SuperPred: drug classification and target prediction

The drug classification scheme of the World Health Organization (WHO) [Anatomical Therapeutic Chemical (ATC)-code] connects chemical classification and therapeutic approach. It is generally accepted that compounds with similar physicochemical properties exhibit similar biological activity. If this hypothesis holds true for drugs, then the ATC-code, the putative medical indication area and potentially the medical target should be predictable on the basis of structural similarity. We have validated that the prediction of the drug class is reliable for WHO-classified drugs. The reliability of the predicted medical effects of the compounds increases with a rising number of (physico-) chemical properties similar to a drug with known function. The web-server translates a user-defined molecule into a structural fingerprint that is compared to about 6300 drugs, which are enriched by 7300 links to molecular targets of the drugs, derived through text mining followed by manual curation. Links to the affected pathways are provided. The similarity to the medical compounds is expressed by the Tanimoto coefficient that gives the structural similarity of two compounds. A similarity score higher than 0.85 results in correct ATC prediction for 81% of all cases. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets

A Chemocentric Approach to the Identification of Cancer Targets

A novel chemocentric approach to identifying cancer-relevant targets is introduced. Starting with a large chemical collection, the strategy uses the list of small molecule hits arising from a differential cytotoxicity screening on tumor HCT116 and normal MRC-5 cell lines to identify proteins associated with cancer emerging from a differential virtual target profiling of the most selective compounds detected in both cell lines. It is shown that this smart combination of differential in vitro and in silico screenings (DIVISS) is capable of detecting a list of proteins that are already well accepted cancer drug targets, while complementing it with additional proteins that, targeted selectively or in combination with others, could lead to synergistic benefits for cancer therapeutics. The complete list of 115 proteins identified as being hit uniquely by compounds showing selective antiproliferative effects for tumor cell lines is provided

Rational methods for the selection of diverse screening compounds.

Traditionally a pursuit of large pharmaceutical companies, high-throughput screening assays are becoming increasingly common within academic and government laboratories. This shift has been instrumental in enabling projects that have not been commercially viable, such as chemical probe discovery and screening against high-risk targets. Once an assay has been prepared and validated, it must be fed with screening compounds. Crafting a successful collection of small molecules for screening poses a significant challenge. An optimized collection will minimize false positives while maximizing hit rates of compounds that are amenable to lead generation and optimization. Without due consideration of the relevant protein targets and the downstream screening assays, compound filtering and selection can fail to explore the great extent of chemical diversity and eschew valuable novelty. Herein, we discuss the different factors to be considered and methods that may be employed when assembling a structurally diverse compound collection for screening. Rational methods for selecting diverse chemical libraries are essential for their effective use in high-throughput screens.We are grateful for financial support from the MRC, Wellcome Trust, CRUK, EPSRC, BBSRC and Newman Trust.This is the author accepted manuscript. The final version is available from American Chemical Society via http://dx.doi.org/10.1021/cb100420

Targeting the ‘Oligomerization Region’ of the Epidermal Growth Factor Receptor using Small Molecule Inhibitors as Novel Anticancer Agents

There are two main types of ErbB-RTK subfamily inhibitors, viz, a) the mAbs and b) the RTKIs, which act at different domains of the receptors. The mAbs act at the ectodomain locale either by interfering with the ligand such as EGF or the receptor such as EGFR, in each case interference with dimerization occurs. The RTKIs inhibit numerous biochemical processes beginning with the recruitment of accessory proteins by the dimerized complexes. However, little has been done specifically in the ‘oligomerization region’ in developing active anti-EGFR anti-oligomeric small molecules that can inhibit the oligomerization processes in spite the ligands are bound to their canonical ErbB receptors. The concept of the oligomerization mechanisms, particularly heterotetramerization, has shown leading clues to untangle some of the probes dimerization has limited explanations for, wherein lies the scope of our work. We have designed four small molecules, namely, 3-(furan-2-yl)-4-(8-hydroxyquinolin-2-yl)-2,4,6,7-tetrahydro-5H-pyrazolo[3,4-c]pyridin-5-one (%IG50,3.98 µM; %IC50, 8.90 µM), 3,3,3-trifluoro-2-hydroxy-N-((2-(4-methylpiperazin-1-yl)pyridin-3-yl)methyl)propanamide (%IG50, 0.25 µM; %IC50, 0.40 µM), 2-((2-(3-isopropyl-1,2,4-oxadiazol-5-yl)pyrrolidin-1-yl)methyl)quinolin-8-ol (%IG50, 1.59 µM; %IC50, 1.50 µM) and 4-(1-cyclopentylpyrrolidin-2-yl)-N-((3,5-dimethyl-1H-pyrazol-4-yl)methyl)thiophene-2-carboxamide (%IG50, 1.59 µM; %IC50, 1.10 µM) that act at the ‘oligomerization region’, using the Schrodinger Software v10.4Maestro, v6.9Glide (Schrödinger, LLC, New York, NY, 2015-4) on scrutinizing ≥ 9 x 106 ligands from different chemical databases

In-silico investigation of CREB-binding protein on castration-resistant prostrate cancer: insight from molecular dynamic simulations and computer aided drug discovery.

Masters degree. University of KwaZulu-Natal, Durban.Prostate cancer has evolved over the years despite various treatment and therapy. One recent threat of the prostate cancer is the Castration- Resistant Prostate Cancer (CRPC). The CRPC is an advanced form of prostate cancer. Despite therapies involving chemical, surgical or hormonal treatment, the cancer cells in CRPC have been discovered to continue growth and development at an alarming rate. This forms the basis for this research. One novel drug (Y08197) aimed towards CBRB-binding protein was reported to have anti- cancer properties and therapeutic effect on CRPC. Their research based on in vivo and in vitro experiments, revealed CREB-binding protein as a therapeutic target. This study majored in a search for the culprit behind this miracle. We used molecular dynamics simulation to gain insights into the mechanistic and selective targeting of the novel drug as well as its similar inhibitory effect to an FDA drug (CPI-637). Also, since little is known about the structural and inhibitory properties of CREB-binding protein, we also commenced a review study to look into its inhibitory history. The use of computational techniques such as molecular modeling, molecular docking, virtual screening protocols and molecular dynamics allows the evaluation and assessment of potential leads compounds. These in-silico techniques as stated above are cost-effective and efficient in research and pivot to fast track drug discovery process. Herein, we used molecular dynamics simulation to gain insights into the mechanistic and selective targeting of Y08197 at the bromodomain active site. Molecular Mechanics/ Poisson-Boltzmann Surface Area (MM/PBSA) analysis revealed a similar inhibitory effect between Y08197 and CPI-637. Furthermore, in exploring the selective affinity of Y08197 towards CBP in combination with Bromodomain and PHD finger-containing protein 1(BRPF1), our result highlighted Asp1116 as the ‘culprit’ residue responsible for this selective targeting. Conclusively, the implementation of the information extracted in this study, can be replicated in future structure based CBP inhibitors and pharmacological research implicated in carcinogenesis

Development and validation of in silico tools for efficient library design and data analysis in high throughput screening campaigns

My PhD project findings have their major application in the early phase of the drug discovery process, in particular we have developed and validated two computational tools (Molecular Assembles and LiGen) to support the hit finding and the hit to lead phases. I have reported here novel methods to first design chemical libraries optimized for HTS and then profile them for a specific target receptor or enzyme. I also analyzed the generated bio-chemical data in order to obtain robust SARs and to select the most promising hits for the follow up. The described methods support the iterative process of validated hit series optimization up to the identification of a lead. In chapter 3, Ligand generator (LiGen), a de novo tool for structure based virtual screening, is presented. The development of LiGen is a project based on a collaboration among Dompé Farmaceutici SpA, CINECA and the University of Parma. In this multidisciplinary group, the integration of different skills has allowed the development, from scratch, of a virtual screening tool, able to compete in terms of performance with long standing, well-established molecular docking tools such as Glide, Autodock and PLANTS. LiGen, using a novel docking algorithm, is able to perform ligand flexible docking without performing a conformational sampling. LiGen also has other distinctive features with respect to other molecular docking programs: • LiGen uses the inverse pharmacophore derived from the binding site to identify the putative bioactive conformation of the molecules, thus avoiding the evaluation of molecular conformations which do not match the key features of the binding site. • LiGen implemenst a de novo molecule builder based on the accurate definition of chemical rules taking account of building block (reagents) reactivity. • LiGen is natively a multi-platform C++ portable code designed for HPC applications and optimized for the most recent hardware architectures like the Xeon Phi Accelerators. Chapter 3 also reports the further development and optimization of the software starting from the results obtained in the first optimization step performed to validate the software and to derive the default parameters. In chapter 4, the application of LiGen in the discovery and optimization of novel inhibitors of the complement factor 5 receptor (C5aR) is reported. Briefly, the C5a anaphylatoxin acting on its cognate G protein-coupled receptor C5aR is a potent pronociceptive mediator in several models of inflammatory and neuropathic pain. Although there has long been interest in the identification of C5aR inhibitors, their development has been complicated, as is the case with many peptidomimetic drugs, mostly due to the poor drug-like properties of these molecules. Herein, we report the de novo design of a potent and selective C5aR noncompetitive allosteric inhibitor, DF2593A. DF2593A design was guided by the hypothesis that an allosteric site, the “minor pocket”, previously characterized in CXCR1 and CXCR2, could be functionally conserved in the GPCR class.DF2593A potently inhibited C5a-induced migration of human and rodent neutrophils in vitro. Moreover, oral administration of DF2593A effectively reduced mechanical hyperalgesia in several models of acute and chronic inflammatory and neuropathic pain in vivo, without any apparent side effects. Chapter 5 describes another tool: Molecular Assemblies (MA), a novel metrics based on a hierarchical representation of the molecule based on different representations of the scaffold of the molecule and pruning rules. The algorithm used by MA, defining a priori a metrics (a set of rules), creates a representation of the chemical structure through hierarchical decomposition of the scaffold in fragments, in a pathway invariant way (this feature is novel with respect to the other algorithms reported in literature). Such structure decomposition is applied to nine hierarchical representation of the scaffold of the reference molecule, differing for the content of structural information: atom typing and bond order (this feature is novel with respect to the other algorithms reported in literature) The algorithm (metrics) generates a multi-dimensional hierarchical representation of the molecule. This descriptor applied to a library of compounds is able to extract structural (molecule having the same scaffold, wireframe or framework) and sub structural (molecule having the same fragments in common) relations among all the molecules. At least, this method generates relations among molecules based on identities (scaffolds or fragments). Such an approach produces a unique representation of the reference chemical space not biased by the threshold used to define the similarity cut-off between two molecules. This is in contrast to other methods which generate representations based in similarities. MA procedure, retrieving all scaffold representation, fragments and fragmentation’s patterns (according to the predefined rules) from a molecule, creates a molecular descriptor useful for several cheminformatics applications: • Visualization of the chemical space. The scaffold relations (Figure 7) and the fragmentation patterns can be plotted using a network representation. The obtained graphs are useful depictions of the chemical space highlighting the relations that occur among the molecule in a two dimensional space. • Clustering of the chemical space. The relations among the molecules are based on identities. This means that the scaffold representations and their fragments can be used as a hierarchical clustering method. This descriptor produces clusters that are independent from the number and similarity among closest neighbors because belonging to a cluster is a property of the single molecule (Figure 8). This intrinsic feature makes the scaffold based clustering much faster than other methods in producing “stable” clusters in fact, adding and removing molecules increases and decreases the number of clusters while adding or removing relations among the clusters. However these changes do not affect the cluster number and the relation of the other molecules in dataset. • Generate scaffold-based fingerprints. The descriptor can be used as a fingerprint of the molecule and to generate a similarity index able to compare single molecules or also to compare the diversity of two libraries as a whole. Chapter 6 reports an application of MA in the design of a diverse drug-like scaffold based library optimized for HTS campaigns. A well designed, sizeable and properly organized chemical library is a fundamental prerequisite for any HTS project. To build a collection of chemical compounds with high chemical diversity was the aim of the Italian Drug Discovery Network (IDDN) initiative. A structurally diverse collection of about 200,000 chemical molecules was designed and built taking into account practical aspects related to experimental HTS procedures. Algorithms and procedures were developed and implemented to address compound filtering, selection, clusterization and plating. Chapter 7 collects concluding remarks and plans for the further development of the tools

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening Computer-aided drug design is an essential part of the modern medicinal chemistry, and has led to the acceleration of many projects. The herein described thesis presents examples for its application in the field of lead optimization and lead identification for three metalloproteins. DOXP-reductoisomerase (DXR) is a key enzyme of the mevalonate independent isoprenoid biosynthesis. Structure-activity relationships for 43 DXR inhibitors are established, derived from protein-based docking, ligand-based 3D QSAR and a combination of both approaches as realized by AFMoC. As part of an effort to optimize the properties of the established inhibitor Fosmidomycin, analogues have been synthesized and tested to gain further insights into the primary determinants of structural affinity. Unfortunately, these structures still leave the active Fosmidomycin conformation and detailed reaction mechanism undetermined. This fact, together with the small inhibitor data set provides a major challenge for presently available docking programs and 3D QSAR tools. Using the recently developed protein tailored scoring protocol AFMoC precise prediction of binding affinities for related ligands as well as the capability to estimate the affinities of structurally distinct inhibitors has been achieved. Farnesyltransferase is a zinc-metallo enzyme that catalyzes the posttranslational modification of numerous proteins involved in intracellular signal transduction. The development of farnesyltransferase inhibitors is directed towards the so-called non-thiol inhibitors because of adverse drug effects connected to free thiols. A first step on the way to non-thiol farnesyltransferase inhibitors was the development of an CAAX-benzophenone peptidomimetic based on a pharmacophore model. On its basis bisubstrate analogues were developed as one class of non-thiol farnesyltransferase inhibitors. In further studies two aryl binding and two distinct specificity sites were postulated. Flexible docking of model compounds was applied to investigate the sub-pockets and design highly active non-thiol farnesyltransferase inhibitor. In addition to affinity, special attention was paid towards in vivo activity and species specificity. The second part of this thesis describes a possible strategy for computer-aided lead discovery. Assembling a complex ligand from simple fragments has recently been introduced as an alternative to traditional HTS. While frequently applied experimentally, only a few examples are known for computational fragment-based approaches. Mostly, computational tools are applied to compile the libraries and to finally assess the assembled ligands. Using the metalloproteinase thermolysin (TLN) as a model target, a computational fragment-based screening protocol has been established. Starting with a data set of commercially available chemical compounds, a fragment library has been compiled considering (1) fragment likeness and (2) similarity to known drugs. The library is screened for target specificity, resulting in 112 fragments to target the zinc binding area and 75 fragments targeting the hydrophobic specificity pocket of the enzyme. After analyzing the performance of multiple docking programs and scoring functions forand the most 14 candidates are selected for further analysis. Soaking experiments were performed for reference fragment to derive a general applicable crystallization protocol for TLN and subsequently for new protein-fragment complex structures. 3-Methylsaspirin could be determined to bind to TLN. Additional studies addressed a retrospective performance analysis of the applied scoring functions and modification on the screening hit. Curios about the differences of aspirin and 3-methylaspirin, 3-chloroaspirin has been synthesized and affinities could be determined to be 2.42 mM; 1.73 mM und 522 μM respectively. The results of the thesis show, that computer aided drug design approaches could successfully support projects in lead optimization and lead identification. fragments in general, the fragments derived from the screening are docke

Computational study of therapeutic targets and ADME-Associated proteins and application in drug design

Ph.DDOCTOR OF PHILOSOPH