In silico search for novel bacterial inhibitors targeting RNA polymerase switch region

The arise of antibiotic-resistant bacterial strains in an alarming rate has increased the interest in the discovery of novel antibiotics. The rifamycins are a valuable class of antibiotics that target bacterial Ribonucleic Acid polymerase (RNAP) and are considered the first-line treatment for tuberculosis. Consequently, bacterial strains resistant to rifamycin constitute a public health threat. RNAP switch region is an attractive target for the development of new antibacterial agents as it lies away from the rifamycin binding region and thus the compounds that target the switch region would not show cross-resistance with rifamycins. In this work, we developed a virtual screening pipeline to identify new bacterial RNAP inhibitors that target the enzyme switch region. The screening pipeline involved docking of the designated libraries using the Maestro Glide docking tool, and the compounds with the best docking scores were submitted for binding free energy calculations using the molecular mechanics-generalised born surface area (MM-GBSA)-based method. Moreover, a quantitative structure-activity relationship (QSAR) model was developed, and it was applied to predict the biological activity of the compounds with the most favourable calculated binding free energies. Based on the results of docking, MM-GBSA binding free energies and the activities predicted by the QSAR model, the most promising compounds were chosen to be evaluated by molecular dynamics (MD) simulations. The results of the MD simulation of each docked candidate compound in the RNAP binding site were compared with the MD simulations carried out with the apo protein and with a reference co-crystallized ligand in the RNAP binding site. The candidate compounds showing comparable binding to the RNAP site to the reference ligand were selected for further biological testing

rational design of functionalized lipids with antioxidant and scavenging activity as components of innovative artificial tears

openDurante i tre anni di dottorato mi sono occupato del design razionale di lipidi funzionalizzati ad attività antiossidante da utilizzare per la formulazione di lacrime artificiali. Abbiamo scelto i liposomi per veicolare le molecole antiossidanti testate; siamo partiti utilizzando l’Edaravone (EDR), il quale è stato opportunamente funzionalizzato (EDR-C18), senza diminuire le proprietà antiossidanti della molecola. Abbiamo condotto studi sperimentali evidenziando che l’EDR-C18 mantiene elevate capacità antiossidanti, solo a questo punto abbiamo condotto simulazioni di dinamica molecolare utilizzando un sistemi lipidici puri contenti fosfatidilcolina (POPC), e differenti concentrazioni di EDR-C18. I dati di simulazione hanno evidenziato una elevata stabilità dell’EDR-C18 in membrana. Abbiamo quindi determinato la concentrazione ottimale al fine di ottenere una intatta fluidità del sistema e allo stesso tempo una elevata quantità di antiossidante. Partendo da questo modello di simulazione, abbiamo creato altri sistemi di simulazione in cui abbiam investigato l’effetto dei Sali. Abbiamo testato soluzioni saline già comunemente utilizzate nella formulazione di lacrime artificiali, ed abbiamo riscontrato nel CaCl2 il sale maggiormente utile per la nostra strategia. Tali sistemi sono estremamente promettenti per la formulazione di gocce oculari. Un’altra molecola testata è l’epigallocatechin3-Gallato (EGCG). Essa ha la capacità di interagire spontaneamente con sistemi lipidici, perciò non è stata funzionalizzata. Abbiamo condotto delle simulazioni di dinamica molecolare creando sistemi lipidici misti per indagare l’effetto della matrice sulla capacità di inglobamento dell’EGCG. Abbiamo inoltre modulato la concentrazione salina ed abbiamo individuato nel sistema lipidico anionico con una quantità di Magnesio pari a 5:1 in rapporto molare con EGCG, il sistema in cui tutto l’EGCG introdotto viene inglobato, aumentando la biodisponibilità della molecola al massimo possibile.During three years of my PhD course, I studied the rational design of functionalized lipid with antioxidant activity to be used for the formulation of artificial tears. We chosen the liposomes to carrier the antioxidant molecules that we studied; we started using the edaravone (EDR), which has been suitably functionalized (EDR-C18), without decrease the antioxidant properties of the molecule. For this purpose, we made experimental studies showing that the EDR-C18 maintains high antioxidant capacity, only at this point we carried out the molecular dynamics (MD) simulations using pure lipid systems containing POPC and different EDR-C18 concentrations. The simulation data showed a high EDR-C18 stability in the membrane. We determined the optimal concentration to obtain an intact fluidity of the system and at the same time a high amount of antioxidant. On the basis on this simulation model, we created other MD systems in which we have investigated the effects of salts. We tested salt solutions already commonly used in the formulation of artificial tears, and we found that CaCl2 is better salt to use for our strategy. Such systems are extremely promising for the formulation of eye drops. Another antioxidant that we studied is the epigallocatechin3-gallate (EGCG). It has the ability to interact spontaneously with lipid system, for this reason, it was not functionalized. We carried out MD simulations creating mixed lipid systems to investigate the effects of the matrix on the EGCG encapsulation. In addition, we modulated on salt concentration and we found that the best model is the anionic lipid system with an amount of magnesium equal to 5:1 in molar ratio with EGCG the system in which all the EGCG introduced is encapsulated, maximizing the bioavailability of the antioxidant compound, that is able to reach the lipid medium of lacrimal tear and to retain in it.SCIENZE DELLA VITA E DELL'AMBIENTEembargoed_20181001Laudadio, EmilianoLaudadio, Emilian

In silico & In vitro study to estimate Plasma Protein Binding of anti-parasitic compounds for Sleeping sickness (Human African trypanosomiasis)

Human African trypanosomiasis (HAT), also known as sleeping sickness, is a disease caused by a group of parasites called Trypanosoma brucei (Tb). The two main types causing HAT are T. brucei gambiense and T. brucei rhodesiense. T. brucei gambiense is the most common form of HAT, accounting for ninety seven percent of all reported cases of sleeping sickness. According to WHO, HAT is endemic in 36 sub-Saharan African countries. The disease can lead to death during the second stage if left untreated. Several drugs have been developed for the first stage such as pentamidine and suramin, and for the second stage such as melarsoprol, nifurtimox-eflornithine combination therapy (NECT). In 2019, fexinidazole was introduced as an oral treatment for the first stage and non-severe second stage of HAT. Several antiparasitic compounds prepared by our collaborator’s research group at the University of Graz, Austria showed varying levels of activity against Tb in vitro, whereas the compounds had only a moderate in vivo effect if at all. The suggested reason for the poor in vivo activities is that the compounds may bind tightly to plasma proteins, or they are metabolized before reaching the target sites for therapeutic effect. The prediction of plasma protein binding is of paramount importance in the pharmacokinetics characterization of drugs, as it causes significant changes in volume of distribution, clearance and drug half-life. Human serum albumin (HSA), an abundant plasma protein, can bind a remarkable variety of drugs impacting their delivery and efficacy and ultimately altering the drug’s pharmacokinetic and pharmacodynamic properties. In this current investigation, the overall aim was to investigate whether a strong HSA binding could be a probable reason for the poor in vivo activity of the provided antiparasitic compounds. The interaction of the antiparasitic compounds with HSA was studied computationally by docking them in the HSA drug binding site I and II. The compounds with the highest docking score were additionally studied using molecular dynamics simulations to evaluate the stability of the binding interactions. Moreover, the HSA binding affinity of the compounds was estimated by calculating the binding free energies using the MM-GBSA approach. In addition, experimental HSA binding studies using Microscale thermophoresis (MST) were conducted for some of the compounds. The results of the in silico studies suggest that majority of the investigated compounds may bind to HSA with varying affinity whereas a few of them did not show favorable binding interactions with HSA. Further, none of the compounds studied in vitro by MST showed HSA binding. In sum, plasma protein binding may be the reason for the in vivo inactivity for some of the investigated antiparasitic compounds

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Experimental and computational methods for identification of novel fungal histone acetyltransferase Rtt109 inhibitors

University of Minnesota Ph.D. dissertation. February 2014. Major: Medicinal Chemistry. Advisor: Elizabeth A. Amin. 1 computer file (PDF); xii, 180 pages.Rtt109 is a fungal-specific histone acetyltransferase that catalyzes histone H3 lysine 56 acetylation and is a promising antifungal drug target. To identify novel Rtt109 inhibitors as potential drug scaffolds, we employed in vitro high throughput screening (HTS) and various computer-assisted strategies, including molecular dynamics, docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis, pharmacophore modeling, and Support Vector Machine (SVM) mining. An initial experimental screening of 82,861 compounds (HTS1) yielded hits with activity ranging from 0.49 - 17.5 µM against Rtt109. The molecular dynamics simulation of Rtt109 suggested that the histone lysine tunnel, a potential inhibitor binding site, was not flexible and thus the use of a rigid protein structure of Rtt109 was appropriate for docking studies. From a virtual screen using Surflex-Dock, we have identified 878 additional compounds as potential hits, with predicted Kd values of 0.1 nm or lower. Based on preliminary experimental data from HTS1, validated pharmacophore maps were developed and used to pinpoint potential Rtt109 ligand-receptor interactions. 3D-QSAR CoMFA and CoMSIA models that were also derived from the hit series generated in the initial experimental HTS display high self-consistency (r2 = 0.985 [CoMFA] and r2 = 0.976 [CoMSIA]) and robust internal predictivity (rcv2 = 0.754 [CoMFA] and rcv2 = 0.654 [CoMSIA]). Importantly, key features identified in both the pharmacophore hypotheses and the 3D-QSAR models agreed well with each other and with experimentally defined structural features in the Rtt109 lysine-binding tunnel. In addition, our optimized SVM models demonstrated high predictive power against the external test sets for Rtt109 with accuracy of 91.1%. We also identified novel features with significant differentiating ability to separate Rtt109 inhibitors from non-inhibitors

Structure-activity approaches for prediction of chemical reactivity and pharmacological properties of some heterocyclic compounds

Benzodiazepine drugs are widely prescribed to treat many psychiatric and neurologic disorders. As its pharmacological action is exerted in a sensitive area of the brain; ''the central nervous system'', it is crucial to provide detailed reports on the chemistry of benzodiazepines, model the mechanism of action that occurs with GABAA receptors, identify the overlap with other modulators, as well as explore the structural requirements that better potentiate the receptor response to benzodiazepines. This dissertation consists of two original studies that consider the new lines of research related to benzodiazepines, particularly the identification of three new TMD binding sites. The first study provides, on the one hand, an overview of the chemistry of six Benzodiazepine basic rings starting from structural characteristics, electronic properties, Global/local reactivities, up to intermolecular interactions with long-range nucleophilic/electrophilic reactants. This was achieved by combining a DFT investigation with a quantitative MEP analysis on the vdW surface. On the other hand, the performed molecular docking simulations allowed identifying the best binding modes, binding interactions, and binding affinities with residues, which helped to validate the quantitative MEP analysis predictions. The second study was conducted on a dataset of [3H]diazepam derivatives. First, molecular docking simulation was used to initially screen the dataset and select the best ligand/target complexes. Afterwise, the best-docked complexes were refined by performing molecular dynamics simulation for 1000 picoseconds. For both simulations, the binding modes, binding interactions, and binding affinities were thoroughly discussed and compared with each other and with outcomes collected from the literature. Additionally, the good pharmacokinetic properties (ADME prediction) as well as compliance with all druglikeness rules were checked via in silico tools for all the dataset compounds. Finally, a QSAR analysis was carried out using an improved version of PLS regression. Briefly, the dataset is randomly split into 10 000 training and test sets that involve, respectively, 80% and 20% of chemicals. Subsequently, 10 000 statistical simulations were conducted that; after excluding outlying observations, yielded 10 000 best training models following the Bayesian Information Criterion. Among these 10 000 best models, the best predictors with the highest probability of occurrence were selected. As a consequence, the derived PLS regression equation explains 63.2% of the variability in BDZ activity around its mean. Furthermore, Internal and external validation metrics assure the robustness and predictability of the developed model. The developed model was interpreted based on literature investigations and a combination of implemented approaches

Design and synthesis of cysmethynil and analogues as inhibitors of isoprenylcysteine carboxyl methyltransferase (ICMT)

Ph.DDOCTOR OF PHILOSOPH

Aqueous solubility of drug-like compounds

New effective experimental techniques in medicinal chemistry and pharmacology have resulted in a vast increase in the number of pharmacologically interesting compounds. However, the possibility of producing drug candidates with optimal biopharmaceutical and pharmacokinetic properties is still improvable. A large fraction of typical drug candidates is poorly soluble in water, which results in low drug concentrations in gastrointestinal fluids and related acceptable low drug absorption. Therefore, gaining knowledge to improve the solubility of compounds is an indispensable requirement for developing compounds with drug-like properties. The main objective of this thesis was to investigate whether computer-based models derived from calculated molecular descriptors and structural fragments can be used to predict aqueous solubility for drug-like compounds with similar structures. For this purpose, both experimental and computational studies were performed. In the experimental work, a novel crystallization method for weak acids and bases was developed and applied for European patent. The obtained crystalline materials could be used for solubility measurements. A novel recognition method was developed to evaluate the tendency of compounds to form amorphous forms. This method could be used to ensure that only solubilities of crystalline materials were collected for the development of solubility prediction. In the development of improved in silico solubility models, lipophilicity was confirmed as the major driving factor and crystal information related descriptors as the second important factor for solubility. Reasons for the limited precision of commercial solubility prediction tools were identified. A general solubility model of high accuracy was obtained for drug-like compounds in congeneric series when lipophilicity was used as descriptor in combination with the structural fragments. Rules were derived from the prediction models of solubility which could be used by chemists or interested scientists as a rough guideline on the contribution of structural fragments on solubility: Aliphatic and polar fragments with high dipole moments are always considered as solubility enhancing. Strong acids and bases usually have lower intrinsic solubility than neutral ones. In summary, an improved solubility prediction method for congeneric series was developed using high quality solubility results of drugs and drug precursors as input parameter. The derived model tried to overcome difficulties of commercially available prediction tools for solubility by focusing on structurally related series and showed higher predictive power for drug-like compounds in comparison to commercially available tools. Parts of the results of this work were protected by a patent application1, which was filed by F. Hoffmann-La Roche Ltd on August 30, 2005

Novel Strategies for Model-Building of G Protein-Coupled Receptors

The G protein-coupled receptors constitute still the most densely populated proteinfamily encompassing numerous disease-relevant drug targets. Consequently, medicinal chemistry is expected to pursue targets from that protein family in that hits need to be generated and subsequently optimized towards viable clinical candidates for a variety of therapeutic areas. For the purpose of rationalizing structure-activity relationships within such optimization programs, structural information derived from the ligand's as well as the macromolecule's perspective is essential. While it is relatively straightforward to define pharmacophore hypotheses based on comparative modelling of structurally and biologically characterized low-molecular weight ligands, a deeper understanding of the molecular recognition event underlying, remains challenging, since the principally available amount of experimentally derived structural data on GPCRs is extremely scarse when compared to, e.g., soluble enzymes. In this context, the protein modelling methodologies introduced, developed, optimized, and applied in this thesis provide structural models that are capable of assisting in the development of structural hypotheses on ligand-receptor complexes. As such they provide a valuable structural framework not only for a more detailed insight into ligand-GPCR interaction, but also for guiding the design process towards next-generation compounds which should display enhanced affinity. The model building procedure developed in this thesis systematically follows a hierarchical approach, sequentially generating a 1D topology, followed by a 2D topology that is finally converted into a 3D topology. The determination of a 1D topology is based on a compartmentalization of the linear amino acid sequence of a GPCR of interest into the extracellular, intracellular, and transmembrane sequence stretches. The entire chapter 3 of this study elaborates on the strengths and weaknesses of applying automated prediction tools for the purpose of identifying the transmembrane sequence domains. Based on an once derived 1D topology, a type of in-plane projection structure for the seven transmembrane helices can be derived with the aide of calculated vectorial property moments, yielding the 2D topology. Thorough bioinformatics studies revealed that only a consensus approach based on a conceptual combination of different methods employing a carefully made selection of parameter sets gave reliable results, emphasizing the danger to fully automate a GPCR modelling procedure. Chapter 4 describes a procedure to further expand the 2D topological findings into 3D space, exemplified on the human CCK-B receptor protein. This particular GPCR was chosen as the receptor of interest, since an enormous experimentally derived and structurally relevant data-set was available. Within the computational refinement procedure of constructed GPCR models, major emphasis was laid on the explicit treatment of a non-isotropic solvent environment during molecular mechanics (i.e. energy minimization and molecular dynamics simulations) calculations. The majority of simulations was therefore carried out in a tri-phasic solvent box accounting for a central lipid environment, flanked by two aqueous compartments, mimicking the extracellular and cytoplasmic space. Chapter 5 introduces the reference compound set, comprising low-molecular weight compounds modulating CCK receptors, that was used for validation purposes of the generated models of the receptor protein. Chapter 6 describes how the generated model of the CCK-B receptor was subjected to intensive docking studies employing compound series introduced in chapter 5. It turned out that by applying the DRAGHOME methodology viable structural hypotheses on putative receptor-ligand complexes could be generated. Based on the methodology pursued in this thesis a detailed model of the receptor binding site could be devised that accounts for known structure-activity relationships as well as for results obtained by site-directed mutagenesis studies in a qualitative manner. The overall study presented in this thesis is primarily aimed to deliver a feasibility study on generating model structures of GPCRs by a conceptual combination of tailor-made bioinformatics techniques with the toolbox of protein modelling, exemplified on the human CCK-B receptor. The generated structures should be envisioned as models only, not necessarily providing a detailed image of reality. However, consistent models, when verified and refined against experimental data, deliver an extremely useful structural contextual platform on which different scientific disciplines such as medicinal chemistry, molecular biology, and biophysics can effectively communicate