Identification, synthesis and evaluation of CSF1R inhibitors using fragment based drug design.

Colony-stimulating factor 1 receptor is a type III receptor protein tyrosine kinase belonging to PDGFR family. CSF1R signaling is essential for differentiation, proliferation and survival of macrophages. Aberrant expression of CSF1R appears to be an attractive target in several cancer types. Higher expression of CSF1R ligands correlates to tumor progression. CSF1R inhibitors have been shown to suppress cancers. We have attempted an in silico fragment derived drug discovery approach by screening ˜25,000 in-house compounds as potential CSF1R inhibitors. Using FBDD approach we have identified six diverse fragments that exhibit affinity towards hinge region of CSF1R. Some of the fragments 5-nitroindole and 7-azaindole and their derivatives were synthesized for further evaluation. The in silico and in vitro enzyme activity studies reveal moderate inhibition of CSF1R kinase activity by 5-nitroindole and good inhibition by 7-azaindole fragments. Bio and chemiinformatics studies have shown that 7-azaindole compounds have better membrane permeability and enzyme inhibition properties. Molecular docking studies show that the amino acid residues 664-666 in the hinge region of the cytosolic domain of CSF1R to be the preferred region of binding for nitroindole and azaindole derivatives. Further optimization and biological analysis would identify these fragments as potential and promising leads as CSF1R inhibitors.

Annotated chemical patent corpus: a gold standard for text mining.

Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, targets, and modes of action. Spelling mistakes and spurious line break due to optical character recognition errors were also annotated. A subset of 47 patents was annotated by at least three annotator groups, from which harmonized annotations and inter-annotator agreement scores were derived. One group annotated the full set. The patent corpus includes 400,125 annotations for the full set and 36,537 annotations for the harmonized set. All patents and annotated entities are publicly available at www.biosemantics.org.

Tracking 20 years of compound-to-target output from literature and patents.

The statistics of drug development output and declining yield of approved medicines has been the subject of many recent reviews. However, assessing research productivity that feeds development is more difficult. Here we utilise an extensive database of structure-activity relationships extracted from papers and patents. We have used this database to analyse published compounds cumulatively linked to nearly 4000 protein target identifiers from multiple species over the last 20 years. The compound output increases up to 2005 followed by a decline that parallels a fall in pharmaceutical patenting. Counts of protein targets have plateaued but not fallen. We extended these results by exploring compounds and targets for one large pharmaceutical company. In addition, we examined collective time course data for six individual protease targets, including average molecular weight of the compounds. We also tracked the PubMed profile of these targets to detect signals related to changes in compound output. Our results show that research compound output had decreased 35% by 2012. The major causative factor is likely to be a contraction in the global research base due to mergers and acquisitions across the pharmaceutical industry. However, this does not rule out an increasing stringency of compound quality filtration and/or patenting cost control. The number of proteins mapped to compounds on a yearly basis shows less decline, indicating the cumulative published target capacity of global research is being sustained in the region of 300 proteins for large companies. The tracking of six individual targets shows uniquely detailed patterns not discernible from cumulative snapshots. These are interpretable in terms of events related to validation and de-risking of targets that produce detectable follow-on surges in patenting. Further analysis of the type we present here can provide unique insights into the process of drug discovery based on the data it actually generates.

Virtual screening studies on HIV-1 reverse transcriptase inhibitors to design potent leads.

The purpose of this study is to identify novel and potent inhibitors against HIV-1 reverse transcriptase (RT). The crystal structure of the most active ligand was converted into a feature-shaped query. This query was used to align molecules to generate statistically valid 3D-QSAR (r(2) = 0.873) and Pharmacophore models (HypoGen). The best HypoGen model consists of three Pharmacophore features (one hydrogen bond acceptor, one hydrophobic aliphatic and one ring aromatic) and further validated using known RT inhibitors. The designed novel inhibitors are further subjected to docking studies to reduce the number of false positives. We have identified and proposed some novel and potential lead molecules as reverse transcriptase inhibitors using analog and structure based studies.

Database systems for knowledge-based discovery.

Several database systems have been developed to provide valuable information from the bench chemist to biologist, medical practitioner to pharmaceutical scientist in a structured format. The advent of information technology and computational power enhanced the ability to access large volumes of data in the form of a database where one could do compilation, searching, archiving, analysis, and finally knowledge derivation. Although, data are of variable types the tools used for database creation, searching and retrieval are similar. GVK BIO has been developing databases from publicly available scientific literature in specific areas like medicinal chemistry, clinical research, and mechanism-based toxicity so that the structured databases containing vast data could be used in several areas of research. These databases were classified as reference centric or compound centric depending on the way the database systems were designed. Integration of these databases with knowledge derivation tools would enhance the value of these systems toward better drug design and discovery.

Discovery of potential ZAP-70 kinase inhibitors: pharmacophore design, database screening and docking studies.

The best ZAP-70 inhibitor model consists of four-pharmacophore features, (1) one hydrogen bond acceptor, (2) one hydrogen bond donor (3) one hydrophobic aliphatic and (4) one hydrophobic aromatic features. This model was validated against 110 known ZAP-70 inhibitors with a correlation of 0.902 as well as enrichment factor of 1.61 against a maximum value of 2. This model picked 4094 hits from a database of 238,819 molecules while 358 molecules were indicated as highly active. Subsequently, docking studies were performed on the hits and novel series of potent leads were suggested based on the interactions energy between ZAP-70 and the putative inhibitors which validated not only the virtual screening potential of the model but also identified the possible new Chemotypes.

Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models.

Monoamine Oxidase B interaction with known ligands was investigated using combined pharmacophore and structure based modeling approach. The docking results suggested that the pharmacophore and docking models are in good agreement and are used to identify the selective MAO-B inhibitors. The best model, Hypo2 consists of three pharmacophore features, i.e., one hydrogen bond acceptor, one hydrogen bond donor and one ring aromatic. The Hypo2 model was used to screen an in-house database of 80,000 molecules and have resulted in 5500 compounds. Docking studies were performed, subsequently, on the cluster representatives of 530 hits from 5500 compounds. Based on the structural novelty and selectivity index, we have suggested 15 selective MAO-B inhibitors for further synthesis and pharmacological screening.

Fragment and knowledge-based design of selective GSK-3beta inhibitors using virtual screening models.

Glycogen Synthase Kinase 3beta is one of the important targets in the treatment of type II diabetes and Alzheimer's disease. Currently this target is in pursuit for type II diabetes and a few GSK-3beta inhibitors have been now advanced to Phases I and II of clinical trials. The best validated HypoGen model consists of four pharmacophore features; 1) two hydrogen bond acceptors, 2) one hydrogen bond donor and 3) one hydrophobic. This pharmacophore model correlates well with the docking model, one hydrogen bond acceptor is necessary for the H-bond interaction with VAL135, and second hydrogen bond acceptor is important for the H-bond interactions with ARG141 and the hydrophobic feature may be required for the weak H-bond interactions with ASP133. The comparative model was developed from analogue and structure-based models like Catalyst, Glide SP & XP, Gold Fitness & ChemScore and Ligand Fit using multiple linear regression analysis. A virtual library of 10,000 molecules was generated employing fragment and knowledge-based approach and the comparative model was used to predict the activities of these molecules. The H-bond with ARG141 appears to be unique to GSK-3beta and explains the high GSK-3beta selectivity observed for 1H-Quinazolin-4-ones and Benzo[e][1,3]oxazin-4-ones. This understanding of protein-ligand interactions and molecular recognition increases the rapid development of potent and selective inhibitors, and also helps to eliminate the increase in number of false positives and negatives.

Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads.

Histone deacetylase is one of the important targets in the treatment of solid tumors and hematological cancers. A total of 20 well-defined inhibitors were used to generate Pharmacophore models using and HypoGen module of Catalyst. These 20 molecules broadly represent 3 different chemotypes. The best HypoGen model consists of four-pharmacophore features--one hydrogen bond acceptor, one hydrophobic aliphatic and two ring aromatic centers. This model was validated against 378 known HDAC inhibitors with a correlation of 0.897 as well as enrichment factor of 2.68 against a maximum value of 3. This model was further used to retrieve molecules from NCI database with 238,819 molecules. A total of 4638 molecules from a pool of 238,819 molecules were identified as hits while 297 molecules were indicated as highly active. Also, a Similarity analysis has been carried out for set of 4638 hits with respect to most active molecule of each chemotypes which validated not only the Virtual Screening potential of the model but also identified the possible new Chemotypes. This type of Similarity analysis would prove to be efficient not only for lead generation but also for lead optimization.

Virtual screening studies to design potent CDK2-cyclin A inhibitors.

The cell division cycle is controlled by cyclin-dependent kinases (CDK), which consist of a catalytic subunit (CDK1-CDK8) and a regulatory subunit (cyclin A-H). Pharmacophore analysis indicates that the best inhibitor model consists of (1) two hydrogen bond acceptors, (2) one hydrogen bond donor, and (3) one hydrophobic feature. The HypoRefine pharmacophore model gave an enrichment factor of 1.31 and goodness of fit score of 0.76. Docking studies were carried out to explore the structural requirements for the CDK2-cyclin A inhibitors and to construct highly predictive models for the design of new inhibitors. Docking studies demonstrate the important role of hydrogen bond and hydrophobic interactions in determining the inhibitor-receptor binding affinity. The validated pharmacophore model is further used for retrieving the most active hits/lead from a virtual library of molecules. Subsequently, docking studies were performed on the hits, and novel series of potent leads were suggested based on the interaction energy between CDK2-cyclin A and the putative inhibitors.