A Novel Two-Step Hierarchical Quantitative Structure–Activity Relationship Modeling Work Flow for Predicting Acute Toxicity of Chemicals in Rodents

BackgroundAccurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening.ObjectiveA wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects.Methods and resultsA database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC50) and in vivo rodent median lethal dose (LD50) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure–activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD50 values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC50 and LD50. However, a linear IC50 versus LD50 correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC50 and LD50 values: One group comprises compounds with linear IC50 versus LD50 relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD50 values from chemical descriptors. All models were extensively validated using special protocols.ConclusionsThe novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only to inform the initial construction of the hierarchical two-step QSAR models. Models resulting from this approach employ chemical descriptors only for external prediction of acute rodent toxicity

Cheminformatics Approaches to Structure Based Virtual Screening: Methodology Development and Applications

Structure-based virtual screening (VS) using 3D structures of protein targets has become a popular in silico drug discovery approach. The success of VS relies on the quality of underlying scoring functions. Despite of the success of structure-based VS in several reported cases, target-dependent VS performance and poor binding affinity predictions are well-known drawbacks in structure-based scoring functions. The goal of my dissertation is to use cheminformatics approaches to address above problems of the existing structure-based scoring methods. In Aim 1, cheminformatics practices are applied to those problems which conventional structure-based scoring functions find difficult (anti-bacterial leads efflux study) or fail to address (AmpC β-lactamase study). Predictive binary classification QSAR models can be constructed to classify complex efflux properties (low vs. high) and to differentiate AmpC β-lactamase binders from binding decoys (i.e., the false positives generated by scoring functions). The above models are applied to virtual screening and many computational hits are experimentally confirmed. In Aim 2, novel statistical binding and pose scoring functions (or pose filter in Aim 3) are developed, to accurately predict protein-ligand binding affinity and to discriminate native-like poses of ligands from pose decoys respectively. In my approach, the proteinligand interface is represented at the atomic level resolution and transformed via a special computational geometry approach called Delaunay tessellation to a collection of atom quadruplet motifs. And individual atom members of the motifs are characterized by conceptual Density Functional Theory (DFT)-based atomic properties. The binding scoring function shows acceptable prediction accuracy towards Community Structure-Activity Resources (CSAR) data sets with diverse protein families. In Aim 3, a two-step scoring protocol for target-specific virtual screening is developed and validated using the challenging Directory of Useful Decoys (DUD) data sets. In the first step our target-specific pose (-scoring) filter developed in Aim 2 is used to filter out/penalize putative pose decoys for every compound. Then in the second step the remaining putative native-like poses are scored with MedusaScore, which is a conventional force-field-based scoring function. This novel screening protocol can consistently improve MedusaScore VS performance, suggesting it possible applications to practical pharmaceutically relevant targets

Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers

Compared to the current knowledge on cancer chemotherapeutic agents, only limited information is available on the ability of organic compounds, such as drugs and/or natural products, to prevent or delay the onset of cancer. In order to evaluate chemical chemopreventive potentials and design novel chemopreventive agents with low to no toxicity, we developed predictive computational models for chemopreventive agents in this study. First, we curated a database containing over 400 organic compounds with known chemoprevention activities. Based on this database, various random forest and support vector machine binary classifiers were developed. All of the resulting models were validated by cross validation procedures. Then, the validated models were applied to virtually screen a chemical library containing around 23,000 natural products and derivatives. We selected a list of 148 novel chemopreventive compounds based on the consensus prediction of all validated models. We further analyzed the predicted active compounds by their ease of organic synthesis. Finally, 18 compounds were synthesized and experimentally validated for their chemopreventive activity. The experimental validation results paralleled the cross validation results, demonstrating the utility of the developed models. The predictive models developed in this study can be applied to virtually screen other chemical libraries to identify novel lead compounds for the chemo-prevention of cancers

The QSARome of the receptorome: Quantitative structure-activity relationship modeling of multiple ligand sets acting at multiple receptors

Recent advances in High Throughput Screening (HTS) led to the rapid growth of chemical libraries of small molecules, which calls for improved computational tools and predictive models for Virtual Screening (VS). Thus this dissertation focuses on both the development and application of predictive Quantitative Structure-Activity Relationship (QSAR) models and aims to discover novel therapeutic agents for certain diseases. First, this dissertation adopts the combinatorial QSAR framework created by our lab, including the first application of the Distance Weighted Discrimination (DWD) method that resulted in a set of robust QSAR models for the 5-HT7 receptor. VS using these models, followed by the experimental test of identified compounds, led to the finding of five known drugs as potent 5-HT7 binders. Eventually, droperidol (Ki = 3.5 nM) and perospirone (Ki = 8.6 nM) proved to be strong 5-HT7 antagonists. Second, we intended to enhance VS hit rate. To that end, we developed a cost/benefit ratio as an evaluation performance metric for QSAR models. This metric was applied in the Decision Tree machine learning method in two ways: (1) as a benchmarking criterion to compare the prediction performances of different classifiers and (2) as a target function to build QSAR classification trees. This metric may be more suitable for imbalanced HTS data that include few active but many inactive compounds. Finally, a novel QSAR strategy was developed in response to the polygenic nature of most psychotic disorders, related mainly to G-Protein-Coupled Receptors (GPCRs), one class of molecular targets of greatest interest to the pharmaceutical industry. We curated binding data for thousands of GPCR ligands, and developed predictive QSAR models to assess the GPCR binding profiles of untested compounds that could be used to identify potential drug candidates. This comprehensive study yielded a compendium of validated QSAR predictors (the GPCR QSARome), providing effective in silico tools to search for novel antipsychotic drugs. The advances in results and procedures achieved in these studies will be integrated into the current computational strategies for rational drug design and discovery boosted by our lab, so that predictive QSAR modeling will become a reliable support tool for drug discovery programs

Prioritizing Small Molecules for Drug Discovery or Chemical Safety Assessments using Ligand- and Structure-based Cheminformatics Approaches

Recent growth in the experimental data describing the effects of chemicals at the molecular, cellular, and organism level has triggered the development of novel computational approaches for the prediction of a chemical's effect on an organism. The studies described in this dissertation research predict chemical activity at three levels of biological complexity: binding of drugs to a single protein target, selective binding to a family of protein targets, and systemic toxicity. Optimizing cheminformatics methods that examine diverse sources of experimental data can lead to novel insight into the therapeutic use and toxicity of chemicals. In the first study, a combinatorial Quantitative Structure-Activity Relationship (QSAR) modeling workflow was successfully applied to the discovery of novel bioactive compound against one specific protein target: histone deacetylase inhibitors (HDACIs). Four candidate molecules were selected from the virtual screening hits to be tested experimentally, and three of them were confirmed active against HDAC. Next, a receptor-based protocol was established and applied to discover target-selective ligands within a family of proteins. This protocol extended the concept of protein/ligand interaction-guided pose selection by employing a binary classifier to discriminate poses of interest from a calibration set. The resulting virtual screening tools were applied for enriching beta2-adrenergic receptor (β2AR) ligands that are selective against other subtypes in the βAR family (i.e. β1AR and β3AR). Moreover, some computational 3D protein structures used in this study have exhibited comparative or even better performance in virtual screening than X-ray crystal structures of β2AR, and therefore computational tools that use these computational structures could complement tools utilizing experimental structures. Finally, a two-step hierarchical QSAR modeling approach was developed to estimate in vivo toxicity effects of small molecules. Besides the chemical structural descriptors, the developed models utilized additional biological information from in vitro bioassays. The derived models were more accurate than traditional QSAR models utilizing chemical descriptors only. Moreover, retrospective analysis of the developed models helped to identify the most informative bioassays, suggesting potential applicability of this methodology in guiding future toxicity experiments. These studies contribute to the development of computational strategies for comprehensive analysis of small molecules' biological properties, and have the potential to be integrated into existing methods for modern rational drug design and discovery.Doctor of Philosoph

Bioinformatics Analysis and Modelling of Therapeutically Relevant Molecules

Ph.DDOCTOR OF PHILOSOPH

Identificación de inhibidores de la actividad de la enzima superóxido dismutasa (SOD-Cu/Zn) del parásito Taenia solium mediante simulación del acoplamiento molecular proteína-ligando (docking)

En el presente trabajo de tesis, se dan a conocer los resultados obtenidos al llevar a cabo la simulación del acoplamiento molecular proteína-ligando (docking) de una quimioteca de aproximadamente 50 mil compuestos con potencial farmacológico (LeadQuest®) y la estructura tridimensional de la enzima dimérica superóxido dismutasa Cu/Zn del parasito Taenia solium, TsSOD–Cu/Zn. Este trabajo tiene el propósito de identificar compuestos líderes que permitan desarrollar inhibidores específicos de la actividad de la enzima del parásito sin afectar a la enzima homologa en humano. Para tal fin, fueron construidos los confórmeros de los compuestos LeadQuest® con energías conformacionales igual o menores a 3 kcal/mol respecto de la más estable, constituyéndose una base de más de 2 millones de confórmeros. La simulación del acoplamiento se realizó sobre los 21 sitios potenciales de unión localizados en la superficie tridimensional de la enzima. Los complejos proteína-ligando con mejor puntaje fueron sometidos a una minimización local de la energía con la finalidad de mejorar las interacciones del ligando con los residuos de contacto en el sitio de unión. Una vez realizado lo anterior, la inspección visual de los complejos minimizados permitió seleccionar aquellos con interacciones intermoleculares con residuos no conservados en la enzima homóloga en humano. Ensayos de inhibición directa sobre la enzima recombinante SOD–Cu/Zn de T. solium pura, con algunos de los compuestos que presentaron buenos puntajes de formación de complejos proteínaligando por simulación computacional, selectividad en la unión hacia residuos no conservados en la secuencia de SOD-Cu/Zn de humano, entre otros aspectos, mostraron inhibir parcial o totalmente la actividad de la enzima a concentraciones del orden micro molar. Particularmente, seis de los cincuenta compuestos seleccionados para realizar ensayos de inhibición presentaron actividad inhibitoria de TsSOD–Cu/Zn y sólo tres afectan la actividad de la enzima superóxido dismutasa de humano a las concentraciones ensayadas en este trabajo. Adicionalmente, se realizó la simulación del acoplamiento molecular del albendazol y tiabendazol, ambos fármacos antihelmínticos, y la estructura tridimensional de la TsSOD–Cu/Zn. El estudio muestra que estos compuestos tienen afinidad por el sito de la catálisis, al cual se unen mediante interacciones tipo puente de hidrógeno y metálica con el cobre catalítico.We describe in this work a successful virtual screening using MOE™ package and experimental testing aimed to the identification of novel inhibitors of superoxide dismutase of the tape worm Taenia solium (TsSOD–Cu/Zn), a human parasite. After conformational search from LeadQuest® database of drug–like compounds, about 2 million structures from about 50 thousand original compounds were selected and then docked on 21 potential binding sites over the surface of X-ray diffraction structure of TsSOD–Cu/Zn. Docking results were screened looking for best protein-ligand complex scores and then, energy minimizations of complexes based on three steps methodology were carried out in order to include the possible structural effect of the ligand on the enzyme binding site. We then searched for ligand with receptor side-chains contacts not conserved in the human homologue structure, aimed to identify lead compounds for in vitro experiments over recombinant pure TsSOD–Cu/Zn. Several other criteria were considered as a second criterion to reduce the subset of potential ligands, for example, LogP, number of hydrogen bonds, shape complementarity, robustness of the results (poses found frequently), etc. Six out of fifty experimentally tested compounds have shown µM inhibitory activities toward TsCu/Zn–SOD. Three of these compounds showed excellent species selectivity to TsSOD–Cu/Zn since they affect the activity of this enzyme but did not show inhibition in the homologous human enzyme when assayed in vitro

I. Comparison of Translesion Bypass of Guanine–N2 Monoadducts of Mitomycin C and Guanine-N7 Monoadducts of 2,7-diaminomitosene by T7 exo-, Klenow exo-, eta and Klenow exo+ DNA Polymerases. II. Structure-based Design, Synthesis, Structure-conformation and Structure-activity Relationships Studies of D-Phe-Pro-D-Arg-P1’-CONH2 Tetrapeptides with Inhibitory Activity for Thrombin.

The guanine (G)-N2 DNA monoadduct of mitomycin C (MC), a cytotoxic anticancer drug, inhibits translesion bypass by DNA polymerases. 2,7-Diaminomitosene (2,7-DAM) is the major metabolite of MC in tumor cells, generated by the reduction of MC. 2,7-DAM alkylates DNA in the cell in situ, forming an adduct at the N7 position of 2\u27-deoxyguanosine (2,7-DAM-dG) and is noncytotoxic. In part I of this study we tested a potential correlation between the lack of cytotoxicity of 2,7-DAM and the relative ease of bypass of this adduct as compared with the MC adduct. 24-mer and 27-mer templates, adducted at a single guanine either with MC or 2,7-DAM were synthesized and submitted to extension of primers by T7 exo-, Klenow exo-, Klenow exo+, and eta DNA polymerases. The G-N7-2,7-DAM adduct was bypassed by all four polymerases, resulting in the production of a fully extended primer. In sharp contrast, the G-N2-MC monoadduct was not bypassed beyond the adduct position under the same conditions by any of the four polymerases. In parallel experiments in cell free systems, template oligonucleotides containing a single 2,7-DAM-dG-N7 adduct directed selective incorporation of cytosine in the 5\u2732P-labeled primer strands opposite the adducted guanine, catalyzed by Klenow (exo-) DNA polymerase. These results showed for the first time that the dG-N7-2, 7-DAM lesion is non-mutagenic in cell-free systems. In part II of this research structure-based design and molecular docking were employed to design in silico libraries of peptides as potential reversible inhibitors of thrombin. The candidate inhibitors were selected from two original classes of amino acids sequences (1)-D-Phe-Pro-Arg (P1)-D-Pro(P1\u27)-P2\u27-P3\u27-CONH2 and (2)-D-Phe-Pro-D-Arg(P1)-P1\u27-P2\u27-P3\u27-CONH2. For the first time in the field of peptides inhibitors for thrombin we showed that the presence of D-Pro at P1\u27 Position and the use of D-Arg instead of L-Arg at P1 Position is responsible for inhibiting hydrolysis of these of peptides by thrombin, causing these sequences to be inhibitors. In vitro kinetics of thrombin inhibition showed a specific structure-activity relationship at P1\u27 position in the peptide sequence space (2)-D-Phe-Pro-D-Arg(P1)-P1\u27-CONH2. The lead peptides (D-Phe-Pro-D-Arg-D-Ala-CONH2, D-Phe-Pro-D-Arg-D-Thr-CONH2, D-Phe-Pro-D-Arg-D-Cys-CONH2, D-Phe-Pro-D-Arg-D-Ser-CONH2) had competitive or mixed inhibition with respect to thrombin and are characterized by inhibitory constant in the 20-0.8 micromolar range