Computer-assisted rational design of immunosuppressive compounds

Probing the opportunities for designing anthelmintic leads by sub-structural topology-based QSAR modelling

A quantitative structure-activity (QSAR) model has been developed for enriched tubulin inhibitors, which were retrieved from sequence similarity searches and applicability domain analysis. Using partial least square (PLS) method and leave-one-out (LOO) validation approach, the model was generated with the correlation statistics of [Formula: see text] and [Formula: see text] of 0.68 and 0.69, respectively. The present study indicates that topological descriptors, viz. BIC, CH_3_C, IC, JX and Kappa_2 correlate well with biological activity. ADME and toxicity (or ADME/T) assessment showed that out of 260 molecules, 255 molecules successfully passed the ADME/T assessment test, wherein the drug-likeness attributes were exhibited. These results showed that topological indices and the colchicine binding domain directly influence the aetiology of helminthic infections. Further, we anticipate that our model can be applied for guiding and designing potential anthelmintic inhibitors.

On the history of the connectivity index: from the connectivity index to the exact solution of the protein alignment problem

We briefly review the history of the connectivity index from 1975 to date. We hope to throw some light on why this unique, by its design, graph theoretical molecular descriptor continues to be of interest in QSAR, having wide use in applications in structure-property and structure-activity studies. We will elaborate on its generalizations and the insights it offered on applications in Multiple Regression Analysis (MRA). Going beyond the connectivity index we will outline several related developments in the development of molecular descriptors used in MRA, including molecular ID numbers (1986), the variable connectivity index (1991), orthogonal regression (1991), irrelevance of co-linearity of descriptors (1997), anti-connectivity (2006), and high discriminatory descriptors characterizing molecular similarity (2015). We will comment on beauty in QSAR and recent progress in searching for similarity of DNA, proteins and the proteome. This review reports on several results which are little known to the structure-property-activity community, the significance of which may surprise those unfamiliar with the application of discrete mathematics to chemistry. It tells the reader many unknown stories about the connectivity index, which may help the reader to better understand the meaning of this index. Readers are not required to be familiar with graph theory.

Prolonged survival time of allografts by the oral administration of RDP58 linked to the cholera toxin B subunit

Oral administration, which has been identified as a tool for boosting physiological immunoregulatory mechanisms in an antigen-specific manner, is a more convenient way than classical parenteral injection methods. RDP58 is derived from specific regions of class-I MHC molecules and is known to have immunomodulatory effects after intraperitoneal injection or intravenous administration. To determine whether the oral administration of RDP58 conjugated to the cholera toxin B subunit (CTB) can better induce peripheral tolerance than the use of traditional methods, we used various feeding regimens and methods of administration using equivalent doses of antigen during rat kidney transplantation. The results showed that RDP58-GC/CTB treatment increased the activity of Haem oxygenase-1 (HO-1) in vivo and significantly improved the survival and histopathology of allograft kidney tissue relative to the oral administration of RDP58 alone. These results suggest that the administration of RDP58 linked to CTB outweighs the benefits of oral administration of RDP58 alone for prolonging the survival time of kidney transplantation. This study supports the potential therapeutic use of oral administration of RDP58 linked to CTB as a platform molecule in the treatment of allograft rejection.

Molecular topology as a novel approach for drug discovery

INTRODUCTION

Molecular topology (MT) has emerged in recent years as a powerful approach for the in silico generation of new drugs. One key part of MT is that, in the process of drug design/discovery, there is no need for an explicit knowledge of a drug's mechanism of action unlike other drug discovery methods.

AREAS COVERED

In this review, the authors introduce the topic by explaining briefly the most common methodology used today in drug design/discovery and address the most important concepts of MT and the methodology followed (QSAR equations, LDA, etc.). Furthermore, the significant results achieved, from this approach, are outlined and discussed.

EXPERT OPINION

The results outlined herein can be explained by considering that MT represents a new paradigm in the field of drug design. This means that it is not only an alternative method to the conventional methods, but it is also independent, that is, it represents a pathway to connect directly molecular structure with the experimental properties of the compounds (particularly drugs). Moreover, the process can be realized also in the reverse pathway, that is, designing new molecules from their topological pattern, what opens almost limitless expectations in new drugs development, given that the virtual universe of molecules is much greater than that of the existing ones.

The latest developments in synthetic peptides with immunoregulatory activities

In the past few years, many researches have provided us with much data demonstrating the abilities of synthetic peptides to impact immune response in vitro and in vivo. These peptides were designed according to the structure of some important protein molecules which play a key role in immune response, so they act with specific targets. The class I and II MHC-derived peptides inhibit the TCR recognition of antigen peptide-MHC complex. Rationally designed CD80 and CD154-binding peptides block the interaction between cell surface costimulatory molecules on antigen-presenting cells (APCs) and T cells. Some peptides were designed to inhibit the activities of cell signal proteins, including JNK, NF-κB and NFAT. Some peptide antagonists competitively bind to important cytokines and inhibit their activities, such as TNF-α, TGF-β and IL-1β inhibitory peptides. Adhesion molecule ICAM-1 derived peptides block the T cell adhesion and activation. These immunoregulatory peptides showed therapeutic effect in several animal models, including collagen-induced arthritis (CIA), autoimmune cystitis model, murine skin transplant model and cardiac allograft model. These results give us important implications for the development of a novel therapy for immune mediated diseases.

Structural determinants of PERK inhibitor potency and selectivity

The unfolded protein response (UPR) is a coordinated program that promotes cell survival under conditions of endoplasmic reticulum stress and is required in tumor progression as well. To date, no specific small molecule inhibitor targeting this pathway has been identified. Pancreatic endoplasmic reticulum kinase (PERK), one of the UPR transducers, is an eIF2α kinase. Compromising PERK function inhibits tumor growth in mice, suggesting that PERK may be a cancer drug target, but identifying a specific inhibitor of any kinase is challenging. The goal of this study was to identify some pair-wise receptor-ligand atomic contacts that confer selective PERK inhibition. Compounds selectively inhibiting PERK-mediated phosphorylation in vitro were identified using an initial virtual library screen, followed by structure-activity hypothesis testing. The most potent PERK selective inhibitors utilize three specific kinase active site contacts that, when absent from chemically similar compounds, abrogates the inhibition: (i) a strong van der Waals contact with PERK residue Met7, (ii) interactions with the N-terminal portion of the activation loop, and (iii) groups providing electrostatic complementarity to Asp144. Interestingly, the activation loop contact is required for PERK selectivity to emerge. Understanding these structure-activity relationships may accelerate rational PERK inhibitor design.

Implementing Molecular Connectivity Theory, a Basic Tool in Modeling Drugs

The concepts of chain graph, general graph, and complete graph have been used to implement the graph framework of molecular connectivity (MC) theory. Some concepts of this theory have been addressed using "external" theoretical concepts belonging mostly to quantum or structural chemistry, with no direct counterpart in graph theory. Thus, while the concept of chain graph can be used to tackle the cis-trans isomerism problem, the concept of pseudograph, or general graph can be used to tackle the description of the sigma-, pi-, and nonbonding n-electrons. The concept of complete graph can instead be used to tackle the electron core problem of the atoms of a molecule. Graph concepts can also be used to tackle the problem of the hydrogen contribution in hydrogen depleted graphs, which are encoded by the aid of a perturbation parameter, which differentiates between compounds with similar hydrogen-suppressed chemical graphs, like the graphs of CH(3)F and BH(2)F. These concepts have allowed redesign of a central parameter of MC theory, the valence delta, giving MC indices with improved model quality as exemplified here with different properties for each treated topic.

On representation of proteins by star-like graphs

To arrive at graphical representations of proteins one is confronted with number of arbitrary decisions how to assign the 20 natural amino acids to equivalent or non-equivalent sites of underlying geometrical objects used for construction of their graphical representation. Here we consider representation of proteins based on generalized star graphs, which are graphs with one vertex of maximal degree in the center to which are attached other vertices of either degree one or two. The matrix representation of proteins based on star-like graphs has an important advantage in that, while its pictorial representation depends on selected assignment of amino acids to various branches of star graph, its properties do not depend on the adopted assignment of vertices to amino acids. Hence, the derived graph invariants, devoid of artifacts associated with graphical representations of biosequences, will better reflect upon the inherent properties of protein structure. We describe several graph invariants, mostly extracted from distance matrices of star-like graphs, which can serve as protein descriptors. The approach is illustrated on strand A of the human insulin.

Generalized Topological Indices. Modeling Gas-Phase Rate Coefficients of Atmospheric Relevance

We develop the idea that the use of ad hoc molecular descriptors in QSAR/QSPR studies is not an optimal solution. Instead, we propose to optimize these descriptors for the specific properties under study. In the case of topological indices (TIs) we propose the use of the generalized topological indices (GTIs), which account for several of the classical TIs in one single graph invariant. GTIs represent points in a six-dimensional space of topological parameters, which can be optimized for describing a specific property. The situation shows some resemblance with the geometry optimization procedures used to minimize molecular energy. Here we study the reaction rate coefficients for the gas-phase radical reactions between alkanes and cycloalkanes with OH, Cl, and NO3 radicals. These reaction rate coefficients were studied recently by using some "classical" TIs, such as Balaban and Randić indices. Despite the fact that the Randić index produces acceptable QSPR models for some of these reactions, the models developed are still far from being optimal. Using the GTI approach we have improved these QSPRs by reducing the standard deviation by almost 50%. In addition, the current approach permits the illustration of the similarities and differences among the different descriptors studied, indicating possible directions for searching new optimal molecular descriptors.

Prediction of h5-HT2A receptor antagonistic activity of arylindoles: Computational approach using topochemical descriptors

Relationship between the topochemical indices and h5-HT2A receptor antagonistic activity of arylindoles has been investigated. Three topochemical indices, Wiener's topochemical index--a distance-based topochemical descriptor, molecular connectivity topochemical index--an adjacency-based topochemical descriptor and eccentric connectivity topochemical index--an adjacency-cum-distance based topochemical descriptor, were used for the present investigation. A data set comprising 31 differently substituted arylindoles was selected for the present study. The values of the Wiener's topochemical index, molecular connectivity topochemical index and eccentric connectivity topochemical index were computed for all the analogues involved in the data set using an in-house computer program. Resultant data was analyzed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analogue using these models, which was then compared with the reported h5-HT2A receptor antagonistic activity. Accuracy of prediction was found to vary from a minimum of approximately 81% to a maximum of approximately 84%.

Complexity of chemical graphs in terms of size, branching, and cyclicity

Chemical graph complexity depends on many factors, but the main ones are size, branching, and cyclicity. Some molecular descriptors embrace together all these three parameters, which cannot then be disentangled. The topological index J (and its refinements that include accounting for bond multiplicity and the presence of heteroatoms) was designed to compensate in a significant measure for graph size and cyclicity, and therefore it contains information mainly on branching. In order to separate these factors, two new indices (F and G) related with J are proposed, which allow to group together graphs with the same size into families of constitutional formulas differing in their branching and cyclicity. A comparison with other topological indices revealed that a few other topological indices vary similarly with index G, notably DN2S4 among the triplet indices, and TOTOP among the indices contained in the Molconn-Z program. This comparison involved all possible chemical graphs (i.e. connected planar graphs with vertex degrees not higher than four) with four through six vertices, and all possible alkanes with four through nine carbon atoms.

RDP58 does not prevent graft-versus-host disease after dog leukocyte antigen-nonidentical canine hematopoietic cell transplantation

Graft-versus-host disease (GVHD) remains a cause of substantial morbidity for patients undergoing allogeneic hematopoietic cell transplantation (HCT). The present study was undertaken to investigate the effectiveness of RDP58, a peptide derived from the human leukocyte antigen class I heavy chain, in preventing GVHD in the established dog leukocyte antigen (DLA)-nonidentical canine model. Dogs underwent HCT from unrelated DLA-nonidentical donors after conditioning with 920 cGy total body irradiation. Engraftment and achievement of full donor chimerism was seen in five of six dogs, whereas one dog showed rejection and died of marrow aplasia. All five dogs with engraftment developed acute GVHD and were euthanized at an average of 20.6 days after HCT. Compared with historical controls, the Suse of RDP58 neither prevented acute GVHD nor significantly prolonged survival of DLA-nonidentical HCT recipients.

Peptide-mediated immunosuppression

There is now a substantial body of data demonstrating the abilities of synthetic peptides and peptide analogues to inhibit the auto- and alloimmune response in vitro and in vivo. We have studied the immunomodulatory role of synthetic peptides derived from highly conserved regions of the class II MHC alpha chain. These MHC-derived peptides inhibit the rat, human, and mouse mixed lymphocyte response (MLR), proliferation to autoantigen, cytokine production, and cytolytic T lymphocyte (CTL) generation. Our studies demonstrated that the inhibitory effect of the MHC class II nonpolymorphic peptides is mediated through the induction of apoptosis in APCs via a nonclassic caspase-independent pathway. In addition, T lymphocytes initially stimulated in the presence of HLA-DQA1 are rendered hyporesponsive to subsequent stimuli. Immunomodulation by HLA-DQA1 was effective in vivo because it prevented both the priming and the effector function of primed allogeneic T cells in a murine DTH model. Our data demonstrate that peptides derived from highly conserved regions of the class II MHC alpha chain can alter T-lymphocyte immune responses both in vitro and in vivo. These results have important implications for the development of a novel therapy for immune mediated diseases.

Contribution of peptides to multiple sclerosis research

Multiple sclerosis (MS) is an autoimmune disease associated with chronic inflammatory demyelination of the central nervous system in genetically susceptible individuals. Because of the disease complexity and heterogeneity, its pathogenesis remains unknown despite extensive research efforts, and specific effective treatments have not yet been developed. Peptide-based research has been important in attempts to unravel particular aspects of this complex disease, including the characterization of the different molecular mechanisms of MS, with the goal of providing useful products for immune-mediated therapies. In fact, in the past decade, peptide-based research has been predominant in research aimed to identify and/or develop target antigens as synthetic probes for specific biomarkers as well as innovative immunomodulating therapies. This review presents an overview of the contributions of peptide science to MS research and discusses future directions of peptide-based investigations.

Topical application of a novel immunomodulatory peptide, RDP58, reduces skin inflammation in the phorbol ester-induced dermatitis model

RDP58 is the first lead compound in a series of immunomodulating decapeptides discovered through activity-based screening and computer-aided, rational design. RDP58 disrupts cellular responses signaled through the Toll-like and tumor necrosis factor (TNF) receptor families and occludes important signal transduction pathways involved in inflammation, inhibiting the production of tumor necrosis factor alpha (TNFalpha), interferon-gamma, interleukin (IL)-2, IL-6, and IL-12. These pro-inflammatory cytokines are thought to be involved in the pathogenesis of several inflammatory and autoimmune diseases, including atopic dermatitis and psoriasis. The goal of this study was to determine the ability of RDP58 to inhibit skin inflammation following exposure to the well-characterized protein kinase C activator and tumor promoter 12-O-tetradecanoylphorbol-13-acetate (TPA). Topical application of RDP58 to the epidermis following TPA treatment resulted in the amelioration of the phorbol ester-induced irritant contact dermatitis. Substantial reductions were observed in skin thickness and tissue weight, neutrophil-mediated myeloperoxidase activity, inflammatory cytokine production, and various histopathological indicators. We also found RDP58 to be effective in reducing the compounding inflammatory damage brought on by chronic TPA exposure, and that it is capable of targeting inflammatory mediators specifically in the keratinocyte. These results demonstrate that topically applied RDP58 is an effective anti-inflammatory treatment in the phorbol ester-induced dermatitis model, and suggest that it may have therapeutic potential in a variety of immune-related cutaneous diseases.

“Anticonnectivity”: A Challenge for Structure−Property−Activity Studies

The higher-order variable connectivity indices were introduced to account for the combination of positive and negative relative contributions of atoms and bonds in the construction of the quantitative structure-property relationships or quantitative structure-activity relationships models. The coding capabilities of modified descriptors were presented on the modeling of the atmospheric reaction rate constants of selected organic compounds with OH radicals. The optimization of diagonal weights of the augmented adjacency matrix pointed out the significant enhancing effect of oxygen and the suppressive effect of chlorine on the overall molecular atmospheric reactivity of organic compounds with OH radicals. The linear regression model, using a single structural descriptor, that is, a variable connectivity index of order one, produced a root-mean-square error of 0.343 log units. Although the obtained calculation error was higher than in previously reported multiple linear regression models, the new model offered important insight into the role of the individual structural components that are influencing the reactivity of organic compounds.

Peptide dynamic fingerprints: a tool for investigating the role of conformational flexibility for GLP-1 analogs affinity

Glucagon-like peptide-1 (GLP-1) is a 30-residue peptide implicated in short-term appetite regulation. Its analogs are presumed to be potential drugs against obesity and non-insulin dependent diabetes mellitus (NIDDM or type 2 diabetes). This study examined how the dynamic fingerprints can be used for establishing dynamics-activity relationships in a series of peptides for which the mechanism of action is unknown and in which mutations can cause an increase or decrease in biological activity. The 3D autocorrelation method was used to generate maps of both active and inactive analogs. As the active conformation of GLP-1 is not yet clearly defined, the dynamic fingerprints of peptides in an aqueous environment were compared to explain the high affinity of the peptide for its receptor. The suggestion that the peptide could bind to the receptor in a folded conformation has been examined. In the case of the GLP-1 analogs, it was shown that the folding tendency cannot be directly related to affinity values and the results do not favor a folded active conformation model of GLP-1.

Predicting anti-HIV activity of 2,3-diaryl-1,3-thiazolidin-4-ones: computational approach using reformed eccentric connectivity index

The eccentric connectivity index, which has recently been employed successfully for the development of numerous mathematical models for the prediction of biological activities of diverse nature, has been reformed to overcome its limitations caused by degeneracy and insensitivity towards heteroatoms. The reformed eccentric connectivity index, termed the eccentric connectivity topochemical index, overcomes the limitations of the eccentric connectivity index by exhibiting very low degeneracy and displaying sensitivity to both the presence and relative position of heteroatoms without compromizing the discriminating power of the eccentric connectivity index. The relationship of the eccentric connectivity topochemical index, eccentric connectivity index and Wiener's index with regard to the anti-HIV activity of 2, 3-diaryl-1, 3-thiazolidin-4-one derivatives was subsequently investigated. The values of the eccentric connectivity topochemical index, the eccentric connectivity index and Wiener's index of each of 31 analogues comprizing the data set were computed using in-house computer program. Resultant data was analyzed and suitable models developed after identification of active ranges. Subsequently, each derivative was assigned a biological activity using these models, which was then compared with the reported anti-HIV activity. The accuracy of prediction using these models was found to vary from 81 to 90%. The proposed index offers a vast potential for virtual screening of combinatorial libraries, structure property/activity studies and drug design. [figure]. Basic structure of 2,3-diaryl-1, 3-thiazoidin-4-ones.

RDP58, a novel immunomodulatory peptide, ameliorates clinical signs of disease in the Lewis rat model of acute experimental autoimmune encephalomyelitis

The therapeutic value of a novel immunomodulatory peptide, RDP58, was investigated in the acute experimental autoimmune encephalomyelitis (EAE) model of Multiple Sclerosis (MS). RDP58 is a 10-amino acid peptide with two major activities: (i) inhibition of inflammatory TH1 cytokines such as TNFalpha, IFNgamma, and IL12 and (ii) up-regulation of heme oxygenase-1 (HO-1) expression. Experiments in which EAE-induced Lewis rats exhibit an acute monophasic episode of disease demonstrated that a single intracerebroventricular injection of RDP58 is effective in preventing clinical signs of disease. The therapeutic effect on disease activity was observed at all pre-onset administration times and at all doses tested. Consistent with disease activity in vivo, RDP58-treated animals had reduced cellular infiltration within the spinal cord along with decreased TNFalpha expression levels. The data in this proof of concept study support the premise that RDP58, as a platform molecule, may be a promising new therapeutic intervention in autoimmune and inflammatory diseases.

Blood-Brain Barrier Permeation Models: Discriminating between Potential CNS and Non-CNS Drugs Including P-Glycoprotein Substrates

The aim of this article is to present the design of a large heterogeneous CNS library (approximately 1700 compounds) from WDI and mapping CNS drugs using QSAR models of blood-brain barrier (BBB) permeation and P-gp substrates. The CNS library finally includes 1336 BBB-crossing drugs (BBB+), 259 molecules non-BBB-crossing (BBB-), and 91 P-gp substrates (either BBB+ or BBB-). Discriminant analysis and PLS-DA have been used to model the passive diffusion component of BBB permeation and potential physicochemical requirement of P-gp substrates. Three categories of explanatory variables (Cdiff, BBBpred, PGPpred) have been suggested to express the level of permeation within a continuous scale, starting from two classes data (BBB+/BBB-), allowing that the degree to which each compound belongs to an activity class is given using a membership score. Finally, statistical data analyses have shown that some very simple descriptors are sufficient to evaluate BBB permeation in most cases, with a high rate of well-classified drugs. Moreover, a "CNS drugs" map, including P-gp substrates and accurately reflecting the in vivo behavior of drugs, is proposed as a tool for CNS drug virtual screening.

Novel map descriptors for characterization of toxic effects in proteomics maps

We consider a novel numerical characterization of proteomics maps based on the construction of a graph obtained by connecting all protein spots in a proteomics map that are at distance equal to, or smaller than, a critical distance D(c). We refer to the so constructed graph as a cluster graph and we calculate four associated characteristic matrices, previously considered in the literature: (1) the Euclidean-distance matrix ED; (2) the neighborhood-distance matrix ND; (3) the path-distance matrix based on the shortest paths between connected spots PD; and (4) the quotient matrix Q, the elements of which are given as the quotient of the corresponding elements of ED and ND matrices. Numerical descriptors for proteomics maps include in particular the leading eigenvalue of the Q matrix and the family of associated "higher order" matrices defined as powers of Q. These map descriptors show considerable sensitivity to perturbations of proteomics maps by toxicants.

On a four-dimensional representation of DNA primary sequences

We consider a four-dimensional representation of DNA primary sequences by assigning to each of the four basic amino acids A, T, G, C directions along the four orthogonal coordinate axes. Advantages and limitations of the novel representation of DNA primary sequences are discussed, and the use of the 4-D representation is illustrated by constructing novel sequence invariants. Comparisons with the similarity/dissimilarity results based on 2-D and 3-D representations for a set of eight short DNA sequences corresponding to the first exon of beta globin in eight species, including human, are considered to illustrate the use of our novel sequence invariants based on the entries in derived sequence matrices restricted to a selected width of a band along the main diagonal.

RDP58, a Novel Immunomodulatory Peptide with Anti-Inflammatory Effects. A Pharmacological Study in Trinitrobenzene Sulphonic Acid Colitis and Crohn Disease

BACKGROUND

Tumour necrosis factor (TNF) plays a key role in the pathogenesis of Crohn disease (CD). RDP58 is a novel anti-inflammatory decapeptide which was developed using a novel rational design strategy. Recently, RDP58 has proved to be a potent inhibitor of TNF production at a post-transcriptional step. The aims of this study were to investigate the anti-inflammatory properties of RDP58 ex vivo in human CD and in vivo in an experimental model colitis.

METHODS

Biopsies and lamina propria mononuclear cells from inflamed colonic mucosa of 18 CD patients were cultured for 24 h in the presence or absence of RDP58. TNF was quantified in a bioassay; interferon (IFN)-γ and interleukin (IL)-1β levels were measured by enzyme-linked immunosorbent assays. Colitis was induced by intra-rectal administration of 2, 4, 6 trinitrobenzene sulphonic acid (TNBS) in rats. Inflammation was assessed following 7 days of oral therapy with RDP58 or vehicle alone.

RESULTS

RDP58 led to decreased TNF and IFN-γ (but not IL-1β) production by biopsies and lamina propria mononuclear cells from CD patients. In rats with TNBS-induced colitis, oral RDP58 therapy reduced weight loss and diarrhoea and improved macroscopic and histological inflammation scores.

CONCLUSIONS

Our results suggest that RDP58 may be an effective therapy for CD with the clinical advantage of an oral administration.

Orally administered RDP58 reduces the severity of dextran sodium sulphate induced colitis

Oral administration of the novel anti-inflammatory peptide RDP58 markedly reduced the severity of dextran sulphate sodium (DSS) colitis as determined by clinical and quantitative histological criteria. The architecture of the colonic epithelium in DSS treated mice receiving RDP58 remained relatively normal compared with that of control DSS treated animals. 5-Bromo-2'-deoxyuridine (BrdU) labelling studies showed a pronounced inhibition of colonic epithelial cell proliferation during DSS treatment, which was partially reversed by RDP58 therapy. Remarkably, RDP58 almost completely prevented colonic epithelial cell death induced by DSS treatment. RDP58 therapy also inhibited the accumulation of neutrophils in the colon of DSS treated mice and effectively down regulated tumour necrosis factor (TNF) expression. Preservation of the intestinal mucosa by RDP58 may thus derive from its influence on TNF expression as well as additional anti-inflammatory properties. These findings indicate that RDP58 represents a new, orally available agent potentially useful in the treatment of inflammatory bowel disease.

Molecular descriptors that influence the amount of drugs transfer into human breast milk

Most drugs are excreted into breast milk to some extent and are bioavailable to the infant. The ability to predict the approximate amount of drug that might be present in milk from the drug structure would be very useful in the clinical setting. The aim of this research was to simplify and upgrade the previously developed model for prediction of the milk to plasma (M/P) concentration ratio, given only the molecular structure of the drug. The set of 123 drug compounds, with experimentally derived M/P values taken from the literature, was used to develop, test and validate a predictive model. Each compound was encoded with 71 calculated molecular structure descriptors, including constitutional descriptors, topological descriptors, molecular connectivity, geometrical descriptors, quantum chemical descriptors, physicochemical descriptors and liquid properties. Genetic algorithm was used to select a subset of the descriptors that best describe the drug transfer into breast milk and artificial neural network (ANN) to correlate selected descriptors with the M/P ratio and develop a QSAR. The averaged literature M/P values were used as the ANN's output and calculated molecular descriptors as the inputs. A nine-descriptor nonlinear computational neural network model has been developed for the estimation of M/P ratio values for a data set of 123 drugs. The model included the percent of oxygen, parachor, density, highest occupied molecular orbital energy (HOMO), topological indices (chiV2, chi2 and chi1) and shape indices (kappa3, kappa2), as the inputs had four hidden neurons and one output neuron. The QSPR that was developed indicates that molecular size (parachor, density) shape (topological shape indices, molecular connectivity indices) and electronic properties (HOMO) are the most important for drug transfer into breast milk. Unlike previously reported models, the QSPR model described here does not require experimentally derived parameters and could potentially provide a useful prediction of M/P ratio of new drugs only from a sketch of their structure and this approach might also be useful for drug information service. Regardless of the model or method used to estimate drug transfer into breast milk, these predictions should only be used to assist in the evaluation of risk, in conjunction with assessment of the infant's response.

The connectivity index 25 years after

We review the developments following introduction of the connectivity indices as molecular descriptors in multiple linear regression analysis (MLRA) for structure-property-activity studies. We end the review with discussion of results obtained with applications of the variable connectivity index. A comparison is made between some results obtained with the traditional topological indices and the variable connectivity index.

Application of the artificial neural network in quantitative structure-gradient elution retention relationship of phenylthiocarbamyl amino acids derivatives

Quantitative structure-retention relationship(QSRR) method was used to model reversed-phase high-performance liquid chromatography (RP-HPLC) separation of 18 selected amino acids. Retention data for phenylthiocarbamyl (PTC) amino acids derivatives were obtained using gradient elution on ODS column with mobile phase of varying acetonitrile, acetate buffer and containing 0.5 ml/l of triethylamine (TEA). Molecular structure of each amino acid was encoded with 36 calculated molecular descriptors. The correlation between the molecular descriptors and the retention time of the compounds in the calibration set was established using the genetic neural network method. A genetic algorithm (GA) was used to select important molecular descriptors and supervised artificial neural network (ANN) was used to correlate mobile phase composition and selected descriptors with the experimentally derived retention times. Retention time values were used as the network's output and calculated molecular descriptors and mobile phase composition as the inputs. The best model with five input descriptors was chosen, and the significance of the selected descriptors for amino acid separation was examined. Results confirmed the dominant role of the organic modifier in such chromatographic systems in addition to lipophilicity (log P) and molecular size and shape (topological indices) of investigated solutes.

Computing wiener-type indices for virtual combinatorial libraries generated from heteroatom-containing building blocks

The expensive and time-consuming process of drug lead discovery is significantly accelerated by efficiently screening molecular libraries with a high structural diversity and selecting subsets of molecules according to their similarity toward specific collections of active compounds. To characterize the molecular similarity/diversity or to quantify the drug-like character of compounds the process of screening virtual and synthetic combinatorial libraries uses various classes of structural descriptors, such as structure keys, fingerprints, graph invariants, and various topological indices computed from atomic connectivities or graph distances. In this paper we present efficient algorithms for the computation of several distance-based topological indices of a molecular graph from the distance invariants of its subgraphs. The procedures utilize vertex- and edge-weighted molecular graphs representing organic compounds containing heteroatoms and multiple bonds. These equations offer an effective way to compute for weighted molecular graphs the Wiener index, even/odd Wiener index, and resistance-distance index. The proposed algorithms are especially efficient in computing distance-based structural descriptors in combinatorial libraries without actually generating the compounds, because only distance-based indices of the building blocks are needed to generate the topological indices of any compound assembled from the building blocks.

ANN modeling of the penetration across a polydimethylsiloxane membrane from theoretically derived molecular descriptors

A quantitative structure-permeability relationship was developed using Artificial Neural Network (ANN) modeling to study penetration across a polydimethylsiloxane membrane. A set of 254 compounds and their experimentally derived maximum steady state flux values used in this study was gathered from the literature. A total of 42 molecular descriptors were calculated for each compound. A genetic algorithm was used to select important molecular descriptors and supervised ANN was used to correlate selected descriptors with the experimentally derived maximum steady-state flux through the polydimethylsiloxane membrane (log J). Calculated molecular descriptors were used as the ANN's inputs and log J as the output. Developed model indicates that molecular shape and size, inter-molecular interactions, hydrogen-bonding capacity of drugs, and conformational stability could be used to predict drug absorption through skin. A 12-descriptor nonlinear computational neural network model has been developed for the estimation of log J values for a data set of 254 drugs. Described model does not require experimental parameters and could potentially provide useful prediction of membrane penetration of new drugs and reduce the need for actual compound synthesis and flux measurements.

On graphical and numerical characterization of proteomics maps

We outlined a mathematical approach suitable for characterization of experimental data given by 2-D densitograms. In particular we consider numerical characterization of proteomics maps. The basis of our approach is to order "spots" of a 2-D map and assign them unique labels (that in general will depend on the criteria used for ordering). In this way a map is "translated" into a sequence. In the next step one associates with the generated sequence a geometrical path and views such a path as a mathematical object that needs characterization. We have ordered spots representing proteins in 2-D gel plates according to their relative intensities which results in a zigzag path that produces a complicated "fingerprint" pattern. Mathematical characterization of zigzag pattern follows similar mathematical characterizations of embedded patterns based on matrices, the elements of which are given as quotients of Euclidean distance between spots and the distance along the zigzag path. The leading eigenvalue of constructed matrices is taken to represent characterization of the original 2-D map. Comparison of different 2-D maps (simulated by using random generator) allows one to construct partial order, which although qualitative in nature gives some insight into perturbation induced by foreign agents to the proteome of the control cell.

Wiener index extension by counting even/odd graph distances

Chemical structures of organic compounds are characterized numerically by a variety of structural descriptors, one of the earliest and most widely used being the Wiener index W, derived from the interatomic distances in a molecular graph. Extensive use of such structural descriptors or topological indices has been made in drug design, screening of chemical databases, and similarity and diversity assessment. A new set of topological indices is introduced representing a partitioning of the Wiener index based on counts of even and odd molecular graph distances. These new indices are further generalized by weighting exponents which can be optimized during the quantitative structure-activity/-property relationship (QSAR/QSPR) modeling process. These novel topological indices are tested in QSPR models for the boiling temperature, molar heat capacity, standard Gibbs energy of formation, vaporization enthalpy, refractive index, and density of alkanes. In many cases, the even/odd distance indices proposed here give notably improved correlations.

3D connectivity indices in QSPR/QSAR studies

Topographic (3D) molecular connectivity indices based on molecular graphs weighted with quantum chemical parameters are used in QSPR and QSAR studies. These descriptors were compared to 2D connectivity indices (vertex and edge ones) and to quantum chemical descriptors in modeling partition coefficient (log P) and antibacterial activity of 2-furylethylene derivatives. In describing log P the 3D connectivity indices produced a significant improvement (more than 29%) in the predictive capacity of the model compared to those derived with topological and quantum chemical descriptors. The best linear discriminant model for classifying antibacterial activity of these compounds was also obtained with the use of 3D connectivity indices. The global percent of good classification obtained with 3D and 2D connectivity as well as quantum chemical descriptors were 94.1, 91.2, and 88.2, respectively. In general, all these models predict correctly the antibacterial activity of a set of nine new 2-furylethylene derivatives. The best result is obtained with 3D connectivity indices that classified correctly 100% of these compounds versus 88.9% obtained with 2D connectivity or quantum chemical descriptors.

QSAR with few compounds and many features

Fitting quantitative structure-activity relationships (QSAR) requires different statistical methodologies and, to some degree, philosophies depending on the "shape" of the data matrix. When few features are used and there are many compounds, it is a reasonable expectation that good feature subset selection may be made and that nonlinearities and nonadditivities can be detected and diagnosed. Where there are many features and few compounds, this is unrealistic. Methods such as ridge regression RR, PLS, and principal component regression PCR, which abjure feature selection and rely on linearity may provide good predictions and fair understanding. We report a development of ridge regression for the underdetermined case by using generalized cross-validation to choose the ridge constant and perform F-tests for additional information. Conventional regression diagnostics can be used in followup to identify nonlinearities and other departures from model. We illustrate the approach with QSAR models of four data sets using calculated molecular descriptors.

Prediction of mutagenicity of aromatic and heteroaromatic amines from structure: a hierarchical QSAR approach

Due to the lack of experimental data, there has been increasing use of theoretical structural descriptors in the hazard assessment of chemicals. We have used a hierarchical approach to develop class-specific quantitative structure-activity relationship (QSAR) models for the prediction of mutagenicity of a set of 95 aromatic and heteroaromatic amines. The hierarchical approach begins with the simplest molecular descriptors, the topostructural, which encode limited chemical information. The complexity is then increased, adding topochemical, geometric, and finally quantum chemical parameters. We have also added log P to the set of independent variables. The results indicate that the topological parameters, i.e., the topostructural and topochemical indices, explain the majority of the variance, and that the inclusion of log P, geometric, and quantum chemical parameters does not result in significantly improved predictive models.

The variable molecular descriptors based on distance related matrices

Recently variable molecular connectivity index and variable paths have been tested as molecular descriptors in several structure-property regressions. Here we outline the construction of several variable molecular descriptors, derived from the distance matrix and the "reversed" distance matrix. This includes the variable Balaban J index and the "reversed" Balaban index 1/J as well as a novel index 1/JJ derived from J and 1/J. All the variable descriptors mentioned were constructed by augmenting the distance matrix by replacing the diagonal zeroes with the variables x, y, z,.

On structural interpretation of several distance related topological indices

We consider the role that individual bonds play in bond-additivities in order to better understand the structural basis of various topological indices. In particular we consider indices closely related to the Wiener index (W) and the distance matrix and search for optimal weights of terminal and interior CC bonds in alkanes for a selection of physicochemical properties. It is interesting to note that different properties are associated with different relative roles of the exterior and the interior CC bonds.

Inhibition of Cutaneous UV Light–induced Tumor Necrosis Factor-α Protein Production by Allotrap 1258, a Novel Immunomodulatory Peptide¶

Peptides derived from the heavy chain of the HLA Class-I molecules have been shown to modulate immune responses both in vivo and in vitro. Using a computer-aided rational drug design approach, novel immunomodulatory peptides were designed based on peptide 2702.75-85, derived from HLA-B2702. Several peptides were identified which had increased immunomodulatory activity, including peptides RDP1258 and its D-isomer the peptide Allotrap 1258. The present study using Skh/hr hairless mouse skin model evaluated the in vivo effects of Allotrap 1258 on acute UVB-induced skin inflammation. Here we demonstrate that intraperitoneal administration of Allotrap 1258 1 h prior to UV exposure resulted in significantly diminished levels of UV-induced tumor necrosis factor (TNF)-alpha protein production in the epidermis but had no effect on other parameters of the acute UV-induced inflammatory response. By virtue of its ability to suppress TNF-alpha protein production, Allotrap 1258 could prove to be an effective modulator of inflammatory responses.

Quantitative structure-property relationships generated with optimizable even/odd Wiener polynomial descriptors

Chemical structures of organic compounds are characterized numerically by a variety of structural descriptors, one of the earliest and most widely used being the Wiener index W, derived from the interatomic distances in a molecular graph. Extensive use of distance-based structural descriptors or topological indices has been made in QSPR and QSAR models, drug design, toxicology, virtual screening of combinatorial libraries, similarity and diversity assessment. Novel topological indices are introduced representing a partitioning of the Wiener polynomial based on counts of even and odd molecular graph distances. During the QSAR/QSPR modeling process the variables of the even and odd power functions are optimized in order to offer an improved mapping of the investigated property. These novel topological indices are tested in QSPR models for the boiling temperature, molar heat capacity, standard Gibbs energy of formation, vaporization enthalpy, refractive index, and density of alkanes. In many cases, the even/odd Wiener polynomial indices proposed here give notably improved correlations or suggest simpler QSPR models.

QSAR comparative study of Wiener descriptors for weighted molecular graphs

Quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) studies use statistical models to compute physical, chemical, or biological properties of a chemical substance from its molecular structure, encoded in a numerical form with the aid of various descriptors. Structural indices derived from molecular graph matrices represent an important group of descriptors used in QSPR and QSAR models; recently, their utilization was extended to molecular similarity and diversity, in database mining and virtual screening of combinatorial libraries. Initially defined from the distance matrix, the Wiener index W was the source of novel graph descriptors derived from recently proposed molecular matrices and of the Wiener graph operator. In this work we present a comparative study of several Wiener-type descriptors computed for vertex- and edge-weighted molecular graphs, corresponding to organic compounds with heteroatoms and multiple bonds. The acute toxicities toward Tetrahymena pyriformis of 47 nitrobenzenes are modeled with multilinear regression equations, using as structural descriptors the hydrophobicity (corrected for ionization) and various Wiener-type indices, with better results than a comparative molecular field analysis model.

A genetic algorithm for the automated generation of small organic molecules: drug design using an evolutionary algorithm

Rational drug design involves finding solutions to large combinatorial problems for which an exhaustive search is impractical. Genetic algorithms provide a novel tool for the investigation of such problems. These are a class of algorithms that mimic some of the major characteristics of Darwinian evolution. LEA has been designed in order to conceive novel small organic molecules which satisfy quantitative structure-activity relationship based rules (fitness). The fitness consists of a sum of constraints that are range properties. The algorithm takes an initial set of fragments and iteratively improves them by means of crossover and mutation operators that are related to those involved in Darwinian evolution. The basis of the algorithm, its implementation and parameterization, are described together with an application in de novo molecular design of new retinoids. The results may be promising for chemical synthesis and show that this tool may find extensive applications in de novo drug design projects.

Inhibition of tumor necrosis factor mRNA translation by a rationally designed immunomodulatory peptide

Based on sequences of immunomodulatory peptides derived from the heavy chain of HLA Class I, novel immunomodulatory peptides with increased potency were developed by computer-aided rational design. Allotrap 1258 was characterized in detail and shown to inhibit cell-mediated immune responses in vitro and in vivo. Immunomodulatory activity was associated with the capability of the peptides to modulate heme oxygenase (HO) activity. In this study we analyzed the effect of Allotrap 1258 on cytokine expression. Allotrap 1258 inhibited concanavalin A- and lipopolysaccharide-induced human and mouse tumor necrosis factor (TNF) production in vitro and in vivo but had no effect on interleukin (IL)-1, IL-2, IL-4, IL-6, or IL-10 expression. Experiments with HO-1/KO and iNOS/KO mice showed that Allotrap 1258-mediated inhibition of TNF was independent of HO-1 and iNOS. Quantitation of TNF protein expression and mRNA steady state levels demonstrated that Allotrap 1258-mediated inhibition occurred at the translational level. Deletion of the AU-rich element in the 3'-untranslated region (UTR) of TNF mRNA, a region known to be involved in TNF mRNA translation, had minimal effect on Allotrap 1258-mediated inhibition. However, replacement of the TNF 3'-UTR with the human globin 3'-UTR rendered the peptide inactive. This demonstrates that besides AU-rich elements, other sequences in the 3'-UTR of TNF mRNA are involved in translational control of TNF expression. Such sequences are necessary for Allotrap 1258-mediated inhibition of TNF production.

Functional diversity of compound libraries

The ideal designed screening library should contain compounds with a variety of structural shapes and molecular properties, while avoiding redundancies. Other requirements involve the need to find structurally distinct leads and to recognise drug-like molecules. Functional diversity analysis is one way in which these objectives can be achieved. For this, molecular descriptions that relate to both structure and properties of molecules are needed, as well as their evaluation in terms of biological relevance.