IDAAPM : integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data

Background: The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process. Description: We have built a relational database (IDAAPM) to integrate available approved drug data such as drug approval information, ADMET and adverse effects, chemical structures and molecular descriptors, targets, bioactivity and related references. The database has been coupled with a searchable web interface and modern data analytics platform (KNIME) to allow data access, data transformation, initial analysis and further predictive modeling. Data were extracted from FDA resources and supplemented from other publicly available databases. Currently, the database contains information regarding about 19,226 FDA approval applications for 31,815 products (small molecules and bio-logics) with their approval history, 2505 active ingredients, together with as many ADMET properties, 1629 molecular structures, 2.5 million adverse effects and 36,963 experimental drug-target bioactivity data. Conclusion: IDAAPM is a unique resource that, in a single relational database, provides detailed information on FDA approved drugs including their ADMET properties and adverse effects, the corresponding targets with bioactivity data, coupled with a data analytics platform. It can be used to perform basic to complex drug-target ADMET or adverse effects analysis and predictive modeling. IDAAPM is freely accessible at http://idaapm.helsinki.fi and can be exploited through a KNIME workflow connected to the database.Peer reviewe

Multisubstituted pyrimidines effectively inhibit bacterial growth and biofilm formation of Staphylococcus aureus

Biofilms are multicellular communities of microorganisms that generally attach to surfaces in a self-produced matrix. Unlike planktonic cells, biofilms can withstand conventional antibiotics, causing significant challenges in the healthcare system. Currently, new chemical entities are urgently needed to develop novel anti-biofilm agents. In this study, we designed and synthesized a set of 2,4,5,6-tetrasubstituted pyrimidines and assessed their antibacterial activity against planktonic cells and biofilms formed by Staphylococcus aureus. Compounds 9e, 10d, and 10e displayed potent activity for inhibiting the onset of biofilm formation as well as for killing pre-formed biofilms of S. aureus ATCC 25923 and Newman strains, with half-maximal inhibitory concentration (IC50) values ranging from 11.6 to 62.0 mu M. These pyrimidines, at 100 mu M, not only decreased the number of viable bacteria within the pre-formed biofilm by 2-3 log(10) but also reduced the amount of total biomass by 30-50%. Furthermore, these compounds were effective against planktonic cells with minimum inhibitory concentration (MIC) values lower than 60 mu M for both staphylococcal strains. Compound 10d inhibited the growth of S. aureus ATCC 25923 in a concentration-dependent manner and displayed a bactericidal anti-staphylococcal activity. Taken together, our study highlights the value of multisubstituted pyrimidines to develop novel anti-biofilm agents.Peer reviewe

In Silico Resources to Assist in the Development and Evaluation of Physiologically-Based Kinetic Models

Since their inception in pharmaceutical applications, physiologically-based kinetic (PBK) models are increasingly being used across a range of sectors, such as safety assessment of cosmetics, food additives, consumer goods, pesticides and other chemicals. Such models can be used to construct organ-level concentration-time profiles of xenobiotics. These models are essential in determining the overall internal exposure to a chemical and hence its ability to elicit a biological response. There are a multitude of in silico resources available to assist in the construction and evaluation of PBK models. An overview of these resources is presented herein, encompassing all attributes required for PBK modelling. These include predictive tools and databases for physico-chemical properties and absorption, distribution, metabolism and elimination (ADME) related properties. Data sources for existing PBK models, bespoke PBK software and generic software that can assist in model development are also identified. On-going efforts to harmonise approaches to PBK model construction, evaluation and reporting that would help increase the uptake and acceptance of these models are also discussed

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro-in vivo -clinical data recorded and suitability for modelling, read-across or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data

ADMET and adverse effects predictive modeling based on FDA-approved drugs Data

Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties and adverse effects determine the success of each drug. These factors play an important role in the late-stage failure of drug candidates and withdrawal of approved drugs from the market. In early drug discovery research, computational methods along with the integration of large, clean and safe compound data are effective approaches to minimizing the risk of late-stage attrition and reducing the number of safety issues. This thesis describes a relational database called Integrated Database of ADMET and Adverse effects for Predictive Modeling based on FDA approved drug data (IDAAPM). This database is designed to integrate approved drug data, including drug approval information, ADMET, adverse effects, chemical structures and molecular descriptors, targets and binding affinity data including their associated scientific literature references. Moreover, the database is connected to a responsive website interface, and coupled with a modern data analytic platform (KNIME). Currently, IDAAPM contains FDA approval applications (19,226), products (31,815), active ingredients (2,505), ADMET properties (1,076), drug adverse effect pairs (2,472,770), molecular structures (1,629), drug targets (2,220) and drug target interactions (36,963). Therefore, IDAAPM is a unique and comprehensive platform that provides safe compound data and enables the researcher to run a predictive analysis of their compounds of interest in terms of ADMET and adverse effect properties. IDAAPM can be accessed through a web browser at http://idaapm.helsinki.fi or a downloaded KNIME workflow at http://idaapm.helsinki.fi/Download.Siirretty Doriast

Development of Integrated Databases and Web Resources for drug discovery

In this thesis, three databases are presented that are connected to web servers. The IDAAPM database integrates approved drugs and associated properties such as molecular descriptors, ADMET, adverse effects, biological targets, and bioactivities (Publication I). Its successor DrugMapper expands the data content to investigational drugs and contains more than 10,000 entries. It displays a major reorganization of the underlying data and major add-ons to the web interface (Publication II). DrugMapper allows users to visualize integrated compound data such as molecular structures, targets, adverse effects, and clinical trials. The search interface allows advanced searches by chemical similarity, using target sequence, target classification level, mechanism of action, and adverse effects. Additionally, knowledge graphs can be generated for in-depth analysis. ChembioMapper (Publication V) was developed to analyse congeneric series produced by medicinal chemists together with biological activity and structural target data. The web server allows the navigation of thousands of congeneric series, exploring their Maximum Common Substructures and associated R-groups. Other databases are also used for two applications: computational analysis work is presented on the ChEMBL database to understand the target co-testing utilizing four major medicinal journals’ data from the ChEMBL database (Publication IV). Compounds active against gram-negative and positive bacteria are analysed with respect to their chemical space as well as similarity to hit compounds (Publication III).I detta examensarbete presenteras tre databaser som är kopplade till webbservrar. IDAAPM-databasen integrerar godkända läkemedel och associerade egenskaper såsom molekylära deskriptorer, ADMET, negativa effekter, biologiska mål och bioaktiviteter (Publikation I). Dess efterträdare DrugMapper utökar datainnehållet till prövningsläkemedel och innehåller mer än 10 000 poster. Den visar en större omorganisation av underliggande data och större tillägg till webbgränssnittet (Publication II). DrugMapper tillåter användare att visualisera integrerade sammansättningsdata som molekylära strukturer, mål, negativa effekter och kliniska prövningar. Sökgränssnittet tillåter avancerade sökningar efter kemisk likhet, med användning av målsekvens, målklassificeringsnivå, verkningsmekanism och negativa effekter. Dessutom kan kunskapsdiagram genereras för djupgående analys. ChembioMapper (Publication V) utvecklades för att analysera kongeneriska serier producerade av läkemedelskemister tillsammans med biologisk aktivitet och strukturella måldata. Webbservern tillåter navigering av tusentals kongeneriska serier och utforskar deras maximala gemensamma understrukturer och tillhörande R-grupper. Andra databaser används också för två tillämpningar: beräkningsanalysarbete presenteras på ChEMBL-databasen för att förstå målsamtestningen med hjälp av fyra stora medicinska tidskrifters data från ChEMBL-databasen (Publication IV). Föreningar som är aktiva mot gramnegativa och positiva bakterier analyseras med avseende på deras kemiska utrymme samt likhet med hitföreningar (Publikation III)