Legacy data sharing to improve drug safety assessment: the eTOX project

Sanz, Ferrán; Philippe, François; Steger‐Hartmann, Thomas; Díaz, Carlos; eTOX,; Cases, Montserrat; Pastor, Manuel; Marc, Philippe; Wichard, Joerg; Briggs, Katharine; Watson, David; Kleinöder, Thomas; Yang, Chihae; Amberg, Alexander; M, Beaumont; Brookes, Anthony J.; Brunak, Søren; Cronin, Mark T.D.; Ecker, Gerhard; Escher, Sylvia; Greene, Nigel; Guzmán, Antonio; Hersey, Anne; Jacques, Pascale; Lammens, Lieve; Mestres, Jordi; Muster, Wolfgang; Northeved, Helle; Pinches, Marc; Sáiz, Javier; Sajot, Nicolas; Valencia, Alfonso; Lei, Johan van der; Vermeulen, Nico; Vock, Esther; Wolber, Gerhard; Zamora, Ismael

doi:10.1038/nrd.2017.177

Cited by 59 publications

(32 citation statements)

References 6 publications

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“…Despite the fact that modern drug discovery has made some successes in offering effective drugs, drug design has been affected by several factors, such as the tremendous chemical space for exploring drug molecules [63]. Further, as a large number of data increase in biological, chemical, and clinical medicine, it is obvious that the drug design should be solved with multiscale optimization methods, and concentrate on the data beyond molecular levels [64]. Thus, it is essential to discuss the function of multiscale models in drug discovery, and how they have predicted multiple biological properties in different biological targets.…”

Section: Multiscale De Novo Drug Design Toward Personalized Medicinementioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure-and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.Molecules 2020, 25, 1375 2 of 17 Biomolecular Simulations in Drug Screening and DesignThe Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt, and Arieh Warshel for their pioneering contributions in the development of multiscale models for complex biochemical systems, recognizing the important role that theory and computational methods play as a direct and necessary complement to experiments [12]. Further, applications of biomolecular simulations in identifying drug binding sites and elucidating the molecular mechanisms of diseases have been subject to rapid developments [13][14][15].Depending on the biological problems to be studied, multiscale models will be used in biomolecular simulations. The combination of quantum mechanics (QM) and molecular mechanics (MM) (QM/MM) can be used to study the electronic properties [16], simulate chemical reactions (e.g., the enzyme catalysis mechanism [17]), and calculate spectra [18] in a single simulation, which can be used to elucidate the action mechanism of certain drugs. Another widely used biomolecular method is molecular dynamics (MD) simulation [19][20][21][22][23][24], which applies empirical molecular mechanics (MM) force fields and is based on classical Newtonian mechanics. According to different accuracy requirements, all-atom (AA), united-atom (UA), and coarse-grained (CG) MD simulations as well as explicit/implicit solvent models, which allow simulations of temporal and spatial scales, can be used to facilitate the drug discovery [25,26]. Generally, MD simulations have been used for the identification of potential drug binding sites on target proteins, the calculation of binding free energy between target proteins and drug molecules, the action mechanism of drug molecules, etc. [27,28]. However, it is worth mentioning that MD simulations are also limited to time and length scales. Currently, AAMD simulations can explore time-dependent phenomena of large systems such as viral capsids in atomic detail as long as microseconds or even milliseconds [21,29,30]. Therefore, different types of ...

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Section: Multiscale De Novo Drug Design Toward Personalized Medicinementioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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“…For example, the recent use of transfer and multitask learning in predicting pharmacokinetic parameters [38] or the application of DL to ADMET prediction [39]. Moreover, public-private partnerships such as the eTOX consortium have been created to recover legacy toxicological data from big pharmaceutical companies and develop better predictive models [40]. Despite promising advances, the lack of sufficient well-curated Big Data in the public domain is currently limiting our capacity to mine ADMET data and develop predictive ADMET models.…”

Section: Admet Modellingmentioning

confidence: 99%

Transforming cancer drug discovery with Big Data and AI

Workman

Antolín

Al‐Lazikani

2019

Expert Opinion on Drug Discovery

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“…The sharing of high-quality data strongly facilitates moving research forward providing the bases for new applications and benefit to society. The IMI "Integrating bioinformatics and chemoinformatics approaches for the development of Expert systems allowing the in silico prediction of toxicities" (eTOX) consortium developed innovative strategies and novel software tools to better predict the safety and side effects of new candidate medicines for patients (Sanz et al 2017).…”

Section: On Data Sharing Collaborative Research and Research Qualitymentioning

confidence: 99%

Research Collaborations and Quality in Research: Foes or Friends?

Vaudano

2019

Good Research Practice in Non-Clinical Pharmacology and Biomedicine

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Collaboration is the cornerstone of nowadays research. Successful collaborative research and high research quality go hand in hand. Collaborative research needs to build on common and upfront expectations for the quality of its outputs. This is necessary to enable a trustful research environment where all are committed to contribute and can share the rewards. A governance and leadership are critical for this to happen as well as a policy for openness and for effective data sharing. Collaborative research is often large-scale research: to be successful it needs good research practice as an enabler. Collaborative projects are ideal vehicles to promote high research quality, among other by enabling the delivery of results of high external validity and the development and implementation of standards. Robustness of results increases when confirmed by combining different methods and tools and even more when results are obtained while sharing and learning different approaches and languages of science. When doing collaborative E. Vaudano (

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Legacy data sharing to improve drug safety assessment: the eTOX project

Cited by 59 publications

References 6 publications

A Review on Applications of Computational Methods in Drug Screening and Design

A Review on Applications of Computational Methods in Drug Screening and Design

Transforming cancer drug discovery with Big Data and AI

Research Collaborations and Quality in Research: Foes or Friends?

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