.0139556.1 2 515 515 515 515 51       pRN ttpt oopo aapa ssps spcS c eee i ttt i uuu i xxx i i xqij       (2) Int. J. Mol. Sci. 2014, 15 17040 After inspection of this equation, we can see that the ALMA model can predict for the same compound different scores for different experimental parameters, targets, assays, or even different organisms. In Table 2 we illustrate the values of probability of drug–target interaction pij(cq) predicted with the previous model, for several examples of known drugs or new promising compounds. These are the probabilities with which the ith compound interact with the jth drug under the assay conditions cq. This is equivalent to pij(cq) > 0.5 ≥ Lij(cq)pred = 1. However, online supplementary material files contain a complete list with many examples of positive and control cases. Table 1. Results of Assessing Links with Moving Averages (ALMA) models for entropy measures vs. different spectral moments. Descriptor Sub-Set Stat. a % Groups Ci(mj)pred = 1 Ci(mj)pred = 0 Reference MI-Entropy Train Sp 79.0 Lij(Cq)obs = 1 1092 290 This work Sn 91.5 Lij(Cq)obs = 0 412 4438 Ac 88.7 Total CV Sp 81.3 Lij(Cq)obs = 1 379 87 Sn 92.6 Lij(Cq)obs = 0 119 1492 Ac 90.1 Total MI spectral moments Train Sp 84.6 Lij(Cq)obs = 1 1172 214 [52] Sn 82.4 Lij(Cq)obs = 0 224 1051 Ac 83.5 Total CV Sp 83.3 Lij(Cq)obs = 1 385 77 Sn 81.6 Lij(Cq)obs = 0 78 347 Ac 82.5 Total TM spectral moments Train Sp 81.3 Lij(Cq)obs = 1 1533 352 [51] Sn 98.0 Lij(Cq)obs = 0 36 1762 Ac 89.5 Total CV Sp 81.0 Lij(Cq)obs = 1 513 120 Sn 97.7 Lij(Cq)obs = 0 14 585 Ac 89.1 Total MI, MARCH-INSIDE; a Sensitivity = Sn = Positive Correct/Positive Total; Specificity = Sp = Negative Correct/Negative Total; Accuracy = Ac = Total Correct/Total; TM, TOPS-MODE. Table 2. Examples predicted with the model. Compound (i) pij(cq)Assay ID Measure (Units) Organism Target Protein Arecoline 0.94 796814 Efficiency (%) rno Muscarinic acetylcholine receptor Bipinnatin-A 1.00 751272 Inhibition (%) mmu Acetylcholine receptor protein β chain Carachol 0.99 796814 Efficiency (%) rno Muscarinic acetylcholine receptor Caulophylline 0.96 838016 EC50 (nM) hsa Neuronal acetylcholine receptor; α4/β2 Citalopram 0.99 740208 Ki (nM) mmu Dopamine transporter Condelphine 1.00 748943 −Log(IC50) (nM) rno Neuronal acetylcholine receptor protein α-7 subunit Int. J. Mol. Sci. 2014, 15 17041 Table 2. Cont. Compound (i) pij(cq)Assay ID Measure (Units) Organism Target Protein Delcorine 1.00 748943 −Log(IC50) (nM) rno Neuronal acetylcholine receptor protein α-7 subunit Delsoline 1.00 748943 −Log(IC50) (nM) rno Neuronal acetylcholine receptor protein α-7 subunit Desipramine 0.99 797692 −Log(IC50) (nM) rno Norepinephrine transporter Elatine 1.00 748943 −Log(IC50) (nM) rno Neuronal acetylcholine receptor protein α-7 subunit Emopamil 1.00 817225 −Log(IC50) (nM) rno Voltage-gated R-type calcium channel α-1E subunit Epibatidine 0.94 838016 EC50 (nM) hsa Neuronal acetylcholine receptor; α4/β2 Epibatidine 0.19 825420 Efficacy (%) hsa Neuronal acetylcholine receptor; α4/β2 Femoxetine 0.99 740206 Ki (nM) mmu Dopamine transporter Femoxetine 0.99 740207 Ki (nM) mmu Norepinephrine transporter Femoxetine 0.99 740208 Ki (nM) mmu Dopamine transporter Fisetin 0.05 1027709 %max (%) mmu HT22 cells Fluoxetine 0.99 740207 Ki (nM) mmu Norepinephrine transporter Fluoxetine 0.99 740208 Ki (nM) mmu Dopamine transporter Imipramine 0.99 740206 Ki (nM) mmu Dopamine transporter Imipramine 0.99 740207 Ki (nM) mmu Norepinephrine transporter Imipramine 0.99 740208 Ki (nM) mmu Dopamine transporter Inuline 1.00 748943 −Log(IC50) (nM) rno Neuronal acetylcholine receptor protein α-7 subunit Karacoline 1.00 748943 −Log(IC50) (nM) rno Neuronal acetylcholine receptor protein α-7 subunit L-Arginine 0.99 755144 Activity (nM) hsa Nitric-oxide synthase, brain L-NIL 0.59 752266 −Log(IC50) (nM) hsa Nitric-oxide synthase, brain L-NMMA 0.99 876477 −Log(IC50) (nM) hsa Nitric-oxide synthase, brain L-NNA 0.98 752385 −Log(IC50) (nM) hsa Nitric-oxide synthase, brain L-NNA 0.86 752276 Ki (nM) hsa Nitric-oxide synthase, brain LY-379268 0.99 714803 Activity (nM) hsa Metabotropic glutamate receptor 4 LY-379268 0.99 877752 Activity (nM) hsa Metabotropic glutamate receptor 2 LY-379268 0.99 718128 Activity (nM) hsa Metabotropic glutamate receptor 6 LY-389795 0.99 718128 Activity (nM) hsa Metabotropic glutamate receptor 6 LY-389795 0.98 715721 Activity (nM) hsa Metabotropic glutamate receptor 5 LY-389795 0.97 714446 Activity (nM) hsa Metabotropic glutamate receptor 3 Int. J. Mol. Sci. 2014, 15 17042 Table 2. Cont. Compound (i) pij(cq)Assay ID Measure (Units) Organism Target Protein Lycoctonine 1.00 748943 −Log(IC50) (nM) rno Neuronal acetylcholine receptor protein α-7 subunit M826 1.00 841780 Ki (nM) hsa Caspase-3 M827 1.00 841780 Ki (nM) hsa Caspase-3 Methyllycaconitine 1.00 750084 Ki (nM) rno Neuronal acetylcholine receptor protein α-10 subunit NBQX 0.99 641893 −Log(IC50) (nM) rno Glutamate receptor ionotropic, AMPA 2 NBQX 0.99 641893 −Log(IC50) (nM) rno Glutamate receptor ionotropic, AMPA 4 NBQX 0.99 641893 −Log(IC50) (nM) rno Glutamate receptor ionotropic, AMPA 3 NBQX 0.99 641893 −Log(IC50) (nM) mmu Glutamate receptor ionotropic, AMPA 1 Nipecotic acid 0.28 785010 −Log(IC50) (nM) rno GABA transporter 1 Nipecotic acid 0.28 785010 −Log(IC50) (nM) rno GABA transporter 2 Nipecotic acid 0.28 785010 −Log(IC50) (nM) rno GABA transporter 3 Nipecotic acid 0.28 785010 −Log(IC50) (nM) rno Betaine transporter NOHA 0.04 755137 NO formation (%) rno Nitric-oxide synthase, brain Norepinephrine 0.98 780755 Concentration (% dose·g−1) rno Nudicauline 1.00 748943 −Log(IC50) (nM) rno Neuronal acetylcholine receptor protein α-7 subunit Omega nitro-arginine 0.99 752258 Ki (nM) hsa Nitric-oxide synthase, brain Oxotremorine 0.84 798083 pD2 rno Muscarinic acetylcholine receptor M1 Paroxetine 1.00 740206 Ki (nM) mmu Dopamine transporter RedAm-Ethyl 0.33 840782 Selectivity hsa Nitric-oxide synthase, endothelial RedAm-Ethyl 0.28 840782 Selectivity hsa Nitric-oxide synthase, brain Resveratrol 0.99 1613870 EC50 (nM) hsa Nuclear factor NF-κB p105 subunit Resveratrol 0.99 1613870 EC50 (nM) hsa Nuclear factor NF-κB p65 subunit Stemofoline 1.00 936299 EC50 (nM) hvi Nicotinic acetylcholine receptor α1 subunit Thiocytisine 0.51 857972 Log Ki rno Neuronal acetylcholine receptor; α4/β2 rno, Rattus norvegicus (Rat); mmu, Mus musculus (Mouse); hsa, Homo sapiens (Human); and hvi, Heliothis virescens. The Table 2 shows predictions of the same drug in different sets of conditions of assay cq, including different targets, organisms, or assays. Therefore, we only have to substitute in the equation the value Int. J. Mol. Sci. 2014, 15 17043 of θi5 of the compound and the respective values p1(cq)·<θi5(cq)> for the MA operators of each condition. In the Table 3 we depict many examples of values of MA operators p1(cq)·<θi5(cq)> for different conditions. 2.1.2. Comparison with Other ALMA Models An interesting exercise is the comparison of the present model and the network predicted with outcomes obtained with other methods. Until the best of our knowledge, there are only two similar models. Both models make use of the spectral moments of a molecular matrix as input variables (Di) to quantify the molecular structure of drugs. The first model [51] applies spectral moments µk of order kth of the bond adjacency matrix (1B) calculated with the TM approach. The equation of this model is the following:         005.07.03683 198684.4·10·84.2·10·16.1 ·10·93.2·10·84.7·10·01.7 5 4 5 4 5 4 5 4 5 4      pRN to ascpcS c ii iii jqij   (3) The second model [52] employs as input the πik values of the Markov matrix (1Π) of atom–atom electron delocalization calculated with the software MI. In the TM method, we weighted the edges of the molecular graph with standard distances of chemical bonds whereas the MI algorithm employs atom standard electronegativities to weighting the nodes of molecular graph. The equation of the second model is:         005.0007.191372.02661 ·001660.0·020189.0 ·434889.0·199322.0·403994.0139556.1 2 15 555    pRN to ascpcS c e i t i u i x ii jqij    (4) In both cases, as well as in the present ALMA-entropy model, we used MA terms to quantify the deviations of the structure of one compound from sub-sets of compounds with a positive outcome in different conditions cq. The three methods showed excellent values of Ac, Sp, and Sn on both training and validation series (see Table 1). Apparently, the TM model shows better values of these parameters but we have to take into consideration the differences in the complexity of the data sets used to train and validate these models. The TM-spectral moment model is able to classify correctly 83%–82% of 4915 cases in total (on training and validation series respectively). The MI-spectral moment model is able to classify correctly 89%–92% of 3598 cases. Notably, the MI-entropy model is able to classify correctly 89%–92% of 8309 cases. Consequently, the statistics for the present model refer to a dataset with more than twice the number of data points present in previous models. Int. J. Mol. Sci. 2014, 15 17050 Table 5. Neuroprotective ability of the new 1,2-rasagiline derivatives. Compound Formula % Neuro-Protection % ANA a e.s.m. Glutamate b e.s.m. H2O2 c e.s.m. 2 0.0 2.8 0.0 6.5 −2.8 1.2 3 4.7 6.0 −0.2 1.6 −12.3 2.1 4 4.2 6.5 −8.1 4.9 −14.2 2.1 5 1.2 5.0 3.8 5.0 2.9 1.0 6 11.5 8.8 −4.0 5.5 −9.1 2.4 7 4.0 4.5 2.6 3.9 -6.1 1.1 8 −1.7 6.9 −5.2 5.9 −8.9 1.9 9 8.4 10.7 −5.2 2.3 −14.0 2.0 a % protection (comp 5 µM), in the Absence of Neurotoxic Agents (ANA); b % protection (comp 5 µM) against Glutamate 100 µM; c % protection (comp 5 µM) against H2O2 100 µM. Firstly, we studied the ability to induce a neuroprotective effect in the absence of any neurotoxic stimulation. Secondly, we studied the neuroprotective effect in the presence of glutamate, a compound that causes a pathological process, in which neurons are damaged leading to apoptosis when its receptors, such as the NMDA and AMPA, are over-activated. Lastly, the ability of the compounds synthesized to protect from damage by H2O2, that causes neuronal death by oxidative stress, was analyzed. The results obtained allow to deduce the existence of a moderate neuroprotective effect in the absence of any toxic stimulus, presenting the best results type 6 and 9 carbamate derivatives, with values of 11.5% and 8.4%, respectively, followed by the compound 3, 4, and 7 with values slightly above 4% (see Figure 3). Int. J. Mol. Sci. 2014, 15 17051 Figure 3. Results of the experimental assay of neuroprotective effect of the new compounds. 2.2.2. Using ALMA-Entropy Model to Predicting New Drugs in Other Assays We used the ALMA-entropy model to predicting the more probable results for all the new rasagiline derivatives synthesized in this work, in >500 assays not carried out experimentally. When the molecular descriptors (entropy indices) of the new rasagiline derivatives were introduced in our model, we obtained the probable interaction with different targets. The model predicts that most of them could interact with the subunits A and B of the 5-hidroxy-tryptamine type 3 receptors (5-HT3Rs), see Table 6. These results seem to be consistent with the literature, since the antagonists of 5-HT3Rs have been related to neuroprotective properties in vitro and in vivo [66]. In fact, this could be a potential mechanism of neuroprotection added to several described mechanisms for rasagiline derivatives [67,68]. Rasagiline is also known for promoting serotoninergic activity by other ways, which is a clinically relevant fact in certain circumstances [69]. All in one highlights the intricate relationships of these drugs with the 5-hidroxy-tryptamine (serotonine) system. Table 6. Some predictive results for interaction between compound 6 with 5HT3Rs and other targets. Si(cj) Meassure Assay ID Target ID Target a Neurotoxic Agent 2.097 pA2 617971 1899 5HT3aR ANA 2.097 pA2 617969 1899 5HT3aR ANA 2.097 pA2 617971 3895 5HT3bR ANA 2.097 pA2 617969 3895 5HT3bR ANA 1.78 Selectivity 848737 3568 bNOS H2O2 1.78 Selectivity 840777 3568 bNOS H2O2 1.78 Selectivity 755901 3568 bNOS H2O2 1.17 Activity (%) 866501 2586 nAChRβ-3 H2O2 0.42 pIC50 (nM) 710048 3772 mGluR1 Glu a nAChRβ-3 = neuronal acethyl-choline receptor β3, mGluR1 = metabotropic glutamate receptor type 1. Int. J. Mol. Sci. 2014, 15 17052 In any case, we need to analyze these results with caution. In our previous works [51,52], we predicted with new models and confirmed experimentally that some rasagiline derivatives (similar to the derivatives studied in this work) presented activity over glutamate receptors (GluRs) pathway. In the first of these works [51], we study experimental measures of neuroprotective capacity of new 1,3-rasagiline derivatives. All the compounds, except one of them, had a high protective activity against damage mediated by H2O2. The best one of all, a monopropargyl trans derivative, showed also a high neuroprotective action in all three type of assays. Our first model predicted for this compound high probability of activity in relationship with acetylcholine and GABA, in addition to GluRs. In coincidence, acetylcholine receptors (AChRs) have been associated with neuroprotective proprieties in several recent experimental works, and there are also reports of association of GABA and GluRs with neuroprotective ability [70,71]. Nuritova et al. [72], discussed a neuroprotective strategy involving retrograde release of glutamate. In our second work [52], we studied two types of substituent groups (propargyl groups attached to the nitrogen and a carbamate or esther group instead of hydroxyl). The compounds also presented two different chirality patterns but with 1,3 substitutions pattern. The compounds of this second series were active experimentally in the absence and presence of neurotoxic agents. The best compound of this second series, a dipropargyl derivative, was predicted to have brain nitric oxide synthase (bNOS) as the most probable target and certain probability of multi-target ligand. Again, bNOS was associated experimentally with neuroprotective action in several works [73,74]. The compounds studied this third work present similar substituent groups and stereochemistry but one 1,2 substitution pattern. Based on the previous results, we should expect a similar experimental activity and predictions. However, in the previous section we shown experimentally that the present set of compounds seems not to be very active over GluRs and the model predicts the higher scores of activity over 5-HT3Rs instead of the expected receptors. As we stated in the previous paragraph, 5-HT3Rs have been related to neuroprotective properties in vitro and in vivo [66]. A plausible hypothesis (pendent of further experimental confirmation) is the variation in receptor affinity (from GluRs to 5-HT3Rs pathway) due to the change from 1,3 to 1,2 substitution pattern. From our point of view, these correspondences between targets that our equations predict, and the references cited from the literature could indicate biological plausibility of our models. 3. Materials and Methods 3.1. Computational Methods 3.1.1. ALMA-Entropy Models ALMA models may be classified as a general type of model to assessing the links in different systems. They are adaptable to all molecular descriptors and/or graphs invariants or descriptors for complex networks. In general, we refer to a descriptor Dik of type kth of the ith system (compound or drug di in this case) represented by a matrix M. In fact, in this work we are going to compare the model based on entropy values θik of a Markov matrix 1Π with other ALMA models based on other invariants of the same matrix 1Π, or invariants of the bond adjacency matrix 1B. Consequently, we describe first the general equations of the model using a generic descriptor, or graph theoretical Int. J. Mol. Sci. 2014, 15 17053 invariant Dik, and later we give the specific equation for the entropy model based on θik values. The aim of this model is to link the scores Sij(cq) with the molecular descriptors Dik of a given compound di and the Box–Jenkins MA operators written in the form of deviation terms ΔDik(cq) = Dik – <Dik(cq)>. The model has the following general form:                            5 1 5 0 5 0 0 5 1 5 0 5 0 0 5 1 5 0 0 '')(' '')(' )('''''' q q k k q k i q k i qk k k k i lk q q k k q k i jk k k k i lk q q q k ijjk k k q k kqij cDcpDaDcpaa cDaDcpaa cSaSaacS (5) The output dependent variable is Sij(cq) = Sij(cl, c2, c3, c4, c5) = Sij(cl, aq, ot, tj, sx). The variable Sij(cq) is a numerical score of the biological activity of the ith drug (di) vs. the jth target measured in one assay carried out under the set of qth conditions cq. Our hypothesis is H0: we can calculate the output Si(cq) as a linear combination of scores. We have two types of scores. The first type are the scores 'Sik = 'ak·p(cl)·iDk that account for the quality of data p(cl) and for contributions of the kth molecular descriptors to the final activity score Sij(cq). In fact, we used the probability p(c1) = 1.0; 0.75; or 0.5 for data curated in CHEMBL database at levels of expert, intermediate, or auto-curation level, respectively. The second type are scores ''Sijk(cq>1) = ''ak·ΔDik(cj) for the contributions of deviations ΔDik(cq) = (Dik − <Dik(cq)>) of the descriptors of di from the average of those of active molecules Lij(cq) = 1 for different cq. In general, cj refers to different Multi-output assay conditions, e.g., targets, assays, cellular lines, organisms, organs, etc. In this sense, c0 = is the accuracy of the data for this assay, c1 = au is the assay per se, c2 = ot is the organism that express the target, c3 = tj is the jth cellular or molecular target, and c5 = sx is standard experimental measure of activity. Then, the parameter Dik and ΔDik(cq) are the input independent variables and Lij(cq) = 1 is the input dependent variable. Here, <Dik(cq)> is the average of the kth descriptors Dik of all ith compounds considered as active (Lij(cq) = 1) in an assay carry out under the set of conditions cq. The parameters ΔDik(cq) are similar to the MA used in time series analysis for Bob–Jenkins ARIMA models and others [42]. This type of MA model has been used before to solve different problems in Cheminformatics before. It means that, firstly, we sum the values of Dik for all the nj drugs with Lij(cq) = 1 in the assay carry out in the conditions cj. Next, we divide this sum by the number of compounds nj with this condition.       q ni i q k i q q k icD n cD 1 1 (6) Int. J. Mol. Sci. 2014, 15 17054 In this model, we used only one molecular descriptor θi5. This is the Shannon entropy of order k = 5 calculated with MI. We do not use low-order entropies k = 0, 1, 2, 3, and 4. Accordingly, the general equation is:                  5 1 555 0 5 1 55 50 5 1 55 50 '')(' '')(' )('''')('' q q q i qqk i lk q q q i jk i l q q qijjkliqij ccpacpaa cacpaa cSacSaacS i   (7) This type of moving average or deviation-like models was coined by us as the ALMA models, and has been used before to solve different problems [54,75–77]. In order to seek the model we used the technique Linear Discriminant Analysis (LDA) implemented in the software package STASTICA 6.0 [78]. The statistical parameters used to corroborate the model were: Number of cases in training (N), and overall values of Sp, Sn, and Ac [54]. 3.1.2. CHEMBL Dataset We downloaded from the public database CHEMBL a general data set composed of >8000 Multi-output assay endpoints (results of multiple assays) [33,34]. We assigned a value of the observed (obs) class variable Lij(cq)obs = 1 (active compound) or Lij(cq)obs = 0 (non-active compounds) to every ith drug biologically assayed in different conditions cj. The dataset used to train and validate the model includes N = 3548 statistical cases, formed by Nd = 3091 unique drugs which have been assayed each one in at least one out of 37 possible standard type measures determined in, at least, one out of 493 assays. Each assay involves, in turn, at least one out of 169 molecular or cellular targets expressed in the tissues of at least one out of 11 different organisms (including human). 3.2. Experimental Methods: Chemistry 3.2.1. Synthesis of 1,2-Rasagiline Derivatives Melting points are uncorrected and were determined in Reichert Kofler Thermopan (Reichert, Vienna, Austria) or in capillary tubes on a Büchi 510 apparatus (BÜCHI Labortechnik AG, Flawil, Switzerland). Infrared spectra were recorded on a JASCO FT/IR-4100 spectrophotometer (JASCO Analytical Instruments, Easton, PA, USA). The 1H-NMR spectra (300 MHz) and 13C-NMR spectra (75 MHz) were recorded in a Bruker AMX spectrometer (Bruker BioSpin Corporation, Fremont, CA, USA), using TMS as internal reference (chemical shifts in δ values, J. in Hz). EI Mass spectra were recorded on a HEWLETT-PACKARD 5988A spectrometer (Hewlett-Packard Company, Palo Alto, CA, USA). FABMS were obtained using MICROMASS AUTOSPEC mass spectrometer (WATERS, Milford, MA, USA) and ESIMS were determined on a BRUKER AMAZON ETD spectrometer (Bruker BioSpin Corporation). We performed microanalyses in a Perkin-Elmer 240B elemental analyzer (PerkinElmer, Waltham, MA, USA) by the Microanalysis Service of the University of Santiago de Compostela. The specific rotation was measured with a PERKIN-ELMER 241 polarimeter Int. J. Mol. Sci. 2014, 15 17055 (PerkinElmer), and it is expressed in (°) (dm−1) (g−1) (mL). Most of the reactions were monitored by TLC on pre-coated silica gel plates (Merck 60 F254, 0.25 mm, Merck KGaA, Darmstadt, Germany). Synthesized products were purified by flash column chromatography on silica gel (Merck 60, 230–240 mesh, Merck KGaA) and crystallized if necessary. Solvents were dried by distillation prior use. Compound (3): (1S,2R)-(+)-cis-1-(N-Propargylamino)-2-indanol (2) and (1S,2R)-(+)-cis-1-(N,N- dipropargylamino)-2-indanol. A mixture of 1 (0.20 g, 1.34 mmol), K2CO3 (0.18 g, 1.34 mmol) and MeCN (7 mL) was stirred at room temperature under argon for 5 min. A solution of propargyl bromide (0.3 mL, 2.7 mmol) dissolved in MeCN (2 mL) was added dropwise with stirring. After being stirred for 24 h, the solvent was evaporated and the residue was dissolved in EtOAc (10 mL). The organic layer was washed with NaOH 2N (3 × 10 mL) and dried (Na2SO4). The removal of excess of solvent to give a white solid, that was purified by flash column chromatography using hexane/EtOAc (3:1) as eluent to give, in first place 3 (170 mg, yield 56%) as a white solid and in second place 2 (90 mg, yield 36%) as a white solid. (+)-cis-2. M.p. 106–108 °C.[∝] º = +38° (25 °C, 0.25, CHCl3). IR ν = 3277, 2906, 1421, 1339, 1140, 1051, 731 cm−1. 1H NMR (300 MHz, CDCl3) δ = 7.32–7.22 (m, 4H, Harom), 4.51–4.47 (m, 1H, 2-H), 4.31–4.29 (m, 1H, 1-H), 3.69–3.52 (AB system , 1H, J = 17.2 Hz, CH2), 3.68–3.51 (AB system, 1H, J. = 17.2 Hz, CH2), 3.11–2.96 (m, 2H, 3α-H, 3β-H), 2.67 (br. s., 1H, D2O exch., OH), 2.31 (t, 1H, J. = 2.2 Hz, CH). 13C RMN (75 MHz, CDCl3) δ = 141.85 (C-3a), 141.05 (C-7a), 128.17, 126.79, 125.58 and 123.94 (CHarom), 82.27 (C≡CH), 71.90 (C-2), 70.87 (C≡CH), 64.78 (C-1), 39.59 (CH2), 37.16 (C-3). MS (EI): m/z (%): 186 (2) [M−1]+, 168 (5) [M+–H2O], 148 (100) [M+–propargyl], 130 (21), 115 (10), 103 (31), 77 (11). Anal. calcd. for C12H13NO (187.24): C 76.98, H 7.00, N 7.48; found C 76.63, H 7.12, N 7.36. (+)-cis-3. M.p. 106–109 °C. [∝] º = +72° (25 °C, 0.25, CHCl3). IR ν = 3279, 2894, 1339, 1244, 1137 cm−1. 1H NMR (300 MHz, CDCl3) δ = 7.52–7.50 (m, 1H, 7-H), 7.29–7.18 (m, 3H, 4-H, 5-H, 6-H), 4.52 (dd, 1H, J. = 13.2, 6.9 Hz, 2-H), 4.42–4.40 (m, 1H, 1-H), 3.74 (br. s., 1H, D2O exch., OH), 3.65–3.39 (AB system, 2H, J. = 17.1 Hz, CH2), 3.64–3.38 (AB system, 2H, J. = 17.1 Hz, CH2), 3.24–2.79 (part AB of an ABM system, 2H, JAB = 16.4 Hz, JAM =7.2 Hz, JBM = 6.1 Hz, 3α-H, 3β-H), 2.29 (t, 2H, J. = 2.3 Hz, 2 × CH). 13C RMN (75 MHz, CDCl3) δ = 141.47 (C-3a), 138.15 (C-7a), 128.68, 127.04, 126.65 and 125.46 (CHarom), 80.37 (2 × C≡CH), 72.87 (C-2), 71.39 (2 × C≡CH), 68.37 (C-1), 41.04 (2 × CH2), 40.31 (C-3). MS (EI): m/z (%): 226 (2) [M+1]+, 225 (5) [M+], 224 (4) [M−1]+, 208 (2) [M+–H2O], 186 (100) [(M−1)+–propargyl], 133 (32), 116 (35), 77 (29). Anal. calcd. for C15H15NO (225.29): C 79.97, H 6.71, N 6.22; found C 79.81, H 6.92, N 6.29. Compound (4): (1S,2R)-(−)-cis-1-(N,N-Dipropargylamino)-2-indanyl acetate. A mixture of 3 (0.08 g, 0.36 mmol), acetic anhydride (66 μL, 0.72 mmol), Et3N (100 μL, 0.72 mmol), DMAP (a catalytic amount) in MeCN (5 mL), under argon, was stirred at room temperature for 3 h. The solvent was removed and the residue was partitioned between EtOAc (10 mL) and H2O (10 mL), and the organic layer was washed with a saturated solution of NaCl (3 × 10 mL), dried (Na2SO4) and evaporated, to give 4 (as a white solid (76 mg, yield 80%). M.p. 52–53 °C. [∝] º = −70.6° (25 °C, 0.25, CHCl3). IR ν = 3239, 2890, 1729, 1210, 1035 cm−1. 1H NMR (300 MHz, CDCl3) δ = 7.49–7.46 (m, 1H, 7-H), 7.31–7.21 (m, 3H, 4-H, 5-H, 6-H), 5.68 (dt, 1H, J. = 5.4, 2.4 Hz, 2-H), 4.61 (d, 1H, J. = 5.4 Hz, 1-H), 3.77–3.63 (AB system, 2H, J. = 17.5 Hz, CH2), 3.76–3.62 (AB system, 2H, J. = 17.3 Hz, CH2), Int. J. Mol. Sci. 2014, 15 17056 3.17–2.93 (part AB of an ABM system, 2H, JAB = 17.2 Hz, JAM = 5.7 Hz, JBM = 2.5 Hz, 3α-H, 3β-H), 2.21 (t, 2H, J. = 2.4 Hz, 2 × CH), 2.02 (s, 3H, CH3). 13C NMR (75 MHz, CDCl3) δ = 170.40 (COCH3), 140.01 (C-3a), 139.48 (C-7a), 128.07, 126.97, 125.24 and 125.13 (CHarom), 81.21 (2 × C≡CH), 77.04 (C-2), 71.74 (2 × C≡CH), 68.94 (C-1), 39.99 (2 × CH2), 37.68 (C-3), 21.70 (CH3). MS (FAB): m/z (%): 269 (6) [M+2]+, 268 (26) [M+1]+, 225 (2) [M+–acetyl], 197 (18), 169 (12), 154 (88), 137 (100). Anal. calcd. for C17H17NO2 (267.32): C 76.38, H 6.41, N 5.24; found C 76.12, H 6.68, N 5.36. Compound (5): (1S,2R)-(−)-cis-1-(N,N-Dipropargylamino)-2-indanyl benzoate. To a solution of of 3 (0.08 g, 0.36 mmol), DMAP (a catalytic amount) in MeCN (5 mL), at 0 °C and under argon, was added dropwise a solution of benzoyl chloride (82 μL, 0.72 mmol) and Et3N (100 μL, 0.72 mmol). The mixture was stirred at room temperature for 2 h. the solvent was evaporated and the residue was dissolved in CH2Cl2 (10 mL). The layer organic was washed with a saturated solution of NaCl (3 × 10 mL), dried (Na2SO4) and evaporated, to give a yellow oil that was purified by flash column chromatography using hexane–EtOAc (6:1) as eluent to give 5 (73 mg, yield 73%) as a yellow oil. [∝] º = −85.6° (25 °C, 0.25, CHCl3). IR ν = 3289, 2842, 1714, 1267, 1108, 1069 cm−1. 1H NMR (300 MHz, CDCl3) δ = 7.95–7.92 (m, 2H, 2'-H, 6'-H), 7.56–7.50 (m, 7H, 3'-H, 4'-H, 5'-H, 4 × Harom), 6.00 (dt, 1H, J. = 5.6, 2.6 Hz, 2-H), 4.74 (d, 1H, J. = 5.3 Hz, 1-H), 3.75 (d, 4H, J. = 2.3 Hz, 2 × CH2), 3.29–3.06 (part AB of an ABM system, 2H, JAB = 17.0 Hz, JAM = 5.7 Hz, JBM = 2.7 Hz, 3α-H, 3β-H), 2.15 (t, 2H, J. = 2.1 Hz, 2 × CH). 13C NMR (75 MHz, CDCl3) δ = 166.36 (CO), 140.30 (C-3a), 139.81 (C-7a), 133.16 (C'-4), 130.72 (C'-1), 129.83, 128.61, 128.39, 127.28, 125.49 and 125.43 (4 × CHarom, 4 × C'-H), 81.30 (2 × C≡CH), 77.75 (C-2), 72.20 (2 × C≡CH), 69.17 (C-1), 40.40 (2 × CH2), 38.13 (C-3). MS (FAB): m/z (%): 331 (11) [M+2]+, 330 (40) [M+1]+, 231 (68), 186 (3), 154 (95), 137 (100), 105 (25). Anal. calcd. for C22H19NO2 (329.39): C 80.22, H 5.81, N 4.25; found C 80.05, H 6.01, N 4.34. 3.2.2. Reaction of Carbamylation To a stirred and ice-cooled solution of 2 or 3 (0.43 mmol) in acetonitrile (5 mL) was added the N,N-dialkylcarbamyl chloride (0.73 mmol), followed by a dropwise addition of NaH (60% in oil, 0.56 mmol). The reaction mixture was stirred for 24 h at room temperature under argon. After evaporation of the solvent in vacuo, water (10 mL) was added and extracted with ether (3 × 10 mL). The organic phase was washed with dilute KOH (pH 10–11), dried and evaporated to dryness in vacuo. Purification by column chromatography (Hexane:EtOAc 4:1) afforded: Compound (6): (1S,2R)-(−)-cis-1-(N-Propargylamino)-2-indanyl dimethylcarbamate. This compound was obtained as a yellow solid (100 mg, yield 73%). M.p. 119–122 °C. [∝] º = −50.4° (23 °C, 0.25, CHCl3). IR ν = 3264, 2923, 1693, 1388, 1184, 1047 cm−1. 1H NMR (300 MHz, CDCl3) δ = 7.40–7.39 (m, 1H, 7-H), 7.28–7.13 (m, 3H, 4-H, 5-H, 6-H), 5.52–5.48 (m, 1H, 2-H), 4.38 (d, 1H, J = 5.0 Hz, 1-H), 3.61–3.46 (AB system, 1H, J. = 16.8 Hz, CH2), 3.60–3.45 (AB system, 1H, J. = 16.8 Hz, CH2), 3.19–3.04 (AB system, 1H, J = 16.5 Hz, 3α-H), 3.17–3.03 (AB system, 1H, J. = 16.5 Hz, 3β-H), 2.90–2.80 (m, 6H, 2 × CH3), 2.62 (t, 1H, J. = 2.5 Hz, CH), 2.25 (br. s., 1H, D2O exch., NH). 13C NMR (75 MHz, CDCl3) δ = 155.97 (CO), 142.09 (C-3a), 139.75 (C-7a), 127.97, 126.69, 124.93 and 124.66 (CHarom), 82.17 (C≡CH), 76.00 (C-2), 71.64 (C≡CH), 63.28 (C-1), 37.46 (CH2), 36.35 (C-3), 29.94 Int. J. Mol. Sci. 2014, 15 17057 and 29.67 (2 × CH3). MS (FAB): m/z (%): 258 (1) [M]+, 257 (6) [M−1]+, 168 (100), 116 (80), 72 (80). Anal. calcd. for C15H18N2O2 (258.32): C 69.74, H 7.02, N 10.84; found C 69.65, H 7.13, N 10.93. Compound (7): (1S,2R)-(−)-cis-1-(N-Propargylamino)-2-indanyl diethylcarbamate. Isa yellow solid (98 mg, yield 66%). M.p. 68–69 °C. [∝] º = −37.6° (23 °C, 0.25, CHCl3). IR ν = 3242, 2972, 1677, 1425, 1270, 1173, 1066 cm−1. 1H NMR (300 MHz, CDCl3) δ = 7.42–7.39 (m, 1H, 7-H), 7.29–7.21 (m, 3H, 4-H, 5-H, 6-H), 5.54 (dt, 1H, J. = 5.3, 3.6 Hz, 2-H), 4.41–4.39 (m, 1H, 1-H), 3.63–3.49 (AB system, 1H, J. = 16.8 Hz, CH2), 3.62–3.48 (AB system, 1H, J. = 16.8 Hz, CH2), 3.29–3.09 (m, 6H, 3α-H, 3β-H, 2 × CH2CH3), 2.25 (t, 1H, J. = 2.4 Hz, CH), 1.93 (br. s., 1H, D2O exch., NH), 1.28–1.01 (m, 6H, 2 × CH2CH3). 13C NMR (75 MHz, CDCl3) δ = 155.18 (CO), 142.24 (C-3a), 139.80 (C-7a), 127.91, 126.65, 124.88 and 124.61 (CHarom), 82.19 (C≡CH), 75.64 (C-2), 71.54 (C≡CH), 63.52 (C-1), 41.92 and 41.30 (2 × CH2CH3), 37.45 (CH2), 36.50 (C-3), 13.99 and 13.51 (2 × CH2CH3). MS (FAB): m/z (%): 288 (18) [M+2]+, 287 (100) [M+1]+, 286 (8) [M]+, 285 (6) [M−1]+, 231 (21), 154 (27), 137 (26). Anal. calcd. for C17H22N2O2 (286.37): C 71.30, H 7.74, N 9.78; found 71.12, H 7.99, N 9.92. Compound (8): (1S,2R)-(−)-cis-1-(N,N-Dipropargylamino)-2-indanyl dimethylcarbamate. Was obtained as a white solid (76 mg, yield 58%). M.p. 109–112 °C. [∝] º = −38° (25 °C, 0.25, CHCl3). IR ν = 3292, 2922, 1685, 1397, 1272, 1186, 1050 cm−1. 1H NMR (300 MHz, CDCl3) δ = 7.48 (t, 1H, J = 3.9 Hz, 7-H), 7.27–7.22 (m, 3H, 4-H, 5-H, 6-H), 5.61 (dt, 1H, J. = 5.6, 3.3 Hz, 2-H), 4.63–4.61 (m, 1H, 1-H), 3.67–3.66 (m, 4H, 2 × CH2), 3.16–2.97 (AB system, 1H, J. = 16.8 Hz, 3α-H), 3.14–2.96 (AB system, 1H, J. = 16.8 Hz, 3β-H), 2.91–2.81 (m. 6H, 2 × CH3), 2.21 (t, 1H, J. = 2.2 Hz, 2 × CH) 13C NMR (75 MHz, CDCl3) δ = 155.98 (CO), 140.05 (C-3a), 139.82 (C-7a), 127.96, 126.76, 125.39 and 125.10 (CHarom), 81.10 (2 × C≡CH), 77.36 (C-2), 71.91 (2 × C≡CH), 68.31 (C-1), 40.24 (2 × CH2), 37.94 (C-3), 36.49 and 36.14 (2 × CH3). MS (FAB): m/z (%): 298 (19) [M+2]+, 297 (100) [M+1]+, 296 (4) [M]+, 295 (9) [M−1]+, 231 (30), 204 (21), 154 (31), 137 (39). Anal. calcd. for C18H20N2O2 (296.36): C 72.95, H 6.80, N 9.45; found 72.78, H 7.01, N 9.53. Compound (9): (1S,2R)-(−)-cis-1-(N,N-Dipropargylamino)-2-indanyl diethylcarbamate. This compound was obtained as an oil (70 mg, yield 49%). [∝] º = −18.6° (25 °C, 0.25, CHCl3). IR ν = 3292, 2928, 1688, 1425, 1270, 1167, 1062 cm−1. 1H NMR (300 MHz, CDCl3) δ = 7.49 (t, 1H, J = 4.2 Hz, 7-H), 7.28–7.23 (m, 3H, 4-H, 5-H, 6-H), 5.59 (dt, 1H, J. = 5.8, 3.9 Hz, 2-H), 4.62 (d, 1H, J. = 5.8 Hz, 1-H), 3.71–3.57 (m, 4H, 2 × CH2), 3.35–3.23 (m, 4H, 2 × CH2CH3), 3.17–2.98 (AB system, 1H, J. = 16.8 Hz, 3α-H), 3.15–2.97 (AB system, 1H, J = 16.8 Hz, 3β-H), 2.21 (t, 2H, J. = 2.2 Hz, 2 × CH), 1.12–1.01 (m, 6H, 2 × CH2CH3). 13C NMR (75 MHz, CDCl3) δ = 155.21 (CO), 139.98 (C-3a), 139.86 (C-7a), 128.00, 126.74, 125.55 and 125.01 (CHarom), 81.06 (2 × C≡CH), 76.58 (C-2), 71.97 (2 × C≡CH), 67.99 (C-1), 41.65 and 41.12 (2 × CH2CH3), 40.14 (2 × CH2), 37.81 (C-3), 13.97 and 13.47 (2 × CH2CH3). MS (FAB): m/z (%): 326 (20) [M+2]+, 325 (92) [M+1]+, 324 (2) [M]+, 323 (8) [M−1]+, 288 (89), 230 (51), 154 (71), 137 (100). Anal. calcd. for C20H24N2O2 (324.42): C 74.04, H 7.46, N 8.64; found 73.89, H 7.61, N 8.75. Int. J. Mol. Sci. 2014, 15 17058 3.3. Experimental Methods: Biology 3.3.1. Culture of Rat Cortical Neurons Embryos were selected from 19 to 20 days pregnant rats by caesarean section. Meninges were removed and cortex was isolated after the dissection of the brain. The fragments obtained from several embryos were subjected to mechanic digestion. We re-suspended the cells in a Neurobasal medium with 2% B-27. We seeded in 48-well plates at a density of 100,000 cells/mL. Neuronal cultures were allowed to grow for 8–10 days. Incubations with different CSF were done when the microscope showed the existence of a dense neuronal network. Embryos were selected from 19 to 20 days pregnant rats, which were decapitated and embryos were extracted from the womb by caesarean section. Meninges were removed and a portion of motor cortex was isolated after the dissection of the brain. Fragments obtained from several embryos were subjected to mechanic digestion and cells were re-suspended in Neurobasal medium with 2% B-27 and seeded in 48-well plates at a density of 100,000 cells/mL. Neuronal cultures were allowed to grow for 8–10 days and when the microscope showed the existence of a dense neuronal network, incubations with different CSF were done [79]. 3.3.2. Measurement of Neuronal Viability We used the MTT reduction assay following the procedure previously described [65]. After the appropriate incubations with the compounds alone, or co-incubated with 100 µM H2O2 or glutamate, 0.5 mg/mL MTT were added to each well and incubation was performed at 37 °C for 2 h. Formazan salt formed was dissolved in DMSO, and colorimetric determination were performed at 540 nm. Control cells without compounds or toxic stimulus were considered 100% viability. Neuronal viability after exposure to compounds or different treatments was expressed as% of control within each individual experiment. Graph Pad Prism Software (GraphPad Software, San Diego, CA, USA) was used to perform statistical analyses and graphical presentation. Experiments were reproduced at least three times. Data were expressed as mean ± S.E.M. values. Groups were compared by ANOVA/Dunnett’s test. A p-value ≤0.05 was accepted as the limit of statistical significance. 4. Conclusions We can use Shannon entropy measures to developing predictive models for multi-target networks of neuroprotective/neurotoxic compounds. In doing so, we can use Box–Jenkins operators of molecular descriptors to obtain multi-target, multi-scale, and multi-output models able to predict different outcomes for multiple combinations of output experimental measures, experimental protocols, organisms, and molecular and cellular targets. One of these models has been demonstrated here to be useful as a complementary tool in the organic synthesis and evaluation of the multi-target biological activity of new compounds with potential neuroprotective activity. The model is also a very useful tool to predict complex networks of drug-target interactions with possible applications to the study of non-linear effects in the biological activity of neuroprotective drugs. Int. J. Mol. Sci. 2014, 15 17059 Acknowledgments The authors thank the Xunta de Galicia for financial support of this work under project 07CSA008203PR. Author Contributions Synthesis, identification, purification, and characterization of new organic compounds: Xerardo García-Mera, Olga Caamaño, and Nerea Alonso; Pharmacological assays: Matilde Yañez; Data retrieval and preprocessing, cheminformatics calculations, statistical analysis, complex network analysis, predictive study: Francisco J. Romero Durán, Francisco J. Prado-Prado and Humberto González-Díaz; Writing of paper: Francisco J. Romero Durán, Xerardo García-Mera, and Humberto González-Díaz. Supplementary Information Supplementary material files which contain detailed lists of the values of parameters are available upon requests to the corresponding author. Conflict of Interest The authors declare no conflict of interest. References 1. Mekenyan, O.; Bonchev, D.; Trinajstic, N. Chemical graph theory modeling the thermodynamic properties of molecules. Intern. J. Quantum Chem. Symp. 1980, 18, 369–380. 2. Bonchev, D.; Trinajstic, N. Information theory, distance matrix, and molecular branching. J. Chem. Phys. 1977, 67, 4517–4533. 3. Bonchev, D.; Kamenski, D.; Kamenska, V. Symmetry and information content of chemical structures. Bull. Math. Biol. 1976, 38, 119–133. 4. Kier, L.B. Use of molecular negentropy to encode structure governing biological activity. J. Pharm. Sci. 1980, 69, 807–810. 5. Stahura, F.L.; Godden, J.W.; Bajorath, J. 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