In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach

Planning and discovery of new molluscicidal compounds for Biomphalaria glabrata (Mollusca, Planorbidae)

Submitted by Erika Demachki ([email protected]) on 2016-08-23T18:34:08Z No. of bitstreams: 2 Dissertação_José Teófilo Moreira Filho.pdf: 3739532 bytes, checksum: 781ef593c7c871e1b885d3e654832a3c (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Approved for entry into archive by Erika Demachki ([email protected]) on 2016-08-23T18:34:31Z (GMT) No. of bitstreams: 2 Dissertação_José Teófilo Moreira Filho.pdf: 3739532 bytes, checksum: 781ef593c7c871e1b885d3e654832a3c (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2016-08-23T18:34:31Z (GMT). No. of bitstreams: 2 Dissertação_José Teófilo Moreira Filho.pdf: 3739532 bytes, checksum: 781ef593c7c871e1b885d3e654832a3c (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-07-29Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPqSchistosomiasis is a neglected tropical disease caused by parasites of the genus Schistosoma. Worldwide, there are about 240 million people infected and have more than 700 million people at risk of infection in 78 countries. Biomphalaria glabrata is the main intermediate host of Schistosoma mansoni in Brazil. Niclosamide is the molluscicide recommended by the WHO. However, this molluscicide is toxic to other species of aquatic animals and plants, has difficult solubilization both in organic solvents and in water and also high cost. The utilization of in silico methods for virtual screening of new compounds rationalizes costs, reduces the time and also the number of animals in the early stages of research and development. The work’s purpose was plan and identify new molluscicidal compounds potentially active against B. glabrata through in silico methods. Known active and inactive molluscicidal compounds against B. glabrata were selected from literature. Decoys were generated to validate the shape-based models and QSAR models. First, the top 10,000 compounds from Chembridge and ZINC "DrugsNow" databases were screened with the best shape-based model, selected using TanimotoCombo socre function. Later, the activity of the compounds was predicted using consensus QSAR models. Finally, the water solubility of the compounds was calculated for the best molluscicidal candidates, leading to the identification of 20 potentially active compounds as molluscicides against B. glabrata.A esquistossomose é uma doença tropical negligenciada causada por parasitos do gênero Schistosoma. No mundo, existem cerca de 240 milhões de pessoas infectadas e mais de 700 milhões de pessoas em 78 países sob risco de infecção. Biomphalaria glabrata é o principal hospedeiro intermediário de Schistosoma mansoni no Brasil. A niclosamida é o moluscicida recomendado pela Organização Mundial de Saúde. No entanto, este moluscicida é tóxico a outras espécies de animais aquáticos e plantas, possui difícil solubilização tanto em solventes orgânicos quanto em água e, também, possui alto custo. A utilização de métodos in silico para triagem virtual de novos compostos racionaliza os custos, diminui o tempo e reduz número de animais nas fases iniciais de pesquisas e desenvolvimento. O objetivo deste trabalho foi planejar e identificar novos compostos moluscicidas potencialmente ativos contra B. glabrata através de métodos in silico. Compostos ativos e inativos como moluscicidas contra B. glabrata foram selecionados na literatura. Decoys foram gerados para a validação dos modelos de similaridade pela forma 3D e modelos de QSAR. Primeiramente, os melhores 10.000 compostos das bases de dados Chembridge e ZINC "DrugsNow" foram triados com o melhor modelo de similaridade pela forma 3D, utilizando a função de score TanimotoCombo. Posteriormente, a atividade dos compostos foi predita utilizando os modelos de QSAR gerados. Ao final, a solubilidade em água dos compostos com melhor atividade predita foi calculada, levando à identificação de 20 compostos potencialmente moluscicidas contra B. glabrata

QSAR-Based Virtual Screening of Natural Products Database for Identification of Potent Antimalarial Hits

With about 400,000 annual deaths worldwide, malaria remains a public health burden in tropical and subtropical areas, especially in low-income countries. Selection of drug-resistant Plasmodium strains has driven the need to explore novel antimalarial compounds with diverse modes of action. In this context, biodiversity has been widely exploited as a resourceful channel of biologically active compounds, as exemplified by antimalarial drugs such as quinine and artemisinin, derived from natural products. Thus, combining a natural product library and quantitative structure–activity relationship (QSAR)-based virtual screening, we have prioritized genuine and derivative natural compounds with potential antimalarial activity prior to in vitro testing. Experimental validation against cultured chloroquine-sensitive and multi-drug-resistant P. falciparum strains confirmed the potent and selective activity of two sesquiterpene lactones (LDT-597 and LDT-598) identified in silico. Quantitative structure–property relationship (QSPR) models predicted absorption, distribution, metabolism, and excretion (ADME) and physiologically based pharmacokinetic (PBPK) parameters for the most promising compound, showing that it presents good physiologically based pharmacokinetic properties both in rats and humans. Altogether, the in vitro parasite growth inhibition results obtained from in silico screened compounds encourage the use of virtual screening campaigns for identification of promising natural compound-based antimalarial molecules

Artificial intelligence systems for the design of magic shotgun drugs

Designing magic shotgun compounds, i.e., compounds hitting multiple targets using artificial intelligence (AI) systems based on machine learning (ML) and deep learning (DL) approaches, has a huge potential to revolutionize drug discovery. Such intelligent systems enable computers to create new chemical structures and predict their multi-target properties at a low cost and in a time-efficient manner. Most examples of AI applied to drug discovery are single-target oriented and there is still a lack of concise information regarding the application of this technology for the discovery of multi-target drugs or drugs with broad-spectrum action. In this review, we focus on current developments in AI systems for the next generation of automated design of multi-target drugs. We discuss how classical ML methods, cutting-edge generative models, and multi-task deep neural networks can help de novo design and hit-to-lead optimization of multi-target drugs. Moreover, we present state-of-the-art workflows and highlight some studies demonstrating encouraging experimental results, which pave the way for de novo drug design and multi-target drug discovery

Synthesis and molecular modelling studies of pyrimidinones and pyrrolo[3,4-d]-pyrimidinodiones as new antiplasmodial compounds

Submitted by Sandra Infurna ([email protected]) on 2018-07-10T15:24:15Z No. of bitstreams: 1 leonardo_carvalho_etal_IOC_2018.pdf: 1246557 bytes, checksum: 48ba2ef748ce43846be623e25cbb48b4 (MD5)Approved for entry into archive by Sandra Infurna ([email protected]) on 2018-07-10T15:37:52Z (GMT) No. of bitstreams: 1 leonardo_carvalho_etal_IOC_2018.pdf: 1246557 bytes, checksum: 48ba2ef748ce43846be623e25cbb48b4 (MD5)Made available in DSpace on 2018-07-10T15:37:52Z (GMT). No. of bitstreams: 1 leonardo_carvalho_etal_IOC_2018.pdf: 1246557 bytes, checksum: 48ba2ef748ce43846be623e25cbb48b4 (MD5) Previous issue date: 2018Universidade Federal Rural do Rio de Janeiro. Departamento de Química. Laboratório de Diversidade Molecular e Química Medicinal. Seropédica, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz.Laboratório de Pesquisas em Malária. Rio de Janeiro, RJ, Brasil.Universidade Federal Rural do Rio de Janeiro. Departamento de Química. Laboratório de Diversidade Molecular e Química Medicinal. Seropédica, RJ, Brasil.Universidade Federal de Goiás. Faculdade de Farmácia. Laboratório de Planejamento de Fármacos e Modelagem Molecular. Goiânia, GO, Brasil / Centro Universitário de Anápolis, UniEvangélica. Laboratório de Quimioinformática. Anápolis, GO, Brasil.Universidade Federal de Goiás. Faculdade de Farmácia. Laboratório de Planejamento de Fármacos e Modelagem Molecular. Goiânia, GO, Brasil.Universidade Federal Rural do Rio de Janeiro. Instituto de Ciências Exatas. Departamento de Química. Seropédica, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz.Laboratório de Pesquisas em Malária. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz.Laboratório de Pesquisas em Malária. Rio de Janeiro, RJ, Brasil.Universidade Federal de Goiás. Faculdade de Farmácia. Laboratório de Planejamento de Fármacos e Modelagem Molecular. Goiânia, GO, Brasil / Universidade de Campinas. Instituto de Biologia. Departamento de Genética, Evolução e Bioagentes. Laboratório de Doenças Tropicais Prof. Dr. Luiz Jacintho da Silva. Campinas, SP, Brasil.Universidade Federal Rural do Rio de Janeiro. Departamento de Química. Laboratório de Diversidade Molecular e Química Medicinal. Seropédica, RJ, Brasil.BACKGROUND Malaria is responsible for 429,000 deaths per year worldwide, and more than 200 million cases were reported in 2015. Increasing parasite resistance has imposed restrictions to the currently available antimalarial drugs. Thus, the search for new, effective and safe antimalarial drugs is crucial. Heterocyclic compounds, such as dihydropyrimidinones (DHPM), synthesised via the Biginelli multicomponent reaction, as well as bicyclic compounds synthesised from DHPMs, have emerged as potential antimalarial candidates in the last few years. METHODS Thirty compounds were synthesised employing the Biginelli multicomponent reaction and subsequent one-pot substitution/cyclisation protocol; the compounds were then evaluated in vitro against chloroquine-resistant Plasmodium falciparum parasites (W2 strain). Drug cytotoxicity in baseline kidney African Green Monkey cells (BGM) was also evaluated. The most active in vitro compounds were evaluated against P. berghei parasites in mice. Additionally, we performed an in silico target fishing approach with the most active compounds, aiming to shed some light into the mechanism at a molecular level. RESULTS The synthetic route chosen was effective, leading to products with high purity and yields ranging from 10-84%. Three out of the 30 compounds tested were identified as active against the parasite and presented low toxicity. The in silico study suggested that among all the molecular targets identified by our target fishing approach, Protein Kinase 3 (PK5) and Glycogen Synthase Kinase 3β (GSK-3β) are the most likely molecular targets for the synthesised compounds. CONCLUSIONS We were able to easily obtain a collection of heterocyclic compounds with in vitro anti-P. falciparum activity that can be used as scaffolds for the design and development of new antiplasmodial drugs