COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology

The U.S. Environmental Protection Agency's (EPA) ToxCast program is testing a large library of Agency-relevant chemicals using in vitro high-throughput screening (HTS) approaches to support the development of improved toxicity prediction models. Launched in 2007, Phase I of the program screened 310 chemicals, mostly pesticides, across hundreds of ToxCast assay end points. In Phase II, the ToxCast library was expanded to 1878 chemicals, culminating in the public release of screening data at the end of 2013. Subsequent expansion in Phase III has resulted in more than 3800 chemicals actively undergoing ToxCast screening, 96% of which are also being screened in the multi-Agency Tox21 project. The chemical library unpinning these efforts plays a central role in defining the scope and potential application of ToxCast HTS results. The history of the phased construction of EPA's ToxCast library is reviewed, followed by a survey of the library contents from several different vantage points. CAS Registry Numbers are used to assess ToxCast library coverage of important toxicity, regulatory, and exposure inventories. Structure-based representations of ToxCast chemicals are then used to compute physicochemical properties, substructural features, and structural alerts for toxicity and biotransformation. Cheminformatics approaches using these varied representations are applied to defining the boundaries of HTS testability, evaluating chemical diversity, and comparing the ToxCast library to potential target application inventories, such as used in EPA's Endocrine Disruption Screening Program (EDSP). Through several examples, the ToxCast chemical library is demonstrated to provide comprehensive coverage of the knowledge domains and target inventories of potential interest to EPA. Furthermore, the varied representations and approaches presented here define local chemistry domains potentially worthy of further investigation (e.g., not currently covered in the testing library or defined by toxicity "alerts") to strategically support data mining and predictive toxicology modeling moving forward.

Structure based drug design for HIV protease: from molecular modeling to cheminformatics

Significant progress over the past decade in virtual representations of molecules and their physicochemical properties has produced new drugs from virtual screening of the structures of single protein molecules by conventional modeling methods. The development of clinical antiviral drugs from structural data for HIV protease has been a major success in structure based drug design. Techniques for virtual screening involve the ranking of the affinity of potential ligands for the target site on a protein. Two main alternatives have been developed: modeling of the target protein with a series of related ligand molecules, and docking molecules from a database to the target protein site. The computational speed and prediction accuracy will depend on the representation of the molecular structure and chemistry, the search or simulation algorithm, and the scoring function to rank the ligands. Moreover, the general challenges in modern computational drug design arise from the profusion of data, including whole genomes of DNA, protein structures, chemical libraries, affinity and pharmacological data. Therefore, software tools are being developed to manage and integrate diverse data, and extract and visualize meaningful relationships. Current areas of research include the development of searchable chemical databases, which requires new algorithms to represent molecules and search for structurally or chemically similar molecules, and the incorporation of machine learning techniques for data mining to improve the accuracy of predictions. Examples will be presented for the virtual screening of drugs that target HIV protease.

Knowledge-based algorithms for chemical structure and property analysis

We have successfully developed 'rule-based' algorithms that efficiently perform sub- and exact-structure searching, as well as accurately describe the chemistry of small molecules. These algorithms use a simple and concise set of rules for information extraction from molecule files. This design is intended to reduce the computational time required for the process, while improving the accuracy in the tasks. The performances of these algorithms have been successfully validated with a wide range of small molecules. Our future goal is to combine these algorithms with our newly designed knowledge-based object database, such that their tasks can be automated with a high efficiency.

An approach in building a chemical compound search engine in oracle database

A searching or identifying of chemical compounds is an important process in drug design and in chemistry research. An efficient search engine involves a close coupling of the search algorithm and database implementation. The database must process chemical structures, which demands the approaches to represent, store, and retrieve structures in a database system. In this paper, a general database framework for working as a chemical compound search engine in Oracle database is described. The framework is devoted to eliminate data type constrains for potential search algorithms, which is a crucial step toward building a domain specific query language on top of SQL. A search engine implementation based on the database framework is also demonstrated. The convenience of the implementation emphasizes the efficiency and simplicity of the framework.

Pluggable application server framework

Building a system based on variants of disparate individual components/programs is usually a challenging task. The components/programs are not designed to communicate with each other but the whole system construction does require a seamless collaboration among them. In this paper, targeting at protein structure prediction, a pluggable application server framework is presented. The framework is capable of combining various existing programs into an efficient unit and the design is devoted to provide a model which is able to integrate heterogeneous components/programs into the system quickly without modifying their codes. Based on the model, different components can be plugged into the system with easy configuration, which would lead to a self-configurable and adaptive system. A protein structure prediction server implementation was developed by applying the design model and the real implementation emphasizes the efficiency and simplicity of the system construction. The method and model are generic and can be applied to other system design as well.

Novel bis-tetrahydrofuranylurethane-containing nonpeptidic protease inhibitor (PI) UIC-94017 (TMC114) with potent activity against multi-PI-resistant human immunodeficiency virus in vitro

We designed, synthesized, and identified UIC-94017 (TMC114), a novel nonpeptidic human immunodeficiency virus type 1 (HIV-1) protease inhibitor (PI) containing a 3(R),3a(S),6a(R)-bis-tetrahydrofuranylurethane (bis-THF) and a sulfonamide isostere which is extremely potent against laboratory HIV-1 strains and primary clinical isolates (50% inhibitory concentration [IC(50)], approximately 0.003 micro M; IC(90), approximately 0.009 micro M) with minimal cytotoxicity (50% cytotoxic concentration for CD4(+) MT-2 cells, 74 micro M). UIC-94017 blocked the infectivity and replication of each of HIV-1(NL4-3) variants exposed to and selected for resistance to saquinavir, indinavir, nelfinavir, or ritonavir at concentrations up to 5 micro M (IC(50)s, 0.003 to 0.029 micro M), although it was less active against HIV-1(NL4-3) variants selected for resistance to amprenavir (IC(50), 0.22 micro M). UIC-94017 was also potent against multi-PI-resistant clinical HIV-1 variants isolated from patients who had no response to existing antiviral regimens after having received a variety of antiviral agents. Structural analyses revealed that the close contact of UIC-94017 with the main chains of the protease active-site amino acids (Asp-29 and Asp-30) is important for its potency and wide spectrum of activity against multi-PI-resistant HIV-1 variants. Considering the favorable pharmacokinetics of UIC-94017 when administered with ritonavir, the present data warrant that UIC-94017 be further developed as a potential therapeutic agent for the treatment of primary and multi-PI-resistant HIV-1 infections.